Spatial downscaling and evaluation of CMORPH analyses over the continental U.S.
Matthew Garcia, Univ. of Maryland Baltimore County/GEST and NASA/GSFC, Greenbelt, MD; and Y. Tian, C. D. Peters-Lidard, J. B. Eylander, C. Daly, R. Joyce, and J. Janowiak
The spatially distributed application of a land surface model (LSM) over a region of interest requires the application of similarly distributed precipitation fields that can be derived from various sources, including surface gauge networks, surface-based radar, and orbital platforms. The spatial variability of precipitation influences the spatial organization of soil temperature and moisture states and, consequently, the spatial variability of land–atmosphere fluxes. The accuracy of spatially-distributed precipitation fields can contribute significantly to the uncertainty of model-based hydrological states and fluxes at the land surface. These issues are currently being investigated in collaborations between NASA, AFWA, NOAA and Oregon State University using the NASA–GSFC Land Information System (LIS; Kumar et al. 2006).
Recent enhancements to the LIS processing of meteorological forcing inputs include the handling of station-based precipitation datasets and the assimilation of climatological information for precipitation and temperature variables. Spatial interpolation methods for the analysis of irregular (e.g. station) datasets and the downscaling of gridded products are examined and their error characteristics are discussed. Climatological information included in the LIS downscaling procedure is provided by a monthly high-resolution PRISM (Daly et al. 1994) dataset for North America. The combination of these methods and sources attempts to address both the strengths and weaknesses of the interpolation and PRISM techniques (Daly 2006), including the need for timely estimation of spatial fields in quasi-operational and near-real-time simulations.
A test input precipitation dataset is provided by the NOAA–NWS CPC Morphing (CMORPH; Joyce et al. 2004) technique. The CMORPH method uses microwave-based satellite precipitation estimates from various polar-orbiting platforms (NOAA POES, DMSP, and NASA's EOS Aqua) as well as the NASA Tropical Rainfall Measurement Mission (TRMM). This technique combines estimates from existing microwave-based precipitation algorithms by way of standard calibration to the TRMM Microwave Imager (TMI; 2A12) product, and is therefore extremely flexible such that precipitation estimates from any microwave-based satellite source can be incorporated. Using these precipitation estimates, identified meteorological features are then transported via spatial propagation information from geostationary (NOAA GOES) infrared-based satellite observations. The CMORPH method thus produces global precipitation analyses at high spatial resolution (8 to 25 km) and temporal frequency (30 minutes to 3 hours).
Results from processing of the CMORPH precipitation dataset over various areas of the U.S. using LIS-based methods for downscaling, both with and without climatological factors, are evaluated against high-resolution surface-based datasets. These evaluation datasets include available NOAA–NWS gauge, radar and Multisensor Precipitation Estimation (MPE; Seo 1998) products as well as analyses based on cooperative and local surface precipitation gauge networks. Error characteristics are generally oriented on known deficiencies of the methods included in the source and evaluation products, including difficulties in microwave-based satellite precipitation estimates over land and in the spatial coverage of surface-based radar over regions of complex terrain.
Daly, C., 2006: Guidelines for assessing the suitability of spatial climate data sets. International J. Climatology, v. 26, pp. 707-721.
Daly, C., R.P. Neilson, and D.L. Phillips, 1994: A statistical–topographic model for mapping climatological precipitation over mountanious terrain. J. Applied Meteorology, v. 33, pp. 140-158.
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. Hydrometeorology, v. 5, pp. 487-503.
Kumar, S.V., C.D. Peters-Lidard, Y. Tian, P.R. Houser, J. Geiger, S. Olden, L. Lighty, J.L. Eastman, B. Doty, P. Dirmeyer, J. Adams, K. Mitchell, E.F. Wood, and J. Sheffield, 2006: Land information system: An interoperable framework for high resolution land surface modeling. Environmental Modeling & Software, v. 21, pp. 1402-1415.
Seo, D.-J., 1998: Real-time estimation of rainfall fields using radar rainfall and rain gage data. J. Hydrology, v. 208, pp. 37-52.Recorded presentation
Session 2, Hydrometeorological Remote Sensing
Tuesday, 16 January 2007, 8:30 AM-5:00 PM, 211
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