• Andreadis, K. M., E. A. Clark, A. W. Wood, A. F. Hamlet, and D. P. Lettenmaier, 2005: Twentieth-century drought in the conterminous United States. J. Hydrometeor., 6, 9851001, doi:10.1175/JHM450.1.

    • Search Google Scholar
    • Export Citation
  • Baird, B. P., and R. R. Robles, 1997: Emergency management issues in the California floods of 1997: Lessons learned or lessons lost? California Specialized Training Institute Doc. G4173 N3, San Luis Obispo, CA, 54 pp. [Available from California Specialized Training Institute, P.O. Box 8123, San Luis Obispo, CA 93403-8123.]

  • Behrangi, A., K. Hsu, B. Imam, S. Sorooshian, G. J. Huffman, and R. J. Kuligowski, 2009: PERSIANN-MSA: A precipitation estimation method from satellite-based multispectral analysis. J. Hydrometeor., 10, 14141429, doi:10.1175/2009JHM1139.1.

    • Search Google Scholar
    • Export Citation
  • Behrangi, A., K. Hsu, B. Imam, and S. Sorooshian, 2010: Daytime precipitation estimation using bispectral cloud classification system. J. Appl. Meteor. Climatol., 49, 10151031, doi:10.1175/2009JAMC2291.1.

    • Search Google Scholar
    • Export Citation
  • Behrangi, A., B. Khakbaz, T. C. Jaw, A. AghaKouchak, K. Hsu, and S. Sorooshian, 2011: Hydrologic evaluation of satellite precipitation products over a mid-size basin. J. Hydrol., 397, 225237, doi:10.1016/j.jhydrol.2010.11.043.

    • Search Google Scholar
    • Export Citation
  • Behrangi, A., M. Lebsock, S. Wong, and B. Lambrigtsen, 2012: On the quantification of oceanic rainfall using spaceborne sensors. J. Geophys. Res., 117, D20105, doi:10.1029/2012JD017979.

    • Search Google Scholar
    • Export Citation
  • Behrangi, A., G. Stephens, R. F. Adler, G. J. Huffman, B. Lambrigtsen, and M. Lebsock, 2014: An update on the oceanic precipitation rate and its zonal distribution in light of advanced observations from space. J. Climate, 27, 39573965, doi:10.1175/JCLI-D-13-00679.1.

    • Search Google Scholar
    • Export Citation
  • Berg, W., T. L'Ecuyer, and C. Kummerow, 2006: Rainfall climate regimes: The relationship of regional TRMM rainfall biases to the environment. J. Appl. Meteor. Climatol., 45, 434454, doi:10.1175/JAM2331.1.

    • Search Google Scholar
    • Export Citation
  • Bitew, M. M., M. Gebremichael, L. T. Ghebremichael, and Y. A. Bayissa, 2012: Evaluation of high-resolution satellite rainfall products through streamflow simulation in a hydrological modeling of a small mountainous watershed in Ethiopia. J. Hydrometeor., 13, 338350, doi:10.1175/2011JHM1292.1.

    • Search Google Scholar
    • Export Citation
  • Cayan, D., E. Maurer, M. Dettinger, M. Tyree, and K. Hayhoe, 2008: Climate change scenarios for the California region. Climatic Change, 87, 2142, doi:10.1007/s10584-007-9377-6.

    • Search Google Scholar
    • Export Citation
  • Daly, C., R. P. Neilson, and D. L. Phillips, 1994: A statistical–topographic model for mapping climatological precipitation over mountainous terrain. J. Appl. Meteor., 33, 140158, doi:10.1175/1520-0450(1994)033<0140:ASTMFM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Defries, R. S., M. C. Hansen, J. R. G. Townshend, A. C. Janetos, and T. R. Loveland, 2000: A new global 1-km dataset of percentage tree cover derived from remote sensing. Global Change Biol., 6, 247254, doi:10.1046/j.1365-2486.2000.00296.x.

