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 non-tropical cyclone events over Darwin, Australia.

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.

The Global precipitation mission is conceptually centered on the deployment of a "core" satellite with an active dual-frequency (Ka/Ku band) precipitation radar, and microwave imager capable of sensing the total precipitation within all cloud layers (http://gpm.gsfc.nasa.gov). Compared to the single-frequency TRMM (Tropical Rainfall Measuring Mission) precipitation radar (PR), the dual-frequency precipitation radar (DPR) onboard GPM core satellite is expected to enhance our understanding on microphysics, and provide more accurate retrieval of rainfall and liquid water content. The goal of the present study is to combine the TMI (TRMM's Microwave Imager), TRMM PR and a mesoscale WRF (Weather Research and Forecast) model to simulate the DPR reflectivity profiles. Prior studies to simulate the DPR profiles were done based on TRMM PR observation using frequency scaling arguments and hydrometeor classification [1]. First, the methodology to retrieve self consistent hydrometeor profiles from a combination of TMI, PR observations as well as WRF model is described. Using the TRMM constrained WRF model profiles, a procedure to simulate DPR observations is described. Attenuation from cloud water and cloud ice is considered. Simulation of cyclone Nargis shows reasonable results using TMI retrieved microphysics. Attenuations from both precipitation and non-precipitation are also compared.

This study compares instantaneous rainfall estimates provided by the current generation of retrieval algorithms for passive microwave sensors using retrievals from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and merged surface radar and gauge measurements over the continental United States as references. The goal is to quantitatively assess surface rain retrievals from cross-track scanning microwave humidity sounders relative to those from conically scanning microwave imagers. The passive microwave sensors included in the study are three operational sounders—the Advanced Microwave Sounding Unit-B (AMSU-B) instruments on the NOAA-15, -16, and -17 satellites—and five imagers: the TRMM Microwave Imager (TMI), the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) instrument on the Aqua satellite, and the Special Sensor Microwave Imager (SSM/I) instruments on the Defense Meteorological Satellite Program (DMSP) F-13, -14, and -15 satellites. The comparisons with PR data are based on “coincident” observations, defined as instantaneous retrievals (spatially averaged to 0.25° latitude and 0.25° longitude) within a 10-min interval collected over a 20-month period from January 2005 to August 2006. Statistics of departures of these coincident retrievals from reference measurements as given by the TRMM PR or ground radar and gauges are computed as a function of rain intensity over land and oceans. Results show that over land AMSU-B sounder rain retrievals are comparable in quality to those from conically scanning radiometers for instantaneous rain rates between 1.0 and 10.0 mm h−1. This result holds true for comparisons using either TRMM PR estimates over tropical land areas or merged ground radar/gauge measurements over the continental United States as the reference. Over tropical oceans, the standard deviation errors are comparable between imager and sounder retrievals for rain intensities above 5 mm h−1, below which the imagers are noticeably better than the sounders; systematic biases are small for both imagers and sounders. The results of this study suggest that in planning future satellite missions for global precipitation measurement, cross-track scanning microwave humidity sounders on operational satellites may be used to augment conically scanning microwave radiometers to provide improved temporal sampling over land without degradation in the quality of precipitation estimates.

Overland rain retrieval using spaceborne microwave radiometer offers a myriad of complications as land presents itself as a radiometrically warm and highly variable background. Hence, land rainfall algorithms of the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) have traditionally incorporated empirical relations of microwave brightness temperature (Tb) with rain rate, rather than relying on physically based radiative transfer modeling of rainfall (as implemented in the TMI ocean algorithm). In this paper, sensitivity analysis is conducted using the Spearman rank correlation coefficient as benchmark, to estimate the best combination of TMI low-frequency channels that are highly sensitive to the near surface rainfall rate from the TRMM Precipitation Radar (PR). Results indicate that the TMI channel combinations not only contain information about rainfall wherein liquid water drops are the dominant hydrometeors but also aid in surface noise reduction over a predominantly vegetative land surface background. Furthermore, the variations of rainfall signature in these channel combinations are not understood properly due to their inherent uncertainties and highly nonlinear relationship with rainfall. Copula theory is a powerful tool to characterize the dependence between complex hydrological variables as well as aid in uncertainty modeling by ensemble generation. Hence, this paper proposes a regional model using Archimedean copulas, to study the dependence of TMI channel combinations with respect to precipitation, over the land regions of Mahanadi basin, India, using version 7 orbital data from the passive and active sensors on board TRMM, namely, TMI and PR. Studies conducted for different rainfall regimes over the study area show the suitability of Clayton and Gumbel copulas for modeling convective and stratiform rainfall types for the majority of the intraseasonal months. Furthermore, large ensembles of TMI Tb (from the most sensitive TMI channel combination) were generated conditional on various quantiles (25th, 50th, 75th, and 95th) of the convective and the stratiform rainfall. Comparatively greater ambiguity was observed to model extreme values of the convective rain type. Finally, the efficiency of the proposed model was tested by comparing the results with traditionally employed linear and quadratic models. Results reveal the superior performance of the proposed copula-based technique.

