text:
Journal of Atmospheric and Oceanic Technology
2. Data and methodology
a. The ER-2 algorithm basis dataset
b. The Peterson et al. (2015) ER-2 algorithm
c. Variations on the satellite passive microwave electric field retrieval algorithm
1) The passive microwave and radar fork
2) The passive-microwave-only fork
3) The microwave cold cloud feature fork
a. Statistics of satellite-retrieved Wilson current
b. Retrieved Wilson currents and LIS total lightning
c. Retrieved Wilson currents and the Carnegie curve
Adlerman E. J., and E. R. Williams, 1996: Seasonal variation of the global electric circuit. J. Geophys. Res., 101, 29 679–29 688, https://doi.org/10.1029/96JD01547.
Adlerman E. J., and E. R. Williams, 1996: Seasonal variation of the global electric circuit. J. Geophys. Res., 101, 29 679–29 688, https://doi.org/10.1029/96JD01547.10.1029/96JD01547)| false
Awaka, J., T. Iguchi, and K. Okamoto, 2007: Rain type classification algorithm. Measuring Precipitation from Space: EURAINSAT and the Future, V. Levizzani, P. Bauer, and F. J. Turk, Eds., Advances in Global Change Research, Vol. 28, Springer, 213–224, https://doi.org/10.1007/978-1-4020-5835-6_17.
Awaka, J., T. Iguchi, and K. Okamoto, 2007: Rain type classification algorithm. Measuring Precipitation from Space: EURAINSAT and the Future, V. Levizzani, P. Bauer, and F. J. Turk, Eds., Advances in Global Change Research, Vol. 28, Springer, 213–224, https://doi.org/10.1007/978-1-4020-5835-6_17.10.1007/978-1-4020-5835-6_17)| false
Bailey, J. C., R. J. Blakeslee, D. E. Buechler, and H. J. Christian, 2007: Diurnal lightning distributions as observed by the Optical Transient Detector (OTD) and the Lightning Imaging Sensor (LIS). Proc. 13th Int. Conf. on Atmospheric Electricity, Beijing, China, International Commission on Atmospheric Electricity, 4 pp., https://ntrs.nasa.gov/search.jsp?R=20070038367.
Barros, A. P., and Coauthors, 2014: IPHEx 2014: Observations of orographic precipitation processes in the Southern Appalachians. 2014 Fall Meeting, San Francisco, CA, Amer. Geophys. Union, Abstract H23P-01.
Bateman, M. G., M. F. Stewart, R. J. Blakeslee, S. J. Podgorny, H. J. Christian, D. M. Mach, J. C. Bailey, and D. Daskar, 2007: A low‐noise, microprocessor‐controlled, internally digitizing rotating‐vane electric field mill for airborne platforms. J. Atmos. Oceanic Technol., 24, 1245–1255, https://doi.org/10.1175/JTECH2039.1.
Bateman, M. G., M. F. Stewart, R. J. Blakeslee, S. J. Podgorny, H. J. Christian, D. M. Mach, J. C. Bailey, and D. Daskar, 2007: A low‐noise, microprocessor‐controlled, internally digitizing rotating‐vane electric field mill for airborne platforms. J. Atmos. Oceanic Technol., 24, 1245–1255, https://doi.org/10.1175/JTECH2039.1.10.1175/JTECH2039.1)| false
Blakeslee, R. J., and Coauthors, 1999: Diurnal lightning distribution as observed by the Optical Transient Detector (OTD). 11th International Conference on Atmospheric Electricity, H. J. Christian, Ed., NASA Conf. Publ. NASA/CP‐1999‐209261, 742–745.
Blyth, A. M, H. J Christian Jr., K. Driscoll, A. M Gadian, and J. Latham, 2001: Determination of ice precipitation rates and thunderstorm anvil ice contents from satellite observations of lightning. Atmos. Res., 59–60, 217–229, https://doi.org/10.1016/S0169-8095(01)00117-X.
Blyth, A. M, H. J Christian Jr., K. Driscoll, A. M Gadian, and J. Latham, 2001: Determination of ice precipitation rates and thunderstorm anvil ice contents from satellite observations of lightning. Atmos. Res., 59–60, 217–229, https://doi.org/10.1016/S0169-8095(01)00117-X.10.1016/S0169-8095(01)00117-X)| false
Brooks, C. E. P., 1925: The distribution of thunderstorms over the globe. Geophys. Mem., 3 (24), 147–164.
Brooks, C. E. P., 1925: The distribution of thunderstorms over the globe. Geophys. Mem., 3 (24), 147–164.)| false
Burns, G. B., A. V. Frank-Kamenetsky, B. A. Tinsley, W. J. R. French, P. Grigioni, D. Camporeale, and E. A. Bering, 2017: Atmospheric global circuit variations from Vostok and Concordia electric field measurements. J. Atmos. Sci., 74, 783–800, https://doi.org/10.1175/JAS-D-16-0159.1.
Burns, G. B., A. V. Frank-Kamenetsky, B. A. Tinsley, W. J. R. French, P. Grigioni, D. Camporeale, and E. A. Bering, 2017: Atmospheric global circuit variations from Vostok and Concordia electric field measurements. J. Atmos. Sci., 74, 783–800, https://doi.org/10.1175/JAS-D-16-0159.1.10.1175/JAS-D-16-0159.1)| false
Cecil, D. J., S. J. Goodman, D. J. Boccippio, E. J. Zipser, and S. W. Nesbitt, 2005: Three years of TRMM precipitation features. Part I: Radar, radiometric, and lightning characteristics. Mon. Wea. Rev., 133, 543–566, https://doi.org/10.1175/MWR-2876.1.
Cecil, D. J., S. J. Goodman, D. J. Boccippio, E. J. Zipser, and S. W. Nesbitt, 2005: Three years of TRMM precipitation features. Part I: Radar, radiometric, and lightning characteristics. Mon. Wea. Rev., 133, 543–566, https://doi.org/10.1175/MWR-2876.1.10.1175/MWR-2876.1)| false
Cecil, D. J., D. E. Buechler, and R. J. Blakeslee, 2014: Gridded lightning climatology from TRMM-LIS and OTD: Dataset description. Atmos. Res., 135-136, 404–414, https://doi.org/10.1016/j.atmosres.2012.06.028.
Cecil, D. J., D. E. Buechler, and R. J. Blakeslee, 2014: Gridded lightning climatology from TRMM-LIS and OTD: Dataset description. Atmos. Res., 135-136, 404–414, https://doi.org/10.1016/j.atmosres.2012.06.028.10.1016/j.atmosres.2012.06.028)| false
Davydenko, S. S., T. C. Marshall, and M. Stolzenburg, 2011: Modeling the electric properties of MCS stratiform regions and their contribution to the global circuit. Proc. XIV Int. Conf. on Atmospheric Electricity, Rio de Janeiro, Brazil, International Commission on Atmospheric Electricity, 7–12.
Deierling, W., C. Kalb, D. Mach, C. Liu, M. Peterson, and R. Blakeslee, 2014: On the variability of Wilson currents by storm type and phase. Proc. 15th Int. Conf. on Atmospheric Electricity (ICAE 2014), Normal, OK, IUGG and IAMAS International Commission on Atmospheric Electricity, O-10-02, http://www.nssl.noaa.gov/users/mansell/icae2014/preprints/Deierling_77.pdf.
Goodman, S. J., and Coauthors, 2013: The GOES-R Geostationary Lightning Mapper (GLM). Atmos. Res., 125–126, 34–49, https://doi.org/10.1016/j.atmosres.2013.01.006.
Goodman, S. J., and Coauthors, 2013: The GOES-R Geostationary Lightning Mapper (GLM). Atmos. Res., 125–126, 34–49, https://doi.org/10.1016/j.atmosres.2013.01.006.10.1016/j.atmosres.2013.01.006)| false
Halverson, J., and T. Rickenbach, 2002: Environmental characteristics of convective systems during TRMM-LBA. Mon. Wea. Rev., 130, 1493–1509, https://doi.org/10.1175/1520-0493(2002)130<1493:ECOCSD>2.0.CO;2.
Halverson, J., and T. Rickenbach, 2002: Environmental characteristics of convective systems during TRMM-LBA. Mon. Wea. Rev., 130, 1493–1509, https://doi.org/10.1175/1520-0493(2002)130<1493:ECOCSD>2.0.CO;2.10.1175/1520-0493(2002)130<1493:ECOCSD>2.0.CO;2)| false
Halverson, J., and Coauthors, 2007: NASA's Tropical Cloud Systems and Processes experiment: Investigating tropical cyclogenesis and hurricane intensity change. Bull. Amer. Meteor. Soc., 88, 867–882, https://doi.org/10.1175/BAMS-88-6-867.
Halverson, J., and Coauthors, 2007: NASA's Tropical Cloud Systems and Processes experiment: Investigating tropical cyclogenesis and hurricane intensity change. Bull. Amer. Meteor. Soc., 88, 867–882, https://doi.org/10.1175/BAMS-88-6-867.10.1175/BAMS-88-6-867)| false
Hollinger, J. P., 1991: DMSP Special Sensor Microwave/Imager calibration/validation. Naval Research Laboratory Final Rep., Vol. II, 314 pp.
Hutchins, M. L., R. H. Holzworth, and J. B. Brundell, 2014: Diurnal variation of the global electric circuit from clustered thunderstorms. J. Geophys. Res. Space Phys., 119, 620–629, https://doi.org/10.1002/2013JA019593.
Hutchins, M. L., R. H. Holzworth, and J. B. Brundell, 2014: Diurnal variation of the global electric circuit from clustered thunderstorms. J. Geophys. Res. Space Phys., 119, 620–629, https://doi.org/10.1002/2013JA019593.10.1002/2013JA019593)| false
Israel, H., 1973: Fields, Charges, Currents. Vol. II, Atmospheric Electricity, Probleme der Kosmischen Physik, Vol. 29, Israel Program for Scientific Translations, Ltd., 478 pp.
Jayaratne, E. R., C. P. R. Saunders, and J. Hallet, 1983: Laboratory studies of the charging of soft hail during ice crystal interactions. Quart. J. Roy. Meteor. Soc., 109, 609–630, https://doi.org/10.1002/qj.49710946111.
Jayaratne, E. R., C. P. R. Saunders, and J. Hallet, 1983: Laboratory studies of the charging of soft hail during ice crystal interactions. Quart. J. Roy. Meteor. Soc., 109, 609–630, https://doi.org/10.1002/qj.49710946111.10.1002/qj.49710946111)| false
Kakar, R., M. Goodman, R. Hood, and A. Guillory, 2006: Overview of the Convection and Moisture Experiment (CAMEX). J. Atmos. Sci., 63, 5–18, https://doi.org/10.1175/JAS3607.1.
Kakar, R., M. Goodman, R. Hood, and A. Guillory, 2006: Overview of the Convection and Moisture Experiment (CAMEX). J. Atmos. Sci., 63, 5–18, https://doi.org/10.1175/JAS3607.1.10.1175/JAS3607.1)| false
Kummerow, C., W. Barnes, T. Kozu, J. Shiue, and J. Simpson, 1998: The Tropical Rainfall Measuring Mission (TRMM) sensor package. J. Atmos. Oceanic Technol., 15, 809–817, https://doi.org/10.1175/1520-0426(1998)015<0809:TTRMMT>2.0.CO;2.
Kummerow, C., W. Barnes, T. Kozu, J. Shiue, and J. Simpson, 1998: The Tropical Rainfall Measuring Mission (TRMM) sensor package. J. Atmos. Oceanic Technol., 15, 809–817, https://doi.org/10.1175/1520-0426(1998)015<0809:TTRMMT>2.0.CO;2.10.1175/1520-0426(1998)015<0809:TTRMMT>2.0.CO;2)| false
Liu, C., E. J. Zipser, D. J. Cecil, S. W. Nesbitt, and S. Sherwood, 2008: A cloud and precipitation feature database from 9 years of TRMM observations. J. Appl. Meteor. Climatol., 47, 2712–2728, https://doi.org/10.1175/2008JAMC1890.1.
Liu, C., E. J. Zipser, D. J. Cecil, S. W. Nesbitt, and S. Sherwood, 2008: A cloud and precipitation feature database from 9 years of TRMM observations. J. Appl. Meteor. Climatol., 47, 2712–2728, https://doi.org/10.1175/2008JAMC1890.1.10.1175/2008JAMC1890.1)| false
Liu, C., E. R. Williams, E. J. Zipser, and G. Burns, 2010: Diurnal variations of global thunderstorms and electrified shower clouds and their contribution to the global electric circuit. J. Atmos. Sci., 67, 309–323, https://doi.org/10.1175/2009JAS3248.1.
Liu, C., E. R. Williams, E. J. Zipser, and G. Burns, 2010: Diurnal variations of global thunderstorms and electrified shower clouds and their contribution to the global electric circuit. J. Atmos. Sci., 67, 309–323, https://doi.org/10.1175/2009JAS3248.1.10.1175/2009JAS3248.1)| false
Liu, C., D. Cecil, and E. J. Zipser, 2011: Relationships between lightning flash rates and passive microwave brightness temperatures at 85 and 37 GHz over the tropics and subtropics. J. Geophys. Res., 116, D23108, https://doi.org/10.1029/2011JD016463.
Liu, C., D. Cecil, and E. J. Zipser, 2011: Relationships between lightning flash rates and passive microwave brightness temperatures at 85 and 37 GHz over the tropics and subtropics. J. Geophys. Res., 116, D23108, https://doi.org/10.1029/2011JD016463.)| false
Mach, D. M., R. J. Blakeslee, M. G. Bateman, and J. C. Bailey, 2009: Electric fields, conductivity, and estimated currents from aircraft overflights of electrified clouds. J. Geophys. Res., 114, D10204, https://doi.org/ 10.1029/2008JD011495.
Mach, D. M., R. J. Blakeslee, M. G. Bateman, and J. C. Bailey, 2009: Electric fields, conductivity, and estimated currents from aircraft overflights of electrified clouds. J. Geophys. Res., 114, D10204, https://doi.org/ 10.1029/2008JD011495.10.1029/2008JD011495)| false
Mach, D. M., R. J. Blakeslee, M. G. Bateman, and J. C. Bailey, 2010: Comparisons of total currents based on storm location, polarity, and flash rates derived from high-altitude aircraft overflights. J. Geophys. Res., 115, D03201, https://doi.org/10.1029/2009JD012240.
Mach, D. M., R. J. Blakeslee, M. G. Bateman, and J. C. Bailey, 2010: Comparisons of total currents based on storm location, polarity, and flash rates derived from high-altitude aircraft overflights. J. Geophys. Res., 115, D03201, https://doi.org/10.1029/2009JD012240.)| false
Mach, D. M., R. J. Blakeslee, and M. G. Bateman, 2011: Global electric circuit implications of combined aircraft storm electric current measurements and satellite-based diurnal lightning statistics. J. Geophys. Res., 116, D05201, https://doi.org/10.1029/2010JD014462.
Mach, D. M., R. J. Blakeslee, and M. G. Bateman, 2011: Global electric circuit implications of combined aircraft storm electric current measurements and satellite-based diurnal lightning statistics. J. Geophys. Res., 116, D05201, https://doi.org/10.1029/2010JD014462.)| false
Mansell, E. R., D. R. MacGorman, C. L. Ziegler, and J. M. Straka, 2005: Charge structure and lightning sensitivity in a simulated multicell thunderstorm. J. Geophys. Res., 110, D12101, https://doi.org/10.1029/2004JD005287.
Mansell, E. R., D. R. MacGorman, C. L. Ziegler, and J. M. Straka, 2005: Charge structure and lightning sensitivity in a simulated multicell thunderstorm. J. Geophys. Res., 110, D12101, https://doi.org/10.1029/2004JD005287.10.1029/2004JD005287)| false
Markson, R., 2007: The global circuit intensity: Its measurement and variation over the last 50 years. Bull. Amer. Meteor. Soc., 88, 223–241, https://doi.org/10.1175/BAMS-88-2-223.
Markson, R., 2007: The global circuit intensity: Its measurement and variation over the last 50 years. Bull. Amer. Meteor. Soc., 88, 223–241, https://doi.org/10.1175/BAMS-88-2-223.10.1175/BAMS-88-2-223)| false
Mezuman, K., C. Price, and E. Galanti, 2014: On the spatial and temporal distribution of global thunderstorm cells. Environ. Res. Lett., 9, 124023, https://doi.org/10.1088/1748-9326/9/12/124023.
Mezuman, K., C. Price, and E. Galanti, 2014: On the spatial and temporal distribution of global thunderstorm cells. Environ. Res. Lett., 9, 124023, https://doi.org/10.1088/1748-9326/9/12/124023.10.1088/1748-9326/9/12/124023)| false
Nesbitt, S. W., E. J. Zipser, and D. J. Cecil, 2000: A census of precipitation features in the tropics using TRMM: Radar, ice scattering, and lightning observations. J. Climate, 13, 4087–4106, https://doi.org/10.1175/1520-0442(2000)013<4087:ACOPFI>2.0.CO;2.
Nesbitt, S. W., E. J. Zipser, and D. J. Cecil, 2000: A census of precipitation features in the tropics using TRMM: Radar, ice scattering, and lightning observations. J. Climate, 13, 4087–4106, https://doi.org/10.1175/1520-0442(2000)013<4087:ACOPFI>2.0.CO;2.10.1175/1520-0442(2000)013<4087:ACOPFI>2.0.CO;2)| false
Peterson, M. J., 2014: Variations of optical and radio lightning characteristics and the relationship between storm convective intensity and above-cloud electric fields. Ph.D. dissertation, University of Utah, 253 pp., https://search.proquest.com/openview/2d481bb6a222ae8958de1016fd602870/1?pq-origsite=gscholar&cbl=18750&diss=y.
Peterson, M. J., C. Liu, D. Mach, W. Deierling, and C. Kalb, 2015: A method of estimating electric fields above electrified clouds from passive microwave observations. J. Atmos. Oceanic Technol., 32, 1429–1446, https://doi.org/10.1175/JTECH-D-14-00119.1.
Peterson, M. J., C. Liu, D. Mach, W. Deierling, and C. Kalb, 2015: A method of estimating electric fields above electrified clouds from passive microwave observations. J. Atmos. Oceanic Technol., 32, 1429–1446, https://doi.org/10.1175/JTECH-D-14-00119.1.10.1175/JTECH-D-14-00119.1)| false
Peterson, M. J., W. Deierling, C. Liu, D. Mach, and C. Kalb, 2017: A TRMM/GPM retrieval of the total mean generator current for the global electric circuit. J. Geophys. Res. Atmos., 122, 10 025–10 049, https://doi.org/10.1002/2016JD026336.
Peterson, M. J., W. Deierling, C. Liu, D. Mach, and C. Kalb, 2017: A TRMM/GPM retrieval of the total mean generator current for the global electric circuit. J. Geophys. Res. Atmos., 122, 10 025–10 049, https://doi.org/10.1002/2016JD026336.10.1002/2016JD026336)| false
