Estimation of Precipitation over the OLYMPEX Domain during Winter 2015/16

Qian Cao Department of Geography, University of California, Los Angeles, Los Angeles, California

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Thomas H. Painter NASA Jet Propulsion Laboratory, Pasadena, California

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William Ryan Currier University of Washington, Seattle, Washington

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Jessica D. Lundquist University of Washington, Seattle, Washington

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Dennis P. Lettenmaier Department of Geography, University of California, Los Angeles, Los Angeles, California

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Abstract

To provide ground validation data for satellite precipitation products derived from the Global Precipitation Measurement (GPM) mission, such as IMERG, in cold seasons and where orographic factors exert strong controls on precipitation, the Olympic Mountain Experiment (OLYMPEX) was conducted during winter 2015/16. By utilizing multiple observational resources from OLYMPEX, estimates of daily and finer-scale precipitation are constructed at 1/32° spatial resolution over the OLYMPEX domain. The estimates are based on NOAA WSR-88D and gauge estimates as incorporated in NOAA’s National Severe Storms Laboratory (NSSL) Q3GC product, augmented with an additional 120 gauges available during OLYMPEX. Few stations are located in the interior of the Olympic Peninsula at elevations higher than about 500 m, and in this part of the domain the Variable Infiltration Capacity (VIC) hydrology model is used to invert the snow water equivalent (SWE) estimates, derived from two NASA JPL Airborne Snow Observatory (ASO) snow depth maps on 8–9 February 2016 and 29–30 March 2016, for precipitation through adjustment of the precipitation-weighting factor on a grid cell by grid cell basis. In comparison with this composite product, both IMERG (version 04A) and its Japanese counterpart GSMaP’s (version 04B) satellite-only products tend to underestimate winter precipitation, by 41% and 28%, respectively, over the entire domain from 1 October 2015 to 30 April 2016. The underestimation is more pronounced for the orographically enhanced mountainous interior of the OLYMPEX domain, by 57% and 48%, respectively. In contrast, IMERG and GSMaP storm interarrival time statistics are quite similar to those estimated from gridded observations.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JHM-D-17-0076.s1.

© 2018 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: Dennis P. Lettenmaier, dlettenm@ucla.edu

Abstract

To provide ground validation data for satellite precipitation products derived from the Global Precipitation Measurement (GPM) mission, such as IMERG, in cold seasons and where orographic factors exert strong controls on precipitation, the Olympic Mountain Experiment (OLYMPEX) was conducted during winter 2015/16. By utilizing multiple observational resources from OLYMPEX, estimates of daily and finer-scale precipitation are constructed at 1/32° spatial resolution over the OLYMPEX domain. The estimates are based on NOAA WSR-88D and gauge estimates as incorporated in NOAA’s National Severe Storms Laboratory (NSSL) Q3GC product, augmented with an additional 120 gauges available during OLYMPEX. Few stations are located in the interior of the Olympic Peninsula at elevations higher than about 500 m, and in this part of the domain the Variable Infiltration Capacity (VIC) hydrology model is used to invert the snow water equivalent (SWE) estimates, derived from two NASA JPL Airborne Snow Observatory (ASO) snow depth maps on 8–9 February 2016 and 29–30 March 2016, for precipitation through adjustment of the precipitation-weighting factor on a grid cell by grid cell basis. In comparison with this composite product, both IMERG (version 04A) and its Japanese counterpart GSMaP’s (version 04B) satellite-only products tend to underestimate winter precipitation, by 41% and 28%, respectively, over the entire domain from 1 October 2015 to 30 April 2016. The underestimation is more pronounced for the orographically enhanced mountainous interior of the OLYMPEX domain, by 57% and 48%, respectively. In contrast, IMERG and GSMaP storm interarrival time statistics are quite similar to those estimated from gridded observations.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JHM-D-17-0076.s1.

