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Bringi, V. N., Gwo-Jong Huang, S. Joseph Munchak, Christian D. Kummerow, David A. Marks, and David B. Wolff. "Comparison of Drop Size Distribution Parameter (D0) and Rain Rate from S-Band Dual-Polarized Ground Radar, TRMM Precipitation Radar (PR), and Combined PR–TMI: Two Events from Kwajalein Atoll." Journal of Atmospheric and Oceanic Technology 29, no. 11 (November 1, 2012): 1603–16. http://dx.doi.org/10.1175/jtech-d-11-00153.1.
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Abstract The estimation of the drop size distribution parameter [median volume diameter (D0)] and rain rate (R) from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) as well as from combined PR–TRMM Microwave Imager (TMI) algorithms are considered in this study for two TRMM satellite overpasses near the Kwajalein Atoll. An operational dual-polarized S-band radar (KPOL) located in Kwajalein is central as the only TRMM ground validation site for measurement of precipitation over the open ocean. The accuracy of the TRMM PR in retrieving D0 and R is better for precipitation over the ocean based on a more stable surface reference technique for estimating the path-integrated attenuation. Also, combined PR–TMI methods are more accurate over the open ocean because of better knowledge of the surface microwave emissivity. Using Zh (horizontal polarized radar reflectivity) and Zdr (differential reflectivity) data for the two TRMM overpass events over Kwajalein, D0 and R from KPOL are retrieved. Herein, the main objective is to see if the D0 retrieved from either PR or the combined PR–TMI algorithms are in agreement with KPOL-derived values. Also, the variation of D0 versus R is compared for convective rain pixels from KPOL, PR, and PR–TMI. It is shown that the PR–TMI optimal estimation scheme does indeed adjust the D0 in the “correct” direction, on average, from the a priori state if the KPOL data are considered to be the ground truth. This correct adjustment may be considered as evidence of the value added by the TMI brightness temperatures in the combined PR–TMI variational scheme, at least for the two overpass events considered herein.
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Wolters, E. L. A., B. J. J. M. van den Hurk, and R. A. Roebeling. "Rainfall retrievals over West Africa using SEVIRI: evaluation with TRMM-PR and monitoring of the daylight time monsoon progression." Hydrology and Earth System Sciences Discussions 7, no. 4 (August 27, 2010): 6351–80. http://dx.doi.org/10.5194/hessd-7-6351-2010.
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Abstract. This paper describes the application of the KNMI cloud physical properties – precipitation properties (CPP-PP) algorithm over West Africa. The algorithm combines condensed water path (CWP), cloud phase (CPH), cloud particle effective radius (re), and cloud-top temperature (CTT) information, retrieved from visible, near-infrared and infrared observations of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard Meteosat-9 to estimate precipitation occurrence and intensity. It is investigated whether the CPP-PP algorithm is capable of retrieving rain occurrence and intensity over West Africa with a sufficient accuracy, using tropical monsoon measurement mission precipitation radar (TRMM-PR) and a small number of rain gauge observations as reference. As a second goal, it is assessed whether SEVIRI is capable of monitoring both the seasonal and synoptical evolution of the West African monsoon (WAM). It is shown that the SEVIRI-detected rainfall area agrees well with TRMM-PR, having a correlation coefficient of 0.86, with the areal extent of rainfall by SEVIRI being ~10% larger than TRMM-PR. The mean retrieved rain rate from CPP-PP is about 8% higher than from TRMM-PR. The frequency distributions of rain rate reveal that the median rain rates of CPP-PP and TRMM-PR are similar. However, rain rates >7 mm h−1 are retrieved more frequently by SEVIRI than by TRMM-PR, which is partly explained by known biases in TRMM-PR. Finally, it is illustrated that both the seasonal and synoptical time scale of the WAM can be well detected from SEVIRI daytime observations. It was found that the daytime westward MCS travel speed fluctuates between 50 and 60 km h−1. Furthermore, the ratio of MCS precipitation to the total precipitation was estimated to be about 27%. Our results indicate that rainfall retrievals from SEVIRI can be used to monitor the West African monsoon.
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Xu, Weixin, and Steven A. Rutledge. "Morphology, Intensity, and Rainfall Production of MJO Convection: Observations from DYNAMO Shipborne Radar and TRMM." Journal of the Atmospheric Sciences 72, no. 2 (February 1, 2015): 623–40. http://dx.doi.org/10.1175/jas-d-14-0130.1.
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Abstract This study uses Dynamics of the Madden–Julian Oscillation (DYNAMO) shipborne [Research Vessel (R/V) Roger Revelle] radar and Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) datasets to investigate MJO-associated convective systems in specific organizational modes [mesoscale convective system (MCS) versus sub-MCS and linear versus nonlinear]. The Revelle radar sampled many “climatological” aspects of MJO convection as indicated by comparison with the long-term TRMM PR statistics, including areal-mean rainfall (6–7 mm day−1), convective intensity, rainfall contributions from different morphologies, and their variations with MJO phase. Nonlinear sub-MCSs were present 70% of the time but contributed just around 20% of the total rainfall. In contrast, linear and nonlinear MCSs were present 10% of the time but contributed 20% and 50%, respectively. These distributions vary with MJO phase, with the largest sub-MCS rainfall fraction in suppressed phases (phases 5–7) and maximum MCS precipitation in active phases (phases 2 and 3). Similarly, convective–stratiform rainfall fractions also varied significantly with MJO phase, with the highest convective fractions (70%–80%) in suppressed phases and the largest stratiform fraction (40%–50%) in active phases. However, there are also discrepancies between the Revelle radar and TRMM PR. Revelle radar data indicated a mean convective rain fraction of 70% compared to 55% for TRMM PR. This difference is mainly due to the reduced resolution of the TRMM PR compared to the ship radar. There are also notable differences in the rainfall contributions as a function of convective intensity between the Revelle radar and TRMM PR. In addition, TRMM PR composites indicate linear MCS rainfall increases after MJO onset and produce similar rainfall contributions to nonlinear MCSs; however, the Revelle radar statistics show the clear dominance of nonlinear MCS rainfall.
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Gingrey, Alexandria, Adam Varble, and Edward Zipser. "Relationships between Extreme Rain Rates and Convective Intensities from the Perspectives of TRMM and WSR-88D Radars." Journal of Applied Meteorology and Climatology 57, no. 6 (June 2018): 1353–69. http://dx.doi.org/10.1175/jamc-d-17-0240.1.
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AbstractTRMM PR 2A25, version 7 (V7), retrievals of reflectivity Z and rainfall rate R are compared with WSR-88D dual-polarimetric S-band radar data for 28 radars over the southeastern United States after matching their horizontal resolution and sampling. TRMM Ku-band measurements are converted to S-band approximations to more directly compare reflectivity estimates. Rain rates are approximated from WSR-88D data using the CSU–hydrometeor identification rainfall optimization (HIDRO) algorithm. Tropics-wide TRMM retrievals confirm previous findings of a low overlap fraction between extreme convective intensity, as approximated by the maximum 40-dBZ height, and extreme near-surface rain rates. WSR-88D data also confirm this low overlap but show that it is likely higher than TRMM PR retrievals indicate. For maximum 40-dBZ echo heights that extend above the freezing level, mean WSR-88D reflectivities at low levels are approximately 2 dB higher than TRMM PR reflectivities. Higher WSR-88D-retrieved rain rates for a given low-level reflectivity combine with these higher low-level reflectivities for a given maximum 40-dBZ height to produce rain rates that are approximately double those retrieved by the TRMM PR for maximum 40-dBZ heights that extend above the freezing level. TRMM PR path-integrated attenuation, and WSR-88D specific differential phase, differential reflectivity, and hail fraction indicate that the TRMM PR 2A25 V7 algorithm is possibly misidentifying low–midlevel hail and/or graupel as greater attenuating liquid, or vice versa. This misidentification, coupled with underestimation of path-integrated attenuation caused by nonuniform beamfilling and higher rain rates produced by specific differential phase (KDP)–R than Z–R relationships, results in low-biased 2A25 V7 rain rates in intense convection.
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Stocker, E. F., F. Alquaied, S. Bilanow, Y. Ji, and L. Jones. "TRMM Version 8 Reprocessing Improvements and Incorporation into the GPM Data Suite." Journal of Atmospheric and Oceanic Technology 35, no. 6 (June 2018): 1181–99. http://dx.doi.org/10.1175/jtech-d-17-0166.1.
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AbstractThe National Aeronautics and Space Administration (NASA) has always included data reprocessing as a major component of every science mission. A final reprocessing is typically a part of mission closeout (known as phase F). The Tropical Rainfall Measuring Mission (TRMM) is currently in phase F, and NASA is preparing for the last reprocessing of all the TRMM precipitation data as part of the closeout. This reprocessing includes improvements in calibration of both the TRMM Microwave Imager (TMI) and the TRMM Precipitation Radar (PR). An initial step in the version 8 reprocessing is the improvement of geolocation. The PR calibration is being updated by the Japan Aerospace Exploration Agency (JAXA) using data collected as part of the calibration of the Dual-Frequency Precipitation Radar (DPR) on the Global Precipitation Measurement (GPM) Core Observatory. JAXA undertook a major effort to ensure TRMM PR and GPM Ku-band calibration is consistent.A major component of the TRMM version 8 reprocessing is to create consistent retrievals with the GPM version 05 (V05) retrievals. To this end, the TRMM version 8 reprocessing uses retrieval algorithms based on the GPM V05 algorithms. This approach ensures consistent retrievals from December 1997 (the beginning of TRMM) through the current ongoing GPM retrievals. An outcome of this reprocessing is the incorporation of TRMM data products into the GPM data suite. Incorporation also means that GPM file naming conventions and reprocessed TRMM data carry the V05 data product version. This paper describes the TRMM version 8 reprocessing, focusing on the improvements in TMI level 1 products.
