JULY 2016 B R O W N E T A L . 1539 Hurricane GPROF: An Optimized Ocean Microwave Rainfall Retrieval for Tropical Cyclones PAULA J. BROWN,CHRISTIAN D. KUMMEROW, AND DAVID L. RANDEL Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado (Manuscript received 17 November 2015, in final form 18 May 2016) ABSTRACT The Goddard profiling algorithm (GPROF) is an operational passive microwave retrieval that uses a Bayesian scheme to estimate rainfall. GPROF 2014 retrieves rainfall and hydrometeor vertical profile in- formation based upon a database of profiles constructed to be simultaneously consistent with Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and TRMM Microwave Imager (TMI) ob- servations. A small number of tropical cyclones are in the current database constructed from one year of TRMM data, resulting in the retrieval performing relatively poorly for these systems, particularly for the highest rain rates. To address this deficiency, a new database focusing specifically on hurricanes but consisting of 9 years of TRMM data is created. The new database and retrieval procedure for TMI and GMI is called Hurricane GPROF. An initial assessment of seven tropical cyclones shows that Hurricane GPROF provides a better estimate of hurricane rain rates than GPROF 2014. Hurricane GPROF rain-rate errors relative to the PR are reduced by 20% compared to GPROF, with improvements in the lowest and highest rain rates es- pecially. Vertical profile retrievals for four hydrometeors are also enhanced, as error is reduced by 30% compared to the GPROF retrieval, relative to PR estimates. When compared to the full database of tropical cyclones, Hurricane GPROF improves the RMSE and MAE of rain-rate estimates over those from GPROF by about 22% and 27%, respectively. Similar improvements are also seen in the overall rain-rate bias for 2 hurricanes in the database, which is reduced from 0.20 to 20.06 mm h 1. 1. Introduction (TRMM) Microwave Imager (TMI) increasing the number of channels and spatial resolution of SSM/I Tropical cyclones are a high-impact meteorological (Kummerow et al. 2001). TRMM was deployed in 1997 event that can have catastrophic impacts, especially if with a Microwave Imager (TMI) and a precipitation landfall occurs. Tropical ocean environments create the radar (PR) on board to provide detailed data on tropical conditions under which tropical storms and tropical cy- rainfall between 358N and 358S, which makes it ideal for clones occur, but these environments are difficult to studying spatial and temporal rainfall characteristics of observe using ground-based instrumentation. Conse- tropical cyclones. Similarly, the recent deployment of quently, satellite-derived measurements are frequently the Global Precipitation Measurement (GPM) Core used to detect and monitor the evolution of tropical Observatory satellite in February 2014 will complement cyclones. Space-based microwave radiometers have the rainfall products generated from TRMM. The GPM provided measures of atmospheric moisture since the Core Observatory’s Microwave Imager (GMI) and dual- Special Sensor Microwave Imager (SSM/I) began op- frequency precipitation radar (DPR) provides near- erating in 1987. Similar instruments are now commonly global coverage and improves upon TRMM’s detection used to estimate both cyclone features and rain rates of light rain, snow, and the microphysical properties of (Benedetti et al. 2005; Lau et al. 2008; Jiang et al. 2011), precipitating particles (Hou et al. 2014). especially with the Tropical Rainfall Measuring Mission Over oceans, physical methods that use statistical re- lationships between brightness temperatures and rain rates/hydrometeors have been developed to retrieve Corresponding author address: Paula J. Brown, Department of Atmospheric Science, Colorado State University, 1371 Campus rainfall for TRMM, the GPM Core Observatory, and Delivery, Fort Collins, CO 80523-1371. other satellites (Wilheit et al. 1991; Petty 2001; Hilburn E-mail: [email protected] and Wentz 2008; Kummerow et al. 2015), often through DOI: 10.1175/JTECH-D-15-0234.1 Ó 2016 American Meteorological Society Unauthenticated | Downloaded 10/09/21 05:54 AM UTC 1540 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY VOLUME 33 Bayesian schemes (Evans et al. 1995; Bauer et al. 2001; also possible, but it was preferable to evaluate the re- Di Michele et al. 2005; Kummerow et al. 2011; Sanò trieval over oceans where radar data are more robust and et al. 2013). Bayesian schemes often use a probabilistic the physical relationships between rain and the radar are approach to match an a priori database of hydrometeor better understood. profiles constructed by requiring consistency between The purpose of this paper is to describe the new re- hydrometeors and radiometer observations (Marzano trieval methodology for tropical cyclones and to show et al. 1999; Kummerow et al. 2011; Sanò et al. 2013). how it improves estimates of tropical cyclone rain rates Retrievals over land are complicated by surface het- over oceans. While this methodology is