• Accadia, C., S. Mariani, M. Casaioli, A. Lavagnini, and A. Speranza, 2003: Sensitivity of precipitation forecast skill scores to bilinear interpolation and a simple nearest-neighbor average method on high-resolution verification grids. Wea. Forecasting, 18, 918932, https://doi.org/10.1175/1520-0434(2003)018<0918:SOPFSS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Adler, R. F., G. Gu, and G. J. Huffman, 2012: Estimating climatological bias errors for the Global Precipitation Climatology Project (GPCP). J. Appl. Meteor. Climatol., 51, 8499, https://doi.org/10.1175/JAMC-D-11-052.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Adler, R. F., G. Gu, M. Sapiano, J.-J. Wang, and G. J. Huffman, 2017: Global precipitation: Means, variations and trends during the satellite era (1979–2014). Surv. Geophys., 38, 679699, https://doi.org/10.1007/s10712-017-9416-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Belward, A., 1996: The IGBP-DIS Global 1 km Land Cover Data Set: Proposal and implementation plans. International Geosphere-Biosphere Program–Data and Information System Working Paper, 13 pp.

  • Bosch, D. D., J. M. Sheridan, and L. K. Marshall, 2007: Precipitation, soil moisture, and climate database, Little River experimental watershed, Georgia, United States. Water Resour. Res., 43, W09472, https://doi.org/10.1029/2006WR005834.

    • Search Google Scholar
    • Export Citation
  • Brodzik, M. J., B. Billingsley, T. Haran, B. Raup, and M. H. Savoie, 2012: EASE-grid 2.0: Incremental but significant improvements for earth-gridded data sets. ISPRS Int. J. Geo-Inf., 1, 3245, https://doi.org/10.3390/ijgi1010032.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Case, J. L., S. V. Kumar, J. Srikishen, and G. J. Jedlovec, 2011: Improving numerical weather predictions of summertime precipitation over the southeastern United States through a high-resolution initialization of the surface state. Wea. Forecasting, 26, 785807, https://doi.org/10.1175/2011WAF2222455.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chan, S. K., and Coauthors, 2016: Assessment of the SMAP passive soil moisture product. IEEE Trans. Geosci. Remote Sens., 54, 49945007, https://doi.org/10.1109/TGRS.2016.2561938.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chan, S. K., and Coauthors, 2018: Development and assessment of the SMAP enhanced passive soil moisture product. Remote Sens. Environ., 204, 931941, https://doi.org/10.1016/j.rse.2017.08.025.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Charba, J. P., D. W. Reynolds, B. E. McDonald, and G. M. Carter, 2003: Comparative verification of recent quantitative precipitation forecasts in the National Weather Service: A simple approach for scoring forecast accuracy. Wea. Forecasting, 18, 161183, https://doi.org/10.1175/1520-0434(2003)018<0161:CVORQP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, F., and R. Avissar, 1994: The impact of land-surface wetness heterogeneity on mesoscale heat fluxes. J. Appl. Meteor., 33, 13231340, https://doi.org/10.1175/1520-0450(1994)033<1323:TIOLSW>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Choudhury, B. J., T. J. Schmugge, and T. Mo, 1982: A parameterization of effective soil temperature for microwave emission. J. Geophys. Res., 87, 13011304, https://doi.org/10.1029/JC087iC02p01301.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Colliander, A., and Coauthors, 2017: Validation of SMAP surface soil moisture products with core validation sites. Remote Sens. Environ., 191, 215231, https://doi.org/10.1016/j.rse.2017.01.021.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Colliander, A., and Coauthors, 2020: Effect of rainfall events on SMAP radiometer-based soil moisture accuracy using core validation sites. J. Hydrometeor., 21, 255264, https://doi.org/10.1175/JHM-D-19-0122.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Coopersmith, E. J., M. H. Cosh, W. A. Petersen, J. Prueger, and J. J. Niemeier, 2015: Soil moisture model calibration and validation: An ARS watershed on the South Fork Iowa River. J. Hydrometeor., 16, 10871101, https://doi.org/10.1175/JHM-D-14-0145.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dai, A., 2006: Precipitation characteristics in eighteen coupled climate models. J. Climate, 19, 46054630, https://doi.org/10.1175/JCLI3884.1.