    • Search Google Scholar
    • Export Citation
  • Dettinger, M., 2011: Climate change, atmospheric rivers, and floods in California—A multimodel analysis of storm frequency and magnitude changes1. J. Amer. Water Resour. Assoc., 47, 514523, doi:10.1111/j.1752-1688.2011.00546.x.

    • Search Google Scholar
    • Export Citation
  • Dinku, T., P. Ceccato, K. Cressman, and S. J. Connor, 2010: Evaluating detection skills of satellite rainfall estimates over desert locust recession regions. J. Appl. Meteor. Climatol., 49, 13221332, doi:10.1175/2010JAMC2281.1.

    • Search Google Scholar
    • Export Citation
  • Ebert, E. E., J. E. Janowiak, and C. Kidd, 2007: Comparison of near-real-time precipitation estimates from satellite observations and numerical models. Bull. Amer. Meteor. Soc., 88, 4764, doi:10.1175/BAMS-88-1-47.

    • Search Google Scholar
    • Export Citation
  • Ferraro, R. R., N. Grody, and G. Marks, 1994: Effects of surface conditions on rain identification using the DMSP-SSM/I. Remote Sens. Rev., 11, 195209, doi:10.1080/02757259409532265.

    • Search Google Scholar
    • Export Citation
  • Ferraro, R. R., F. H. Weng, N. C. Grody, and L. M. Zhao, 2000: Precipitation characteristics over land from the NOAA-15 AMSU sensor. Geophys. Res. Lett., 27, 26692672, doi:10.1029/2000GL011665.

    • Search Google Scholar
    • Export Citation
  • Ferraro, R. R., and Coauthors, 2005: NOAA operational hydrological products derived from the advanced microwave sounding unit. IEEE Trans. Geosci. Remote Sens.,43, 10361049.

    • Search Google Scholar
    • Export Citation
  • Ferraro, R. R., and Coauthors, 2013: An evaluation of microwave land surface emissivities over the continental United States to benefit GPM-Era precipitation algorithms. IEEE Trans. Geosci. Remote Sens.,51, 378398.

    • Search Google Scholar
    • Export Citation
  • Gebregiorgis, A. S., Y. Tian, C. D. Peters-Lidard, and F. Hossain, 2012: Tracing hydrologic model simulation error as a function of satellite rainfall estimation bias components and land use and land cover conditions. Water Resour. Res., 48, W11509, doi:10.1029/2011WR011643.

    • Search Google Scholar
    • Export Citation
  • Gopalan, K., N.-Y. Wang, R. Ferraro, and C. Liu, 2010: Status of the TRMM 2A12 land precipitation algorithm. J. Atmos. Oceanic Technol., 27, 13431354, doi:10.1175/2010JTECHA1454.1.

    • Search Google Scholar
    • Export Citation
  • Guan, B., N. P. Molotch, D. E. Waliser, E. J. Fetzer, and P. J. Neiman, 2010: Extreme snowfall events linked to atmospheric rivers and surface air temperature via satellite measurements. Geophys. Res. Lett., 37, L20401, doi:10.1029/2010GL044696.

    • Search Google Scholar
    • Export Citation
  • Haddad, Z. S., E. A. Smith, C. D. Kummerow, T. Iguchi, M. R. Farrar, S. L. Durden, M. Alves, and W. S. Olson, 1997: The TRMM “day-1” radar/radiometer combined rain-profiling algorithm. J. Meteor. Soc. Japan, 75, 799809.

    • 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
  • Hidalgo, H. G., and Coauthors, 2009: Detection and attribution of streamflow timing changes to climate change in the western United States. J. Climate, 22, 38383855, doi:10.1175/2009JCLI2470.1.

    • Search Google Scholar
    • Export Citation
  • Hogue, T. S., S. Sorooshian, H. Gupta, A. Holz, and D. Braatz, 2000: A multistep automatic calibration scheme for river forecasting models. J. Hydrometeor., 1, 524542, doi:10.1175/1525-7541(2000)001<0524:AMACSF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hong, Y., K. L. Hsu, S. Sorooshian, and X. G. Gao, 2004: Precipitation estimation from remotely sensed imagery using an artificial neural network cloud classification system. J. Appl. Meteor., 43, 18341852, doi:10.1175/JAM2173.1.

