CMORPH: A Method that Produces Global Precipitation Estimates from Passive Microwave and Infrared Data at High Spatial and Temporal Resolution

Abstract

A new technique is presented in which half-hourly global precipitation estimates derived from passive microwave satellite scans are propagated by motion vectors derived from geostationary satellite infrared data. The Climate Prediction Center morphing method (CMORPH) uses motion vectors derived from half-hourly interval geostationary satellite IR imagery to propagate the relatively high quality precipitation estimates derived from passive microwave data. In addition, the shape and intensity of the precipitation features are modified (morphed) during the time between microwave sensor scans by performing a time-weighted linear interpolation. This process yields spatially and temporally complete microwave-derived precipitation analyses, independent of the infrared temperature field. CMORPH showed substantial improvements over both simple averaging of the microwave estimates and over techniques that blend microwave and infrared information but that derive estimates of precipitation from infrared data when passive microwave information is unavailable. In particular, CMORPH outperforms these blended techniques in terms of daily spatial correlation with a validating rain gauge analysis over Australia by an average of 0.14, 0.27, 0.26, 0.22, and 0.20 for April, May, June-August, September, and October 2003, respectively. CMORPH also yields higher equitable threat scores over Australia for the same periods by an average of 0.11, 0.14, 0.13, 0.14, and 0.13. Over the United States for June-August, September, and October 2003, spatial correlation was higher for CMORPH relative to the average of the same techniques by an average of 0.10, 0.13, and 0.13, respectively, and equitable threat scores were higher by an average of 0.06, 0.09, and 0.10, respectively.

The Salinity Retrieval Algorithms for the NASA Aquarius Version 5 and SMAP Version 3 Releases

“…2018, 10, 1121 4 of 25 ancillary data set that aids users of the V5.0 Aquarius Level 2 data to better understand the salinity stratification changes due to rain. The product uses as input the surface rain rates from the NOAA CMORPH (CPC-Climate Prediction Center-Morphing technique) global precipitation data set [16]. The average instantaneous rain rate is calculated using a structure of 13 CMORPH pixels around the center of the Aquarius IFOV, where the weight associated with each pixel is based on the antenna gain.…”

The quasi-stationary feature of nocturnal precipitation in the Sichuan Basin and the role of the Tibetan Plateau

“…This dataset, produced by the National Meteorological Information Center of China Meteorological Administration, is used to study the diurnal cycle of precipitation. It combines the National Oceanic and Atmospheric Administration (NOAA)/Climate Prediction Center morphing technique (CMORPH) precipitation data (Joyce et al 2004) and the hourly precipitation dataset observed by more than thirty thousand automatic meteorological stations in China ( Fig. 1) (Shen et al 2010a).…”

Evaluation of Drought Indices Based on Thermal Remote Sensing of Evapotranspiration over the Continental United States

“…While real-time precipitation analyses of reasonable quality are available over most of the United States (e.g., McEnery et al 2005), many parts of world lack sufficiently dense radar and rain gauge networks. Satellite-derived global precipitation products provide improved spatial coverage (Huffman et al 2007;Joyce et al 2004), but they are known to exhibit seasonally and spatially dependent biases (Villarini et al 2009;Zeweldi and Gebremichael 2009). Drought classifications from prognostic water balance models (e.g., NLDAS) depend strongly on the assumed model physics, dynamic forcings, and subsurface properties (Mo 2008), requiring information about soil-moisture-holding capacity and retention characteristics that is difficult to obtain with adequate accuracy over large areas.…”