TRMM) Microwave Imager Research Articles

Modeling on the Aspects of Thermal Response of Bay of Bengal to Tropical Cyclone TC05B 1999 using Princeton Ocean Model (POM): Preliminary Results

Tropical cyclones (TC’s), while moving over the ocean bring about significant changes in ocean thermal structure and other ocean environment. These oceanic thermal responses provide vital information for understanding the air-sea interaction processes. This study used Princeton Ocean Model (POM) to investigate the changes in the oceanic thermal characteristics of Bay of Bengal (BOB) associated with TC05B (Orissa super cyclone) 1999. Model was forced with the wind and heat plus salinity fluxes as surface forcing in different experiments. In order to provide reasonable and realistic cyclonic winds to the model as forcing, the tropical cyclone wind model (TCWM) was developed to generate the synthetic cyclonic winds and superimposed with the analyzed blended QSCAT/NCEP wind field. Results show significant drop in sea surface temperature (SST) beneath the storm centre and to the right of the track confirming the earlier findings and are in qualitative agreement with the available Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) satellite SST imageries. Subsurface thermal structures also reflect significant impact of cyclonic vortex over BOB.

Non-parametric rain/no rain screening method for satellite-borne passive microwave radiometers at 19–85 GHz channels with the Random Forests algorithm

This paper presents a novel non-parametric pattern recognition method to screen rain/no rain status for satellite-borne passive microwave radiometers in the 19–85 GHz channels. The method is based on randomized decision trees with bootstrap aggregation (Random Forests (RF) algorithm). It relies on pragmatic associations between the input features using Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) calibrated brightness temperatures and precipitation radar (PR) rain/no rain information as targets. Both these instruments are carried on board the TRMM satellite. In order to develop the method, first, the 10 most significant input features are selected by using feature importance criteria through out-of-bag (OOB) statistics from a total of 17 input features. The input features include the brightness temperatures, as well as some computed signatures – polarization differences (PD), polarization-corrected temperatures (PCT), and scattering indices (SI) at in the 19–85 GHz channels. The feature selection is carried out for different types of surface terrain (ocean, land, and coast), and the selected features are then used for final RF algorithm development. During the dichotomous statistical assessment of the method against the PR rain/no rain status as ‘truth’, the presented method produced reasonable threat scores of 0.50, 0.43, and 0.39, respectively, over ocean, land, and coast surface terrains. Furthermore, the results are compared with the dichotomous scores derived by the Goddard profiling algorithm (GPROF) and, remarkably, the RF-based method corroborated better statistical scores than that of the GPROF. The presented method does not rely on any a priori information and is applicable to other passive microwave radiometers at similar frequencies.

Identification of convective/stratiform dominance over surface rainfall

In order to find out the characteristics of stratiform/convective dominance over surface rainfall, upper-air meteorological elements like cloud liquid water (CLW), precipitation water (PW) and latent heat (LH) have been analysed from the surface to a height of 18 km, for a few selected stations in India and a few other global locations. The data required for the study are the CLW, PW, LH and rainfall data from the data product 2A12 of the Tropical Rainfall Measuring Mission satellite (TRMM) Microwave Imager (TMI) for the period 1999-2002, 2007 and 2008; bright band height (BBH) and freezing level height (HFL) data from 2A23 of the Precipitation Radar (PR) onboard TRMM for the period 1999 - 2002. Vertical profiles of PW, LH and CLW have been presented. Two new parameters, called height of peak cloud liquid water (HPCL) and precipitation water at HPCL have been introduced. HPCL is defined as the height where CLW shows its peak. It is found that absorption and evolution of LH along the vertical column, PW values at HPCL and the level of LH absorption peak are able to explain stratiform/convective dominance over surface rainfall.

