• Berg, W., T. L’Ecuyer, and J. M. Haynes, 2010: The distribution of rainfall over oceans from spaceborne radars. J. Appl. Meteor. Climatol., 49, 535543, doi:10.1175/2009JAMC2330.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cao, Q., Y. Hong, S. Chen, J. J. Gourley, J. Zhang, and P. Kirstetter, 2014: Snowfall detectability of NASA’s CloudSat: The first cross-investigation of its 2C-SNOW-PROFILE product and National Multi-Sensor Mosaic QPE (NMQ) snowfall data. Prog. Electromagnetics Res., 148, 5561, doi:10.2528/PIER14030405.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, S., and Coauthors, 2016: Comparison of snowfall estimates from the NASA CloudSat Cloud Profiling Radar and NOAA/NSSL Multi-Radar Multi-Sensor System. J. Hydrol., 541, 862872, doi:10.1016/j.jhydrol.2016.07.047.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Doviak, R., and D. Zrnić, 1993: Doppler Radar and Weather Observations. 2nd ed. Dover Publications, 562 pp.

  • Ellis, T. D., T. L’Ecuyer, J. M. Haynes, and G. L. Stephens, 2009: How often does it rain over the global oceans? The perspective from CloudSat. Geophys. Res. Lett., 36, L03815, doi:10.1029/2008GL036728.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gottschalck, J., J. Meng, M. Rodell, and P. Houser, 2005: Analysis of multiple precipitation products and preliminary assessment of their impact on Global Land Data Assimilation System land surface states. J. Hydrometeor., 6, 573598, doi:10.1175/JHM437.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gourley, J. J., Y. Hong, Z. L. Flamig, L. Li, and J. Wang, 2010: Intercomparison of rainfall estimates from radar, satellite, gauge, and combinations for a season of record rainfall. J. Appl. Meteor. Climatol., 49, 437452, doi:10.1175/2009JAMC2302.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gourley, J. J., and Coauthors, 2017: The FLASH project: Improving the tools for flash flood monitoring and prediction across the United States. Bull. Amer. Meteor. Soc., 98, 361372, doi:10.1175/BAMS-D-15-00247.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Haynes, J. M., T. S. L’Ecuyer, G. L. Stephens, S. D. Miller, C. Mitrescu, N. B. Wood, and S. Tanelli, 2009: Rainfall retrieval over the ocean with spaceborne W-band radar. J. Geophys. Res., 114, D00A22, doi:10.1029/2008JD009973.

    • Search Google Scholar
    • Export Citation
  • Kirstetter, P.-E., and Coauthors, 2012: Toward a framework for systematic error modeling of spaceborne precipitation radar with NOAA/NSSL ground radar-based National Mosaic QPE. J. Hydrometeor., 13, 12851300, doi:10.1175/JHM-D-11-0139.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kirstetter, P.-E., Y. Hong, J. Gourley, Q. Cao, M. Schwaller, and W. Petersen, 2014: Research framework to bridge from the Global Precipitation Measurement mission core satellite to the constellation sensors using ground-radar-based National Mosaic QPE. Remote Sensing of the Terrestrial Water Cycle, Geophysical Monogr., Vol. 206, Amer. Geophys. Union, 61–79, doi:10.1002/9781118872086.ch4.

    • Crossref
    • Export Citation
  • Kirstetter, P.-E., Y. Hong, J. Gourley, and M. Schwaller, 2015: Impact of sub-pixel rainfall variability on spaceborne precipitation estimation: Evaluating the TRMM 2A25 product. Quart. J. Roy. Meteor. Soc., 141, 953966, doi:10.1002/qj.2416.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kulie, M. S., L. Milani, N. B. Wood, S. A. Tushaus, R. Bennartz, and T. S. L’Ecuyer, 2016: A shallow cumuliform snowfall census using spaceborne radar. J. Hydrometeor., 17, 12611279, doi:10.1175/JHM-D-15-0123.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lakshmanan, V., A. Fritz, T. Smith, K. Hondl, and G. J. Stumpf, 2007: An automated technique to quality control radar reflectivity data. J. Appl. Meteor. Climatol., 46, 288305, doi:10.1175/JAM2460.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lakshmanan, V., J. Zhang, and K. Howard, 2010: A technique to censor biological echoes in radar reflectivity data. J. Appl. Meteor. Climatol., 49, 453462, doi:10.1175/2009JAMC2255.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lebsock, M. D., and T. S. L’Ecuyer, 2011: The retrieval of warm rain from CloudSat. J. Geophys. Res., 116, D20209, doi:10.1029/2011JD016076.

