The Fourth TRMM Latent Heating Workshop

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.

Rainfall is a fundamental process within the Earth's hydrological cycle because it represents a principal forcing term in surface water budgets, while its energetics corollary, latent heating, is the principal source of atmospheric diabatic heating well into the middle latitudes. Latent heat production itself is a consequence of phase changes between the vapor, liquid, and frozen states of water. The properties of the vertical distribution of latent heat release modulate large-scale meridional and zonal circulations within the Tropics, as well as modify the energetic efficiencies of midlatitude weather systems. This paper highlights the retrieval of latent heating from satellite measurements generated by the Tropical Rainfall Measuring Mission (TRMM) satellite observatory, which was launched in November 1997 as a joint American–Japanese space endeavor. Since then, TRMM measurements have been providing credible four-dimensional accounts of rainfall over the global Tropics and subtropics, information that can be used to estimate the space–time structure of latent heating across the Earth's low latitudes. A set of algorithm methodologies for estimating latent heating based on precipitation-rate profile retrievals obtained from TRMM measurements has been under continuous development since the advent of the mission s research program. These algorithms are briefly described, followed by a discussion of the latent heating products that they generate. The paper then provides an overview of how TRMM-derived latent heating information is currently being used in conjunction with global weather and climate models, concluding with remarks intended to stimulate further research on latent heating retrieval from satellites.

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.

Launched in 2014, the Global Precipitation Measurement (GPM) mission aimed at ensuring the continuity with the Tropical Rainfall Measuring Mission (TRMM) launched in 1997 that has provided unprecedented accuracy in Satellite Precipitation Estimates (SPEs) on the near-global scale. Since then, various SPE versions have been successively made available from the GPM mission. The present study assesses the potential benefits of the successive GPM based SPEs product versions that include the Integrated Multi–Satellite Retrievals for GPM (IMERG) version 3 to 5 (–v03, –v04, –v05) and the Global Satellite Mapping of Precipitation (GSMaP) version 6 to 7 (–v06, –v07). Additionally, the most effective TRMM based SPEs products are also considered to provide a first insight into the GPM effectiveness in ensuring TRMM continuity. The analysis is conducted over different geomorphic and meteorological regions of Pakistan while using 88 precipitations gauges as the reference. Results show a clear enhancement in precipitation estimates that were derived from the very last IMERG–v05 in comparison to its two previous versions IMERG–v03 and –v04. Interestingly, based on the considered statistical metrics, IMERG–v03 provides more consistent precipitation estimate than IMERG–v04, which should be considered as a transition IMERG version. As expected, GSMaP–v07 precipitation estimates are more accurate than the previous GSMaP–v06. However, the enhancement from the old to the new version is very low. More generally, the transition from TRMM to GPM is successful with an overall better performance of GPM based SPEs than TRMM ones. Finally, all of the considered SPEs have presented a strong spatial variability in terms of accuracy with none of them outperforming the others, for all of the gauges locations over the considered regions.

Latent heating (LH) is an important quantity in both weather forecasting and climate analysis, being the essential factor driving convective systems. Yet, inferring LH rates from our current observing systems is challenging at best. For climate studies, LH has been retrieved from the Precipitation Radar on the Tropical Rainfall Measuring Mission (TRMM) using model simulations in the look-up table (LUT) that relates instantaneous radar profiles to corresponding heating profiles. These radars, first on TRMM and then Global Precipitation Measurement Mission (GPM), provide a continuous record of LH. However, temporal resolution is too coarse to have a significant impacts on forecast models. In operational forecast models such as High-Resolution Rapid Refresh, convection is initiated from LH derived from ground based radar. Despite the high spatial and temporal resolution of ground-based radars, one disadvantage of using these sources is that its data are only available over well observed land areas. This study develops a method to derive LH from the Geostationary Operational Environmental Satellite-16 (GOES-16) in near-real time. Even though the visible and infrared channels on the Advanced Baseline Imager (ABI) provide mostly cloud top information, rapid changes in cloud top visible and infrared properties, when formulated as a LUT similar to those used by the TRMM and GPM radars, can equally be used to derive LH profiles for convective regions based on model simulations with a convective classification scheme and channel 14 (11.2 μm) brightness temperatures. Convective regions detected by GOES-16 are assigned LH from the LUT, and they are compared with LH from the Next Generation Weather Radar (NEXRAD) and one of the Dual-frequency Precipitation Radar (DPR) products, the Goddard Convective-Stratiform Heating (CSH). LH obtained from GOES-16 show similar magnitude with NEXRAD and CSH, and vertical distribution of LH is also very similar with CSH. One month analysis of total LH from convective clouds from GOES-16 and NEXRAD shows good correlation between the two products. Finally LH profiles from GOES-16 and NEXRAD are applied to WRF simulations for convective initiation and their results are compared to investigate their impacts in precipitation forecasts. Results show that LH from GOES-16 have similar impacts as NEXRAD, and improves the forecast significantly.