An exploratory investigation of an adaptive neuro fuzzy inference system (ANFIS) for estimating hydrometeors from TRMM/TMI in synergy with TRMM/PR

The authors have investigated an adaptive neuro fuzzy inference system (ANFIS) for the estimation of hydrometeors from the TRMM microwave imager (TMI). The proposed algorithm, named as Hydro-Rain algorithm, is developed in synergy with the TRMM precipitation radar (PR) observed hydrometeor information. The method retrieves rain rates by exploiting the synergistic relations between the TMI and PR observations in twofold steps. First, the fundamental hydrometeor parameters, liquid water path (LWP) and ice water path (IWP), are estimated from the TMI brightness temperatures. Next, the rain rates are estimated from the retrieved hydrometeor parameters (LWP and IWP). A comparison of the hydrometeor retrievals by the Hydro-Rain algorithm is done with the TRMM PR 2A25 and GPROF 2A12 algorithms. The results reveal that the Hydro-Rain algorithm has good skills in estimating hydrometeor paths LWP and IWP, as well as surface rain rate. An examination of the Hydro-Rain algorithm is also conducted on a super typhoon case, in which the Hydro-Rain has shown very good performance in reproducing the typhoon field. Nevertheless, the passive microwave based estimate of hydrometeors appears to suffer in high rain rate regimes, and as the rain rate increases, the discrepancies with hydrometeor estimates tend to increase as well.

Investigating Hector Convective Development and Microphysical Structure Using High-Resolution Model Simulations, Ground-Based Radar Data, and TRMM Satellite Data

Abstract One event of a tropical thunderstorm typically observed in northern Australia, known as Hector, is investigated using high-resolution model output from the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) observations from a ground-based weather radar located in Berrimah (Australia) and data from the Tropical Rainfall Measuring Mission (TRMM) satellite. The analysis is carried out by tracking the full life cycle of Hector from prestorm stage to the decaying stage. In both the prestorm stage, characterized by nonprecipitating cells, and the triggering stage, when the Hector storm is effectively initiated, an analysis is performed with the aid of high-spatial-and-temporal-resolution MM5 output and the Berrimah ground-based radar imagery. During the mature (“old”) stage of Hector, considering the conceptual model for tropical convection suggested by R. Houze, TRMM Microwave Imager satellite-based data were added to ground-based radar data to analyze the storm vertical structure (dynamics, thermodynamics, and hydrometeor contents). Model evaluation with respect to observations (radar reflectivity and TRMM data) suggests that MM5 performed fairly well in reproducing the dynamics of Hector, providing support to the assertion that the strength of convection, in terms of vertical velocity, largely contributes to the vertical distribution of hydrometeors. Moreover, the stages of the storm and its vertical structure display good agreement with Houze’s aforementioned conceptual model. Finally, it was found that the most important triggering mechanisms for this Hector event are topography, the sea breeze, and a gust front produced by previous convection.

Principal Components of Multifrequency Microwave Land Surface Emissivities. Part II: Effects of Previous-Time Precipitation

Abstract The microwave land surface emissivity (MLSE) over the continental United States was examined during 2011 as a function of prior rainfall conditions using two independent emissivity estimation techniques, one providing instantaneous estimates based on a clear-scene emissivity principal component (PC) analysis and the other based on physical radiative transfer modeling. Results show that over grass, closed shrub, and cropland, prior rainfall can cause the horizontally polarized 10-GHz brightness temperature (TB) to drop by as much as 20 K, with a corresponding emissivity drop of approximately 0.06, whereby prior rain exhibited little influence on the emissivity over forest because of the dense vegetation. The correlation between emissivity and its leading principal components and the prior rainfall over grass, closed shrub, and cropland is −0.6, while it is only −0.1 over forested areas. Forward-simulated TB using the PC-based emissivity derived from instantaneous Tropical Rainfall Measuring Mission (TRMM) satellite overpasses agrees much better with TRMM Microwave Imager (TMI) observations relative to a climatologically based emissivity, especially after a period of heavy rain. Two potential applications of the PC-based emissivity are demonstrated. The first exploits the time history change of the MLSE to estimate the amount of prior rainfall. The second application is a method to estimate the emissivity underneath precipitating radiometric scenes by first adjusting the surface-sensitive principal components that were derived under clear-sky scenes and then by reconstructing the joint emissivity (all channels simultaneously) from the modified PC structure. The results are applicable to future overland passive microwave rainfall retrieval algorithms to simultaneously detect and estimate precipitation amounts under dynamically changing surface conditions.