  • L’Ecuyer, T. S., and J. H. Jiang, 2010: Touring the atmosphere aboard the A-Train. Phys. Today, 63, 36, doi:10.1063/1.3463626.

  • L’Ecuyer, T. S., and Coauthors, 2015: The observed state of the energy budget in the early twenty-first century. J. Climate, 28, 83198346, doi:10.1175/JCLI-D-14-00556.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, X., and A. Y. Hou, 2012: Estimation of rain intensity spectra over the continental United States using ground radar–gauge measurements. J. Climate, 25, 19011915, doi:10.1175/JCLI-D-11-00151.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, Y., and K. E. Mitchell, 2005: The NCEP stage II/IV hourly precipitation analyses: Development and applications. 19th Conf. on Hydrology, San Diego, CA, Amer. Meteor. Soc., 1.2. [Available online at https://ams.confex.com/ams/Annual2005/techprogram/paper_83847.htm.]

  • Maddox, R., J. Zhang, J. Gourley, and K. Howard, 2002: Weather radar coverage over the contiguous United States. Wea. Forecasting, 17, 927934, doi:10.1175/1520-0434(2002)017<0927:WRCOTC>2.0C.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mott, R., D. Scipión, M. Schneebeli, N. Dawes, A. Berne, and M. Lehning, 2014: Orographic effects on snow deposition patterns in mountainous terrain. J. Geophys. Res. Atmos., 119, 13631385, doi:10.1002/2013JD019880.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • NASA, 2007: Level 2 GEOPROF product process description and interface control document algorithm version 5.3. CloudSat Data Processing Center, 44 pp. [Available online at http://www.cloudsat.cira.colostate.edu/sites/default/files/products/files/2B-GEOPROF_PDICD.P_R04.20070628.pdf.]

  • Nijssen, B., and D. Lettenmaier, 2004: Effect of precipitation sampling error on simulated hydrological fluxes and states: Anticipating the Global Precipitation Measurement satellites. J. Geophys. Res., 109, D02103, doi:10.1029/2003JD003497.

    • Search Google Scholar
    • Export Citation
  • Norin, L., A. Devasthale, T. S. L’Ecuyer, N. B. Wood, and M. Smalley, 2015: Intercomparison of snowfall estimates derived from the CloudSat Cloud Profiling Radar and the ground-based weather radar network over Sweden. Atmos. Meas. Tech., 8, 50095021, doi:10.5194/amt-8-5009-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smalley, M., T. L’Ecuyer, M. Lebsock, and J. Haynes, 2014: A comparison of precipitation occurrence from the NCEP stage IV QPE product and the CloudSat Cloud Profiling Radar. J. Hydrometeor., 15, 444458, doi:10.1175/JHM-D-13-048.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stephenson, D. B., 2000: Use of the “odds ratio” for diagnosing forecast skill. Wea. Forecasting, 15, 221232, doi:10.1175/1520-0434(2000)015<0221:UOTORF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tanelli, S., S. L. Durden, K. S. Pak, D. G. Reinke, P. Partain, J. M. Haynes, and R. T. Marchand, 2008: CloudSat’s Cloud Profiling Radar after two years in orbit: Performance, calibration, and processing. IEEE Trans. Geosci. Remote Sens., 46, 35603573, doi:10.1109/TGRS.2008.2002030.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tanelli, S., G. F. Sacco, S. L. Durden, and Z. S. Haddad, 2012: Impact of non-uniform beam filling on spaceborne cloud and precipitation radar retrieval algorithms. Remote Sensing of the Atmosphere, Clouds, and Precipitation, T. Hayasaka, K. Nakamura, and E. Im, Eds., International Society for Optical Engineering (SPIE Proceedings, Vol. 8523), 852308, doi:10.1117/12.977375.