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.

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.

Precipitation plays a crucial role to the global water and energy cycle that governs the weather, climate, and ecological systems. Thorough understanding and accurate forecasting of precipitation is essential to the affairs of humans. The Tropical Rainfall Measuring Mission (TRMM), launched in 1997, is a joint space mission between NASA and the Japan Aerospace Exploration Agency (JAXA) designed to monitor and study tropical rainfall. The Precipitation Radar (PR) on board the TRMM satellite is the first space borne instrument, capable of providing high-resolution vertical profile of precipitation on a global scale. TRMM-PR operates at a single frequency of Ku- (13.6 GHz) band. The microphysical retrieval algorithms for TRMM-PR rely on the surface-reference technique (SRT) to estimate path attenuation and correct the measured Ku-band reflectivity. With the attenuation-corrected reflectivities, a modified Hitschfeld-Bordan method [1] is then used to retrieve limited drop size distribution (DSD) information, and the rainfall rate [2]. One disadvantage of single-frequency space borne radar such as TRMM-PR is that it is not easy to retrieve the DSD parameters completely. Therefore, k-Z and Z-R relationships, with their inherent assumptions, are used to estimate rainfall rate which is not sufficient to capture the variability of precipitation and has large uncertainty. Global Precipitation Measurement (GPM) mission is poised to be the next generation observations from space after the TRMM mission. GPM is a science mission with integrated applications goals for advancing the knowledge of the global water/energy cycle variability as well as improving weather, climate, and hydrological prediction capabilities through more accurate and frequent measurements of global precipitation. The GPM core satellite will be equipped with a dual-frequency precipitation radar (DPR) operating at Ku- (13.6 GHz) and Ka- (35.5 GHz) band [3]. Taking two independent sets of observation, DPR on aboard the GPM is expected to improve our knowledge of precipitation processes relative to the single-frequency (Ku- band) radar used in TRMM by providing greater dynamic range, more detailed information on microphysics. Two parameters of DSDs can be retrieved from dual-frequency observations and better accuracies in rainfall estimation can be achieved. Theoretically, rainfall rate is a function of rain drop size distribution and rain drop terminal velocity, R=0.67π∗10−3∫ v(D)D3N(D)dD. The most critical component in rainfall rate estimation is the time-space variation of drop size distribution. Le and Chandrasekar (2014) [4] developed a hybrid method to retrieve drop size distribution parameters for GPM-DPR. The hybrid method is a profile-based optimization algorithm with the philosophy to combine the attributes of forward method and linear constraints of DSDs in rain. Two of the gamma distribution parameters [5], Do and Nw, at surface are optimized when the deviation between estimates and observations are minimized. The hybrid method can be used to estimate DSDs at each space and temporal resolution of GPM-DPR observation. In this paper, rainfall rate is calculated using DSDs retrieved through the hybrid method [4] based on assumptions of particle terminal velocity. Data collected by GPM-DPR is capability to cover ±65° latitude of the earth with every 2–4 hours. Thus, a global rainfall map can be generated. In polarimetric radar system, rainfall rate can be estimated through dual-polarized radar parameter such as Zdr [6][7]. Zdr is called differential reflectivity and it is a function of particle characteristics itself. Similar of Zdr to the dual-polarization radar retrieval, there is a parameter called dual-frequency ratio (DFR) that plays an important role in the dual-frequency radar retrievals. DFR is defined as the difference of the radar reflectivity at two frequencies in decibels which carries information of single particle characteristics. In this study, we investigate potential relation between rainfall rate, reflectivity at Ku- band and DFR using theoretical simulation [8] and curve fitting. Figure 1 shows a scattergram of rainfall rate estimation from R(Zh, DFR) versus true rainfall rate. Both Zku and DFR are in linear form in this equation. This provides a direct estimation of rainfall rate without prior knowledge of drop size distribution. Since both reflectivity and dual frequency ratio are intrinsic values, attenuation correction is needed before rain rate can be estimated through R(Zh, DFR) equation. The hybrid method [4] described in previous paragraph could be one of the algorithms to perform attenuation correction. The global rainfall map generated through R(Zh, DFR) approach will be cross-compared with DSD approach described in the previous paragraph, as well as other approaches such as attenuation based rainfall rate. The comprehensive analysis of the three techniques are presented.

