TRMM) Microwave Imager Brightness Temperatures Research Articles

Final Updates of Tropical Rainfall Measuring Mission Microwave Imager Emissive-Reflector Bias Correction for the Global Precipitation Measurement V07 Reprocessing

Typically, every 3 - 5 years, the brightness temperature data products for the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) were reprocessed to incorporate improvements in signal processing algorithms. In 2017, the TMI 1B data product, Global Precipitation Measurement (GPM) version 5 (V05), was updated with an improved correction for a slightly emissive main reflector antenna that was physics-based and it incorporated empirical coefficients derived from additional deep-space calibration tests performed. Subsequently, it was discovered that TMI brightness temperatures still contain a consistent bias of the order of 0.6 K in all channels that varied with orbital phase and sun angle in a way that resembled the orbital pattern of the estimated main reflector physical temperature. In this paper, we present an analysis of this bias and provide a correction, which has been included in GPM version 7 (V07) reprocessing. First, an updated inter-satellite radiometric calibration (XCAL) radiative transfer model was used to derive the physical temperature of TMI’s main reflector for all possible solar orientations throughout the 17-year mission. Then, the corrected TMI brightness temperatures were derived using these new physical temperature tables. Finally, these updated Tbs were validated by intercomparison with collocated 13-month GMI measurements using the XCAL double difference technique. A quantitative and qualitative analysis of the improvements is provided.

Hot Load Temperature Correction for TRMM Microwave Imager in the Legacy Brightness Temperature

The Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), launched in late November 1997, has provided a > 17-year record of tropical precipitation measurements, and prior to the launch of the global precipitation measurements (GPM) satellite in February 2014, the TMI served as the radiometric transfer standard for the TRMM constellation radiometers. Thus, the radiometric calibration of the TRMM constellation radiometers depends upon that of TMI. Therefore, the level-1 TMI brightness temperatures have been reprocessed to produce the TMI legacy brightness temperature product namely 1B11 version 8 (also known as GPM05), which has been released by NASA in late 2017. During this data reprocessing, a radiometric calibration anomaly, associated with the hot load, was discovered. This error was caused by an occasional transient solar intrusion, which introduced a systematic error in the brightness temperature. To remove this error, an independent hot load correction algorithm was implemented. In this paper, the process of identifying the issue, developing the solution, and evaluating the results is described.

Copula-Based Modeling of TMI Brightness Temperature With Rainfall Type

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.

Improved Passive Microwave Retrievals of Rain Rate over Land and Ocean. Part II: Validation and Intercomparison

Abstract A new passive microwave rainfall retrieval algorithm for the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) that relies on an a priori database derived from matchups between TMI brightness temperatures and precipitation radar (PR)-derived surface rain rates has been developed. In addition to implementing a fairly conventional Bayesian approach to precipitation estimation, it exploits a dimensional reduction technique designed to increase the effective sample density in the database and also to improve the detectability of precipitation over problem surface types. The details of the algorithm itself are described in a companion paper. In this paper, the algorithm is validated against independent PR–TMI matchups from calendar year 2002. The validation results are benchmarked against results obtained for the same scenes from the current standard (version 7) 2A12 rainfall product for TRMM. Validation statistics considered include the biases, correlation coefficients, and root-mean-square (RMS) differences for annual precipitation totals on a 1° grid as well as two-threshold Heidke skill scores (HSS) for instantaneous (pixel level) retrievals, determined separately for each of seven surface classes, including ocean, coast, and five other basic land surface types as well as for cold (<275 K) and warm surface skin temperatures. Overall, the University of Wisconsin (UW) algorithm exhibits markedly reduced RMS error and bias in the annual total rainfall and markedly improved instantaneous skill at delineating light rain rates, especially over land and the coast. To ensure that the improved results were not due to both the training and validation data having been taken from the same calendar year, the validation of the UW algorithm is repeated using 2005 matchup data.