© 2018 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: Dennis P. Lettenmaier, dlettenm@ucla.edu

Supplementary Materials

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  • AghaKouchak, A., A. Mehran, H. Norouzi, and A. Behrangi, 2012: Systematic and random error components in satellite precipitation data sets. Geophys. Res. Lett., 39, L09406, https://doi.org/10.1029/2012GL051592.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Anders, A., G. Roe, D. Durran, and J. Minder, 2007: Small-scale spatial gradients in climatological precipitation on the Olympic peninsula. J. Hydrometeor., 8, 10681081, https://doi.org/10.1175/JHM610.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Andreadis, K., P. Storck, and D. Lettenmaier, 2009: Modeling snow accumulation and ablation processes in forested environments. Water Resour. Res., 45, W05429, https://doi.org/10.1029/2008WR007042.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Aonashi, K., and Coauthors, 2009: GSMaP passive microwave precipitation retrieval algorithm: Algorithm description and validation. J. Meteor. Soc. Japan, 87A, 119136, https://doi.org/10.2151/jmsj.87A.119.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barros, A. P., and D. P. Lettenmaier, 1993: Dynamic modeling of the spatial distribution of precipitation in remote mountainous areas. Mon. Wea. Rev., 121, 11951214, https://doi.org/10.1175/1520-0493(1993)121<1195:DMOTSD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Berndt, C., E. Rabiei, and U. Haberlandt, 2014: Geostatistical merging of rain gauge and radar data for high temporal resolutions and various station density scenarios. J. Hydrol., 508, 88101, https://doi.org/10.1016/j.jhydrol.2013.10.028.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, F., and X. Li, 2016: Evaluation of IMERG and TRMM 3B43 monthly precipitation products over mainland China. Remote Sens., 8, 472, https://doi.org/10.3390/rs8060472.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Coccia, G., A. L. Siemann, M. Pan, and E. F. Wood, 2015: Creating consistent datasets by combining remotely-sensed data and land surface model estimates through Bayesian uncertainty post-processing: The case of Land Surface Temperature from HIRS. Remote Sens. Environ., 170, 290305, https://doi.org/10.1016/j.rse.2015.09.010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Colle, B., and C. Mass, 1996: An observational and modeling study of the interaction of low-level southwesterly flow with the Olympic Mountains during COAST IOP 4. Mon. Wea. Rev., 124, 21522175, https://doi.org/10.1175/1520-0493(1996)124<2152:AOAMSO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cosgrove, B., and Coauthors, 2003: Real-time and retrospective forcing in the North American Land Data Assimilation System (NLDAS) project. J. Geophys. Res., 108, 8842, https://doi.org/10.1029/2002JD003118.

    • Search Google Scholar
    • Export Citation
  • Currier, W. R., T. Thorson, and J. D. Lundquist, 2017: Independent evaluation of frozen precipitation from WRF and PRISM in the Olympic Mountains. J. Hydrometeor., 18, 26812703, https://doi.org/10.1175/JHM-D-17-0026.1.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Daly, C., M. Halbleib, J. I. Smith, W. P. Gibson, M. K. Doggett, G. H. Taylor, J. Curtis, and P. P. Pasteris, 2008: Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. Int. J. Climatol., 28, 20312064, https://doi.org/10.1002/joc.1688.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dettinger, M., F. Ralph, T. Das, P. Neiman, and D. Cayan, 2011: Atmospheric rivers, floods and the water resources of California. Water, 3, 445478, https://doi.org/10.3390/w3020445.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Durand, M., N. P. Molotch, and S. A. Margulis, 2008: A Bayesian approach to snow water equivalent reconstruction. J. Geophys. Res., 113, D20117, https://doi.org/10.1029/2008JD009894.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gebregiorgis, A., P.-E. Kirstetter, Y. Hong, N. Carr, J. J. Gourley, and Y. Zheng, 2017: Understanding overland multisensor satellite precipitation error in TMPA-RT products. J. Hydrometeor., 18, 285306, https://doi.org/10.1175/JHM-D-15-0207.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Girotto, M., G. Cortés, S. A. Margulis, and M. Durand, 2014a: Examining spatial and temporal variability in snow water equivalent using a 27 year reanalysis: Kern River watershed, Sierra Nevada. Water Resour. Res., 50, 67136734, https://doi.org/10.1002/2014WR015346.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Girotto, M., S. A. Margulis, and M. Durand, 2014b: Probabilistic SWE reanalysis as a generalization of deterministic SWE reconstruction techniques. Hydrol. Processes, 28, 38753895, https://doi.org/10.1002/hyp.9887.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gourley, J., Y. Hong, Z. 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, https://doi.org/10.1175/2009JAMC2302.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hamlet, A., and D. Lettenmaier, 2005: Production of temporally consistent gridded precipitation and temperature fields for the continental United States. J. Hydrometeor., 6, 330336, https://doi.org/10.1175/JHM420.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hasan, M., A. Sharma, F. Johnson, G. Mariethoz, and A. Seed, 2016: Merging radar and in situ rainfall measurements: An assessment of different combination algorithms. Water Resour. Res., 52, 83848398, https://doi.org/10.1002/2015WR018441.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Henn, B., M. P. Clark, D. Kavetski, and J. D. Lundquist, 2015: Estimating mountain basin-mean precipitation from streamflow using Bayesian inference. Water Resour. Res., 51, 80128033, https://doi.org/10.1002/2014WR016736.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Henn, B., M. P. Clark, D. Kavetski, B. McGurk, T. H. Painter, and J. D. Lundquist, 2016: Combining snow, streamflow, and precipitation gauge observations to infer basin-mean precipitation. Water Resour. Res., 52, 87008723, https://doi.org/10.1002/2015WR018564.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Henn, B., A. J. Newman, B. Livneh, C. Daly, and J. D. Lundquist, 2018: An assessment of differences in gridded precipitation datasets in complex terrain. J. Hydrol., 556, 12051219, https://doi.org/10.1016/j.jhydrol.2017.03.008.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hou, A., 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
  • Houze, R. A., Jr., and Coauthors, 2017: The Olympic Mountains Experiment (OLYMPEX). Bull. Amer. Meteor. Soc., 98, 21672188, https://doi.org/10.1175/BAMS-D-16-0182.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huffman, G. J., D. T. Bolvin, and E. J. Nelkin, 2015: Integrated Multi-satellitE Retrievals for GPM (IMERG) technical documentation. NASA/GSFC Code 612 Tech. Doc., 48 pp., http://pmm.nasa.gov/sites/default/files/document_files/IMERG_doc.pdf.

