Rain Rate Estimation Research Articles

Simultaneous X-band and K-band study of precipitation to derive specific Z– R relationships

Using a simple reflectivity X-band scanning radar and a vertically looking K-band Micro Rain Radar, we investigate the high spatial and temporal variability of precipitating systems at scales internal to the system itself. In order to improve rain rate estimates, we propose to classify rain regimes such that specific Z–R relationships can be applied. These selection schemes are based either on the trend of the reflectivity intensity (increasing, decreasing, stagnating) or on its intensity itself (reflectivity classes) while a last method make use of a statistical approach. Our study shows that using the trend of the rain offers good prospect for improved rain rate restitution but still needs some work to refine the selection criteria. Furthermore, intensity scheme better accommodates the micro-physical properties of the corresponding rain regimes, although possible calibration effects on the high reflectivity periods produce some underestimates of rain rates. Finally, the statistical approach proves efficient but can only be applied on sub-sets of the data for which there is good stability of the reflectivity signal which means that it cannot be applied alone to an entire data set.

Vertical profiling of precipitation using passive microwave observations: The main impediment and a proposed solution

Several methods have been proposed to train microwave radiometers to retrieve precipitation rates estimated by a radar that observed the same location at the same time. These radar‐trained passive microwave algorithms differ in the quantities that are estimated; some estimate the vertically integrated liquid water, whereas others estimate the near‐surface precipitation. Since it is no more or less credible to estimate the rain rate at the surface than it is to estimate the rain rate at any discrete altitude, it is particularly interesting to quantify to what extent it is indeed feasible to estimate vertical profiles of precipitation from a passive microwave radiometer, what the obstacles are, and what vertical resolution would be achievable. To that end, we selected five study regions and started by quantifying the vertical variability of rainfall as derived from the Tropical Rainfall Measuring Mission (TRMM) radar. Two cases emerged: a monsoon‐like case where the first principal component of the vertical precipitation accounts for about 90% of the variability and a Mediterranean‐like case where the first principal component accounts for about 80% of the variability. A Bayesian approach was applied to the TRMM Microwave Imager measurements colocated with the radar profiles. For the monsoon‐like regions, it produced estimates of rain rates at 250‐meter vertical increments, which compared well with the TRMM radar estimates. For the Mediterranean‐like regions, the retrieval errors were very large. We therefore proceeded to identify the main reason for the failure of the straightforward training method. It turns out to be the unknown signature of the sea surface in the portion of the beam that does not contain precipitation. In the problematic Mediterranean case, our original straightforward approach can still be applied to measurements that do not suffer from this identifiable partial beam filling. For measurements that do, we derive a filtering approach to neutralize the variability of the partial surface signature and thus overcome the problem.

Precipitation and microphysical studies with a low cost high resolution X-band radar: an innovative project prospective

Abstract. This paper describes an innovative project which has just been launched at the "Laboratoire de Météorologie Physique" (LaMP) in Clermont-Ferrand in collaboration with the "Meteorologische Institut" in Hamburg, where a low cost X-band high resolution precipitation radar is combined with supporting measurements and a bin microphysical cloud resolving model in order to develop adapted Z–R relationships for accurate rain rate estimates over a local area such as a small catchment basin, an urban complex or even an agriculture domain. In particular, the use of K-band micro rain radars which can retrieve vertical profiles of drop size distribution and the associated reflectivity will be used to perform direct comparisons with X-band radar volume samples while a network of rain-gauges provides ground truth to which our rain estimates will be compared. Thus, the experimental suite of instrumentation should provide a detailed characterization of the various rain regimes and their associated Z–R relationship. Furthermore, we will make use of the hilly environment of the radar to test the use of novel attenuation methods in order to estimate rainfall rates. A second important aspect of this work is to use the detailed cloud modeling available at LaMP. Simulations of precipitating clouds in highly resolved 3-D dynamics model allow predicting the spectra of rain drops and precipitating ice particles. Radar reflectivity determined from these model studies will be compared with the observations in order to better understand which raindrop size spectrum shape factor should be applied to the radar algorithms as a function of the type of precipitating cloud. Likewise, these comparisons between the modeled and the observed reflectivity will also give us the opportunity to further improve our model microphysics and the parameterizations for meso-scale models.