A new methodology for rain identification from passive microwave data in the Tropics using neural networks

AbstractThe detection of rainfall remains a challenge for the monitoring of precipitation from space. A methodology is presented to identify rain events from spaceborne passive microwave data using neural networks. We focus on BRAIN, the algorithm that provides instantaneous quantitative precipitation estimates at the surface, based on the MADRAS radiometer onboard the Megha‐Tropiques satellite. A version of BRAIN using data from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) has been used to compare several multilayer perceptrons (MLP) trained on different combinations of TMI brightness temperatures with the conventional GSCAT‐2 algorithm approach used for rainfall detection. These classifiers were compared at a global scale to reference values from the TRMM Precipitation Radar (PR). They were also compared to ground measurements using two 1° × 1° dense rain‐gauge networks from different climatic zones in West Africa to assess the influence of rainfall types. At the global scale the MLPs provide better Probability of Detection than the GSCAT‐2 decision tree but tend to have a higher False Alarm Rate. While no unique solution exists given the strong regional dependence of the classifiers' performances, the screen based on the 19, 21 and 85 GHz channels provides the best detection results at the instantaneous scales. As to accumulated rainfall, the screen that exhibits the lower bias relative to the PR makes use of the 37 and 85 GHz channels. The evaluation over West Africa using 10 years of TRMM overpasses shows that MLPs are in better agreement with both the PR and the gauges than GSCAT‐2. The MLP trained on the 37 and 85 GHz channels increases the Probability of Detection by nearly 35% compared to the former screening over the two studied regions. Better results are obtained in the case of organized systems. Copyright © 2013 Royal Meteorological Society

Comparison of Drop Size Distribution Parameter (D0) and Rain Rate from S-Band Dual-Polarized Ground Radar, TRMM Precipitation Radar (PR), and Combined PR–TMI: Two Events from Kwajalein Atoll

Abstract The estimation of the drop size distribution parameter [median volume diameter (D0)] and rain rate (R) from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) as well as from combined PR–TRMM Microwave Imager (TMI) algorithms are considered in this study for two TRMM satellite overpasses near the Kwajalein Atoll. An operational dual-polarized S-band radar (KPOL) located in Kwajalein is central as the only TRMM ground validation site for measurement of precipitation over the open ocean. The accuracy of the TRMM PR in retrieving D0 and R is better for precipitation over the ocean based on a more stable surface reference technique for estimating the path-integrated attenuation. Also, combined PR–TMI methods are more accurate over the open ocean because of better knowledge of the surface microwave emissivity. Using Zh (horizontal polarized radar reflectivity) and Zdr (differential reflectivity) data for the two TRMM overpass events over Kwajalein, D0 and R from KPOL are retrieved. Herein, the main objective is to see if the D0 retrieved from either PR or the combined PR–TMI algorithms are in agreement with KPOL-derived values. Also, the variation of D0 versus R is compared for convective rain pixels from KPOL, PR, and PR–TMI. It is shown that the PR–TMI optimal estimation scheme does indeed adjust the D0 in the “correct” direction, on average, from the a priori state if the KPOL data are considered to be the ground truth. This correct adjustment may be considered as evidence of the value added by the TMI brightness temperatures in the combined PR–TMI variational scheme, at least for the two overpass events considered herein.

Rain-Rate Characteristics over the Korean Peninsula and Improvement of the Goddard Profiling (GPROF) Database for TMI Rainfall Retrievals

AbstractSummer rainfall characteristics over the Korean Peninsula are examined using six years of Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) measurements and surface rain measurements from the densely populated rain gauges spread across South Korea. A comparison of the TMI brightness temperature at 85 GHz with the measured surface rain rate reveals that a significant portion of rainfall over the peninsula occurs at warmer brightness temperatures than would be expected from the Goddard profiling (GPROF) database. By incorporating the locally observed rain characteristics into the GPROF algorithm, efforts are made to test whether locally appropriate hydrometeor profiles may be used to improve the retrieved rainfall. Profiles are obtained by simulating rain cases using the cloud-resolving University of Wisconsin Nonhydrostatic Modeling System (UW-NMS) model and matching the calculated radar reflectivities to TRMM precipitation radar (PR) reflectivities. Selected profiles and the corresponding simulated TMI brightness temperatures (limited in this study to values that are larger than 235 K) are added to the GPROF database to form a modified database that is considered to be more suitable for local application over the Korean Peninsula. The rainfall retrieved from the new database demonstrates that heavy-rainfall events—in particular, those associated with warmer clouds—are better captured by the new algorithm as compared with the official TRMM GPROF version-6 retrievals. The results suggest that a more locally suitable rain retrieval algorithm can be developed if locally representative rain characteristics are included in the GPROF algorithm.

