Intercomparison of MAX-DOAS vertical profile retrieval algorithms: studies using synthetic data MAX-DOAS vertical profiling comparison Udo Frieß et al.
Frieß, U., Beirle, S., Alvarado Bonilla, L., Bösch, T., Friedrich, M. M., Hendrick, F., Piters, A., Richter, A., van Roozendael, M., Rozanov, V. V., Spinei, E., Tirpitz, J.-L., Vlemmix, T., Wagner, T., and Wang, Y.: Intercomparison of MAX-DOAS vertical profile retrieval algorithms: studies using synthetic data, Atmos. Meas. Tech., 12, 2155-2181, https://doi.org/10.5194/amt-12-2155-2019, 2019.
The planetary boundary layer (PBL) is the part of the atmosphere that is in direct contact with the terrestrial biosphere. Its chemical composition is determined by anthropogenic and natural emissions. Monitoring of both chemical composition and aerosol content of the PBL is crucial for the understanding of the chemical and physical processes and the spatio-temporal evolution of PBL composition. A versatile tool for the monitoring of atmospheric trace gases and aerosol content of the PBL is the well-known multi-axis differential optical absorption spectroscopy (MAX-DOAS) (e.g. Hönninger et al., 2004; Wagner et al., 2004; Heckel et al., 2005; Frieß et al., 2006; Platt and Stutz, 2008; Irie et al., 2008; Clémer et al., 2010; Wagner et al., 2011; Vlemmix et al., 2015b). It relies on the spectral analysis of scattered sunlight and enables the simultaneous detection of numerous trace gases, such as nitrogen dioxide (NO2), formaldehyde (HCHO), nitrous acid (HONO), water vapour (H2O), sulfur dioxide (SO2), ozone (O3), and halogen oxides. Measurements along different lines of sight, with elevation angles (EAs) ranging from near the horizon to the zenith, allow for the reconstruction of vertical profiles of the measured trace gases and, using the oxygen collision complex O4 as a proxy for the light path, also of aerosol extinction. Using suitable inverse models, trace gas and aerosol profiles can be retrieved in the lowermost ≈2 km with a vertical resolution of about 50–100 m at the surface and a lower resolution above. Up to four independent pieces of information can be retrieved.
Algorithms for the retrieval of vertical profiles from MAX-DOAS measurements can be separated into those that retrieve vertical profiles on a finite vertical grid (usually with layers of 50–200 m in thickness) using the optimal estimation method (OEM) (Rodgers, 2000) and parametrised algorithms that use a small number of parameters (typically two to four) to describe the shape of the atmospheric profile. Parametrised algorithms are typically faster than OEM algorithms since they are usually based on precalculated look-up tables (LUTs), while OEM algorithms rely on online radiative transfer modelling (RTM). Being based on Bayesian statistics, OEM algorithms have the advantage of providing a thorough error analysis as well as a quantitative characterisation of the vertical resolution and the information content (Rodgers, 2000). However, the results of OEM algorithms critically depend on the appropriate choice of a priori constraints, which are in many cases difficult to assess. In addition to OEM and parametrised approaches, the present study also includes a fast algorithm developed by NASA, which relies only on geometrical considerations and only invokes radiative transfer modelling for a pure Rayleigh atmosphere.
Testing the performance of algorithms for the retrieval of the atmospheric state using remote-sensing measurements on the basis of synthetic data is a method that has been widely used in the scientific community. In particular, numerous synthetic studies that investigated the performance of MAX-DOAS retrieval algorithms were published in the past (Wagner et al., 2004; Frieß et al., 2006; Hay, 2010; Vlemmix et al., 2011; Yilmaz, 2012; Hartl and Wenig, 2013; Holla, 2013; Zielcke, 2015). This paper presents the first intercomparison of eight state-of-the-art algorithms for the retrieval of vertical profiles of aerosols and trace gases using synthetic MAX-DOAS measurements. Synthetic measurements have the advantage over ambient measurements that the true atmospheric state is exactly known, and thus a quantitative comparison of true and retrieved atmospheric states is straightforward. This study is part of the Fiducial Reference Measurements for Ground-Based DOAS Air-Quality Observations (FRM4DOAS) project funded by the European Space Agency (see http://frm4doas.aeronomie.be, last access: 8 April 2019). One of the main objectives of this project is the development of a community algorithm for a harmonised near-real-time processing of MAX-DOAS data, including spectral analysis as well as the retrieval of tropospheric profiles of aerosols, HCHO and NO2, and stratospheric NO2 profiles as well as total ozone columns. As part of the FRM4DOAS project, the aim of the study presented here has been the selection of suitable algorithms for the retrieval of tropospheric profiles to be integrated in the FRM4DOAS community algorithm.
The paper is structured as follows: Sect. 2 briefly describes the inverse modelling theory and outlines the strategy for the intercomparison of the profile retrieval algorithms. A short description of the individual retrieval algorithms is provided in Sect. 3. The model scenarios and RTM settings are specified in Sect. 4. Slant column densities (SCDs) of NO2, HCHO, and the oxygen collision complex O4, simulated by the different RTMs serving as forward models for the retrieval algorithms, are compared in Sect. 5. Comparisons of the quantities derived by the participating retrieval algorithms are presented in Sect. 6. These include averaging kernels from the OEM algorithms (Sect. 6.2), a posteriori modelled dSCDs (Sect. 6.3), vertical profiles (Sect. 6.4), total columns (Sect. 6.5), and aerosol extinctions and trace gas concentrations near the surface (Sect. 6.6). Finally, the numerical performance of the individual retrieval algorithms is assessed in Sect. 6.7.
with the SCD S being the integrated concentration along the (average) light path through the atmosphere.
Here, ρ is the number density of the trace gas and s parametrises the light path length through the atmosphere. The vector b in Eq. (1) represents additional forward model parameters, which are not to be retrieved, such as atmospheric pressure, temperature, aerosol optical properties, etc. The aim of an inverse model is to provide an estimate of the atmospheric state x for a given measurement y. However, inverse problems are often poorly constrained, and the inverse of the forward model function F−1, for which x=F-1(y) either does not exist or the finite measurement error ϵ leads to unstable estimates of the state vector.
a Optimal estimation. b OEM(log): optimal estimation with logarithmic state vector. c Parametrised retrieval. d Analytical retrieval with RTM only for Rayleigh atmosphere.
OEM algorithms have the advantages that the approach, based on the well-established Bayesian statistics, is mathematically stringent and that important parameters, such as the retrieval covariance matrix (separated into smoothing and noise covariance), AVK, and information content, can be readily derived. Parametrised algorithms are usually faster than OEM algorithms since the small number of parameters allows for the usage of precalculated LUTs, whereas OEM algorithms, with a larger number of state vector elements, usually perform radiative transfer calculations online. The calculation of the Jacobian matrix (weighting function), which is required by OEM algorithms for the minimisation of the cost function, can be quite time consuming, especially for non-linear problems, such as the aerosol profile retrieval. Parametrised algorithms have the disadvantage that the parametrisation limits the possible representations of the state vector to a certain subspace of the state vector space when characterising the state vector with a limited number of parameters. Conversely, OEM algorithms tend to be biased to the a priori, in particular in regions where the sensitivity to the atmospheric state is low.
The overall strategy for the comparison of aerosol and trace gas vertical profiles from MAX-DOAS measurements within this study is depicted in Fig. 1. It consists of the following steps.
The first step of the intercomparison exercise consists of a comparison of the forward models of the individual retrieval algorithms. Simulations of SCDs of HCHO, NO2 and O4 are performed for a variety of atmospheric scenarios and viewing geometries as described in Sect. 4. In the following, these prescribed scenarios are referred to as the true atmospheric state, which is represented by the atmospheric state vector x. This forward model intercomparison exercise allows for a quantitative comparison of the individual RTMs on which the inversions are based (see Sect. 5).
The medians of the ensemble of dSCDs of HCHO, NO2 and O4 from the different forward models derived from step 1 serve as a reference dataset that represents the measurement vectors for the different atmospheric scenarios. This dataset was distributed among the participants of this study and serves as input for the individual retrieval algorithms. This dataset is referred to below as the (synthetic) measurements y.
The comparison of profiles x^ retrieved by each participant on the basis of the reference dataset with the true aerosol and trace gas profiles x allows for a quantitative assessment of the inverse models. The results of this intercomparison will be presented in Sect. 6.
Finally, a comparison of the numerical performance of the individual retrieval algorithms is performed (Sect. 6.7).
Figure 1Flow diagram depicting the strategy for the retrieval algorithm intercomparison.
This section briefly describes the eight MAX-DOAS retrieval algorithms whose main features are listed in Table 1. Five of the algorithms are based on OEM, two on a parametrised approach, and one (NASA) on an analytical approach without radiative transfer modelling. Each of the participants is free to decide on criteria for flagging particular retrieval results as invalid as described in the following subsections. If not stated otherwise, the comparison of retrieved quantities within this study is based on all data, without considering the validity flags.
The Belgian Profile (bePRO) OEM inversion algorithm was developed at the Royal Belgian Institute for Space Aeronomy (BIRA-IASB) by Clémer et al. (2010). The forward model used in bePRO is the linearised discrete ordinate radiative transfer (LIDORT) code (Spurr et al., 2008). LIDORT is a multiple-scattering multilayer discrete ordinate scattering code with a simultaneous linearisation facility for the generation of both radiances and analytical Jacobians (intensity partial derivatives with respect to any atmospheric or surface parameter). It uses the Green's function method for solving the layer radiative transfer equations (RTEs), for both solar and thermal-emission sources. The analytical calculation of the weighting functions allows near-real-time MAX-DOAS profile inversion without the use of precalculated weighting function LUTs. The bePRO algorithm uses a two-step approach. First, the aerosol extinction vertical profiles are retrieved for each MAX-DOAS scan from the corresponding measured O4 dSCDs. Since it is a non-linear inversion problem, the iterative OEM approach is used to minimise the cost function defined in Eq. (4). The standard aerosol retrieval output includes the following parameters: aerosol extinction profiles (per kilometre), aerosol optical depth, AVKs, smoothing and noise measurement error matrices, and O4 dSCDs calculated from the retrieved aerosol extinction profiles using the forward model F. In a second step, trace gas vertical profiles are retrieved using retrieved aerosol extinction profiles as input for the calculation of the corresponding weighting functions. Since trace gases (except ozone) are optically thin absorbers, the OEM equation for the linear case is used (Rodgers, 2000).
The standard retrieval vertical grid used in bePRO is the following: 10 layers of 200 m in thickness starting from the altitude of the station, followed by two layers of 500 m in thickness and one layer of 1 km in thickness. This grid can be modified if needed.
A bePRO retrieval (aerosol or trace gas) is flagged as valid if the three following criteria are fulfilled: (1) the root mean square (RMS) of the difference between measured and simulated dSCDs <30 %, (2) DFS ≥1, and (3) no negative value in the retrieved profile.
The Bremen Optimal estimation REtrieval for Aerosols and trace gaseS (BOREAS) OEM algorithm (Bösch et al., 2018) developed at the Institute of Environmental Physics (IUP) Bremen uses the SCIAMACHY radiative transfer model (SCIATRAN) version 4.0.1 for the forward model calculations and the SCIATRAN retrieval mode for the inversion of the aerosol extinction profile (Rozanov et al., 2014). However, since BOREAS is still under development, a previous version of SCIATRAN was used (v3.8.12) together with a preliminary version of BOREAS within this study. SCIATRAN is run in BOREAS using the discrete ordinate mode with full multiple-scattering, full spherical geometry for single-scattering light and plane-parallel geometry for multiple-scattering light. Polarisation and rotational Raman scattering are usually not included in the forward model calculations. The weighting functions are calculated analytically assuming an optically thin atmosphere.
The BOREAS aerosol retrieval is based on the minimisation of the difference in O4 optical depth between measurements and the forward model within a Tikhonov regularisation by varying the aerosol extinction profile until convergence or an iteration limit is reached. In a second step, so-called box air mass factors are calculated for the chosen absorber using the previously obtained aerosol extinction profile. Again SCIATRAN in its full-spherical mode is applied. With the box air mass factors, the relation between the measurement and the absorber's profile in the atmosphere can be expressed as a linear system, which is then solved by applying the OEM to the measured slant columns and a priori profile information to retrieve trace gas profiles.
The profile retrieval is calculated based on SCIATRAN's main grid and can be set by the user. The grid itself describes homogeneous layers around the grid points, with the exception of the uppermost (lowermost) layer, which is considered to be half of the grid steps. BOREAS retrieval results are routinely calculated on equidistant grid levels from the surface up to the maximum retrieval height. For each level, the retrieved value is the average of the corresponding layer, e.g. in the case of the 200 m level, the altitude range from 100 to 300 m defines this specific layer. Two exceptions are the boundary levels, 0 and 4000 m, which cover only half the altitude range compared to the other layers (0–100 and 3900–4000 m). Since the grid was defined at the centre of the levels in this intercomparison study, BOREAS results were interpolated on these altitude values. Due to this interpolation, the lowest values in the submitted profiles are the interpolation between the retrieved surface value and the 200 m result.
