Academic literature on the topic 'TRMM PR'

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.

Abstract. This paper describes the application of the KNMI cloud physical properties – precipitation properties (CPP-PP) algorithm over West Africa. The algorithm combines condensed water path (CWP), cloud phase (CPH), cloud particle effective radius (re), and cloud-top temperature (CTT) information, retrieved from visible, near-infrared and infrared observations of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard Meteosat-9 to estimate precipitation occurrence and intensity. It is investigated whether the CPP-PP algorithm is capable of retrieving rain occurrence and intensity over West Africa with a sufficient accuracy, using tropical monsoon measurement mission precipitation radar (TRMM-PR) and a small number of rain gauge observations as reference. As a second goal, it is assessed whether SEVIRI is capable of monitoring both the seasonal and synoptical evolution of the West African monsoon (WAM). It is shown that the SEVIRI-detected rainfall area agrees well with TRMM-PR, having a correlation coefficient of 0.86, with the areal extent of rainfall by SEVIRI being ~10% larger than TRMM-PR. The mean retrieved rain rate from CPP-PP is about 8% higher than from TRMM-PR. The frequency distributions of rain rate reveal that the median rain rates of CPP-PP and TRMM-PR are similar. However, rain rates >7 mm h−1 are retrieved more frequently by SEVIRI than by TRMM-PR, which is partly explained by known biases in TRMM-PR. Finally, it is illustrated that both the seasonal and synoptical time scale of the WAM can be well detected from SEVIRI daytime observations. It was found that the daytime westward MCS travel speed fluctuates between 50 and 60 km h−1. Furthermore, the ratio of MCS precipitation to the total precipitation was estimated to be about 27%. Our results indicate that rainfall retrievals from SEVIRI can be used to monitor the West African monsoon.

Abstract This study uses Dynamics of the Madden–Julian Oscillation (DYNAMO) shipborne [Research Vessel (R/V) Roger Revelle] radar and Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) datasets to investigate MJO-associated convective systems in specific organizational modes [mesoscale convective system (MCS) versus sub-MCS and linear versus nonlinear]. The Revelle radar sampled many “climatological” aspects of MJO convection as indicated by comparison with the long-term TRMM PR statistics, including areal-mean rainfall (6–7 mm day−1), convective intensity, rainfall contributions from different morphologies, and their variations with MJO phase. Nonlinear sub-MCSs were present 70% of the time but contributed just around 20% of the total rainfall. In contrast, linear and nonlinear MCSs were present 10% of the time but contributed 20% and 50%, respectively. These distributions vary with MJO phase, with the largest sub-MCS rainfall fraction in suppressed phases (phases 5–7) and maximum MCS precipitation in active phases (phases 2 and 3). Similarly, convective–stratiform rainfall fractions also varied significantly with MJO phase, with the highest convective fractions (70%–80%) in suppressed phases and the largest stratiform fraction (40%–50%) in active phases. However, there are also discrepancies between the Revelle radar and TRMM PR. Revelle radar data indicated a mean convective rain fraction of 70% compared to 55% for TRMM PR. This difference is mainly due to the reduced resolution of the TRMM PR compared to the ship radar. There are also notable differences in the rainfall contributions as a function of convective intensity between the Revelle radar and TRMM PR. In addition, TRMM PR composites indicate linear MCS rainfall increases after MJO onset and produce similar rainfall contributions to nonlinear MCSs; however, the Revelle radar statistics show the clear dominance of nonlinear MCS rainfall.