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Hirose, M., and K. Okada. "A 0.01° Resolving TRMM PR Precipitation Climatology." Journal of Applied Meteorology and Climatology 57, no. 8 (August 2018): 1645–61. http://dx.doi.org/10.1175/jamc-d-17-0280.1.
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AbstractIn this study, rainfall data are prepared at a 0.01° scale using 16-yr spaceborne radar data over the area of 36.13°S–36.13°N as provided by the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR). A spatial resolution that is finer than the field of view is obtained by assuming rainfall uniformity within an instantaneous footprint centered on the PR footprint geolocation. These ultra-high-resolution data reveal local rainfall concentrations over slope areas. A new estimate of the maximum rainfall at Cherrapunji, India, was observed on the valley side, approximately 5 km east of the gauge station, and is approximately 50% higher than the value indicated by the 0.1°-scale data. A case study of Yakushima Island, Japan, indicates that several percent of the sampling error arising from the spatial mismatch may be contained in conventional 0.05°-scale datasets generated without footprint areal information. The differences attributable to the enhancement in the resolution are significant in complex terrain such as the Himalayas. The differences in rainfall averaged for the 0.1° and 0.01° scales exceed 10 mm day−1 over specific slope areas. In the case of New Guinea, the mean rainfall on a mountain ridge can be 30 times smaller than that on an adjacent slope at a distance of 0.25°; this is not well represented by other high-resolution datasets based on gauges and infrared radiometers. The substantial nonuniformity of rainfall climatology highlights the need for a better understanding of kilometer-scale geographic constraints on rainfall and retrieval approaches.
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SETO, Shinta, Toshio IGUCHI, Nobuyuki UTSUMI, and Taikan OKI. "HEAVY RAIN ESTIMATES IN TRMM/PR STANDARD PRODUCT VERSION 7." Journal of Japan Society of Civil Engineers, Ser. B1 (Hydraulic Engineering) 68, no. 4 (2012): I_373—I_378. http://dx.doi.org/10.2208/jscejhe.68.i_373.
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Prat, O. P., and A. P. Barros. "Assessing satellite-based precipitation estimates in the Southern Appalachian mountains using rain gauges and TRMM PR." Advances in Geosciences 25 (June 8, 2010): 143–53. http://dx.doi.org/10.5194/adgeo-25-143-2010.
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Abstract. A study was performed using the first full year of rain gauge records from a newly deployed network in the Southern Appalachian mountains. This is a region characterized by complex topography with orographic rainfall enhancement up to 300% over small distances (<8 km). Rain gauge observations were used to assess precipitation estimates from the Precipitation Radar (PR) on board of the TRMM satellite, specifically the TRMM PR 2A25 precipitation product. Results show substantial differences between annual records and isolated events (e.g. tropical storm Fay). An overall bias of −27% was found between TRMM PR 2A25 rain rate and rain gauge rain rates for the complete one year of study (−59% for tropical storm Fay). Besides differences observed for concurrent observations by the satellite and the rain gauges, a large number of rainfall events is detected independently by either one of the observing systems alone (rain gauges: 50% of events are missed by TRMM PR; TRMM PR: 20% of events are not detected by the rain gauges), especially for light rainfall conditions (0.1–2mm/h) that account for more than 80% of all the missed satellite events. An exploratory investigation using a microphysical model along with TRMM reflectivity factors at selected heights was conducted to determine the shape of the drop size distribution (DSD) that can be applied to reduce the difference between TRMM estimates and rain gauge observations. The results suggest that the critical DSD parameter is the number concentration of very small drops. For tropical storm Fay an increase of one order of magnitude in the number of small drops is apparently needed to capture the observed rainfall rate regardless of the value of the measured reflectivity. This is consistent with DSD observations that report high concentrations of small and/or midsize drops in the case of tropical storms.
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Sekaranom, Andung Bayu, and Hirohiko Masunaga. "Comparison of TRMM-Derived Rainfall Products for General and Extreme Rains over the Maritime Continent." Journal of Applied Meteorology and Climatology 56, no. 7 (July 2017): 1867–81. http://dx.doi.org/10.1175/jamc-d-16-0272.1.
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AbstractProperties of the rain estimation differences between Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) 2A25, TRMM Microwave Imager (TMI) 2A12, and TRMM Multisatellite Precipitation Analysis (TMPA) 3B42 are investigated with a focus on distinguishing between nonextreme and extreme rains over the Maritime Continent from 1998 to 2014. Statistical analyses of collocated TMI 1B11 85-GHz polarization-corrected brightness temperatures, PR 2A23 storm-top heights, and PR 2A25 vertical rain profiles are conducted to identify possible sources of the differences. The results indicate that a large estimation difference exists between PR and TMI for the general rain rate (extreme and nonextreme events). The PR–TMI rain-rate differences are larger over land and coast than over ocean. When extreme rain is isolated, a higher frequency of occurrence is identified by PR over ocean, followed by TMI and TMPA. Over land, TMI yields higher rain frequencies than PR with an intermediate range of rain rates (between 15 and 25 mm h−1), but it gives way to PR for the highest extremes. The turnover at the highest rain rates arises because the heaviest rain depicted by PR does not necessarily accompany the strongest ice-scattering signals, which TMI relies on for estimating precipitation over land and coast.
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Wang, Jianxin, and David B. Wolff. "Comparisons of Reflectivities from the TRMM Precipitation Radar and Ground-Based Radars." Journal of Atmospheric and Oceanic Technology 26, no. 5 (May 1, 2009): 857–75. http://dx.doi.org/10.1175/2008jtecha1175.1.
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Abstract Given the decade-long and highly successful Tropical Rainfall Measuring Mission (TRMM), it is now possible to provide quantitative comparisons between ground-based radars (GRs) and the spaceborne TRMM precipitation radar (PR) with greater certainty over longer time scales in various tropical climatological regions. This study develops an automated methodology to match and compare simultaneous TRMM PR and GR reflectivities at four primary TRMM Ground Validation (GV) sites: Houston, Texas (HSTN); Melbourne, Florida (MELB); Kwajalein, Republic of the Marshall Islands (KWAJ); and Darwin, Australia (DARW). Data from each instrument are resampled into a three-dimensional Cartesian coordinate system. The horizontal displacement during the PR data resampling is corrected. Comparisons suggest that the PR suffers significant attenuation at lower levels, especially in convective rain. The attenuation correction performs quite well for convective rain but appears to slightly overcorrect in stratiform rain. The PR and GR observations at HSTN, MELB, and KWAJ agree to about ±1 dB on average with a few exceptions, whereas the GR at DARW requires +1 to −5 dB calibration corrections. One of the important findings of this study is that the GR calibration offset is dependent on the reflectivity magnitude. Hence, it is proposed that the calibration should be carried out by using a regression correction rather than by simply adding an offset value to all GR reflectivities. This methodology is developed to assist TRMM GV efforts to improve the accuracy of tropical rain estimates, but can also be applied to the proposed Global Precipitation Measurement and other related activities over the globe.
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Fisher, Brad L. "Statistical Error Decomposition of Regional-Scale Climatological Precipitation Estimates from the Tropical Rainfall Measuring Mission (TRMM)." Journal of Applied Meteorology and Climatology 46, no. 6 (June 1, 2007): 791–813. http://dx.doi.org/10.1175/jam2497.1.
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Abstract Monthly rainfall estimates inferred from the NASA Tropical Rainfall Measuring Mission (TRMM) satellite contain errors due to discrete temporal sampling and remote spaceborne rain retrievals. This paper develops a regional-scale error model that uses the rain information in the ground data to disentangle the sampling and retrieval errors in the satellite estimate statistically. The proposed method computes a mean rain rate from monthly rainfall statistics for each TRMM rain sensor by subsampling high-resolution ground-based rain data at satellite overpass times. This additional rain-subsampled parameter plays an essential role in the statistical decomposition of the total error distribution into its sampling and retrieval error components. Using the statistical formalism developed in this study, an error analysis was performed on 5 yr of monthly rain estimates produced by the TRMM Microwave Imager (TMI) and precipitation radar (PR) rain sensors aboard TRMM over a quasi 2° × 2° region of the TRMM ground validation (GV) site at Melbourne, Florida. Annual retrieval and sampling error statistics were computed for the TMI and PR using monthly rainfall estimates derived from two independent ground-based sensors: a regional rain gauge network and the Next-Generation Weather Radar (NEXRAD). Subsampled ground-based rainfall estimates produced for the radar and the gauges were highly correlated with the TMI and PR rainfall estimates, and both GV sensors produced relatively consistent error estimates. The PR-to-TMI sampling error ratio was equal to about 1.3, which was in close agreement with prelaunch predications, and the TMI-to-PR retrieval error ratio was about 2.0. For the TMI, a seasonally alternating rainfall bias was also observed that was negative during winter and positive during summer.
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Hirose, Masafumi, Shuji Shimizu, Riko Oki, Toshio Iguchi, David A. Short, and Kenji Nakamura. "Incidence-Angle Dependency of TRMM PR Rain Estimates." Journal of Atmospheric and Oceanic Technology 29, no. 2 (February 1, 2012): 192–206. http://dx.doi.org/10.1175/jtech-d-11-00067.1.
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Abstract The incidence-angle differences of estimated surface rainfall obtained from the precipitation radar (PR) on board the Tropical Rainfall Measuring Mission (TRMM) satellite were investigated. The bias before the orbit boost in August 2001 relative to the near-nadir statistics was 2.7% over the ocean and −5.8% over land. After the boost, the bias was −3.2% and −9.5%, respectively. These biases were further quantified with respect to error sources, that is, the beam mismatch correction error, detection capability of storms with low-level storm-top height, and residual effects. For shallow storms lower than 3 km, most incidence-angle differences were caused by main lobe contamination. For nonshallow storms, several error factors resulted in 5.3% overestimates over the ocean and 5.1% underestimates over land for the period before the boost. The remaining uncertainty in local low-level profiles was identified as a controversial issue. The bias-corrected dataset updates the interannual variation in rainfall obtained from the TRMM PR. The increasing rainfall features and recent high-rainfall years were consistent with prior studies based on other microwave sensors. The coherent signals and slight differences in the temporal variation compared with the Global Precipitation Climatology Project (GPCP) data indicate the importance of further internal and cross validations based on long-term observation by multiple sensors.