Four different quality filters were applied. A profile is flagged as invalid if (1) the retrieved vertical column density is negative, (2) the profile contains more than 10 negative values, (3) the RMS between simulated and measured dSCD is larger than 2×1016 molec cm−2 for NO2 and HCHO and 5×1043 molec2 cm−5 for O4, and (4) the relative difference between the simulated and the measured dSCD for each line of sight is smaller than 5 %.
The Heidelberg Profile Retrieval Algorithm (HEIPRO) is an updated version of the algorithm already described in detail in Frieß et al. (2006, 2011). It uses the SCIATRAN radiative transfer model version 2.1.5 (Rozanov et al., 2014) in discrete ordinate mode with full multiple-scattering, full spherical geometry for single-scattering and plane-parallel geometry for multiple-scattering light. The weighting functions for trace gases (box air mass factors) are calculated analytically; the weighting functions for aerosols are calculated using the finite difference method.
HEIPRO is based on OEM and retrieves the most probable state vector by minimising the cost function given by Eq. (4). The vertical extinction and aerosol profiles are represented in the state vector as the logarithm of extinction and trace gas concentration, respectively. This has the advantages that (1) negative values, which cannot be processed by the RTM, are avoided, (2) the retrieval can reconstruct a larger range of atmospheric conditions, and (3) the retrieval is generally more stable with less tendency to oscillations. As most MAX-DOAS retrieval algorithms, HEIPRO uses a two-step approach, where the aerosol extinction profile is retrieved in a first step using O4 dSCDs as the measurement vector, and the trace gas profile is retrieved in a second step using the according trace gas dSCDs as the measurement vector, together with the aerosol profile from the first step. The radiative transfer model SCIATRAN version 2.1.5 serves as a forward model for the retrieval.
No filtering of the HEIPRO data has been performed, and all profiles are flagged as valid.
The Mexican MAX-DOAS Fit (MMF) algorithm (Friedrich et al., 2018) uses VLIDORT (Spurr, 2006) version 2.7, the vectorised version of LIDORT (see also Sect. 3.1), in pseudo-spherical multiple-scattering setting as the forward model. Only the intensity information and its analytically calculated Jacobians, not the other Stokes parameters, are currently used. MMF can operate in linear or logarithmic state vector space. While different regularisation matrices are possible in the linear space, only Sa matrices of the form used in this study are currently tested in logarithmic mode. The results presented here were performed in logarithmic mode. MMF also uses a two-step approach, as outlined above in Sect. 3.3. Quantities such as aerosol single-scattering albedo, aerosol asymmetry factor, surface albedo, and temperature and pressure profiles can be supplied. In addition to the custom retrieval grid, a simulation grid can be supplied. The algorithm has so far been applied in linear mode with Tikhonov regularisation for aerosol retrieval on an almost equal distance-in-pressure grid at UNAM (National Autonomous University of Mexico) for the retrieval of NO2 profiles from four stations in the Mexico City area.
MMF flagging for this study was based on the mean of the ratio of the absolute value of the difference between measured and simulated dSCD and the dSCD measurement error. The limit for flagging scans as invalid was 10.
The OEM-based profile inversion algorithm of aerosol extinction and trace gas concentration (PriAM) developed by the Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences (AIOFM, CAS), in cooperation with Max Planck Institute for Chemistry (MPIC), is introduced in Wang et al. (2013) and Frieß et al. (2016). A two-step inversion procedure is used in PriAM. In the first step, profiles of aerosol extinction are retrieved from the O4 dSCDs. The single-scattering albedo and asymmetry factor should be defined for the aerosol retrieval based on other auxiliary measurements. Afterwards, profiles of volume mixing ratios (VMRs) of trace gases are retrieved from the respective dSCDs in each MAX-DOAS EA sequence. The retrieval problem is solved by the Levenberg–Marquardt modified Gauss–Newton numerical iteration procedure (Levenberg, 1944; Marquardt, 1963; Rodgers, 2000). PriAM uses the SCIATRAN 2.2 RTM (Rozanov et al., 2005) to calculate the weighting function and other simulated quantities. To avoid meaningless negative values, the original aerosol extinction and VMR of trace gases are transformed to the logarithms of these quantities. Because of the conversion, it is necessary to use the nonlinear optimal inverse method to retrieve the profiles of trace gases instead of the linear method. PriAM can retrieve trace gas and aerosol profiles on any arbitrary vertical grid.
The Mainz Profile Algorithm (MAPA) developed at the MPIC is a two-step algorithm based on a parametrised approach. First, the aerosol profile is retrieved based on O4 dSCDs, and second, trace gas profiles are retrieved based on trace gas dSCDs and aerosol profiles from the first step. The forward model is provided as a LUT, relating the profile parameters to O4 and trace gas differential air mass factors (dAMFs) for given solar zenith angles (SZAs) and relative azimuth angles (RAAs) between the Sun and the instrument's field of view. The LUT is calculated with the Monte Carlo Atmospheric Radiative Transfer Inversion Model (McARTIM) (v1), a full spherical Monte Carlo model without polarisation (Deutschmann et al., 2011). Up to four parameters are determined independently: the integrated column (aerosol optical thickness, AOT; vertical column density, VCD), layer height, profile shape, and, in the case of aerosols, the O4 scaling factor (optional). The profile shape is described by the shape parameter s, with s=1 representing a box profile, s<1 representing a combined box profile with an exponential profile on top (s describes the fraction of the box profile on the total profile), and s>1 representing elevated profiles.
Previous versions of the parameter-based profile inversions, as described e.g. in Wagner et al. (2011), were based on a Levenberg–Marquardt least-squares algorithm. In MAPA (from v0.6 on), however, the profile retrieval is based on a Monte Carlo approach yielding an ensemble (instead of only one set) of best matching parameters. This approach is much faster (about 3 s per profile), accounts for correlations between the parameters, can deal with multiple minima, and allows the uncertainty of the resulting profiles to be determined. MAPA is described in detail in Beirle et al. (2019).
MAPA uses RTM parameters for the LUT generation that slightly differ from those prescribed within this study (see Sect. 2), with a phase function asymmetry parameter of 0.68, aerosol single-scattering albedo of 0.95, and surface albedo of 0.05.
The MAPA flagging scheme is as follows. For each elevation sequence, MAPA determines the parameter combinations yielding the best match of modelled and measured dSCDs, no matter how good this best match actually is. Thus, flagging is required in order to evaluate which MAPA results should be considered as meaningful and which not. MAPA provides a two-stage flagging scheme: moderate exceedance of the thresholds results in a warning, while large deviations raise an error. Flagging is based on different criteria. (1) The level of agreement between forward model and measurement compared to the dSCD error (for details see Beirle et al., 2019), (2) the consistency of column parameter within the Monte Carlo ensemble, and (3) the shape of the resulting profile, which is requested to be located in the lower troposphere. In addition, scenes with high AOT (>2) are flagged as invalid. For RAA <15∘, even scenes with AOT >0.5 are flagged. All flags are determined and stored individually and merged into one total flag defined as any of the flag criteria for both warnings and errors.
Flag criteria and thresholds have been developed and optimised based on both the synthetic dSCDs presented in this study and real measurements during the Second Cabauw Intercomparison of Nitrogen Dioxide Measuring Instruments (CINDI-2). For details on the MAPA flagging scheme and a discussion on the impact of the a priori thresholds see Beirle et al. (2019).
The MAX-DOAS retrieval KNMI (MARK) developed at the Royal Dutch Meteorological Institute (KNMI) is described in Vlemmix et al. (2011, 2015a). It makes use of a profile shape parametrisation with just a few (two to four) free parameters. A LUT of differential slant column simulations is produced by the Doubling-Adding KNMI (DAK) model (de Haan et al., 1987; Stammes et al., 1989). A standard least-squares algorithm is used to minimise the deviations between simulated and measured differential slant columns at the different EAs. Uncertainties in the parameters are estimated from the spread in the results of an ensemble approach in which the retrieval is performed multiple times with disturbed measured differential slant column densities, based on the DOAS retrieval uncertainties. For each individual retrieval the aerosol profile is retrieved first, and the outcome is used in the trace gas retrieval. The ensemble retrieval is performed for four different parameterisations, and the reduced χ2 distribution of the ensembles is used to make an a posteriori composite of the profile and its corresponding uncertainties. The fitted profile shape parameters are (1) the tropospheric column density (or AOT); (2) the top height of the mixing layer; (3) the shape parameter, which determines the linear increase or decrease in the mixing layer; and (4) the fraction of the total trace gas column density, which resides above the mixing layer.
MARK data are flagged as invalid if the variability in the AOT or the trace gas column within an ensemble is larger than 15 % of the value itself.
The National Aeronautics and Space Administration (NASA) real-time algorithm was developed as a quick look algorithm that relies on the fact that atmospheric scattering strongly affects DOAS-measured O4 absorption (Spinei et al., 2019). Two separate approaches are used for aerosol and trace gas profile retrieval. The aerosol profile algorithm determines the layer aerosol extinction coefficients by comparing measured ring and O4 absorption with ring and O4 absorption under pure Rayleigh conditions. Air mass factors and ring absorption for the Rayleigh case are precalculated using the VLIDORT v2.8 and LIDORT-LRRS v2.5 radiative transfer models, respectively (Spurr et al., 2008; Spurr, 2008), assuming the US standard atmosphere. Since ring simulations were not provided in this study, aerosol analysis was performed only at 477 nm. O4 dSCDs are corrected for SZA dependence. Equation (9) is the simplified equation used in this study to calculate aerosol scattering extinction coefficients at each layer for specific observation geometry (EA and RAA) Θ. We also assume an aerosol single-scattering albedo of ωaer(λ)=1.
with SO4(αi) being the O4 slant column density at elevation angle αi and VO4 the O4 vertical column density.
The maximum number of vertical layers is equal to the number of elevation angles. Profiles are considered invalid if fewer than four measurements are used in the profile calculation, with all of the synthetic data analysed here satisfying this test. Within this study, an exponential profile decreasing to 0.01 % of the last altitude layer extinction coefficient at 4 km was added for consistency with the other algorithms. The resulting profile was then linearly interpolated on the common grid (200 m up to 4 km).
The rest of the profile VMR is calculated using O4 and trace gas dSCDs at multiple EAs. The layer altitude is calculated similar to the aerosol case. The derived profile is then converted to partial columns and scaled by the total VCD. As in the aerosol case, the layer grid is condition specific and was adjusted to the common grid in this study.
A first important step for the comparison of retrieval algorithms is the assessment of their capability to realistically simulate the underlying physical processes using appropriate forward models, which are in this case atmospheric radiative transfer models. This section describes the forward model parameters and atmospheric scenarios for the modelling of O4, NO2, and HCHO SCDs, while the comparison of forward modelled SCDs is presented in Sect. 5.
The model atmosphere for the forward calculations consists of 67 layers, with a resolution of 100 m at altitudes between the surface and 4 km and a coarser resolution above. Note that the retrieval of extinction and trace gas profiles is performed on a coarser grid with 200 m resolution in the lowermost 4 km. The choice of a finer grid for the forward modelling than for the inverse modelling allows for the investigation of the impact of sub-grid trace gas and aerosol variabilities on the retrieved profiles. For the forward modelling, a constant concentration within each layer has been implemented for the model calculations whenever possible.
Figure 2Trace gas number concentration (a) and aerosol extinction (b) vertical profiles as input for the forward modelling of HCHO, NO2, and O4. The shaded areas in (b) indicate the location of fog and cloud layers with an extinction coefficient of 10 km−1. The properties of the individual profiles are described in Tables 2 and 3.
The trace gas concentration and aerosol extinction profile scenarios for the forward modelling of HCHO, NO2 and O4 SCDs are shown in Fig. 2. The same set of trace gas profiles is assumed for both HCHO and NO2, in accordance with ambient measurements in mid-latitudes, where typical concentrations of both species are of the same order of magnitude (e.g. Vlemmix et al., 2015b). For simplicity, it is assumed that aerosol extinction does not change with wavelength (Ångström exponent of zero). The model profiles are chosen in order to represent a large variety of different atmospheric conditions, including trace gas and aerosol free atmospheres as well as moderate to high trace gas and aerosol loads up to cloudy and foggy conditions. The shapes of the different profiles include near-surface box profiles, profiles exponentially decreasing with altitude, uplifted profiles with a Gaussian shape, and profiles without trace gases and aerosols. Additionally, the trace gas scenarios include two NO2 profiles measured with a balloon sonde during the CINDI-2 campaign conducted in the Netherlands in September 2016 (Kreher et al., 2019; see also http://www.tropomi.eu/data-products/cindi-2, last access: 8 April 2019). The aerosol scenarios include three extreme cases with an extinction of 10 km−1 located at the surface, as well as at altitudes around 1.3 and 5.25 km, representing fog and low-lying and high-lying clouds, respectively. In total, the scenarios include 10 trace gas and 11 aerosol profiles whose features are listed in Tables 2 and 3, respectively. The forward calculations of O4 at 360 and 477 nm are performed for each of the 11 aerosol profiles. NO2 and HCHO SCDs are simulated for each combination of aerosol and trace gas profiles, yielding in total 110 different trace gas scenarios.