  • Du, Y., F. T. Ulaby, and M. C. Dobson, 2000: Sensitivity to soil moisture by active and passive microwave sensors. IEEE Trans. Geosci. Remote Sens., 38, 105114, https://doi.org/10.1109/36.823905.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dunbar, R. S., 2013: SMAP Ancillary Data Report: Precipitation. NASA Jet Propulsion Laboratory, 11 pp., https://smap.jpl.nasa.gov/system/internal_resources/details/original/291_049_precip.pdf.

  • Entekhabi, D., and Coauthors, 2014: SMAP Handbook—Soil Moisture Active Passive: Mapping soil moisture and freeze/thaw from space. NASA Jet Propulsion Laboratory, 182 pp., https://smap.jpl.nasa.gov/system/internal_resources/details/original/178_SMAP_Handbook_FINAL_1_JULY_2014_Web.pdf.

  • Ferrazzoli, P., S. Paloscia, P. Pampaloni, G. Schiavon, D. Solimini, and P. Coppo, 1992: Sensitivity of microwave measurements to vegetation biomass and soil moisture content: A case study. IEEE Trans. Geosci. Remote Sens., 30, 750756, https://doi.org/10.1109/36.158869.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Flores, A. N. V. Y. Ivanov, D. Entekhabi, and R. L. Bras, 2009: Impact of hillslope-scale organization of topography, soil moisture, soil temperature, and vegetation on modeling surface microwave radiation emission. IEEE Trans. Geosci. Remote Sens., 47, 25572571, https://doi.org/10.1109/TGRS.2009.2014743.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Friedl, M. A., and Coauthors, 2002: Global land cover mapping from MODIS: Algorithms and early results. Remote Sens. Environ., 83, 287302, https://doi.org/10.1016/S0034-4257(02)00078-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gerrity, J. P., 1992: A note on Gandin and Murphy’s equitable skill score. Mon. Wea. Rev., 120, 27092712, https://doi.org/10.1175/1520-0493(1992)120<2709:ANOGAM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hanson, C. L., 2001: Long-term precipitation database, Reynolds creek experimental watershed, Idaho, United States. Water Resour. Res., 37, 28312834, https://doi.org/10.1029/2001WR000415.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hong, Y., K.-L. Hsu, S. Sorooshian, and X. Gao, 2004: Precipitation estimation from remotely sensed imagery using an artificial neural network cloud classification system. J. Appl. Meteor., 43, 18341853, https://doi.org/10.1175/JAM2173.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hou, A. Y., and Coauthors, 2014: The Global Precipitation Measurement Mission. Bull. Amer. Meteor. Soc., 95, 701722, https://doi.org/10.1175/BAMS-D-13-00164.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huffman, G. J., 2019: IMERG V06 quality index. NASA, 5 pp., https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/IMERGV06_QI.pdf.

  • Huffman, G. J., and Coauthors, 2007: The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J. Hydrometeor., 8, 3855, https://doi.org/10.1175/JHM560.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huffman, G. J., D. T. Bolvin, D. Braithwaite, K. Hsu, R. Joyce, P. Xie, and S.-H. Yoo, 2015: NASA Global Precipitation Measurement (GPM) Integrated Multi-Satellite Retrievals for GPM (IMERG). NASA Algorithm Theoretical Basis Doc., version 4, 30 pp.

  • Jackson, T. J., and T. J. Schmugge, 1991: Vegetation effects on the microwave emission of soils. Remote Sens. Environ., 36, 203212, https://doi.org/10.1016/0034-4257(91)90057-D.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jackson, T. J., D. M. Le Vine, A. Y. Hsu, A. Oldak, P. J. Starks, C. T. Swift, J. D. Isham, and M. Haken, 1999: Soil moisture mapping at regional scales using microwave radiometry: The Southern Great Plains Hydrology Experiment. IEEE Trans. Geosci. Remote Sens., 37, 21362151, https://doi.org/10.1109/36.789610.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jackson, T. J., D. Chen, M. Cosh, F. Li, M. Anderson, C. Walthall, P. Doriaswamy, and E. R. Hunt, 2004: Vegetation water content mapping using Landsat data derived normalized difference water index for corn and soybeans. Remote Sens. Environ., 92, 475482, https://doi.org/10.1016/j.rse.2003.10.021.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jackson, T. J., and Coauthors, 2010: Validation of advanced microwave scanning radiometer soil moisture products. IEEE Trans. Geosci. Remote Sens., 48, 42564272, https://doi.org/10.1109/TGRS.2010.2051035.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Joyce, R. J., and P. Xie, 2011: Kalman filter–based CMORPH. J. Hydrometeor., 12, 15471563, https://doi.org/10.1175/JHM-D-11-022.1.