A validation of passive microwave–adjusted rainfall analyses of tropical cyclones using spaceborne radar data is presented. This effort is part of the one-dimensional plus four-dimensional variational (1D+4D-Var) rain assimilation project that is being carried out at the European Centre for Medium-Range Weather Forecasts (ECMWF). Brightness temperatures or surface rain rates from the Tropical Rainfall Measuring Mission (TRMM) satellite are processed through a 1D-Var retrieval to derive values of total column water vapor that can be ingested into the operational ECMWF 4D-Var. As an indirect validation, the precipitation fields produced at the end of the 1D-Var minimization process are converted into equivalent radar reflectivity at the frequency of the TRMM precipitation radar (13.8 GHz) and are compared with the observations averaged at model resolution. The averaging process is validated using a sophisticated downscaling/upscaling approach that is based on wavelet decomposition. The precipitation radar measurements are ideal for this validation exercise, being approximately collocated with but completely independent of the TRMM Microwave Imager (TMI) radiometer measurements. Qualitative and statistical comparisons between radar observations and retrievals from the TMI-derived surface rain rates and from TMI radiances are made using 17 well-documented tropical cyclone occurrences between January and April of 2003. Several statistical measures, such as bias, root-mean-square error, and Heidke skill score, are introduced to assess the 1D-Var skill as well as the model background skill in producing a realistic rain distribution. Results show a good degree of skill in the retrievals, especially near the surface and for medium–heavy rain. The model background produces precipitation in the domain that is sometimes in excess with respect to the observations, and it often shows an error in the location of precipitation maxima. Differences between the two 1D-Var approaches are not large enough to make final conclusions regarding the advantages of one method over the other. Both methods are capable of redistributing the rain patterns according to the observations. It appears, however, that the brightness temperature approach is in general more effective in increasing precipitation amounts at moderate-to-high rainfall rates.

Satellite remote sensing is an indispensable means of measuring and monitoring precipitation on a global scale. The Tropical Rainfall Measuring Mission (TRMM) is continuing to make significant progress in helping the global features of precipitation to be understood, particularly with the help of a pair of spaceborne microwave sensors, the TRMM Microwave Imager (TMI) and precipitation radar (PR). The TRMM version-5 standard products, however, are known to have a systematic inconsistency in mean monthly rainfall. To clarify the origin of this inconsistency, the authors investigate the zonal mean precipitation and the regional trends in the hydrometeor profiles in terms of the precipitation water content (PWC) and the precipitation water path (PWP) derived from the TMI profiling algorithm (2A12) and the PR profile (2A25). An excess of PR over TMI in near-surface PWC is identified in the midlatitudes (especially in winter), whereas PWP exhibits a striking excess of TMI over PR around the tropical rainfall maximum. It is shown that these inconsistencies arise from TMI underestimating the near-surface PWC in midlatitude winter and PR underestimating PWP in the Tropics. This conclusion is supported by the contoured-frequency-by-altitude diagrams as a function of PWC. Correlations between rain rate and PWC/PWP indicate that the TMI profiling algorithm tends to provide a larger rain rate than the PR profile under a given PWC or PWP, which exaggerates the excess by TMI and cancels the excess by PR through the conversion from precipitation water to rain rate. As a consequence, the disagreement in the rainfall products between TMI and PR is a combined result of the intrinsic bias originating from the different physical principles between TMI and PR measurements and the purely algorithmic bias inherent in the conversion from precipitation water to rain rate.