Dual-polarization C-band weather radar algorithms for rain rate estimation and hydrometeor classification in an alpine region

Abstract. Dual polarization is becoming the standard for new weather radar systems. In contrast to conventional weather radars, where the reflectivity is measured in one polarization plane only, a dual polarization radar provides transmission in either horizontal, vertical, or both polarizations while receiving both the horizontal and vertical channels simultaneously. Since hydrometeors are often far from being spherical, the backscatter and propagation are different for horizontal and vertical polarization. Comparing the reflected horizontal and vertical power returns and their ratio and correlation, information on size, shape, and material density of cloud and precipitation particles can be obtained. The use of polarimetric radar variables can therefore increase the accuracy of the rain rate estimation compared to standard Z-R relationships of non-polarimetric radars. It is also possible to derive the type of precipitation from dual polarization parameters, although this is not an easy task, since there is no clear discrimination between the different values. Fuzzy logic approaches have been shown to work well with overlapping conditions and imprecisely defined class output. In this paper the implementation of different polarization algorithms for the new Austrian weather radar on Mt. Valluga is described, and first results from operational use are presented. This study also presents first observations of rain events in August 2007 during the test run of the radar. Further, the designated rain rate estimation and hydrometeor classification algorithms are explained.

Availability of High-Quality TRMM Ground Validation Data from Kwajalein, RMI: A Practical Application of the Relative Calibration Adjustment Technique

Abstract Since the Tropical Rainfall Measuring Mission (TRMM) satellite launch in November 1997, the TRMM Satellite Validation Office (TSVO) at NASA Goddard Space Flight Center (GSFC) has been performing quality control and estimating rainfall from the KPOL S-band radar at Kwajalein, Republic of the Marshall Islands. Over this period, KPOL has incurred many episodes of calibration and antenna pointing angle uncertainty. To address these issues, the TSVO has applied the relative calibration adjustment (RCA) technique to eight years of KPOL radar data to produce Ground Validation (GV) version 7 products. This application has significantly improved stability in KPOL reflectivity distributions needed for probability matching method (PMM) rain-rate estimation and for comparisons to the TRMM precipitation radar (PR). In years with significant calibration and angle corrections, the statistical improvement in PMM distributions is dramatic. The intent of this paper is to show improved stability in corrected KPOL reflectivity distributions by using the PR as a stable reference. Intermonth fluctuations in mean reflectivity differences between the PR and corrected KPOL are on the order of ±1–2 dB, and interyear mean reflectivity differences fluctuate by approximately ±1 dB. This represents a marked improvement in stability with confidence comparable to the established calibration and uncertainty boundaries of the PR. The practical application of the RCA method has salvaged eight years of radar data that would have otherwise been unusable and has made possible a high-quality database of tropical ocean–based reflectivity measurements and precipitation estimates for the research community.

An Algorithm for Real-Time Rainfall Rate Estimation by Using Polarimetric Radar: RIME

Abstract Polarimetric radars provide measurements that describe the shape and dimensions of hydrometeors and are unaffected by calibration, attenuation, and the presence of ice. These measurements can potentially lead to a more detailed description of hydrometeors and to an improvement in quantitative rainfall rate estimation. The authors present an algorithm that exploits polarimetric measures for rain-rate estimation and investigate its application in a real-time framework by using measurements from the C-band polarimetric radar at Monte Settepani in Savona, Italy. It is based on a flowchart decision tree that allows the use of the best rain-rate retrieval algorithm, depending on the value of polarimetric variables. The methodology was applied to a real-time framework for more than a year, and the results were presented for all the significant events observed during the test period. To evaluate the performance of the algorithm, a comparison was made with rain gauge observation from a dense regional network. The performances of the algorithm were compared with those obtained by standard operational Z–R formulations to evaluate the benefit of this approach for operational applications.