  • Joyce, R. J., J. E. Janowiak, P. A. Arkin, and P. Xie, 2004: CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. J. Hydrometeor., 5, 487503, https://doi.org/10.1175/1525-7541(2004)005<0487:CAMTPG>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kerr, Y. H., and Coauthors, 2012: The SMOS soil moisture retrieval algorithm. IEEE Trans. Geosci. Remote Sens., 50, 13841403, https://doi.org/10.1109/TGRS.2012.2184548.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kidd, C., and G. Huffman, 2011: Global precipitation measurement. Meteor. Appl., 18, 334353, https://doi.org/10.1002/met.284.

  • Kidd, C., A. Becker, G. J. Huffman, C. L. Muller, P. Joe, G. Skofronick-Jackson, and D. B. Kirschbaum, 2017: So, how much of the earth’s surface is covered by rain gauges? Bull. Amer. Meteor. Soc., 98, 6978, https://doi.org/10.1175/BAMS-D-14-00283.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Koster, R. D., and M. J. Suarez, 1995: Relative contributions of land and ocean processes to precipitation variability. J. Geophys. Res., 100, 13 77513 790, https://doi.org/10.1029/95JD00176.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Koster, R. D., and Coauthors, 2004: Regions of strong coupling between soil moisture and precipitation. Science, 305, 11381140, https://doi.org/10.1126/science.1100217.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lucchesi, R., 2013: File Specification for GEOS-5 FP-IT (Forward Processing for Instrument Teams). NASA Tech. Rep., 63 pp, https://ntrl.ntis.gov/NTRL/dashboard/searchResults/titleDetail/N150001438.xhtml.

  • Maggioni, V., P. C. Meyers, and M. D. Robinson, 2016: A review of merged high-resolution satellite precipitation product accuracy during the Tropical Rainfall Measuring Mission (TRMM) Era. J. Hydrometeor., 17, 11011117, https://doi.org/10.1175/JHM-D-15-0190.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McNairn, H., and Coauthors, 2014: The Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12): Prelaunch calibration and validation of the SMAP soil moisture algorithms. IEEE Trans. Geosci. Remote Sens., 53, 27842801, https://doi.org/10.1109/TGRS.2014.2364913.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mo, T., B. J. Choudhury, T. J. Schmugge, J. R. Wang, and T. J. Jackson, 1982: A model for microwave emission from vegetation-covered fields. J. Geophys. Res., 87, 11 22911 237, https://doi.org/10.1029/JC087iC13p11229.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Moran, M. S., C. D. Holifield Collins, D. C. Goodrich, J. Qi, D. T. Shannon, and A. Olsson, 2008: Long-term remote sensing database, Walnut Gulch experimental watershed, Arizona, United States. Water Resour. Res., 44, W05S10, https://doi.org/10.1029/2006WR005689.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Neelam, M., and B. P. Mohanty, 2015: Global sensitivity analysis of the radiative transfer model. Water Resour. Res., 51, 24282443, https://doi.org/10.1002/2014WR016534.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Neelam, M., A. Colliander, B. P. Mohanty, M. H. Cosh, S. Misra, and T. J. Jackson, 2020: Multiscale surface roughness for improved soil moisture estimation. IEEE Trans. Geosci. Remote Sens., 58, 52645276, https://doi.org/10.1109/TGRS.2019.2961008.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Olson, W. S., and Coauthors, 2011: GPM Combined Radar-Radiometer Precipitation Algorithm Theoretical Basis Document (version 4). NASA Algorithm Theoretical Basis Doc., 63 pp., https://gpm.nasa.gov/sites/default/files/document_files/Combined_algorithm_ATBD.V04.rev_.pdf.

  • O’Neill, P. E., E. G. Njoku, T. J. Jackson, S. Chan, and R. Bindlish, 2019: SMAP algorithm theoretical basis document: Level 2 & 3 soil moisture (passive) data products. NASA Jet Propulsion Laboratory, D-66480, 100 pp., https://smap.jpl.nasa.gov/system/internal_resources/details/original/484_L2_SM_P_ATBD_rev_F_final_Aug2020.pdf.

