TRMM) Microwave Imager Observations Research Articles

Evaluation of WRF Model Hydrometeors Based on TMI Observations Using an Indirect Method

Using a microwave radiative transfer (MWRT) model with microwave brightness temperatures (TBs) observed from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), an indirect appro...

Combining Satellite Microwave Radiometer and Radar Observations to Estimate Atmospheric Heating Profiles

Abstract In this study, satellite passive microwave sensor observations from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) are utilized to make estimates of latent + eddy sensible heating rates (Q1 − QR) where Q1 is the apparent heat source and QR is the radiative heating rate in regions of precipitation. The TMI heating algorithm (herein called TRAIN) is calibrated or “trained” using relatively accurate estimates of heating based on spaceborne Precipitation Radar (PR) observations collocated with the TMI observations over a one-month period. The heating estimation technique is based on a previously described Bayesian methodology, but with improvements in supporting cloud-resolving model simulations, an adjustment of precipitation echo tops to compensate for model biases, and a separate scaling of convective and stratiform heating components that leads to an approximate balance between estimated vertically integrated condensation and surface precipitation. Estimates of Q1 − QR from TMI compare favorably with the PR training estimates and show only modest sensitivity to the cloud-resolving model simulations of heating used to construct the training data. Moreover, the net condensation in the corresponding annual mean satellite latent heating profile is within a few percent of the annual mean surface precipitation rate over the tropical and subtropical oceans where the algorithm is applied. Comparisons of Q1 produced by combining TMI Q1 − QR with independently derived estimates of QR show reasonable agreement with rawinsonde-based analyses of Q1 from two field campaigns, although the satellite estimates exhibit heating profile structures with sharper and more intense heating peaks than the rawinsonde estimates.

Advanced Rain/No-Rain Classification Methods for Microwave Radiometer Observations over Land

Abstract Seto et al. developed rain/no-rain classification (RNC) methods over land for the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI). In this study, the methods are modified for application to other microwave radiometers. The previous methods match TMI observations with TRMM precipitation radar (PR) observations, classify the TMI pixels into rain pixels and no-rain pixels, and then statistically summarize the observed brightness temperature at the no-rain pixels into a land surface brightness temperature database. In the modified methods, the probability distribution of brightness temperature under no-rain conditions is derived from unclassified TMI pixels without the use of PR. A test with the TMI shows that the modified (PR independent) methods are better than the RNC method developed for the Goddard profiling algorithm (GPROF; the standard algorithm for the TMI) while they are slightly poorer than corresponding previous (PR dependent) methods. M2d, one of the PR-independent methods, is applied to observations from the Advanced Microwave Scanning Radiometer for Earth Observing Satellite (AMSR-E), is evaluated for a matchup case with PR, and is evaluated for 1 yr with a rain gauge dataset in Japan. M2d is incorporated into a retrieval algorithm developed by the Global Satellite Mapping of Precipitation project to be applied for the AMSR-E. In latitudes above 30°N, the rain-rate retrieval is compared with a rain gauge dataset by the Global Precipitation Climatology Center. Without a snow mask, a large amount of false rainfall due to snow contamination occurs. Therefore, a simple snow mask using the 23.8-GHz channel is applied and the threshold of the mask is optimized. Between 30° and 60°N, the optimized snow mask forces the miss of an estimated 10% of the total rainfall.

TRMM Calibration of SSM/I Algorithm for Overland Rainfall Estimation

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.

Bayesian Estimation of Precipitation from Satellite Passive Microwave Observations Using Combined Radar–Radiometer Retrievals