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Wolff, David B., and Brad L. Fisher. "Comparisons of Instantaneous TRMM Ground Validation and Satellite Rain-Rate Estimates at Different Spatial Scales." Journal of Applied Meteorology and Climatology 47, no. 8 (August 1, 2008): 2215–37. http://dx.doi.org/10.1175/2008jamc1875.1.
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Abstract This study provides a comprehensive intercomparison of instantaneous rain rates observed by the two rain sensors aboard the Tropical Rainfall Measuring Mission (TRMM) satellite with ground data from two regional sites established for long-term ground validation: Kwajalein Atoll and Melbourne, Florida. The satellite rain algorithms utilize remote observations of precipitation collected by the TRMM Microwave Imager (TMI) and the Precipitation Radar (PR) aboard the TRMM satellite. Three standard level II rain products are generated from operational applications of the TMI, PR, and combined (COM) rain algorithms using rain information collected from the TMI and the PR along the orbital track of the TRMM satellite. In the first part of the study, 0.5° × 0.5° instantaneous rain rates obtained from the TRMM 3G68 product were analyzed and compared to instantaneous Ground Validation (GV) program rain rates gridded at a scale of 0.5° × 0.5°. In the second part of the study, TMI, PR, COM, and GV rain rates were spatiotemporally matched and averaged at the scale of the TMI footprint (∼150 km2). This study covered a 6-yr period (1999–2004) and consisted of over 50 000 footprints for each GV site. In the first analysis, the results showed that all of the respective rain-rate estimates agree well, with some exceptions. The more salient differences were associated with heavy rain events in which one or more of the algorithms failed to properly retrieve these extreme events. Also, it appears that there is a preferred mode of precipitation for TMI rain rates at or near 2 mm h−1 over the ocean. This mode was noted over ocean areas of Kwajalein and Melbourne and has been observed in TRMM tropical–global ocean areas as well.
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Sekaranom, Andung Bayu, and Hirohiko Masunaga. "Origins of Heavy Precipitation Biases in the TRMM PR and TMI Products Assessed with CloudSat and Reanalysis Data." Journal of Applied Meteorology and Climatology 58, no. 1 (January 2019): 37–54. http://dx.doi.org/10.1175/jamc-d-18-0011.1.
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AbstractThis study aims to characterize the background physical processes in the development of those heavy precipitation clouds that contribute to the Tropical Rainfall Measuring Mission (TRMM) active and passive sensor differences. The combined global observation data from TRMM, CloudSat, and European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim) from 2006 to 2014 were utilized to address this issue. Heavy rainfall events were extracted from the top 10% of the rain events from the Precipitation Radar (PR) and TRMM Microwave Imager (TMI) rain-rate climatology. Composite analyses of CloudSat and ERA-Interim were conducted to identify the detailed cloud structures and the background environmental conditions. Over tropical land, TMI tends to preferentially detect deep isolated precipitation clouds for relatively drier and unstable environments, while PR identifies more organized systems. Over the tropical ocean, TMI identifies heavy rainfall events with notable convective organization and clear regional gradients between the western and eastern Pacific Ocean, while PR fails to capture the eastward shallowing of convective systems. The PR–TMI differences for the moist and stable environments are reversed over tropical land.
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Kummerow, Christian D., Sarah Ringerud, Jody Crook, David Randel, and Wesley Berg. "An Observationally Generated A Priori Database for Microwave Rainfall Retrievals." Journal of Atmospheric and Oceanic Technology 28, no. 2 (February 1, 2011): 113–30. http://dx.doi.org/10.1175/2010jtecha1468.1.
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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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Henderson, David S., Christian D. Kummerow, David A. Marks, and Wesley Berg. "A Regime-Based Evaluation of TRMM Oceanic Precipitation Biases." Journal of Atmospheric and Oceanic Technology 34, no. 12 (December 2017): 2613–35. http://dx.doi.org/10.1175/jtech-d-16-0244.1.
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AbstractOver 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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Katsanos, D., N. Viltard, K. Lagouvardos, and V. Kotroni. "Performance of a rain retrieval algorithm using TRMM data in the Eastern Mediterranean." Advances in Geosciences 7 (May 2, 2006): 321–25. http://dx.doi.org/10.5194/adgeo-7-321-2006.
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Abstract. This study aims to make a regional characterization of the performance of the rain retrieval algorithm BRAIN. This algorithm estimates the rain rate from brightness temperatures measured by the TRMM Microwave Imager (TMI) onboard the TRMM satellite. In this stage of the study, a comparison between the rain estimated from Precipitation Radar (PR) onboard TRMM (2A25 version 5) and the rain retrieved by the BRAIN algorithm is presented, for about 30 satellite overpasses over the Central and Eastern Mediterranean during the period October 2003–March 2004, in order to assess the behavior of the algorithm in the Eastern Mediterranean region. BRAIN was built and tested using PR rain estimates distributed randomly over the whole TRMM sampling region. Characterization of the differences between PR and BRAIN over a specific region is thus interesting because it might show some local trend for one or the other of the instrument. The checking of BRAIN results against the PR rain-estimate appears to be consistent with former results i.e. a somewhat marked discrepancy for the highest rain rates. This difference arises from a known problem that affect rain retrieval based on passive microwave radiometers measurements, but some of the higher radar rain rates could also be questioned. As an independent test, a good correlation between the rain retrieved by BRAIN and lighting data (obtained by the UK Met. Office long range detection system) is also emphasized in the paper.
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Wang, Jianxin, and David B. Wolff. "Evaluation of TRMM Rain Estimates Using Ground Measurements over Central Florida." Journal of Applied Meteorology and Climatology 51, no. 5 (May 2012): 926–40. http://dx.doi.org/10.1175/jamc-d-11-080.1.
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AbstractThis study evaluates space-based rain estimates from the Tropical Rainfall Measuring Mission (TRMM) satellite using ground-based measurements from the radar (GR) and tipping-bucket rain gauges (TG) over the TRMM Ground Validation (GV) site at Melbourne, Florida. The satellite rain products are derived from the TRMM Microwave Imager (TMI), precipitation radar (PR), and combined (COM) rain algorithms using observations from both instruments. The TRMM satellite and GV rain products are spatiotemporally matched and are intercompared at multiple time scales over the 12-yr period from 1998 to 2009. On monthly and yearly scales, the TG agree excellently with the GR because the GR rain rates are generated using the TG data as a constraint on a monthly basis. However, large disagreements exist between the GR and TG at shorter time scales because of their significantly different spatial and temporal sampling modes. The yearly biases relative to the GR for the PR and TMI are generally negative, with a few exceptions. The COM bias fluctuates from year to year over the 12-yr period. The PR, TMI, and COM are in good overall agreement with the GR in the lower range of rain rates, but the agreement is notably worse at higher rain rates. The diurnal cycle of rainfall is captured well by all products, but the peak satellite-derived rainfall (PR, TMI, and COM) lags the peak from the ground measurements (GR and TG) by ~1 h.
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Deo, Anil, S. Joseph Munchak, and Kevin J. E. Walsh. "Cross Validation of Rainfall Characteristics Estimated from the TRMM PR, a Combined PR–TMI Algorithm, and a C-POL Ground Radar during the Passage of Tropical Cyclone and Nontropical Cyclone Events over Darwin, Australia." Journal of Atmospheric and Oceanic Technology 35, no. 12 (December 2018): 2339–58. http://dx.doi.org/10.1175/jtech-d-18-0065.1.
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AbstractThis study cross validates the radar reflectivity Z; the rainfall drop size distribution parameter (median volume diameter Do); and the rainfall rate R estimated from the Tropical Rainfall Measuring Mission (TRMM) satellite Precipitation Radar (PR), a combined PR and TRMM Microwave Imager (TMI) algorithm (COM), and a C-band dual-polarized ground radar (GR) for TRMM overpasses during the passage of tropical cyclone (TC) and non-TC events over Darwin, Australia. Two overpass events during the passage of TC Carlos and 11 non-TC overpass events are used in this study, and the GR is taken as the reference. It is shown that the correspondence is dependent on the precipitation type whereby events with more (less) stratiform rainfall usually have a positive (negative) bias in the reflectivity and the rainfall rate, whereas in the Do the bias is generally positive but small (large). The COM reflectivity estimates are similar to the PR, but it has a smaller bias in the Do for most of the greater stratiform events. This suggests that combining the TMI with the PR adjusts the Do toward the “correct” direction if the GR is taken as the reference. Moreover, the association between the TRMM estimates and the GR for the two TC events, which are highly stratiform in nature, is similar to that observed for the highly stratiform non-TC events (there is no significant difference), but it differs considerably from that observed for the majority of the highly convective non-TC events.
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Dinku, Tufa, and Emmanouil N. Anagnostou. "Regional Differences in Overland Rainfall Estimation from PR-Calibrated TMI Algorithm." Journal of Applied Meteorology and Climatology 44, no. 2 (February 1, 2005): 189–205. http://dx.doi.org/10.1175/jam2186.1.