A Combined Multisensor Optimal Estimation Retrieval Algorithm for Oceanic Warm Rain Clouds

Abstract The complicated interactions between cloud processes in the tropical hydrologic cycle and their responses to changes in environmental variables have been the focus of many recent investigations. Most studies that examine the response of the hydrologic cycle to temperature changes focus on deep convection and cirrus production, but recent results suggest that warm rain clouds may be more sensitive to temperature changes. These clouds are prevalent in the tropics and make considerable contributions to the radiation budget and to total tropical rainfall, as well as serving to moisten and precondition the atmosphere for deep convection. A change in the properties of these clouds in climate-change scenarios could have significant implications for the hydrologic cycle. Existing microwave and visible retrievals of warm rain cloud liquid water path (LWP) disagree over the range of sea surface temperatures (SST) observed in the tropical western Pacific Ocean. Although both retrieval methods show similar behavior for nonraining clouds, the two methods show very different warm-rain-cloud LWP responses to SST, both in magnitude and trend. This makes changes to the relationship between precipitation and cloud properties in changing temperature regimes difficult to interpret. A combined optimal estimation retrieval algorithm that takes advantage of the strengths of the different satellite measurements available on the Tropical Rainfall Measuring Mission (TRMM) satellite has been developed. Deconvolved TRMM Microwave Imager brightness temperatures are combined with cloud fraction from the Visible and Infrared Scanner and rainwater estimates from the TRMM precipitation radar to retrieve the cloud LWP in warm rain systems. This algorithm is novel in that it takes into account the water in the rain and estimates the LWP due to only the cloud water in a raining cloud, thus allowing investigation of the effects of precipitation on cloud properties.

Comparison of TRMM TMI and PR Version 5 and 6 Precipitation Data Products Under Cyclonic Weather Conditions

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.

Alongfront Variability of Precipitation Associated with a Midlatitude Frontal Zone: TRMM Observations and MM5 Simulation

Abstract On 19 February 2001, the Tropical Rainfall Measuring Mission (TRMM) satellite observed complex alongfront variability in the precipitation structure of an intense cold-frontal rainband. The TRMM Microwave Imager brightness temperatures suggested that, compared to the northern and southern ends of the rainband, a greater amount of precipitation ice was concentrated in the middle portion of the rainband where the front bowed out. A model simulation conducted using the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) is examined to explain the distribution of precipitation associated with the cold-frontal rainband. The simulation reveals that the enhanced precipitation ice production and the implied mean ascent along the central part of the front were associated with a synergistic interaction between a low-level front and an upper-level front associated with an intrusion of high-PV stratospheric air. The low-level front contributed to an intense bow-shaped narrow cold-frontal rainband (NCFR). The upper-level front was dynamically active only along the central to northern portion of the NCFR, where the upper-level PV advection and Q-vector convergence were most prominent. The enhanced mean ascent associated with the upper-level front contributed to a wide cold-frontal rainband (WCFR) that trailed or overlapped with the NCFR along its central to northern segments. Because of the combination of the forcing from both lower- and upper-level fronts, the ascent was deepest and most intense along the central portion of the front. Thus, a large concentration of precipitation ice, attributed to both the NCFR and WCFR, was produced.

Use of Numerical Forecasts for Improving TMI Rain Retrievals over the Mountainous Area in Korea

Abstract Topographical influences on the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) rain retrievals over the terrain area of the Korean peninsula were examined using a training dataset constructed from numerical mesoscale model simulations in conjunction with radiative transfer calculations. By relating numerical model outputs to rain retrievals from simulated brightness temperatures, a positive relationship between topographically forced vertical motion and rain retrievals in the upstream region over the mountainous area was found. Based on the relationship obtained, three topographical correction methods were developed by incorporating slope-forced vertical motion and its associated upward vapor flux, and vapor flux convergence in the surface boundary layer into a scattering-based TMI rain retrieval algorithm. The developed correction methods were then applied for the rain retrievals from simulated TMI brightness temperatures with model outputs and measured TMI brightness temperatures. Results showed that orographic influences on the rain formation can be included in the TMI rainfall algorithms, which tend to underestimate rainfall over the complex terrain area. It was shown that topographical corrections surely improve the rain retrieval when a strong rain event is present over the upslope region. Among various elements, moisture convergence in the boundary layer appears to be an important factor needed in the topographical correction. Overall topography-corrected estimates of rainfall showed a better agreement with ground measurements than those without correction, suggesting that satellite rain retrieval over the terrain area can be improved when accurate numerical forecast outputs are incorporated into the rain retrieval algorithm.