    • Search Google Scholar
    • Export Citation
  • Hong, Y., R. F. Adler, F. Hossain, S. Curtis, and G. J. Huffman, 2007: A first approach to global runoff simulation using satellite rainfall estimation. Water Resour. Res., 43, W08502, doi:10.1029/2006WR005739.

    • Search Google Scholar
    • Export Citation
  • Hossain, F., and E. N. Anagnostou, 2004: Assessment of current passive-microwave- and infrared-based satellite rainfall remote sensing for flood prediction. J. Geophys. Res., 109, D07102, doi:10.1029/2003JD003986.

    • Search Google Scholar
    • Export Citation
  • Hou, A. Y., and Coauthors, 2013: The Global Precipitation Measurement (GPM) mission. Bull. Amer. Meteor. Soc., 95, 701–722, doi:10.1175/BAMS-D-13-00164.1.

    • Search Google Scholar
    • Export Citation
  • Hsu, K. L., X. G. Gao, S. Sorooshian, and H. V. Gupta, 1997: Precipitation estimation from remotely sensed information using artificial neural networks. J. Appl. Meteor., 36, 11761190, doi:10.1175/1520-0450(1997)036<1176:PEFRSI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Huffman, G. J., and D. T. Bolvin, 2014: TRMM and other data precipitation data set documentation. NASA GSFC, 42 pp. [Available online at ftp://precip.gsfc.nasa.gov/pub/trmmdocs/3B42_3B43_doc.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, doi:10.1175/JHM560.1.

    • Search Google Scholar
    • Export Citation
  • Huffman, G. J., D. Bolvin, D. Braithwaite, K. Hsu, R. Joyce, and P. P. Xie, 2013: Integrated Multi-satellite Retrievals for GPM (IMERG), algorithm theoretical basis document (ATBD), version 4.1. NASA, 25 pp. [Available online at http://pmm.nasa.gov/sites/default/files/document_files/IMERG_ATBD_V4.1.pdf .]

  • Kongoli, C., P. Pellegrino, R. R. Ferraro, N. C. Grody, and H. Meng, 2003: A new snowfall detection algorithm over land using measurements from the Advanced Microwave Sounding Unit (AMSU). Geophys. Res. Lett.,30, 1756, doi:10.1029/2003GL017177.

  • 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, doi:10.1175/1525-7541(2004)005<0487:CAMTPG>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc., 77, 437471, doi:10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Kidd, C., P. Bauer, J. Turk, G. J. Huffman, R. Joyce, K. L. Hsu, and D. Braithwaite, 2012: Intercomparison of high-resolution precipitation products over northwest Europe. J. Hydrometeor., 13, 6783, doi:10.1175/JHM-D-11-042.1.

    • Search Google Scholar
    • Export Citation
  • Kidd, C., E. Dawkins, and G. Huffman, 2013: Comparison of precipitation derived from the ECMWF operational forecast model and satellite precipitation datasets. J. Hydrometeor., 14, 14631482, doi:10.1175/JHM-D-12-0182.1.

    • Search Google Scholar
    • Export Citation
  • Krakauer, N., S. Pradhanang, T. Lakhankar, and A. Jha, 2013: Evaluating satellite products for precipitation estimation in mountain regions: A case study for Nepal. Remote Sens., 5, 41074123, doi:10.3390/rs5084107.

    • Search Google Scholar
    • Export Citation
  • Kucera, P., and B. Lapeta, 2013: IPWG recent accomplishments and future directions. Expert Team on Satellite Utilization and Products (ET-SUP 7), Coordination Group for Meteorological Satellites (CGMS), Geneva, Switzerland, 13 pp. [Available online at http://www.wmo.int/pages/prog/sat/meetings/documents/ET-SUP-7_Doc_15-02_IPWG.pdf.]

  • Kucera, P., E. E. Ebert, F. J. Turk, V. Levizzani, D. Kirschbaum, F. J. Tapiador, A. Loew, and M. Borsche, 2013: Precipitation from space: Advancing earth system science. Bull. Amer. Meteor. Soc.,94, 365–375, doi:10.1175/BAMS-D-11-00171.1.