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Abstract The Tropical Rainfall Measuring Mission (TRMM) satellite carries a combination of active [precipitation radar (PR)] and multichannel passive microwave [the TRMM Microwave Imager (TMI)] sensors, which advance our ability to estimate rainfall over land. Rain retrieval from the TRMM PR is associated with an unprecedented accuracy and resolution but is limited in terms of sampling because of the narrow PR swath width (215 km). TMI provides wider coverage (760 km), but its observations are associated with a more complex relationship to precipitation in comparison with PR (especially over land). The PR rain estimates are used here for calibrating an overland TMI rain algorithm. The algorithm consists of 1) multichannel-based rain screening and convective/stratiform (C/S) classification schemes, and 2) nonlinear (linear) regressions for the rain-rate retrieval of stratiform (convective) rain regimes. This study examines regional differences in the algorithm performance. Four geographic regions consisting of central Africa (AFC), the Amazon (AMZ), the U.S. southern Plains (USA), and the Ganges–Brahmaputra–Meghna River basin (GBM) in south Asia are selected. Data from three summer months of 2000 and 2001 are used for calibration; validation is done using summer 2002 data. The current algorithm is also compared with the latest [version 6 (V6)] TRMM 2A12 product in terms of rain detection, and rain-rate retrieval error statistics on the basis of PR reference rainfall. The performance of the algorithm is different for the different regions. For instance, the reduction in random error (relative to 2A12 V6) is about 24%, 36%, 57%, and 165% for USA, AFC, AMZ, and GBM, respectively. However, significant difference between global (the four regions combined) and regional calibration is observed only for the GBM region.
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Dinku, Tufa, and Emmanouil N. Anagnostou. "TRMM Calibration of SSM/I Algorithm for Overland Rainfall Estimation." Journal of Applied Meteorology and Climatology 45, no. 6 (June 1, 2006): 875–86. http://dx.doi.org/10.1175/jam2379.1.
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Abstract This paper extends the work of Dinku and Anagnostou overland rain retrieval algorithm for use with Special Sensor Microwave Imager (SSM/I) observations. In Dinku and Anagnostou, Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) rainfall estimates were used to calibrate TRMM Microwave Imager (TMI) retrieval. Regional differences in PR-based TMI calibration were investigated by testing the algorithm over four geographic regions, consisting of Africa, northern South America (containing the Amazon basin), the continental United States, and south Asia. In this paper the performance of Dinku and Anagnostou's technique applied on SSM/I data over three of these regions (Africa, Amazon, and South Asia) is demonstrated. Two approaches are investigated for using PR rainfall products to calibrate the algorithm parameters. In the first approach, TMI channels are remapped to the spatial resolutions of the corresponding SSM/I channels; then, PR is used to calibrate the rain retrieval on the remapped TMI data. In the second approach, the PR-based TMI algorithm calibration is performed at a coarser (0.25°) resolution. To assess the quality of algorithm estimates with respect to PR, rainfall fields derived from Dinku and Anagnostou, applied to SSM/I observations (using parameters determined from both approaches), are compared with matched (within ±15 min of the satellites' overpass time difference) PR surface rain rates. Calibration data come from the wet seasons (January–March) of 2000 and 2001. To assess the quality of the estimates with respect to PR, data from a 5-month period (December–April) of 2002, 2003, and 2004 are used. In comparison with the latest version of the Goddard profiling (GPROF) algorithm rain estimates, the current algorithm shows significant improvements in terms of both bias and random error reduction. The paper also shows that rain estimation based on TMI observations is associated with lower error statistics in comparison with the corresponding SSM/I retrievals.
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Rui, Li, and Fu Yunfei. "Tropical precipitation estimated by GPCP and TRMM PR observations." Advances in Atmospheric Sciences 22, no. 6 (November 2005): 852–64. http://dx.doi.org/10.1007/bf02918685.
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Biscaro, Thiago S., and Carlos A. Morales. "Continental Passive Microwave-Based Rainfall Estimation Algorithm: Application to the Amazon Basin." Journal of Applied Meteorology and Climatology 47, no. 7 (July 1, 2008): 1962–81. http://dx.doi.org/10.1175/2007jamc1744.1.
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Abstract This paper presents a new statistical algorithm to estimate rainfall over the Amazon Basin region using the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI). The algorithm relies on empirical relationships derived for different raining-type systems between coincident measurements of surface rainfall rate and 85-GHz polarization-corrected brightness temperature as observed by the precipitation radar (PR) and TMI on board the TRMM satellite. The scheme includes rain/no-rain area delineation (screening) and system-type classification routines for rain retrieval. The algorithm is validated against independent measurements of the TRMM–PR and S-band dual-polarization Doppler radar (S-Pol) surface rainfall data for two different periods. Moreover, the performance of this rainfall estimation technique is evaluated against well-known methods, namely, the TRMM-2A12 [the Goddard profiling algorithm (GPROF)], the Goddard scattering algorithm (GSCAT), and the National Environmental Satellite, Data, and Information Service (NESDIS) algorithms. The proposed algorithm shows a normalized bias of approximately 23% for both PR and S-Pol ground truth datasets and a mean error of 0.244 mm h−1 (PR) and −0.157 mm h−1 (S-Pol). For rain volume estimates using PR as reference, a correlation coefficient of 0.939 and a normalized bias of 0.039 were found. With respect to rainfall distributions and rain area comparisons, the results showed that the formulation proposed is efficient and compatible with the physics and dynamics of the observed systems over the area of interest. The performance of the other algorithms showed that GSCAT presented low normalized bias for rain areas and rain volume [0.346 (PR) and 0.361 (S-Pol)], and GPROF showed rainfall distribution similar to that of the PR and S-Pol but with a bimodal distribution. Last, the five algorithms were evaluated during the TRMM–Large-Scale Biosphere–Atmosphere Experiment in Amazonia (LBA) 1999 field campaign to verify the precipitation characteristics observed during the easterly and westerly Amazon wind flow regimes. The proposed algorithm presented a cumulative rainfall distribution similar to the observations during the easterly regime, but it underestimated for the westerly period for rainfall rates above 5 mm h−1. NESDIS1 overestimated for both wind regimes but presented the best westerly representation. NESDIS2, GSCAT, and GPROF underestimated in both regimes, but GPROF was closer to the observations during the easterly flow.
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Liao, Liang, and Robert Meneghini. "Validation of TRMM Precipitation Radar through Comparison of Its Multiyear Measurements with Ground-Based Radar." Journal of Applied Meteorology and Climatology 48, no. 4 (April 1, 2009): 804–17. http://dx.doi.org/10.1175/2008jamc1974.1.
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Abstract A procedure to accurately resample spaceborne and ground-based radar data is described and then is applied to the measurements taken from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and the ground-based Weather Surveillance Radar-1988 Doppler (WSR-88D or WSR) for the validation of the PR measurements and estimates. Through comparisons with the well-calibrated, nonattenuated WSR at Melbourne, Florida, for the period 1998–2007, the calibration of the PR aboard the TRMM satellite is checked using measurements near the storm top. Analysis of the results indicates that the PR, after taking into account differences in radar reflectivity factors between the PR and WSR, has a small positive bias of 0.8 dB relative to the WSR, implying a soundness of the PR calibration in view of the uncertainties involved in the comparisons. Comparisons between the PR and WSR reflectivities are also made near the surface for evaluation of the attenuation-correction procedures used in the PR algorithms. It is found that the PR attenuation is accurately corrected in stratiform rain but is underestimated in convective rain, particularly in heavy rain. Tests of the PR estimates of rainfall rate are conducted through comparisons in the overlap area between the TRMM overpass and WSR scan. Analyses of the data are made both on a conditional basis, in which the instantaneous rain rates are compared only at those pixels at which both the PR and WSR detect rain, and an unconditional basis, in which the area-averaged rain rates are estimated independently for the PR and WSR. Results of the conditional rain comparisons show that the PR-derived rain is about 9% greater and 19% less than the WSR estimates for stratiform and convective storms, respectively. Overall, the PR tends to underestimate the conditional mean rain rate by 8% for all rain categories, a finding that conforms to the results of the area-averaged rain (unconditional) comparisons.
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Seo, Eun-Kyoung. "Characteristics of Summer Rainfall over East Asia as Observed by TRMM PR." Journal of the Korean earth science society 32, no. 1 (February 28, 2011): 33–45. http://dx.doi.org/10.5467/jkess.2011.32.1.33.
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Li, Nan, Zhenhui Wang, Xi Chen, and Geoffrey Austin. "Studies of General Precipitation Features with TRMM PR Data: An Extensive Overview." Remote Sensing 11, no. 1 (January 4, 2019): 80. http://dx.doi.org/10.3390/rs11010080.
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The Precipitation Radar (PR), the first space-borne precipitation radar onboard the Tropical Rainfall Measuring Mission (TRMM) satellite, could observe three-dimensional precipitation in global tropical regions and acquire continuous rainfall information with moderate temporal and high spatial resolutions. TRMM PR had carried out 17 years of observations and ended collecting data in April, 2015. So far, comprehensive and abundant research results related to the application of PR data have been analyzed in the current literature, but overall precipitation features are not yet identified, a gap that this review intends to fill. Studies on comparisons with ground-based radars and rain gauges are first introduced to summarize the reliability of PR observations or estimates. Then, this paper focuses on general precipitation features abstracted from about 130 studies, from 2000 to 2018, regarding lightning analysis, latent heat retrieval, and rainfall observation by PR data. Finally, we describe the existing problems and limitations as well as the future prospects of the space-borne precipitation radar data.
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Furuzawa, Fumie A., and Kenji Nakamura. "Differences of Rainfall Estimates over Land by Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) and TRMM Microwave Imager (TMI)—Dependence on Storm Height." Journal of Applied Meteorology 44, no. 3 (March 1, 2005): 367–83. http://dx.doi.org/10.1175/jam-2200.1.