An Evaluation of Over-Land Rain Rate Estimates by the GSMaP and GPROF Algorithms: The Role of Lower-Frequency Channels

This paper presents an evaluation of over-land rain-rate estimates by both the Global Satellite Mapping of Precipitation (GSMaP) algorithm and the standard (GPROF) algorithm for the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), by comparing them with estimates by the standard algorithm for TRMM Precipitation Radar (PR). This study has the following advantages over previous studies: (1) the errors in rain-rate estimates are decomposed into those caused by rain/no-rain classification and those caused by rain-rate retrieval, (2) the quantitative effects of bright band height (BBH) and land surface physical temperature on retrieval are evaluated; and (3) the role of lower frequency channels (37.0 GHz and lower) for retrieval is investigated. GSMaP yields monthly average and zonal mean rain-rate estimates close to those estimated by the standard algorithm for PR, as it refers to a database produced with PR data. However, GSMaP and GPROF overestimate (underestimate) rain rates for tall (shallow), stratiform (convective), and evening (morning) rainfall. Dependence on storm height (SH) is unavoidable as long as the algorithm relies on the scattering signal caused by solid precipitation for higher frequency channels (85.5 GHz). Lower frequency channels are secondarily used in some algorithms to mitigate the above bias characteristics to some degree. In GSMaP Version 4.7, the severe overestimation seen in GSMaP Version 4.5 when SH is higher than 10 km is mitigated by using 37.0 GHz observations as a scattering signal. The following are indicated for stratiform rainfall with a bright band (SRBB). While the rain-rate estimates are negatively dependent on BBH in GSMaP, the use of 21.3 GHz and 10.7 GHz observations resulted in the cancellation of the dependence on the BBH in GPROF. Although lower-frequency observations are subject to variation in land surface physical temperature, no significant effects of land surface physical temperature on the rain-rate estimates were observed in this study. To improve over-land rain-rate estimates, it is important to make the most effective use of lower-frequency observations.

Evaluation of the Effects of the Orbit Boost of the TRMM Satellite on PR Rain Estimates

The Precipitation Radar (PR) on the Tropical Rainfall Measuring Mission (TRMM) satellite, which was launched on 28 November 1997, has been collecting global rainfall data for more than 10 years. The monthly estmated-surface rainfall (e_SurfRain) amounts estimated by PR have decreased on average since the satellite altitude was changed from 350 km to 402.5 km in August 2001 to extend its lifetime. There is no significant decrease in e_SurfRain in the five angle bins around the nadir (near-nadir) or in rainfall amounts at a 2-km height. The major causes of the changes in rain estimates due to the orbit boost are (1) sensitivity degradation, owing to the increase of satellite altitude, (2) an increase in the range of surface clutter owing to the increase in the footprint size, and (3) a mismatch between the transmission and reception angles of adjacent radar beams for one in every 32 pulses. The decrease in the echo from a distributed target near the surface attributable to the change of the satellite altitude from 350 km to 402.5 km is 1.21 dB (= 20log(402.5/350)). From a simulation result, we estimate a decrease of 0.5% in surface-rainfall estimates due to the sensitivity degradation. Because the height of the clutter-free bottom increases at off-nadir angles due to the increase in the footprint size from 4.3 km to 5.0 km, the sampling range bins for near-surface rain have been raised, causing the PR to miss low-rain systems more often after the boost than before the boost. Although a beam-mismatch correction is executed in the current 1B21 algorithm, the correction algorithm still results in a negative bias in rain-rate estimates near the surface in the second half of the PR scan. The changes in the surface-rainfall estimates after the boost due to the second and third causes can be estimated as -2.5% and -2.9% by comparing the estimates at off-nadir bins with those at near-nadir bins with the assumption that the true rain rates measured at all angle bins should be the same, on average. It is concluded that the total effect of the orbit boost is a decrease of 5.9% (= 0.5 + 2.5 + 2.9%) in the e_SurfRain amounts in PR version 6 products.