Principal Components of Multifrequency Microwave Land Surface Emissivities. Part I: Estimation under Clear and Precipitating Conditions

Abstract The upcoming Global Precipitation Measurement mission will provide considerably more overland observations over complex terrain, high-elevation river basins, and cold surfaces, necessitating an improved assessment of the microwave land surface emissivity. Current passive microwave overland rainfall algorithms developed for the Tropical Rainfall Measuring Mission (TRMM) rely upon hydrometeor scattering-induced signatures at high-frequency (85 GHz) brightness temperatures (TBs) and are empirical in nature. A multiyear global database of microwave surface emissivities encompassing a wide range of surface conditions was retrieved from Advanced Microwave Scanning Radiometer for Earth Observing System (EOS; AMSR-E) radiometric clear scenes using companion A-Train [CloudSat, Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), and Atmospheric Infrared Sounder (AIRS)] data. To account for the correlated emissivity structure, the procedure first derives the TRMM Microwave Imager–like nine-channel emissivity principal component (PC) structure. Relations are derived to estimate the emissivity PCs directly from the instantaneous TBs, which allows subsequent TB observations to estimate the PC structure and reconstruct the emissivity vector without need for ancillary data regarding the surface or atmospheric conditions. Radiative transfer simulations matched the AMSR-E TBs within 5–7-K RMS difference in the absence of precipitation. Since the relations are derived specifically for clear-scene conditions, discriminant analysis was performed to find the PC discriminant that best separates clear and precipitation scenes. When this technique is applied independently to two years of TRMM data, the PC-based discriminant demonstrated superior relative operating characteristics relative to the established 85-GHz scattering index, most notably during cold seasons.

An Updated TRMM Composite Climatology of Tropical Rainfall and Its Validation

Abstract An updated 15-yr Tropical Rainfall Measuring Mission (TRMM) composite climatology (TCC) is presented and evaluated. This climatology is based on a combination of individual rainfall estimates made with data from the primary TRMM instruments: the TRMM Microwave Imager (TMI) and the precipitation radar (PR). This combination climatology of passive microwave retrievals, radar-based retrievals, and an algorithm using both instruments simultaneously provides a consensus TRMM-based estimate of mean precipitation. The dispersion of the three estimates, as indicated by the standard deviation σ among the estimates, is presented as a measure of confidence in the final estimate and as an estimate of the uncertainty thereof. The procedures utilized by the compositing technique, including adjustments and quality-control measures, are described. The results give a mean value of the TCC of 4.3 mm day−1 for the deep tropical ocean belt between 10°N and 10°S, with lower values outside that band. In general, the TCC values confirm ocean estimates from the Global Precipitation Climatology Project (GPCP) analysis, which is based on passive microwave results adjusted for sampling by infrared-based estimates. The pattern of uncertainty estimates shown by σ is seen to be useful to indicate variations in confidence. Examples include differences between the eastern and western portions of the Pacific Ocean and high values in coastal and mountainous areas. Comparison of the TCC values (and the input products) to gauge analyses over land indicates the value of the radar-based estimates (small biases) and the limitations of the passive microwave algorithm (relatively large biases). Comparison with surface gauge information from western Pacific Ocean atolls shows a negative bias (~16%) for all the TRMM products, although the representativeness of the atoll gauges of open-ocean rainfall is still in question.

Evaluation of Version-7 TRMM Multi-Satellite Precipitation Analysis Product during the Beijing Extreme Heavy Rainfall Event of 21 July 2012