AbstractTRMM PR 2A25, version 7 (V7), retrievals of reflectivity Z and rainfall rate R are compared with WSR-88D dual-polarimetric S-band radar data for 28 radars over the southeastern United States after matching their horizontal resolution and sampling. TRMM Ku-band measurements are converted to S-band approximations to more directly compare reflectivity estimates. Rain rates are approximated from WSR-88D data using the CSU–hydrometeor identification rainfall optimization (HIDRO) algorithm. Tropics-wide TRMM retrievals confirm previous findings of a low overlap fraction between extreme convective intensity, as approximated by the maximum 40-dBZ height, and extreme near-surface rain rates. WSR-88D data also confirm this low overlap but show that it is likely higher than TRMM PR retrievals indicate. For maximum 40-dBZ echo heights that extend above the freezing level, mean WSR-88D reflectivities at low levels are approximately 2 dB higher than TRMM PR reflectivities. Higher WSR-88D-retrieved rain rates for a given low-level reflectivity combine with these higher low-level reflectivities for a given maximum 40-dBZ height to produce rain rates that are approximately double those retrieved by the TRMM PR for maximum 40-dBZ heights that extend above the freezing level. TRMM PR path-integrated attenuation, and WSR-88D specific differential phase, differential reflectivity, and hail fraction indicate that the TRMM PR 2A25 V7 algorithm is possibly misidentifying low–midlevel hail and/or graupel as greater attenuating liquid, or vice versa. This misidentification, coupled with underestimation of path-integrated attenuation caused by nonuniform beamfilling and higher rain rates produced by specific differential phase (KDP)–R than Z–R relationships, results in low-biased 2A25 V7 rain rates in intense convection.

AbstractThe National Aeronautics and Space Administration (NASA) has always included data reprocessing as a major component of every science mission. A final reprocessing is typically a part of mission closeout (known as phase F). The Tropical Rainfall Measuring Mission (TRMM) is currently in phase F, and NASA is preparing for the last reprocessing of all the TRMM precipitation data as part of the closeout. This reprocessing includes improvements in calibration of both the TRMM Microwave Imager (TMI) and the TRMM Precipitation Radar (PR). An initial step in the version 8 reprocessing is the improvement of geolocation. The PR calibration is being updated by the Japan Aerospace Exploration Agency (JAXA) using data collected as part of the calibration of the Dual-Frequency Precipitation Radar (DPR) on the Global Precipitation Measurement (GPM) Core Observatory. JAXA undertook a major effort to ensure TRMM PR and GPM Ku-band calibration is consistent.A major component of the TRMM version 8 reprocessing is to create consistent retrievals with the GPM version 05 (V05) retrievals. To this end, the TRMM version 8 reprocessing uses retrieval algorithms based on the GPM V05 algorithms. This approach ensures consistent retrievals from December 1997 (the beginning of TRMM) through the current ongoing GPM retrievals. An outcome of this reprocessing is the incorporation of TRMM data products into the GPM data suite. Incorporation also means that GPM file naming conventions and reprocessed TRMM data carry the V05 data product version. This paper describes the TRMM version 8 reprocessing, focusing on the improvements in TMI level 1 products.

AbstractIn this study, rainfall data are prepared at a 0.01° scale using 16-yr spaceborne radar data over the area of 36.13°S–36.13°N as provided by the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR). A spatial resolution that is finer than the field of view is obtained by assuming rainfall uniformity within an instantaneous footprint centered on the PR footprint geolocation. These ultra-high-resolution data reveal local rainfall concentrations over slope areas. A new estimate of the maximum rainfall at Cherrapunji, India, was observed on the valley side, approximately 5 km east of the gauge station, and is approximately 50% higher than the value indicated by the 0.1°-scale data. A case study of Yakushima Island, Japan, indicates that several percent of the sampling error arising from the spatial mismatch may be contained in conventional 0.05°-scale datasets generated without footprint areal information. The differences attributable to the enhancement in the resolution are significant in complex terrain such as the Himalayas. The differences in rainfall averaged for the 0.1° and 0.01° scales exceed 10 mm day−1 over specific slope areas. In the case of New Guinea, the mean rainfall on a mountain ridge can be 30 times smaller than that on an adjacent slope at a distance of 0.25°; this is not well represented by other high-resolution datasets based on gauges and infrared radiometers. The substantial nonuniformity of rainfall climatology highlights the need for a better understanding of kilometer-scale geographic constraints on rainfall and retrieval approaches.