Quantitative Precipitation Estimation over Ocean Using Bayesian Approach from Microwave Observations during the Typhoon Season

We have developed a new Bayesian approach to retrieve oceanic rain rate from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), with an emphasis on typhoon cases in the West Pacific. Retrieved rain rates are validated with measurements of rain gauges located on Japanese islands. To demonstrate improvement, retrievals are also compared with those from the TRMM/Precipitation Radar (PR), the Goddard Profiling Algorithm (GPROF), and a multi-channel linear regression statistical method (MLRS). We have found that qualitatively, all methods retrieved similar horizontal distributions in terms of locations of eyes and rain bands of typhoons. Quantitatively, our new Bayesian retrievals have the best linearity and the smallest root mean square (RMS) error against rain gauge data for 16 typhoon overpasses in 2004. The correlation coefficient and RMS of our retrievals are 0.95 and ~2 mm hr-1, respectively. In particular, at heavy rain rates, our Bayesian retrievals outperform those retrieved from GPROF and MLRS. Overall, the new Bayesian approach accurately retrieves surface rain rate for typhoon cases. Accurate rain rate estimates from this method can be assimilated in models to improve forecast and prevent potential damages in Taiwan during typhoon seasons.

Development of Nonuniform Beamfilling Correction Method in Rainfall Retrievals for Passive Microwave Radiometers over Ocean Using TRMM Observations

Nonuniform beamfilling (NUBF) is a major error source in physical retrieval algorithms for estimating rain rates using satellite-borne passive microwave radiometers. The NUBF effects over the ocean within the beam effective field of view in the 10 GHz channel (FOV10) for the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) are investigated from simultaneous measurements made by the TMI and the Precipitation Radar (PR) aboard the TRMM satellite. They are investigated with respect to variability of a non-uniform parameter defined by the lognormal assumption from January to December 2000. The parameter computed using surface rain rates estimated from PR data tends to be small (large) for stratiform (convective) rainfall in the FOV10. The parameter computed using surface rain rates estimated from brightness temperature (Tb) at 85 GHz is systematically lower in stratiform rainfall than the reference derived by the PR. Systematic differences are not found for the convective cases. To evaluate the effects on rain retrievals due to the systematic differences of the non-uniform parameters, a simple adjustment is applied, and rain retrievals from the observed TMI Tb at 10 GHz vertical polarization are performed over the ocean. Relatively large increases in rain rates retrieved using corrected parameters are found in the tropics, while the increases of the corrected parameters are similar for all latitudes. Effects of the non-uniform parameter differences on the rain retrievals are nonlinear and could be closely related to high background values of the non-uniform parameter in the tropics and more frequent high rainfall intensities.

An Intercomparison of Model Simulations and VPR Estimates of the Vertical Structure of Warm Stratiform Rainfall during TWP-ICE

Abstract A model of rain shaft microphysics that solves the stochastic advection–coalescence–breakup equation in an atmospheric column was used to simulate the evolution of a stratiform rainfall event during the Tropical Warm Pool-International Cloud Experiment (TWP-ICE) in Darwin, Australia. For the first time, a dynamic simulation of the evolution of the drop spectra within a one-dimensional rain shaft is performed using realistic boundary conditions retrieved from real rain events. Droplet size distribution (DSD) retrieved from vertically pointing radar (VPR) measurements are sequentially imposed at the top of the rain shaft as boundary conditions to emulate a realistic rain event. Time series of model profiles of integral parameters such as reflectivity, rain rate, and liquid water content were subsequently compared with estimates retrieved from vertically pointing radars and Joss–Waldvogel disdrometer (JWD) observations. Results obtained are within the VPR retrieval uncertainty estimates. Besides evaluating the model’s ability to capture the dynamical evolution of the DSD within the rain shaft, a case study was conducted to assess the potential use of the model as a physically based interpolator to improve radar retrieval at low levels in the atmosphere. Numerical results showed that relative improvements on the order of 90% in the estimation of rain rate and liquid water content can be achieved close to the ground where the VPR estimates are less reliable. These findings raise important questions with regard to the importance of bin resolution and the lack of sensitivity for small raindrop size (<0.03 cm) in the interpretation of JWD data, and the implications of using disdrometer data to calibrate radar algorithms.