  • Kuligowski, R. J., 2002: A self-calibrating real-time GOES rainfall algorithm for short-term rainfall estimates. J. Hydrometeor., 3, 112130, doi:10.1175/1525-7541(2002)003<0112:ASCRTG>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Kummerow, C. D., S. Ringerud, J. Crook, D. Randel, and W. Berg, 2011: An observationally generated a priori database for microwave rainfall retrievals. J. Atmos. Oceanic Technol., 28, 113130, doi:10.1175/2010JTECHA1468.1.

    • 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.

  • Liang, X., and Z. Xie, 2001: A new surface runoff parameterization with subgrid-scale soil heterogeneity for land surface models. Adv. Water Resour., 24, 11731193, doi:10.1016/S0309-1708(01)00032-X.

    • Search Google Scholar
    • Export Citation
  • Liang, X., D. P. Lettenmaier, and E. F. Wood, 1996: One-dimensional statistical dynamic representation of subgrid spatial variability of precipitation in the two-layer variable infiltration capacity model. J. Geophys. Res., 101, 21 40321 422, doi:10.1029/96JD01448.

    • 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/pdfpapers/83847.pdf.]

  • Livneh, B., E. A. Rosenberg, C. Lin, B. Nijssen, V. Mishra, K. M. Andreadis, E. P. Maurer, and D. P. Lettenmaier, 2013: A long-term hydrologically based dataset of land surface fluxes and states for the conterminous United States: Update and extensions. J. Climate, 26, 93849392, doi:10.1175/JCLI-D-12-00508.1.

    • Search Google Scholar
    • Export Citation
  • Lohmann, D., E. Raschke, B. Nijssen, and D. P. Lettenmaier, 1998: Regional scale hydrology: I. Formulation of the VIC-2L model coupled to a routing model. Hydrol. Sci. J., 43, 131141, doi:10.1080/02626669809492107.

    • Search Google Scholar
    • Export Citation
  • Lohmann, D., and Coauthors, 2004: Streamflow and water balance intercomparisons of four land surface models in the North American Land Data Assimilation System project. J. Geophys. Res., 109, D07S91, doi:10.1029/2003JD003517.

    • Search Google Scholar
    • Export Citation
  • Maurer, E. P., A. W. Wood, J. C. Adam, D. P. Lettenmaier, and B. Nijssen, 2002: A long-term hydrologically based dataset of land surface fluxes and states for the conterminous United States. J. Climate, 15, 32373251, doi:10.1175/1520-0442(2002)015<3237:ALTHBD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Miller, D. A., and R. A. White, 1998: A conterminous United States multilayer soil characteristics dataset for regional climate and hydrology modeling. Earth Interact.,2, doi:10.1175/1087-3562(1998)002<0001:ACUSMS>2.3.CO;2.

  • Neiman, P. J., F. M. Ralph, G. A. Wick, J. D. Lundquist, and M. D. Dettinger, 2008: Meteorological characteristics and overland precipitation impacts of atmospheric rivers affecting the west coast of North America based on eight years of SSM/I satellite observations. J. Hydrometeor., 9, 2247, doi:10.1175/2007JHM855.1.

    • Search Google Scholar
    • Export Citation
  • Nijssen, B., R. Schnur, and D. P. Lettenmaier, 2001: Global retrospective estimation of soil moisture using the variable infiltration capacity land surface model, 1980–93. J. Climate, 14, 17901808, doi:10.1175/1520-0442(2001)014<1790:GREOSM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Ralph, F. M., and M. D. Dettinger, 2011: Storms, floods, and the science of atmospheric rivers. Eos, Trans. Amer. Geophys. Union, 92, 265266, doi:10.1029/2011EO320001.

    • Search Google Scholar
    • Export Citation
  • Shiklomanov, A. I., R. B. Lammers, and C. J. Vörösmarty, 2002: Widespread decline in hydrological monitoring threatens pan-Arctic research. Eos, Trans. Amer. Geophys. Union, 83, 1317, doi:10.1029/2002EO000007.