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Abstract It is well known that precipitation rate estimation is poor over land. Using the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and TRMM Microwave Imager (TMI), the performance of the TMI rain estimation was investigated. Their differences over land were checked by using the orbit-by-orbit data for June 1998, December 1998, January 1999, and February 1999, and the following results were obtained: 1) Rain rate (RR) near the surface for the TMI (TMI-RR) is smaller than that for the PR (PR-RR) in winter; it is also smaller from 0900 to 1800 LT. These dependencies show some variations at various latitudes or local times. 2) When the storm height is low (<5 km), the TMI-RR is smaller than the PR-RR; when it is high (>8 km), the PR-RR is smaller. These dependencies of the RR on the storm height do not depend on local time or latitude. The tendency for a TMI-RR to be smaller when the storm height is low is more noticeable in convective rain than in stratiform rain. 3) Rain with a low storm height predominates in winter or from 0600 to 1500 LT, and convective rain occurs frequently from 1200 to 2100 LT. Result 1 can be explained by results 2 and 3. It can be concluded that the TMI underestimates rain with low storm height over land because of the weakness of the TMI algorithm, especially for convective rain. On the other hand, it is speculated that TMI overestimates rain with high storm height because of the effect of anvil rain with low brightness temperatures at high frequencies without rain near the surface, and because of the effect of evaporation or tilting, which is indicated by a PR profile and does not appear in the TMI profile. Moreover, it was found that the PR rain for the cases with no TMI rain amounted to about 10%–30% of the total but that the TMI rain for the cases with no PR rain accounted for only a few percent of the TMI rain. This result can be explained by the difficulty of detecting shallow rain with the TMI.
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Seo, Eun-Kyoung. "Rainfall Characteristics in the Tropical Oceans: Observations using TRMM TMI and PR." Journal of the Korean earth science society 33, no. 2 (April 30, 2012): 113–25. http://dx.doi.org/10.5467/jkess.2012.33.2.113.
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Fu, Yunfei, and Guosheng Liu. "Possible Misidentification of Rain Type by TRMM PR over Tibetan Plateau." Journal of Applied Meteorology and Climatology 46, no. 5 (May 1, 2007): 667–72. http://dx.doi.org/10.1175/jam2484.1.
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Abstract Rain-type statistics derived from Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) standard product show that some 70% of raining pixels in the central Tibetan Plateau summer are stratiform—a clear contradiction to the common knowledge that rain events during summer in this region are mostly convective, as a result of the strong atmospheric convective instability resulting from surface heating. In examining the vertical distribution of the stratiform rain-rate profiles, it is suspected that the TRMM PR algorithm misidentifies weak convective rain events as stratiform rain events. The possible cause for this misidentification is believed to be that the freezing level is close to the surface over the plateau, so that the ground echo may be mistakenly identified as the melting level in the PR rain classification algorithm.
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Barros, Ana P., and Kun Tao. "A Space-Filling Algorithm to Extrapolate Narrow-Swath Instantaneous TRMM Microwave Rain-Rate Estimates Using Thermal IR Imagery." Journal of Atmospheric and Oceanic Technology 25, no. 11 (November 1, 2008): 1901–20. http://dx.doi.org/10.1175/2008jtecha1019.1.
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Abstract A space-filling algorithm (SFA) based on 2D spectral estimation techniques was developed to extrapolate the spatial domain of the narrow-swath near-instantaneous rain-rate estimates from Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and TRMM Microwave Imager (TMI) using thermal infrared imagery (Meteosat-5) without making use of calibration or statistical fitting. A comparison against rain gauge observations and the original PR 2A25 and TMI 2A12 estimates in the central Himalayas during the monsoon season (June–September) over a 3-yr period of 1999–2001 was conducted to assess the algorithm’s performance. Evaluation over the continental United States was conducted against the NCEP stage IV combined radar and gauge analysis for selected events. Overall, the extrapolated PR and TMI rainfall fields derived using SFA exhibit skill comparable to the original TRMM estimates. The results indicate that probability of detection and threat scores of the reconstructed products are significantly better than the original PR data at high-elevation stations (>2000 m) on mountain ridges, and specifically for rainfall rates exceeding 2–5 mm h−1 and for afternoon convection. For low-elevation stations located in steep narrow valleys, the performance varies from year to year and deteriorates strongly for light rainfall (false alarm rates significantly increase). A preliminary comparison with other satellite products (e.g., 3B42, a TRMM-adjusted merged infrared-based rainfall product) suggests that integrating this algorithm in currently existing operational multisensor algorithms has the potential to improve significantly spatial resolution, texture, and detection of rainfall, especially in mountainous regions, which present some of the greatest challenges in precipitation retrieval from satellites over land, and for hydrological operations during extreme events.
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Nesbitt, Stephen W., Robert Cifelli, and Steven A. Rutledge. "Storm Morphology and Rainfall Characteristics of TRMM Precipitation Features." Monthly Weather Review 134, no. 10 (October 1, 2006): 2702–21. http://dx.doi.org/10.1175/mwr3200.1.
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Abstract Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR), TRMM Microwave Imager (TMI), and Visible and Infrared Scanner (VIRS) observations within the Precipitation Feature (PF) database have been analyzed to examine regional variability in rain area and maximum horizontal extent of rainfall features, and role of storm morphology on rainfall production (and thus modes where vertically integrated heating occurs). Particular attention is focused on the sampling geometry of the PR and the resulting impact on PF statistics across the global Tropics. It was found that 9% of rain features extend to the edge of the PR swath, with edge features contributing 42% of total rainfall. However, the area (maximum dimension) distribution of PR features is similar to the wider-swath TMI up until a truncation point of nearly 30 000 km2 (250 km), so a large portion of the feature size spectrum may be examined using the PR as with past ground-based studies. This study finds distinct differences in land and ocean storm morphology characteristics, which lead to important differences in rainfall modes regionally. A larger fraction of rainfall comes from more horizontally and vertically developed PFs over land than ocean due to the lack of shallow precipitation in both relative and absolute frequency of occurrence, with a trimodal distribution of rainfall contribution versus feature height observed over the ocean. Mesoscale convective systems (MCSs) are found to be responsible for up to 90% of rainfall in selected land regions. Tropicswide, MCSs are responsible for more than 50% of rainfall in almost all regions with average annual rainfall exceeding 3 mm day−1. Characteristic variability in the contribution of rainfall by feature type is shown over land and ocean, which suggests new approaches for improved convective parameterizations.
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Wolters, E. L. A., B. J. J. M. van den Hurk, and R. A. Roebeling. "Evaluation of rainfall retrievals from SEVIRI reflectances over West Africa using TRMM-PR and CMORPH." Hydrology and Earth System Sciences 15, no. 2 (February 3, 2011): 437–51. http://dx.doi.org/10.5194/hess-15-437-2011.
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Abstract. This paper describes the evaluation of the KNMI Cloud Physical Properties – Precipitation Properties (CPP-PP) algorithm over West Africa. The algorithm combines condensed water path (CWP), cloud phase (CPH), cloud particle effective radius (re), and cloud-top temperature (CTT) retrievals from visible, near-infrared and thermal infrared observations of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation (MSG) satellites to estimate rain occurrence frequency and rain rate. For the 2005 and 2006 monsoon seasons, it is investigated whether the CPP-PP algorithm is capable of retrieving rain occurrence frequency and rain rate over West Africa with sufficient accuracy, using Tropical Monsoon Measurement Mission Precipitation Radar (TRMM-PR) as reference. As a second goal, it is assessed whether SEVIRI is capable of monitoring the seasonal and daytime evolution of rainfall during the West African monsoon (WAM), using Climate Prediction Center Morphing Technique (CMORPH) rainfall observations. The SEVIRI-detected rainfall area agrees well with TRMM-PR, with the areal extent of rainfall by SEVIRI being ~10% larger than from TRMM-PR. The mean retrieved rain rate from CPP-PP is about 8% higher than from TRMM-PR. Examination of the TRMM-PR and CPP-PP cumulative frequency distributions revealed that differences between CPP-PP and TRMM-PR are generally within +/−10%. Relative to the AMMA rain gauge observations, CPP-PP shows very good agreement up to 5 mm h−1. However, at higher rain rates (5–16 mm h−1) CPP-PP overestimates compared to the rain gauges. With respect to the second goal of this paper, it was shown that both the accumulated precipitation and the seasonal progression of rainfall throughout the WAM is in good agreement with CMORPH, although CPP-PP retrieves higher amounts in the coastal region of West Africa. Using latitudinal Hovmüller diagrams, a fair correspondence between CPP-PP and CMORPH was found, which is reflected by high correlation coefficients (~0.7) for both rain rate and rain occurrence frequency. The daytime cycle of rainfall from CPP-PP shows distinctly different patterns for three different regions in West Africa throughout the WAM, with a decrease in dynamical range of rainfall near the Inter Tropical Convergence Zone (ITCZ). The dynamical range as retrieved from CPP-PP is larger than that from CMORPH. It is suggested that this results from both the better spatio-temporal resolution of SEVIRI, as well as from thermal infrared radiances being partly used by CMORPH, which likely smoothes the daytime precipitation signal, especially in case of cold anvils from convective systems. The promising results show that the CPP-PP algorithm, taking advantage of the high spatio-temporal resolution of SEVIRI, is of added value for monitoring daytime precipitation patterns in tropical areas.
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Amitai, Eyal, Carl L. Unkrich, David C. Goodrich, Emad Habib, and Bryson Thill. "Assessing Satellite-Based Rainfall Estimates in Semiarid Watersheds Using the USDA-ARS Walnut Gulch Gauge Network and TRMM PR." Journal of Hydrometeorology 13, no. 5 (October 1, 2012): 1579–88. http://dx.doi.org/10.1175/jhm-d-12-016.1.