A Space-Filling Algorithm to Extrapolate Narrow-Swath Instantaneous TRMM Microwave Rain-Rate Estimates Using Thermal IR Imagery

Abstract A space-filling algorithm (SFA) based on 2D spectral estimation techniques was developed to extrapolate the spatial domain of the narrow-swath near-instantaneous rain-rate estimates from Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and TRMM Microwave Imager (TMI) using thermal infrared imagery (Meteosat-5) without making use of calibration or statistical fitting. A comparison against rain gauge observations and the original PR 2A25 and TMI 2A12 estimates in the central Himalayas during the monsoon season (June–September) over a 3-yr period of 1999–2001 was conducted to assess the algorithm’s performance. Evaluation over the continental United States was conducted against the NCEP stage IV combined radar and gauge analysis for selected events. Overall, the extrapolated PR and TMI rainfall fields derived using SFA exhibit skill comparable to the original TRMM estimates. The results indicate that probability of detection and threat scores of the reconstructed products are significantly better than the original PR data at high-elevation stations (>2000 m) on mountain ridges, and specifically for rainfall rates exceeding 2–5 mm h−1 and for afternoon convection. For low-elevation stations located in steep narrow valleys, the performance varies from year to year and deteriorates strongly for light rainfall (false alarm rates significantly increase). A preliminary comparison with other satellite products (e.g., 3B42, a TRMM-adjusted merged infrared-based rainfall product) suggests that integrating this algorithm in currently existing operational multisensor algorithms has the potential to improve significantly spatial resolution, texture, and detection of rainfall, especially in mountainous regions, which present some of the greatest challenges in precipitation retrieval from satellites over land, and for hydrological operations during extreme events.

Validation of Envisat Rain Detection and Rain Rate Estimates by Comparing With TRMM Data

This letter provides validation results on both Envisat altimeter rain detection flag and rain rate estimates. This assessment was performed based on 18-month collocated data sets between Envisat with two sensor measurements from the Tropical Rainfall Measuring Mission, i.e., the microwave imager and the precipitation radar. Along with the comparison between closest sensor measurements with strict spatial and temporal criteria, discussion is also provided on environmental context insight when interpreting comparison results based on collocated data.

Improved Forecasts of the Diurnal Cycle in the Tropics Using Multiple Global Models. Part I: Precipitation

Abstract The Tropical Rainfall Measuring Mission (TRMM) satellite supplemented with the Defense Meteorological Satellites Program (DMSP) microwave dataset provides accurate rain-rate estimates. Furthermore, infrared radiances from the geostationary satellites provide the possibility for mapping the diurnal change of tropical rainfall. Modeling of the phase and amplitude of the tropical rainfall is the theme of this paper. The present study utilizes a suite of global multimodels that are identical in all respects except for their cumulus parameterization algorithms. Six different cumulus parameterizations are tested in this study. These include the Florida State University (FSU) Modified Kuo Scheme (KUO), Goddard Space Flight Center (GSFC) Relaxed Arakawa–Schubert Scheme (RAS1), Naval Research Laboratory–Navy Operational Global Atmospheric Prediction System (NRL–NOGAPS) Relaxed Arakawa–Schubert Scheme (RAS2), NCEP Simplified Arakawa–Schubert Scheme (SAS), NCAR Zhang–McFarlane Scheme (ZM), and NRL–NOGAPS Emanuel Scheme (ECS). The authors carried out nearly 600 experiments with these six versions of the T170 Florida State University global spectral model. These are 5-day NWP experiments where the diurnal change datasets were archived at 3-hourly intervals. This study includes the estimation of skills of the phase and amplitudes of the diurnal rain using these member models, their ensemble mean, a multimodel superensemble, and those from a single unified model. Test results are presented for the global tropics and for some specific regions where the member models show difficulty in predicting the diurnal change of rainfall. The main contribution is the considerable improvement of the modeling of diurnal rain by deploying a multimodel superensemble and by constructing a single unified model. The authors also present a comparison of these findings on the modeling of diurnal rain from another suite of multimodels that utilized different versions of cloud radiation algorithms (instead of different cumulus parameterization schemes) toward defining the suite of multimodels. The principal result is that the superensemble does provide a future forecast for the total daily rain and for the diurnal change of rain through day 5 that is superior to forecasts provided by the best model. The training of the superensemble with good observed estimates of rain, such as those from TRMM, is necessary for such forecasts.

