3 Remote Sensing of the Terrestrial Water Cycle, Geophysical Monograph 206. First Edition. Edited by Venkat Lakshmi. © 2015 American Geophysical Union. Published 2015 by John Wiley & Sons, Inc. 1.1. INTRODUCTION Precipitation is a critical variable driving the atmosphere’s general circulation through latent heat release. As such, accurate quantification of the spatiotemporal variability of precipitation is essential for applications involving environmental, atmospheric, water resource, and related science and engineering disciplines. The increased availability of data products from microwave (passive and active) remote sensing has contributed toward our understanding of the spatiotemporal distribution of pre- cipitation by providing near-real-time spatially continuous precipitation estimates at smaller temporal sampling inter- vals [Petty, 1994; Ferraro, 1997; Bauer, 2001; Grecu and Anagnostou, 2001; Kummerow et al., 2001; Turk et al., 2002; McCollum and Ferraro, 2003; Wilheit et al., 2003; Ferraro et al., 2005; Levizzani and Gruber, 2007]. These include data products from the Special Sensor Microwave Imager (SSM/I) on Defense Meteorological Satellite Program satellites [Ferraro, 1997], Advanced Microwave Sounding Unit (AMSU) on National Oceanic and Atmospheric Agency (NOAA) Polar Orbiting environmental satellites [Ferraro et al., 2005], Tropical Rainfall Measuring Mission (TRMM) microwave imager (TMI) and precipitation radar (PR) [Kummerow et al., 2001; Wang et al., 2009], Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) [Wilheit et al., 2003] on National Aeronautics and Space Administration (NASA) and Japan Aerospace and Exploration Agency (JAXA) joint satellites, etc. Along with the widespread acceptance of microwave-based precipitation products, it has also been recognized that these products contain large uncertainties [Petty, 1994; Smith et al., 1998; Kummerow et al., 1998, 2005; Coppens et al., 2000]. Studies quantifying global uncertainty offered by microwave rainfall algorithms show climatologically dis- tinct space/time domains that contribute approximately 25% uncertainty to rainfall product that goes undetected by a microwave radiometer [Kummerow et al., 2005]. Of these, nearly 20% is attributed to changes in cloud morphology and microphysics and 5% to changes in the rain/no-rain thresholds. The purpose of this chapter is to describe the foundations of rain/no-rain classification (RNC) based on passive microwave brightness temperatures, outstanding issues, areas of future research, and a comprehensive review of the existing RNC algorithms, based on the works by Grody [1991], Adler et al. [1993], Ferraro et al. [1998], Seto et al. [2005, 2009], Kida et al. [2009], and Kubota et al. [2007]. The physically based overland rainfall retrieval algorithms incorporate rainfall screening as an integral part, without which the succeeding overland rain retrieval technique gets corrupted easily. From the work by Grody [1991], “the physics of rain detection and screen- ing are every bit as important as those of conversion.” Studies by rainfall intercomparison projects including algorithm intercomparison projects sponsored by the Global Precipitation Climatology Project and NASA WetNet Precipitation Intercomparison Projects con- clude that inadequate screening of nonraining pixels complicates the simplest to the most complex of retrieval algorithms. To date, various approaches exist to detect raining areas within a radiometer footprint. While some of these tech- niques are easy to implement, some others involve sophisti- cated programming logic for correct implementation. 1 Rain/No-Rain Classification Using Passive Microwave Radiometers J. Indu 1 and D. Nagesh Kumar 1,2 1 Department of Civil Engineering, Indian Institute of Science, Bangalore, India 2 Centre for Earth Sciences, Indian Institute of Science, Bangalore, India COPYRIGHTED MATERIAL