Prigent, C., E. Defer, J. R. Pardo, C. Pearl, W. B. Rossow, and J.-P. Pinty, 2005: Relations of polarized scattering signatures observed by the TRMM Microwave Instrument with electrical processes in cloud systems. Geophys. Res. Lett., 32, L04810, https://doi.org/10.1029/2004GL022225.
Prigent, C., E. Defer, J. R. Pardo, C. Pearl, W. B. Rossow, and J.-P. Pinty, 2005: Relations of polarized scattering signatures observed by the TRMM Microwave Instrument with electrical processes in cloud systems. Geophys. Res. Lett., 32, L04810, https://doi.org/10.1029/2004GL022225.10.1029/2004GL022225)| false
Reynolds, S. E., M. Brook, and M. F. Gourley, 1957: Thunderstorm charge separation. J. Meteor., 14, 426–436, https://doi.org/10.1175/1520-0469(1957)014<0426:TCS>2.0.CO;2.
Reynolds, S. E., M. Brook, and M. F. Gourley, 1957: Thunderstorm charge separation. J. Meteor., 14, 426–436, https://doi.org/10.1175/1520-0469(1957)014<0426:TCS>2.0.CO;2.10.1175/1520-0469(1957)014<0426:TCS>2.0.CO;2)| false
Rycroft, M. J., R. G. Harrison, K. A. Nicoll, and E. A. Mareev, 2008: An overview of Earth's global electric circuit and atmospheric conductivity. Space Sci. Rev., 137, 83–105, https://doi.org/10.1007/s11214-008-9368-6.
Rycroft, M. J., R. G. Harrison, K. A. Nicoll, and E. A. Mareev, 2008: An overview of Earth's global electric circuit and atmospheric conductivity. Space Sci. Rev., 137, 83–105, https://doi.org/10.1007/s11214-008-9368-6.10.1007/s11214-008-9368-6)| false
Saunders, C. P. R., and S. L. Peck, 1998: Laboratory studies of the influence of the rime accretion rate on charge transfer during crystal/graupel collisions. J. Geophys. Res., 103, 13 949–13 956, https://doi.org/10.1029/97JD02644.
Saunders, C. P. R., and S. L. Peck, 1998: Laboratory studies of the influence of the rime accretion rate on charge transfer during crystal/graupel collisions. J. Geophys. Res., 103, 13 949–13 956, https://doi.org/10.1029/97JD02644.10.1029/97JD02644)| false
Saunders, C. P. R., W. D. Keith, and R. P. Mitzeva, 1991: The effect of liquid water content on thunderstorm charging. J. Geophys. Res., 96, 11 007–11 017, https://doi.org/10.1029/91JD00970.
Saunders, C. P. R., W. D. Keith, and R. P. Mitzeva, 1991: The effect of liquid water content on thunderstorm charging. J. Geophys. Res., 96, 11 007–11 017, https://doi.org/10.1029/91JD00970.10.1029/91JD00970)| false
Spencer, R. W., H. G. Goodman, and R. E. Hood, 1989: Precipitation retrieval over land and ocean with the SSM/I: Identification and characteristics of the scattering signal. J. Atmos. Oceanic Technol., 6, 254–273, https://doi.org/10.1175/1520-0426(1989)006<0254:PROLAO>2.0.CO;2.
Spencer, R. W., H. G. Goodman, and R. E. Hood, 1989: Precipitation retrieval over land and ocean with the SSM/I: Identification and characteristics of the scattering signal. J. Atmos. Oceanic Technol., 6, 254–273, https://doi.org/10.1175/1520-0426(1989)006<0254:PROLAO>2.0.CO;2.10.1175/1520-0426(1989)006<0254:PROLAO>2.0.CO;2)| false
Spencer, R. W., R. E. Hood, F. J. LaFontaine, E. A. Smith, R. Platt, J. Galliano, V. L. Griffin, and E. Lobl, 1994: High-resolution imaging of rain systems with the Advanced Microwave Precipitation Radiometer. J. Atmos. Oceanic Technol., 11, 849–857, https://doi.org/10.1175/1520-0426(1994)011<0849:HRIORS>2.0.CO;2.
Spencer, R. W., R. E. Hood, F. J. LaFontaine, E. A. Smith, R. Platt, J. Galliano, V. L. Griffin, and E. Lobl, 1994: High-resolution imaging of rain systems with the Advanced Microwave Precipitation Radiometer. J. Atmos. Oceanic Technol., 11, 849–857, https://doi.org/10.1175/1520-0426(1994)011<0849:HRIORS>2.0.CO;2.10.1175/1520-0426(1994)011<0849:HRIORS>2.0.CO;2)| false
Takahashi, T., 1978: Riming electrification as a charge generation mechanism in thunderstorms. J. Atmos. Sci., 35, 1536–1548, https://doi.org/10.1175/1520-0469(1978)035<1536:REAACG>2.0.CO;2.
Takahashi, T., 1978: Riming electrification as a charge generation mechanism in thunderstorms. J. Atmos. Sci., 35, 1536–1548, https://doi.org/10.1175/1520-0469(1978)035<1536:REAACG>2.0.CO;2.10.1175/1520-0469(1978)035<1536:REAACG>2.0.CO;2)| false
Takahashi, T., and K. Miyawaki, 2002: Reexamination of riming electrification in a wind tunnel. J. Atmos. Sci., 59, 1018–1025, https://doi.org/10.1175/1520-0469(2002)059<1018:ROREIA>2.0.CO;2.
Takahashi, T., and K. Miyawaki, 2002: Reexamination of riming electrification in a wind tunnel. J. Atmos. Sci., 59, 1018–1025, https://doi.org/10.1175/1520-0469(2002)059<1018:ROREIA>2.0.CO;2.10.1175/1520-0469(2002)059<1018:ROREIA>2.0.CO;2)| false
Toracinta, R., D. Cecil, E. Zipser, and S. Nesbitt, 2002: Radar, passive microwave, and lightning characteristics of precipitating systems in the tropics. Mon. Wea. Rev., 130, 802–824, https://doi.org/10.1175/1520-0493(2002)130<0802:RPMALC>2.0.CO;2.
Toracinta, R., D. Cecil, E. Zipser, and S. Nesbitt, 2002: Radar, passive microwave, and lightning characteristics of precipitating systems in the tropics. Mon. Wea. Rev., 130, 802–824, https://doi.org/10.1175/1520-0493(2002)130<0802:RPMALC>2.0.CO;2.10.1175/1520-0493(2002)130<0802:RPMALC>2.0.CO;2)| false
Vivekanandan, J., J. Turk, and V. N. Bringi, 1991: Ice water path estimation and characterization using passive microwave radiometry. J. Appl. Meteor., 30, 1407–1421, https://doi.org/10.1175/1520-0450(1991)030<1407:IWPEAC>2.0.CO;2.
Vivekanandan, J., J. Turk, and V. N. Bringi, 1991: Ice water path estimation and characterization using passive microwave radiometry. J. Appl. Meteor., 30, 1407–1421, https://doi.org/10.1175/1520-0450(1991)030<1407:IWPEAC>2.0.CO;2.10.1175/1520-0450(1991)030<1407:IWPEAC>2.0.CO;2)| false
Whipple, F. J. W., 1929: On the association of the diurnal variation of electric potential gradient in fine weather with the distribution of thunderstorms over the globe. Quart. J. Roy. Meteor. Soc., 55, 1–18, https://doi.org/10.1002/qj.49705522902.
Whipple, F. J. W., 1929: On the association of the diurnal variation of electric potential gradient in fine weather with the distribution of thunderstorms over the globe. Quart. J. Roy. Meteor. Soc., 55, 1–18, https://doi.org/10.1002/qj.49705522902.10.1002/qj.49705522902)| false
Whipple, F. J. W., and F. J. Scrase, 1936: Point discharge in the electric field of the earth. Geophys. Mem., 7 (68), 1–20.
Whipple, F. J. W., and F. J. Scrase, 1936: Point discharge in the electric field of the earth. Geophys. Mem., 7 (68), 1–20.)| false
Wilheit, T., W. Berg, H. Ebrahimi, R. Kroodsma, D. McKague, V. Payne, and J. Wang, 2015: Intercalibrating the GPM constellation using the GPM Microwave Imager (GMI). 2015 IEEE International Geoscience and Remote Sensing Symposium: Proceedings, IEEE, 5162–5165, https://doi.org/10.1109/IGARSS.2015.7326996.
Wilheit, T., W. Berg, H. Ebrahimi, R. Kroodsma, D. McKague, V. Payne, and J. Wang, 2015: Intercalibrating the GPM constellation using the GPM Microwave Imager (GMI). 2015 IEEE International Geoscience and Remote Sensing Symposium: Proceedings, IEEE, 5162–5165, https://doi.org/10.1109/IGARSS.2015.7326996.10.1109/IGARSS.2015.7326996)| false
Williams, E. R., M. E. Weber, and R. E. Orville, 1989: The relationship between lightning type and convective state of thunderclouds. J. Geophys. Res., 94, 13 213–13 220, https://doi.org/10.1029/JD094iD11p13213.
Williams, E. R., M. E. Weber, and R. E. Orville, 1989: The relationship between lightning type and convective state of thunderclouds. J. Geophys. Res., 94, 13 213–13 220, https://doi.org/10.1029/JD094iD11p13213.10.1029/JD094iD11p13213)| false
Wilson, C. T. R., 1921: Investigation on lightning discharges and on the electric field of thunderstorms. Philos. Trans. Roy. Soc. London, 221A, 73–115, https://doi.org/10.1098/rsta.1921.0003.
Wilson, C. T. R., 1921: Investigation on lightning discharges and on the electric field of thunderstorms. Philos. Trans. Roy. Soc. London, 221A, 73–115, https://doi.org/10.1098/rsta.1921.0003.)| false
An example AMPR overflight with LIP measurements and AMPR retrieved electric fields showing the 85-GHz passive microwave brightness temperature field (a) on the native AMPR grid and (b) regridded to the resolution of the TMI, and (c) the strength of the measured and estimated electric field along the flight track.
ER-2 cumulative distribution functions (CDFs) of the strengths of observed LIP electric fields and passive microwave estimates at the native AMPR grid size and AMPR data regridded to the resolution of TMI.
ER-2 estimated Wilson current strengths from AMPR and LIP for the same overflights. (a) Pixel-integrated retrieved currents are compared to a feature-level proxy current estimate and the LIP-only estimate from Mach et al. (2009). (b) Histograms of AMPR and LIP estimated currents.
The global average distribution of Wilson current retrieved from GPM passive microwave and precipitation radar observations. (a) The map of mean current, (b) zonal current distribution, and (c) meridional current distribution are shown as a fraction of the total.
As in Fig. 4, but using SSM/I passive microwave observations only to construct the global current distributions. GPM distributions are overlaid in (b) and (c).
Histograms and cumulative distributions of the Wilson current retrieved using the radar-added algorithm with TMI and PR observations (gray), the passive-only algorithm using TMI observations (blue), and feature-level algorithm (yellow). Separate distributions are shown for (a),(c),(e) ESCs and (b),(d),(f) thunderstorms and for (a),(b) land-based, (c),(d) coastal ocean, and (e),(f) open ocean storms.
The fraction of features with a given total current that are thunderstorms for (a) land, (b) coastal ocean, and (c) open ocean features. Separate curves are shown for the radar-added algorithm with TMI and PR (gray), the passive-only algorithm with TMI (blue), and the feature-level algorithm (yellow).
Cumulative two-dimensional distributions of LIS total flash rate and the total Wilson current retrieved by the radar-added algorithm with (a)–(c) TMI and PR, (d)–(f) the passive-only algorithm with TMI, and (g)–(i) the feature-level algorithm. Separate distributions are shown for (left) land-based, (middle) coastal ocean, and (right) open ocean thunderstorms. Quartiles for the distribution of current at a given flash rate and a statistical fit to the median current are overlaid.
Diurnal cycles of the retrieved current across the TRMM domain calculated using the radar-added algorithm with (a) TMI and PR data, (b) the passive-only algorithm with TMI, and (c) the feature-level algorithm. The total current (gray dashed) is compared with the land-only current (blue dashed) and both the Carnegie curve (gray solid) and the LIS/OTD climatology from Cecil et al. (2014) (blue solid). The current contributions from longitude quadrants centered on the tropical chimneys are also shown (thin solid).
Retrieving Global Wilson Currents from Electrified Clouds Using Satellite Passive Microwave Observations
Michael Peterson 1 , Wiebke Deierling 2 , Chuntao Liu 3 , Douglas Mach 4 , and Christina Kalb 5
1 National Center for Atmospheric Research, Boulder, Colorado
2 University of Colorado Boulder, Boulder, Colorado
3 Texas A&M University–Corpus Christi, Corpus Christi, Texas
4 Global Hydrology and Climate Center, University of Alabama in Huntsville, Huntsville, Alabama
https://doi.org/10.1175/JTECH-D-18-0038.1
High-altitude atmospheric electricity measurements have been used to calculate the conduction (Wilson) currents that are supplied to the global electric circuit (GEC) by individual electrified clouds. Quantifying the global average current and assessing its temporal variability is a challenge, however, because it requires measurements in every stormy region of the world. Thus, a retrieval algorithm has been developed to infer the electric fields and Wilson currents above electrified weather from NASA ER-2 passive microwave high-altitude aircraft observations that are also common satellite products.
This study documents the adaptation of the passive microwave electric field and the Wilson current retrieval algorithm for use with satellite platforms. Three distinct variants on the algorithm are produced to respond to specific use cases that differ in 1) whether swath or microwave feature data are available to describe the lateral extent of electrified clouds, 2) the availability of coincident radar data to characterize the vertical structure of electrified clouds, and 3) the prioritization of scientific accuracy or computational expense and product latency. The Wilson currents produced by the satellite retrievals are compared with each other and also with coincident lightning measurements and the Carnegie curve. The advantages, caveats, and limitations of each variant are discussed.
a Current affiliation: University of Maryland, College Park, College Park, Maryland.
Corresponding author: Michael Peterson, michaeljp24@gmail.com
Keywords: Atmosphere; Atmospheric electricity; Algorithms; Cloud retrieval; Microwave observations; Satellite observations
The global electric circuit (GEC) describes a series of electrical connections within the Earth system that maintains the electrical potential of the ionosphere relative to Earth's surface (Adlerman and Williams 1996; Markson 2007). The GEC is powered by conduction currents (Wilson currents; Wilson 1921) generated by electrified weather across the globe. The load on the circuit from the fair-weather atmosphere responds to variations in the generator current supplied by global electrified weather. Changes in the global integrated generator current resulting from solar heating as Earth rotates create a pronounced climatological diurnal cycle in the fair-weather electric field known as the Carnegie curve (Brooks 1925; Whipple 1929; Whipple and Scrase 1936; Israel 1973). The distinct shape of the Carnegie curve changes throughout the year as a result of the uneven distribution of land north and south of the equator (Burns et al. 2017).
Measuring changes to the circuit load can be accomplished with a single well-sited ground station or research vessel, but quantifying differing contributions to the supply side of the circuit requires frequent global measurements of all types of electrified weather. This includes electrified shower clouds (ESCs; Wilson 1921), which are difficult to detect because they do not initiate lightning. Wilson current contributions to the GEC from individual thunderstorms and ESCs have been quantified using the Lighting Instrument Package (LIP; Bateman et al. 2007) measurements taken by the NASA ER-2 high-altitude aircraft (Mach et al. 2009). LIP consists of multiple electric field mills positioned throughout the aircraft fuselage as well as an atmospheric conductivity probe. These combined measurements at the nominal 20-km ER-2 cruising altitude make it possible to calculate the Wilson current density, which is then integrated spatially to infer the total Wilson current supplied by the storm.
Aircraft measurements are limited to a collection of field campaign overflights, however, that tend to occur in certain key regions of the globe and often have a focus on a particular type of storm. They are not well suited for constructing a representative sample of all types of electrified clouds on global scales. For this reason, remote satellite and long-range ground-based measurements have been used to approximate the composition and overall behavior of the generator current that supplies the GEC. Mach et al. (2011) used ER-2 overflight electric fields to modify the diurnal cycle of total lightning provided by the Lightning Imaging Sensor (LIS) and Optical Transient Detector (OTD) orbital lightning imagers (Bailey et al. 2007; Blakeslee et al. 1999; Cecil et al. 2014) to account for Wilson current contributions from ESCs. Similarly, Hutchins et al. (2014) and Mezuman et al. (2014) each used ER-2 measurements to add ESC contributions to their diurnal cycles of World Wide Lightning Location Network (WWLLN) thunderstorm clusters. Liu et al. (2010), meanwhile, identified thunderstorms and ESCs in precipitation feature (PF; Nesbitt et al. 2000; Liu et al. 2008) data and then used the near-surface rainfall produced by these storm features as a proxy for Wilson current.