Table 2Description of the trace gas profiles shown in Fig. 2. The VCD and the maximum number concentration ρmax are given in units of 1015 molec cm−2 and 1011 molec cm−3, respectively.
The viewing geometry for the forward model simulations is specified by the EA α, the SZAs θ, and the RAA ϕ between the Sun and the viewing direction of the instrument. Simulations are performed using any combination of values for EA, SZAs, and RAA as listed in Table 4. The EA sequence, consisting of 10 angles between the horizon and zenith, is identical to the measurement sequence during the CINDI-2 campaign. Together with three SZAs and three RAA values, this yields 90 different viewing geometries to be simulated for each model atmosphere.
Table 3Description of the aerosol extinction profiles shown in Fig. 2. The maximum extinction kmax is given per kilometre.
Table 4Viewing geometry for the model calculations. SCDs are simulated for each combination of EA, SZAs, and RAA.
Further RTM parameters specified for both forward and inverse modelling are listed in Table 5, and the wavelengths for the simulation of the different trace gases can be found in Table 6. The RTM parameters include vertical profiles of temperature and pressure derived from average ozone-sondes for the month of September from 2013 to 2015 in De Bilt, the Netherlands, as well as surface albedo, aerosol optical properties, and trace gas literature absorption cross sections. The aerosol scattering was parametrised using a Henyey–Greenstein phase function (Henyey and Greenstein, 1941) with the asymmetry parameter as listed in Table 5.
Table 5RTM parameters for the radiative transfer modelling.
Table 6Assumed measurement errors of the O4, HCHO, and NO2 dSCDs, as well as total columns of the respective a priori profiles. Units for the errors are molec2 cm−5 for O4 and molec cm−2 for HCHO and NO2.
In total, all combinations of viewing geometries, aerosol profiles, and trace gas profiles yield 990 O4 SCD simulations each at 360 and 477 nm and 9900 simulations for each of the trace gases NO2 and HCHO.
In this section, the ability of the forward models to realistically simulate trace gas SCDs is assessed based on a comparison of the individual simulations with the median of the SCDs from the different RTMs. The median SCDs also serve as a reference dataset and provide the synthetic measurements for the profile retrieval comparison presented in Sect. 6. Figure 3 shows the comparison of the simulated O4, NO2, and HCHO SCDs to the respective ensemble median. The respective parameters of a linear regression of forward modelled SCDs versus median SCDs are shown in Fig. 4. The SCDs simulated by most forward models agree under all conditions, with Pearson's coefficients and slopes very close to unity (R>0.999 and 0.99<slope<1.001) for a regression between SCDs from the individual models and the median from all models. Exceptions are HEIPRO and PRIAM, both using an older version of the SCIATRAN RTM (version 2.1) as the forward model. These models show deviations for the fog and cloud scenarios (AER8 … AER10), the shallow box profile (AER4), and in the case of HEIPRO also for the exponential profile AER2 with a high AOT of 1. MAPA with McARTIM as the forward model yields significantly smaller slopes of 0.955 and 0.959 than the other models for O4 at 477 nm and for NO2, respectively. This is probably due to the different treatment of sphericity within the McARTIM model. Significant biases are found for MAPA–McARTIM and PRIAM–SCIATRAN for NO2 and O4 at 477 nm, as well as for HEIPRO–SCIATRAN for HCHO. Apart from possible differences in the implementation and the approaches of the individual RTMs, some of the differences are likely due to the different representations of the trace gas and aerosol profiles on the prescribed vertical grid.
Figure 3Correlation of SCDs of O4 (360 and 477 nm), HCHO, and NO2 simulated by the different forward models with the corresponding median SCDs from all models. Colours indicate the underlying aerosol and trace gas profiles as denoted in the legend. For HCHO and NO2, results for all scenarios with different aerosol profiles are included.
Figure 4Slope, intercept, regression coefficient, and root-mean-square difference (RMS) of the correlation between the SCDs from the individual forward models and the median SCDs from all models. RMS and intercept values are in units of 1043 molec2 cm−5 for O4 and 1017 molec cm−2 for HCHO and NO2.
In summary, the SCDs simulated by the different radiative transfer models agree well under all conditions, as within previous RTM intercomparisons (e.g. Wagner et al., 2007). This implies that possible discrepancies in the retrieved trace gas and aerosol profiles are mainly caused by differences in the implementation of the retrieval algorithms, rather than by differences in the forward models.
This section presents the results of the aerosol and trace gas retrieval algorithm intercomparison. Section 6.1 describes the measurement vectors, forward model parameters, and a priori constraints of the profile retrieval. An important diagnostic tool is provided by the AVKs from the OEM algorithms, which quantify the sensitivity of the retrieval to the atmospheric state. AVKs and information content are discussed in Sect. 6.2. The comparison of modelled and measured dSCDs presented in Sect. 6.3 provides a measure for the level of convergence of the retrieval algorithms. Results of the intercomparison of vertical profiles, trace gas and aerosol total columns, and surface extinction and trace gas concentrations are presented in Sect. 6.4, 6.5, and 6.6, respectively. Finally, the computational speed of the retrieval algorithms is discussed in Sect. 6.7.
Figure 5The 360 nm aerosol AVKs. Each subplot shows the mean AVK over all SZAs and RAA for a specific aerosol scenario (columns) and retrieval algorithm (rows). Filled circles indicate the nominal altitude of the corresponding AVK plotted in the same colour. Colour-shaded areas (barely visible in most cases) indicate the standard deviation of the AVKs, i.e. the variation for different SZAs and RAA. Also shown are the DFS. AVKs stem from retrievals with noisy measurements (v1n).
Based on the simulated SCDs from the atmospheric scenarios described in Sect. 5, a reference dataset of O4 at 360 and 477 nm, NO2, and HCHO dSCDs has been compiled based on the ensemble median dSCDs from all participants. In the following, we refer to this synthetic dataset as the measured dSCDs. Table 6 lists the according dSCD errors, which were chosen to represent typical values based on ambient measurements by various instruments during the CINDI-2 campaign. As already discussed in Sect. 4, the reference dataset contains dSCDs at nine EAs for 990 different combinations of trace gas profiles, aerosol profiles, and solar geometry (SZAs and RAA).
Two reference datasets were created: dataset v1 contains the median dSCDs without any noise, and dataset v1n contains the median dSCDs with a noise component consisting of the sum of (1) normally distributed noise with a standard deviation according to the errors listed in Table 6, and (2) additional noise with a standard deviation of 5 % of the dSCD values. This second noise component, which is in the case of O4 and NO2 much higher than typical measurement noise, is supposed to represent random variations in the measured dSCDs due to horizontal inhomogeneities of aerosols and trace gases as well as temporal changes in the atmospheric state (in particular cloud properties) during a single elevation scan with a typical duration of several minutes. The second noise component is of magnitude similar to the RMS differences between dSCDs measured by more than 30 instruments during the CINDI-2 Campaign (Kreher et al., 2019).
Figure 6Same as Fig. 5, but for aerosols at 477 nm.
Figure 7Same as Fig. 5, but for HCHO.
Each participant performed retrievals with settings being as close as possible to the prescribed settings described below. The results based on noise-free and noisy dSCDs are labelled "v1" and "v1n", respectively. Each measurement vector consists of O4, HCHO, or NO2 dSCDs of a single EA sequence for a given atmospheric scenario specified by aerosol and trace gas profiles, as well as solar zenith and relative azimuth angles. Retrievals are performed using the same forward model parameters as for the forward model calculations (see Table 5), with the exception of MAPA that uses slightly different surface albedo and aerosol optical properties (see Sect. 3.6). The retrieval grid consists of 20 layers with 200 m in thickness between the surface and 4 km in altitude, which means that the atmospheric layers are twice as thick as for the forward modelling. This choice allows for the investigation of the impact of sub-grid variations on the retrieval, in particular in the case of scenarios TG4 and AER4 (100 m thick box profiles).
Figure 8Same as Fig. 5, but for NO2.
In contrast to the output of OEM algorithms, which directly retrieve trace gas and aerosol profiles on this vertical grid, profiles from the parametrised algorithms are interpolated onto the prescribed grid. The OEM algorithms use aerosol and trace gas a priori profiles exponentially decreasing with altitude with a scale height of 1 km, with a priori vertical columns for each species as listed in Table 6. The diagonal elements of the a priori covariance matrices consist of the square of 50 % of the corresponding a priori profiles. Off-diagonal terms consist of a Gaussian function with a correlation length of 200 m as described by Clémer et al. (2010).
Optionally, each participant can define criteria for the validity of the retrieved profiles, and can provide corresponding Boolean validity flags for each profile.
Figure 9A posteriori versus measured dSCDs of O4 (360 and 477 nm), HCHO, and NO2 for the v1n dataset. Colours and symbols indicate the underlying aerosol and trace gas profiles, respectively, as denoted in the legend. Data flagged as invalid are shown as smaller symbols. Also shown are the RMS differences between measured and modelled dSCDs with the RMS for valid data only being shown in brackets, as well as the percentage of valid data. Units of the RMS are 1044 molec2 cm−5 for O4 and 1017 molec cm−2 for HCHO and NO2.
Figure 10Slope, intercept, regression coefficient, and RMS of the dSCD correlation. Each of the circular symbols for the filtered data represents a pie chart that quantifies the fraction of data flagged as valid. RMS and intercept values are in units of 1043 molec2 cm−5 for O4 and 1017 molec cm−2 for HCHO and NO2. Cloud and fog scenarios (AER8, AER9, AER10) are excluded from the regression analysis.
Figures 5, 6, 7, and 8 show the AVKs from the OEM algorithms for aerosols at 360 and 477 nm, HCHO, and NO2, respectively. The AVKs confirm that MAX-DOAS measurements yield only information on the lowermost ≈2 km of the atmosphere when using a zenith-sky reference spectrum from the same EA sequence. This is a result of the measurement geometry and, in the case of aerosols, also of the fact that the O4 vertical distribution is heavily weighted to the surface. The DFS range from ds<0.5 during foggy conditions (AER8) to ds>3 for aerosol-free atmospheres (AER0), with the information content being generally higher for aerosols than for trace gases. The dependency of the information content on wavelength is inconsistent: for a pure Rayleigh atmosphere (AER0), bePRO, BOREAS, and MMF report smaller DFS for aerosols at 477 nm than at 360 nm, while the opposite is true for HEIPRO and PRIAM. The information content for trace gases increases with wavelength for all algorithms, except for bePRO in the case of the AER0 scenario.
The shapes of the AVKs from the different models have a high degree of similarity, except for BOREAS aerosols, where the AVKs show a much smaller information content than all other algorithms and indicate that there is very little height sensitivity for aerosol profiles from the BOREAS algorithm. The respective BOREAS aerosol vertical profiles are, however, in good agreement with the results from the other algorithms (see Sect. 6.4). The reason for this apparent discrepancy is that BOREAS is not a standard OEM retrieval but includes additional regularisation terms, making interpretation of the AVKs less straightforward (Bösch et al., 2018). Significant differences between the AVKs can be found for the rather extreme scenarios AER8 and AER9 (fog and low-lying cloud). PRIAM aerosol AVKs are much less affected by fog (AER8) than the other algorithms, which show a strong reduction in information content in the case of high extinction at the surface. The AVKs for a high-lying cloud at ≈5 km in altitude (AER10) are very similar to those of a Rayleigh atmosphere (AER0), indicating that horizontally homogeneous free tropospheric clouds have little impact on the sensitivity of MAX-DOAS retrievals.
Apart from the fog scenario (AER8), there is only a moderate dependency of vertical resolution and information content on the aerosol content of the atmosphere. Interestingly, the information content for trace gases, in particular NO2, is highest for the uplifted aerosol layer (AER7). This layer probably increases the sensitivity for trace gases near the surface due to the fact that the majority of scattering occurs within the cloud. The light paths below the cloud are thus well defined and, in the case of low EAs, can be higher than for the clear-sky case.
Figure 11Comparison of retrieved (red solid line, version v1n) and true (green solid line) vertical profiles of aerosol extinction at 360 nm for each aerosol scenario (columns) and algorithm (rows). The retrieved profiles are the medians for all SZA–RAA combinations. The (25 %–75 %) and (5 %–95 %) percentiles are shown as grey areas and whiskers, respectively. The a priori profile is shown as the blue line (OEM algorithms only).
The variability of the AVKs with the position of the Sun (shown as shaded areas in Figs. 6–8) is generally very small, indicating little dependency on SZAs and RAA.
An important indicator for the level of convergence of the retrieval, and subsequently the accuracy of the retrieved profile, is the agreement between the measurement vector y and the measurement vector F(x^) modelled for the retrieved state x^ (a posteriori dSCDs). The comparison of a posteriori and measured dSCDs for the v1n dataset is shown in Fig. 9, and the corresponding linear regression parameters are depicted in Fig. 10. The NASA algorithm does not rely on the forward modelling of dSCDs; thus no dSCD data are available for this algorithm.
Figure 12Same as Fig. 11, but for aerosols at 477 nm.