  • Hunter, S. M., 1996: WSR-88D radar rainfall estimation: Capabilities, limitations and potential improvements. Natl. Wea. Dig., 20, 2638.

    • Search Google Scholar
    • Export Citation
  • Kim, K., J. Park, J. Baik, and M. Choi, 2017: Evaluation of topographical and seasonal feature using GPM IMERG and TRMM 3B42 over Far-East Asia. Atmos. Res., 187, 95105, https://doi.org/10.1016/j.atmosres.2016.12.007.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kubota, T., and Coauthors, 2007: Global precipitation map using satelliteborne microwave radiometers by the GSMaP project: Production and validation. IEEE Trans. Geosci. Remote Sens., 45, 22592275, https://doi.org/10.1109/TGRS.2007.895337.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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, 809817, https://doi.org/10.1175/1520-0426(1998)015<0809:TTRMMT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lettenmaier, D. P., 2017: Observational breakthroughs lead the way to improved hydrological predictions. Water Resour. Res., 53, 25912597, https://doi.org/10.1002/2017WR020896.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lettenmaier, D. P., D. Alsdorf, J. Dozier, G. Huffman, M. Pan, and E. Wood, 2015: Inroads of remote sensing into hydrologic science during the WRR era. Water Resour. Res., 51, 73097342, https://doi.org/10.1002/2015WR017616.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Leung, L., and Y. Qian, 2003: The sensitivity of precipitation and snowpack simulations to model resolution via nesting in regions of complex terrain. J. Hydrometeor., 4, 10251043, https://doi.org/10.1175/1525-7541(2003)004<1025:TSOPAS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liang, X., D. P. Lettenmaier, E. Wood, and S. Burges, 1994: A simple hydrologically based model of land-surface water and energy fluxes for general-circulation models. J. Geophys. Res., 99, 14 41514 428, https://doi.org/10.1029/94JD00483.

    • 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, https://ams.confex.com/ams/Annual2005/techprogram/paper_83847.htm.