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Abstract The rain gauge network associated with the Walnut Gulch Experimental Watershed (WGEW) in southeastern Arizona provides a unique opportunity for direct comparisons of in situ measurements and satellite-based instantaneous rain rate estimates like those from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR). The WGEW network is the densest rain gauge network in the PR coverage area for watersheds greater than 10 km2. It consists of 88 weighing rain gauges within a 149-km2 area. On average, approximately 10 gauges can be found in each PR field of view (~5-km diameter). All gauges are very well synchronized with 1-min reporting intervals. This allows generating very-high-temporal-resolution rain rate fields and obtaining accurate estimates of the area-average rain rate for the entire watershed and for a single PR field of view. In this study, instantaneous rain rate fields from the PR and the spatially interpolated gauge measurements (on a 100 m × 100 m grid, updated every 1 min) are compared for all TRMM overpasses in which the PR recorded rain within the WGEW boundaries (25 overpasses during 1999–2010). The results indicate very good agreement between the fields with low bias values (<10%) and high correlation coefficients, especially for the near-nadir cases (>0.9). The correlation is high at overpass time but the peak occurs several minutes after the overpass, which can be explained by the fact that it takes several minutes for the raindrops to reach the gauge from the time they are observed by the PR. The correlation improves with the new version of the TRMM algorithm (V7). The study includes assessment of the accuracy of the reference products.
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Gebremichael, Mekonnen, Thomas M. Over, and Witold F. Krajewski. "Comparison of the Scaling Characteristics of Rainfall Derived from Space-Based and Ground-Based Radar Observations." Journal of Hydrometeorology 7, no. 6 (December 1, 2006): 1277–94. http://dx.doi.org/10.1175/jhm549.1.
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Abstract In view of the importance of tropical rainfall and the ubiquitous need for its estimates in climate modeling, the authors assess the ability of the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) to characterize the scaling characteristics of rainfall by comparing the derived results with those obtained from the ground-based radar (GR) data. The analysis is based on 59 months of PR and GR rain rates at three TRMM ground validation (GV) sites: Houston, Texas; Melbourne, Florida; and Kwajalein Atoll, Republic of the Marshall Islands. The authors consider spatial scales ranging from about 4 to 64 km at a fixed temporal scale corresponding to the sensor “instantaneous” snapshots (∼15 min). The focus is on the scaling of the marginal moments, which allows estimation of the scaling parameters from a single scene of data. The standard rainfall products of the PR and the GR are compared in terms of distributions of the scaling parameter estimates, the connection between the scaling parameters and the large-scale spatial average rain rate, and deviations from scale invariance. The five main results are as follows: 1) the PR yields values of the rain intermittence scaling parameter within 20% of the GR estimate; 2) both the PR and GR data show a one-to-one relationship between the intermittence scaling parameter and the large-scale spatial average rain rate that can be fit with the same functional form; 3) the PR underestimates the curvature of the scaling function from 20% to 50%, implying that high rain-rate extremes would be missed in a downscaling procedure; 4) the majority of the scenes (>85%) from both the PR and GR are scale invariant at the moment orders q = 0 and 2; and 5) the scale-invariance property tends to break down more likely over ocean than over land; the rainfall regimes that are not scale invariant are dominated by light storms covering large areas. Our results further show that for a sampling size of one year of data, the TRMM temporal sampling does not significantly affect the derived scaling characteristics. The authors conclude that the TRMM PR has the ability to characterize the basic scaling properties of rainfall, though the resulting parameters are subject to some degree of uncertainty.
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Brown, Paula J., Christian D. Kummerow, and David L. Randel. "Hurricane GPROF: An Optimized Ocean Microwave Rainfall Retrieval for Tropical Cyclones." Journal of Atmospheric and Oceanic Technology 33, no. 7 (July 2016): 1539–56. http://dx.doi.org/10.1175/jtech-d-15-0234.1.
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AbstractThe 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 information 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) observations. 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 especially. 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 hurricanes in the database, which is reduced from 0.20 to −0.06 mm h−1.
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Wang, Yadong, Jian Zhang, Pao-Liang Chang, and Qing Cao. "Radar Vertical Profile of Reflectivity Correction with TRMM Observations Using a Neural Network Approach." Journal of Hydrometeorology 16, no. 5 (October 1, 2015): 2230–47. http://dx.doi.org/10.1175/jhm-d-14-0136.1.
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Abstract Complex terrain poses challenges to the ground-based radar quantitative precipitation estimation (QPE) because of partial or total blockages of radar beams in the lower tilts. Reflectivities from higher tilts are often used in the QPE under these circumstances and biases are then introduced due to vertical variations of reflectivity. The spaceborne Precipitation Radar (PR) on board the Tropical Rainfall Measuring Mission (TRMM) satellite can provide good measurements of the vertical structure of reflectivity even in complex terrain, but the poor temporal resolution of TRMM PR data limits their usefulness in real-time QPE. This study proposes a novel vertical profile of reflectivity (VPR) correction approach to enhance ground radar–based QPEs in complex terrain by integrating the spaceborne radar observations. In the current study, climatological relationships between VPRs from an S-band Doppler weather radar located on the east coast of Taiwan and the TRMM PR are developed using an artificial neural network (ANN). When a lower tilt of the ground radar is blocked, higher-tilt reflectivity data are corrected with the trained ANN and then applied in the rainfall estimation. The proposed algorithm was evaluated with three typhoon precipitation events, and its preliminary performance was evaluated and analyzed.
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Hamada, Atsushi, Yuki Murayama, and Yukari N. Takayabu. "Regional Characteristics of Extreme Rainfall Extracted from TRMM PR Measurements." Journal of Climate 27, no. 21 (October 24, 2014): 8151–69. http://dx.doi.org/10.1175/jcli-d-14-00107.1.
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Abstract Characteristics and global distribution of regional extreme rainfall are presented using 12 yr of the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) measurements. By considering each rainfall event as a set of contiguous PR rainy pixels, characteristic values for each event are obtained. Regional extreme rainfall events are defined as those in which maximum near-surface rainfall rates are higher than the corresponding 99.9th percentile on a 2.5° × 2.5° horizontal-resolution grid. The geographical distribution of extreme rainfall rates shows clear regional differences. The size and volumetric rainfall of extreme events also show clear regional differences. Extreme rainfall rates show good correlations with the corresponding rain-top heights and event sizes over oceans but marginal or no correlation over land. The time of maximum occurrence of extreme rainfall events tends to be during 0000–1200 LT over oceans, whereas it has a distinct afternoon peak over land. There are also clear seasonal differences in which the occurrence over land is largely coincident with insolation. Regional extreme rainfall is classified by extreme rainfall rate (intensity) and the corresponding event size (extensity). Regions of “intense and extensive” extreme rainfall are found mainly over oceans near coastal areas and are likely associated with tropical cyclones and convective systems associated with the establishment of monsoons. Regions of “intense but less extensive” extreme rainfall are distributed widely over land and maritime continents, probably related to afternoon showers and mesoscale convective systems. Regions of “extensive but less intense” extreme rainfall are found almost exclusively over oceans, likely associated with well-organized mesoscale convective systems and extratropical cyclones.
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Hirose, Masafumi, Yukari N. Takayabu, Atsushi Hamada, Shoichi Shige, and Munehisa K. Yamamoto. "Spatial Contrast of Geographically Induced Rainfall Observed by TRMM PR." Journal of Climate 30, no. 11 (May 8, 2017): 4165–84. http://dx.doi.org/10.1175/jcli-d-16-0442.1.
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Abstract In this study, the spatial variability in precipitation at a 0.1° scale is investigated using long-term data from the Tropical Rainfall Measuring Mission Precipitation Radar. Marked regional heterogeneities emerged for orographic rainfall on characteristic scales of tens of kilometers, high concentrations of small-scale systems (<10 km) over alpine areas, and sharp declines around mountain summits. In detecting microclimates, an additional concern is suspicious echoes observed around certain geographical areas with relatively low rainfall. A finescale land–river contrast can be extracted in the diurnal behavior of rainfall in medium-scale systems (10–100 km), corresponding to the course of the Amazon River. In addition, rainfall enhancement over small islands (0.1°–1°) was identified in terms of the storm scale. Even 0.1°-scale flat islands experience more rainfall than the adjacent ocean, primarily as a result of localized small or moderate systems. By contrast, compared with small islands, high-impact large-scale systems (>100 km) result in more rainfall over the adjacent ocean. Finescale hourly data represented the abrupt asymmetric fluctuation in rainfall across the coastline in the tropics and subtropics (30°S–30°N). Significant diurnal modulations in the rainfall due to large-scale systems are found over tropical offshore regions of vast landmasses but not over small islands or in the midlatitudes between 30° and 36°. Rainfall enhancement over small tropical islands is generated by abundant afternoon rainfall, which results from medium-scale storms that are regulated by the island size and inactivity of rainfall over coastal waters.
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Wen, Yixin, Qing Cao, Pierre-Emmanuel Kirstetter, Yang Hong, Jonathan J. Gourley, Jian Zhang, Guifu Zhang, and Bin Yong. "Incorporating NASA Spaceborne Radar Data into NOAA National Mosaic QPE System for Improved Precipitation Measurement: A Physically Based VPR Identification and Enhancement Method." Journal of Hydrometeorology 14, no. 4 (August 1, 2013): 1293–307. http://dx.doi.org/10.1175/jhm-d-12-0106.1.
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Abstract This study proposes an approach that identifies and corrects for the vertical profile of reflectivity (VPR) by using Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) measurements in the region of Arizona and southern California, where the ground-based Next Generation Weather Radar (NEXRAD) finds difficulties in making reliable estimations of surface precipitation amounts because of complex terrain and limited radar coverage. A VPR identification and enhancement (VPR-IE) method based on the modeling of the vertical variations of the equivalent reflectivity factor using a physically based parameterization is employed to obtain a representative VPR at S band from the TRMM PR measurement at Ku band. Then the representative VPR is convolved with ground radar beam sampling properties to compute apparent VPRs for enhancing NEXRAD quantitative precipitation estimation (QPE). The VPR-IE methodology is evaluated with several stratiform precipitation events during the cold season and is compared to two other statistically based correction methods, that is, the TRMM PR–based rainfall calibration and a range ring–based adjustment scheme. The results show that the VPR-IE has the best overall performance and provides much more accurate surface rainfall estimates than the original ground-based radar QPE. The potential of the VPR-IE method could be further exploited and better utilized when the Global Precipitation Measurement Mission's dual-frequency PR is launched in 2014, with anticipated accuracy improvements and expanded latitude coverage.