The algorithms show significant differences in the level of convergence. BOREAS, HEIPRO, MMF, and MAPA show good agreement between measured and modelled dSCDs for all scenarios with slopes and Pearson correlation coefficients close to unity. The same holds true for MARK except for the HCHO retrieval, where poor convergence is achieved for the TG9 scenario, but this has only a little effect on the regression parameters (see Fig. 10). PRIAM achieves good convergence for the trace gas retrievals, but shows a larger scatter than other algorithms for O4 at 360 nm and significantly underestimates O4 at 477 nm, where the slope of the linear regression is only 0.585. In many cases, bePRO yields only poor agreement between measured and modelled dSCDs, in particular in the visible range where the RMS between measurement and modelling is significantly higher than for the other algorithms, and regression coefficients are only 0.67 for O4 dSCDs and 0.24 for NO2 dSCDs. The bePRO algorithm furthermore has problems retrieving the AER10 scenario (high-lying cloud) at 360 nm. The poor convergence of the bePRO algorithm is the result of numerous outliers, and the RMS strongly improves after discarding about 15 % of the 477 nm O4 data points and 23 % of the NO2 data points that are flagged as invalid. The reason for the instabilities of the bePRO algorithm, which strongly affect the accuracy of the retrieved profiles (see Sect. 6.4), is the fact that bePRO uses a Gauss–Newton inversion scheme. Sensitivity studies have shown that this problem could be overcome by using a Levenberg–Marquardt inversion scheme instead. More than 55 % of data from MAPA are flagged as invalid although the retrievals show a high level of convergence. This might indicate that the MAPA quality flagging criteria could be too strict.
Figure 13Comparison of retrieved (red solid line, version v1n) and true (green solid line) vertical profiles of HCHO for each trace gas scenario (columns) and algorithm (rows). In addition to the median profiles for all aerosol scenarios, SZAs and SAAs shown in red, the median concentration profile for each aerosol scenario is shown as coloured symbols as denoted in the legend. The RMS (true – retrieved extinction) is shown in units of 1011 molec cm−3.
In this section, the overall ability of the retrieval algorithms to reproduce the true atmospheric aerosol and trace gas profiles is discussed. Figures 11 and 12 show the comparison between true and retrieved aerosol profiles at 360 and 477 nm, and HCHO and NO2 profile comparisons are shown in Figs. 13 and 14, respectively. The regression parameters for the comparison of true and retrieved aerosol extinction and trace gas number concentration for all target species are presented in Fig. 15. All data presented here are based on retrievals with noisy dSCDs (v1n). The corresponding profiles with valid data only, and with noise-free measurements, are shown in the Supplement.
Figure 14Same as Fig. 13, but for NO2.
Figure 15Slope, intercept, regression coefficient, and RMS for the correlation between true and retrieved aerosol extinction as well as HCHO and NO2 number concentrations at all altitudes. Intercept and RMS values are per kilometre for aerosols and 1011 molec cm−3 for HCHO and NO2. Cloud and fog scenarios (AER8, AER9, AER10) are excluded from the regression analysis.
Most algorithms are capable of realistically retrieving the shape of the true aerosol extinction profiles for moderate conditions (AER0 … AER7), with slopes close to unity and Pearson regression coefficients for the correlation between true and retrieved extinction of R>0.8 (see Fig. 15). Exponential profiles (AER1, AER2, and AER3) are well reproduced, but the extinction above ≈1 km is underestimated by all algorithms for AER2 (high aerosols, exponentially decreasing) owing to a reduced sensitivity of MAX-DOAS measurements to high altitudes. In the case of OEM algorithms, this leads to a bias towards the a priori as discussed in Sect. 6.2, whereas parametrised algorithms tend to become unstable at altitudes where the sensitivity is low. The shallow aerosol layers AER4 and AER5 are captured well by all algorithms except NASA (which provides profiles at 477 nm only). The retrievals are performed on a 200 m grid, and the 100 m thick layer (AER4) yields results similar to the 200 m thick layer (AER5), which has the same AOT but only half the extinction coefficient. As expected, the 1 km thick aerosol layer AER6 is significantly smoothed by the retrievals, except by MARK, which reproduces the sharp edges of this profile better than the other algorithms. The uplifted Gaussian profile with a centre altitude of 1 km (AER7) is well reproduced by all OEM algorithms, and to a lesser extent also by NASA. The parametrised algorithms MAPA and MARK also retrieve uplifted profiles, but significantly overestimate the extinction above 1 km.
Figure 16Box–whisker plots of the retrieved AOT at 360 (a) and 477 nm (b) for the different aerosol scenarios (rows) and retrieval algorithms (columns) based on retrievals using noisy dSCDs. Crosses show the mean and dashed horizontal lines the median. Shaded areas indicate the (25 %–75 %) percentile, and whiskers show the (5 %–95 %) percentile. The true AOT is shown as the blue horizontal line. For each algorithm, all data are shown on the left in black, and data marked as valid are shown on the right in green. Also shown is the percentage of valid data.
The 200 m thick fog layer (AER8) is very well reproduced by the parametrised algorithms MAPA and MARK, and to a lesser extent also by the OEM algorithms MMF, HEIPRO, and PRIAM, which retrieve the logarithm of the aerosol extinction profile and are therefore capable of retrieving a higher range of aerosol extinction values than bePRO, which operates in linear space and therefore is subject to a stronger bias towards the a priori. The cloud above 1 km (AER9) is very well retrieved by MMF. MAPA and MARK capture the cloud bottom altitude but overestimate the extinction above the cloud. The bePRO, BOREAS, HEIPRO, and PRIAM algorithms retrieve only a small enhancement of the aerosol extinction (<2 km−1) around the altitude of the cloud, and NASA appears to be insensitive to the cloud.
The retrieved extinction profiles for scenario AER10, which consists of a cloud above 5 km altitude and no aerosols elsewhere, are very similar to the cloud- and aerosol-free scenario AER0, although the RMS difference between true and retrieved profiles is higher for AER10 than for AER0 in some cases. As already discussed in Sect. 6.3, measured and a posteriori dSCDs are also in good agreement for AER10. It can therefore be concluded that high-lying clouds have very little effect on MAX-DOAS aerosol retrievals, in contradiction to the findings of Ortega et al. (2016), who suggest that free-tropospheric aerosols and clouds would strongly affect MAX-DOAS measurements of O4.
Figure 17Box–whisker plots of the retrieved HCHO (a) and NO2 (b) VCD for the different trace gas scenarios (rows) and retrieval algorithms (columns) based on retrievals using noisy dSCDs. Crosses show the mean, and dashed horizontal lines show the median. Shaded areas indicate the (25 %–75 %) percentile, and whiskers show the (5 %–95 %) percentile. The true VCD is shown as a blue horizontal line. For each algorithm, all data are shown on the left in black, and data marked as valid are shown on the right in green. Also shown is the percentage of valid data. The median for each aerosol scenario is shown as a coloured symbol as indicated in the legend.
As can be seen from the width of the 50 % and 90 % confidence intervals (shaded areas and error bars in Figs. 11 and 12), the parametrised algorithms (MAPA and MARK) produce a significant number of outliers of the retrieved aerosol profiles, in particular for the scenarios AER1, AER2, AER6, and AER7. This effect is larger for retrievals with noisy than with noise-free dSCDs (see Supplement Figs. S5 and S6). A likely reason for this behaviour is that MAPA and MARK do not rely on a priori constraints as a regularisation, but use a minimisation method for the differences between measured and modelled dSCDs. In some cases the ensemble of possible solutions with similar minima can be widespread. However, in most cases the outliers do not significantly affect the overall accuracy as expressed by the RMS difference between retrieved and true aerosol profiles (see Fig. 15).
Note that for MAPA the spread of the retrieved profiles is far smaller when only filtered results are considered (see Figs. S1–S4), indicating that the MAPA filter is successfully removing outliers. This requires, however, excluding 17 % and 37 % of the aerosol profiles at 360 and 477 nm, respectively, as well as 47 % of the HCHO profiles and 44 % of the NO2 profiles of the AER0–AER7 scenarios, while almost 93 % of the profiles for scenarios with cloud and fog layers (AER8–AER10) are discarded.
Figure 18Slope, intercept, regression coefficient, and RMS for the correlation between true and retrieved AOT as well as HCHO and NO2 VCD. Intercept and RMS values are in dimensionless units for aerosols and 1016 molec cm−2 for HCHO and NO2. Cloud and fog scenarios (AER8, AER9, AER10) are excluded from the regression analysis.
Figure 19Box–whisker plots of the retrieved surface aerosol extinction at 360 nm (a) and 477 nm (b) for the different aerosol scenarios (rows) and retrieval algorithms (columns) based on retrievals using noisy dSCDs. Crosses show the mean, and dashed horizontal lines show the median. Shaded areas indicate the (25 %–75 %) percentile, and whiskers show the (5 %–95 %) percentile. The true AOT is shown as the blue horizontal line. For each algorithm, all data are shown on the left in black, and data marked as valid are on the right in green. Also shown is the percentage of valid data.
As a result of a poor convergence of modelled and measured dSCDs (see Sect. 6.3), bePRO fails to retrieve the extinction profile at 477 nm for aerosol-free atmospheres (AER0) and for a high-lying cloud (AER10). The RMS difference between true and retrieved extinction is significantly higher for bePRO than for other algorithms for AER4 (100 m thick aerosol layer). As a result of these instabilities, the linear regression of retrieved and true aerosol extinction coefficients at 477 nm yields a smaller slope (0.76) and regression coefficient (0.26) for bePRO than for the other algorithms when including all data. After filtering out ≈75 % of data flagged as invalid, the accuracy of the bePRO results is similar to the other algorithms.
PRIAM and NASA underestimate the aerosol extinction at 477 nm with slopes of only 0.72 and 0.63, respectively. Furthermore, PRIAM falsely retrieves a non-existing uplifted aerosol layer around 1 km in altitude with a peak extinction >0.1 km−1 for aerosol-free atmospheres (AER0 and AER10) at 477 nm, whereas MAPA retrieves elevated aerosol extinction at the top of the retrieval domain between 3.5 and 4 km for these scenarios at both 360 and 477 nm. This is a consequence of the fact that there are no a priori constraints on extinction profiles made in MAPA, and the resulting dSCDs are almost unaffected by high cloud layers. Most of the MAPA profiles for the AER10 scenario are, however, flagged as invalid, leaving only 2 % of valid profiles (see Figs. S1 and S2).
The retrieval is generally more stable for trace gases (Figs. 13 and 14) than for aerosols. Obviously, it is sufficient to constrain the trace gas retrieval with an aerosol profile that realistically reproduces the light path, even if the extinction profile differs from the truth. Furthermore, trace gas retrievals are probably more stable because they constitute a linear problem in contrast to the non-linear aerosol retrieval.
With the exception of NO2 from bePRO, the algorithms reproduce exponential trace gas profiles (TG1, TG2, and TG3) well, with little bias towards the a priori at high altitudes (Figs. 13 and 14). As for aerosols at 477 nm, bePRO NO2 profiles suffer from significant instabilities, and BOREAS shows oscillating features with negative values for altitudes between 1 and 2 km. These discrepancies of the bePRO and BOREAS results are probably due to the fact that they operate in linear space. The vertical extent and trace gas concentration of the shallow trace gas layers TG4 and TG5 are well reproduced by all algorithms except NASA. HEIPRO NO2 results for TG4 suffer from outliers for the AER9 scenario, where NO2 above the cloud is overestimated. As for aerosols, the degree of smoothing of the 1 km box profile (TG6) is higher for the OEM than for the parametrised algorithms. The peak altitude of the uplifted profile (TG7) at 360 nm is underestimated by all algorithms except MAPA and MARK, with the latter overestimating the altitude of the trace gas layer. The agreement between retrieved and true TG7 profiles is better for NO2, where HEIPRO, MMF, and MAPA retrieve the peak altitude at the right location, than for HCHO. This is probably owing to the lower information content and lower sensitivity for high altitudes in the UV range compared to the visible range (see Sect. 6.2). The uplifted trace gas profile (TG7) is subject to a larger degree of smoothing than the corresponding uplifted aerosol profile (AER7). Owing to the limited vertical resolution of MAX-DOAS measurements, the fine structure of the trace gas profiles measured by a NO2 balloon sonde (TG8 and TG9) is not well reproduced by the retrievals. The lowest level of agreement between true and retrieved trace gas profiles is found for the fog scenario (AER8) since the high extinction at the ground leads to a very small information content and a low sensitivity for trace gases except for the lowermost atmospheric layers (see Sect. 6.2).
MAPA, MARK, and NASA algorithms retrieve profiles close to zero for the trace gas free atmospheres (TG0), whereas the OEM algorithms either exhibit a slight bias towards the a priori (HEIPRO, MMF, PRIAM) or oscillate around zero (bePRO, BOREAS) for this scenario. Sensitivity studies based on the MMF algorithm have shown that these oscillations are suppressed if the logarithm of the profile is retrieved, as is the case for HEIPRO, MMF, and PRIAM. This representation also prevents the retrieval of negative values, which occur for bePRO and BOREAS, in particular for TG0 and TG4.
Except for bePRO NO2, a linear regression between true and retrieved trace gas concentrations yields slopes between 0.9 and 1.08 (see Fig. 15). Regression coefficients usually exceed 0.8, except for NO2 in the case of bePRO (R=0.36), BOREAS (R=0.75), PRIAM (R=0.72), and MARK (R=0.77). In particular at 360 nm, MMF and MARK aerosols appear to be more sensitive to noise than the other algorithms, with Pearson's R decreasing from 0.93 to 0.76 (MMF) and from 0.84 to 0.77 (MARK) after adding noise to the synthetic measurements. In general, NO2 profiles from the OEM algorithms appear to be more sensitive to measurement noise than HCHO profiles.
Figure 20Same as Fig. 19, but for HCHO (a) and NO2 (b) surface concentrations. The median for each aerosol scenario is shown as coloured symbol as indicated in the legend.
Figure 21Slope, intercept, regression coefficient, and RMS for the correlation between true and retrieved aerosol extinction as well as HCHO and NO2 surface concentration. Intercept and RMS values are in dimensionless units for aerosols and 1011 molec cm−3 for HCHO and NO2. Cloud and fog scenarios (AER8, AER9, AER10) are excluded from the regression analysis.
