text:
DELTA user manual
DELTA Version 5.0
Concepts / User's Guide / Diagrams
P. Thunis, C. Cuvelier
A. Pederzoli, E. Georgieva,
D. Pernigotti, B. Degraeuwe, M. Marioni
Joint Research Centre, Ispra
Concepts __________________________________________________________________ 3
Introduction_______________________________________________________________ 4
Basic principles ____________________________________________________________ 4
Overview _________________________________________________________________ 5
Model Quality Objective and Performance criteria ________________________________ 7
Model Quality Objective (MQO) ___________________________________________________ 7
Performance criteria for Bias, R and SD ______________________________________________ 8
Performance criteria for high percentile values ______________________________________ 10
An expression for the measurement uncertainty _____________________________________ 10
An alternative formulation for the observation uncertainty ____________________________ 13
The 90% principle ______________________________________________________________ 13
Benchmarking report ______________________________________________________ 14
Exploration ____________________________________________________________________ 6
Benchmarking __________________________________________________________________ 7
Hourly frequency ______________________________________________________________ 14
Yearly frequency _______________________________________________________________ 17
References _______________________________________________________________ 20
User's Guide ______________________________________________________________ 21
What's new ______________________________________________________________ 22
From version 4.0 to 5.0 __________________________________________________________ 22
Installation and running steps _______________________________________________ 23
Preparation of input files ___________________________________________________ 24
Init.ini _______________________________________________________________________ 24
Startup.ini ____________________________________________________________________ 25
Observation file ________________________________________________________________ 27
Yearly Frequency_______________________________________________________________ 29
Model file ____________________________________________________________________ 30
Using DELTA with yearly output ___________________________________________________ 33
Delta Tool top menu _______________________________________________________ 33
Exploration mode _________________________________________________________ 34
The data selection interface ______________________________________________________ 35
The analysis interface ___________________________________________________________ 37
The main graphical interface _____________________________________________________ 39
DELTA functionalities and user's tuning options _________________________________ 40
"Playing" with uncertainty parameters: the "goals_criteria_oc" input file _________________ 40
Saving summary statistics information in ASCII _______________________________________ 41
Mouse-driven recognize functionality ______________________________________________ 41
Managing multiple datasets: the "MyDeltaInput" option ______________________________ 41
Benchmarking mode _______________________________________________________ 42
Demo Dataset: Po-Valley ___________________________________________________ 43
Utility programs __________________________________________________________ 43
Data-Check Integrity Tool ________________________________________________________ 43
Interactive format conversion tool ________________________________________________ 44
Preproc-CDF __________________________________________________________________ 44
DIAGRAMS Overview _______________________________________________________ 45
TEMPLATE: Diagram name (Elaboration name) ______________________________________________ 46
BARPLOT (Mean, Stddev, Exc. Days) _______________________________________________________ 47
BARPLOT (Spatial Correlation) ___________________________________________________________ 48
BARPLOT (R, Mbias, RMSE, IOA, RDE, NMB, RPE, FAC2, NMSD) _________________________________ 49
BARPLOT (CUMUL) ____________________________________________________________________ 50
SCATTER (Mean mod vs. mean obs) _______________________________________________________ 51
SCATTER (One station – All time values) ___________________________________________________ 52
TIME SERIES __________________________________________________________________________ 53
TARGET (8H Max, Daily, Hourly) __________________________________________________________ 54
SUMMARY REPORT (8H Max, Daily, Hourly) ________________________________________________ 55
MPC correlation (8H Max, Daily, Hourly) ___________________________________________________ 56
MPC std. Dev. (8H Max, Daily, Hourly) _____________________________________________________ 57
Taylor _______________________________________________________________________________ 58
Q-Q plot (One station All values) _________________________________________________________ 59
Dynamic evaluation (Day-Night) __________________________________________________________ 60
Dynamic evaluation (Summer-Winter) _____________________________________________________ 61
Dynamic evaluation (Weekdays – Weekends) _______________________________________________ 62
GeoMap (Target) ______________________________________________________________________ 63
Google Earth (Mean, Exc. Days, Bias, NMB, Std. Dev, R, RMSE, RDE, σM/σO, NMSD) ________________ 64
This document describes version 5 of the DELTA Tool. This tool is an IDL-based evaluation
software which includes the main assets of the EuroDelta, CityDelta, and POMI tools
(Cuvelier et al. 2007; Thunis et al. 2007). It allows the user to perform rapid diagnostics of air
quality and meteorological model performances. Although DELTA focuses on the air
pollutants mentioned in the Air Quality Directive 2008 (AQD) it can be used for other
variables as well. It works on the comparison of time series at specific locations and therefore
addresses all relevant spatial scales (from local to regional). Some material about DELTA
has been already presented in different documents:
METHOD2012: Performance criteria to evaluate air quality modeling applications,
P. Thunis, A. Pederzoli, D. Pernigotti. Atmospheric Environment, Volume 59, November
2012, Pages 476-482
UNCERT2012: Set of 3 peer-reviewed publications and a working note:
 Performance criteria to evaluate air quality modeling applications, P. Thunis, A.
Pederzoli, D. Pernigotti. Atmospheric Environment, Volume 59, November 2012, Pages
 Model quality objectives based on measurement uncertainty: Part 1: Ozone, P. Thunis, D.
Pernigotti and M. Gerboles, 2012, Atmospheric Environment, Volume 79, November
 Model quality objectives based on measurement uncertainty: Part II:PM10 and NO2. D.
Pernigotti, P. Thunis, M. Gerboles and C. Belis, Atmospheric Environment, Volume 79,
November 2013, Pages 869-878
 Modeling quality objectives in the framework of the FAIRMODE project: working
document. D. Pernigotti, M. Gerboles and P. Thunis, April 2014. Available on the
fairmode webpage: http://fairmode.jrc.ec.europa.eu/wg1.html.
PROCBENCH: A procedure for air quality models benchmarking. 2011.
P. Thunis, E. Georgieva, S. Galmarini (document available on DELTA web site)
We will here recall the main concepts and details of the DELTA Tool, as well as the
improvements made in version 4 with respect to previous versions (see what"s new section).
DELTA works with modelled-observed data pairs at surface level, i.e. temporal series of
modelled and monitored data at selected ground level locations (e.g. monitoring stations).
In theory the software works therefore independently of model gridding and spatial scale.
Of course the user must use an appropriate methodology to ensure comparability between
grid-cell averaged model results and punctual measurements.
A minimum data availability is required for statistics to be produced at a given station.
Presently the requested percentage of available data over the selected period is 75% as
defined in the AQD 2008. For other variables than discussed in the AQD the same
percentage threshold applies. Statistics for a single station are only produced in DELTA
when data availability of paired modelled and observed data is at least of 75% for the
time period considered. When time averaging operations are performed the same
availability criteria of 75% applies. For example daily averages will be performed only if
data for 18 hours are available. Similarly O3 daily maximum 8-hour means will be
performed only when 6 hourly values are available per set of 8 hours.
Although DELTA focuses mostly on the evaluation of single model results, it allows
analysing multiple model results. This is intended to help in the comparison of the results
from different model versions.
The current statistical diagrams and indicators proposed in DELTA have been selected
based on literature review (see PROCBENCH). Usage of composite diagrams (e.g.
Taylor, Target,…) has been favoured.
Model results are assessed (when possible) with respect to "performance criteria" or
model quality objectives which indicate the level of accuracy considered to be acceptable
for regulatory applications (see METHOD2012 and UNCERT2012 for more details). In
this new version of the DELTA tool uncertainty-based performance criteria have been
inserted for O3, NO2, PM10, PM2.5, WS and TEMP. For the latter two the criteria are
proposed currently for testing purposes only.
Both meteorological (scalars only) and air quality data can be handled by DELTA.
Benchmarking is included in the DELTA software to allow the production of model
performance summary reports by the users (see Concepts Section 5). For this
benchmarking DELTA focuses on the evaluation of modelling applications related to the
AQD. Pollutants and temporal scales are therefore those relevant to the AQD, i.e. O3,
PM10, PM2.5 and NO2 data covering an entire calendar year.
The structure of the software is schematically presented in Figure 1. There are four main
Input module – refers to air quality and meteorological data, both from modelling and
monitoring, prepared in a specific format. Instructions on how to prepare these input
files are given in the User"s Guide;
Configuration module - includes configuration files, which link the input to the
desired statistical elaboration. One of these files is the startup.ini file (to be prepared
by the user) which contains details on the monitoring stations and measured variables
(see User"s Guide). Other important configuration files, embedded in the tool are the
performance and goal criteria file which lists the performance criteria used in
DELTA for the different species and the myDeltaInput file which facilitates the
management of multiple datasets;
Analysis module – is the core of the DELTA where different statistical indicators and
diagrams are produced. This module can be operated in two modes – exploration and
Output module – includes the results of the selected statistical elaborations (graphics
or statistics values). For the benchmarking mode this output follows a predefined
template, not modifiable by the user (see Concepts Section 5).
Figure 1. Structure of the DELTA software
Within the analysis two main modes exist: exploration and benchmarking mode. They are
described in the next sections.
This mode allows the user to analyse different statistical metrics and diagrams, using various
time intervals, various stations, various parameters (meteorological variables or pollutants)
from one or more models. Different types of analysis can be performed:
o Temporal analysis can be performed with different options (running averages, daily
min/max/mean, selection of seasons, week days/ week-end, and daylight/ night time
o Spatial analysis can be performed in two ways: (1) indirectly: based on the
classification of the monitoring stations in different geographical entities (different
colors are then used for each defined geographical entity) or (2) directly: by using the
Google Earth (or GeoMap) option, a functionality which permits to visualise a
statistical parameter at each station as a point on a 2D map
o Multidimensional analysis can be performed. Dimensions here refer to monitoring
parameters, models, scenarios and stations. One or more elements for each of those
dimensions can be chosen and overlaid on a single diagram.
This mode allows to produce summary reports containing performance criteria for different
statistical indicators related to a given model application in the frame of the AQD. The
reports are obtained through an automatic procedure and follow a pre-defined template
structured around core indicators and diagrams (see Concepts Section 5). Some bounds for
specific statistical indicators (performance criteria and model quality objective) are included,
aiming to help in the assessment of the model performance.
Contrary to the exploration mode described above, the freedom left to the user in
benchmarking mode is minimal, i.e. DELTA automatically produces the performance report.
The template for reporting model performances is application specific (assessment or
planning). In the current prototype version only assessment templates are considered and
have been prepared for O3, NO2 and PM10. In terms of diagrams and indicators, the template
is independent of spatial scale and pollutant but performance criteria and model quality
objectives (see next Section) can be pollutant and/or scale specific. Note that specific
templates are proposed for models delivering annual averages only.
4. Model Quality Objective and Performance criteria
The main statistical indicators referred to in the follow-up analysis are:
Mean Bias
MB  M  O
RMSE 
Correlation Coefficient
R   Mi  M
 M
 Oi 
i 1
 O  O
Centred Root Mean
Square error
CRMSE 
M
 M
 