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.

Observations of brightness temperature, Tb, made over land regions by the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) radiometer are analyzed with the help of nearly simultaneous measurements of the vertical profiles of reflectivity factor, Z, made by the Precipitation Radar (PR) onboard the TRMM satellite. Furthermore, this analysis is done separately over convective and stratiform rain regions. This examination reveals a clear relationship between TMI and PR data. Possible explanation for this relationship is explored with the help of radiative transfer calculations. With this approach, we demonstrate that the 85 GHz observations of TMI can be simulated crudely from the observations of Z. However, the 37 and 19 GHz observations are not as well simulated, possibly because of horizontal non-uniformity in the hydrometeor distribution in the broad footprints of these channels and contamination introduced by land-surface emissivity. On the other hand, from TMI and PR observations, we find that the brightness temperature difference (T19-T37) minimizes these sources of error. Our simulations of (T19-T37) over convective rain regions are in reasonable agreement with this finding. This investigation indicates that the TMI 85 GHz channel yields the best information about rain over tropical land, because it has minimal surface contamination, strong extinction, and a fine footprint. The brightness temperature difference (T19-T37) can supplement the information given by the 85 GHz channel.

The Tropical Rainfall Measuring Mission (TRMM) version‐6 rainfall products show the reduced bias between Precipitation Radar (PR) and TRMM Microwave Imager (TMI) rainfall estimates during the 1997/1998 El Niño event noted in version 5, but need to be verified. We investigate consistency between TMI‐observed brightness temperatures (TBs) at 10 and 19 GHz channels and those simulated from the PR and TMI rainfall estimates using a radiative transfer model. Simulated TBs from PR V6 exhibits better agreement with observed ones than those from PR V5, implying the algorithm improvements. However, discrepancies at 19 GHz suggest that uncertainty in the assumed drop size distribution still remains in PR V6. Simulated TBs from TMI V6 also exhibits better agreement with observed ones than those from TMI V5. However, the simulated 10‐GHz TBs from TMI V6 exhibits more scatter against TMI‐observed ones than those from PR V6 do.

A technique has been developed for retrieving ocean surface winds using surface backscatter measurements from the Precipitation Radar (PR) of the Tropical Rainfall Measuring Mission (TRMM). Though limited by the small incidence angles and the single look capability of the scan geometry, TRMM PR offers a distinct advantage over conventional spaceborne scatterometer systems through the fine scale vertical and horizontal resolution of its normalized radar cross section, /spl sigma//spl deg/. The wind retrieval algorithm developed for TRMM PR makes use of a maximum likelihood estimation technique to compensate for the low /spl sigma//spl deg/ sensitivity associated with the PR configuration. The narrow vertical resolution of the PR range bins serves to filter out rain contaminated cells normally integrated into scatterometer surface measurements. The algorithm was developed and validated through remotely measured winds from other spaceborne sensors, namely the TRMM Microwave Imager (TMI) radiometer and the NASA QuikSCAT scatterometer Further algorithm validation is presented using in-situ observations from oceanographic buoys. All buoy data are acquired from-the NOAA National Buoy Data Center. The TRMM PR geophysical model function used in the retrieval process is verified through comparison of PR /spl sigma//spl deg/ measurements and buoy wind measurements. Ocean surface wind speeds derived from the new PR retrieval technique are then compared with collocated buoy winds, revealing excellent agreement in wind speed estimation.

Revised versions of previous passive microwave land rainfall algorithms are developed for the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), the Special Sensor Microwave/Imager (SSM/I), and the new Advanced Microwave Sounding Radiometer-Earth Observing System (EOS) (AMSR-E). The relationships between rainfall rate and 85 GHz brightness temperature are re-calibrated with respect to previous algorithms using collocated TMI and TRMM Precipitation Radar (PR) data. Another new feature is a procedure to estimate the probability of convective rainfall, as convective/stratiform classification can reduce the abmiguity of possible rainfall rates for a given brightness temperature. These modifications essentially eliminate the global high bias found in studies of previous versions of the SSM/I and TMI algorithms. However, many regional and seasonal biases still exist, and these are identified. The applicability of the new features to the other microwave sensors is studied using SSM/I data. The AMSR-E algorithm is the same as the TMI, as the footprint resolutions and frequencies of these instruments are very similar. The TMI algorithm will be used in the land portion of the offical Version 6 TMI instantaneous rainfall rate product, to be released in 2003, while the AMSR algorithm will be used for future AMSR-E products.