The latest Version-7 (V7) Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) products were released by the National Aeronautics and Space Administration (NASA) in December of 2012. Their performance on different climatology, locations, and precipitation types is of great interest to the satellite-based precipitation community. This paper presents a study of TMPA precipitation products (3B42RT and 3B42V7) for an extreme precipitation event in Beijing and its adjacent regions (from 00:00 UTC 21 July 2012 to 00:00 UTC 22 July 2012). Measurements from a dense rain gauge network were used as the ground truth to evaluate the latest TMPA products. Results are summarized as follows. Compared to rain gauge measurements, both 3B42RT and 3B42V7 generally captured the rainfall spatial and temporal pattern, having a moderate spatial correlation coefficient (CC, 0.6) and high CC values (0.88) over the broader Hebei, Beijing and Tianjin (HBT) regions, but the rainfall peak is 6 h ahead of gauge observations. Overall, 3B42RT showed higher estimation than 3B42V7 over both HBT and Beijing. At the storm center, both 3B42RT and 3B42V7 presented a relatively large deviation from the temporal variation of rainfall and underestimated the storm by 29.02% and 36.07%, respectively. The current study suggests that the latest TMPA products still have limitations in terms of resolution and accuracy, especially for this type of extreme event within a latitude area on the edge of coverage of TRMM precipitation radar and microwave imager. Therefore, TMPA users should be cautious when 3B42RT and 3B42V7 are used to model, monitor, and forecast both flooding hazards in the Beijing urban area and landslides in the mountainous west and north of Beijing.

Rainfall asymmetry in the southeast Indian Ocean between positive and negative IODs and its local impact

AbstractThe analysis of Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) satellite observations and European Centre for Medium‐Range Weather Forecasts (ECMWF) reanalysis data identify an asymmetric rainfall pattern in the southeast Indian Ocean (SEIO) between the positive and negative Indian Ocean Dipoles (IODs). During positive (negative) IODs, the precipitation anomaly is more confined to equatorial (off‐equatorial) regions, even though their associated sea surface temperature anomalies almost reside in the same region. It is argued that the rainfall pattern asymmetry is attributed to the effect of the rainfall mean state. While the negative rainfall anomalies associated with positive IODs are confined in the region of mean precipitation, the positive rainfall anomalies associated with negative IODs do not have such a constraint. The so‐induced asymmetric rainfall pattern exerts a great impact on the upper ocean salinity and bushfires over the western maritime continent.

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.

Improved Passive Microwave Retrievals of Rain Rate over Land and Ocean. Part I: Algorithm Description

Abstract A new approach to passive microwave retrievals of precipitation is described that relies on an objective dimensional reduction procedure to filter, normalize, and decorrelate geophysical background noise while retaining the majority of radiometric information concerning precipitation. The dimensional reduction also sharply increases the effective density of any a priori database used in a Bayesian retrieval scheme. The method is applied to passive microwave data from the Tropical Rainfall Measuring Mission (TRMM), reducing the original nine channels to three “pseudochannels” that are relatively insensitive to most background variations occurring within each of seven surface classes (one ocean plus six land and coast) for which they are defined. These pseudochannels may be used in any retrieval algorithm, including the current standard Goddard profiling algorithm (GPROF), in place of the original channels. The same methods are also under development for the Global Precipitation Measurement (GPM) Core Observatory Microwave Imager (GMI). Starting with the pseudochannel definitions, a new Bayesian algorithm for retrieving the surface rain rate is described. The algorithm uses an a priori database populated with matchups between the TRMM precipitation radar (PR) and the TRMM Microwave Imager (TMI). The explicit goal of the algorithm is to retrieve the PR-derived best estimate of the surface rain rate in portions of the TMI swath not covered by the PR. A unique feature of the new algorithm is that it provides robust posterior Bayesian probabilities of pixel-averaged rain rate exceeding various thresholds. Validation and intercomparison of the new algorithm is the subject of a companion paper.

Cloud cover, cloud liquid water and cloud attenuation at Ka and V bands over equatorial climate

Cloud cover statistics and their diurnal variation have been obtained from in situ and satellite measurements for three equatorial locations. Cloud liquid water content, 0 °C isotherm height and cloud attenuation have also been obtained from radiosonde measurement using the so-called Salonen model at Kuala Lumpur (Malaysia). The results show a strong seasonal variation of cloud cover and cloud liquid water content on the two monsoon seasons. The Liquid Water Content (LWC) obtained from radiosonde and the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) is higher during the Northeast Monsoon season, which corresponds to the period of higher percentage cloud cover and high rainfall accumulation. The International Telecommunication Union—Region (ITU-R) model underestimates the cumulative distribution of LWC values at the present station. The relationship of the cloud attenuation, derived from the profiles of liquid water density and temperature within the cloud, shows an underestimate by the data obtained from the ITU-R model. The cloud attenuation at Kuala Lumpur is somewhat underestimated by the ITU-R model up to about 1.2 dB at Ka (30 GHz) and 3.4 dB at V (50 GHz) bands. The results of the specific attenuation can be used for the estimation of cloud attenuation at microwave and millimetre wave over earth-space paths. The present data are important for planning and design of satellite communications at Ka and V bands on the Earth–space path in the equatorial region.