In this section, the ability of the retrieval algorithms to retrieve aerosol and trace gas total columns is discussed. Box–whisker plots comparing retrieved and true AOT at 360 and 477 nm are shown in Fig. 16. The corresponding plots for HCHO and NO2 VCDs are shown in Fig. 17. The parameters of a linear regression between retrieved and true total columns are shown in Fig. 18. Note that the total column is defined here as the integral of extinction coefficient and number concentration within the retrieval domain, i.e. from the surface to 4 km in altitude. This means that, for example, a high-altitude cloud above the retrieval domain as in the AER10 scenario is not considered in the calculation of the AOT.
Figure 22Time in seconds for the retrieval of a single profile on a single CPU core for each participant, separated by the target species as denoted in the legend.
The total column of both trace gases and aerosols is retrieved accurately by most algorithms. Except for foggy conditions (AER8), there is little dependency of the accuracy of the retrieved trace gas VCD on the aerosol profile. As expected from the limited sensitivity to high altitudes, the total columns from OEM algorithms tend to be biased towards the a priori. For the aerosol and trace gas free scenarios AER0 and TG0, a positive bias of (0.02–0.06) for AOT and (0.2–0.35)×1016 molec cm−2 for trace gases is found for the OEM algorithms. Ranging from 0.05 to 0.15, the AOT bias is somewhat higher for AER10 with a cloud above 5 km in altitude than for AER0. The OEM algorithms show a negative bias for the high-extinction scenario AER2 (AOT of 1) and a positive bias for the shallow layers AER4, AER5, and TG4, TG5. The bias towards the a priori is furthermore reflected by a smaller slope of the linear regression between retrieved and true total column for the OEM algorithms compared to the parametrised and analytical algorithms MAPA, MARK, and NASA for all species except NO2 (see Fig. 18). Again, bePRO shows poor performance in the visible range with regression coefficients of 0.02 and 0.71 for aerosols at 477 nm and NO2, respectively, while all other algorithms yield R>0.85 and R>0.93 for aerosols and trace gases, respectively. These discrepancies mainly occur for the AER0 scenario, and to a lesser extent also for AER4.
The parametrised algorithms MAPA and MARK accurately retrieve the total column in most cases. Exceptions are the AER6 and AER7 scenarios (1 km box profile and uplifted profile), where both algorithms show a positive bias. Both MAPA and MARK show a significant scatter of the AOT for AER0, AER1, AER2, AER7, and AER10 that, in the case of MAPA, is reduced by filtering out a significant fraction of the data (28 % at 360 nm and 37 % at 477 nm). Furthermore, MAPA significantly overestimates the AOT at 477 nm with a slope of 1.4, and yields a regression coefficient of only 0.56 for the AOT at 360 nm when considering all data. Regression parameters for MAPA are, however, comparable to those from the other algorithms after removing data flagged as invalid. MARK flags almost all TG0 data as invalid, although the agreement between true and retrieved VCD is very good for this algorithm. The NASA algorithm retrieves the HCHO and NO2 VCD accurately, but shows a significant negative bias for all aerosol scenarios at 477 nm (except for AER6), where the regression coefficient is less than 0.6.
In this section, the agreement between true and retrieved surface extinction and surface concentration (i.e. the values in the lowermost layer of the respective profiles with a thickness of 200 m) are discussed. As for the total column discussed in the previous section, Figs. 19 and 20 show box–whisker plots for the aerosol and trace gas surface values, and Fig. 21 lists the parameters of the linear regression between true and retrieved aerosol extinction and trace gas concentration in the lowermost retrieval layer.
Surface aerosol extinction and trace gas profiles are generally well reproduced. For bePRO retrievals in the visible, however, a negative slope and no significant correlation (R=-0.098) between true and retrieved values are found for surface aerosol extinction at 477 nm. Furthermore, the regression coefficient for bePRO NO2 is only 0.24 due to the aforementioned deviations in case of the AER0 and AER4 scenarios. However, the regression parameters of the bePRO algorithm significantly improve after discarding about 20 % of the profiles marked as invalid. The OEM algorithms yield a small positive bias for scenarios with no or only small surface extinction coefficients and trace gas concentrations (AER0, AER7, AER9, AER10, TG0, and TG7). PRIAM and NASA both underestimate the aerosol extinction at 477 nm (slope between 0.6 and 0.65). In contrast, HEIPRO, MAPA, and MARK overestimate the surface extinction, in particular at 360 nm (slope >1.16), and yield smaller regression coefficients (R<0.9 at 360 nm) than the other algorithms.
For the prescribed profile scenarios, retrieved aerosol surface extinction (mean regression coefficient from all algorithms R‾=0.903 at 360 nm and R‾=0.825 at 477 nm) shows a better agreement with the true value than the AOT (R‾=0.874 at 360 nm and R‾=0.803 at 477 nm). The opposite is true for trace gases, where the agreement of the total column (R‾=0.969 and R‾=0.955 for HCHO and NO2, respectively) is better than for the surface mixing ratio (R‾=0.894 and R‾=0.780).
In order to assess the numerical performance of the different retrieval algorithms, the duration for a single profile retrieval was reported by each participant. For multiprocessor systems, the total time has been multiplied by the number of processor kernels used for the retrieval. It is important to note that the retrievals were performed individually by each participant, using computers with different performances. A more accurate comparison would require to run all algorithms on the same computer, which is outside the scope of this study. The results of the benchmark test are shown in Fig. 22.
The OEM algorithms, which rely on online radiative transfer calculations, require between 4 (MMF) and 23 s (PRIAM) for the retrieval of a single trace gas profile. The duration for the retrieval of an aerosol profile ranges between 6 s for MMF and more than 3.5 min for BOREAS. The large range of computational effort for aerosols probably results from the different approaches for the calculation of the weighting functions. The BOREAS aerosol retrieval relies on radiative transfer simulations at several wavelengths, resulting in the lowest computational performance, followed by HEIPRO, whose aerosol weighting function calculation is based on the finite difference method, leading to about 1 min for an aerosol retrieval, while MMF and bePRO are significantly faster since they rely on analytically calculated aerosol weighting functions.
The parametrised algorithms MAPA and MARK show significant differences in computational performance, although both rely on LUTs for the weighting functions. MARK requires 13 and 24 s for aerosol and trace gas retrievals, respectively, while MAPA aerosol and trace gas retrievals are executed within 3 and 2 s, respectively.
The NASA algorithm, which does not rely on radiative transfer modelling but on an analytical approach, is outstanding in terms of computational performance. The retrieval of a single aerosol or trace gas profile requires less than 5 ms, and is thus almost 3 orders of magnitude faster than the second fastest algorithm, MAPA.
Eight different algorithms for the retrieval of aerosol and trace gas vertical profiles from MAX-DOAS measurements have been compared under a large variety of atmospheric conditions by using synthetic measurements. Both OEM and parametrised algorithms, and also the analytic approach by NASA, show equally good performance in terms of the reproduction of the true atmospheric state, with a typical accuracy (in terms of RMS difference between true and retrieved state) of (0.08–0.25) km−1 for surface aerosol extinction and (5.9–15.0) ×1010 molec cm−3 (or about 2.4–6 ppb) for HCHO and NO2 surface concentrations. These deviations, and also a potential positive bias towards the a priori for OEM algorithms, can be quite significant for clean air conditions, but are relatively small in polluted areas where several tens of parts per billion of NO2 and HCHO and aerosol extinction of up to 5 km−1 can be present (e.g. Vlemmix et al., 2015b). Total columns of trace gases can be retrieved with a higher accuracy than for aerosols, with slopes >0.85 and correlation coefficients >0.95 in most cases. However, the accuracy is expected to be higher for real atmospheric measurements (e.g. Irie et al., 2011) since some of the scenarios within this study are quite arbitrary with the intention to test the algorithms under extreme conditions.
There are only a few exceptions from this high level of agreement between the retrieved atmospheric state from the different algorithms. As a result of lack of convergence between true and modelled slant column densities, bePRO profiles are subject to a high degree of instability in the visible wavelength range for the aerosol scenarios AER0, AER8, AER9, and AER10 and to a lesser extent also AER4. Up to 25 % of the bePRO profiles need to be discarded in order to achieve an accuracy similar to the other algorithms. However, bePRO performs well when convergence is reached. The synthetic data used for the study are not necessarily representative of real measurements, especially in terms of dSCDs errors, and sensitivity tests performed by increasing the dSCDs errors but also previous publications (e.g. Hendrick et al., 2014; Vlemmix et al., 2015b) have shown that bePRO performs generally well with real measurement data, also in terms of convergence.
Aerosol AVKs from BOREAS differ from those of other OEM retrievals as additional regularisation is applied. They can therefore not be compared to those from the other retrievals and also do not comply with Eq. (6). About 54 % of the data from MAPA are flagged as invalid (39 % of the moderate scenarios AER1–AER7, and 93 % of the cloud/fog scenarios AER8–AER10), although the level of agreement of all MAPA profiles with the truth is comparable to the other algorithms, indicating that the MAPA flagging criteria might be too strict. However, the MAPA flagging successfully removes almost all outliers.
OEM algorithms tend to produce profiles biased towards the a priori, in particular at high altitudes where the sensitivity to the atmospheric state is small. OEM algorithms retrieving the logarithm of the target parameters show a higher degree of stability with fewer oscillations than those operating in linear space. Parametrised algorithms do not suffer from this disadvantage, but the possible results can be wide-spread when the sensitivity to the atmospheric state is low, which is particularly the case at high altitude or above layers with high extinction. However, despite these conceptual differences, the overall accuracy of OEM and parametrised algorithms is very similar.
Based on an analytical approach without using RTM calculations, the NASA algorithm is by far the fastest, with the retrieval of a single profile requiring less than 5 µs, followed by MAPA as the second fastest algorithm, requiring 2–3 s for a single retrieval based on LUTs. Involving online radiative transfer calculations, most OEM algorithms are slower, but their computational performance covers a wide range, mainly owing to the different approaches for the calculation of weighting functions. Being only about 2–3 times slower than the parametrised MAPA algorithm, MMF is by far the fastest OEM algorithm.
In summary, it can be concluded that, with only a few exceptions, the algorithms presented here are capable of realistically retrieving aerosol and trace gas profiles in the lowermost ≈2 km of the atmosphere, yielding 1.5–3.5 independent pieces of information depending on the target species and the atmospheric conditions. The comparison using synthetic measurements of course represents an idealisation in many respects, and the agreement between true and retrieved state might be worse for true atmospheric measurements. In particular, it has been assumed that forward model parameters, such as surface albedo, aerosol optical properties, etc., are perfectly well known, which is usually not the case for ambient measurements. Furthermore, the atmosphere has been assumed to be horizontally homogeneous, while in reality inhomogeneities, in particular broken clouds, can have a significant influence on MAX-DOAS measurements. Also, several instrumental aspects, such as a finite instrumental field of view, instrumental stray light, pointing inaccuracies, or difficulties when pointing the telescope closely towards the Sun, were not considered here. Furthermore, the idealised set of atmospheric states considered in this synthetic intercomparison exercise does not cover all atmospheric conditions.
As a result of this study, the MMF and the MAPA algorithms, both showing best performance in terms of reconstruction of the atmospheric state and computational speed, were selected as profile algorithms for the FRM4DOAS centralised near-real-time algorithm for a harmonised processing of MAX-DOAS data, which are planned to be made available to the community in the near future.
A detailed comparison of vertical profiles of trace gases and aerosols from MAX-DOAS field measurements performed during the CINDI-2 (http://www.tropomi.eu/data-products/cindi-2) campaign with co-located independent measurements, which comprises retrievals from nine algorithms and 16 workgroups, will be the subject of a companion publication.
The bePRO and HEIPRO algorithms are available from the authors upon request (Francois Hendrick, francois.hendrick@aeronomie.be, and Udo Frieß, udo.friess@iup.uni-heidelberg.de).
The reference database of synthetic dSCDs is available on the FRM4DOAS web page (http://frm4doas.aeronomie.be/index.php/documents, Frieß et al., 2019).
The supplement related to this article is available online at: https://doi.org/10.5194/amt-12-2155-2019-supplement.
The intercomparison strategy and retrieval settings were developed by FH and UF. Forward modelling of dSCDs and retrieval of vertical profiles was performed by UF, SB, LAB, TB, MMF, FH, AP, AR, MvR, ES, TV, TW, and YW. The visualisation of the data was performed by UF and J-LT. UF performed the statistical analysis of the data. VVR developed the SCIATRAN radiative transfer model.
The funding of this study by the FRM4DOAS project (ESA contract no. 4000118181/16/I-EF) is gratefully acknowledged. The activities of the IUP Heidelberg were supported by the DFG project RAPSODI (grant no. PL 193/17-1). We are also thankful to Marc Allaart (KNMI) for providing the ozone-sonde data from De Bilt for constructing the temperature and pressure profiles used as input for the RT modelling.
This paper was edited by Andreas Hofzumahaus and reviewed by three anonymous referees.
Multi-axis differential optical absorption spectroscopy (MAX-DOAS) is a widely used measurement technique for the detection of a variety of atmospheric trace gases. It enables the retrieval of aerosol and trace gas vertical profiles in the atmospheric boundary layer using appropriate retrieval algorithms. In this study, the ability of eight profile retrieval algorithms to reconstruct vertical profiles is assessed on the basis of synthetic measurements.