Reasonable agreements between these lightning-based and rainfall-based proxies and the Carnegie curve provide evidence that the global total generator current is linked to microphysical processes in electrified weather. However, while this exercise in comparisons with the fair-weather electric field measured at ground level is useful for quantifying how the global distribution of electrified weather drives the GEC, it does not draw direct connections between microphysics, electrification, and the generation of Wilson current in individual storms.
To this end, Peterson et al. (2015) developed a retrieval algorithm from ER-2 overflight data that estimates the electric field that would be measured by the LIP field mills from the spatial distribution of Advanced Microwave Precipitation Radiometer (AMPR; Spencer et al. 1994) 37- or 85-GHz passive microwave brightness temperatures. As with the LIP electric field measurements, multiplying the retrieved electric fields by the atmospheric conductivity yields an estimate of the Wilson current density across the AMPR swath. The total Wilson current for an ER-2 overpass can then be quantified by integrating the retrieved current density geospatially.
Passive microwave measurements at 37 and 85 GHz are commonly available on satellite platforms in low-Earth orbit. The Global Precipitation Measurement (GPM) constellation, for example, consists of 10 partner satellites with intercalibrated measurements in the 85–91-GHz band (Wilheit et al. 2015) that complete a combined ~140 orbits daily. If the ER-2 retrieval can be successfully adapted to general satellite passive microwave datasets, then the level of coverage provided by such orbital systems would allow for an unprecedented direct comparison between Wilson currents retrieved from global electrified weather and the measured fair-weather electric field in Antarctica (Burns et al. 2017) at a regular interval. Additionally, the multiple decade passive microwave record provided by the Special Sensor Microwave Imager (SSM/I: Hollinger 1991) could shed light on the long-term variability of the generator current that supplies the GEC.
This study documents the adaptation of the Peterson et al. (2015) passive microwave electric field and Wilson current retrieval algorithm for satellite datasets. Example use cases include gauging the lightning threat from electrified weather, and constructing long-term climatologies and near-real-time analyses of global thunderstorms and ESCs. A single-satellite algorithm would be ill-equipped to respond to this wide array of applications and the diverse collection of passive microwave sensors in orbit. Thus, three variants on the Peterson et al. (2015) algorithm are presented that balance the availability of geospatial data to describe the three-dimensional extent of electrified clouds with the computational expense of the algorithm. The currents retrieved from satellite observations are quantified herein and compared for each of these "forks" in the satellite algorithm codebase.
Developing a satellite version of the Peterson et al. (2015) algorithm opens the retrieval to use cases that are not possible with aircraft overflight data. However, a single unified algorithm would be unable to account for the range of computational constraints, data types, and potential applications. Thus, we divide our development into three forks, with each one leveraging the Peterson et al. (2015) algorithm to fulfill specific balances of scientific accuracy, ancillary data availability, and computational efficiency. While all forks share a common core premise and are derived from the same ER-2 collocated AMPR and LIP dataset from Peterson et al. (2015), the codebase in each fork is treated independently from the others to satisfy its specific role.
Section 2a documents the common underlying ER-2 data that are used by the aircraft and satellite versions of the electric field and the Wilson current retrieval algorithm. Section 2b describes the Peterson et al. (2015) ER-2 algorithm on which the satellite algorithm is based. Finally, section 2c discusses the motivations behind each fork of the satellite algorithm and highlights the differences between them.
This study uses the ER-2 algorithm basis dataset from Peterson et al. (2015) with a few notable improvements in artifact removal and cloud type identification. These changes affect the underlying data but not the computations in the Peterson et al. (2015) algorithm. As in Peterson et al. (2015), we resample the LIP electric field measurements to the AMPR scan interval, removing LIP lightning spikes and instrument drift from charging on the aircraft, and then integrate the LIP measurements into the AMPR record at one LIP electric field sample per AMPR scan. The result is a time-matched collocated dataset for both ER-2 instruments. Four field projects are considered: the third and the fourth Convection and Moisture Experiments (CAMEX-3 and CAMEX-4, respectively; Kakar et al. 2006), the Tropical Cloud Systems and Processes (TCSP) mission (Halverson et al. 2007), and the Tropical Rainfall Measuring Mission Large-Scale Biosphere-Atmosphere Experiment (TRMM-LBA; Halverson and Rickenbach 2002). The combined dataset includes over 200 000 points of comparison between the AMPR scene below the ER-2 and the LIP electric field measured at the aircraft position.
One of the challenges of working with the AMPR passive microwave data is that cold brightness temperatures over the ocean surface introduce artifacts that resemble cold cloud regions. These ocean surface artifacts were a significant source of error in Peterson et al. (2015). The retrieval works with passive microwave brightness temperatures exclusively over electrified clouds and does not handle surface artifacts well. Even a few contaminated pixels cause errors in the retrieved electric fields that become spread over a large area. Thus, it is necessary to apply a strict filter over the ocean to remove as many artifacts as possible before the AMPR data are passed to the retrieval. In Peterson et al. (2015), we were able to identify individual oceanic cases without notable surface artifact contamination, and in these cases the retrieval was shown to produce electric field estimates that agree with LIP.
To reduce the number of ocean cases that are impacted by surface artifacts, we have developed an improved surface feature filter that removes thousands of clear-air ocean pixels and all of the identifiable false cloud features from the ER-2 dataset. The filter consists of two components. First, a mask is applied to identify oceanic regions where the 10-GHz AMPR brightness temperature is below 160 K and the 37-GHz brightness temperature is below 215 K. These regions are flagged as possible ocean surface artifacts. Next, microwave features are constructed that bound contiguous oceanic regions in the AMPR swath that are either flagged as ocean surface artifacts or have 85-GHz brightness temperatures above 270 K. These features may include combinations of clear-air regions and nonconvective portions of electrified oceanic clouds. Any such oceanic feature >500 km2 in size is considered to be contaminated by the ocean surface and removed from the sample.
We emulate the scan geometry of an orbital passive microwave sensor by regridding the high-resolution AMPR brightness temperatures to the spatial resolution of the 85-GHz channel of the TRMM Microwave Imager (TMI) at 5-km cross track and 7-km down track (Kummerow et al. 1998). LIP measurements are also regridded by recording the average electric field in each larger pixel. The purpose of this regridding is to use the large volume of AMPR measurements rather than a small number of coincident TRMM overpasses to determine coefficients in the Peterson et al. (2015) algorithm that are compatible with the TMI scan geometry.
The passive microwave electric field retrieval algorithm from Peterson et al. (2015) uses the AMPR 37- or 85-GHz observations of clouds below the aircraft to estimate the electric field that is measured by LIP at the ER-2 location and cruising altitude (nominally 20 km). The premise of the algorithm stems from the role of ice in cloud electrification. According to noninductive charging (NIC; Reynolds et al. 1957; Takahashi 1978; Jayaratne et al. 1983; Saunders et al. 1991; Saunders and Peck 1998; Takahashi and Miyawaki 2002; Mansell et al. 2005), electrons are transferred between small ice particles and large graupel pellets rimed with supercooled liquid water when they collide. The small ice particles typically lose electrons to the graupel pellets. When they are lofted by the convective updraft and collect near the top of the cloud, the small ice particles create an upper-level positive charge region. The negatively charged graupel pellets, meanwhile, remain near in the midlevels of the storm. This results in a dipole charge structure that generates Wilson current.
Real-world charge structures are more complicated than this simple dipole model (i.e., Williams et al. 1989). However, we still assume that the more ice that exists in a storm cloud, the greater the potential for collisions and charge transfer, resulting in stronger 20-km electric fields and Wilson currents. Passive microwave observations at 37 and 85 GHz are diagnostic of column-integrated ice water path (Vivekanandan et al. 1991). Thus, the algorithm supposes that passive microwave observations at these frequencies are related to ice content, ice content to charging, and charging to electric field and Wilson current generation. Evidence for connections between passive microwave measurements and the thunderstorm electrification can be found in another consequence of charge separation: lightning activity. Total lightning production has been repeatedly linked to passive microwave measurements in LIS and TMI measurements on the TRMM satellite (Toracinta et al. 2002; Liu et al. 2011; Prigent et al. 2005; Blyth et al. 2001; Cecil et al. 2005).
The Peterson et al. (2015) algorithm computes the electric field across the passive microwave swath in a series of three processing steps. The first step is to approximate the geospatial distribution of charge from the passive microwave data. This is accomplished by using the passive microwave brightness temperature in each cloud pixel as a proxy for charge separation. Each pixel is assigned a charge whose strength is based on the local brightness temperature. Peterson et al. (2015) considered multiple proxy functions for charging and found that a second-power polynomial for this relationship was able to best capture how the electric fields change relative to the passive microwave brightness temperatures between weak ESCs and intense thunderstorms. The charging proxy function (f) is defined as follows:
where is the brightness temperature of the environment outside of the cloud region, is the brightness temperature of the pixel of interest, and n = 2. The environmental temperature is arbitrary, but the selected value will affect the coefficients derived in the third processing step. We do not define empirical coefficients at this stage since this relation is a proxy still in passive microwave brightness temperature units (K2). We generally use a value of 300 K for the environmental temperature.
Computational constraints and a lack of information on the vertical charge distribution of the overflown storms motivated Peterson et al. (2015) to simplify the algorithm's model of electrified cloud charge structure using a single layer of net charges. Ideally, each AMPR pixel would contain a positive charge nearer to the aircraft and a lower negative charge, and these two charges would generate a net positive electric field at the aircraft location. Instead, a single net positive charge is placed near the top of the radar graupel signature that separates the small ice particles above from the graupel below. Three-dimensional radar data are not available for most ER-2 overflights, however. In these cases, the heights of the net charges are assigned using a lookup table that provides the mean 30-dBZ echo-top height in TRMM Precipitation Radar (PR) measurements associated with a specified TMI brightness temperature. Though 85-GHz brightness temperatures and 30-dBZ echo-top heights are both diagnostic of convective vigor, it is important to note that the relationship between these two parameters is not unique (Toracinta et al. 2002). We account for such differences by creating separate lookup tables for land and ocean storms. The assigned altitudes range from 8 to 14 km.
Once the charge distribution is approximated, the second processing step applies Coulomb's law to create a proxy for the full three-dimensional electric field vector at each grid point across the passive microwave swath. For each individual pixel, the electric field contribution from every surrounding net charge is calculated based on its approximate strength from Eq. (1) and the distance between the charge and the observer. The results are then integrated to approximate the net electric field vector that would be measured at that position and the ER-2 altitude.
The final step is to estimate the measured electric field from the proxy electric field vector. To do this, electric field proxy values at the ER-2 location in each AMPR scan are compared with their counterparts in the LIP measurements. A total of 200 000 points of comparison between the two instruments are used to construct a transfer function that provides the best fit to the recorded LIP data. However, the transfer function depends on the passive microwave scan geometry and must be recomputed for radiometers that have different pixel sizes. The transfer function for AMPR at its native resolution is
where is the estimated electric field (V m−1) and is the proxy electric field (K2 m−2) from Eq. (1). The transfer function for the TMI 85-GHz channel is
Figure 1 shows how the Peterson et al. (2015) algorithm is applied to an ER-2 overflight case. The 85-GHz cold cloud features in the AMPR swath that serve as input data for the algorithm are depicted in Fig. 1b at the native AMPR resolution and in Fig. 1b regridded to the resolution of the TMI. Each of the convective features in Fig. 1a contains dozens of high-resolution AMPR pixels with minimum brightness temperatures as low as 160 K. By contrast, the convective features in the coarse TMI grid in Fig. 1b are divided between a handful of pixels. Minimum brightness temperatures are ~50 K warmer in Fig. 1b because the TMI pixels contain both convective and clear-air AMPR pixels. The transfer function in Eq. (3) takes into account the reduced pixel counts and beamfilling effects to produce the best overall match to the LIP measurements at the lower spatial resolution.
Citation: Journal of Atmospheric and Oceanic Technology 35, 7; 10.1175/JTECH-D-18-0038.1
