Using satellite measurements in microwave bands to retrieve precipitation over land requires proper discrimination of the weak rainfall signals from strong and highly variable background Earth surface emissions. Traditionally, land retrieval methods rely on a weak signal of rainfall scattering on high-frequency channels and make use of empirical thresholding and regression-based techniques. Because of the increased surface signal interference, retrievals over radiometrically complex land surfaces-snow-covered lands, deserts, and coastal areas-are particularly challenging for this class of retrieval techniques. This paper evaluates the results by the recently proposed Shrunken Locally Linear Embedding Algorithm for Retrieval of Precipitation (ShARP) using data from the Tropical Rainfall Measuring Mission (TRMM) satellite. The study focuses on a radiometrically complex region, partly covering the Tibetan highlands, Himalayas, and Ganges-Brahmaputra-Meghna River basins, which is unique in terms of its diverse land surface radiation regime and precipitation type, within the TRMM domain. Promising results are presented using ShARP over snow-covered land surfaces and in the vicinity of coastlines, in comparison with the land rainfall retrievals of the standard TRMM 2A12, version 7, product. The results show that ShARP can significantly reduce the rainfall overestimation due to the background snow contamination and markedly improve detection and retrieval of rainfall in the vicinity of coastlines. During the calendar year 2013, compared to TRMM 2A25, it is demonstrated that over the study domain the root-mean-square difference can be reduced up to 38% annually, while the improvement can reach up to 70% during the cold months of the year.
Bibliographical noteFunding Information:
The first author would like to thank Professor Christian Kummerow and Dr. Dave Randel at Colorado State University for the provided insight and fruitful discussion through the course of this research. The authors also gratefully acknowledge the financial support provided by the K. Harrison Brown Family Chair; the Joseph T. and Rose S. Ling Chair; two NASA Global Precipitation Measurement Grants (NNX13AG33G and NNX13AH35G); and the USDA National Institute of Food and Agriculture, project 1008517, through the Agricultural Experiment Station at Utah State University. The TRMM 2A12 and 2A25 data were obtained through the anonymous File Transfer Protocol publicly available at ftp://trmmopen.gsfc.nasa.gov/pub/trmmdata. TheMODIS retrievals (i.e., MYD13C2 and MYD10CM) were also obtained from the Land Processes Distributed Active Archive Center (LP DAAC), which are publicly accessible at https://lpdaac.usgs.gov/data_access/data_pool.
© 2016 American Meteorological Society.
- Atm/Ocean Structure/Phenomena
- Observational techniques and algorithms
- Remote sensing
- Satellite observations