The Reticulated Spine is the northern region of Shadowfen. The city of Stormhold sits at its center, and the villages of Stillrise and Mud Tree are also found here to the southwest and south of the city. The Reticulated Spine Dolmen lies to the west and slightly south of Stormhold. Deshaan borders to the north. To the south are the Venomous Fens and Leafwater.
 The Skin-Stealer's Lair 5 Destroy the Dominion's ability to create Skin-Stealers.
This page was last modified on 10 November 2017, at 19:19.


text:
Predict customer behaviour in seconds? Danish startup Neurons picks up €6 million to show how
The company combines neuroscience and AI to predict consumer behaviour such as attention, focus, cognitive responses, and engagement with at least 95% accuracy
Megha Paul 25 August 2022
Throughout history, we've dabbled in trying to predict human behaviour, with crystal balls and oracles. Enter the new era where startups are using a new combination of neuroscience and AI to make it possible.
Denmark-based software prediction services platform Neurons Inc has raised over €6 million in funding to predict customer behaviour. According to the company, the seed investment is one of the largest global seed round deals ever seen in the country.
Founded in 2013, the Danish consumer neuroscience company combines neuroscience and AI to predict consumer behaviour in seconds. Neurons has provided Fortune-500 companies such as Google, Coca Cola, Mercedes, Capital One, and Ikea. The startup claims its technology can be used to predict customer behaviours such as attention, focus, cognitive responses, and engagement with at least 95% accuracy in a matter of seconds.
Neurons AI models are built and constantly updated using their leading consumer neuroscience database and because of this, reflect market trends. Post the funding, the startup will roll out sentiment AI software to enable companies to predict not only what customers will look at, but also how they will feel.
The funding was backed by Fairpoint Capital, Vækstfonden and Finance Zealand.
Thomas Zoega Ramsøy, CEO and founder of Neurons said: "This seed investment allows us to scale our predictive tools and take something that is extremely complex like the brain, and AI and give it to customers in the palm of their hand. In doing so, we're giving companies the power to create, market and sell the products and services they already know their customers want."
Filip Petersson, investment director of Fairpoint Capital said: "We are focused on investing in amazing deep tech and making it accessible to the global market, so the decision to partner with Neurons was an easy one, even in these challenging economic times. The ability to predict customer behaviors in a matter of seconds will revolutionise the way brands do business for many years to come."
Neurons Inc
Celonis rakes in $1 billion at $13 billion valuation to combat supply chain challenges
Munich-based process mining platform Celonis…
Megha Paul