Abstract. A study was performed using the first full year of rain gauge records from a newly deployed network in the Southern Appalachian mountains. This is a region characterized by complex topography with orographic rainfall enhancement up to 300% over small distances (<8 km). Rain gauge observations were used to assess precipitation estimates from the Precipitation Radar (PR) on board of the TRMM satellite, specifically the TRMM PR 2A25 precipitation product. Results show substantial differences between annual records and isolated events (e.g. tropical storm Fay). An overall bias of −27% was found between TRMM PR 2A25 rain rate and rain gauge rain rates for the complete one year of study (−59% for tropical storm Fay). Besides differences observed for concurrent observations by the satellite and the rain gauges, a large number of rainfall events is detected independently by either one of the observing systems alone (rain gauges: 50% of events are missed by TRMM PR; TRMM PR: 20% of events are not detected by the rain gauges), especially for light rainfall conditions (0.1–2mm/h) that account for more than 80% of all the missed satellite events. An exploratory investigation using a microphysical model along with TRMM reflectivity factors at selected heights was conducted to determine the shape of the drop size distribution (DSD) that can be applied to reduce the difference between TRMM estimates and rain gauge observations. The results suggest that the critical DSD parameter is the number concentration of very small drops. For tropical storm Fay an increase of one order of magnitude in the number of small drops is apparently needed to capture the observed rainfall rate regardless of the value of the measured reflectivity. This is consistent with DSD observations that report high concentrations of small and/or midsize drops in the case of tropical storms.

AbstractProperties of the rain estimation differences between Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) 2A25, TRMM Microwave Imager (TMI) 2A12, and TRMM Multisatellite Precipitation Analysis (TMPA) 3B42 are investigated with a focus on distinguishing between nonextreme and extreme rains over the Maritime Continent from 1998 to 2014. Statistical analyses of collocated TMI 1B11 85-GHz polarization-corrected brightness temperatures, PR 2A23 storm-top heights, and PR 2A25 vertical rain profiles are conducted to identify possible sources of the differences. The results indicate that a large estimation difference exists between PR and TMI for the general rain rate (extreme and nonextreme events). The PR–TMI rain-rate differences are larger over land and coast than over ocean. When extreme rain is isolated, a higher frequency of occurrence is identified by PR over ocean, followed by TMI and TMPA. Over land, TMI yields higher rain frequencies than PR with an intermediate range of rain rates (between 15 and 25 mm h−1), but it gives way to PR for the highest extremes. The turnover at the highest rain rates arises because the heaviest rain depicted by PR does not necessarily accompany the strongest ice-scattering signals, which TMI relies on for estimating precipitation over land and coast.

Abstract Given the decade-long and highly successful Tropical Rainfall Measuring Mission (TRMM), it is now possible to provide quantitative comparisons between ground-based radars (GRs) and the spaceborne TRMM precipitation radar (PR) with greater certainty over longer time scales in various tropical climatological regions. This study develops an automated methodology to match and compare simultaneous TRMM PR and GR reflectivities at four primary TRMM Ground Validation (GV) sites: Houston, Texas (HSTN); Melbourne, Florida (MELB); Kwajalein, Republic of the Marshall Islands (KWAJ); and Darwin, Australia (DARW). Data from each instrument are resampled into a three-dimensional Cartesian coordinate system. The horizontal displacement during the PR data resampling is corrected. Comparisons suggest that the PR suffers significant attenuation at lower levels, especially in convective rain. The attenuation correction performs quite well for convective rain but appears to slightly overcorrect in stratiform rain. The PR and GR observations at HSTN, MELB, and KWAJ agree to about ±1 dB on average with a few exceptions, whereas the GR at DARW requires +1 to −5 dB calibration corrections. One of the important findings of this study is that the GR calibration offset is dependent on the reflectivity magnitude. Hence, it is proposed that the calibration should be carried out by using a regression correction rather than by simply adding an offset value to all GR reflectivities. This methodology is developed to assist TRMM GV efforts to improve the accuracy of tropical rain estimates, but can also be applied to the proposed Global Precipitation Measurement and other related activities over the globe.