 M  Oi  O
  O  O

Normalised mean
NMSD   M   O  O
Table 1: List of the main statistical indices related to the MQO
Model Quality Objective (MQO)
As described in METHOD2012 and UNCERT2012 the Model Quality Objective (MQO)
used to test model results for a given application is defined as:
RMSE
MQO 

2 RM SU 2
 O  M 
U
1
where RMSU is the quadratic mean of the expanded measurement uncertainty U. With this
formulation for the MQO the error between observed and modelled values (numerator) is
compared to the absolute measured uncertainty (denominator).
1) MQO ≤ 0.5. In this case the RMSE between observed and modeled values is less than
the observation uncertainty. Model results are in average within the range of the
observation uncertainty for that station and it is meaningless to further improve model
performances.
2) 0.5 < MQO ≤ 1. In this case the RMSE between observed and modeled values is in
average larger than the range of observation uncertainty but the model still is in the
fulfillment zone.
3) MQO > 1. In this case differences between observations and model results become
significant and the model is not fulfilling the criteria.
This approach is flexible as it allows introducing more detailed information on observation
uncertainty as they become available. Such an analysis is proposed in UNCERT2012 and is
briefly summarized in Concepts Section 4.4 below. Note also that the MQO threshold
remains always unity regardless of the pollutant or scale considered. Details on these
interpretations are available in METHOD2012.
For annual average values, the MQO expressed in (1) reduces to
 1 (2)
2U(O)
Performance criteria for Bias, R and SD
As described in METHOD2012 the equation relating some statistical indicators among
themselves:
σ  σO   2σOσ M 1 - R 
RMSE2
 M
2RMSU  2RMSU  2RMSU 2 2RMSU 2
can be used to derive performance criteria for 3 other indicators: R, NMB NMSD as follows:
Performance criteria
2 RMSU


 RMSU
R  1  2
 O
 M   O  2RMSU
Table 2: Model performance criteria for Bias, Correlation and standard deviation
It is important to note that the performance criteria for R, NMB and NMSD do not represent
sufficient conditions to ensure that the MQO is fulfilled. They are used here to indicate
which aspects of the modeling application need to be improved. Indicative values for these
indicators as a function of geographic area or station type are provided in METHOD2012.
Since the performance criteria for R, NMSD and NMB are station and time dependent
(through σO and the mean concentration), normalized criteria can also be defined from
Equation (4), (5) and (6) as follows:
1  R  02
M O
Table 3: Normalized model performance criteria for Bias, Correlation and standard deviation
One of the main advantages of this approach is to provide a selection of statistical indicators
with a consistent set of performance criteria based on one single input: the measurement
uncertainty U. The main RMSE-based performance criteria (i.e. the MQO) provides a general
overview of the model performances while the associated Performance criteria for
correlation, standard deviation and Bias can be used to highlight which of the model
performances aspects need to be improved.
In previous DELTA versions (up to 3.4) only conditions (7), (8) and (9) have been used for
visualization purposes. From version 3.5 onwards we differentiate different zones based on
the following criteria:
2 RMSU 2
0.5 
 0.5
 M   O 2  1
   O 2  1
0.5  M
2RMSU 2
 M   O 2  0.5
 O2 (1  R)
  (1  R)
0.5  M O
 M  O (1  R)
MQO
MQO  1
MQO  0.5
Table 4: Criteria used to differentiate the different zones (orange, green and dashed lines limits) in the
MQO and MPC diagrams
Zone 1: This is the fulfillment zone (green in diagrams). For the bias, correlation and
standard deviation the criteria is calculated by assuming a perfect behavior for the two other
statistical indexes (e.g. the criteria for bias is built on the assumption that R=1 and  M   O ).
For yearly averaged values the bias criteria becomes the MQO.
Zone 2: This zone (orange in diagrams) is built from Equation (11) and checks which of the
three error types is dominating (i.e. which term in Equation (3) could be larger than 0.5).
This zone still indicates fulfillment of the MQO but the error is dominated by this particular
indicator.
Zone 3: The error between modeled and observed values lies within the measurement
uncertainty range. This zone is indicated by a dashed line within the green shaded area
Performance criteria for high percentile values
The model quality objective described above provides insight on the quality of the model
average performances but does not inform on the model capability to reproduce extreme
events (e.g. exceedances). For this purpose a specific MQO indicator is proposed as:
MQOperc 
M perc  O perc
2U (O perc )
where "perc" is a selected percentile value and Mperc and Operc are the modelled and observed
values corresponding to the selected percentile. The denominator is directly given as a
function of the measurement uncertainty characterizing the Operc value. The default percentile
value is currently set to 95% excepted for hourly NO2 which is automatically set to 99.8%
(19th occurrence in 8760 hours), for the 8h daily maximum O3 set to 92.9% (26th occurrence
in 365 days) and for daily PM10 and PM25 both set to 90.1% (36th occurrence in 365 days).
Note that this indicator is only used in the summary report.
An expression for the measurement uncertainty
In equation (1), (2) and (13) the observation uncertainty (normalization factor) appears either
as a quadratic mean RMSU, U( ̅ ) or Uperc, respectively. The derivation of these uncertainty
expressions, allowing for a simple implementation and calculation in DELTA is detailed in
UNCERT2012 and only the final formulations are provided here. The uncertainty of a single
observation value is expressed as:
U (O)  kurRV (1   )O 2    RV 2
represents the estimated relative measurement uncertainty around a reference
value (RV) for a reference time averaging, e.g. the daily/hourly Limit Values of the
AQD.
α is the fraction of the uncertainty around the reference value (RV) which is nonproportional to the concentration level.
k is the coverage factor. Each value of k gives a particular confidence level that the
true value lies within the interval of confidence consisting in Oi ± U. Most commonly,
the expanded uncertainty is scaled by using the coverage factor k = 2, to give a level
of confidence of approximately 95 percents. Levels of confidence of 90% and 99%
would lead to coverage factors around k=1.40 and k=2.6, respectively. More details
are provided in Section 4.6 regarding the link between the confidence levels
associated to the measurements and model results on one hand and the confidence
level associated to the model-to-measure differences as used in the MQO (see Eq. 1).
From Equation (14) it is possible to derive an expression for RMSU as:
RMSU  kurRV (1   )(O   02 )    RV 2
where  0 is the standard deviation of the measured time series.
For model producing annual averages, the uncertainty is expressed as:

U O  kurRV
(1   ) 2
  RV 2
(1   ) 2   RV 2

O 
N *p
N np
where Np and Nnp are used for annual averages only and account for the compensation of
errors (and therefore a smaller uncertainty) due to random noise and other factors like
periodic re-calibration of the instruments. As seen in equation (16) the standard deviation
term is assumed to be linearly related to the observed mean value in the annual average
formulation. The calculation of the Np coefficient accounts for the correction resulting from
this assumption (see Method 2012 and UNCERT2012 (working document) for more details).
For the percentile uncertainty used in equation ( MQOperc 
(13),
equation (14) is used with O=Operc.
The following values are currently proposed (UNCERT2012). Note that the value of alpha
for PM2.5 referred to in the UNCERT2012 – working note has been arbitrarily modified
from 0.018 to 0.050 to avoid larger uncertainties for PM10 than PM2.5 in the lowest range of
concentrations.
WS (test)
TEMP (test)
200 ug/m3
50 ug/m3
Table 5: List of the parameters used to calculate the uncertainty (see formulas (15) and (16))
The values reported in this table can be used to produce uncertainty curves for each
compound (see figures below). Parameters for other species than those mentioned in the
above table can be implemented easily in DELTA (see next Concepts Section 6.1 for more
details)
Figure 2: Relative uncertainties for daily averaged
PM10 and PM2.5, hourly NO2 and 8h daily
maximum O3 as a function of concentration.
Figure 3: Absolute uncertainties for daily PM10
and PM2.5, hourly NO2 and 8h daily maximum O3
as a function of the concentration.
Figure 4: Absolute uncertainties for hourly wind
speed and temperature.
Figure 5: Relative uncertainties for yearly PM10,
PM2.5 and NO2 as a function of the concentration.
Figure 6: Absolute uncertainties for yearly PM10,
PM2.5 and NO2 as a function of the concentration
Figure 7: Comparison of daily averaged PM10
uncertainty for a single value and for an entire time
series. [comparison of formulations (14) referred
to as (O) in figure) and (15) referred to as (O and
SD) in figure)]
In Figure 7 we compare formulations (14) and (15) for a single value and a time series
respectively. For visualisation purposes we assume that a linear relationship holds between
the squared mean and the sum of the squared mean and variance of the observations, i.e.
O   02   O . Based on 2009 observations, the following values of  have been used (0.3
for NO2, 0.6 for PM10 and PM2.5 and 0.1 for O3). Note that this linear simplification is done
for visualization purposes only whereas the full formulation (Eq. 15) is implemented in
DELTA.
An alternative formulation for the observation uncertainty
The uncertainty formulation (14) requires two parameters to be defined: the proportionality
coefficient  and the relative expanded uncertainty U RV
 kurRV , around an arbitrarily fixed
reference concentration (RV). Because the first of these two parameters is not always
straightforward to establish, we propose here an alternative formulation.
Equation (14) can be re-written as a linear relationship between the squared uncertainty (U2)
and the squared concentration (O2):
U U
 (1   )O
   RV
    U 
 U RV
 
 RV
 (1   )O 2
But this linear relationship can also be simply expressed as:
U   U 
 U  
RV  L
where L is a low range concentration value (i..e close to zero) and UL its associated absolute
expanded uncertainty. Comparing the two formulations we get:
 UL
   RV
U
U   U 
 
