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The article reports on the global precipitation measurement (GPM) mission which collects essential rain and snow data for scientific studies and societal benefit. Discussed is the Tropical Rainfall Measuring Mission (TRMM) spacecraft launched by U.S. National Aeronautics & Space Administration and Japan Aerospace Exploration Agency which has a passive microwave multifrequency imaging radiometer and Ku-band radar channel capable of generating three-dimensional views of precipitation structure.

As a component of Earth's hydrologic cycle, and especially at higher latitudes, falling snow creates snowpack accumulation that in turn provides a large proportion of the freshwater resources required by many communities throughout the world. To assess the relationships between remotely sensed snow measurements with in situ measurements, a winter field project, termed the Global Precipitation Measurement (GPM) Cold Season Precipitation Experiment (GCPEx), was carried out in the winter of 2011/12 in Ontario, Canada. Its goal was to provide information on the precipitation microphysics and processes associated with cold season precipitation to support GPM snowfall retrieval algorithms that make use of a dual-frequency precipitation radar and a passive microwave imager on board the GPM core satellite and radiometers on constellation member satellites. Multiparameter methods are required to be able to relate changes in the microphysical character of the snow to measureable parameters from which precipitation detection and estimation can be based. The data collection strategy was coordinated, stacked, high-altitude, and in situ cloud aircraft missions with three research aircraft sampling within a broader surface network of five ground sites that in turn were taking in situ and volumetric observations. During the field campaign 25 events were identified and classified according to their varied precipitation type, synoptic context, and precipitation amount. Herein, the GCPEx field campaign is described and three illustrative cases detailed. [ABSTRACT FROM AUTHOR]

Abstract: During the middle part of this decade, a wide variety of passive microwave imagers and sounders will be unified in the Global Precipitation Measurement (GPM) mission to provide a common basis for frequent (3h) global precipitation monitoring. The ability of these sensors to detect precipitation by discerning it from non-precipitating background depends upon the channels available and characteristics of the surface and atmosphere. This study quantifies the minimum detectable precipitation rate and fraction of precipitation detected for four representative instruments (TMI, GMI, AMSU-A, and AMSU-B) that will be part of the GPM constellation. Observations for these instruments were constructed from equivalent channels on the SSMIS instrument on DMSP satellites F16 and F17 and matched to precipitation data from NOAA's National Mosaic and QPE (NMQ) during 2009 over the continuous United States. A variational optimal estimation retrieval of non-precipitation surface and atmosphere parameters was used to determine the consistency between the observed brightness temperatures and these parameters, with high cost function values shown to be related to precipitation. The minimum detectable precipitation rate, defined as the lowest rate for which probability of detection exceeds 50%, and the detected fraction of precipitation are reported for each sensor, surface type (ocean, coast, bare land, snow cover) and precipitation type (rain, mix, snow). The best sensors over ocean and bare land were GMI (0.22mmh−1 minimum threshold and 90% of precipitation detected) and AMSU (0.26mmh−1 minimum threshold and 81% of precipitation detected), respectively. Over coasts (0.74mmh−1 threshold and 12% detected) and snow-covered surfaces (0.44mmh−1 threshold and 23% detected), AMSU again performed best but with much lower detection skill, whereas TMI had no skill over these surfaces. The sounders (particularly over water) benefited from the use of re-analysis data (vs. climatology) to set the a priori atmospheric state and all instruments benefited from the use of a conditional snow cover emissivity database over land. It is recommended that real-time sources of these data be used in the operational GPM precipitation algorithms. [Copyright &y& Elsevier]

There is an increased interest in detecting and estimating the amount of falling snow reaching the Earth's surface in order to fully capture the global atmospheric water cycle. An initial step toward global spaceborne falling snow algorithms for current and future missions includes determining the thresholds of detection for various active and passive sensor channel configurations and falling snow events over land surfaces and lakes. In this paper, cloud resolving model simulations of lake effect and synoptic snow events were used to determine the minimum amount of snow (threshold) that could be detected by the following instruments: the W-band radar of CloudSat, Global Precipitation Measurement (GPM) Dual-Frequency Precipitation Radar (DPR) Ku- and Ka-bands, and the GPM Microwave Imager. Eleven different nonspherical snowflake shapes were used in the analysis. Notable results include the following: 1) The W-band radar has detection thresholds more than an order of magnitude lower than the future GPM radars; 2) the cloud structure macrophysics influences the thresholds of detection for passive channels (e.g., snow events with larger ice water paths and thicker clouds are easier to detect); 3) the snowflake microphysics (mainly shape and density) plays a large role in the detection threshold for active and passive instruments; 4) with reasonable assumptions, the passive 166-GHz channel has detection threshold values comparable to those of the GPM DPR Ku- and Ka-band radars with \sim\! 0.05\ \g\cdot\m^-3 detected at the surface, or an \sim\! 0.5-1.0\-\mm\cdot \h^-1 melted snow rate. This paper provides information on the light snowfall events missed by the sensors and not captured in global estimates. [ABSTRACT FROM AUTHOR]

The article notes the importance of satellite-based global precipitation measures and a forum at the University of California, Irvine which focused on remote sensing. Research priorities mentioned at the Advanced Concepts Workshop on Remote Sensing of Precipitation at Multiple Scales include improving satellite-based precipitation retrieval algorithms, developing validation metrics for research, and creating a near-real-time model of probabilistic uncertainty.