Comparisons of Instantaneous TRMM Ground Validation and Satellite Rain-Rate Estimates at Different Spatial Scales

Abstract This study provides a comprehensive intercomparison of instantaneous rain rates observed by the two rain sensors aboard the Tropical Rainfall Measuring Mission (TRMM) satellite with ground data from two regional sites established for long-term ground validation: Kwajalein Atoll and Melbourne, Florida. The satellite rain algorithms utilize remote observations of precipitation collected by the TRMM Microwave Imager (TMI) and the Precipitation Radar (PR) aboard the TRMM satellite. Three standard level II rain products are generated from operational applications of the TMI, PR, and combined (COM) rain algorithms using rain information collected from the TMI and the PR along the orbital track of the TRMM satellite. In the first part of the study, 0.5° × 0.5° instantaneous rain rates obtained from the TRMM 3G68 product were analyzed and compared to instantaneous Ground Validation (GV) program rain rates gridded at a scale of 0.5° × 0.5°. In the second part of the study, TMI, PR, COM, and GV rain rates were spatiotemporally matched and averaged at the scale of the TMI footprint (∼150 km2). This study covered a 6-yr period (1999–2004) and consisted of over 50 000 footprints for each GV site. In the first analysis, the results showed that all of the respective rain-rate estimates agree well, with some exceptions. The more salient differences were associated with heavy rain events in which one or more of the algorithms failed to properly retrieve these extreme events. Also, it appears that there is a preferred mode of precipitation for TMI rain rates at or near 2 mm h−1 over the ocean. This mode was noted over ocean areas of Kwajalein and Melbourne and has been observed in TRMM tropical–global ocean areas as well.

Rain-Area Identification Using TRMM/TMI Data by Data Mining Approach

Rain-area identification distinguishes between rainy and non-rainy areas, which is the first step in some critical real-world problems, such as rain intensity identification and rain-rate estimation. We develop a data mining approach for oceanic rain-area identification during typhoon season, using microwave data from the Tropical Rainfall Measuring Mission (TRMM) satellite. Three schemes tailored for the problem are developed, namely (1) association rule analysis for uncovering the set of potential attributes relevant to the problem, (2) three-phase outlier removal for cleaning data and (3) the neural committee classifier (NCC) for achieving more accurate results. We created classification models from 1998-2004 TRMM Microwave Imager (TRMM-TMI) satellite data and used Automatic Rainfall and Meteorological Telemetry System (ARMTS) rain gauge data measurements to evaluate the model. Experimental results show that our approach achieves high accuracy for the rain-area identification problem. The classification accuracy of our approach, 96%, outperforms the 78.6%, 77.3%, 83.3% obtained by the scattering index, threshold check, and rain flag methods, respectively.

Simulation of polarimetric radar variables in rain at S-, C- and X-band wavelengths

Abstract. Polarimetric radar variables of rainfall events, like differential reflectivity ZDR, or specific differential phase KDP, are better suited for estimating rain rate R than just the reflectivity factor for horizontally polarized waves, ZH. A variety of physical and empirical approaches exist to estimate the rain rate from polarimetric radar observables. The relationships vary over a wide range with the location and the weather conditions. In this study, the polarimetric radar variables were simulated for S-, C- and X-band wavelengths in order to establish radar rainfall estimators for the alpine region of the form R(KDP), R(ZH, ZDR), and R(KDP), ZDR. For the simulation drop size distributions of hundreds of 1-minute-rain episodes were obtained from 2D-Video-Distrometer measurements in the mountains of Styria, Austria. The sensitivity of the polarimetric variables to temperature is investigated, as well as the influence of different rain drop shape models – including recently published ones – on radar rainfall estimators. Finally it is shown how the polarimetric radar variables change with the elevation angle of the radar antenna.