    • 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, 444–458, doi:10.1175/JHM-D-13-048.1.

    • 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, doi:10.1175/1520-0477(2000)081<2035:EOPSSE>2.3.CO;2.

    • Search Google Scholar
    • Export Citation
  • Stampoulis, D., E. N. Anagnostou, and E. I. Nikolopoulos, 2013: Assessment of high-resolution satellite-based rainfall estimates over the Mediterranean during heavy precipitation events. J. Hydrometeor., 14, 15001514, doi:10.1175/JHM-D-12-0167.1.

    • Search Google Scholar
    • Export Citation
  • Stokstad, E., 1999: Scarcity of rain, stream gages threatens forecasts. Science, 285, 11991200, doi:10.1126/science.285.5431.1199.

  • Tian, Y., and C. D. Peters-Lidard, 2010: A global map of uncertainties in satellite-based precipitation measurements. Geophys. Res. Lett., 37, L24407, doi:10.1029/2010GL046008.

    • Search Google Scholar
    • Export Citation
  • Tian, Y., C. D. Peters-Lidard, B. J. Choudhury, and M. Garcia, 2007: Multitemporal analysis of TRMM-based satellite precipitation products for land data assimilation applications. J. Hydrometeor., 8, 11651183, doi:10.1175/2007JHM859.1.

    • Search Google Scholar
    • Export Citation
  • Tobin, K. J., and M. E. Bennett, 2010: Adjusting satellite precipitation data to facilitate hydrologic modeling. J. Hydrometeor., 11, 966978, doi:10.1175/2010JHM1206.1.

    • Search Google Scholar
    • Export Citation
  • Turk, F. J., and S. D. Miller, 2005: Toward improved characterization of remotely sensed precipitation regimes with MODIS/AMSR-E blended data techniques. IEEE Trans. Geosci. Remote Sens., 43, 10591069, doi:10.1109/TGRS.2004.841627.

    • Search Google Scholar
    • Export Citation
  • Vila, D., R. Ferraro, and R. Joyce, 2007: Evaluation and improvement of AMSU precipitation retrievals. J. Geophys. Res., 112, D20119, doi:10.1029/2007JD008617.

    • Search Google Scholar
    • Export Citation
  • Weng, F. Z., L. M. Zhao, R. R. Ferraro, G. Poe, X. F. Li, and N. C. Grody, 2003: Advanced Microwave Sounding Unit cloud and precipitation algorithms. Radio Sci.,38, 8068, doi:10.1029/2002RS002679.

  • Westerling, A. L., H. G. Hidalgo, D. R. Cayan, and T. W. Swetnam, 2006: Warming and earlier spring increase western U.S. forest wildfire activity. Science, 313, 940943, doi:10.1126/science.1128834.

    • Search Google Scholar
    • Export Citation
  • Wilheit, T., C. D. Kummerow, and R. Ferraro, 2003: NASDA rainfall algorithms for AMSR-E. IEEE Trans. Geosci. Remote Sens., 41, 204214, doi:10.1109/TGRS.2002.808312.

    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., 2011. Statistical Methods in the Atmospheric Sciences. 3rd ed. Academic Press, 676 pp.

  • Wu, H., R. F. Adler, Y. Hong, Y. Tian, and F. Policelli, 2012: Evaluation of global flood detection using satellite-based rainfall and a hydrologic model. J. Hydrometeor., 13, 12681284, doi:10.1175/JHM-D-11-087.1.

    • Search Google Scholar
    • Export Citation
  • Yilmaz, K. K., T. S. Hogue, K. L. Hsu, S. Sorooshian, H. V. Gupta, and T. Wagener, 2005: Intercomparison of rain gauge, radar, and satellite-based precipitation estimates with emphasis on hydrologic forecasting. J. Hydrometeor., 6, 497517, doi:10.1175/JHM431.1.

    • Search Google Scholar
    • Export Citation
  • Zhang, X., E. N. Anagnostou, M. Frediani, S. Solomos, and G. Kallos, 2013: Using NWP simulations in satellite rainfall estimation of heavy precipitation events over mountainous areas. J. Hydrometeor., 14, 18441858, doi:10.1175/JHM-D-12-0174.1.