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

Physically based statistical integration of TRMM microwave measurements for precipitation profiling

A statistical methodology to combine measurements from space‐borne microwave radar and radiometers is proposed. The approach is fairly general, even though the combination technique is here tailored for the two instruments onboard the Tropical Rainfall Measuring Mission (TRMM) satellite specifically devoted to rainfall measurements, that is, the Precipitation Radar (PR) and the TRMM Microwave Imager (TMI). Two combined retrieval algorithms are proposed, both derived from the previously developed Bayesian algorithm for microwave‐based precipitation retrieval from passive sensors (BAMPR‐P), which is based on a Bayesian inversion method and is trained by a modeled cloud radiation database. The first combined technique, called BAMPR‐C (BAMPR combined), operates in the narrower common swath aiming at exploiting the simultaneous measurements of PR and TMI instruments. Within BAMPR‐C the hydrometeor profiles, retrieved from TMI, are used as a constraint for the PR‐based inversion: this two step cascade allows us to overcome the difficulty to take into account the different scan geometries of TMI and PR. The second combined technique is called BAMPR‐B (BAMPR broadening) and aims at improving the TMI‐only retrieval outside the common swath. In this approach, first optimal retrieved profiles are generated by reversing the order of the previous two steps of BAMPR‐C cascade within the common swath. Then the resulting profile data set and the corresponding TMI brightness temperatures are used to define the cloud radiation database to be employed outside the common swath for the TMI‐only retrieval, achieving the so‐called radar swath synthetic broadening. Numerical internal tests, using simulated data, are illustrated to quantify the features of the proposed synergetic algorithms. Finally, an application to measured TRMM data for a selected case study (hurricane Bonnie on August 1998) is shown and discussed.

An uncertainty model for Bayesian Monte Carlo retrieval algorithms: Application to the TRMM observing system

AbstractAn error model for Bayesian Monte Carlo retrieval algorithms which explicitly accounts for uncertainty introduced by the use of a finite database of realizations, as well as uncertainties associated with the modelling and measurement components of the retrieval is described. The model provides a rigorous estimate of the uncertainty in all retrieved parameters as well as a breakdown of this uncertainty into two components attributable to an imperfect database, and modelling and measurement uncertainties, respectively. This error information is critical for algorithm development, model validation and, in particular, in variational data assimilation where the relative accuracy of the observations and the background forecast determines how much the latter is modified in the assimilation process.Using the error model, uncertainties in the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) instantaneous surface rainfall product (2A12) are found to range from 40 to 60 percent in rainfall up to 20 mm h−1.. In heavier rain, uncertainties rapidly increase due to heavy attenuation in all TMI channels which requires surface rainfall to be inferred solely from the non‐unique relationship between surface rain rate and the ice‐scattering signature in the strongly coupled 37 and 85 GHz brightness temperatures. In light rain, the fact that the database attempts to approximate nature's infinite cloud probability density function by a finite set of realizations, and the inherent inability of the TMI brightness temperatures to completely distinguish between all cloud profiles in the database, dominate retrieval uncertainties. Between 4 and 10 mm h−1 both error components are comparable while measurement and model uncertainties dominate in heavier rainfall. Preliminary attempts to incorporate radar reflectivity data to reduce profile database uncertainties show promise, but can lead to a compensating increase in the modelling and measurement‐error component.Results highlight the need for studying sources of systematic error in the cloud database such as errors in cloud microphysical assumptions, beam‐filling errors, or biases in the radiative‐transfer calculations used to simulate brightness temperatures for each profile. In addition, the utility of the error model for estimating uncertainties in any Bayesian Monte Carlo retrieval algorithm is demonstrated. Copyright © 2002 Royal Meteorological Society.