Rainfall measured by Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) is important for studying precipitation distribution in the tropical regions. The ground validation of TRMM PR is difficult because the ground sensing systems have different characteristics from TRMM PR in terms of resolution, scale, view aspect and sensing environments. In this paper, we introduce a machine learning system to train ground radars for rainfall estimation using rain gauge data and subsequently using the trained ground radar rainfall estimation to train TRMM PR. This system can build a connection between ground gauge measurements and ground radar observations, and transfer this connection to TRMM PR observations for rainfall estimation. The rain gauge, ground radar and satellite data collected from Melbourne, Florida are used for demonstration purposes. The rainfall estimation product derived from this new system is compared against the TRMM standard products, which shows improvement brought by the new machine learning system.

In this study, 9 years (1998–2006) of monthly precipitation data from Tropical Rainfall Measuring Mission (TRMM) are used to examine the relations between tropical rainfall and surface temperature using measurements from both passive and active microwave sensors. These relations are compared to those derived from Global Precipitation Climatology Project (GPCP) analyses. A technique is first developed to adjust the TRMM Precipitation Radar (PR) monthly rainfall data in the tropics (whole ocean and whole land) to account for the effect of the TRMM orbit boost from 350 to 402 km in August 2001. The postboost PR rainfall is adjusted by adding 6.5, 6.0, and 1.0% to the monthly PR rainfall data over the ocean at the estimated surface, the near surface, and the 2 km level, respectively. No adjustment is made for data over land or above the 4 km level. The relationships between the tropical rainfall and surface temperature are then examined with both the TRMM Microwave Imager (TMI) and adjusted PR data. Comparing tropical (25°N–25°S) ocean precipitation to mean sea surface temperature (SST) over the same area, the GPCP and TMI rainfall data have large and similar slopes (∼15%/°C) against ocean‐wide SST anomalies, while the surface monthly rainfall anomalies derived from the TRMM PR exhibit much shallower slopes (∼4%/°C) against the SST anomalies. At the 4 km level the PR data exhibit a larger slope (12%/°C) comparable to the passive microwave value. Over the tropical land, all rainfall data except TRMM PR at 6 km have similar, but negative, slopes against surface temperature anomalies, in contrast to the positive slopes over the ocean. Over the total tropics (ocean and land combined), TRMM TMI and GPCP rainfall data have rather similar smaller positive slopes (6%/°C), when compared to ocean plus land surface temperature, but the PR rainfall data slopes are slightly negative, except at the 4 km level (4%/°C). Overall, the PR‐based surface precipitation‐temperature slopes do not confirm slopes based on passive microwave observations. This may be the result of PR retrieval error or inherent passive/active retrieval differences. Further research is needed to advance the use of TRMM data in this regard.

With 15 yr of the Tropical Rainfall Measuring Mission (TRMM) observations, the passive microwave radiometers [TRMM Microwave Imager (TMI)] and the precipitation radar (PR) report a close geographical distribution of annual precipitation between 36°S and 36°N. However, large discrepancies between PR and TMI precipitation retrievals are also found over several specific regions, such as central Africa, the Amazon, the tropical east Pacific, and north Indian Ocean. To understand these discrepancies, the PR near-surface and the TMI surface precipitation retrievals are compared at both pixel and precipitation system levels using collocated pixels and a precipitation feature database from 1998 to 2012. Over land, the TMI overestimates precipitation in deep and intense convective systems, but misses significant amounts of warm rainfall in shallow systems. Over the ocean, because of the partial beam filling of large footprints of the lower-frequency sensors, the TMI reports a larger precipitation area than the PR and underestimates the precipitation rate in the convective precipitation region. The TMI tends to overestimate precipitation compared to the PR in a large proportion of shallow systems over the tropical east Pacific and trade wind regions with large-scale descent. The PR tends to overestimate precipitation compared to the TMI in a large proportion of shallow systems over rainy oceans, such as the west Pacific and the Atlantic ITCZ. All these findings imply that there are still large uncertainties in the precipitation climatology over some regions. Further ground validation campaigns are still needed, especially over the ocean.

This 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.