Abstract Precipitation estimation from satellite passive microwave radiometer observations is a problem that does not have a unique solution that is insensitive to errors in the input data. Traditionally, to make this problem well posed, a priori information derived from physical models or independent, high-quality observations is incorporated into the solution. In the present study, a database of precipitation profiles and associated brightness temperatures is constructed to serve as a priori information in a passive microwave radiometer algorithm. The precipitation profiles are derived from a Tropical Rainfall Measuring Mission (TRMM) combined radar–radiometer algorithm, and the brightness temperatures are TRMM Microwave Imager (TMI) observed. Because the observed brightness temperatures are consistent with those derived from a radiative transfer model embedded in the combined algorithm, the precipitation–brightness temperature database is considered to be physically consistent. The database examined here is derived from the analysis of a month-long record of TRMM data that yields more than a million profiles of precipitation and associated brightness temperatures. These profiles are clustered into a tractable number of classes based on the local sea surface temperature, a radiometer-based estimate of the echo-top height (the height beyond which the reflectivity drops below 17 dBZ), and brightness temperature principal components. For each class, the mean precipitation profile, brightness temperature principal components, and probability of occurrence are determined. The precipitation–brightness temperature database supports a radiometer-only algorithm that incorporates a Bayesian estimation methodology. In the Bayesian framework, precipitation estimates are weighted averages of the mean precipitation values corresponding to the classes in the database, with the weights being determined according to the similarity between the observed brightness temperature principal components and the brightness temperature principal components of the classes. Because the classes are stratified by the sea surface temperature and the echo-top-height estimator, the number of classes that are considered for retrieval is significantly smaller than the total number of classes, making the algorithm computationally efficient. The radiometer-only algorithm is applied to TMI observations, and precipitation estimates are compared with combined TRMM precipitation radar (PR)–TMI reference estimates. The TMI-only algorithm, supported by the empirically derived database, produces estimates that are more consistent with the reference values than the precipitation estimates from the version-6 TRMM facility TMI algorithm. Cloud-resolving model simulations are used to assign a latent heating profile to each precipitation profile in the empirically derived database, making it possible to estimate latent heating using the radiometer-only algorithm. Although the evaluation of latent heating estimates in this study is preliminary, because realistic conditional probability distribution functions are attached to latent heating structures in the algorithm's database, a generally positive impact on latent heating estimation from passive microwave observations is expected.

Rain/No-Rain Classification Methods for Microwave Radiometer Observations over Land Using Statistical Information for Brightness Temperatures under No-Rain Conditions

Abstract One of the goals of the Global Precipitation Measurement project, the successor to the Tropical Rainfall Measuring Mission (TRMM), is to produce a 3-hourly global rainfall map using several spaceborne microwave radiometers. It is important, although often difficult, to classify radiometer observations over land as either “rain” or “no rain” because background land surface conditions change significantly with time and location. In this study, a no-rain brightness temperature database was created to infer land surface conditions using simultaneous observations by TRMM Microwave Imager (TMI) and precipitation radar (PR) with a resolution of 1 month and 1° latitude × 1° longitude. This paper proposes new rain/no-rain classification (RNC) methods that use the database to determine the background brightness temperature. The proposed RNC methods and the RNC method developed for the Goddard profiling algorithm (GPROF; the standard rain-rate retrieval algorithm for TMI) are applied to all TMI observations for the entire year of 2000, and the results are evaluated against the RNC made by PR as the “truth.” The first method (M1) simply uses the average brightness temperature at 85-GHz vertical polarization [denoted as TB (85 V)] under no-rain conditions as the background brightness temperature at 85-GHz vertical polarization [denoted as TBe (85 V)]. The second method (M2) uses a regression equation between TB (85 V) and TB (22 V) under no-rain conditions from the database. Here, TBe (85 V) is calculated by substituting the observed TB (22 V) into the regression equation. The ratio of accurate rain detection by GPROF to all rain occurrences detected by PR was 59%. This ratio was 57% for M1 and 63% for M2. The ratio with the weight of the rain rate was 81% for M1 and 86% for M2; it was 80% for GPROF. These comparisons were made by setting a threshold using a constant coefficient k0 to make the ratio of false rain detection to all no-rain occurrences detected by PR almost the same (approximately 0.85%) for all three methods. Further comparisons among the methods are made, and the reasons for the differences are investigated herein.

Experimental use of TRMM precipitation radar observations in 1D+4D−Var assimilation