Microwave measurement of rain and sea surface temperature by the TRMM Microwave Imager (TMI)

The Tropical Rainfall Measuring Mission (TRMM) is important for studies of the global hydrological cycle and for testing the realism of climate models and their ability to simulate and predict climate accurately; the effect of El Nino on climate could be addressed as well. This paper investigates the microwave rain measurement using satellite data from the TRMM Microwave Imager (TMI). The physical bases of rainfall estimation algorithms, vertical structure of rain and its physical processes are explained. The algorithms for processing TMI radiance and brightness temperature data are presented. Various rain maps and sea surface temperature (SST) maps are produced using TMI microwave data. The performance, calibration, analysis of results and sources of errors in the averaged monthly surface rain rate estimation are discussed.

Remote Sensing of Precipitation on the Tibetan Plateau Using the TRMM Microwave Imager

The Tibetan Plateau is a unique location for studying the global climate and China’s severe weather. The precipitation on the Tibetan Plateau can be studied conveniently with the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI). It is shown that the TMI brightness temperature at 85 GHz in the vertical polarization (TB 85V) is negatively correlated to the surface rain rate, but a very low value of TB 85V does not correspond to very intense surface rain rates on the Tibetan Plateau, a result that is different from what is observed in other areas of the world. For surface precipitation retrieval on the Tibetan Plateau from TMI, the effect from snow cover on precipitation retrieval is removed before analysis of precipitation. Using the dynamic cluster K-mean method, five categories of surface types and rain areas are identified on the Tibetan Plateau: dry soil, wet soil, water area, stratiform rain area, and convective rain area. The precipitation areas are screened by classification before the precipitation retrieval. Two datasets of rain-free areas and precipitation areas are formed after surface classification. Based on the dataset of rain-free areas, the value of TB85V can be simulated well by TB10V ,T B 19V, and TB21V when it is not raining. By means of the dataset of precipitation areas, it is revealed that the scattering index over land (SI L) is positively correlated and the polarization-corrected brightness temperature at 85 GHz (PCT85) is negatively correlated with the surface rain rate. With SI L, PCT85, and their combinations as retrieval algorithms, three precipitation retrieval formulas are proposed in which the SI L algorithm is most suitable for small rain retrieval, the PCT 85 algorithm is most suitable for moderate rain retrieval, and the combined SIL and PCT85 algorithm is most suitable for relatively large rain retrieval on the Tibetan Plateau. By means of two thresholds, 265 and 245 K, for TB85V, the combination of the three formulas is applied to precipitation retrieval on the Tibetan Plateau during the Tibetan Plateau Experiment Intensive Observing Period of 1998, resulting in acceptable and encouraging surface rain-rate retrievals. Intercomparison among the TMI algorithms and the 17 Special Sensor Microwave Imager algorithms from the second Precipitation Intercomparison Project demonstrates that the comprehensive application of the TMI algorithms has good precision and error index and is suitable for precipitation retrieval on the Tibetan Plateau.

PIn. II. Comparative evaluation of SSM/I and TMI precipitable water estimate for the Mediterranean Sea

For pt.I see ibid., vol.39, no.12, p.2566-74 (2001). To estimate integrated precipitable water vapor along with liquid water path and water vapor effective profile (i.e. standard atmospheric profile approximation), utilizing the Special Sensor Microwave/Imager (SSM/I) and Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) radiometers, an operative procedure was developed and assessed. This procedure is based on a fast nonlinear physical inversion algorithm (PIn) developed by the authors. A large data set of near-coincident TMI and SSM/I data acquisitions were collected and used to supply the procedure. Retrieved parameters were compared against retrievals achieved with well-accepted statistical algorithms, and consistency between TMI and SSM/I retrievals was confirmed. As far as TMI and SSM/I precipitable water retrieving consistency is concerned, this research revealed a linear relationship up to 20 kg/m/sup 2/ and a general overestimate of TMI retrieving, for higher values. A new algorithm for obtaining integrated precipitable water from TMI brightness temperatures was introduced and the goodness of its accuracy was reported. The procedure proved to be reliable and portable and its integrated precipitable water vapor retrieving was assessed to be as accurate as the best radiometric retrieving algorithms, reported in literature. For SSM/I data, developed-procedure liquid water path estimates seemed to be in good agreement with statistical retrievals. Eventually the procedure provided effective water vapor vertical profiles which belong to a deterministic distribution area characterized by an upper and lower limit; it was confirmed that SSM/I and TMI vertical profile distribution areas mainly overlap even if they are characterized by different sensitivities to profile parameters.