  • Livneh, B., J. Deems, D. Schneider, J. Barsugli, and N. Molotch, 2014: Filling in the gaps: Inferring spatially distributed precipitation from gauge observations over complex terrain. Water Resour. Res., 50, 85898610, https://doi.org/10.1002/2014WR015442.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Livneh, B., T. J. Bohn, D. W. Pierce, F. Munoz-Arriola, B. Nijssen, R. Vose, D. R. Cayan, and L. Brekke, 2015: A spatially comprehensive, hydrometeorological data set for Mexico, the US, and Southern Canada 1950–2013. Sci. Data, 2, https://doi.org/10.1038/sdata.2015.42.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lundquist, J., J. Minder, P. Neiman, and E. Sukovich, 2010: Relationships between barrier jet heights, orographic precipitation gradients, and streamflow in the Northern Sierra Nevada. J. Hydrometeor., 11, 11411156, https://doi.org/10.1175/2010JHM1264.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lundquist, J., M. Hughes, B. Henn, E. Gutmann, B. Livneh, J. Dozier, and P. Neiman, 2015: High-elevation precipitation patterns: Using snow measurements to assess daily gridded datasets across the Sierra Nevada, California. J. Hydrometeor., 16, 17731792, https://doi.org/10.1175/JHM-D-15-0019.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maddox, R. A., J. Zhang, J. J. Gourley, and K. W. Howard, 2002: Weather radar coverage over the contiguous United States. Wea. Forecasting, 17, 927934, https://doi.org/10.1175/1520-0434(2002)017<0927:WRCOTC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mass, C., and Coauthors, 2003: Regional environmental prediction over the Pacific Northwest. Bull. Amer. Meteor. Soc., 84, 13531366, https://doi.org/10.1175/BAMS-84-10-1353.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maurer, E. P., J. D. Rhoads, R. O. Dubayah, and D. P. Lettenmaier, 2003: Evaluation of the snow-covered area data product from MODIS. Hydrol. Processes, 17, 5971, https://doi.org/10.1002/hyp.1193.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mehran, A., and A. AghaKouchak, 2014: Capabilities of satellite precipitation datasets to estimate heavy precipitation rates at different temporal accumulations. Hydrol. Processes, 28, 22622270, https://doi.org/10.1002/hyp.9779.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mei, Y., E. Anagnostou, E. Nikolopoulos, and M. Borga, 2014: Error analysis of satellite precipitation products in mountainous basins. J. Hydrometeor., 15, 17781793, https://doi.org/10.1175/JHM-D-13-0194.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Miao, C., H. Ashouri, K. Hsu, S. Sorooshian, and Q. Duan, 2015: Evaluation of the PERSIANN-CDR daily rainfall estimates in capturing the behavior of extreme precipitation events over China. J. Hydrometeor., 16, 13871396, https://doi.org/10.1175/JHM-D-14-0174.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Minder, J., D. Durran, G. Roe, and A. Anders, 2008: The climatology of small-scale orographic precipitation over the Olympic Mountains: Patterns and processes. Quart. J. Roy. Meteor. Soc., 134, 817839, https://doi.org/10.1002/qj.258.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Minder, J., P. Mote, and J. Lundquist, 2010: Surface temperature lapse rates over complex terrain: Lessons from the Cascade Mountains. J. Geophys. Res., 115, D14122, https://doi.org/10.1029/2009JD013493.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Molotch, N., 2009: Reconstructing snow water equivalent in the Rio Grande headwaters using remotely sensed snow cover data and a spatially distributed snowmelt model. Hydrol. Processes, 23, 10761089, https://doi.org/10.1002/hyp.7206.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nasrollahi, N., 2015: False alarm in satellite precipitation data. Improving Infrared-Based Precipitation Retrieval Algorithms Using Multi-Spectral Satellite Imagery, N. Nasrollahi, Ed., Springer, 7–12.

    • Crossref
    • Export Citation
  • Neiman, P., L. Schick, F. Ralph, M. Hughes, and G. Wick, 2011: Flooding in western Washington: The connection to atmospheric rivers. J. Hydrometeor., 12, 13371358, https://doi.org/10.1175/2011JHM1358.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Newman, A., and Coauthors, 2015: Gridded ensemble precipitation and temperature estimates for the contiguous United States. J. Hydrometeor., 16, 24812500, https://doi.org/10.1175/JHM-D-15-0026.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nikolopoulos, E., N. Bartsotas, E. Anagnostou, and G. Kallos, 2015: Using high-resolution numerical weather forecasts to improve remotely sensed rainfall estimates: The case of the 2013 Colorado flash flood. J. Hydrometeor., 16, 17421751, https://doi.org/10.1175/JHM-D-14-0207.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Okamoto, K., T. Iguchi, N. Takahashi, K. Iwanami, and T. Ushio, 2005: The Global Satellite Mapping of Precipitation (GSMaP) project. Proc. 25th IEEE Int. Geoscience and Remote Sensing Symp., Seoul, South Korea, IEEE, 3414–3416, https://doi.org/10.1109/IGARSS.2005.1526575.