Estimating Rain Rates from Tipping-Bucket Rain Gauge Measurements

Abstract This paper describes the cubic spline–based operational system for the generation of the Tropical Rainfall Measuring Mission (TRMM) 1-min rain-rate product 2A-56 from tipping-bucket (TB) gauge measurements. A simulated TB gauge from a Joss–Waldvogel disdrometer is employed to evaluate the errors of the TB rain-rate estimation. These errors are very sensitive to the time scale of rain rates. One-minute rain rates suffer substantial errors, especially at low rain rates. When 1-min rain rates are averaged over 4–7-min intervals or longer, the errors dramatically reduce. Estimated lower rain rates are sensitive to the event definition whereas the higher rates are not. The median relative absolute errors are about 22% and 32% for 1-min rain rates higher and lower than 3 mm h−1, respectively. These errors decrease to 5% and 14% when rain rates are used at the 7-min scale. The radar reflectivity–rain-rate distributions drawn from the large amount of 7-min rain rates and radar reflectivity data are mostly insensitive to the event definition. The time shift due to inaccurate clocks can also cause rain-rate estimation errors, which increase with the shifted time length. Finally, some recommendations are proposed for possible improvements of rainfall measurements and rain-rate estimations.

Improvement of Microwave Rainfall Retrievals in Bayesian Retrieval Algorithms

This note describes a method to improve the current Tropical Rainfall Measuring Mission (TRMM) passive microwave rain rate estimates by adopting a modified probability density function (PDF) of rain rates in Bayesian retrieval algorithms. Unlike the original database that was generated from cloud model simulations mostly for more "eventful" cloud/rain cases, the modified PDF includes the probability of zero rain rates derived from actual observations. Based on analysis of one year TRMM data, this simple remedy lowers the passive microwave rain rate estimates by 3% and reduces the discrepancy between radiometer and radar estimates by 6%.

A Method to Estimate Rain Rate over Tropical Oceans with the TRMM Microwave Imager Radiometer

In this preliminary study, we have developed a method to retrieve rain rate on a scale of 20 km from the brightness temperatures measured by the TRMM microwave imaging radiometer (TMI) over the tropical oceans, using the estimates of rain rate RPR made by the TRMM Precipitation Radar (PR) as a benchmark. The purpose of this study is to demonstrate with a limited amount of PR and TMI data the feasibility of improving the TRMM operational rain retrieval method V6 over the tropical oceans. This study utilizes the TMI-measured brightness temperatures T19H, T37H, and T85H of horizontally polarized microwave radiances at 19, 37 and 85 GHz, respectively, to deduce a salient non-linear parameter ξ that is highly correlated with RPR over the oceans. Two additional parameters generated from TMI data, ω and γ, add significant amounts of rain information to our retrieval method. The parameter &omega is based on T19V and T21V, the brightness temperatures measured by TMI for vertically polarized microwave radiances at 19 and 21 GHz respectively. This parameter takes advantage of the independent information contained in T21V. The parameter Γ depends on the average horizontal gradient of the TMI-measured T85V (vertically-polarized 85 GHz radiance) in a 20 km footprint. Initially our TMI rain retrieval algorithm is tuned with the help of RPR for seven cases of 2° × 3° area over tropical oceans. Then it is applied to 13 other independent tropical ocean cases. For these independent cases, the rain rate R* estimated from our method correlates better with RPR than the rain rate RV6 retrieved from the present TMI V6 operational retrieval method. On a 20 km scale, the correlation between RPR and R* is better by about 6% compared to that between RPR and RV6. The slope of the regression line between the rain rates RPR and RV6 is about 0.5. With respect to RPR, the rain rate RV6 retrieved from operational V6 method tends to underestimate high rain rates and overestimate low rain rates. The slope of the regression line between RPR and the rain rate R* retrieved with our method is about 0.8, another indication of the improvement of R* over RV6. In addition, the area average rain rate on a scale of 2° × 3° deduced from our method agrees better with that of PR by about 7%.