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Chen, S., P. E. Kirstetter, Y. Hong, J. J. Gourley, Y. D. Tian, Y. C. Qi, Q. Cao, et al. "Evaluation of Spatial Errors of Precipitation Rates and Types from TRMM Spaceborne Radar over the Southern CONUS." Journal of Hydrometeorology 14, no. 6 (November 22, 2013): 1884–96. http://dx.doi.org/10.1175/jhm-d-13-027.1.
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Abstract In this paper, the authors estimate the uncertainty of the rainfall products from NASA and Japan Aerospace Exploration Agency's (JAXA) Tropical Rainfall Measurement Mission (TRMM) Precipitation Radar (PR) so that they may be used in a quantitative manner for applications like hydrologic modeling or merging with other rainfall products. The spatial error structure of TRMM PR surface rain rates and types was systematically studied by comparing them with NOAA/National Severe Storms Laboratory's (NSSL) next generation, high-resolution (1 km/5 min) National Mosaic and Multi-Sensor Quantitative Precipitation Estimation (QPE; NMQ/Q2) over the TRMM-covered continental United States (CONUS). Data pairs are first matched at the PR footprint scale (5 km/instantaneous) and then grouped into 0.25° grid cells to yield spatially distributed error maps and statistics using data from December 2009 through November 2010. Careful quality control steps (including bias correction with rain gauges and quality filtering) are applied to the ground radar measurements prior to considering them as reference data. The results show that PR captures well the spatial pattern of total rainfall amounts with a high correlation coefficient (CC; 0.91) with Q2, but this decreases to 0.56 for instantaneous rain rates. In terms of precipitation types, Q2 and PR convective echoes are spatially correlated with a CC of 0.63. Despite this correlation, PR's total annual precipitation from convection is 48.82% less than that by Q2, which points to potential issues in the PR algorithm's attenuation correction, nonuniform beam filling, and/or reflectivity-to-rainfall relation. Finally, the spatial analysis identifies regime-dependent errors, in particular in the mountainous west. It is likely that the surface reference technique is triggered over complex terrain, resulting in high-amplitude biases.
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Schwaller, Mathew R., and K. Robert Morris. "A Ground Validation Network for the Global Precipitation Measurement Mission." Journal of Atmospheric and Oceanic Technology 28, no. 3 (March 1, 2011): 301–19. http://dx.doi.org/10.1175/2010jtecha1403.1.
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Abstract A prototype Validation Network (VN) is currently operating as part of the Ground Validation System for NASA’s Global Precipitation Measurement (GPM) mission. The VN supports precipitation retrieval algorithm development in the GPM prelaunch era. Postlaunch, the VN will be used to validate GPM spacecraft instrument measurements and retrieved precipitation data products. The period of record for the VN prototype starts on 8 August 2006 and runs to the present day. The VN database includes spacecraft data from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and coincident ground radar (GR) data from operational meteorological networks in the United States, Australia, Korea, and the Kwajalein Atoll in the Marshall Islands. Satellite and ground radar data products are collected whenever the PR satellite track crosses within 200 km of a VN ground radar, and these data are stored permanently in the VN database. VN products are generated from coincident PR and GR observations when a significant rain event occurs. The VN algorithm matches PR and GR radar data (including retrieved precipitation data in the case of the PR) by calculating averages of PR reflectivity (both raw and attenuation corrected) and rain rate, and GR reflectivity at the geometric intersection of the PR rays with the individual GR elevation sweeps. The algorithm thus averages the minimum PR and GR sample volumes needed to “matchup” the spatially coincident PR and GR data types. The result of this technique is a set of vertical profiles for a given rainfall event, with coincident PR and GR samples matched at specified heights throughout the profile. VN data can be used to validate satellite measurements and to track ground radar calibration over time. A comparison of matched TRMM PR and GR radar reflectivity factor data found a remarkably small difference between the PR and GR radar reflectivity factor averaged over this period of record in stratiform and convective rain cases when samples were taken from high in the atmosphere. A significant difference in PR and GR reflectivity was found in convective cases, particularly in convective samples from the lower part of the atmosphere. In this case, the mean difference between PR and corrected GR reflectivity was −1.88 dBZ. The PR–GR bias was found to increase with the amount of PR attenuation correction applied, with the PR–GR bias reaching −3.07 dBZ in cases where the attenuation correction applied is >6 dBZ. Additional analysis indicated that the version 6 TRMM PR retrieval algorithm underestimates rainfall in case of convective rain in the lower part of the atmosphere by 30%–40%.
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Marks, David A., David B. Wolff, David S. Silberstein, Ali Tokay, Jason L. Pippitt, and Jianxin Wang. "Availability of High-Quality TRMM Ground Validation Data from Kwajalein, RMI: A Practical Application of the Relative Calibration Adjustment Technique." Journal of Atmospheric and Oceanic Technology 26, no. 3 (March 1, 2009): 413–29. http://dx.doi.org/10.1175/2008jtecha1174.1.
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Abstract Since the Tropical Rainfall Measuring Mission (TRMM) satellite launch in November 1997, the TRMM Satellite Validation Office (TSVO) at NASA Goddard Space Flight Center (GSFC) has been performing quality control and estimating rainfall from the KPOL S-band radar at Kwajalein, Republic of the Marshall Islands. Over this period, KPOL has incurred many episodes of calibration and antenna pointing angle uncertainty. To address these issues, the TSVO has applied the relative calibration adjustment (RCA) technique to eight years of KPOL radar data to produce Ground Validation (GV) version 7 products. This application has significantly improved stability in KPOL reflectivity distributions needed for probability matching method (PMM) rain-rate estimation and for comparisons to the TRMM precipitation radar (PR). In years with significant calibration and angle corrections, the statistical improvement in PMM distributions is dramatic. The intent of this paper is to show improved stability in corrected KPOL reflectivity distributions by using the PR as a stable reference. Intermonth fluctuations in mean reflectivity differences between the PR and corrected KPOL are on the order of ±1–2 dB, and interyear mean reflectivity differences fluctuate by approximately ±1 dB. This represents a marked improvement in stability with confidence comparable to the established calibration and uncertainty boundaries of the PR. The practical application of the RCA method has salvaged eight years of radar data that would have otherwise been unusable and has made possible a high-quality database of tropical ocean–based reflectivity measurements and precipitation estimates for the research community.
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Imaoka, Keiji, and Kenji Nakamura. "Statistical Analysis of the Life Cycle of Isolated Tropical Cold Cloud Systems Using MTSAT-1R and TRMM Data." Monthly Weather Review 140, no. 11 (November 1, 2012): 3552–72. http://dx.doi.org/10.1175/mwr-d-11-00364.1.
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Abstract Observations from the Multifunctional Transport Satellite-1R (MTSAT-1R) and the Tropical Rainfall Measuring Mission (TRMM) satellites are analyzed to show the universal view of the cloud life cycle, including the changes of vertical structure of rainfall, over the Maritime Continent and a part of the tropical western Pacific, with a focus on the isolated cold cloud systems. Temporally connected cold cloud systems are identified by a cloud tracking procedure and compared with the collocated observations from TRMM. Clear life cycle changes of the average reflectivity profile from the Precipitation Radar (PR), such as those of radar echo height and the brightband feature, are statistically confirmed over the ocean area. Systems with a lifetime of 5 h show a behavior similar to those of typical mesoscale convective systems, with an extension of anvil clouds up to an area of about 6000 km2 as a delayed response to the earlier intense convection, indicated by the peaks of rain rates and radar echo height at the early stages. In contrast, the 2-h lifetime systems decay rapidly and do not produce an extension of cloud and precipitation. The results also show that the difference between rainfall estimates of the TRMM Microwave Imager (TMI) and PR depends on the phase in the lifetime. TMI tends to provide higher conditional average rain rates at the mature phase than that of PR.
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Qi, Youcun, Jian Zhang, Qing Cao, Yang Hong, and Xiao-Ming Hu. "Correction of Radar QPE Errors for Nonuniform VPRs in Mesoscale Convective Systems Using TRMM Observations." Journal of Hydrometeorology 14, no. 5 (October 1, 2013): 1672–82. http://dx.doi.org/10.1175/jhm-d-12-0165.1.
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Abstract Mesoscale convective systems (MCSs) contain both regions of convective and stratiform precipitation, and a bright band (BB) is often found in the stratiform region. Inflated reflectivity intensities in the BB often cause positive biases in radar quantitative precipitation estimation (QPE). A vertical profile of reflectivity (VPR) correction is necessary to reduce such biases. However, existing VPR correction methods for ground-based radars often perform poorly for MCSs owing to their coarse resolution and poor coverage in the vertical direction, especially at far ranges. Spaceborne radars such as the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR), on the other hand, can provide high resolution VPRs. The current study explores a new approach of incorporating the TRMM VPRs into the VPR correction for the Weather Surveillance Radar-1988 Doppler (WSR-88D) radar QPE. High-resolution VPRs derived from the Ku-band TRMM PR data are converted into equivalent S-band VPRs using an empirical technique. The equivalent S-band TRMM VPRs are resampled according to the WSR-88D beam resolution, and the resampled (apparent) VPRs are then used to correct for BB effects in the WSR-88D QPE when the ground radar VPR cannot accurately capture the BB bottom. The new scheme was tested on six MCSs from different regions in the United States and it was shown to provide effective mitigation of the radar QPE errors due to BB contamination.
