Comparison of Rainfall Products Derived from TRMM Microwave Imager and Precipitation Radar

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.

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.

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.

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.

Empirical orthogonal function (EOF) analysis of radiance vectors associated with emission and scattering indices for the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) has been performed to examine the regional variability in relations between brightness temperature and rain rate over portions of the tropical oceans known to exhibit regional differences due to different thermodynamic environments and different large-scale forcing. The TMI indices and rain rates used in this study are the products of the Goddard profiling algorithm (GPROF), version 6. The EOF framework reduces the nine-dimensional space of the brightness temperatures and their polarizations to just two dimensions associated with the EOF coefficients. Vertical profiles of reflectivity from the TRMM precipitation radar (PR) are used to show that the statistically obtained EOFs represent bulk physical characteristics of raining clouds. Hence, EOF analysis provides an efficient framework for diagnosing regional differences in cloud structures that affect brightness temperature–rain-rate relations. The EOF framework revealed fundamental differences in the behavior of TMI surface rain-rate retrievals versus retrievals that are based on the PR aboard the TRMM satellite. In EOF space, TMI rain rates were bimodally distributed, with one mode indicating higher rain rates with greater high-density ice and rainwater content in the cloud and the other mode being consistent with moderately heavy warm rain from shallow convection. In contrast, the PR rain-rate distribution showed high rain rates being assigned over a much greater diversity of cloud structures. The manifold of EOF space constructively shows that, of the regions examined here, the tropical northwestern Pacific Ocean region produces the greatest occurrence of particularly strong cumulonimbus clouds.

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.

Microwave radiation with the inherent advantage of its ability to partially penetrate clouds is ideally suited for remote measurements of precipitation, especially over the oceanic regions. The retrieval problem is of great practical interest, as precipitation over the oceans has to be necessarily remotely sensed. More importantly, precipitation is also a crucial input in many weather and climate models. A downward looking space borne radiometer such as the TRMM's Microwave Imager (TMI), however, does not sense the surface rain fall directly. Rather, it measures the upwelling radiation coming from the top-of-atmosphere which depends on the total quantities of the parameters that can affect the radiation in the chosen frequency range. In addition to precipitation, the attenuation and augmentation of total cloud content and integrated precipitable water content by absorption and emission, respectively, affect the microwave brightness temperatures in various frequencies. In the present work, a systematic study has been conducted to investigate the effect of total cloud and precipitable water contents on surface rainrate retrievals from the TMI measured brightness temperatures (BT). While a Bayesian framework is used to assimilate radar reflectivities into hydrometeor structures, a neural network is used to correlate rain and cloud parameters with brightness temperatures. The correlation between the TMI brightness temperatures and the TRMM's Precipitation Radar (TRMM-PR) is used as the benchmark for comparison. A community developed software meso-scale Weather Research and Forecast (WRF) is used to simulate the cloud and precipitable water content, along with the surface rainfall rate for several rain events in the past. Four cases are considered: (a) TRMM PR's near surface rain rate is correlated with TMI brightness temperatures directly; (b) the total cloud and precipitable water contents along with surface rainfall simulated using WRF are correlated with TMI BTs; (c) similar to case (b) with near surface rainfall taken from TRMM PR measurements; (d) total cloud and precipitable water contents and the surface rain rate are corrected with PR vertical reflectivity profile in a Bayesian framework. The freely available QuickBeam software has been used for simulation of reflectivities at the TRMM PR frequency. The corrected data are then correlated with TMI BTs. Results show that surface rain fall retrievals can be radically improved by using TRMM PR vertical rain corrected total cloud and precipitable water content.

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.

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.

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

Properties 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.

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.

This paper describes improvements to the Tropical Rainfall Measurement Mission (TRMM) Microwave Imager (TMI) land rainfall algorithm in version 7 (v7) of the TRMM data products. The correlations between rain rates and TMI 85-GHz brightness temperatures (Tb) for convective and stratiform rain are generated using 7 years of collocated TMI and TRMM precipitation radar (PR) data. The TMI algorithm for estimating the convective ratio of rainfall is also modified. This paper highlights both the improvements in the v7 algorithm and the continuing problems with the land rainfall retrievals. It is demonstrated that the proposed changes to the algorithm significantly lower the overestimation by TMI globally and over large sections of central Africa and South America. Also highlighted are the problems with the 2A12 land algorithm that have not been addressed in the version 7 algorithm, such as large regional and seasonal dependence of biases in the TMI rain estimates, and potential changes to the algorithm to resolve these problems are discussed.

In 2004, the Tropical Rainfall Measuring Mission (TRMM) Science Project released a newer version of precipitation products, i.e., version 6 (V6), from its various instruments. The V6 data products are expected to be more accurate than the previous version 5 (V5). In this letter, we have attempted to analyze V5 and V6 products from two primary sensors on the TRMM, namely, the TRMM Microwave Imager (TMI) and the Precipitation Radar (PR), to unravel the quality of the V6 products vis-a-vis that of the V5 products. It is found that there are significant changes in the TMI-derived precipitation values, but the TMI brightness temperature (Tb) has not undergone any change from V5 to V6. Thus, the retrieval algorithm must have undergone some changes from V5 to V6. While the total number of TMI raining pixels is nearly unchanged, V5 moderate rain events (1 - 5 mm ldr h-1) are more often classified as low rain events (< 1 mm ldr h-1) in V6. Thus, TMI-based precipitation shows larger bias, particularly at low to moderate rain rates than that in V5. This results in a depression in the average Tb at around 2 mm ldr h-1 in the Tb-rain-rate relationship with V6 measurements, which is implausible. This dip in average Tb value further complicates the Tb-rain-rate relationship by showing a continuous rise in Tb with rain rate, which is again implausible. On the other hand, PR-based rain rates have not undergone much change from V5 to V6. The relationship between Tb from TMI and rain rate from PR also does not show any anomalous behavior.

Tropical Rainfall Measuring Mission (TRMM) rainfall retrieval algorithms are evaluated in tropical cyclone (TC) inner cores (IC), inner bands (IB), and outer rainbands (OB). In total, 1329 IC, 2149 IB, and 4627 OB storm regions are analyzed using data from a 12‐year TRMM Tropical Cyclone Precipitation Feature (TCPF) database containing 1013 TCs viewed from December 1997 to December 2009. Attention is focused on the difference between the Precipitation Radar (PR) 2A25 and the TRMM Microwave Imager (TMI) 2A12 rainfall algorithms. The PR 2A25 produces larger mean rain rates than the TMI 2A12 in inner cores and inner bands, with the greatest difference occurring in hurricanes. This discrepancy is caused mostly by the TMI 2A12 significantly underestimating regions of moderate to heavy rain &gt;15 mm hour−1 or when the PR reflectivity is greater than 30 dBZ. The TMI 2A12 rain rates are most closely related to the percentage coverage of 85 GHz polarization‐corrected brightness temperature (PCT) &lt;225 K in the IC and 85 GHz PCT &lt;250 K in the IB and OB. These convective parameters are good predictors of the mean TMI 2A12 rain rate, but significant ice scattering is not always present in areas of heavy rain that are often widespread in TC inner regions. As a result, the TMI 2A12 algorithm may poorly measure the rain rate, particularly in the inner core of hurricanes.