Long‐term mean rainfall rates from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and Precipitation Radar (PR) are compared with in situ measurements by rain gauges on the NOAA TAO/TRITON buoy array in the tropical Pacific. The buoy rain gauges have an advantage over most of the available ground truth data in that they are not influenced by the local meteorological effects of islands. Instantaneous TRMM retrievals averaged over 1° × 1° longitude‐latitude boxes are matched with 12‐hour‐averaged buoy rain amounts centered on the TRMM overpasses. The TMI has near‐zero bias with respect to the gauges when compared across climatologically different gauge locations (dry to wet). The PR is biased about 30% low relative to the gauges. These results are consistent with other ground truth studies and the known biases between the TMI and PR in the TRMM version 5 algorithms. Because rain gauges are thought to underestimate rainfall during windy conditions, further study of gauge biases would be useful.

We evaluated the performance of the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) at‐launch algorithm for monthly oceanic rain rate using six months (January–June 1998) of TMI data. Comparison with oceanic monthly rain rates derived from the Special Sensor Microwave Imager (SSM/I) data shows statistically significant differences. The TMI rain rates are lower than the SSM/I rain rates by about 10% overall, except for rain rates lower than 1.4 mm/day. The low TMI bias may be due to an overestimate of the columnar water vapor as indicated by the estimated rain layer thickness. The superior sampling by the TMI improves the algorithm statistics at the low rain rates. The averaged monthly rain rates over the latitudes between 40°S and 40°N are 2.78 and 3.17 mm/day, respectively for TMI and SSM/I, with RMS difference of 1.35 mm/day and correlation coefficient of 0.94.

The Global Precipitation Measurement (GPM) mission started as an expanded follow-on mission of the Tropical Rainfall Measuring Mission (TRMM) project to obtain more accurate and frequent observations of precipitation than TRMM. An important goal for the GPM mission is the frequent measurement of global precipitation using a GPM core satellite and a constellation of multiple satellites. The GPM core satellite is developed by the US and Japan as like as TRMM, while the constellation satellites are developed by various countries. The accurate measurement of precipitation will be achieved by the Dual-frequency Precipitation Radar (DPR) installed on the GPM core satellite. DPR consists of two radars, which are Ku-band (13.6 GHz) precipitation radar (KuPR) and Ka-band (35.5 GHz) radar (KaPR). KaPR will detect snow and light rain, and the KuPR will detect heavy rain. In an effective dynamic range in both KaPR and KuPR, drop size distribution (DSD) information and more accurate rainfall estimates will be provided by a dual-frequency algorithm. The frequent precipitation measurement every three hours at any place on the globe will be achieved by several constellation satellites with microwave radiometers (MWRs). JAXA/EORC is responsible for the GPM/DPR algorithm development for engineering values (Level 1) and physical products (e.g. precipitation estimation) (Level 2 and 3) and the quality control of the products as the sensor provider. It is also important for us to produce and deliver 3-hourly global precipitation map in real time in order to make useful for various research and application areas (i.e., the prediction of the floods). To secure the quality of estimates, the mission must place emphasis on validation of satellite data and retrieval algorithms.

Performance evaluation of the TRMM precipitation estimation using ground-based radars from the GPM validation network

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.

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.

Two radiometers of the Tropical Rainfall Measuring Mission (TRMM) were used to retrieve parameters that were necessary to determine the latent heat flux density at the sea surface. The Visible and Infrared Radiometer System (VIRS) was used to measure sea surface temperature. The TRMM Microwave Imager (TMI) measured near-surface wind speed and air-specific humidity. The retrieval schemes for these parameters were derived theoretically by means of radiative transfer simulations and were validated by TRMM data matched with buoy measurements. The validation study revealed deficiencies in the description of the infrared water vapour absorption continuum and in the TMI calibration. Both effects were corrected for so that the desired parameters could be retrieved with adequate accuracy. A global application of the retrieval method to TRMM data for April 1998 is shown.