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The precipitation, due to its importance, is considered one of the main variables of the hydrological cycle. An alternative to collect rainfall data is using rainfall measurements by sensors/satellites. Among these kinds of alternative, the Tropical Rainfall Measuring Mission (TRMM) allows the collection with a spatial resolution 0.25º × 0.25º. Thus, the objective of the study is to understand, in more detail, the behavior and distribution of rainfall throughout the Upper São Francisco basin (Sub-40), in a recent period (1998-2013). The survey was conducted in the following steps: collection of TRMM rainfall data from 1998 to 2013 organizing them monthly and yearly; getting Três Marias reservoir in flow data; getting altimetry SRTM data; analyzing the correlation between the TRMM precipitation and the inflows to Três Marias reservoir; generating surface profiles to compare with the precipitation data, analyzing the number of consecutive dry days and consecutive wet days; computing the Standardized Precipitation Index - SPI for each point (169 points); spatial distribution of annual and monthly accumulated rainfall data, the correlation of rainfall and flow, of consecutive wet and dry days, the SPI index and cluster analysis. According to the results, it can be concluded that the years of highest and lowest value of annual rainfall depth was respectively 2009 and 2012. The driest months are June to August, and July is the driest one. In contrast, the wettest months are from November to January, and most rainy month is December. The annual and monthly precipitation depth showed that the northeast region of the basin rains less and the western and southern basin are wetter. The precipitation response to the inflow to the Três Marias reservoir is four days after the rainfall occurs. The relation between precipitation and altimetry shows that most of the annual rainfall depth is directly proportional to altimetry, but in some cases it shows little variability in the ground. Regarding the SPI, it was possible to calculate the beginning, end, intensity and magnitude of each dry and wet season. From the maps, SPI spatial information was produced in order to identify for each period the regions with highest and lowest values. By means of the map and dendrogram clusters, regions with higher and lower similarity between the monthly accumulated rainfall data were identified. Finally, the TRMM sensor proved useful in the analysis of spatial and temporal variability of precipitation over the studied basin, accounting satisfactorily dry and rainy periods. With easy acquisition and handling, satellite data is a viable alternative to collect precipitation data with spatial and temporal quality, especially in areas of difficult access or in developing countries.
A precipitação, pela sua importância, é considerada uma das principais variáveis do ciclo hidrológico. Uma alternativa para coletar dados de chuva é utilizar precipitações obtidas por sensores/satélites. Dentre estes, a Tropical Rainfall Measuring Mission (TRMM) permite a coleta com resolução espacial de 0,25º × 0,25º. Com isso, o objetivo do estudo é compreender, de forma mais detalhada, o comportamento e a distribuição da precipitação ao longo da bacia do Alto São Francisco (Sub-40), em período recente (1998−2013). A pesquisa foi realizada nas seguintes etapas: coleta de dados do TRMM para o período de 1998 a 2013 organizando-os em acumulados mensais e anuais; coleta dos dados de vazão do reservatório Três Marias; coleta de dados de altimetria SRTM; correlação diária entre os elementos de precipitação TRMM; analisar o comportamento da precipitação anual da Sub-40 frente aos dados altimétricos; identificar a quantidade de dias secos e úmidos consecutivos de cada ponto da grade utilizado; extração do Índice Padronizado de Precipitação - SPI para cada ponto (169 pontos); espacialização dos dados de precipitação acumulada anual e mensal, da correlação de chuva e vazão, dos dias secos e úmidos consecutivos, do Índice SPI e da análise de cluster. De acordo com os resultados obtidos, pode-se concluir que os anos de maior e menor valor de precipitações acumuladas anuais foram respectivamente os anos de 2009 e 2012. Os meses mais secos foram os de junho a agosto, sendo julho o mês mais seco. Em contrapartida, os meses mais úmidos foram de novembro a janeiro, com maior precipitação ocorrendo em dezembro. Os dados de precipitação acumuladas anuais e mensais mostraram que a região nordeste da bacia chove menos e que o oeste e sul da bacia são mais úmidos. O tempo de resposta da precipitação frente à vazão afluente ao reservatório Três Marias é de quatro dias após a ocorrência de chuvas. A relação entre a precipitação e altimetria mostra que a maioria dos dados de precipitações acumuladas anuais é diretamente proporcional à altimetria, mas que em alguns casos ela apresenta pouca variabilidade em relação ao terreno. Com relação ao SPI, a partir dos mapas, produziram-se informações de SPI de maneira espacializada identificando a cada período as regiões de maiores e menores valores. Observando os mapas de clusters e dendrograma identificaram-se as regiões com maior e menor similaridade entre os dados de precipitação acumulada mensal. Por fim, o sensor TRMM se mostrou hábil na análise da variabilidade espaço-temporal da precipitação sobre a bacia, representando de forma satisfatória os períodos secos e chuvosos. Com fácil aquisição e manuseio, os dados do satélite são uma alternativa viável para se coletar informações pluviométricas com qualidade espacial e temporal, principalmente em regiões de difícil acesso ou em países em desenvolvimento.