applied to erogeneity, affecting emissivities and increasing error in tropical cyclones here, it can also be applied to other the rainfall estimates (Wang et al. 2009; Petty and Li meteorological phenomenon. An overview of the GPROF 2013). Despite these complexities, passive microwave 2014 retrieval, its use of databases, and their limitations rain products over land show good agreement with other are identified in section 2. Section 2 also describes the products (Kummerow et al. 2011; 2015), especially as new ocean-only database and the adaptations made to improvements are made to address weaknesses previ- the GPROF 2014 rain-rate retrieval for tropical storms ously identified in the algorithms (Wang et al. 2009; and tropical cyclones. Hurricane GPROF is then eval- Gopalan et al. 2010; Ferraro et al. 2013). uated in section 3. Section 4 assesses the effect of in- TRMM’s suitability for monitoring tropical cyclones creasing the error term in the Bayesian retrieval to has led to extensive development and use of databases, optimize channel uncertainty in order to account for the such as the tropical cyclone cloud and precipitation incomplete population of tropical storms in the data- feature databases (Liu et al. 2008; Jiang et al. 2011), base. Vertical profile retrievals are evaluated in section 5, tropical cyclone track databases (Knapp et al. 2010) and and then a summary of the new Hurricane GPROF re- numerous studies of meteorological characteristics of trieval and conclusions from this work are contained in tropical cyclones (Lonfat et al. 2004; Jiang 2012; Ren section 6. et al. 2014). However, deficiencies have been shown when comparing estimates from independent radar ob- servations of precipitation and TMI precipitation esti- 2. TRMM and the GPROF retrieval procedure mates, with TMI underestimating the inner core and a. TRMM instruments high-rain-rate regions of tropical cyclones (Viltard et al. 2006; Zagrodnik and Jiang 2013a,b). These underesti- Active and passive microwave instruments are on mates are associated with one of the limitations of the board the TRMM satellite. The precipitation properties Bayesian probability theory applied in the retrieval, of the PR and the TMI instruments are estimated re- which produces a rain-rate estimate based on its likeli- motely from the reflectivity and brightness tempera- hood. In the case of tropical cyclones, their hydrome- tures, respectively. The PR operates at 13.8 GHz, with a teorological environments and high rain rates are surface resolution of 5 km and a 247-km (after boost) uncommon in the a priori database and are therefore swath. The PR 2A25 algorithm (Iguchi et al. 2000) de- unlikely to be highly weighted by the retrieval’s Bayes- termines rain rates based on the relationship between ian scheme. As TMI has limited information from its reflectivity and rain rate, using a drop size distribution observations, it cannot fully distinguish between the Tbs model, an attenuation correction, and a nonuniform of different rain systems, so identifying these rain sys- beamfilling correction. The sensitivity of the PR is 2 tems (e.g., tropical cyclones) using auxillary data adds roughly 0.7 mm h 1, although it can produce rain rates of 2 information to benefit the retrieval. as low as approximately 0.3 mm h 1. Consequently, light Creating application-specific versions of the TMI re- rain rates, especially those from shallow stratiform rain, trieval should result in improvements to the rain-rate are not always detected (Schumacher and Houze estimates for tropical cyclones. In particular, the a priori 2000, 2003). databases used in the Bayesian scheme of Kummerow TMI is a nine-channel passive microwave radiometer et al. (2011) can be populated with data from tropical that observes brightness temperatures (Tb) at five dif- cyclones. Running a retrieval specifically for tropical ferent frequencies (10.65, 19.35, 21.3, 37.0, and 85.5 GHz). cyclones over oceans, based on oceanic tropical cyclone Vertical and horizontal polarization measurements rainfall data alone, should produce better rain-rate esti- are taken at all but the 21-GHz (vertical only) channel mates than a more generalized global scheme. Adapting (Table 1). A spatial resolution of 5.1 km is achieved at the Bayesian retrieval for TMI to improve its perfor- 85 GHz and a full swath scan covers 878 km. The overlap mance under oceanic tropical cyclone conditions is the between coincident PR and TMI observations is re- premise of this work. A comparable retrieval over land is stricted by the much narrower width of the PR swath. A Unauthenticated | Downloaded 10/09/21 05:54 AM UTC JULY 2016 B R O W N E T A L . 1541 TABLE 1. Temperature sensitivity of NEDT (K) for the nine TMI channels (Kummerow et al. 1998). Center frequency (GHZ) 10.65V 10.65H 19.35V 19.35H 21.3V 37.0V 37.0H 85.5V 85.5H Sensitivity, NEDT (K) 0.63 0.54 0.50 0.47 0.71 0.36 0.31 0.52 0.93 small temporal offset also results from the nadir- Ancillary datasets provide the SST data, which are es- oriented PR and the 538 conical scan angle of TMI.