The retrieved electric fields along the ER-2 flight track (solid thick line in Figs. 1a,b) are compared with LIP in Fig. 1c. The retrieved electric field at the native AMPR resolution (thin solid line) generally agrees with LIP measurements (solid thick line) throughout the overflight. There are two peaks: one over the western feature at 200 V m−1 and another that reaches 300 V m−1 over the stronger eastern feature. The TMI-resolution retrieved electric fields, meanwhile, peak at 200 V m−1 in the center of the LIP feature. The coarse microwave grid prevents the retrieval from reproducing the dual-peak curve across the overflight, while higher brightness temperatures from beamfilling keep the retrieved electric fields from reaching the 300 V m−1 peak of the retrieval at the native AMPR resolution. Beamfilling is an important source of uncertainty for the satellite algorithm because of the comparably large pixel sizes of orbital instruments. Small isolated cases like the features in Fig. 1a can have peak electric fields and current densities that are underrepresented by the algorithm. For the case in Fig. 1, the peak electric field is underreported by ~33% in the TMI-resolution retrieval.
The algorithm is constructed under the assumption of a convective charge structure that produces positive (upward) Wilson currents. This is consistent with the ER-2 overflights, 93% of which recorded positive current (Mach et al. 2010). However, an anomalously high fraction of negative Wilson currents is found when only the ER-2 overflights of a mature system with large stratiform regions are considered (Deierling et al. 2014). Because the Peterson et al. (2015) algorithm has no mechanism for inferring the polarity of the storm, retrieved electric fields over stratiform regions may have the correct strength but the wrong sign. This can lead to total integrated Wilson currents in mesoscale convective systems (MCSs) that are notably stronger than what is considered a physical range.
We apply a simple convective and stratiform partitioning scheme to the AMPR data that assigns a cloud type based on the intensity and texture of the microwave signals in the 85-GHz cloud scene. AMPR pixels that either have brightness temperatures below 200 K or are 20 K colder than the average brightness temperature of the surrounding >200-K cloud feature are considered convective while the remaining cloud pixels are defined as stratiform.
Electric fields produced by the retrieval at the native AMPR resolution and the regridded TMI resolution in convective and stratiform clouds are compared in Fig. 2. Cumulative electric field distributions for all cloud types over land are shown in Fig. 2a. Overall, the range of electric fields provided by the retrieval agree with the LIP measurements. AMPR estimates at the native resolution (dashed line) fall slightly lower than LIP, but the TMI-resolution retrieved electric field distribution (dotted line) is nearly indistinguishable from the LIP curve (solid line). Beamfilling effects become apparent in the convection-only distributions (Fig. 2b). The retrieved electric fields at the native AMPR resolution agree with LIP in all but the most extreme cases, where the limited AMPR swath width often misses part of the storm, as noted in Peterson et al. (2015). By comparison, the TMI-resolution retrieval produces electric fields in the 200–500 V m−1 range less frequently than the native AMPR resolution retrieval.
Distributions of the retrieved electric fields over land-based stratiform clouds (Fig. 2c) are in better agreement with LIP than convection. This is partly due to the relatively large extent of stratiform regions compared to convective features and thus less beamfilling effects. What is important to note here is that the range of retrieved electric fields either agrees with LIP (TMI resolution) or slightly favors weaker retrieved values (AMPR resolution). The fact that stratiform retrieved electric fields are not significantly higher than LIP measurements suggests that the lack of polarity information is responsible for anomalously high stratiform currents and not-higher-than-expected electric fields produced by the retrieval.
Distributions for oceanic overflights are shown in Figs. 2d–f. The ocean surface filtering in Peterson et al. (2015) was ineffective to the point that the slope between 37-GHz retrieved electric fields and LIP measurements was actually negative overall and the strongest retrieved electric fields were generally associated with clear-air cases. For this reason, we ignored the ocean results in Peterson et al. (2015) except in carefully selected cases where ocean surface artifacts were not evident.
With the new artifact removal scheme in this study, the 85-GHz retrieved electric fields at least resemble the LIP measurements, but it is clear that ocean surface artifacts still remain. Such artifacts are particularly pronounced in the convective category and increase the retrieved electric fields in Fig. 2e to the point that 66% of the retrievals are greater than 400 V m−1 compared to 50% of the LIP measurements. Such a distinct separation between the curves in this region is not evident in land-based convection (Fig. 2b).
More recent field programs with ER-2 overflights, such as the Integrated Precipitation and Hydrology Experiment (IPHEx; Barros et al. 2014) in 2014 and the Geostationary Operational Environmental Satellite (GOES-R) field campaign in 2017 (Goodman et al. 2013), have reported AMPR polarization-corrected temperatures (PCTs; Spencer et al. 1989) that compensate for land and ocean emissivity differences and place passive microwave measurements in both regions on an even field. It was not possible to incorporate these measurements into the present study, but they will be considered in future work.
We develop three variants of the algorithm outlined in section 2b for use with satellite data that are designed to meet specific operational needs. These forks ingest similar passive microwave data but differ in the ancillary data that they require and in their computational expense. The motivations behind each fork and their deviations from the Peterson et al. (2015) algorithm are discussed below.
The first fork of the satellite algorithm uses coincident radar measurements to construct an accurate representation of the three-dimensional precipitation structure of electrified clouds and is intended for general scientific use. It is computationally identical to the Peterson et al. (2015) algorithm, but it uses the coincident radar profiles to characterize the vertical extent of the electrified clouds rather than making assumptions based on a lookup table of average values. This allows the algorithm to represent the proximity of charges to the observer at 20-km altitude in all types of electrified weather and to account for clouds that may have the same 85-GHz brightness temperature but vastly different vertical extents, for example, land and ocean storms, or summer and winter convection. We use this version of the algorithm to compute the global generator current from TRMM and GPM observations, and to assess its temporal variability on many time scales in Peterson et al. (2017).
There are four caveats with this fork that limit its utility. First, the requirement of coincident radar profiles reduces the number of satellite platforms with which it can be used. Global coverage, for example, is practical only with the TRMM and GPM Core Observatory satellites, while regional analyses require data fusion with ground-based radar networks. Second, the added radar data increase the computational requirements of the algorithm in terms of data storage and CPU utilization. The pixel integration method used by this algorithm is computationally inefficient and adding the radar data further increases demand on the system. Third, radar profiles for the most intense convection may have 30-dBZ echoes that encroach upon the 20-km observer. Placing charges too close to the observer (i.e., at 19 km) invalidates our charge layer simplification. The distance-squared term in Coulomb's law then produces unrealistic retrieved electric fields and Wilson currents for these cases. Such storms are rare but have a notable impact on the global retrieved current. We mitigate this effect by defining a maximum charge height at 18 km.
Finally, the fourth limitation is the lack of polarity information provided by the algorithm. We combat this by looking only at convection and assuming a negligible overall net contribution from clouds identified as stratiform by the TRMM 2A23 algorithm (Awaka et al. 2007). Most electrified clouds (93%) observed by the ER-2 have positive vertical electric fields resulting in upward-directed Wilson currents (Mach et al. 2010). Deierling et al. (2014) partitioned the ER-2 overflights by storm type and phase and found high fractions of downward currents with dissipating stage storms and stratiform clouds. Modeling results by Davydenko et al. (2011), meanwhile, found that whether an MCS contributes positive or negative current depends on its charge structure. Of the five "type A" charge structure MCSs sampled, all produced negative Wilson currents ranging from −3 to −9 A. An additional 10 "type B" MCSs were examined with some producing positive Wilson currents and others producing negative Wilson currents. Overall, more than 50% of stratiform clouds produced negative Wilson currents.
This is also supported by the analysis of positive and negative polarity lightning flashes recorded by the National Lightning Detection Network (NLDN) collocated with TRMM LIS and PR measurements in Peterson (2014). NLDN strokes in the southern portion of the United States within the TRMM PR swath are assigned to LIS flashes if they occur within the cloud region illuminated by the flash during the LIS flash duration. Illuminated cloud pixels are considered "convective" if they have a convective PR rain type and rainfall is detected by the PR near the surface, and "stratiform" if they have a stratiform PR rain type and near-surface rain. Of all NLDN strokes that occur within LIS flashes illuminating primarily (>70%) stratiform cloud, 40%–60% were anomalous positive polarity. These results all suggest that we might expect a large fraction of the positive Wilson current contributed by electrified stratiform clouds to be nullified by inverted-polarity cases.
The second fork is computationally identical to the Peterson et al. (2015) algorithm. Since it lacks vertical radar profiles, the passive-only fork is unable to represent the hydrometeor configurations in each cloud surveyed by the satellite, however. As in Peterson et al. (2015), this passive-only fork approximates the vertical extent of observed storms with average values from a TRMM-derived lookup table. The charge layer height will be assigned based on the geographical location of the storm and the local 85 GHz PCT of the microwave pixel. Possible charge layer heights range from 8 to 14 km. Because it does not depend on ancillary data, however, this fork can be used with any passive microwave radiometer in orbit that has an 85-GHz channel.
In addition to the lack of measured cloud height information, the passive-only fork shares two of the limitations of the first radar-added fork: the computational expense of integrating over satellite swaths and the lack of polarity information that motivates us to focus on convective precipitation.
While the first two forks have a research focus and provide the most accurate current estimates possible for a given storm, the third fork prioritizes computational efficiency in order to minimize product latency. Rather than integrating pixel by pixel over hundreds to hundreds of thousands of orbital swaths, the third fork queries a database of passive microwave cold cloud features (<250 K at 85 GHz) and uses the reported feature areal extents and minimum brightness temperatures to infer the amount of current the feature should produce. Because it relies on the least detailed representation of the distribution of charge across the electrified cloud, it is intended for near-real-time applications as a "first guess" to be replaced by the passive-only or radar-added retrieved currents once they finish processing. It can also be used to quantify global changes in the overall frequency, intensity, and scale of electrified weather on long time scales.
The feature-level fork is constructed from AMPR 85 GHz < 250 K features. Total Wilson currents are computed by integrating the Peterson et al. (2015) pixel-level current densities over the feature area. These pixel-integrated currents are then compared with a feature-level proxy that depends on extent and convective intensity. The feature-level current proxy is defined as follows:
where Tb-min is the minimum 85-GHz brightness temperature of the feature; ATb<250K, ATb<200K, and ATb<150K are the feature areas with brightness temperatures below 250, 200, and 150 K, respectively; and a1, a2, c1, c2, c3, and n are constants that we tune to find the best fit with the total integrated current provided by the Peterson et al. (2015) algorithm.
We find an optimal fit for the pixel-integrated current with coefficients of n = 2, a1 = 1.12 × 10−8 A m−2 K−2, a2 = 0, c1 = c2 = 1, and c3 = 3. Feature-level currents are compared with pixel-integrated AMPR currents (r2 = 0.98) and LIP-only estimates (r2 = 0.75) in Fig. 3. The LIP current estimates are provided by Mach et al. (2010) and collocated to the nearest AMPR feature. Methodological differences limit the goodness of fit with the LIP currents (Fig. 3b), but the two AMPR estimates agree across the ER-2 current spectrum. Current distributions are shown for the three estimation methods in Fig. 3b. All three distributions peak between 100 and 200 mA. The key difference between the Peterson et al. (2015) integrated currents and the feature-level currents is that the latter is more likely to miss weak events. Features with no discernible feature-level current account for 22% of all AMPR features compared to 2% for passive-only AMPR pixel-integrated currents. However, these weaker features that produce <0.1 A are primarily weakly electrified shower clouds that are not evident in the LIP Wilson current estimates, either.
We compare the different satellite estimates to one another and to known measures of atmospheric electricity in the following sections. Retrieved currents are compared between the forks in section 3a. Retrieved currents are then compared to LIS total lightning activity in section 3b. Finally, the diurnal cycles for the total retrieved current are compared with the Carnegie curve in section 3c.
The key computational difference between the three forks lies in how much information each has about the three-dimensional structure of the electrified clouds. The radar-added fork has the most detailed representation of the horizontal and vertical extents of the cloud. The passive-only fork has the same picture of the electrified cloud horizontal extent with additional ambiguity in the vertical extent as a result of the use of a lookup table. The feature-level fork has an unclear picture of the horizontal and vertical geometry of the cloud. It is informed that there is an electrified cloud at a geographical location and is provided information on its spatial extent and peak intensity, but it has no information on how charge is distributed over the feature area. It attempts to infer the Wilson current using only these feature-level properties.
The result of these algorithm differences can be seen in the global distributions of total current. Figure 4 shows the global (Fig. 4a), zonal (Fig. 4b), and meridional (Fig. 4c) distributions of current retrieved by the radar-added algorithm using the first 2 years of GPM Core Observatory data. The global generator current is concentrated in the tropics (Fig. 4b) with separate peaks above and below the equator. Secondary maxima can be noted in the midlatitudes at ~30° north or south before falling off to zero in the high latitudes. The current is not distributed evenly along the equator, but it is instead clustered into three key regions known as the continental chimneys of the Americas, Africa and Europe, and Asia (Fig. 4c).