 1    RV 2  L2
The two above relations (17) and (18) allow switching easily from one formulation to the
other. The first formulation requires defining values for both  and U RV
 kurRV , around an
arbitrarily fixed reference value (RV), while the second formulation requires defining
uncertainties around two arbitrarily fixed concentrations (RV and L). An equivalent to Table
5 for the second methodology is provided below with L=0.
15.1 ug/m3
9 ug/m3
9.6 ug/m3
1.87 ug/m3
A practical example of introduction of the uncertainty parameters using the second
methodology is provided in User"s Guide section 6.1
The 90% principle
For all statistical indicators used in DELTA for benchmarking purposes the approach
currently used in the AQD has been followed. This means that the model quality objective
must be fulfilled for at least 90% of the available stations. Given the integer nature of the
station number this criteria sometimes means a larger than 90% of the available stations to
fulfil the criteria. For example all stations will need to fulfil the criteria if the number of
stations is lower than 10. This point is also relevant when considering group of stations (see
User"s Guide Section 5.1) when the 90% option is selected; the number of stations which can
be discarded and the effective percentage of stations kept within a given group depends on
the number of stations composing this group.
5. Benchmarking report
These reports are currently available for the hourly NO2, the 8h daily maximum O3 and daily
PM10 and PM2.5.
Hourly frequency
Target Diagram (Fig9 Upper diagram)
The MQO as described by Equation (1) is used as main indicator. In the normalised Target
diagram, it represents the distance between the origin and a given station point. As mentioned
above the performance criterion for the target indicator is set to unity (circle limit) regardless
of spatial scale and pollutant and it is expected to be fulfilled by at least 90% of the
available stations. The normalised bias (first term on the right hand side of Equation [3]) is
use for the vertical axis while the centred root mean square error (CRMSE) (sum of the two
last terms on the right hand side of Equation [3]) ius used to define the X axis.
The percentage of stations fulfilling the target criterion is indicated in the upper left corner
and is meant to be used as the main indicator in the benchmarking procedure. As mentioned
above, values higher than 90% must be reached. The uncertainty parameters ( U rRV ,  , and RV
) used to produce the diagram are listed on the top right-hand side.
The four quadrants in the Target diagram correspond to the following conditions, all based on
Equation (3):
Condition I
σ M  σ O 2 2σ O σ M 1 - R 
2RMSU 2 2RMSU 2 2RMSU 2
NMB 2

σ M  σ O 2 2σ O σ M 1 - R  NMB 2
2RMS U 2
2RMS U 2 2RMS U 2
Condition II
NMB  0
Top quadrant
NMB  0
Bottom quadrant

Right quadrant
Left quadrant
The equation used to distinguish the right (SD) from the left quadrants (R) (condition II) can
be rewritten as:
σ M  σ O 2
2σ O σ M 1 - R 