Characterization of the Boreal Summer Upwelling at the Northern Coast of the Gulf of Guinea Based on the PROPAO In Situ Measurements Network and Satellite Data

The boreal summer upwelling along the northern coast of the Gulf of Guinea (GG) is characterized using new in situ sea surface temperature (SST) from onset sensor and satellite TRMM Microwave Imager (TMI) datasets. This study aims to encourage intensive in situ SST measurements at the northern coast of the Gulf of Guinea. It shows good agreement between daily in situ SST and TMI SST and similar coastal upwelling onset date, end date, and durations calculated using both datasets. Interannual evolution of the onset date at four stations along the northern coast of GG indicates that the upwelling can be initiated at one cape or simultaneously at both the cape of palms and the cape of three points. It can be also initiated eastward towards Cotonou or globally off all the northern coasts of GG. Nonsignificant trend is found on upwelling onset date and end date variability. Moreover, this study shows that SST is significantly warm or cold some years. Ocean conditions during these years are related to known physical processes.

The Diurnal Cycle of Cloud-Top Height and Cloud Cover over the Southeastern Pacific as Observed by GOES-10

AbstractThe diurnal cycles in cloud-top height Htop and cloud fraction (CF) in the southeastern Pacific stratocumulus region were determined for October–November 2008 by analyzing data from Geostationary Operational Environmental Satellite-10 (GOES-10) according to a diurnal/semidiurnal harmonic fitting technique. The value of Htop was obtained by applying a formula based on a linear regression of the differences between GOES-10 cloud-top temperature and Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) satellite sea surface temperature, with a common 0.25° × 0.25° spatial resolution. A satellite liquid water path (LWP) climatology complemented this dataset.Southwestward transects of Htop and LWP anomalies reveal a coherent propagating signal from the coast in the afternoon, with a typical phase speed of 25 m s−1. This pattern is preceded by a subsidence wave that reaches its peak a few hours before the maximum in Htop and LWP anomalies. Coincident increases in LWP and Htop after the subsidence wave passes suggest that the boundary layer deepening promotes cloud thickening and increased LWP, which are likely maintained through a well-mixed boundary layer and sufficient moisture fluxes that can counteract the effect of dry air entrainment. The interference between the radiatively and subsidence wave–driven cycles gives rise to a semidiurnal cycle in Htop along the coast. While the semidiurnal amplitude is near 80 m close to the coast with a fraction of explained variance greater than 0.4, it decreases to 30 m offshore (80°W). Similar to Htop, CF also exhibits contrasting zonal differences, but with a smaller semidiurnal component. The phase of the semidiurnal harmonic resembles the subsidence propagation westward, and the noticeable land–sea breeze circulation at 26°S that extends 200 km offshore.

MT-MADRAS Brightness Temperature analysis for Terrain Characterization and Land Surface Microwave Emissivity Estimation

This article reports the potential of the 'MADRAS' payload on-board the Megha-Tropiques satellite for land surface studies. The analysis has been divided into two parts as application of MADRAS data for studying the land surface properties and estimation of microwave emissivity directly from MADRAS brightness temperature (TB) data by applying an in-house developed Microwave Radiative Transfer Computation Code. The derived emissivity is further used to characterize the microwave emissivity of different land surface classes. The polarization difference (PD) parameters, the difference between horizontal ( H-) and vertical ( V-) polarization of TBs at 18 and 36 GHz clearly discern surface features of different surface classes such as deserts, arid/semi-arid and vegetated regions. Land surface microwave emissivity for MADRAS channels is derived on a global basis. These are inter-compared with the emissivity derived from the operational TRMM Microwave Imager and are in reasonably good agreement. The analysis based on emissivity shows spectral variation for different surface classes.

