remote sensing Article Applications of a CloudSat-TRMM and CloudSat-GPM Satellite Coincidence Dataset F. Joseph Turk 1,* , Sarah E. Ringerud 2,3, Andrea Camplani 4,5, Daniele Casella 5, Randy J. Chase 6,7, Ardeshir Ebtehaj 8, Jie Gong 3,9 , Mark Kulie 10, Guosheng Liu 11 , Lisa Milani 2,3 , Giulia Panegrossi 5 , Ramon Padullés 12,13 , Jean-François Rysman 14, Paolo Sanò 5 , Sajad Vahedizade 8 and Norman B. Wood 15 1 Jet Propulsion Laboratory (JPL), California Institute of Technology, Pasadena, CA 91101, USA 2 Earth Systems Science Interdisciplinary Center (ESSIC), University of Maryland, College Park, MD 20740, USA; [email protected] (S.E.R.); [email protected] (L.M.) 3 NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA; [email protected] 4 Department of Civil, Construction and Environmental Engineering, Geodesy and Geomatics Division, Sapienza University of Rome, 00185 Rome, Italy; [email protected] 5 National Research Council of Italy, Institute of Atmospheric Sciences and Climate (CNR-ISAC), 00133 Rome, Italy; [email protected] (D.C.); [email protected] (G.P.); [email protected] (P.S.) 6 School of Computer Science, University of Oklahoma, Norman, OK 73072, USA; [email protected] 7 School of Meteorology, University of Oklahoma, Norman, OK 73072, USA 8 Department of Civil, Environmental and Geo-Engineering, University of Minnesota, Minneapolis, MN 55455, USA; [email protected] (A.E.); [email protected] (S.V.) 9 Universities Space Research Association, Columbia, MD 21046, USA 10 National Oceanic and Atmospheric Administration (NOAA), National Environmental Satellite, Data and Information Service (NESDIS), Center for Satellite Applications and Research (STAR), Advanced Satellite Products Branch (ASPB), Madison, WI 53706, USA; [email protected] 11 Department of Earth, Ocean and Atmospheric Science, Florida State University, Tallahassee, FL 32306, USA; [email protected] Citation: Turk, F.J.; Ringerud, S.E.; 12 Instituto de Ciencias del Espacio (ICE-CSIC/IEEC), 08193 Barcelona, Spain; [email protected] Camplani, A.; Casella, D.; Chase, R.J.; 13 Institut d’Estudis Espacials de Catalunya (IEEC), 08034 Barcelona, Spain Ebtehaj, A.; Gong, J.; Kulie, M.; Liu, 14 LMD/IPSL, École Polytechnique, Institut Polytechnique de Paris, ENS, PSL Université, Sorbonne Université, G.; Milani, L.; et al. Applications of a CNRS, 91128 Palaiseau, France; [email protected] CloudSat-TRMM and CloudSat-GPM 15 Space Science and Engineering Center, University of Wisconsin, Madison, WI 53706, USA; Satellite Coincidence Dataset. Remote [email protected] Sens. 2021, 13, 2264. https://doi.org/ * Correspondence: [email protected] 10.3390/rs13122264 Abstract: The Global Precipitation Measurement (GPM) Dual-Frequency Precipitation Radar (DPR) Academic Editor: Ismail Gultepe (Ku- and Ka-band, or 14 and 35 GHz) provides the capability to resolve the precipitation structure under moderate to heavy precipitation conditions. In this manuscript, the use of near-coincident Received: 30 April 2021 observations between GPM and the CloudSat Profiling Radar (CPR) (W-band, or 94 GHz) are Accepted: 7 June 2021 demonstrated to extend the capability of representing light rain and cold-season precipitation from Published: 9 June 2021 DPR and the GPM passive microwave constellation sensors. These unique triple-frequency data have opened up applications related to cold-season precipitation, ice microphysics, and light rainfall and Publisher’s Note: MDPI stays neutral surface emissivity effects. with regard to jurisdictional claims in published maps and institutional affil- Keywords: GPM; TRMM; CloudSat; ice; radar; radiometer; microwave; precipitation; snow; emissiv- iations. ity; microphysics Copyright: © 2021 by the authors. 1. Introduction Licensee MDPI, Basel, Switzerland. The Global Precipitation Measurement (GPM) Dual-Frequency (Ku- and Ka-band, or This article is an open access article distributed under the terms and 14 and 35 GHz) Precipitation Radar (DPR) provides the capability to resolve the condensed conditions of the Creative Commons water content profile through all but the heaviest precipitation and light rain and snow- Attribution (CC BY) license (https:// fall [1]. With its 65-degree orbit inclination, the DPR observes a wider variety of weather creativecommons.org/licenses/by/ systems than its predecessor (35-degree orbit inclination) Tropical Rainfall Measuring 4.0/). Mission (TRMM) satellite Ku-band Precipitation Radar (PR) [2]. At extreme precipitation Remote Sens. 2021, 13, 2264. https://doi.org/10.3390/rs13122264 https://www.mdpi.com/journal/remotesensing Remote Sens. 2021, 13, 2264 2 of 32 rates (exceeding 50 mm h−1), propagation through heavy precipitation attenuates the radar transmit power below this level before the