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Kuligowski, Robert J., Yaping Li, and Yu Zhang. "Impact of TRMM Data on a Low-Latency, High-Resolution Precipitation Algorithm for Flash-Flood Forecasting." Journal of Applied Meteorology and Climatology 52, no. 6 (June 2013): 1379–93. http://dx.doi.org/10.1175/jamc-d-12-0107.1.
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AbstractData from the Tropical Rainfall Measuring Mission (TRMM) have made great contributions to hydrometeorology from both a science and an operations standpoint. However, direct application of TRMM data to short-fuse hydrologic forecasting has been challenging because of the data refresh and latency issues inherent in an instrument in low Earth orbit (LEO). To evaluate their potential impact on low-latency satellite rainfall estimates, rain rates from both the TRMM Microwave Imager (TMI) and precipitation radar (PR) were ingested into a multisensor framework that calibrates high-refresh, low-latency IR brightness temperature data from geostationary platforms against the more accurate but low-refresh, higher-latency rainfall rates available from microwave (MW) instruments on board LEO platforms. The TRMM data were used in two ways: to bias adjust the other MW data sources to match the distribution of the TMI rain rates, and directly alongside the MW rain rates in the calibration dataset. The results showed a significant reduction in false alarms and also a significant reduction in bias for those pixels for which rainfall was correctly detected. The MW bias adjustment was found to have much greater impact than the direct use of the TMI and PR rain rates in the calibration data, but this is not surprising since the latter represented perhaps only 10% of the calibration dataset.
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Masunaga, Hirohiko, and Christian D. Kummerow. "Combined Radar and Radiometer Analysis of Precipitation Profiles for a Parametric Retrieval Algorithm." Journal of Atmospheric and Oceanic Technology 22, no. 7 (July 1, 2005): 909–29. http://dx.doi.org/10.1175/jtech1751.1.
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Abstract A methodology to analyze precipitation profiles using the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and precipitation radar (PR) is proposed. Rainfall profiles are retrieved from PR measurements, defined as the best-fit solution selected from precalculated profiles by cloud-resolving models (CRMs), under explicitly defined assumptions of drop size distribution (DSD) and ice hydrometeor models. The PR path-integrated attenuation (PIA), where available, is further used to adjust DSD in a manner that is similar to the PR operational algorithm. Combined with the TMI-retrieved nonraining geophysical parameters, the three-dimensional structure of the geophysical parameters is obtained across the satellite-observed domains. Microwave brightness temperatures are then computed for a comparison with TMI observations to examine if the radar-retrieved rainfall is consistent in the radiometric measurement space. The inconsistency in microwave brightness temperatures is reduced by iterating the retrieval procedure with updated assumptions of the DSD and ice-density models. The proposed methodology is expected to refine the a priori rain profile database and error models for use by parametric passive microwave algorithms, aimed at the Global Precipitation Measurement (GPM) mission, as well as a future TRMM algorithms.
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Mariani, Stefano, Christophe Accadia, Nazario Tartaglione, Marco Casaioli, Marco Gabella, Silas Chr Michaelides, and Antonio Speranza. "Multisensor Comparison and Numerical Modeling of Atmospheric Water Fields: A VOLTAIRE Case Study over Cyprus." Weather and Forecasting 23, no. 4 (August 1, 2008): 674–701. http://dx.doi.org/10.1175/2007waf2007032.1.
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Abstract This paper presents a study performed within the framework of the European Union’s (EU) VOLTAIRE project (Fifth Framework Programme). Among other tasks, the project aimed at the integration of the Tropical Rainfall Measuring Mission (TRMM) data with ground-based observations and at the comparison between water fields (precipitation and total column water vapor) as estimated by multisensor observations and predicted by NWP models. In particular, the VOLTAIRE project had as one of its main objectives the goal of assessing the application of satellite-borne instrument measures to model verification. The island of Cyprus was chosen as the main “test bed,” because it is one of the few European territories covered by the passage of the TRMM Precipitation Radar (PR) and it has a dense rain gauge network and an operational weather radar. TRMM PR provides, until now, the most reliable space-borne spatial high-resolution precipitation measurements. Attention is focused on the attempt to define a methodology, using state-of-the-art diagnostic methods, for a comprehensive evaluation of water fields as forecast by a limited area model (LAM). An event that occurred on 5 March 2003, associated with a slow cyclone moving eastward over the Mediterranean Sea, is presented as a case study. The atmospheric water fields were forecast over the eastern Mediterranean Sea using the Bologna Limited Area Model (BOLAM). Data from the Cyprus ground-based radar, the Cyprus rain gauge network, the Special Sensor Microwave Imager (SSM/I), and the TRMM PR were used in the comparison. Ground-based radar and rain gauge data were merged together in order to obtain a better representation of the rainfall event over the island. TRMM PR measurements were employed to range-adjust the ground-based radar data using a linear regression algorithm. The observed total column water vapor has been employed to assess the forecast quality of large-scale atmospheric patterns; such an assessment has been performed by means of the Hoffman diagnostic method applied to the entire total column water vapor field. Subsequently, in order to quantify the spatial forecast error at the finer BOLAM scale (0.09°), the object-oriented contiguous rain area (CRA) analysis was chosen as a comparison method for precipitation. An assessment of the main difficulties in employing CRA in an operational framework, especially over such a small verification domain, is also discussed in the paper.
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Li, Wei, and Courtney Schumacher. "Thick Anvils as Viewed by the TRMM Precipitation Radar." Journal of Climate 24, no. 6 (March 15, 2011): 1718–35. http://dx.doi.org/10.1175/2010jcli3793.1.
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Abstract This study investigates anvils from thick, nonprecipitating clouds associated with deep convection as observed in the tropics by the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) during the 10-yr period, 1998–2007. Anvils observable by the PR occur, on average, 5 out of every 100 days within grid boxes with 2.5° resolution and with a conditional areal coverage of 1.5%. Unconditional areal coverage is only a few tenths of a percent. Anvils also had an average 17-dBZ echo top of ∼8.5 km and an average thickness of ∼2.7 km. Anvils were usually higher and thicker over land compared to ocean, and occurred most frequently over Africa, the Maritime Continent, and Panama. Anvil properties were intimately tied to the properties of the parent convection. In particular, anvil area and echo-top heights were highly correlated to convective rain area. The next best predictor for anvil areal coverage and echo tops was convective echo tops, while convective reflectivities had the weakest correlation. Strong upper-level wind shear also may be associated with anvil occurrence over land, especially when convection regularly attains echo-top heights greater than 7 km. Some tropical land regions, especially those affected by monsoon circulations, experience significant seasonal variability in anvil properties—strong interannual anvil variability occurs over the central Pacific because of the El Niño–Southern Oscillation. Compared to the CloudSat Cloud Profiling Radar, the TRMM PR underestimates anvil-top height by an average of ∼5 km and underestimates their horizontal extent by an average factor of 4.
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Short, David A., and Kenji Nakamura. "Effect of TRMM Orbit Boost on Radar Reflectivity Distributions." Journal of Atmospheric and Oceanic Technology 27, no. 7 (July 1, 2010): 1247–54. http://dx.doi.org/10.1175/2010jtecha1426.1.
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Abstract Probability distributions of measured radar reflectivity from the precipitation radar (PR) on board the Tropical Rainfall Measuring Mission (TRMM) satellite show a small, counterintuitive increase in the midrange, 20–34 dBZ, when comparing data from periods before and after the orbit altitude was boosted in August 2001. Data from two 2-yr time periods, 1999–2000 (preboost) and 2002–03 (postboost), show statistically significant differences of 2%–3% at altitudes of 2, 4, and 10 km and for path-averaged reflectivity. The bivariate Gaussian function, used to model idealized radar response functions, has mathematical properties that indicate an increase in field-of-view (FOV) size associated with an increase in satellite altitude can be expected to result in a narrowing of observed dBZ distributions, with a resulting increase in midrange values. Numerical simulations with echo areas much smaller and larger than the TRMM PR FOV before (4.3 km) and after (5.0 km) boost are used to demonstrate basic characteristics of the observed and expected distribution changes.
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Park, Myung-Sook, and Russell L. Elsberry. "Latent Heating and Cooling Rates in Developing and Nondeveloping Tropical Disturbances during TCS-08: TRMM PR versus ELDORA Retrievals*." Journal of the Atmospheric Sciences 70, no. 1 (January 1, 2013): 15–35. http://dx.doi.org/10.1175/jas-d-12-083.1.
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Abstract Unique sets of Electra Doppler Radar (ELDORA) observations in both developing and nondeveloping tropical disturbances in the western North Pacific are used to retrieve latent heating and cooling rates. During the reintensification of Sinlaku, maximum heating rates of about 80 K h−1 are diagnosed in the upper troposphere in the region of a strong updraft and maximum cooling rates of about −45 K h−1 are diagnosed in the lower troposphere in the region of a strong convective-scale downdraft. The southern convective burst in the pre-Nuri mission had a lower-tropospheric maximum in latent heating that was a more favorable condition for tropical cyclone formation than was the upper-tropospheric maximum in heating and the lower-tropospheric maximum in cooling in the northern convective burst. Two nondeveloping tropical disturbances had deeper layers of more uniform heating and of cooling rates, and some evidence of more shallow cloud tops, that distinguished them from the developing cases. Although the Shige et al. Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) algorithm was only intended to be applied over large areas on longer time scales, the PR-derived latent heating profiles were compared with the ELDORA-derived profiles to reveal important mesoscale effects. Because all six cases indicated near-zero cooling rates, a new TRMM PR algorithm should be developed that would include the effects of saturated convective-scale downdrafts in tropical mesoscale convective systems (MCSs). Production of a legacy TRMM PR dataset with this improvement would be useful for diagnosing tropical cyclone formation dating back to 1998, and for specifying initial and validation conditions for numerical models in the tropics.