Impact of TRMM Data on a Low-Latency, High-Resolution Precipitation Algorithm for Flash-Flood Forecasting

AbstractData from the Tropical Rainfall Measuring Mission (TRMM) have made great contributions to hydrometeorology from both a science and an operations standpoint. However, direct application of TRMM data to short-fuse hydrologic forecasting has been challenging because of the data refresh and latency issues inherent in an instrument in low Earth orbit (LEO). To evaluate their potential impact on low-latency satellite rainfall estimates, rain rates from both the TRMM Microwave Imager (TMI) and precipitation radar (PR) were ingested into a multisensor framework that calibrates high-refresh, low-latency IR brightness temperature data from geostationary platforms against the more accurate but low-refresh, higher-latency rainfall rates available from microwave (MW) instruments on board LEO platforms. The TRMM data were used in two ways: to bias adjust the other MW data sources to match the distribution of the TMI rain rates, and directly alongside the MW rain rates in the calibration dataset. The results showed a significant reduction in false alarms and also a significant reduction in bias for those pixels for which rainfall was correctly detected. The MW bias adjustment was found to have much greater impact than the direct use of the TMI and PR rain rates in the calibration data, but this is not surprising since the latter represented perhaps only 10% of the calibration dataset.

Impact of Oceansat-2 Scatterometer Winds and TMI Observations on Phet Cyclone Simulation

The Indian Space Research Organisation launched the Oceansat-2 scatterometer (OSCAT) for atmospheric and oceanographic applications. In this paper, a case study has been performed to assess the impact of OSCAT-retrieved wind vectors on the simulation of tropical cyclone Phet over the Arabian Sea. Three-dimensional variational data assimilation of the Weather Research and Forecasting model is used for this purpose. In addition to OSCAT winds, wind speed and precipitable water derived from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) are also used for assimilation to evaluate the impact of scatterometer and radiometer data on tropical cyclone prediction. Results show that an ~ 60-km track error is observed in control and TMI experiments when compared with Joint Typhoon Warning Center observed cyclone center at 1800 UTC 01 June 2010. An approximately 40-km track error is determined in the initial center position of OSCAT experiments. The mean track error forecast is less in OSCAT experiments ( ~ 80 km) in comparison with TMI experiments ( ~ 110 km). Only OSCAT data experiments are able to predict the track of the cyclone toward the Oman coast. Assimilation of scatterometer wind direction improves the track forecast; but it degrades the forecast of the intensity, maximum magnitude, and evolution of the cyclone. None of these experiments are able to capture the observed minimum sea level pressure (964 hPa at 1200 UTC 02 June 2010) accurately. TMI experiments are slightly better than OSCAT experiments in capturing the intensity of cyclone Phet, whereas wind direction from OSCAT improves the track forecast of the cyclone.

Composite life cycle of tropical mesoscale convective systems from geostationary and low Earth orbit satellite observations: method and sampling considerations

AbstractThe ability of the current and upcoming space‐borne microwave observing systems to document precipitation processes during the life cycle of tropical convective systems is investigated with emphasis on sampling considerations. A composite technique is introduced that will serve as a Day 1 algorithm for the Megha‐Tropiques mission. It is exemplified using the Tropical Rainfall Measurement Mission (TRMM) satellite observations from the TRMM Microwave Imager (TMI) instrument and the fleet of operational geostationary infrared images for the boreal summer 2009 over the whole intertropical belt. At the system scale, over both land and oceanic regions, rainfall is overall strong at the beginning (the first third) of the life cycle and then smoothly decreases as the system shrinks and dissipates. Larger rain yields are observed for the land systems (∼6 mm h−1 maximum) compared to the systems over ocean (∼4 mm h−1 maximum). An in‐depth analysis of the sensitivity of the results to various aspects of the sampling is performed using simulated observations. The benefit of using various platforms is discussed, including considerations of constellation configuration. The entire Tropics as well as regional scales are explored, revealing the expected improvements from the inclusion of the Megha‐Tropiques observations. The sampling results are also strongly supportive of the use of multiple‐platform microwave observations from the upcoming Global Precipitation Mission constellation to build a mesoscale convective system precipitation composite life cycle, although the merging of the parameters derived from various resolution radiometers would deserve further investigations. Copyright © 2013 Royal Meteorological Society

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