σ o2
2σ M 1 - R 
NMSD 2
 R 1
σO
2(1  NMSD)
Or in graphical terms:
Figure 8: Split between R- and SD-dominated errors in the Target diagram. (R,SD) indices couples falling
in the blue shaded area will be located on the left quadrant, others on the right quadrant.
It is straightforward from this diagram to identify which couples of SD and R indices will
lead the station to be within the left or right quadrants
In addition the Target diagram also allows distinguishing easily the performances for single
stations or group of stations (e.g. different geographical regions in this example) by the use of
different symbols and/or colours.
More details on this adapted Target diagram can be found in METHOD2012.
Summary Report (Fig.9 Lower diagram)
The summary statistics table provides information on model performances. It is meant as a
complementary source of information to the MQO (upper diagram) to identify model
strengths and weaknesses. The summary report is structured as follows:
o ROWS 1-2 (OBS) provide the measured observed means and number of exceedances for
the selected stations. In benchmarking mode, the threshold values for calculating the
exceedances are set automatically to 50, 120 and 200 for the daily PM10, the hourly NO2
and the 8h daily O3 maximum, respectively. For other variables (PM25, WS…) for which
no threshold exists, the value is set to 1000 so that no exceedance will be shown.
o ROWS 3-6 (TIME) provide an overview of the temporal statistics for bias, correlation
and standard deviation as well as information on the ability of the model to capture the
highest range of concentration values. Each point represents a specific station. Values for
these four parameters are estimated via equations (7), (8), (9) and (13) respectively. The
green shaded area represents criteria fulfilment. The orange shaded area (for the three first
indicators) represents fulfilment but the error associated to the particular statistical
indicator is dominant (see Concepts Section 4.2 and Table 4 in particular for more
details). Note again that fulfilment of the bias, correlation, standard deviation and
high percentile related indicators does not guarantee that the overall MQO based on
RMSE is fulfilled.
o ROWS 7-8 (SPACE) provide an overview of spatial statistics for correlation and standard
deviation. Average values over the selected time period are first calculated for each
station and these values are then used to compute the spatial correlation and standard
deviation. Fulfilment of the performance criteria (8) and (9) is then checked for these
values. As a result only one point representing the spatial correlation of all selected
stations is plotted. Colour shading follows the same rules as for rows 3-5.
Note that for indicators in rows 3 to 8, values beyond the proposed scale will be represented
by the station symbol being plotted in the middle of the dashed zone on the right/left side of
the proposed scale
For all indicators, the third column provides information on the number of stations fulfilling
the performance criteria (green beyond 90% of the stations fulfilling, red below 90%).
Figure 9: Example of benchmarking performance summary report
Yearly frequency
Scatter Diagram (Fig.10 Upper diagram)
The MQO described in Concepts Section 4.1 for yearly averaged results (i.e. based on the
bias) is used as main indicator. In the scatter plot, it is used to represent the distance from the
1:1 line. As mentioned above it is expected to be fulfilled by at least 90% of the available
stations. The uncertainty parameters ( U rRV ,  , RV, N p and N np ) used to produce the diagram
are listed on the top right-hand side
The Scatter diagram also provides information on performances for single stations or group
of stations (e.g. different geographical regions in this example below) by the use of symbols
and colours.
More details on the scatter diagram and possible options can be found in METHOD2012.
Summary Report (Fig.10 Lower diagram)
complementary source of information to the bias-based MQO to identify model strengths
and weaknesses. It is structured as follows:
o ROW 1 (OBS) provides the measured observed means for the selected stations.
o ROW 2 (TIME) provides information on the fulfilment of the bias-based MQO for each
selected stations. Note that this information is redundant with the scatter diagram but kept
if the summary report is used independently from the scatter diagram.
deviation. Annual values are used to calculate the spatial correlation and standard
deviation. Criteria (8) and (9) are here used to check fulfilment of the performance
criteria. The same explanation for the green and orange shaded areas as for the hourly
report holds.
The third column provides information on the number of stations fulfilling the performance
criteria, Green beyond 90% of the stations fulfilling and red below.
Figure 10: Example of benchmarking performance summary report
Cuvelier C., P. Thunis, R. Vautard, M. Amann, B. Bessagnet, M. Bedogni, R. Berkowicz, J. Brandt,
F. Brocheton, P. Builtjes, C. Carnavale, A. Coppalle, B. Denby, J. Douros, A. Graf, O. Hellmuth, A.
Hodzic, C. Honoré, J. Jonson, A. Kerschbaumer, et al., 2007: CityDelta: A model intercomparison
study to explore the impact of emission reductions in European cities in 2010. Atmospheric
Environment, Volume 41, Issue 1, Pages 189-207
Thunis P., L. Rouil, C. Cuvelier, R. Stern, A. Kerschbaumer, B. Bessagnet, M. Schaap, P. Builtjes, L.
Tarrason, J. Douros, N. Moussiopoulos, G. Pirovano, M. Bedogni, 2007, Analysis of model responses
to emission-reduction scenarios within the CityDelta project, Atmospheric Environment, Volume 41,
Issue 1, January 2007, Pages 208-220
Thunis P., E. Georgieva, S. Galmarini, 2010: A procedure for air quality models benchmarking.
(http://fairmode.ew.eea.europa.eu/fol568175/work-groups)
P. Thunis, A. Pederzoli, D. Pernigotti, 2010: Performance criteria to evaluate air quality modeling
applications, Atmospheric Environment, Volume 59, November 2012, Pages 476-482
Thunis P., D. Pernigotti and M. Gerboles, 2012: Model quality objectives based on measurement
uncertainty: Part 1: Ozone. 2012 Atmospheric Environment, Volume 79, November 2013, Pages 861868
Pernigotti D., P. Thunis, M. Gerboles and C. Belis.2012. Model quality objectives based on
measurement uncertainty: Part II: PM10 and NO2. Atmospheric Environment, Volume 79, November
1. What's new
From version 4.0 to 5.0
An installer is now provided for DELTA under Windows environment. No prior installation of
the IDL virtual machine is any more requested. A demo dataset is provided within this
installer. See installation instruction in the next section.
A Linux version is available for download. See installation instruction in the next section.
The utility function "Data-Check Integrity Tool" is automatically run by default with new
datasets to check the consistency of the input data files. This run is performed once only.
Note that at first application, this function will also convert automatically the observation
data from csv to cdf format to speed-up future use with DELTA.
Modelling data entered in "csv" format can be converted to "cdf" format through a
convertion functionality incorporated in the DELTA tool (old csv2cdf).
Percentiles value for O3 and PM10/PM2.5 used to calculate the high percentile indicator
included in the summary report have been set to correct values (from 90.4 to 92.9% for O3
and from 93.1 to 90.1% for PM2.5 and PM10). Values have been corrected in the first part of
this guide accordingly.
Paths to existing applications (Word, Excel, Google Earth…) need to be set in the init.ini file
in the resource directory. This operation can now be done automatically through the "Find
external application paths" in the help menu. Note that this operation requires a substantial
amount of time but will be performed once only.
Some minor bugs in the formula of Tables 2, 3 and 4 in the first Section have been corrected.
For yearly models:
o The mouse recognize functionality has been re-activated for the summary report
(bug fix)
o Monitoring data can be formatted in one single "csv" file
Inclusion of a new diagram "geomap" for hourly/daily model results.
The X-axis of the target diagram is positive in both directions.
Uncertainty parameters are now indicated on the target diagram and on the scatter
Addition of new MQO for PM2.5, WS and TEMP. Parameters for the PM2.5 MQO
have been revised to avoid uncertainties smaller than PM10 in the lower
concentrations range.
Update of uncertainty parameters for NO2 and PM10 (yearly and hourly)
Inclusion of the myDeltaInput option to facilitate the management of multiple
datasets. Note that DELTA can run in absence of this new input file.
Inclusion of MQO for SO4, NH4, NO3, EC and TOM for testing purposes.
Uncertainty parameters are available in the "goalscriteria_oc.dat" configuration file.
Correction of geomap SD and R error symbol types: switch to be consistent with
Correction of the counting of valid station in the yearly scatter diagram
Modifications of the hourly/daily summary report: the RDE indicator has been
suppressed and substituted by a threshold indicator
The bug in the summary report (calculation of the spatial correlation and spatial
standard deviation – no point appearing) has been fixed.
The legend of the summary report has been re-designed
Modification of the yearly summary report: RDE has been dropped.
Correction of Target diagram: SD and R related errors were assigned the wrong side
of the diagram (left vs. right)
Uncertainty values for PM10 TEOM and beta-ray measurement techniques have been
included in the "goalsandcriteria_oc" configuration file. See here for more details.
Addition of a "save main statistical indices" option. This option runs automatically
when the summary report diagram is selected. See here for more details.
Correction: The generation of performance reports in pdf format did not work
properly in version 3.6.
The MQO for 3h average NO2 has been removed
2. Installation and running steps
The current version of the Delta Tool is available for Windows and Linux environments.
Installation and running steps under WINDOWS
Download and run the setup.exe file available on the Delta web page. This will create
a "Delta Tool" icon on the desktop as well a "JRC_DELTA" menu in the Windows
start menu (lower left icon on your desktop). You can launch the application by
double-clicking on the icon.
After the first installation the software is configured to operate with a demo dataset. If
you wish to re-use data you produced with an earlier version of the software, please
follow the below steps:
o Access the $home$ directory through the JRC_DELTA menu.
o Create a sub-directory under data/monitoring, e.g. "Mydata" (parallel to demo)
and include in it your monitoring data.
o Create a sub-directory under data/modeling, e.g. "Mydata" (parallel to demo)
and include in it your modeling data
o Include your startup.ini file and rename it into startup_MyData.ini in the
resource sub-directory
o Adapt the names and paths in the MyDeltaInput file (change demo into
Mydata). The MyDeltaInput is placed on the resource subdirectory but is also
accessible through the start menu.
o Re-start the Delta application
A "JRC-DELTA" program item in the start menu gives you access to 1) the home
installation directory, 2) the MyDeltaInput configuration file, 3) the user"s guide and
4) the web-site.
Installation and running steps under LINUX
Download and unzip the setup_linux.tar file available on the Delta web page in a new
directory (e.g. DeltaTool). This will create a DeltaTool.exe as well as a sub-directory
structure (resource, configuration, data...) You can then launch the application by
running the DeltaTool executable.
Mydata). The MyDeltaInput is placed in the resource subdirectory.
o Paths will need to be updated in the init.ini file (under the resource subdirectory) to allow some external applications to run (Word, pdf reader…).
The user"s guide is available in the help sub-directory
3. Preparation of input files
In order to run the Tool, the following files have to be prepared by the user
 The configuration file <startup.ini>. This file is located in folder ...\resource. For
handling different data (obs – mod) sets, see Users Guide Section 6.4
 Files with observed data (one file for each monitoring station). These files should be
in "csv" or "cdf" format and be placed in folder ...\data\monitoring
 Files with modeled data at the locations of the stations (one file per model and
scenario). Such files should be in "csv" or "cdf" format. If only "csv" files are
available, DELTA will automatically create a "cdf" version at first use. Each .cdf file
may contain model results for several locations (stations). The .cdf files should be
placed in folder ...\data\modeling. If results from more than one model are used, the
utility to create cdf files from csv files should be used (available from help menu, see
Section 9.2).
 The file "MyDeltaInput" in the resource directory should then be adapted to the paths
and file names selected by the user.
Init.ini
The resource folder contains an ASCII file named init.ini where specific software (WORD,
ADOBE...) location information should be provided. The user should modify the paths
according to his personal installation settings. This is needed, e.g, to be able to use the help in
the Delta Tool. The right hand side of the following lines (end of the init.ini file) should be
adapted. This updating operation can be done manually or automatically through the help
menu ("find external application paths"). Note that this operation might require a
substantial amount of time but will be performed once only on a given computer.
BROWSER_LOCATION=C:\Program Files\Mozilla Firefox\firefox.exe
WORKSHEET_LOCATION=C:\Program Files\Microsoft Office\OFFICE11\EXCEL.EXE
DOCUMENTSREADER_LOCATION=C:\Program Files\Microsoft Office\OFFICE11\WINWORD.EXE
NOTEPAD_LOCATION=notepad.exe
PDFREADER_LOCATION=C:\Program Files\Adobe\Acrobat 7.0\Acrobat\Acrobat.exe
GOOGLEEARTH_LOCATION=C:\Program Files\Google\Google Earth\client\googleearth.exe
Startup.ini
The configuration file (startup.ini) is common to both inputs with hourly and yearly
frequencies. It is located in ...\resource. The file is in ASCII format and contains some
general information about the spatial scale, the parameters selected for evaluation and the
characteristics of the monitoring stations. The file has three main sections:
MODEL – includes information about the year, spatial scale and input frequency.
PARAMETERS - includes variable names and measurement units
MONITORING – includes list of all stations with their siting characteristics and
parameters measured.
The following conventions apply:
 Each blank row or each line beginning with "[", ";" or "#" will be discarded
 No blanks between fields are permitted
 Line breaks are not allowed.
 The three section headers: "[MODEL]", "[PARAMETERS]" and "[MONITORING]"
are compulsory,
 Station codes and abbreviation codes must be unique.
 The station names should not include blanks and special characters such as "."," " ",
";","-"
 Only the symbol "_" is allowed.
 Variables must be separated by an asterisk.
 The station names must be EXACTLY (including case sensitivity) the same used in
the observation data files and modeled data files.
;Year
;frequency
;Scale
[PARAMETERS]
;Species;type;measure unit
SO2;POL;gm-3
NO2;POL; gm-3
PM25;POL; gm-3
WS;MET; m/s
TEMP;MET; C
[MONITORING]
Stat_Code;Stat_Name;Stat_Abbreviation;Altitude;Lon;Lat;GMTlag;Region;Stat_Type;Area_Type;Siting; listOfvariables
IT00000;station0;STAT0;681.;8.931;44.31;GMT+1;Lombardia;Background;Urban;Plane;TEMP*PM10*O3;
IT00001;station1;STAT1;962.;10.03;44.97;GMT+1;Veneto;Traffic;SubUrban;Hilly;TEMP*O3;
IT00002;station2;STAT2;851.;11.34;44.18;GMT+1;Piemonte;traffic;urban;Mountain;WS*PM10*O3*SO2;
IT00003;station3;STAT3;806.;7.597;46.02;GMT+1;Emilia-Romagna;Industrial;Rural;Valley;WS;
IT00004;station4;STAT4;769.;8.222;44.29;GMT+1;Lombardia;Background;Urban;Plane;TEMP*O3;
IT00005;station5;STAT5;163.;9.193;45.85;GMT+1;Friuli Venezia Giulia;Unknown;Unknown;Coastal;PM10;
<EOF>
[MODEL] section:
The first three lines are just comments
Year: year of interest
Frequency (lowercase): Either hour or year. This parameter should be set to "hour"
for models delivering outputs with an hourly or daily frequency and set to "year" for
models delivering outputs as annual averages (see User"s Guide Section 3.5).
Scale (lowercase): Either local (traffic), urban or regional. But not used currently
[PARAMETERS] section:
The first line is a comment which gives a hint of the contents of the following lines:
Species: name of the variable (lower or upper case but should be consistent with
observation and modeling files)
Type: "POL" and "MET" indicate air quality and meteorological variables
respectively. These categories are created to facilitate filtering during the selection
phase and can be defined by the user at his convenience.
Measure units: the units MUST be μgm-3 for concentrations. For the other variables,
see the notes below.
Each line contains the name of a parameter, the type and the measurement unit,
separated by semicolons. The parameters are those available in the observation
dataset. It is permitted to have lines with parameters not present in the dataset. The
sequence of parameters is irrelevant.
Some parameter names and units are pre-assigned and should be obligatory followed
(since they are used in the benchmarking procedure): O3 [μgm-3], NO2 [μgm-3],
PM10 [μgm-3], WS [ms-1] (wind speed), TEMP [degC] - temperature, SH [g/kg]
(specific humidity)
[MONITORING] section
The first row contains the labels. The labels currently referred to as: region, station type, area
type and siting can be modified by the user and will appear as modified in the data selection
window. Each subsequent row refers to a given station, where:
Stat_Code: national identification of the station e.g. AT0001ST, or VEN00356, or
user"s assigned code (e.g. STAT001)
Stat_Name (case sensitive): combination of letters and/or numbers ; only the symbol
"_" is allowed blanks and special characters are not allowed
Stat_Abbreviation: station name abbreviation (4 letters). The abbreviation will be the
one identifying the station on the DELTA output graphs and statistics
Altitude: height above sea level (in meters)
Lon, Lat: Longitude and Latitude (in decimal degrees)
GMTlag: Time zone (currently not used)
Region: Name of the administrative region to which the station belongs. In alternative
– a user defined region (Naming rules similar to "Stat_Name")
Stat_Type: background, traffic, industrial
Area_Type: urban, suburban, rural
Siting: Categories are proposed: mountain, hilly, plane, valley or coastal. They will be
used eventually to group stations and calculate average statistics for each group; If
other categories suit better user"s stations, they can be defined here.
listOfvariables..: The variables measured at each station , (PM10, O3, WS etc). The
variables are separated by an asterisk.
Note: It is left to the user to assign appropriate fields to classify stations. In our example,
REGION, STAT_type, Area_Type and Siting are selected but other choices could have
been made. These choices will configure the widget menus to help with the selection of
stations according to the chosen fields.
Observation file
3.3.1. Hourly Frequency
Monitoring stations to be used with the Tool may have either air quality data, either
meteorological data or both.
Files names and type:
Each station must have an associated file containing the data in comma separated
format and with extension .csv, e.g. "station1.csv"
The file names should be consistent (including case sensitivity) with the naming rules
used in the configuration file (startup.ini).
Files location:
….\data\monitoring
Files structure:
The first row must contain the labels of the columns: year (4 digits), month (1-12), hour
(0-23) and the names of the observed parameters at each station. Following lines should
include the observed values on an hourly basis (8760 rows (or 8784 for leap year) if entire
year is available). If for a given hour data are missing for all parameters, the line can be
omitted. Data are recognized by their associated date and time.
Example: filename <station1.csv>
year;month;day;hour;O3;PM10;WS;WD;TEMP;
2005;1;1;0;40.1;55.4;0.75;310;15.6;
2005;1;1;1; 40.1;55.4;0.75;310;15.6;
2005;12;31;23; 40.1;55.4;0.75;310;15.6;
Particular requirements:
The station names used in startup.ini must be used for each one of these files.
For non-annual average values each file must contain observation values on an hourly
basis. For leap years, data for February 29th may be included in the files.
 Data will be read by dates. Missing dates (i.e. lines) will automatically be treated by
DELTA as -999.
 If data are monitored on a daily basis (e.g PM10), please put the daily value at all
hours from 0 to 23 for this day.
Remark: Daily deposition observations (for example rain) should be distributed over the
24 hours of the particular day.
 If both air quality and meteorological measurements are available for the same site,
the data must be included in the same file (as in the example above)
 Each blank row or beginning with "[", ";" or "#" will be discarded
 Spaces are not permitted between the fields.
 The semi-column ending each lines is not mandatory
cdf format
The "cdf" format is identical to the one specified for modeling result data (option 2). If
provided as "csv", the conversion from to "cdf" will be performed automatically when
running DELTA if your set of data is new. If not done automatically, you can always perform
this operation by running the "check integrity tool" available under the help menu.
3.3.2. Yearly Frequency
Option 1: Each station monitoring data is assigned a specific file
The file names should be consistent with the naming rules used in the configuration
file <startup.ini> (see Section 3.2).
The first row must start with the label "YearlyAvg" to indicate that yearly averaged
results are used. Should follow the year (4 digits) and species of interest. All fields should
be separated by semi-column. Row 2 should contain the numeric observed values for the
parameters mentioned in row 1.
YearlyAvg;2009;PM10;NO2;WS
; parameter1;parameter2;parameter3
55.1;15.6; 2.1
If data are missing the gaps should be filled by -999.
If both air quality and meteorological measurements are available for the same site,
Each blank row or beginning with "[", ";" or "#" will be discarded
Spaces are not permitted between the fields.
Line breaks are not allowed.
Option 2: All station monitoring data are assigned to a single file
This "csv" format should be identical to the one specified for yearly modeling result data.
Important: the name of the file is mandatory: "OBS_Yearly.csv"
Modeled data can be prepared in one of the following formats:
netcdf (option 1) format (one single file for a given model and time period. A ncdfvariable must be generated for each station/species combination
netcdf (option 2) format (one single file for a given model and time period. A ncdfvariable must be generated for each station.
csv format (similar to the one described for the observations). Such files can then be
converted to "cdf" format through the conversion utility functionality available under
the help menu.
Description of the netcdf (option 1) format
One single netcdf file should be provided for a given model. It must contain a time
series for each station and variable listed in <startup.ini >.
The names of the parameters should be the same as in the configuration file
<startup.ini>.
The units in the netcdf file should be the same as specified in startup.ini
….\data\modeling
Each data block inside the netCDF file should be named as "StatName_Parameter" (see
examples below) where "StatName" is the name of the station corresponding to the one set
in the <startup.ini >, and "Parameter" refers to the modeled pollutants and meteorological
variables, as indicated in the <startup.ini >
Each data block should contain either (a) 1 year of hourly data for each station and parameter
(1dimensional array with 8760 [8784 for leap years] hourly data) or (b) a specific time period
smaller than the entire year. In the latter case an additional attribute should be included in the
netCDF file to set the initial starting time (in hours) as follows (global attributes: StartHour =
1320 indicating that the period of interest starts at hour=1320). Within the specific time
period data should be continuous, i.e. include missing values as "-999".
In the case of a leap year model results for February 29th should be included (or set to -999)
if the period contains this day.
Modeled data at a given station may contain either air quality fields, meteorological fields or
Example: <2008_WRFCHIM_TIME.cdf>
netcdf 2008_WRFCHIM_TIME.cdf {
T = 8760 ;
float station0_CO2(T);
float station1_NO2(T);
float station1_WS(T);
float station1_WD(T);
Example: <2008_WRFCHIM_TIME.cdf> with time period less than entire year
T = 744 ;
global attributes:
StartHour = 1320s ;
Particular requirements: If a parameter is entirely missing (i.e. not provided by the model)
for a station, but the same parameter is present in the monitoring dataset for the same station,
the user must include that parameter in the *.cdf file as an hourly series of "-999".
One single netcdf file should be provided for a given model. For each station it must
contain a time series for each variable listed in the <startup.ini> file.
All parameters (i.e. variables, e.g. NO2, PM10...) should be defined in byte format in
a systematic order defined in a global attribute.
<startup.ini> (see Section 2.2).
in the <startup.ini >
(2 dimensional array with 8760 [or 8784 for leap years] hourly data). Or (b) a specific time
period smaller than the entire year. In the latter case an additional attribute should be included
in the netCDF file to set the initial starting time (in hours) as follows (global attributes:
StartHour = 1320 indicating that the period of interest starts at hour=1320). Within the
specific time period data should be continuous, i.e. include missing values as "-999".
Modelled data at a given station may contain either air quality fields, meteorological fields or
Example: <2008_CHIM_TIME.cdf>
netcdf 2008_CHIM_TIME.cdf {
V = 3 ;
float station0 (T,V);
// global attributes :
: Parameters = 78b, 79b, 50b, 32b, 80b, 77b, 49b, 48b, 32b, 79b, 51b ;
Here '78b, 79b, 50b, 32b, 80b, 77b, 49b, 48b, 32b, 79b, 51b' is the byte
format of 'NO2 PM10 O3'.
Example: <2008_CHIM_TIME.cdf> with given time period (less than entire year)
Modeled data should be prepared in ASCII (csv) format. One single file should be provided
for a given model. It must contain annual average values for each station listed in
<YEAR_MODELNAME_TIME.csv>
Files location: .\data\modeling
YearlyAvg;2009;O3;PM10...
;Station;ValueParam1;ValueParam2...
Illmitz;40.3;45.34
Pillers;78;54.54
Using DELTA with yearly output
By default the input files are configured for hourly frequency models but for models
delivering annual averages it is possible to tune all configuration files to keep only relevant
diagrams and elaborations within the selection menus (e.g. all diagrams using correlation will
be discarded). For doing this, go in your startup.ini file and set the frequency parameter to
4. Delta Tool top menu
When starting Delta Tool the upper right-hand corner contains a menu that allows you, e.g. to
run a Benchmark, and to save and retrieve selections you have made.
o Save image: Save main window diagram in various format (jpeg, tif...).
Images are saved in the subdirectory "save"
o Exit
Benchmark (see Section 4)
o Assessment
 daily 8h maximum O3
 Daily averaged PM10
 Hourly NO2
 Yearly PM10
 Yearly NO2
o Planning (not available yet)
o Select mode (inactive)
o Hide/Show Recognize Info: Mouse recognize window is turned on/off
o Select data: Opens the "Data selection" window (similar to "data selection").
o Save data: Save current "data selection"
o Restore data: Restore "data selection" from existing ones.
o Select Analysis: Opens the "Analysis" window (similar to "Analysis").
o Save Analysis: Save current analysis choices.
o Restore Analysis: Restore "analysis" from existing ones
o Help file: Open the current DELTA version User"s guide (pdf format). The
correct directory in which "acrobat.exe" is located should be specified in the
"init.ini" file in the "resource" directory (but this can be performed
automatically – see option below).
o Data check Integrity Tool: Open an independent window with the Check-IO
processor to check consistency of the input data (see User"s Guide Section
o Delta WWW: Open the DELTA WWW homepage. The correct directory in
which the browser executable is located should be specified in the "init.ini"
file in the "resource" directory.
o About: Version information
o Find external application paths: Automatically update the paths to external
applications (Word, Excel…). This operation might require a substantial
amount of time but only needs to be performed once.
o Licence: End user licence agreement
5. Exploration mode
In order to calculate a given statistical indicator and visualize it by a diagram the user has
first to make selections in two interface windows – "data selection" and "analysis window"
(activated through the starting window, see User"s Guide Section 5). The data selection and
analysis interfaces are described in User"s Guide Sections 5.1 and 5.2 respectively. Finally
the main DELTA graphical interface, which reflects the options previously selected by the
user in the two interface windows, is described in User"s Guide Section 5.3.
Figure 11 The DELTA main interface (starting window)
The data selection interface
A selection has to be made by the user in terms of
o a model/scenario (year) pair
o a parameter (e.g. NO2)
o a monitoring station
An example is given in Figure 12.
In brief, the selections are made in the following way:
 Model selection: In the left pane select one or more models + scenarios.
 Parameter selection: In the right pane first select Type, then Parameters (you may
select several elements by Ctrl + Click).
 Station selection: The panels Region, Station Type, Area Type and Siting indicate
some filters, which may help you in selection of stations. Apply relevant filters, so the
panel Available becomes populated with some stations. Use Ctrl + Click on those you
wish to select. Finally, click the Add button to make the selection effective.
 Optionally save: You may save the list of stations by clicking the "Save Obs" button
(the "Load Obs" button allows you to retrieve a previously saved list).
Some more details follow.
Various filters are available to facilitate the selection of the appropriate monitoring stations in
terms of regions, types. These filters are defined in the configuration file <startup.ini>, where
the user can make the station classification categories case specific.
Note: When a user selects a parameter (e.g. O3) in the "data selection" window, all stations
measuring that parameter automatically appear in the "available" section. The user can then
make his selection among these available stations and add them in the "selected" section. At
this stage the user can still change his mind and select another parameter (e.g. PM10). The
list of selected stations will be updated after warning the user.
The user has the possibility to save his choices and to reload them at a later time. Two
modalities exist which can be useful to avoid repeating frequently used selections.
Modality (1): In order to save the selections in the data selection window, choose "save data"
from the top "data selection" pop up menu. A new window appears with the request to put a
file name. File extension must be *.elb. By default the file is saved in the dir…. \save. To
reload the saved selections, -choose "restore data" from the top "data selection" pop up menu.
Modality (2): In order to save the station selection only, press the button "save obs" in the
lower right corner of the data selection window. A new window appears with the request to
put a file name. File extension will be *.obl. By default the file is saved in the dir…. \save. To
reload the saved selections, -press the button "Load Obs".
A set of stations can either be treated as a number of single entities or as a group. In the case
of groups the user will be asked to select between "mean" and "90% percentile" options. In
the first case the mean of the stations statistical indicators will be represented as a single
dot/symbol in the diagram whereas in the second option the worst statistical indicator among
90% of the available stations (rejecting 10%) is selected. This latter choice must be used
with diagrams in which performance criteria are present and indicate whether this
criterion is fulfilled for the selected group of stations.
Figure 12: DELTA data selection interface.
The analysis interface
The analysis interface (Figure 13) allows the user to select the type of statistics and diagram,
as well as the desired temporal operations to be performed on the original data ("Time Avg"
and "Daily Stats"). Available diagrams are described in the Diagram overview Section (Part
III).
Each of these plot types can be selected to illustrate different statistical metrics (statistics
column). This is especially true for the barplots which is the common way to visualise single
statistical metrics (Mean, RMSE, bias, IOA, Exceedance days...). Some of these statistics
require threshold values which can be included (e.g. SOMO, Exceedance days…) on the
same window. The field for threshold values should contain numbers separated by an #.
Figure 13: DELTA analysis selection interface
The lower left part of the analysis selection interface ("multiple choice info") gives
information on the different possibilities offered to the user in terms of combination of
parameters, stations, and models to generate the diagram. These possibilities give the degree
of freedom in selecting items of the four main entities: scenario (year); model; parameter;
monitoring stations. The allowed multiple choices for a given diagram are pre-defined in the
tool and are described in the Diagram overview Section.
On the right side of the analysis selection interface, time operations can be chosen to be
performed on the selected modelled-observed data pairs, i.e.:
Time Avg.: Time series kept as originally formatted (preserve or 1h) or 8h running
Daily Stats: Statistical operation applied for each day: mean, max or min.
Season: choice between summer, winter and entire year
Day: Selection between night time hours, daylight hours, entire 24h day, week-ends
and week days.
Note that for some statistics and pollutant choices, these flags will be automatically filled to
the adequate values.
This feature can be useful if you repeatedly use the same set of selections. In order to save the
selections in the analysis window, use the top menu in Delta Tool: click the item "Analysis"
and choose "Save Analysis" in the drop-down menu. A new window appears with the request
to put a file name. File extension must be *.elb. By default the file is saved in the dir…. \save.
To reload the saved selections, click the item "Analysis" on the top menu and choose
"Restore Analysis".
The main graphical interface
When the user has made his selections in the data selection window and the analysis window
the "Execute" tab can be pressed. The Delta Tool"s main graphical interface will then pop-up
(unless you have made selections that the tool does not support).
The screen is divided into two main areas:
The left side recapitulates the choices made by the user in the previous interfaces which
lead to the generation of a given diagram.
The right side hosts the diagram and accompanying legend (which also summarizes the
options selected by the user). Only one diagram is shown at a time (i.e. no multiple
windows).
Figure 14: DELTA main graphical window.
6. DELTA functionalities and user's tuning options
6.1. "Playing" with uncertainty parameters: the "goals_criteria_oc"
input file
In the configuration file "goals_criteria_oc.dat" the user can find lines of the type:
3;PM10;ALL;OU;PMEAN;28*0.018*40*1*50*;Descr of: GC 56
Lines with "OU" as fourth parameter contain all information required to calculate the value
of the observation uncertainty used to derive the model quality objectives for one particular
species and time average. The numbers separated by asterisks can be modified by the user to
test alternative uncertainty estimates. By order, these numbers represent (see equation 16 for
details):
 kurRV (28 in our example) expressed in percentage. This is the expanded
relative uncertainty ( U rRV  kurRV )
 (0.018 in our example)
Np and Nnp (40 and 1 in our example)
RL the reference value (50 in our example)
Testing different PM10 measurement uncertainties
Different experimental methodologies exist to measure PM, each characterized by a different
level of uncertainty. The user can test the impact of switching between different uncertainty
estimates, corresponding to values representative of the gravimetric (default reference), betaray and teom techniques. In the "goals_criteria_oc.dat" configuration file, the following lines
are visible:
26;PM10g;ALL;OU;PMEAN;28*0.018*40*1*50
27;PM10t;ALL;OU;PMEAN;38*0.027*40*1*50
28;PM10b;ALL;OU;PMEAN;38*0.009*40*1*50
To switch among the different PM uncertainty estimates it is necessary to copy the number in
between asterisks for the selected uncertainty estimate (PM10g for gravimetric, PM10t for
TEOM and PM10b for beta-ray) and use them in the PM10 line used by DELTA.
Introducing uncertainties for new variables
Two methodologies have been described in Sections 4.4 and 4.5. In the first case the user
knows all required parameters and can introduce a new line at the end of the
"goals_criteria_oc.dat" file as follows:
35;NEWSPEC;ALL;OU;PMEAN;28*0.018*40*1*50*;Descr of: GC 56
The first field is an index number which should increment the index of the previous line by 1.
The second field should contain the name of the new species. Note that the same name should
be kept in all monitoring, modeling and startup files for consistency. The third and fourth
fields should be kept as is (i.e. ALL;OU). The fifth field (PMEAN) is a contraction of the
time average (Preserve, 8H running) and daily stat (Preserve, MEAN, MAX and MIN)
options. The fifth field should concatenate the bold identified letters of the two options
according to the choices made. For example O3 8h daily max would lead to "8HMAX" for
the fifth field while daily PM10 would lead to "PMEAN". The following five fields have
been described above while the last field is a comment to be used at convenience by the user.
In section 4.5 an alternative methodology is described to set these uncertainty parameters. We
provide here an example: assuming that we can estimate that the PM10 uncertainties around 0
and 50 ug/m3 would be 1.88 and 14 ug/m3, respectively. We can calculate easily the
following numbers needed within DELTA.
U rRV 
 0.28 which if k  2 implies that urRV  0.14
 1.88 
 