  • Ookouchi, Y., M. Segal, R. C. Kessler, and R. A. Pielke, 1984: Evaluation of soil moisture effects on the generation and modification of mesoscale circulations. Mon. Wea. Rev., 112, 22812292, https://doi.org/10.1175/1520-0493(1984)112<2281:EOSMEO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Owe, M., R. de Jeu, and J. Walker, 2001: A methodology for surface soil moisture and vegetation optical depth retrieval using the microwave polarization difference index. IEEE Trans. Geosci. Remote Sens., 39, 16431654, https://doi.org/10.1109/36.942542.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Peterson, T. C., and Coauthors, 1998: Homogeneity adjustments of in situ atmospheric climate data: A review. Int. J. Climatol., 13, 14931517, https://doi.org/10.1002/(SICI)1097-0088(19981115)18:13<1493::AID-JOC329>3.0.CO;2-T.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pyle, M. E., and K. F. Brill, 2018: A comparison of two methods for bias correcting precipitation skill scores. Wea. Forecasting, 34, 313, https://doi.org/10.1175/WAF-D-18-0109.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reichle, R. H., Q. Liu, R. D. Koster, C. S. Draper, S. P. P. Mahanama, and G. S. Partyka, 2017: Land surface precipitation in MERRA-2. J. Climate, 30, 16431664, https://doi.org/10.1175/JCLI-D-16-0570.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rienecker, M. M., and Coauthors, 2008: The GEOS-5 Data Assimilation System—Documentation of versions 5.0.1, 5.1.0, and 5.2.0. Vol. 27, Tech. Rep. Series on Global Modeling and Data Assimilation, M. J. Suarez, Ed., NASA Tech. Memo. 104606, 118 pp., accessed 9 July 2020, https://gmao.gsfc.nasa.gov/pubs/docs/Rienecker369.pdf.

  • Schmugge, T. J., and B. J. Choudhury, 1981: A comparison of radiative transfer models for predicting the microwave emission from soils. Radio Sci., 16, 927938, https://doi.org/10.1029/RS016i005p00927.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seneviratne, S. I., T. Corti, E. L. Davin, M. Hirschi, E. B. Jaeger, I. Lehner, B. Orlowsky, and A. J. Teuling, 2010: Investigating soil moisture–climate interactions in a changing climate: A review. Earth-Sci. Rev., 99, 125161, https://doi.org/10.1016/j.earscirev.2010.02.004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Skofronick-Jackson, G., and Coauthors, 2017: The Global Precipitation Measurement (GPM) mission for science and society. Bull. Amer. Meteor. Soc., 98, 16791695, https://doi.org/10.1175/BAMS-D-15-00306.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sorooshian, S., K.-L. Hsu, X. Gao, H. V. Gupta, B. Imam, and D. Braithwaite, 2000: Evaluation of PERSIANN system satellite-based estimates of tropical rainfall. Bull. Amer. Meteor. Soc., 81, 20352046, https://doi.org/10.1175/1520-0477(2000)081<2035:EOPSSE>2.3.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Steiner, J. L., P. J. Starks, J. D. Garbrecht, D. N. Moriasi, X. Zhang, J. M. Schneider, J. A. Guzman, and E. Osei, 2014: Long-term environmental research: The upper Washita River experimental watersheds, Oklahoma, USA. J. Environ. Qual., 43, 12271238, https://doi.org/10.2134/jeq2014.05.0229.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sun, Q., C. Miao, Q. Duan, H. Ashouri, S. Sorooshian, and K.-L. Hsu, 2018: A review of global precipitation data sets: Data sources, estimation, and intercomparisons. Rev. Geophys., 56, 79107, https://doi.org/10.1002/2017RG000574.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sunilkumar, K., A. Yatagai, and M. Masuda, 2019: Preliminary evaluation of GPM-IMERG rainfall estimates over three distinct climate zones with APHRODITE. Earth Space Sci., 6, 13211335, https://doi.org/10.1029/2018EA000503.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ulaby, F. T., M. Razani, and M. C. Dobson, 1983: Effects of vegetation cover on the microwave radiometric sensitivity to soil moisture. IEEE Trans. Geosci. Remote Sens., GE-21, 5161, https://doi.org/10.1109/TGRS.1983.350530.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vrac, M., and P. Friederichs, 2014: Multivariate—intervariable, spatial, and temporal—bias correction. J. Climate, 28, 218237, https://doi.org/10.1175/JCLI-D-14-00059.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wigneron, J.-P., and Coauthors, 2017: Modelling the passive microwave signature from land surfaces: A review of recent results and application to the L-band SMOS & SMAP soil moisture retrieval algorithms. Remote Sens. Environ., 192, 238262, https://doi.org/10.1016/j.rse.2017.01.024.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xu, R., F. Tian, L. Yang, H. Hu, H. Lu, and A. Hou, 2017: Ground validation of GPM IMERG and TRMM 3B42V7 rainfall products over southern Tibetan Plateau based on a high-density rain gauge network. J. Geophys. Res. Atmos., 122, 910924, https://doi.org/10.1002/2016JD025418.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, S., and E. A. Smith, 2006: Mechanisms for diurnal variability of global tropical rainfall observed from TRMM. J. Climate, 19, 51905226, https://doi.org/10.1175/JCLI3883.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Evaluation of GEOS Precipitation Flagging for SMAP Soil Moisture Retrieval Accuracy