To contrast the global current from the radar-added fork in Fig. 4, the global current estimated using the feature-level fork applied to 25 years of SSM/I data is shown in Fig. 5. The zonal and meridional total current distributions from GPM are displayed as dashed lines for reference. The same features from Fig. 4 can be identified in Fig. 5, including a concentration of current along the equator and in the three continental chimney regions. Unlike the pixel-integrated retrieved currents in Fig. 4, however, the feature-level fork produces significant current contributions in the northern expanses of Europe and North America. Additionally, the Africa chimney produces almost as much current as the Americas rather than coming in a distant third as before. Thus, while the broad strokes of the current map remain the same, differences in how the forks describe storm structure affect the fraction of the total current contributed by a given region of the world.
Differences between the algorithm variants can also be noted in the distributions of current from individual satellite PFs (Liu et al. 2008). We define electrified cloud features (ECFs) as contiguous regions of rainfall near the surface measured by the TRMM PR (radar precipitation features) that are at least four PR pixels in size and produce Wilson current according to the retrieval. Histograms and cumulative distributions for land and ocean thunderstorms and shower cloud ECFs are shown in Fig. 6. An ECF is identified as a thunderstorm if the LIS detected a lightning flash within its boundaries. Land is partitioned from the coastal ocean at the shoreline, while the open ocean is distinguished from the coastal ocean at a constant 1000 km from shore.
Generally speaking, the total Wilson currents supplied by electrified shower clouds can be described in terms of milliamps, while thunderstorms typically produce currents in the range of amps. The shower cloud Wilson currents retrieved using either the radar-added or passive-only algorithm converge after 100 mA, but the passive-only algorithm is more likely to produce weaker currents (<1 mA) than the radar-added algorithm. The feature-level fork, in contrast, produces shower cloud currents in excess of 100 mA more frequently than either of the pixel-integrated forks. Though the peaks of the feature-level and radar-added histograms overlap, the propensity of the feature-level algorithm to produce strong ESC currents and its inability to resolve currents below the milliamp range leads to a cumulative ESC distribution that is higher than the other forks—especially over the ocean (Figs. 6c,e).
The feature-level retrieval that frequently produced strong ESC currents also produces the weakest thunderstorm currents. The feature-level thunderstorm current histogram eventually agrees with the passive-only curve, but only for the strongest land and ocean thunderstorms. The median thunderstorm Wilson current from the feature-level algorithm is 0.04 A over land, 0.09 A over the coastal ocean, and 0.1 A over the open ocean compared to 0.15, 0.3, and 0.5 A for the passive-only fork, and 0.3, 0.7, and 1 A for the radar-added fork. The radar-added algorithm routinely produces stronger thunderstorm currents than the passive-only fork because the radar profiles account for high echo tops in individual cases of intense convection. The lookup table used in the passive-only fork provides only an average height across many storms.
The combination of passive microwave observations and total lightning detection on the TRMM satellite makes it an ideal platform to examine the link between lightning production and the microphysical properties of the parent thunderstorm. We are particularly interested in how steady-state Wilson currents (~1 A; > ~600 s) that drive the direct current (dc) branch of the global circuit relate to the rapid high-current lightning discharges (~1000 A; <1 s) that drive the alternating current (ac) branch of the GEC (Rycroft et al. 2008). As both processes stem from the same separation of charged ice particles in an electrified cloud, there is likely some connection between them.
The first question that we address is how much current is produced by a storm before it begins to pose a lightning threat. Figure 7 computes the fraction of features with a particular current that are thunderstorms in land, coastal, and open ocean regions. Thunderstorm probabilities are computed separately for each fork of the satellite algorithm applied to the same set of TRMM ECFs. In each case, the probability of lightning increases with Wilson current on a broad range from 10−5 to 10 A. The radar-added and passive-only forks approach 100% beyond 10 A.
We will focus on the radar-added fork because it has the best representation of the three-dimensional structure of the storm. If we consider a 1% chance of lightning to be a reasonable level of risk, then we should pay attention to land-based storms that produce >1.7 mA, coastal oceanic storms that produce >27 mA, and open ocean storms that produce >100 mA. These thresholds also include 80% of ESCs over land, 15% of ESCs over the coastal ocean, and 5% of ESCs over the open ocean as false detections (Figs. 6a–c). Increasing the acceptable risk to 5% results in a lightning threshold of 12, 80, and 400 mA for land, coastal ocean, and open ocean regions, respectively, while increasing the acceptable risk further to 10% places the lightning threshold at 27, 180, and 960 mA, respectively. Meanwhile, 75% of land-based storms that produce 1 A of current are thunderstorms compared to just 36% of coastal ocean storms and 12% of open ocean storms.
ECF retrieved Wilson currents produced by the three forks are compared with LIS flash rates in Fig. 8. Two-dimensional cumulative histograms are shown. The first, second (median), and third quartiles of the current distribution for a given flash rate are overlaid. A statistical fit to the median is also shown to highlight the difference in slope between the distributions. The histograms for each fork follow a power-law relationship that varies considerably between land and ocean regions. A typical land-based thunderstorm that is producing one flash per minute tends to generate tens of milliamps. Its coastal counterparts, however, are likely to produce >100 mA, while thunderstorms with the same flash rate over the open ocean often produce hundreds of milliamps or more of Wilson current.
While the one-flash-per-minute intercept increases offshore, the slopes of the distributions decrease. The retrievals suggest that high-flash-rate storms—with 100 or more flashes per minute—produce the same amount of current whether they occur in continental or oceanic regions. Another way of interpreting this trend is that while lightning may not be triggered until storms produce considerably stronger Wilson currents in an oceanic environment, the increase in current that accompanies each additional flash per minute is lower over the ocean than over land.
The Carnegie curve tracks the diurnal change in the GEC through the fair-weather load on the circuit. It is a robust climatological variation that has been documented for nearly a century (Whipple 1929; Whipple and Scrase 1936). The Carnegie curve is the best available diagnostic for the performance of the satellite retrieval because it integrates total Wilson current contributions from across the globe. If the Wilson currents produced by the algorithm are reasonable for individual ECFs, then integrating them over 13 years of TRMM observations should produce a good agreement with the Carnegie curve.
Figure 9 computes the diurnal cycle of the total generator current across the TRMM domain retrieved using the radar-added fork (Fig. 9a), the passive-only fork (Fig. 9b), and the feature-level fork (Fig. 9c). The overall total current (gray dashed) and land-only total current (blue dashed) are compared with the classic annual-averaged Carnegie curve (gray) and the LIS/OTD lightning climatology (blue). The globe is divided into longitude quadrants aligned with the continental chimneys. Current contributions from the Americas (124.5°–34.5°W), Africa and Europe (34.5°W–55.5°E), Asia (55.5°–145.5°E), and the Pacific (145.5°E–124.5°W) are shown as thin solid lines.
The diurnal cycles produced by the radar-added fork are shown in Fig. 9a. The retrieved global current agrees in both phase and amplitude with the Carnegie curve, while the total land-only current agrees in overall phase and amplitude with the diurnal cycle of lightning, which primarily occurs over land. The key difference between the retrieved current curve and the Carnegie curve is that the retrieval produces more current between 0500 and 1200 UTC and less current between 1200 and 1800 UTC than the Carnegie curve would suggest. Peterson et al. (2017) links this discrepancy to the simplification of the electrified cloud charge structure to a single layer of net charges. If the radar profiles place charges too close to the observer, then the distance-squared term in Coulomb's law generates unrealistic electric fields. This was remedied using an observer at a higher altitude that was not too close to the cloud top and that produced an excellent agreement with modern observations of the Carnegie curve (Burns et al. 2017) in all seasons.
The passive-only retrieval is identical to the radar-added retrieval in all aspects other than how it handles the vertical extent of electrified clouds. It produces an improved fit with the Carnegie curve even without the added radar profile information in Fig. 6b. The chimney contributions (solid thin lines) are nearly identical between Figs. 6a and 6b with two key exceptions: the Africa peak at 1500 UTC accounts for a larger share of the total current in the passive-only fork, while the current contributed by nocturnal storms over Asia at 2000 UTC rivals the collocated Americas' peak in the radar-added fork only.
Unlike the first two forks that use a pixel-integration technique, the feature-level retrieval underestimates the amplitude of the Carnegie curve and peaks too early in the afternoon. This is largely due to overestimating the oceanic contribution to the total, as the land-based curve (dot–dashed line) peaks at the same time as the Carnegie curve. Much of the bias comes from the Pacific quadrant, which contributes more than Africa and almost as much as the Americas and Asia at all UTC hours. Improving the representation of oceanic and possibly stratiform contributions to the feature-level retrieval will be necessary for it to be used beyond a first-guess estimate.
The degree to which each retrieval agrees with the Carnegie curve is quantified in Table 1. Root-mean-square (RMS) and maximum difference error statistics are reported for the diurnal cycles of the total current retrieved by each fork over the entire TRMM domain and over land only. Error statistics were reported in Peterson et al. (2017) that summarized previous approximations in the literature. The two closest approximations to the Carnegie curve came from Mach et al. (2011) and Liu et al. (2010). Mach et al. (2011) modified the diurnal cycle in total lightning activity to account for Wilson current contributions from ESCs, resulting in a 4.4% RMS difference and an 11% maximum difference. These results depend on ER-2 sampling biases of thunderstorms relative to ESCs, however. Assuming that the ER-2 undersamples ESCs by a factor of 3 improves the agreement with the Carnegie curve to 3.4% RMS error and a 6.9% maximum difference.
RMS and maximum differences from the Carnegie curve for the total current retrieved by the radar-added, passive only, and feature-level forks applied to TRMM observations.
Liu et al. (2010), meanwhile, approximates the Carnegie curve using thunderstorm and ESC rainfall contributions as a proxy for Wilson current. The diurnal cycle of rainfall from all regions and storm types produces a curve with a 4.1% RMS difference and a 9% maximum difference from the Carnegie curve. If only land-based thunderstorms are considered, however, then the fit improves to 3.3% RMS error and a 6.4% maximum difference.
The radar-added fork applied to the TRMM dataset approximates the Carnegie curve with a 3.8% RMS error and a 9.6% maximum difference. Unlike Liu et al. (2010), looking at land only worsens our error statistics to 11.2% RMS error and a 20% maximum difference. Thus, land and ocean currents are necessary for the retrieval to capture the Carnegie curve. The passive-only fork, meanwhile, reproduces the Carnegie curve to within 3.2% RMS error and with a 6.1% maximum difference. These statistics are notably better than the optimal Carnegie curve matches in Liu et al. (2010) and Mach et al. (2011), but without the tuning they employ to achieve an excellent fit.
Finally, the feature-level fork produces the worst error statistics with 7.3% RMS error and a 12.9% maximum difference. Thus, the feature-level approximation is best suited as an interim product that can map and monitor global thunderstorms and ESCs while the other forks are still processing.
The Peterson et al. (2015) electric field retrieval algorithm is adapted for use with passive microwave satellite datasets. Three variants, or "forks," are defined that balance the availability of coincident ancillary data with computational efficiency and product latency. These forks primarily differ in how they represent the three-dimensional distribution of charge in electrified clouds. The first fork emulates the original Peterson et al. (2015) algorithm while requiring coincident radar profiles to directly prescribe the vertical extent of the electrified cloud. The second fork is a direct application of the Peterson et al. (2015) and uses a lookup table to infer the vertical cloud extent. Finally, the third fork estimates Wilson currents from the properties of passive microwave cold cloud features. The feature-level fork requires the least amount of input data and is devised to provide a "first guess" approximation of the Wilson current from an observed electrified cloud feature.
We apply these algorithms to SSM/I, TRMM, and GPM Core Observatory measurements to compare the retrieved current from individual storms, global maps of the retrieved current, and the diurnal cycle of the global retrieved current. We also compare the retrieved currents to LIS total lightning measurements and the Carnegie curve. Familiar elements of global atmospheric electricity, such as high contributions from the tropical chimneys and the distinct diurnal cycle of the Carnegie curve, are found in the satellite-retrieved currents. The retrieval likewise reinforces findings from the ER-2 aircraft data that oceanic thunderstorms produce more Wilson current than their land-based counterparts.
We plan to use these algorithms in a variety of applications that describe the global state of electrified weather. Long-term passive microwave records provided by instruments such as SSM/I will allow us to document the variability of the global generator current over multiple decades. Intercalibrated passive microwave measurements by the radiometers of the GPM constellation can be used to construct global current maps every ~3 h and to describe changes in the supply side of the GEC from one day to the next. These retrieved currents will also allow us to make direct comparisons between the GEC generator current provided by electrified weather and ground-based fair-weather electric field and global temperature measurements.
This work was partly funded by the National Science Foundation Frontiers in Earth System Dynamics Grant AGS-1135446 and NSF Grant 1519006. The National Center for Atmospheric Research is sponsored by the National Science Foundation.