    • Crossref
    • Export Citation
  • Tesfagiorgis, K., S. E. Mahani, N. Y. Krakauer, and R. Khanbilvardi, 2011: Bias correction of satellite rainfall estimates using a radar–gauge product: A case study in Oklahoma (USA). Hydrol. Earth Syst. Sci., 15, 26312647, doi:10.5194/hess-15-2631-2011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Westrick, K. J., C. F. Mass, and B. A. Colle, 1999: The limitations of the WSR-88D radar network for quantitative precipitation measurement over the coastal western United States. Bull. Amer. Meteor. Soc., 80, 22892298, doi:10.1175/1520-0477(1999)080<2289:TLOTWR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wood, N. B., T. S. L’Ecuyer, A. J. Heymsfield, G. L. Stephens, D. R. Hudak, and P. Rodriguez, 2014: Estimating snow microphysical properties using collocated multisensor observations. J. Geophys. Res. Atmos., 119, 89418961, doi:10.1002/2013JD021303.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, W., D. Kitzmiller, and S. Wu, 2012: Evaluation of radar precipitation estimates from the National Mosaic and Multisensor Quantitative Precipitation Estimation system and the WSR-88D precipitation processing system over the conterminous United States. J. Hydrometeor., 13, 10801093, doi:10.1175/JHM-D-11-064.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, J., and Coauthors, 2011: National Mosaic and Multi-Sensor QPE (NMQ) system description, results, and future plans. Bull.C Amer. Meteor. Soc., 92, 13211338, doi:10.C1175/2011BAMS-D-11-00047.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, J., and Coauthors, 2016: Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimation: Initial operating capabilities. Bull. Amer. Meteor. Soc., 97, 621638, doi:10.1175/BAMS-D-14-00174.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 179 107 5
PDF Downloads 82 36 1

How Frequent is Precipitation over the Contiguous United States? Perspectives from Ground-Based and Spaceborne Radars

View More View Less
  • 1 NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California
  • | 2 Advanced Radar Research Center, University of Oklahoma, and National Severe Storms Laboratory, Norman, Oklahoma
  • | 3 University of Wisconsin–Madison, Madison, Wisconsin
Restricted access

Abstract

High temporal and spatial resolution observations of precipitation occurrence from the NEXRAD-based Multi-Radar Multi-Sensor (MRMS) system are compared to matched observations from CloudSat for 3 years over the contiguous United States (CONUS). Across the CONUS, precipitation is generally reported more frequently by CloudSat (7.8%) than by MRMS (6.3%), with dependence on factors such as the NEXRAD beam height, the near-surface air temperature, and the surface elevation. There is general agreement between ground-based and satellite-derived precipitation events over flat surfaces, especially in widespread precipitation events and when the NEXRAD beam heights are low. Within 100 km of the nearest NEXRAD site, MRMS reports a precipitation frequency of 7.54% while CloudSat reports 7.38%. However, further inspection reveals offsetting biases between the products, where CloudSat reports more snow and MRMS reports more rain. The magnitudes of these discrepancies correlate with elevation, but they are observed in both the complex terrain of the Rocky Mountains and the relatively flat midwestern areas of the CONUS. The findings advocate for caution when using MRMS frequency and accumulations in complex terrain, when temperatures are below freezing, and at ranges greater than 100 km. A multiresolution analysis shows that no more than 1.88% of CloudSat pixels over flat terrain are incorrectly identified as nonprecipitating as a result of shallow showers residing the CloudSat clutter-filled blind zone when near-surface air temperatures are above 15°C.

© 2017 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: Mark Smalley, mark.a.smalley@jpl.nasa.gov

Abstract

High temporal and spatial resolution observations of precipitation occurrence from the NEXRAD-based Multi-Radar Multi-Sensor (MRMS) system are compared to matched observations from CloudSat for 3 years over the contiguous United States (CONUS). Across the CONUS, precipitation is generally reported more frequently by CloudSat (7.8%) than by MRMS (6.3%), with dependence on factors such as the NEXRAD beam height, the near-surface air temperature, and the surface elevation. There is general agreement between ground-based and satellite-derived precipitation events over flat surfaces, especially in widespread precipitation events and when the NEXRAD beam heights are low. Within 100 km of the nearest NEXRAD site, MRMS reports a precipitation frequency of 7.54% while CloudSat reports 7.38%. However, further inspection reveals offsetting biases between the products, where CloudSat reports more snow and MRMS reports more rain. The magnitudes of these discrepancies correlate with elevation, but they are observed in both the complex terrain of the Rocky Mountains and the relatively flat midwestern areas of the CONUS. The findings advocate for caution when using MRMS frequency and accumulations in complex terrain, when temperatures are below freezing, and at ranges greater than 100 km. A multiresolution analysis shows that no more than 1.88% of CloudSat pixels over flat terrain are incorrectly identified as nonprecipitating as a result of shallow showers residing the CloudSat clutter-filled blind zone when near-surface air temperatures are above 15°C.

© 2017 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: Mark Smalley, mark.a.smalley@jpl.nasa.gov
Save