signal has reached the surface and can also introduce unwanted second order scattering effects [3]. At light precipitation rates (below 1 mm h−1), the DPR low-end sensitivity of ~15 and (~19 dBz) at Ku-band (Ka-band) implies that a significant fraction of light precipitation and solid-phase (falling snow) pre- cipitation remains unobserved [4,5]. Additional observations are needed to indicate the presence or absence of snow and light rain, to further assess the capability of GPM and other sensors to adequately represent cold-season precipitation (see review article by [6]); in particular, a more complete top-to-bottom radar-observed vertical structure, including portions of the clouds that extend below the DPR (PR) sensitivity. Owing to the unique asynchronous orbits of the GPM and TRMM satellites, their orbital ground tracks periodically intersect the orbits of many sun-synchronous satellites. Of particular interest are the many intersections (coincidences) that occur between the W-band (94-GHz) CloudSat Profiling Radar (CPR), and either the TRMM satellite or the GPM core satellite, with a short time separation. For GPM, these combined radar data offer pseudo three-frequency radar profiles (W-band from CPR, Ku/Ka-band from DPR), and the 13-channel (10-183 GHz) GPM Microwave Imager (GMI) passive microwave (MW) radiometer. For TRMM, these combined data offer pseudo dual-frequency radar profiles (W-band from CPR, Ku-band from PR), and the nine-channel (10–85 GHz) TRMM Microwave Imaging (TMI) radiometer. The purpose of this manuscript is two-fold. First, a description of the content of the CloudSat-TRMM and CloudSat-GPM coincidence dataset is provided in Section2. The latest version of these coincidence data products is processed across both the TRMM (CloudSat + PR/TMI) and GPM (CloudSat + DPR/GMI) observational periods, and are available from the NASA Precipitation Processing System (PPS). These combined multi-frequency radar/passive MW data are useful for many applications related to precipitation estimation. The second purpose of this manuscript is to highlight examples of various past and current investigations that have been enabled through the availability of these coincidence data. Examples include cold-season precipitation (snowfall) estimation (Section3), upper-level ice microphysics (Section4), and light rain and land surface emissivity effects (Section5). It is noted that while a full presentation of all uses of these data is beyond the scope of this manuscript, it includes the evaluation of the DPR [7] and the combined radar– radiometer (DPR+GMI) algorithms (CORRA) [8] and the identification of their deficiencies, radiative transfer simulations to account for the presence of cloud water, and studying land surface effects on the passive MW radiometer (or combined radar–radiometer) precipitation retrieval algorithms. These “pseudo” (in the sense that all three radars are not precisely beam matched) triple-frequency spaceborne observations may serve as a useful proxy for future observations related to the cloud convective precipitation (CCP) designated observable in the recent NASA Earth Science Decadal Survey [9]. 2. Dataset Preparation The processing flow is driven by a pre-generated list of the ±15-min time coincidences between the orbital ground trace of CloudSat and TRMM, and CloudSat and GPM, ob- tained from orbital propagation using daily two-line elements (TLE) ephemeris, providing sufficient accuracy for aligning orbital locations to within several seconds. CloudSat was initially placed into the tightly controlled A-Train sun-synchronous satellite formation (owing to the afternoon 1330 Local Time of Ascending Node, or LTAN). The operation of the CloudSat Profiling Radar (CPR) began in June 2006. Due to satellite power issues in April 2011 which temporarily suspended science data collection, CloudSat was operated in a “daytime only” (CPR operations during the ascending orbit) mode data after mid-2012, with periodic data outages since that time [10–12]. The CloudSat-TRMM dataset spans the period between 16 June 2006 (first day of CloudSat data) and 30 September 2014. A total of 19,610 ± 15-min orbit coincidences files are available within this time span. In 2019, the CloudSat Release-5 (R05) data products Remote Sens. 2021, 13, 3 of 34 Remote Sens. 2021, 13, 2264 3 of 32 The CloudSat-TRMM dataset spans the period between 16 June 2006 (first day of CloudSat data) and 30 September 2014. A total of 19,610 ± 15-min orbit coincidences files are available within this time span. In 2019, the CloudSat Release-5 (R05) data products were released, released, and and all all data data reprocessed reprocessed back back to to the the beginning beginning of of the the mission mission in inJune