redpajama_set_name: RedPajamaCommonCrawl


There are so many products. Hair spray, mousse, wet-look gel and powder promising that tousled surfer look. Often, the queue to wash your hands is endlessly drawn out by people preening in the mirror. Body image is an issue for many men, regardless of sexuality. But it seems to be more of a problem in the gay community. When it comes to appearance, gay men hold themselves - and one another - to far more exacting standards.
Stevie: I am cheerful, purposeful and kind. I hope it is from you. I work as a teacher, so I have a lot of patience. I can be soft and affectionate, but I can be playful and obstinate at the same time.
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Huck: Hope this gives you some impression about me)) I believe in all those beautiful things in life. Nature means a lot to me. I am very outgoing and at least i try everything once. I am active and purposeful.
Publisher: mp3mobilephone Cellular phone phones take happen to a sheerest urgent quota of our life. Within the MS Windows-equipped diligence, together Intel in addition to AMD introduce ignoble dominance, verve competent processors down plus their private movable before compact (M) designs built headed for bring down intensify also too draw out series dazzle devoid of sacrificing electricity.
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Speaking from multiple friendzones i can confirm this.
And not only boys will be boys excuses bad behavior. It also just paints all boys as sexual predators which is not true.
Wilfred: Family is on the first place for me. I love this job, I`ve been working here since my graduation. It is very hard to describe my features of character, but I'll do my best. I am very sincere to find a loving, caring, responsible man, to share my rest of my life and my warm love with him.


text:
namespace cc {

static ResourceProvider::ResourceId ResourceRemapHelper(
    bool* invalid_frame,
    const ResourceProvider::ResourceIdMap& child_to_parent_map,
    ResourceProvider::ResourceIdArray* resources_in_frame,
    ResourceProvider::ResourceId id) {
  ResourceProvider::ResourceIdMap::const_iterator it =
      child_to_parent_map.find(id);
  if (it == child_to_parent_map.end()) {
    *invalid_frame = true;
    return 0;
  }

  DCHECK_EQ(it->first, id);
  ResourceProvider::ResourceId remapped_id = it->second;
  resources_in_frame->push_back(id);
  return remapped_id;
}

Display::Display(DisplayClient* client,
                 SurfaceManager* manager,
                 SharedBitmapManager* bitmap_manager)
    : client_(client),
      manager_(manager),
      aggregator_(manager),
      bitmap_manager_(bitmap_manager) {
}

Display::~Display() {
}

void Display::Resize(const gfx::Size& size) {
  current_surface_.reset(new Surface(manager_, this, size));
}

void Display::InitializeOutputSurface() {
  if (output_surface_)
    return;
  scoped_ptr<OutputSurface> output_surface = client_->CreateOutputSurface();
  if (!output_surface->BindToClient(this))
    return;

  int highp_threshold_min = 0;
  bool use_rgba_4444_texture_format = false;
  size_t id_allocation_chunk_size = 1;
  bool use_distance_field_text = false;
  scoped_ptr<ResourceProvider> resource_provider =
      ResourceProvider::Create(output_surface.get(),
                               bitmap_manager_,
                               highp_threshold_min,
                               use_rgba_4444_texture_format,
                               id_allocation_chunk_size,
                               use_distance_field_text);
  if (!resource_provider)
    return;

  if (output_surface->context_provider()) {
    TextureMailboxDeleter* texture_mailbox_deleter = NULL;
    scoped_ptr<GLRenderer> renderer =
        GLRenderer::Create(this,
                           &settings_,
                           output_surface.get(),
                           resource_provider.get(),
                           texture_mailbox_deleter,
                           highp_threshold_min);
    if (!renderer)
      return;
    renderer_ = renderer.Pass();
  } else {
    scoped_ptr<SoftwareRenderer> renderer = SoftwareRenderer::Create(
        this, &settings_, output_surface.get(), resource_provider.get());
    if (!renderer)
      return;
    renderer_ = renderer.Pass();
  }

  output_surface_ = output_surface.Pass();
  resource_provider_ = resource_provider.Pass();
  child_id_ = resource_provider_->CreateChild(
      base::Bind(&Display::ReturnResources, base::Unretained(this)));
}

bool Display::Draw() {
  if (!current_surface_)
    return false;

  InitializeOutputSurface();
  if (!output_surface_)
    return false;

  // TODO(jamesr): Use the surface aggregator instead.
  scoped_ptr<DelegatedFrameData> frame_data(new DelegatedFrameData);
  CompositorFrame* current_frame = current_surface_->GetEligibleFrame();
  frame_data->resource_list =
      current_frame->delegated_frame_data->resource_list;
  RenderPass::CopyAll(current_frame->delegated_frame_data->render_pass_list,
                      &frame_data->render_pass_list);

  if (frame_data->render_pass_list.empty())
    return false;

  const ResourceProvider::ResourceIdMap& resource_map =
      resource_provider_->GetChildToParentMap(child_id_);
  resource_provider_->ReceiveFromChild(child_id_, frame_data->resource_list);

  bool invalid_frame = false;
  ResourceProvider::ResourceIdArray resources_in_frame;
  DrawQuad::ResourceIteratorCallback remap_resources_to_parent_callback =
      base::Bind(&ResourceRemapHelper,
                 &invalid_frame,
                 resource_map,
                 &resources_in_frame);
  for (size_t i = 0; i < frame_data->render_pass_list.size(); ++i) {
    RenderPass* pass = frame_data->render_pass_list[i];
    for (size_t j = 0; j < pass->quad_list.size(); ++j) {
      DrawQuad* quad = pass->quad_list[j];
      quad->IterateResources(remap_resources_to_parent_callback);
    }
  }

  if (invalid_frame)
    return false;
  resource_provider_->DeclareUsedResourcesFromChild(child_id_,
                                                    resources_in_frame);

  float device_scale_factor = 1.0f;
  gfx::Rect device_viewport_rect = gfx::Rect(current_surface_->size());
  gfx::Rect device_clip_rect = device_viewport_rect;
  bool disable_picture_quad_image_filtering = false;

  renderer_->DrawFrame(&frame_data->render_pass_list,
                       device_scale_factor,
                       device_viewport_rect,
                       device_clip_rect,
                       disable_picture_quad_image_filtering);
  CompositorFrameMetadata metadata;
  renderer_->SwapBuffers(metadata);
  return true;
}

