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Anita D. Rapp, Christian Kummerow, Wesley Berg, and Brian Griffith

Abstract

Significant controversy surrounds the adaptive infrared iris hypothesis put forth by Lindzen et al., whereby tropical anvil cirrus detrainment is hypothesized to decrease with increasing sea surface temperature (SST). This dependence would act as an iris, allowing more infrared radiation to escape into space and inhibiting changes in the surface temperature. This hypothesis assumes that increased precipitation efficiency in regions of higher sea surface temperatures will reduce cirrus detrainment. Tropical Rainfall Measuring Mission (TRMM) satellite measurements are used here to investigate the adaptive infrared iris hypothesis. Pixel-level Visible and Infrared Scanner (VIRS) 10.8-μm brightness temperature data and precipitation radar (PR) rain-rate data from TRMM are collocated and matched to determine individual convective cloud boundaries. Each cloudy pixel is then matched to the underlying SST. This study examines single- and multicore convective clouds separately to directly determine if a relationship exists between the size of convective clouds, their precipitation, and the underlying SSTs. In doing so, this study addresses some of the criticisms of the Lindzen et al. study by eliminating their more controversial method of relating bulk changes of cloud amount and SST across a large domain in the Tropics. The current analysis does not show any significant SST dependence of the ratio of cloud area to surface rainfall for deep convection in the tropical western and central Pacific. Results do, however, suggest that SST plays an important role in the ratio of cloud area and surface rainfall for warm rain processes. For clouds with brightness temperatures between 270 and 280 K, a net decrease in cloud area normalized by rainfall of 5% per degree SST was found.

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David S. Henderson, Christian D. Kummerow, and Wesley Berg

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Discrepancies between Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and Precipitation Radar (PR) oceanic rainfall retrievals are prevalent between El Niño and La Niña conditions with TMI exhibiting systematic shifts in precipitation. To investigate the causality of this relationship, this paper focuses on the evolution of precipitation organization between El Niño and La Niña and their impacts on TRMM precipitation. The results indicate that discrepancies are related to shifts from isolated deep convection during La Niña toward organized precipitation during El Niño with the largest variability occurring in the Pacific basins. During El Niño, organized systems are more frequent, have increased areal coverage of stratiform rainfall, and penetrate deeper into the troposphere compared to La Niña. The increased stratiform raining fraction leads to larger increases in TMI rain rates than PR rain rate retrievals. Reanalysis and water vapor data from the Atmospheric Infrared Sounder (AIRS) indicate that organized systems are aided by midtropospheric moisture increases accompanied by increased convective frequency. During La Niña, tropical rainfall is dominated by isolated deep convection due to drier midtropospheric conditions and strong mid- and upper-level zonal wind shear. To examine tropical rainfall–sea surface temperature relations, regime-based bias corrections derived using ground validation (GV) measurements are applied to the TRMM rain estimates. The robust connection with GV-derived biases and oceanic precipitation leads to a reduction in TMI-PR regional differences and tropics-wide precipitation anomalies. The improved agreement between PR and TMI estimates yields positive responses of precipitation to tropical SSTs of 10% °C−1 and 17% °C−1, respectively, consistent with 15% °C−1 from the Global Precipitation Climatology Project (GPCP).

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Hilawe Semunegus, Wesley Berg, John J. Bates, Kenneth R. Knapp, and Christian Kummerow

Abstract

The National Oceanic and Atmospheric Administration National Climatic Data Center has served as the archive of the Defense Meteorological Satellite Program Special Sensor Microwave Imager (SSM/I) data from the F-8, F-10, F-11, F-13, F-14, and F-15 platforms covering the period from July 1987 to the present. Passive microwave satellite measurements from SSM/I have been used to generate climate products in support of national and international programs. The SSM/I temperature data record (TDR) and sensor data record (SDR) datasets have been reprocessed and stored as network Common Data Form (netCDF) 3-hourly files. In addition to reformatting the data, a normalized anomaly (z score) for each footprint temperature value was calculated by subtracting each radiance value with the corresponding monthly 1° grid climatological mean and dividing it by the associated climatological standard deviation. Threshold checks were also used to detect radiance, temporal, and geolocation values that were outside the expected ranges. The application of z scores and threshold parameters in the form of embedded quality flags has improved the fidelity of the SSM/I TDR/SDR period of record for climatological applications. This effort has helped to preserve and increase the data maturity level of the longest satellite passive microwave period of record while completing a key first step before developing a homogenized and intercalibrated SSM/I climate data record in the near future.