  0.018
 14 
Saving summary statistics information in ASCII
Delta saves by default all information contained in each diagram in ASCII format by
generating a file named "dumpFile.txt" which can be found in the dump directory. This file
will be overwritten at each new generated diagram
Frequently used statistical indicators (e.g. correlation, bias…) can be automatically created by
selecting the "summary report & print" analysis diagram option. DELTA will then generate
the summary report in the graphical window but also create an ASCII file in the dump
directory using the selected model and species to create the name.
Note that both options will create information relative to the selected time period.
Mouse-driven recognize functionality
For most graphics a small additional window appears with the title "Info on data points
(click!)". This allows the user to click on the diagram and retrieve information about the
quantitative values of the different points/stations represented. This feature can be switched
on and off through the top menu in Delta Tool: click the item "Mode" and choose
"Hide/Show Recognizer info".
Managing multiple datasets: the "MyDeltaInput" option
As mentioned before there are three different types of input information required to run the
DELTA tool:
 The startup.ini file located in the ./resource directory
 The YEAR_MODEL_TIME.cdf file located in the ./data/modeling directory
 The station.csv files located in the ./data/monitoring directory
When dealing with different statup.ini files, different sets of modelling results, and different
sets of monitoring stations, it may be useful to be able to overwrite the defaults locations of
these three inputs.
The file MyDeltaInput.dat in the resource directory contains three lines:
1 - First line contains the name of the startup.ini file in the ./resource directory
2 - Second line contains the location of the model data in the ./data directory
3 - Third line contains the location of the monitoring data in the ./data directory
startupED2007.ini
modelling_ED2007
monitoring_ED2007
; the startup.ini file
; subdirectory in ,/data
; subdirectory in ./data
The default (if the MyDeltaInput.dat file is not found) is:
After completion of "Execute" the name of the used startup.ini file, the modelling data
location, and the monitoring data location are mentioned beneath the plot.
Note that DELTA can run with/without this new configuration input file.
7. Benchmarking mode
From the Delta Tool top menu select the item "Benchmarking" (this is only possible when
the "Data Selection" window and the "Analysis" window are closed).
At present the automatic production of performance reports is available for the following
pollutant concentrations and time frequencies:
daily maximum 8h mean O3
Daily averaged PM10
Hourly NO2
Yearly PM10
Yearly NO2
Reports are produced similar to those presented in Section 5 of the "Concepts" part of this
Currently the performance report is produced automatically for one single model. This single
model is selected by default and corresponds to the top-of-the-list model when opening the
data-selection interface. For applying the procedure to other models the user is requested to
play with the model data files in the ../ data/modeling directory and leave in this directory
only the model on which the performance report should be produced.
8. Demo Dataset: Po-Valley
This dataset contains the results from a model inter-comparison exercise performed by two
air quality models for year 2005. The model domain covers the Po Valley (Italy) with at 6 x6
km2 resolution (95x65 cells) grid. Pollutant concentrations have been simulated by 5
transport chemical (CHIMERE, TCAM, CAMX, RCG, MINNI ) of which two are made
available in this demonstration dataset.
Observations from 63 monitoring sites located in the Po Valley are also provided. Sites have
been classified in regions and station types (suburban, urban and rural).
9. Utility programs
Data-Check Integrity Tool
Checking the consistency among the modeling results file (cdf, csv), the observation files
(csv, cdf) and the main configuration file (startup.ini).
Important: The data check integrity step will be performed automatically each time a new
startup.ini is used. If modifications are made in the monitoring or modelling data without
affecting the startup.ini file, please perform this data-check integrity manually to identify
potential consistency issues.
From the Delta Tool top menu select the item "Help" > " Data-Check Integrity Tool" (this
is only possible when the "Data Selection" window and the "Analysis" window are closed).
There are currently 19 steps included in this consistency check:
Checking existence of relevant directories
Checking existence of "startup.ini" file
Checking existence of appropriate sections within startup.ini
Checking correctness of "PARAMETERS" section within Startup.ini
Checking correctness of "MONITORING" section within Startup.ini
Checking for possible redundancy in station names in startup.ini
Checking consistency of the number of stations between startup.ini and obs files
Checking consistency of the station names between startup.ini and obs files
Checking consistency of the species names between startup.ini and obs files
Checking number of lines in obs files
Conversion of observations files from csv to cdf
Checking for extreme values in obs files
Checking for zero values (information check only)
Checking for existence of model file
Checking for correct attribute in model netCdf file
Checking correctness of time dimension in model file
Checking model extreme values
18) Checking consistency between species available in startup.ini and model file
19) Producing statistic report
Check_IO produces a log report, as well as a summary report with details concerning the
various checks. The reports are located in the main directory where Delta Tool is installed.
Interactive format conversion tool
This tool available under the help menu allows to produce a model file in "cdf" format from
files in csv format similar to the one described for monitoring data. This program works only
for data produced with an hourly frequency. If more than one model is used, this conversion
operation will need to be performed for all models. The user needs to specify the input
directory (where the csv are located), the output directory (where the cdf file will be created)
and the startup.ini file which is used as reference.
Important: User"s modifications in the widget input lines will only be considered if you type
the "return" key after changes.
Preproc-CDF
The Deltapreprocessor is an idl-based tool for the extraction of time series at observational
locations from meteorological or air quality model output for use in the DELTA Tool. Input
to the PreProcessor is the configuration file 'startup.ini' containing the variables
(meteorological variables , and pollutants) to be treated, as well as geographical information
about the observational stations. Model output (i.e. input to the pre-processor) should be in
netCDF format with all the variables defined on longitude-latitude coordinates at ground
level and hourly frequency. Three interpolation techniques are available for producing the
modeled time series at the observational stations:
(i) NN (Nearest Neighbour) where the values at a station are taken from the nearest lon-lat
grid point.
(ii) BIL (Bilinear) where a bilinear interpolation is performed on the grid cell in which the
station is located; for this the gridcell is first transformed into a square using a bilinear
mapping.
(iii) DW (Distance Weighted) where a weighted mean value is calculation in the station gridcell. The weights are the inverse of the distance from the station to the 4 gridpoints.
Output of the PreProcessor is written to a netCDF file.
During the PreProcessing a number of checks are performed to guarantee the conformity with
the DELTA Tool conventions.
In this section all diagrams/elaborations available in DELTA are detailed according to the
following template:
TEMPLATE: Diagram name (Elaboration
numbers (internal
use)
Time Avg
options in the
Required or
Brief description of diagram
DIAGRAM example
If Model quality objective and/or model performance criteria are available for the
diagram/elaboration a description of the criteria used is provided here. The list of
parameters together with specific requirements (time averaging…) is also provided. If
groups are allowed to be selected, this will as well be mentioned.
OBS MOD PAR SCEN
Possible options in terms of multiple
choices. A cross in OBS means that
more than one station can be selected
(similar for MOD, PAR and SCEN). If
other options are available these will be
mentioned under the "other" column.
Foe example, O-M means that more
than one model and more than one
station can be selected at the same time.
Can single
stations be
selected?
Can station
groups be
BARPLOT (Mean, Stddev, Exc. Days)
Limit Value
for Exc. days
Bar-plots with observed values represented with grey filled bars. Modeled values are
indicated with colored circles. In case of multiple choices involving three entities
(e.g. models, observations and parameters), multiple bars will appear.
P-M
S-O
P-M-O
P-S-O
M-S-O
BARPLOT (Spatial Correlation)
For all stations included in a selected group (Note that this diagram only works with
groups!) a time average value is calculated for the time period selected. All observed
and modeled values within a given group are then correlated to each other to provide
a single value per selected group of stations.
OBS MOD PAR SCEN Other
2, 3, 4, 7, 8, 23, 30,
N|A
For all stations the selected characteristic is plotted as a colored circle. In case of
multiple choice involving two entities (e.g. models and observations) two colored
circles will appear while for multiple choice involving three entities (e.g. models,
parameters and observations) multiple bars will appear.
BARPLOT (R, Mbias, RMSE, IOA, RDE, NMB,
RPE, FAC2, NMSD)
BARPLOT (CUMUL)
Offset value
For all stations the cumulative total of the selected variable is plotted. This option is
useful for deposition variables like rain or deposited pollutants. The total is the sum
of the hourly values over the selected period of time, In case of multiple choice
involving two entities (e.g. models and observations) two colored circles will appear
while for multiple choice involving three entities (e.g. models, parameters and
observations) multiple bars will appear.
Take offset value equal to 0.
Example gives quantity of rain at some Spanish stations in 1000*mm/m2 over the
period 25/02/2009 – 26/03/2009
SCATTER (Mean mod vs. mean obs)
Mean Model
The scatter diagram plots mean modeled values against mean measurements. If only
one model, one parameter and one scenario selected, different symbols and colors are
used to represent the different stations. Otherwise dots are used for each station (or
group of stations) with colors depending on scenario, model or parameter (see
example).
Dashed and solid lines indicate NMB/2RMSU ratios of 0.5 and 1, respectively. The
orange region (only for hourly/daily) delimited by ratio sqrt(0.5)=0.71 (see Concepts
equations (10 to (12))
MQO are valid for the following parameters/ time statistic choices
MOD PAR SCEN Other
O-M
SCATTER (One station – All time values)
Model values
The scatter diagram plots all time modeled values against measurements for one single
station/group selected. If a group is selected, the average of all station values at each
time are averaged and plotted against the corresponding modeled value.
Plot of the time series for observations and model results at 1 station for 1 or more
models and scenarios. The 75% minimum coverage of the data is not taken into
account here, all values are plotted.
TARGET (8H Max, Daily, Hourly)
CRMSE /
2RMSU
BIAS / 2RMSU
Parameters NO2, O3,
See MQO
The Target diagram plots for each station the normalized CRMSE against the
normalized BIAS. The distance from the origin represents the normalized RMSE.
The screen is divided into four areas distinguishing the main source of error type for
each station (Negative and positive bias, correlation (R), and standard deviation
(SD). Different symbols and colors are used to represent the different stations.
The CRMSE related error is examined to see whether it is dominated by R or by SD.
The following ratio is used to assign a given station either to the left or right side of
the target diagram (see METHOD2012 for more details)
 M   O 
 1  SD dominates on R (right)