SurfaceId Display::CurrentSurfaceId() {
  return current_surface_ ? current_surface_->surface_id() : SurfaceId();
}

void Display::ReturnResources(const ReturnedResourceArray& resources) {
}

}  // namespace cc

redpajama_set_name: RedPajamaGithub


text:
Fareham: Welsh Water selects HBS as solar delivery partner
Welsh Water has appointed HBS New Energies, based at Whiteley, near Fareham, as sole contractor to deliver phase 1 of its Glas Advantage Renewable Energy Programme. The awarded contract will see HBS complete the feasibility, design, supply, installation and commissioning of rooftop solar arrays for six of Welsh Water's operational water treatment facilities.
As one of the largest energy users in Wales with an annual energy bill of £44 million, Welsh Water needs huge amounts of electricity to pump the 800 million litres of water it supplies every day to over three million people across its service network.
To help reduce these increasing energy costs, the company has taken steps to become carbon neutral in its electricity consumption and is planning to be carbon neutral in its gas consumption by 2020 – with ambitions to be energy neutral by 2050. Currently it generates more than 25% of its own energy needs through a combination of solar, wind, hydro and advance anaerobic digestion technologies across 50 of its sites and aims to boost this to 30% by 2019.
Delivering solar electricity for Welsh Water's operations
With surveys on all sites complete our in-house design team are now at the final stages of providing construction issue drawings. HBS New Energies has started construction at the first water treatment facility and plan to have all solar systems commissioned and operational by end of March 2019.
James Bull, managing director, HBS New Energies, commented: "We're delighted to be supporting Welsh Water in the delivery of this solar programme to provide considerable energy and carbon savings across their operations. The roll out of rooftop solar installations will help Welsh Water take a step closer to achieving their energy reduction targets and reduce operating costs."
HBS New Energies has focused on building long-term partnerships with several major water utility companies, including Anglian Water, United Utilities, Bristol Water and Northumbrian Water, helping them harness the commercial benefits of solar electricity. This latest contract award from Welsh Water further demonstrates HBS' creditability in the UK water industry.
By unlocking the value of otherwise unused roof space, land or reservoir, HBS can provide a long-term strategy that will increase a water company's on-site renewable generation capacity and help them combat today's volatile energy market.
Tags: Fareham, Whiteley
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Fareham: Corporate technology honour for Taylor Ma...
Fareham: CW Fellowes appoints two new directors


Was Welcomed as soon as i walked in. Was taken early and Megan who cut my hair was lovely and friendly. She offered loads of advice and even gave me a bouncy blow dry rather than a standard. Couldnt find fault with the place. Clean tidy and friendly happy staff.
Great service and value for money. Nice place and team.


Some homeowners get so excited about remodeling jobs that they don't think through the process.
Focused on the end result, they jump into the project and make major mistakes like choosing a contractor simply because he's the cheapest, hiring someone who doesn't specialize in the work they're having done or trying to oversee the job themselves to save money.
Other homeowners let fear stop them from undertaking the projects that would make their homes more beautiful, more comfortable and more enjoyable places to live.
They wonder whether all of the money they're spending will really increase the value of their home, even if the job goes well.
Regardless of which category you fall into, our expert tips will help you avoid the biggest remodeling mistakes. We want you to proceed with confidence and get a successful result.


text:
Goat cheese cake: Tourteau de Chevre
October 27, 2010 in baking
This is my friend Leandra. It was her birthday last week, and, once again, I volunteered (read: bulldozed) my services to make a cake. Except making a cake for Leandra is slightly difficult as she is allergic to cow's milk protein. It's a relatively unknown allergy-- most people hear 'allergic to dairy' and think that it's the lactose, so most people in the natural health community would recommend drinking raw milk. Except for poeple with a milk protein allergy it doesn't matter what kind of milk they drink, because it all contains casein.
Needless to say, Leandra didn't want to eat a casein infested birthday cake because, well, I'm not going into it here. But I will say that I wish more people with allergies were more like this woman, who is, quite possibly, the easiest going person I've ever met. In fact, I've never once seen her kick up a fuss in public. I've never once seen her complain about anything. If there's something she doesn't want to eat, she just doesn't eat it, and for the most part, nobody ever finds out why. Which, living in Los Angeles where everybody wants to tell you why they're eating what they're eating, and why they won't or can't eat what they can't eat, is most refreshing. Not only THAT, but she can whip together a three course meal without breaking a sweat or having a headless-chicken-moment, and still look like a supermodel when it's ready. Compare to my headless-chicken, need a drink at 530pm, emerge from kitchen hot and sweaty and smelling less than desirable and you can see why this easy-going attitude can be found admirable. Yes. Leandra is cool as shit.
I had just read about this TOURTEAU DE CHEVRE in Dorie Greenspan's new book (I promise that one of these days I won't go on and on about DG so much) and with a simple tweak to my favourite tart crust recipe, the entire thing was casein free.
Whether you make the crust with regular or goat butter (because I've now tried both), let me just say this:
15 people. Ten minutes. All completely gone.
It was that good.
Tourteau de Chevre
From Dorie Greenspan's Around My French Table
Serves up to 15, as it turns out.
1 part SWEET TART CRUST, substituting goat butter for regular if you want it to be casein free.
5 eggs, separated, at room temp
9 oz goat cheese
3 tb cornstarch
Preheat the oven to 400. Roll out the tart dough, and line the bottom of a 9" springform pan, so that the pastry comes about halfway up the sides. Place the pastry-lined pan in the fridge.
Whisk up the egg whites until they form soft peaks, add 2 tb of the sugar and whisk another few seconds, then set aside. If using a mixer, switch to the paddle attachment (if not using a mixer, beat in a bowl) and throw in the goat cheese, the rest of the sugar, egg yolks, vanilla, salt and cornstarch. Beat until thick and creamy with no lumps-- a couple of minutes. Slowly start to fold in the egg whites, a third at a time. Do not over fold. When mixed together (and after a taste because it's basically like a goat cheese mousse, and who can resist a spoonful of that?) scrape the contents of the bowl into the pan with the pastry.
Bake for 15 minutes at 400, then turn the oven down to 350 and bake another 35 minutes or so, until the top cracks, and a knife inserted comes out clean.
I decorated it with strawberries, but it's not at all necessary...
This post has been shared at Fight back Friday.
← Pumpkin spiced chaiBasic sweet tart crust →


Tony Lawson has been working at Orange City Council's Water Treatment Plant in Icely Road for four years as an operator and mechanical fitter. The plant supplies all of Orange with treated water pumped up from Suma Park dam.
"I reckon it's a big responsibility working here. You're looking after a lot of people's lives. We do the water tests every day to make sure the fluoride pH and chlorine levels are ok."
The pumps are down at Suma Dam. We've got three pumps which pump 300-321 litres a second.
When the water comes up to the plant it gets treated with aluminium chlorohydrate – it forms a chemical that attracts itself to dirt. It then goes into flash mixers and an impellor, then through the clarifiers. Then the water goes back – it collects the dirt and this drops to the ground.
The scrapers collect all the dirt through the valves to the sludge pit. The dirty water gets sent to the sewage treatment works and we sell it to Cadia [mine].
The water then goes into the eight filters. Water comes into site basins which are white tiled so we can see any dirt. The water overflows into a cellar where it gets treated with soda ash to correct the pH, and fluoride.
The water is then pumped to the ozone treatment plant. The ozone water is then pumped into the 20 megalitre tank. Just before it goes, chlorine gets injected into it. From these tanks it gets gravity fed into eight storage tanks around town.
I reckon it's a big responsibility working here. You're looking after a lot of people's lives. We do the water tests every day to make sure the fluoride pH and chlorine levels are ok.


"Any further increases of CO2 in the atmosphere will have minimal, if not negligible, effects on the temperature (see "Carbon dioxide is already absorbing almost all it can")."
"The YeZ is a concept car designed to photosynthesize carbon dioxide from the air, much like a plant."
"And carbon dioxide is the predominant greenhouse gas."


All About Counseling's directory of Maplewood marriage counseling professionals is your trusted resource to find a top professional marriage counselor provider to save a marriage, stop my divorce or get related help.
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World Series is an exciting new concept from the IPA, giving both Professional and Amateur players the opportunities to play in new upcoming and established Blackball playing countries.
All events are double elimination. There is no seeding. These are ranking events.


This luxe bench offers outstanding style and support with self-welted upholstery and a pillow-top seat with plentiful foam-and-fiber padding. Bracket-style legs add an element of structure to the design.
Lightly distressed, this pretty perch stands tall on a coarse rubberwood frame with floral medallions, ballerina legs, and a fluted apron. A foam-filled velvet seat with deep button tufts complements the look beautifully.


Join us at 2pm SLT today, Monday 30th January, for the new episode of Designing Worlds at our studio in Garden of Dreams, when we'll be visiting the beautiful estate of Rosehaven, which has recently celebrated its tenth anniversary.
Perhaps the name may be a little unfamiliar to you? That is because until recently, the estate was known as Winterfell, a name it took in 2016, long before a certain television programme had made the name widely known.
We talk to the estate owner Selena Anansi about the reasons for the name change, and about why her voice sounds strangely familiar!
Rosehaven is a estate of mystery and romance, with some dark undertones. We take a look at builds old and new (including some new mews cottages), and discuss the different aspects of this lovely fantasy estate. We visit some of the residents in their homes – including Elizabeth, the Duchess of Trikassi, Mr Podruly Peccable and our very own Wildstar Beaumont who was one of the very earliest residents of the estate!
And we take several tours of the waterways to see some of the magnificent castles and other builds.
As we go we talk about roleplay as opposed to lifestyle, and talk about the role that storytelling plays in Rosehaven.
We can promise you a really lovely show so do join us to watch it at 2pm SLT.
If you would like to visit the Rosehaven Estate for yourself, the recommended starting point is Rosehaven Castle.