Using co-located space-borne satellite (TRMM PR, ESCAT on ERS 1/2) measurements, and numerical predicted wind fields (ECMWF), the sensitivity of C-band backscatter measurement to rain is evaluated. It is demonstrated that C-band radar backscatter can be significantly altered by rain surface perturbation, an effect that has been previously neglected. A low-order wind/rain backscatter model is developed that has inputs of surface rain rate, incidence angle, wind speed, wind direction, and azimuth angle. The wind/rain backscatter model is accurate enough for describing the total backscatter in raining areas with relatively low variance. Rain has a more significant impact on measurements at high incidence angles than at low incidence angles. Using three distinct regimes, the conditions for which wind, rain, and both wind and rain can be retrieved from scatterometer backscatter measurements are determined. The effects of rain on ESCAT wind-only retrieval are evaluated. The additional scattering from rain causes estimated wind speeds to be biased high and estimated wind directions to be biased toward the along-track direction in heavy rains. To compensate for rain-induced backscatter, we develop a simultaneous wind/rain retrieval method (SWRR), which simultaneously estimates wind and rain from ESCAT backscatter measurements with an incidence angle of over 40 degrees. The performance of SWRR under typical wind/rain conditions is evaluated through simulation and validation with collocated TRMM PR and ECMWF data sets. SWRR is shown to significantly improve wind velocity estimates and the SWRR-estimated rain rate has relatively high accuracy in moderate to heavy rain cases. RADARSAT-1 ScanSAR SWA images of Hurricane Katrina are used to retrieve surface wind vectors over the ocean. Collocated H*wind wind directions are used as the wind direction estimate and the wind speed is derived from SAR backscatter measurements by inversion of a C-band HH-polarization Geophysical Model Function (GMF) that is derived from the VV-polarization GMF, CMOD5, using a polarization ratio model. Because existing polarization models do not fit the ScanSAR SWA data well, a recalibration model is proposed to recalibrate the ScanSAR SWA images. Validated with collocated H*wind wind speed estimates, the mean difference between SAR-retrieved and H*wind speed is small and the root mean square (RMS) error is below 4 m/s. Rain effects on the ScanSAR measurements are analyzed for three different incidence angle ranges using collocated ground-based Doppler weather radar (NEXRAD) rain measurements. Compared with the scatterometer-derived model, the rain-induced backscatter observed by the ScanSAR at incidence angles 44 to 45.7 degrees is consistent with the scatterometer-derived model when the polarization difference between HH and VV polarizations is considered.

Inclui apêndices
Orientadora: Sonia Buck
Co-orientadora: Helga Loos
Dissertação (mestrado) - Universidade Federal do Paraná, Setor de Educação, Programa de Pós-Graduação em Educação. Defesa: Curitiba, 2006
Inclui bibliografia