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  • 1 aHydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland
  • | 2 bScience Systems and Applications, Inc., Lanham, Maryland
  • | 3 cMesoscale Atmospheric Processes Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland
  • | 4 dGlobal Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, Maryland
  • | 5 eNASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California
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Abstract

The precipitation flag in the Soil Moisture Active Passive (SMAP) Level 2 passive soil moisture (L2SMP) retrieval product indicates the presence or absence of heavy precipitation at the time of the SMAP overpass. The flag is based on precipitation estimates from the Goddard Earth Observing System (GEOS) Forward Processing numerical weather prediction system. An error in flagging during an active or recent precipitation event can produce either 1) an overestimation of soil moisture due to short-term surface wetting of vegetation and/or surface ponding (if soil moisture retrieval was attempted in the presence of rain) or 2) an unnecessary nonretrieval of soil moisture and loss of data (if retrieval is flagged due to an erroneous indication of rain). Satellite precipitation estimates from the Integrated Multisatellite Retrievals for GPM (IMERG), version 06, Early Run (latency of ~4 h) precipitationCal product are used here to evaluate the GEOS-based precipitation flag in the L2SMP product for both the 1800 local time (LT) ascending and 0600 LT descending SMAP overpasses over the first five years of the mission (2015–20). Consisting of blended precipitation measurements from the Global Precipitation Mission (GPM) satellite constellation, IMERG is treated as the “truth” when comparing to the GEOS model forecasts of precipitation used by SMAP. Key results include (i) IMERG measurements generally show higher spatial variability than the GEOS forecast precipitation, (ii) the IMERG product has a higher frequency of light precipitation amounts, and (iii) the effect of incorporating IMERG rainfall measurements in lieu of GEOS precipitation forecasts are minimal on the L2SMP retrieval accuracy (determined vs in situ soil moisture measurements at core validation sites). Our results indicate that L2SMP retrievals continue to meet the mission’s accuracy requirement [standard deviation of the unbiased RMSE (ubRMSE) less than 0.04 m3 m−3].

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Maheshwari Neelam, maheshwari.neelam@nasa.gov

Abstract

The precipitation flag in the Soil Moisture Active Passive (SMAP) Level 2 passive soil moisture (L2SMP) retrieval product indicates the presence or absence of heavy precipitation at the time of the SMAP overpass. The flag is based on precipitation estimates from the Goddard Earth Observing System (GEOS) Forward Processing numerical weather prediction system. An error in flagging during an active or recent precipitation event can produce either 1) an overestimation of soil moisture due to short-term surface wetting of vegetation and/or surface ponding (if soil moisture retrieval was attempted in the presence of rain) or 2) an unnecessary nonretrieval of soil moisture and loss of data (if retrieval is flagged due to an erroneous indication of rain). Satellite precipitation estimates from the Integrated Multisatellite Retrievals for GPM (IMERG), version 06, Early Run (latency of ~4 h) precipitationCal product are used here to evaluate the GEOS-based precipitation flag in the L2SMP product for both the 1800 local time (LT) ascending and 0600 LT descending SMAP overpasses over the first five years of the mission (2015–20). Consisting of blended precipitation measurements from the Global Precipitation Mission (GPM) satellite constellation, IMERG is treated as the “truth” when comparing to the GEOS model forecasts of precipitation used by SMAP. Key results include (i) IMERG measurements generally show higher spatial variability than the GEOS forecast precipitation, (ii) the IMERG product has a higher frequency of light precipitation amounts, and (iii) the effect of incorporating IMERG rainfall measurements in lieu of GEOS precipitation forecasts are minimal on the L2SMP retrieval accuracy (determined vs in situ soil moisture measurements at core validation sites). Our results indicate that L2SMP retrievals continue to meet the mission’s accuracy requirement [standard deviation of the unbiased RMSE (ubRMSE) less than 0.04 m3 m−3].

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Maheshwari Neelam, maheshwari.neelam@nasa.gov
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