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Wesley Berg, William Olson, Ralph Ferraro, Steven J. Goodman, and Frank J. LaFontaine

Abstract

Rainfall estimates produced from the Special Sensor Microwave/Imager (SSM/I) data have been utilized operationally by the United States Navy since the launch of the first SSM/I sensor in June of 1987. The navy initially contracted Hughes Aircraft Company to develop a rainfall-retrieval algorithm prior to the launch of SSM/I. This first-generation operational navy rainfall retrieval algorithm, referred to as the D-Matrix algorithm, was used until the development of the second-generation algorithm by the SSM/I Calibration/Validation team, which has subsequently been replaced by a third-generation algorithm developed by the National Oceanic and Atmospheric Administration/National Environmental Satellite, Data and Information System. Results from both the D-Matrix and Cal/Val algorithms have been included in a total of five algorithm intercomparison projects conducted through the Global Precipitation Climatology Project and WetNet. A comprehensive summary of both quantitative and qualitative results from these intercomparisons is given detailing many of the strengths and weaknesses of the algorithms. Based on these results, the D-Matrix algorithm was found to produce excessively large estimates over land and to poorly represent the spatial structure of rainfall systems, especially at higher latitudes. The Cal/Val algorithm produces more realistic structure within storm systems but appears to overestimate the region of precipitation for many systems and significantly underestimates regions of intense rainfall. While the Cal/Val algorithm appears to provide better instantaneous rainfall estimates in the Tropics, the D-Matrix algorithm provides reasonable time-averaged results for monthly or longer periods.

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Ruanyu Zhang, Christian D. Kummerow, David L. Randel, Paula J. Brown, Wesley Berg, and Zhenzhan Wang

Abstract

This study focuses on the tropical cyclone rainfall retrieval using FY-3B Microwave Radiation Imager (MWRI) brightness temperatures (Tbs). The GPROF, a fully parametric approach based on the Bayesian scheme, is adapted for use by the MWRI sensor. The MWRI GPROF algorithm is an ocean-only scheme used to estimate rain rates and hydrometeor vertical profiles. An a priori database is constructed from MWRI simulated Tbs, the GPM Microwave Imager (GMI) and Dual-Frequency Precipitation Radar (DPR) combined data, and ancillary data resulting in about 100 000 rainfall profiles. The performance of MWRI retrievals is consistent with DPR observations, even though MWRI retrievals slightly overestimate low rain rates and underestimate high rain rates. The total bias of MWRI retrievals is less than 13% of the mean rain rate of DPR precipitation. Statistical comparisons over GMI GPROF, GMI Hurricane GPROF (HGPROF), and MWRI GPROF retrievals show MWRI GPROF retrievals are consistent in terms of spatial distribution and rain estimates for TCs compared with the other two estimates. In terms of the global precipitation, the mean rain rates at different distances from best track locations for five TC categories are used to identify substantial differences between mean MWRI and GMI GPROF retrievals. After correcting the biases between MWRI and GMI retrievals, the performance of MWRI retrievals shows slight overestimate for light rain rates while underestimating rain rates near the eyewall for category 4 and 5 only.

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Ralph R. Ferraro, Eric A. Smith, Wesley Berg, and George J. Huffman

Abstract

The success of any passive microwave precipitation retrieval algorithm relies on the proper identification of rain areas and the elimination of surface areas that produce a signature similar to that of precipitation. A discussion on the impact of and on methods that identify areas of rain, snow cover, deserts, and semiarid conditions over land, and rain, sea ice, strong surface winds, and clear, calm conditions over ocean, are presented. Additional artifacts caused by coastlines and Special Sensor Microwave/Imager data errors are also discussed, and methods to alleviate their impact are presented. The strengths and weaknesses of the “screening” techniques are examined through application on various case studies used in the WetNet PIP-2. Finally, a methodology to develop a set of screens for use as a common rainfall indicator for the intercomparison of the wide variety of algorithms submitted to PIP-2 is described.

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Christian D. Kummerow, Sarah Ringerud, Jody Crook, David Randel, and Wesley Berg

Abstract

The combination of active and passive microwave sensors on board the Tropical Rainfall Measuring Mission (TRMM) satellite have been used to construct observationally constrained databases of precipitation profiles for use in passive microwave rainfall retrieval algorithms over oceans. The method uses a very conservative approach that begins with the operational TRMM precipitation radar algorithm and adjusts its solution only as necessary to simultaneously match the radiometer observations. Where the TRMM precipitation radar (PR) indicates no rain, an optimal estimation procedure using TRMM Microwave Imager (TMI) radiances is used to retrieve nonraining parameters. The optimal estimation methodology ensures that the geophysical parameters are fully consistent with the observed radiances. Within raining fields of view, cloud-resolving model outputs are matched to the liquid and frozen hydrometeor profiles retrieved by the TRMM PR. The profiles constructed in this manner are subsequently used to compute brightness temperatures that are immediately compared to coincident observations from TMI. Adjustments are made to the rainwater and ice concentrations derived by PR in order to achieve agreement at 19 and 85 GHz, vertically polarized brightness temperatures at monthly time scales. The database is generated only in the central 11 pixels of the PR radar scan, and the rain adjustment is performed independently for distinct sea surface temperature (SST) and total precipitable water (TPW) values. Overall, the procedure increases PR rainfall by 4.2%, but the adjustment is not uniform across all SST and TPW regimes. Rainfall differences range from a minimum of −57% for SST of 293 K and TPW of 13 mm to a maximum of +53% for SST of 293 K and TPW of 45 mm. These biases are generally reproduced by a TMI retrieval algorithm that uses the observationally generated database. The algorithm increases rainfall by 5.0% over the PR solution with a minimum of −99% for SST of 293 K and TPW of 14 mm to a maximum of +11.8% for an SST of 294 K and TPW of 50 mm. Some differences are expected because of the algorithm mechanics.