2 M O (1  R)   1  R dominates on SD (left) 
Dashed and solid lines indicate RMSE/2RMSU ratios of 0.5 and 1, respectively. The
number of stations fulfilling RMSE/2RMSU < 1 is indicated in the upper left part of
the diagram and should be larger than 90%. The Target diagram is only available
with associated MQO (see below). This diagram is not available for yearly average
values. More details can be found in Concepts Section 5.1.
Only 90%
SUMMARY REPORT (8H Max, Daily, Hourly)
Parameters O3,
PM10,NO2
exceedance
The summary report is available for both hourly and yearly frequency (in this case a
simplified version is produced). All details can be found in Section 5.1and 5.2
For details on how green and orange areas are defined, see Section 4.2.
MPC correlation (8H Max, Daily, Hourly)
RMSU/σO
O3, PM10,
This diagram plots correlation as function of the quadratic mean of the uncertainty
divided by the station observed standard deviation. It provides for each station
(represented by a symbol) an indication of whether the time correlation fulfills a
minimum level of quality (green/orange area)
For details on how green and orange areas are defined, see Concepts equations (10 to
MPC std. Dev. (8H Max, Daily, Hourly)
NMSD
Parameters O3, PM10,
This diagram plots NMSD as function of the quadratic mean of the uncertainty
(represented by a symbol) an indication of whether the normalized standard
deviation (NMSD) fulfills a minimum level of quality.
σM/σO
Parameters FREE
The Taylor diagram provides for each station an indication on Correlation (angular
distance), standard deviation (model standard deviation larger than observed one
when the station symbol lies beyond the dashed line) and CRMSE (distance between
the station symbol and the black asterisk on the X axis.
Q-Q plot (One station All values)
Modelled
Same as scatter but both observed and modeled values are independently sorted.
Dynamic evaluation (Day-Night)
Observed day/
night difference
Modeled day/
Scatter plots of modeled vs. observed day-night mean differences. Well behaving
results should lie along the 1 to 1 line. Lower right and upper left part of the graphs
indicate poor results.
Dynamic evaluation (Summer-Winter)
Time Avg.
Dynamic evaluation (Weekdays – Weekends)
weekdays/
GeoMap (Target)
Parameters O3, NO2,
GeoMap map showing the locations of the selected stations, together with the Target
value for O3 (8hr mean), hourly NO2, daily PM10, hourly WS and TEMP. Colors
indicate whether or not the uncertainty criterium is satisfied yes or no; and if not
satisfied the location in the target (Bias >0, Bias <0, Correlation dominated or
NMSD dominated
For details, see Section 4.2.
58,59,60,61,62,
63, 64,67,68,69
eters
in Analysis
Google Earth map showing the locations of the selected stations, as well as all
requested observational and model results in pop-up windows. The location of the GE
application must be inserted in the init.ini file in the resource directory
Google Earth (Mean, Exc. Days, Bias, NMB, Std.
Dev, R, RMSE, RDE, σM/σO, NMSD)
Delta 2000 re-emerging in community
news INSIDE >> Monday, February 11, 2013
news INSIDE >> Wednesday, April 25, 2012
Report 1 - Protezione Civile
Jurnal lttlanaiemen |(eperatruatan
sheriff`s activity report, page 9 classifieds, page 12
Validation report for the 2010 Air Quality Assessment Report
Delts RISE to the challenge
Quantification of population exposure to NO2, PM2.5 and PM10 and
ORGANSKA SNOV V PRSTI NA OBMOČJU CELJSKE KOTLINE
June 2015 - NWA History Centre
The Art of the WordPress Startup
senarai kuantiti pengurusan keselamatan dan kesihatan pekerjaan
Executive summary - Ajuntament de Girona
content - Intellectual Property Corporation of Malaysia
2010 Medical Options
Dirty Little Secrets of the Record Business
THE ED MIRVISH THEATRE BETWEEN: Mirvish Productions
Still Not the News
JSSH Vol. 10 (2) Sep. 2002 - Pertanika Journal