    • Search Google Scholar
    • Export Citation
  • Painter, T., and Coauthors, 2016: The Airborne Snow Observatory: Fusion of scanning lidar, imaging spectrometer, and physically-based modeling for mapping snow water equivalent and snow albedo. Remote Sens. Environ., 184, 139152, https://doi.org/10.1016/j.rse.2016.06.018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Prat, O., and B. Nelson, 2015: Evaluation of precipitation estimates over CONUS derived from satellite, radar, and rain gauge data sets at daily to annual scales (2002–2012). Hydrol. Earth Syst. Sci., 19, 20372056, https://doi.org/10.5194/hess-19-2037-2015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rabiei, E., and U. Haberlandt, 2015: Applying bias correction for merging rain gauge and radar data. J. Hydrol., 522, 544557, https://doi.org/10.1016/j.jhydrol.2015.01.020.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ralph, F., P. Neiman, G. Wick, S. Gutman, M. Dettinger, D. Cayan, and A. White, 2006: Flooding on California’s Russian River: Role of atmospheric rivers. Geophys. Res. Lett., 33, L13801, https://doi.org/10.1029/2006GL026689.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shepard, D. S., 1984: Spatial statistics and models. Computer Mapping: The SYMAP Interpolation Algorithm, G. L. Gaile and C. J. Willmott, Eds., D. Reidel, 133–145.

    • Crossref
    • Export Citation
  • Shige, S., S. Kida, H. Ashiwake, T. Kubota, and K. Aonashi, 2013: Improvement of TMI rain retrievals in mountainous areas. J. Appl. Meteor. Climatol, 52, 242254, https://doi.org/10.1175/JAMC-D-12-074.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sinclair, S., and G. Pegram, 2005: Combining radar and rain gauge rainfall estimates using conditional merging. Atmos. Sci. Lett., 6, 1922, https://doi.org/10.1002/asl.85.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Storck, P., 1999: Trees, snow and flooding: An investigation of forest canopy effects on snow accumulation and melt at the plot and watershed scales in the Pacific Northwest. Water Resources Series Tech. Rep. 161, 176 pp, http://hdl.handle.net/1957/5136.

  • Tang, G., Y. Ma, D. Long, L. Zhong, and Y. Hong, 2016: Evaluation of GPM Day-1 IMERG and TMPA Version-7 legacy products over Mainland China at multiple spatiotemporal scales. J. Hydrol., 533, 152167, https://doi.org/10.1016/j.jhydrol.2015.12.008.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thornton, P., S. Running, and M. White, 1997: Generating surfaces of daily meteorological variables over large regions of complex terrain. J. Hydrol., 190, 214251, https://doi.org/10.1016/S0022-1694(96)03128-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tian, Y., and Coauthors, 2009: Component analysis of errors in satellite-based precipitation estimates. J. Geophys. Res., 114, D24101, https://doi.org/10.1029/2009JD011949.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ushio, T., and Coauthors, 2009: A Kalman filter approach to the Global Satellite Mapping of Precipitation (GSMaP) from combined passive microwave and infrared radiometric data. J. Meteor. Soc. Japan, 87A, 137151, https://doi.org/10.2151/jmsj.87A.137.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Velasco-Forero, C., D. Sempere-Torres, E. Cassiraga, and J. Gomez-Hernandez, 2009: A non-parametric automatic blending methodology to estimate rainfall fields from rain gauge and radar data. Adv. Water Resour., 32, 9861002, https://doi.org/10.1016/j.advwatres.2008.10.004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wayand, N. E., A. Massmann, C. Butler, E. Keenan, J. Stimberis, and J. D. Lundquist, 2015: A meteorological and snow observational data set from Snoqualmie Pass (921 m), Washington Cascades, U.S. Water Resour. Res., 51, 10 09210 103, https://doi.org/10.1002/2015WR017773.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zagrodnik, J. P., L. A. McMurdie, and R. A. Houze Jr., 2018: Stratiform precipitation processes in cyclones passing over a coastal mountain range. J. Atmos. Sci., https://doi.org/10.1175/JAS-D-17-0168.1, in press.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, J., 2011: National Mosaic and Multi-Sensor QPE (NMQ) system: Description, results, and future plans. Bull. Amer. Meteor. Soc., 92, 13211338, https://doi.org/10.1175/2011BAMS-D-11-00047.1.

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    • Export Citation
  • Zhang, J., and Coauthors, 2016: Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimation: Initial operating capabilities. Bull. Amer. Meteor. Soc., 97, 621637, https://doi.org/10.1175/BAMS-D-14-00174.1.

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    • Search Google Scholar
    • Export Citation
  • Zhu, Y., and R. E. Newell, 1994: Atmospheric rivers and bombs. Geophys. Res. Lett., 21, 19992002, https://doi.org/10.1029/94GL01710.

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