text:
Further ECB rate cut expected this summer
Tracker mortage holders could be set for another rate cut this summer. A report in today's Irish Independent claims that the European Central Bank will be forced to reduce its key lending rate in June. That will mean cheaper repayments for the 375,000 people who…
Further cut to ECB rate could be in the offing
General, Home, Mortgages & Money, Property News
Homeowners with tracker mortgages could be set to benefit from further cuts in their monthly repayments this autumn. The head of the European Central Bank, Mario Draghi, signaled yesterday that rates would stay at their historic low of 0.5% for an extended period and may…
Tracker mortgages are now so valuable they may be affecting the market
Yesterday's ECB rate cut will be a welcome boost for the 400,000 or so homeowners who will see their mortgage repayments fall by 0.25%. However, the latest intervention by the European Central Bank to try and drive economic growth has only driven a bigger wedge…
ECB rate left unchanged at 0.75%
The European Central Bank has left its main interest rate at 0.75%, following their meeting today. The move means there will be no increase or reduction in the cost of servicing a mortgage for those on a tracker. Indeed, those on tracker mortgages could…
McGrath accuses banks and government of abandoning variable rate customers
The decision of AIB to hike up their variable mortgage interest rate by another 0.5% is further evidence that the banks and the government have completely abandoned variable rate customers, according to Fianna Fáil finance spokesperson Michael McGrath. Speaking following the announcement yesterday, Deputy McGrath…
Permanent TSB cut variable rate by 0.35%
Home, Mortgages & Money, Property News
Permanent TSB has announced that it is to reduce its standard variable mortgage rate following the decision by the ECB to cut rates yesterday. The bank has been one of the most expensive mortgage lenders in the country and was recently criticised by customers at…
The mortgage divide, is it right?
Mortgages & Money, Property News
Yesterday's ECB rate cut of 0.25% will be welcome news to many mortgage holders but for a large proportion, they will never benefit from it. Tracker mortgage holders will automatically benefit from the new 0.75% rate but the cut is unlikely to be passed on…
Gap between variable and tracker mortgages widens
Mortgages & Money
Research from the Central Bank has found that the average interest rate on variable rate mortgage loans is just under two percentage points higher than the average for tracker loans. The research examined movements in variable rate home loans over the past few years. The…
Should banks pass on rate cuts?
Today's topic may be a little contentious... In a surprise move yesterday Mario Draghi, the brand new ECB President, reduced interest rates and indications are that they may go lower again in the near future (he had been president for a mere 3 days to…