AbstractThis paper presents a new application of the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) observations for indirect assimilation into the European Centre for Medium‐Range Weather Forecasts (ECMWF) model. The PR reflectivities are first processed using a one‐dimensional variational (1D‐Var) method to adjust model temperature and specific humidity. The retrieved Total Column Water Vapour (TCWV) is then assimilated into the operational four‐dimensional variational (4D‐Var) system. The applicability of the 1D+4D−Var approach to the radar observations is discussed in detail.Several case studies were run to assess the feasibility and the effectiveness of assimilating PR reflectivities with a 1D‐Var approach. Results show good behaviour of the 1D‐Var system in terms of convergence and stability. Its performance in terms of retrieved TCWV is comparable to that of other 1D‐Vars which make use of TRMM Microwave Imager (TMI) observations. When the 1D‐Var TCWV pseudo‐observations are input into the 4D‐Var system, a positive impact is shown in the analysis and the subsequent forecasts, both on moisture‐related fields and also on winds and surface pressure. The quality of the forecast is verified using track observations for the tropical cyclones. The track forecasts from the experiments which include 1D‐Var TCWV are generally closer to the observed track than a control run. Despite their much smaller spatial coverage than TMI observations, it is found that the PR data have a comparable impact, provided the satellite samples a meaningful portion of the storm, possibly its centre. This is possibly due to the fact that TCWV increments from PR and from TMI brightness temperature have similar magnitudes.These results show that active sensor data can provide indirect yet useful information on the moisture field and that this information can effectively be assimilated to improve the analysis and the forecast of tropical disturbances. Although this is a sub‐optimal use of PR observations, due to the underexploitation of the vertical information contained in the reflectivity profiles, it is still a step forward towards using active sensor data in preparation for future satellite missions which will deliver this type of data on a global scale and at higher temporal resolution. Copyright © 2005 Royal Meteorological Society

Combined Radar and Radiometer Analysis of Precipitation Profiles for a Parametric Retrieval Algorithm

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.

Soil moisture estimates from TRMM Microwave Imager observations over the Southern United States

The lack of continuous soil moisture fields at large spatial scales, based on observations, has hampered hydrologists from understanding its role in weather and climate. The most readily available observations from which a surface wetness state could be derived is the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) observations at 10.65 GHz. This paper describes the first attempt to map daily soil moisture from space over an extended period of time. Methods to adjust for diurnal changes associated with this temporal variability and how to mosaic these orbits are presented. The algorithm for deriving soil moisture and temperature from TMI observations is based on a physical model of microwave emission from a layered soil–vegetation–atmosphere medium. An iterative, least-squares minimization method, which uses dual polarization observations at 10.65 GHz, is employed in the retrieval algorithm. Soil moisture estimates were compared with ground measurements over the U.S. Southern Great Plains (SGP) in Oklahoma and the Little River Watershed, Georgia. The soil moisture experiment in Oklahoma was conducted in July 1999 and Little River in June 2000. During both the experiments, the region was dry at the onset of the experiment, and experienced moderate rainfall during the course of the experiment. The regions experienced a quick dry-down before the end of the experiment. The estimated soil moisture compared well with the ground observations for these experiments (standard error of 2.5%). The TMI-estimated soil moisture during 6–22 July over Southern U.S. was analyzed and found to be consistent with the observed meteorological conditions.

TRMM Precipitation Radar and Microwave Imager Observations of Convective and Stratiform Rain over Land and their Theoretical Implications.

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.

Over-Ocean Rainfall Retrieval from Multisensor Data of the Tropical Rainfall Measuring Mission. Part I: Design and Evaluation of Inversion Databases

Abstract A new methodology for the combination of active and passive microwave measurements for near-surface precipitation retrieval from the Tropical Rainfall Measuring Mission (TRMM) data was developed. The approach consists of a stand-alone passive microwave algorithm that is calibrated by collocated radar estimates. The passive microwave technique was based on combined cloud model–radiative transfer simulations including varying surface conditions, a melting layer parameterization, and approximative three-dimensional radiative transfer. The representativeness of the simulations with respect to the TRMM Microwave Imager (TMI) observations was evaluated replacing brightness temperatures by empirical orthogonal functions. Thus, nine TMI correlated channels may be replaced by two to three empirical orthogonal functions representating 97%–98% of total variability. Comparing the principal components to those from TMI observations containing precipitation revealed that the 85.5-GHz brightness temperatures fr...

Post-launch calibration of the TRMM microwave imager

Three post-launch calibration methods are used to examine the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) on-orbit performance. The first method is a statistical analysis of TMI ocean observations that reveals a systematic along-scan error. The second method is an intercomparison of TMI and SSM/I observations that shows a warm bias in TMI. The last method is an analysis of TMI observations taken during TRMMs deep-space maneuver. These deep-space observations confirm both the along-scan error found from method 1 and the warm bias found from method 2. The along-scan error exhibits distinctive features having amplitudes near 1 K. The warm bias, which is related to the scene temperature, can be as large are 5 K for ocean measurements. The physical explanation of a slightly emissive main reflector is proposed to explain the calibration errors.