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Stephen M. Saleeby, Wesley Berg, Susan van den Heever, and Tristan L’Ecuyer

Abstract

Cloud-nucleating aerosols emitted from mainland China have the potential to influence cloud and precipitation systems that propagate through the region of the East China Sea. Both simulations from the Spectral Radiation-Transport Model for Aerosol Species (SPRINTARS) and observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) reveal plumes of pollution that are transported into the East China Sea via frontal passage or other offshore flow. Under such conditions, satellite-derived precipitation estimates from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and Precipitation Radar (PR) frequently produce discrepancies in rainfall estimates that are hypothesized to be a result of aerosol modification of cloud and raindrop size distributions. Cloud-resolving model simulations were used to explore the impact of aerosol loading on three identified frontal-passage events in which the TMI and PR precipitation estimates displayed large discrepancies. Each of these events was characterized by convective and stratiform elements in association with a frontal passage. Area-averaged time series for each event reveal similar monotonic cloud and rain microphysical responses to aerosol loading. The ratio in the vertical distribution of cloud water to rainwater increased. Cloud droplet concentration increased and the mean diameters decreased, thereby reducing droplet autoconversion and collision–coalescence growth. As a result, raindrop concentration decreased, while the drop mean diameter increased; furthermore, average rainwater path magnitude and area fraction both decreased. The average precipitation rate fields reveal a complex modification of the timing and spatial coverage of rainfall. This suggests that the warm-rain microphysical response to aerosols, in addition to the precipitation life cycle, microphysical feedbacks, and evaporative effects, play an important role in determining surface rainfall.

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David S. Henderson, Christian D. Kummerow, David A. Marks, and Wesley Berg

Abstract

Over the tropical oceans, large discrepancies in TRMM passive and active microwave rainfall retrievals become apparent during El Niño–Southern Oscillation (ENSO) events. This manuscript describes the application of defined precipitation regimes to aid the validation of instantaneous rain rates from TRMM using the S-band radar located on the Kwajalein Atoll. Through the evaluation of multiple case studies, biases in rain-rate estimates from the TRMM radar (PR) and radiometer (TMI) are best explained when derived as a function of precipitation organization (e.g., isolated vs organized) and precipitation type (convective vs stratiform). When examining biases at a 1° × 1° scale, large underestimates in both TMI and PR rain rates are associated with predominately convective events in deep isolated regimes, where TMI and PR retrievals are underestimated by 37.8% and 23.4%, respectively. Further, a positive bias of 33.4% is observed in TMI rain rates within organized convective systems containing large stratiform regions. These findings were found to be consistent using additional analysis from the DYNAMO field campaign. When validating at the TMI footprint scale, TMI–PR differences are driven by stratiform rainfall variability in organized regimes; TMI overestimates this stratiform precipitation by 92.3%. Discrepancies between TMI and PR during El Niño events are related to a shift toward more organized convective systems and derived TRMM rain-rate bias estimates are able to explain 70% of TMI–PR differences during El Niño periods. An extension of the results to passive microwave retrievals reveals issues in discriminating convective and stratiform rainfall within the TMI field of view (FOV), and significant reductions in bias are found when convective fraction is constrained within the Bayesian retrieval.

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Yalei You, Veljko Petkovic, Jackson Tan, Rachael Kroodsma, Wesley Berg, Chris Kidd, and Christa Peters-Lidard

Abstract

This study assesses the level-2 precipitation estimates from 10 radiometers relative to Global Precipitation Measurement (GPM) Ku-band precipitation radar (KuPR) in two parts. First, nine sensors—four imagers [Advanced Microwave Scanning Radiometer 2 (AMSR2) and three Special Sensor Microwave Imager/Sounders (SSMISs)] and five sounders [Advanced Technology Microwave Sounder (ATMS) and four Microwave Humidity Sounders (MHSs)]—are evaluated over the 65°S–65°N region. Over ocean, imagers outperform sounders, primarily due to the usage of low-frequency channels. Furthermore, AMSR2 is clearly superior to SSMISs, likely due to the finer footprint size. Over land all sensors perform similarly except the noticeably worse performance from ATMS and SSMIS-F17. Second, we include the Sondeur Atmospherique du Profil d’Humidite Intertropicale par Radiometrie (SAPHIR) into the evaluation process, contrasting it against other sensors in the SAPHIR latitudes (30°S–30°N). SAPHIR has a slightly worse detection capability than other sounders over ocean but comparable detection performance to MHSs over land. The intensity estimates from SAPHIR show a larger normalized root-mean-square-error over both land and ocean, likely because only 183.3-GHz channels are available. Currently, imagers are preferred to sounders when level-2 estimates are incorporated into level-3 products. Our results suggest a sensor-specific priority order. Over ocean, this study indicates a priority order of AMSR2, SSMISs, MHSs and ATMS, and SAPHIR. Over land, SSMIS-F17, ATMS and SAPHIR should be given a lower priority than the other sensors.

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