redpajama_set_name: RedPajamaCommonCrawl


text:
class Bar {

}

const makeBar = function () {
 return new Bar(...arguments);
};

redpajama_set_name: RedPajamaGithub


Forget smartwatches. This simple wristband buzzes when your phone rings so you never miss another call.
I really don't want a smartwatch. What I want is something that notifies me when a call or text message comes in. That way I'll stop missing them because I can't hear my phone ring or feel it vibrate.
The Vybe, as described, is a simple rubberized wristband with an embedded (but removable) gizmo that connects to your smartphone via Bluetooth.
When a text message comes in, it vibrates once. When a call comes in, it vibrates at 1-second intervals until you answer the call or it goes to voice mail. You can also reject a call just by pressing the Vybe. And if you get out of range of your phone (at least 50 feet, according to the WearVybe folks), it vibrates rapidly -- the idea being to remind you that you've left it behind somewhere.
The wristband itself comes in your choice of seven different colors, and you should be able to easily move the little vibrating chip from one to another. There's also an optional extender that lets you wear the Vybe around your ankle, nice if you'd rather keep an actual wristwatch on your wrist or just don't like it from a fashion standpoint.
The Vybe is said to be compatible with all smartphones that incorporate Bluetooth 4.0, including the iPhone 4S and later, any Android phone running OS 4.3, some Windows Phone devices, and the BlackBerry Z10.
You can preorder the Vybe for $39, which includes your choice of wristband color. For $49, you get the Vybe and three wristbands, while $74.50 gets you the Vybe and all seven colors. Individual bands run $8 apiece, and the extenders cost $3.
WearVybe promises delivery by February 28, 2014, with a guaranteed refund of your preorder payment if the date is missed.
I love, love, love the concept of the Vybe, but the expected retail price of $55 is way too high. Even $39 seems a little steep, though I will probably go ahead and order one because this is a product I've been wanting for some time.


The Series Z 5cwt van, a derivative of the Morris 8 Series E introduced in late 1938, was designed as a replacement for the Series 1 van. However, many features of the van had more in common with the earlier 8 Series 1 & 2 cars than the Series E. The engine was similar to that in the 8 Series E but used a crankshaft from the Series 1 & 2, albeit with shell bearings. This may have been a necessity to match the engine to the 3 speed gearbox used in earlier 8 models rather than the 4 speed box used in the 8 Series 8 cars. Unlike the cars, with their novel headlight mounting arrangement in the front wings, the Series Z van used traditional headlight mounting arrangements. The instrumentation used was the same as earlier 8 models rather than that used in the Series E. The vast majority of early production went to military and public service use. The Post Office used variants of the van which had detail differences and also purchased a batch with coach-built body work (a practice used previously with the Eight/Minor hybrid vans). Production continued until 1953, by which time in excess of 51,000 vans had been manufactured.