text:
Q: How to disable swiping fancybox slides by mousemove I'm using fancybox 3. I need to turn off swiping fancybox slides by mousemove. I would like to leave only control buttons next\prev.
How can I do that?
Thanks.

A: Simply set touch:false to disable touch events.

A: The previous answer did not work for me on Fancybox 3.5.*. I had to move the touch option to inside the opts object, like as follows:
$.fancybox.open({
    src: yoursource,
    opts: {
        touch: false
    }
});

A: So, the full answer will be:
Using data-options attribute
Click me

Or when initializing fancybox
$('selector').fancybox({
    touch: false
});

From @Janis answer here: https://github.com/fancyapps/fancybox/issues/1277

redpajama_set_name: RedPajamaStackExchange


This was one of the most incredibly surreal shoots that we have done. From the moment we got the call to shoot the film we went from our office in Leeds, to the Gipsy Kings' private rehearsal in a remote country ranch outside of Arles, France in the space of 24 hours. The next evening we were on stage shooting their 25th anniversary gig in a huge Roman amphitheatre to a capacity crowd. Like the music itself, it was a truly amazing free flowing improvising experience.
Go to The Gipsy Kings' Website for more music.


text:
Éliane Comelade, dite Éliane Thibaut-Comelade, née le  à Rigarda (Pyrénées-Orientales) et morte le  à Perpignan, est une historienne de la gastronomie, autrice de livres de cuisine et journaliste française, experte de la cuisine catalane et de l'hygiène alimentaire. Elle est également professeur en sciences de l'alimentation. Son ouvrage majeur est La cuisine catalane, régulièrement réédité depuis 1978.

Biographie
Éliane Comelade est la fille d'instituteurs originaires du Conflent.

En février 1939 à Argelès lors de la Retirada, elle découvre l'horreur des camps de réfugiés républicains survivants dans un extrême dénuement. Pendant la guerre, ses parents l'envoient à Murat (dans le Cantal) dont la déportation d'une partie de la population en juin 1944 la marque aussi terriblement.

Après son baccalauréat à Perpignan, elle continue ses études à Toulouse puis à Paris. Elle sera professeur d'enseignement ménager en lycée technique à Montpellier jusqu'au milieu des années 1970. En parallèle, elle écrit un ouvrage scolaire sur les techniques alimentaires, ce sera les prémices de sa carrière littéraire.

Enfin, la rencontre décisive avec l'historien , politique catalan en exil, l'amène à la culture catalane. Elle rédige son premier livre en catalan en 1968, Cuina rossellonesa i de la Costa Brava, avec une préface de Jordi Pere Cerdà et publié à Barcelone chez l'éditeur Barcino. C'est le début d'un long travail de récupération et de divulgation de la cuisine traditionnelle roussillonnaise et catalane. Le peintre languedocien Pierre Fournel illustre nombre de ses ouvrages. Elle collabore aussi avec de nombreux restaurateurs.

Dans les années 68-70 elle rencontre Lluís Llach, l'écrivain Pere Verdaguer, la chanteuse Teresa Rebull, qui représentent une culture catalane vivante et dynamique. Elle commence un travail de recherches sur la période médiévale, aux archives de Montpellier et Barcelone, qui lui permettent d'écrire une série d'ouvrages sur la cuisine catalane du Moyen Âge.