    • Search Google Scholar
    • Export Citation
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Satellite-Based Precipitation Estimation and Its Application for Streamflow Prediction over Mountainous Western U.S. Basins

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  • 1 Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California
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Abstract

Recognizing the importance and challenges inherent to the remote sensing of precipitation in mountainous areas, this study investigates the performance of the commonly used satellite-based high-resolution precipitation products (HRPPs) over several basins in the mountainous western United States. Five HRPPs [Tropical Rainfall Measuring Mission 3B42 and 3B42-RT algorithms, the Climate Prediction Center morphing technique (CMORPH), Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks (PERSIANN), and the PERSIANN Cloud Classification System (PERSIANN-CCS)] are analyzed in the present work using ground gauge, gauge-adjusted radar, and CloudSat precipitation products. Using ground observation of precipitation and streamflow, the skill of HRPPs and the resulting streamflow simulations from the Variable Infiltration Capacity hydrological model are cross-compared. HRPPs often capture major precipitation events but seldom capture the observed magnitude of precipitation over the studied region and period (2003–09). Bias adjustment is found to be effective in enhancing the HRPPs and resulting streamflow simulations. However, if not bias adjusted using gauges, errors are typically large as in the lower-level precipitation inputs to HRPPs. The results using collocated Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) and CloudSat precipitation data show that missing data, often over frozen land, and limitations in retrieving precipitation from systems that lack frozen hydrometeors contribute to the observed microwave-based precipitation errors transferred to HRPPs. Over frozen land, precipitation retrievals from infrared sensors and microwave sounders show some skill in capturing the observed precipitation climatology maps. However, infrared techniques often show poor detection skill, and microwave sounding in dry atmosphere remains challenging. By recognizing the sources of precipitation error and in light of the operation of the Global Precipitation Measurement mission, further opportunity for enhancing the current status of precipitation retrievals and the hydrology of cold and mountainous regions becomes available.

Corresponding author address: Ali Behrangi, Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Dr., MS 233-304, Pasadena, CA 91109. E-mail: ali.behrangi@jpl.nasa.gov

Abstract

Recognizing the importance and challenges inherent to the remote sensing of precipitation in mountainous areas, this study investigates the performance of the commonly used satellite-based high-resolution precipitation products (HRPPs) over several basins in the mountainous western United States. Five HRPPs [Tropical Rainfall Measuring Mission 3B42 and 3B42-RT algorithms, the Climate Prediction Center morphing technique (CMORPH), Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks (PERSIANN), and the PERSIANN Cloud Classification System (PERSIANN-CCS)] are analyzed in the present work using ground gauge, gauge-adjusted radar, and CloudSat precipitation products. Using ground observation of precipitation and streamflow, the skill of HRPPs and the resulting streamflow simulations from the Variable Infiltration Capacity hydrological model are cross-compared. HRPPs often capture major precipitation events but seldom capture the observed magnitude of precipitation over the studied region and period (2003–09). Bias adjustment is found to be effective in enhancing the HRPPs and resulting streamflow simulations. However, if not bias adjusted using gauges, errors are typically large as in the lower-level precipitation inputs to HRPPs. The results using collocated Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) and CloudSat precipitation data show that missing data, often over frozen land, and limitations in retrieving precipitation from systems that lack frozen hydrometeors contribute to the observed microwave-based precipitation errors transferred to HRPPs. Over frozen land, precipitation retrievals from infrared sensors and microwave sounders show some skill in capturing the observed precipitation climatology maps. However, infrared techniques often show poor detection skill, and microwave sounding in dry atmosphere remains challenging. By recognizing the sources of precipitation error and in light of the operation of the Global Precipitation Measurement mission, further opportunity for enhancing the current status of precipitation retrievals and the hydrology of cold and mountainous regions becomes available.

Corresponding author address: Ali Behrangi, Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Dr., MS 233-304, Pasadena, CA 91109. E-mail: ali.behrangi@jpl.nasa.gov
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