CloudSat Precipitation Profiling Algorithm—Model Description

Abstract

Identifying and quantifying the intensity of light precipitation at global scales is still a difficult problem for most of the remote sensing algorithms in use today. The variety of techniques and algorithms employed for such a task yields a rather wide spectrum of possible values for a given precipitation event, further hampering the understanding of cloud processes within the climate. The ability of CloudSat's millimeter-wavelength Cloud Profiling Radar (CPR) to profile not only cloud particles but also light precipitation brings some hope to the above problems. Introduced as version zero, the present work uses basic concepts of detection and retrieval of light precipitation using spaceborne radars. Based on physical principles of remote sensing, the radar model relies on the description of clouds and rain particles in terms of a drop size distribution function. Use of a numerical model temperature and humidity profile ensures the coexistence of mixed phases otherwise undetected by the CPR. It also provides grounds for evaluating atmospheric attenuation, important at this frequency. Related to the total attenuation, the surface response is used as an additional constraint in the retrieval algorithm. Practical application of the profiling algorithm includes a 1-yr preliminary analysis of global rainfall incidence and intensity. These results underscore once more the role of CloudSat rainfall products for improving and enhancing current estimates of global light rainfall, mostly at higher latitudes, with the goal of understanding its role in the global energy and water cycle.

NASA GPM-Ground Validation: Integrated Precipitation and Hydrology Experiment 2014 Science Plan

“…Wilson and Barros (2014) propose that these missed light rainfall events as well as underestimation of rainfall in the case of stratiform systems and shallow convection can be attributed to seeder -feeder interactions among local fog banks and cap clouds and incoming weather systems. Mitrescu et al (2010) report a preliminary CloudSat climatology for light and moderate rainfall events that is consistent with local observations, thus suggesting that, although for different sensors, there is great opportunity to improve light rainfall estimation over complex terrain with the GPM DPR Dual Polarization Radar). Recently, an error analysis of TRMM PR 2A25 by Duan and Barros (2014) indicates that there is significant spatial and temporal organization of retrieval errors that is associated to topographic features and can be explained in relation to the predominant hydrometeorological regimes.…”

Snowfall Detectability of Nasa's Cloudsat: The First Cross-Investigation of Its 2c-Snow-Profile Product and National Multi-Sensor Mosaic Qpe (Nmq) Snowfall Data

“…It provides good sensitivity for measuring the vertical structure of cloud liquid and solid water distribution. While CPR is optimized for observing clouds, the sensor has proven itself capable of identifying and retrieving both light rainfall and snowfall [14,15]. In February 2013 NASA released a novel snow profile product, 2C-SNOW-PROFILE, to the scientific community [16].…”

Assessing the impact of the Kuroshio Current on vertical cloud structure using CloudSat data

“…One granule consists of approximately 36 383 profiles, and one profile has 125 vertical bins, each of which is 240 m thick. In this study, we used the CloudSat product 2B-GEOPROF 240 m resolution vertical distribution of the cloud mask, radar reflectivity (Mace et al, 2007;Marchand et al, 2008), altitude, and temperature profiles from the European Center for Medium-Range Weather Forecasts (ECMWF-AUX; Partain, 2007); precipitating liquid and ice water content (PLWC and PIWC) from 2C-RAIN-PROFILE (Mitrescu et al, 2010;Lebsock et al, 2011) Fig. 1).…”