De retour à Perpignan, là encore une rencontre sera déterminante pour elle : Manuel Vasquez Montalbán. Éliane Comelade crée en 1985 les Ateliers départementaux de cuisine catalane à Ille-sur-Têt où des chefs tels Patricia Gomez, Laurent Del Prat, viennent se former. Elle poursuit son inventaire du patrimoine culinaire du pays catalan.

Vie privée
Éliane Comelade est la mère du pianiste auteur compositeur Pascal Comelade.

Distinctions

Publications
 .
 .
 .
 .
 .
 .
 .
 Technologie et hygiène alimentaire :  cahier, les nutriments, éd. Lanore, 1987
 .
La table médiévale des catalans, 1995
 Ma cuisine catalane au fil des saisons, éd. Edisud, 1998
La cuisine du foie gras, 2001
Les salades de la Méditerranée, 2002

Les saisons de la cuisine, 2009
 La Cuisine catalane (2 volumes), rééd. Lanore-Delagrave, 2010
Terrines, pâtés et croustades, 2011
Recettes Catalanes de ma grand-mère, 2011
 La cuisine des Pyrénées catalanes : Cerdagne, Capcir, Andorre, éd. Nouvelles Presses du Languedoc, 2013
 Le riz en pays catalan, le riz à la paella ou en cassola, éd. Sud Ouest, 2014
 La cuisine catalane : 300 recettes d'hier et d'aujourd'hui, éd. Trabucaire, 2015
 Ma cuisine catalane, illustrations Cécile Colombo, éd. Edisud, 2015
 Almanach du Catalan 2017, coauteur Gérard Bardon, éd. CPE, 2016
 Petit traité savant de l'artichaut, éd. Equinoxe, 2016

Notes et références

Liens externes

Naissance en avril 1928
Naissance dans les Pyrénées-Orientales
Journaliste française du XXe siècle
Personnalité catalane de la gastronomie
Gastronomie dans les Pyrénées-Orientales
Gastronome français
Lauréat de la Creu de Sant Jordi 2009
Décès à 92 ans
Décès à Perpignan
Décès en avril 2021

redpajama_set_name: RedPajamaWikipedia


When people ask about your business there's usually a mix of positive and neutral feelings. They may be impressed, sometimes they're asking to be polite, and other times they're just making conversation.
Mention that you run your business out of your home and that mixed bag of feelings can turn negative. Some may believe you're going to fail. The assuption masquerades in a number of different ways: that you're not serious, you're inexperienced, or you simply don't have what it takes.
Many home-based businesses fail, but it's not because of the usual problems (e.g. sales ability, time, dedication, money, etc.). These problems don't make things easier, but they're not the business killers entrepreneurs have made them out to be. Here's some of the main reasons behind businesses' failure.
Generating sales leads is a struggle for most home-based businesses. It's common for the well to run dry after family, friends and connections have been used up. Some entrepreneurs can afford to set and maintain an advertising budget but most can't – at least, not at first. Many of them are told they'll have to do sales and marketing on their own. The part that's left out? The learning curve is steep, the cost is high and the skills needed are difficult to master.
Most businesses run out of money before they've achieved the sales and traction their business needs to survive. It's the chicken and the egg problem. You need money to advertise, but you can't do that without sales (or can you?).
When it comes to generating a massive amount of leads, money is optional. Entrepreneurs tend to rely on the usual tactics (advertising, content, social media, etc.) they're time consuming, but they work. Most entrepreneurs don't know how to go about using non-traditional channels to attract customers without spending their money. Work to identify methods (like joint venture partnerships or the dragnet method) that give you the amount of leads you're looking for without having to invest your own money upfront. A systematic approach can generate lots of leads – especially if you're short on cash.
Most entrepreneurs understand the importance of marketing. What they usually miss is the importance of having a marketing system. They work hard to make the phone ring and calls start coming in. They're suddenly flooded with more customers than they can handle. They close as many as they can, ignore the rest and get to work. Eventually, demand comes to a screeching halt.
At that point, they're out of leads and they're struggling. So when a new customer comes in they do whatever it takes to close the sale – which usually means deep discounts, poor terms, and abuse. Their cash flow drops like a rock.
Create a marketing system that removes you from the day-to-day equation. Instead of chasing after each and every lead, create content, tools, and resources that draws customers in to you. These items act as lead magnets and they give you leverage. So even if you stop marketing, the pieces you've already created continue to work hard for you, generating leads day in, day out.
The right marketing system draws customers in, but it also nurtures the relationship. It continues to help customers solve their problems. How do you know you've got the right marketing system? When it turns you and your team into order takers.
If there's one thing entrepreneurs are afraid of it's losing the business. No one wants to lose a customer or the opportunity to make a sale. So what do we do as entrepreneurs? We censor ourselves. We keep things quiet, and we avoid conflict. If it's going to create a problem we avoid it. The fear is, if you rock the boat customers will leave. But interestingly enough, this "yes man" behavior causes customers to leave. Customers don't want a yes man. They want someone that's going to "tell them the kind truth." Someone who's willing to ask dumb questions and look out for them.
A large part of the concern stems from a fear of selling. Many entrepreneurs would sign-up for a root canal before volunteering to sell. But there's a simple and easy method you can use immediately to eliminate this fear. Stop selling. Consult with customers instead; customers don't want to hear another sales pitch and they're not looking for a product to buy. They want a solution to their problem and an advocate. Be willing to walk away if it's not a great fit (for you or them). Be willing to give away small things in order to make the big things (e.g. a long term relationship) work. If you've cultivated your lead sources and created a marketing system, giving or walking away shouldn't be a problem. Drop the pitch, focus on serving customers and solving their problems.
unprepared for. When these entrepreneurs run into a problem they haven't seen before, many of them fall apart. Without a support system in place, there's no one to help them overcome. So they panic, making their problems worse.
When I tell entrepreneurs to create a support system they almost inevitably respond with "How? I don't know anyone." First things first. Most people have friends of friends, some kind of indirect connection they reach out to, so it's a good idea to start there. If you're one of those rare people who don't have any connections to speak of start with the Ben Franklin effect.
Ask for the help and favor you need. Use the Ben Franklin effect to surround yourself with people that can help you when you need it. Do the same for them whenever you can.
Some home-based business don't go through this. A few of them have the resources and know-how they need from the start to avoid these problems. But most don't. You could say these problems are inevitable for those that don't. But it doesn't have to be that way. Using the tips I've shared here, you can find, fix or change the potential points of failure you come across in your business. Follow the strategies and tips I've outlined here and create your own success story.


The award winning "Queen Of Nollywood Tourism" Omolara stews her media page with new logo designed for her beauty fashion world.
The Queen signals more of what beauty entails with her condensed company handworks. Omolara have been making beauty proud stylishly for far better days back and still she slacks not but giving it brighter.
Omolara Fashion World now displays wider with fashionable wears that can wipe out lifetime boredom.
See What She Says On Her Media Page.
"#OMOLARA: Means a child is everything. Seemingly unemotional, Omolara is calm and reserved, haughty and detached. Most certainly, she is sober and very deep, but also shy, honest, and of impeccable morality. Patient, secure, disciplined, determined and level-headed, Omolara is nonetheless apprehensive and uncertain. She can be quite introverted. She isn´t afraid of solitude and prefers to maintain few friendships than several less authentic relationships. Far from being dissipated or superficial, she is only really interested by that which is profound and genuine and you will enjoy every moment you spend with her, not particularly spontaneous or demonstrative, she can be over-sensitive and emotional although she conceals this part of her nature behind a thick layer of ice. Her incredible self-control could easily be mistaken for indifference, and she often has the impression that she is misunderstood. She is humble and doesn´t like to show her vulnerability, which she perceives as a sign of weakness. Likewise, she would rather try and manage for herself what she has at hand. She loves nature, likes simplicity and can´t stand insincerity, deceit or pretence. Omolara is a woman with principles, creativity and understanding.
#FASHION: Means a popular or latest style of clothing, hair, decoration or behavior. It is in the sky, in the street and has to do with ideas, the way we live and what's happening.
I officially bring you OmolaraFashionWorld new logo. …. Looking good means feeling good!


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Q: tensorflow: ValueError: Shape must be rank 2 but is rank 4 for 'MatMul_1' I met with an error in the following code, when I'm trying to multiply a dense matrix with a sparse matrix.
import tensorflow as tf
import numpy as np

A = tf.sparse_placeholder(tf.float32)
B = tf.sparse_placeholder(tf.float32)

IsFoc = tf.placeholder(tf.float32, [None, 1])
IsFoc_Diag = tf.diag(IsFoc)

IsUnFoc = tf.placeholder(tf.float32, [None, 1])
IsUnFoc_Diag = tf.diag(IsUnFoc)

b = tf.matmul(IsFoc_Diag,IsUnFoc_Diag)
a = tf.matmul(IsFoc_Diag,tf.sparse_tensor_to_dense(A),a_is_sparse=False,b_is_sparse=True)

The error says:
Traceback (most recent call last):
  File "a.py", line 14, in <module>
    a = tf.matmul(IsFoc_Diag,tf.sparse_tensor_to_dense(A),a_is_sparse=False,b_is_sparse=True)
  File "/home/mypath.pfc/anaconda2/lib/python2.7/site-packages/tensorflow/python/ops/math_ops.py", line 1813, in matmul
    name=name)
  File "/home/mypath.pfc/anaconda2/lib/python2.7/site-packages/tensorflow/python/ops/gen_math_ops.py", line 2245, in _sparse_mat_mul
    b_is_sparse=b_is_sparse, name=name)
  File "/home/mypath.pfc/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.py", line 767, in apply_op
    op_def=op_def)
  File "/home/mypath.pfc/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2508, in create_op
    set_shapes_for_outputs(ret)
  File "/home/mypath.pfc/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1873, in set_shapes_for_outputs
    shapes = shape_func(op)
  File "/home/mypath.pfc/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1823, in call_with_requiring
    return call_cpp_shape_fn(op, require_shape_fn=True)
  File "/home/mypath.pfc/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/common_shapes.py", line 610, in call_cpp_shape_fn
    debug_python_shape_fn, require_shape_fn)
  File "/home/mypath.pfc/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/common_shapes.py", line 676, in _call_cpp_shape_fn_impl
    raise ValueError(err.message)
ValueError: Shape must be rank 2 but is rank 4 for 'MatMul_1' (op: 'SparseMatMul') with input shapes: [?,1,?,1], ?.

I know there are similar questions discussing similar errors, however, I cannot get the key reason for this type of error.
The error only happens to the line a = tf.matmul(IsFoc_Diag,tf.sparse_tensor_to_dense(A),a_is_sparse=False,b_is_sparse=True). I hope someone could kindly answer my question.
Thank you very much!


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