Charlene, Samantha and family, I am so saddened to hear of Tyler's passing. I am so fortunate to have had the privilege of having taught such a kind, caring, respectful and dedicated student. The world has lost a wonderful person. May your special memories help you heal in the days and months ahead.
Sharing in your sadness as you remember Tyler. Tyler's mom was a caregiver to my siblings and I for the majority of our childhood. Because of that, Tyler and his sister were a part of our life during that time too. Lots of afternoons spent playing together in our backyard that we'll look back on with fond memories. Extending my deepest sympathies during this time.
Charlene and family, I was heartbroken to hear the sad news of Tyler's passing and you have been in my thoughts every day since. You were an important part of our family when the children were little and I watched you nurture and care for your children and mine with love, patience and kindness. While I have not seen much of Tyler since those days, he was well loved by his friends and family and was smart and kind. Sending you love, friendship and my sincere condolences.
My thoughts and prayers are with each and everyone of you during this difficult time.
I went to NSCC with Tyler. Every day in and after class Tyler and I would chat, mostly about schoolwork, but not always. Tyler was always considerate to me, offering greetings in the mornings or whenever we crossed paths. Tyler was never unpleasant to speak with, he did not judge, or condescend to anyone. I will always remember Tyler's smile, it did not always surface, but that meant that the times when it did stood out more. Tyler was a smart and good person, he will certainly be missed.
So very sorry to hear of Tyler's passing. My son knew Tyler through Elec Eng Tech. Deepest condolences.
My heart goes out to all of the family. So very sorry for the loss of this young soul. Sending prayers and strength to you all. May he forever live on in your hearts.
Charlene,I'm so sorry for your loss.
thoughts are with the family ,,So sorry to hear of the loss of Tyler.
Sorry to hear about the loss of your son, grandson, brother and friend. Our thoughts are with you at this difficult time.
So sorry for your loss. Tyler was always such a pleasant student in music class at Maple Ridge.
Tyler was a great friend. He will be missed.
So sorry to hear of the loss of Tyler. We are thinking of you all at this difficult time.
Charlene and Dion, I am so sorry to hear about the loss of your son. My heart goes out to you.
Thinking of you all at this difficult time. Condolences to the entire family.
Proud to say we were friends, forever in my heart! ❤ rip Tyler!
Dear Charlene. I am so very sorry for your loss. Thinking of you all at this sad time. Our thoughts and prayers are being sent your way. Tylers' sweet smile will always be etched in my mind.


text:
18 MayamTue, 14 May 2019 11:28:39 +08002019-05-14T11:28:39+08:0011 2014 by Les Everett
Hunchback jester schemes to bring down Duke by Richard Jones
A look at Opera Australia's Rigoletto
I've seen a few operatic performances over the years but never one where the lead character is performed by a Mongolian.
But such is the case with the latest opus from Opera Australia where the lead character, the hunchback and jester Rigoletto, is performed by Amartuvshin Enkhbat. And as well Rigoletto's master the Duke of Mantua is played by an Armenian: Liparit Averisyan.
When you consider the entire cast sing Verdi's masterpiece in Italian (with sub-titles flashing on the ceiling screen above the orchestra pit) it's an astonishing feat from all concerned. Just as astonishing are the sets designed for Melbourne's State Theatre with the interior of the Duke's palace, the hired assassin Sparafucile's sleazy inn and the interior of Rigoletto's own home where his daughter Gilda (Stacey Alleaume) is under protective custody, almost house arrest – they're all superb.
Baritone Enkhbat sets the scene right from the beginning. He's wearing his jester's mask at the Duke's ball and although hampered by his double walking stick gait taunts one of the Duke's senior courtiers.
This is Count Ceprano and when he falls a-foul of the Duke, courtesy of Rigoletto's mocking, he curses both the Duke and his court jester as he's led off to prison. Terrified that the curse will strike him down Rigoletto scurries – or rather, limps – home to his daughter Gilda. En route he meets innkeeper and hired killer Sparafucile (a great bass role sung by Robert Scandiuzzi) and they come to an arrangement. Rigoletto reflects both men are paid to destroy others: the court jester with words and Sparafucile with daggers.
After a bit of to-ing and fro-ing inside the house between Rigoletto, Gilda and their housekeeper-maid the disguised Duke (he's wearing just a jacket and trousers) is let in by the maid. Hiding upstairs he's amazed to discover that the young woman he's been pursuing is the hunchback's daughter. The Duke confesses his love for Gilda and also tells her he's a student. But the imprisoned count's friends have discovered the house and blindfold the hunchback convincing him they're kidnapping Ceprano's wife.
Verdi has a delicious twist here. Rigoletto actually assists in the capture of his own daughter.
Rigoletto might be unaware of the twist but the Duke isn't. Once he's told his men have captured Rigoletto's 'lover' he immediately realises the 'lover' must be Gilda – and his men have brought her back to his palace. The jester tries to search the palace but he's hampered by his affliction. Fortunately Gilda finds him and after she unburdens herself Rigoletto realises that the Duke has raped his daughter.
So the final scenes are set. Off we go to Sparafucile's inn where the assassin is paid to kill the Duke.
Of course the Duke, again attired in civvies, is this time already there and pursuing another love interest: Sparafucile's sister Maddalena (Sian Sharp). Rigoletto and Gilda (dressed as a man) arrive outside the inn where, inside, Maddalena suggests her brother kill the hunchback instead.
Maddalena has fallen for the Duke – in a conveniently shortened time span – but the assassin still isn't convinced. Eventually Sparafucile proposes a compromise with his sister. If a stranger calls into the inn before midnight he'll kill him instead of the Duke.
And we enjoyed a little bit of off-stage drama as well. Just as the conductor was about to take his place in the orchestra pit, 90-year-old composer George Dreyfus rose from his front row seat. He's been mightily unhappy that although Opera Australia commissioned his 1970 work The Gilt-Edged Kid it's never been performed. With lights dimmed for the opening night start George couldn't read his two-page screed. And his loud hailer wasn't working – we could see the red light flashing from our seats in the middle of the stalls area. Gradually, and quite gently, the nonagenarian was coaxed towards the side front doors and apparently police took him out of the front entrance and to hospital for observation. George's antics delayed the start of Rigoletto by about 20 minutes. But the front row patrons who'd been transferred to the side aisle could at least move back to their allotted seats before the orchestra started up.
This entry was posted in Uncategorized and tagged Amartuvshin Enkhbat, George Dreyfus, Liparit Averisyan, Opera Australia's Rigoletto. Bookmark the permalink.
Speedy Croweaters get the job done
Top four teams all post convincing victories by Richard Jones


text:
Q: Google chrome dependency error - libnss3 I downloaded the .deb file from Google chrome website. Then started installing. But suddenly the software center shows a dependency not satisfied error. Some thing like given below.
Dependency not satisfiable:libnss3 (>=3.14.3)
Then I have tried all these.

*

*http://www.webupd8.org/2009/09/dependency-is-not-satisfiable-libnss3.html

*https://askubuntu.com/questions/220960/cannot-install-google-chrome-how-do-i-fix-it

*Can't install Google Chrome on Ubuntu 11.04
But not yet solved..Still the same issue.
Please give an advice on this.

A: Got it solved from this link
http://packages.ubuntu.com/precise/amd64/libnss3/download.

*

*As it says added this line deb http://security.ubuntu.com/ubuntu precise-security main at the end of /etc/apt/sources.list

*sudo apt-get update

*This may cause gpg key error.

*sudo apt-key adv --keyserver keyserver.ubuntu.com --recv-keys 8BAF9A6F. (Refer: https://askubuntu.com/questions/13065/how-do-i-fix-the-gpg-error-no-pubkey).

*Again run sudo apt-get update if gpg error had occured.

*sudo apt-get install libnss3.

This installed me with latest libnss3 and it solved issue with installing google-chrome.

redpajama_set_name: RedPajamaStackExchange


Yesterday it was 80 degrees, and today it was 50; so of course I had to pull out the cute new sweater and beanie! This outfit is perfect for running errands, lunch dates with the ladies, or even a casual dinner date (minus the beanie). I absolutely LOVE this look! I will link the items below with the exact/similar links for y'all!
Bun Beanie: this one is super cute! This came in handy, because I just really did not want to fix my hair this morning.. We all have those days, right? Haha! I was gifted this at Christmas and was so excited. I'm unsure where it was purchased, but the one linked is one I found!
Sweater: This is my absolute favorite, not kidding. This sweater is so soft and I love the lace up design on the front! I can't find it online, but it was purchased from The Hidden Trunk recently! This one and this one are similar!
Jeans: These were purchased from a local boutique. This pair and this pair are some cute ones! I feel that a pair of distressed denim can easily dress any outfit down for the day!
Booties: These were actually purchased from Charlotte Russe! They are so easy to pair with almost anything! They don't have the exact pair listed online, but these and these are super cute and under $20 right now!
I had so much fun creating this look and hope you enjoyed reading! Don't forget to subscribe to stay up-to-date on future posts!


When Mary-Kate and Ashley Olsen walk a red carpet, there's something reassuring about the fact that you'll never be surprised: The duo often wear long, flowing dresses they complement with messy-chic hair and smoky eyes. The Olsens, who turn 30 today, have inadvertently, perhaps, created an enviable, consistent aesthetic that women have been inspired by for a decade now. "Their whole look is about being monochromatic," says Ana Marie Rizzieri, the Olsens' longtime makeup artist, but "they also like to keep it raw." And while we thought the whole trick was to sleep in some black liner (which is such a no-no), rub our eyes, and head out the door, it turns out there's a same-day approach to their beloved style that we can get on board with—and definitely feel hygienic about.
Pull together the proper tool kit. Rizzieri says to get the signature MK and Ashley smoky eye, you'll need a luminizing eye shadow, a black pencil liner, a brown pencil liner, and a blending brush. The makeup artist recommends Dior Diorshow Fusion Mono Shadow in Mirror. "Any kind of luminizing shadow will work—you just want to make sure it has a cream base," she says. "You also want it to have a sheer shimmer. No glitter; it's more of just a sheen." For liners, Rizzieri keeps her makeup kit stocked with Urban Decay 24/7 Glide-on Eye Pencil in Perversion and Laura Mercier Caviar Stick Eye Color in Chocolate Laura. She also says that she's "in love" (her words) with the small blending brush from the luxe Artis Brush Collection.
Smear cream shadow over your lid—and under your eye, too.
"I put it on my fingers and dab it all over," says Rizzieri. "The reason I prime their eyes with this is [to keep the] look smooth and blended." When smudged across lid first, "the shadow helps blend and melt the black and brown liners," she says.
Next up: Black liner. Take your ring finger and gently pull up your upper lid, penciling the liner directly into upper waterline before smudging it with the makeup brush. "I don't line all the way into into the inner corners of eyes, because it creates that raccoon look." Instead, stop midway, approximately above the pupil, and use your brush to smudge liner toward the inner corner. Continue to blend, softening the harsh line. When in doubt, blend more.
Then bust out the brown liner for the lower lash line. Rizzieri draws brown liner onto the lower waterline, then "diffuses the liner out with the brush," says Rizzieri. Work from the inner corner of the eye out, stopping at the outer corner. "That's when you know not to go any further. It's also kind of nice when it collects in that because it gives you that kind of 'leftover makeup look' that's cool and creasy."
Get extra credit by adding shimmer to the inner corner. "[The twins] always like a little bit of shimmer, but just in the inner corners of the eyes." Rizzieri's current favorite is Marc Jacobs Beauty Glow Stick Glistening Illuminator in Spotlight, which has a subtle sheen that's not at all glittery.
Bottom line: Mary-Kate and Ashley's signature smoky eye is pretty easy to pull off. And they make 30 look pretty damn good.


text:
Stanley de la Courtte Snooke (11 November 1878 – 6 April 1959) was a South African cricketer who played in one Test match in 1907.

His brother, Tip, also played Test cricket for South Africa.

1878 births
1959 deaths
South Africa Test cricketers
South African cricketers
Gauteng cricketers
Western Province cricketers

redpajama_set_name: RedPajamaWikipedia