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Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior
The great demand of accidents resulting in victims with rachi-medullar traumatism (RMT) and the need for indetifying how they are being assisted, led us to proceed with this investigation. It had as its goal to identify and analyze the knowledge of nurses and nurse assistents regarding the nursing assistance to these patients. It consists of a descriptive exploratory study, with a quantitative method and prospective data. For its execution, 193 subjects were interviewed, 37 of them nurses and 157 nurse assistents in 02 hospitals of the metropolitan zone of Natal. The results reveal that the subjects are not coherent when questioned whether they are prepared to assist victim of RMT and the content described by order of piority regarding the steps followed in the assistance of these victims, both in the pre-hospital and hospital care. Thus, we observe that only 06 nurses and 07 assistents, described correctly all the steps necessary to pre-hospital care and only 01 nurse and 02 assistents registered all the steps in the correct sequence regarding the hospital care. We conclude that, in face of the obtained results, we can urgently modify this reality, improving the nursing staff and giving them better work conditions
A grande demanda de acidentes produtores de v?timas com traumatismo raquimedular (TRM) e a necessidade de identificar como est?o sendo assistidas, levaram-nos a fazer esta investiga??o. Teve como objetivo identificar e analisar o conhecimento dos enfermeiros e os auxiliares de enfermagem acerca da assist?ncia de enfermagem a estes acientes.Trata-se de um estudo explorat?rio descritivo, com abordagem quantitativa e dados prospectivos. Para a sua realiza??o, foram entrevistados 193 sujeitos sendo 37 enfermeiros e 157 auxiliares de enfermagem de 02 hospitais da grande Natal. Os resultados revelam que os sujeitos n?o s?o coerentes quando questionados se est?o preparados para assistir ?s v?timas de TRM e o conte?do descrito por ordem de prioridade em rela??o aos passos utilizados na assist?ncia, tanto no atendimento pr?-hospitalar como hospitalar. Assim sendo, observamos que apenas 06 enfermeiros e 07 auxiliares descreveram corretamente todos os passos necess?rios ao atendimento pr?-hospitalar e somente 01 enfermeiro e 02 auxiliares registraram todos os passos na seq??ncia correta, no que se refere ao atendimento hospitalar. Conclu?mos que diante dos resultados obtidos, faz-se necess?rio modificar esta realidade, aperfei?oando a equipe de enfermagem e dando-lhe melhores condi??es de trabalho

碩士
國防大學中正理工學院
應用物理研究所
93
Though there is a dense weather radar observation network in Taiwan area, there exists inconsistent rainfall measurements from different radar systems due to the different radar parameters. It is an urgent necessary to solve this kind of problems for the access of correct rainfall measurements. However owing to the blocking of mountains, especially the Central Mountain Range, it was difficult to observe a same weather system at the same time with all the radar systems and to find the discrepancies between them. TRMM (Tropical Rainfall Measuring Mission) /PR(Precipitation Radar) has potential to be the common standard of ground-based radar system because of its fine horizontal resolution(5 km), vertical resolution (250 m), and the advantage of downward observation type which could effectively eliminates the blind angle view while observing with ground-based radar. Before PR to be the common standard of radar system, it should be validated through the rain gauge measurements which are regarded as ground truth. This study collected all the TRMM/PR data covering the Taiwan island area from 2002 to 2004 and validated the satellite rainfall retrievals with the rain gauge data. Results show that in general the correlation coefficient between rain gauge and PR rainfall retrieval is from 0.34 to 0.63 for monthly averaging, from 0.33 to 0.63 for seasonly averaging, and from 0.43 to 0.47 for annual averaging. It is also shown that the correlation coefficient depends on the amount of rainfall, better one with huge accumulated rainfall and poor one with little accumulated rainfall. One another factor which affects the value of correlation coefficient is terrain. In addition, the vertical rainfall structures for convective and stratiform were analyzed for the understanding of the microphysical rainfall processes. It is shown that in general the height of development depends on season and on location that precipitation occurs. The height of development for convective rainfall is larger that that for straitform rainfall and there is a bright band appeared on the stratiform rainfall profile. Straitfall rainfall is the primary rain type in the spring and winter seasons, while convective rainfall is significantly dominant and the amount of rainfall is usually more than that of stratiform both in summer and in fall, especially in the south of Taiwan. It is easily to distinguish convective from stratiform rainfall from the vertical rainfall profile and the understanding of their microphysical precipitation processes.

Error characteristics associated with satellite-derived precipitation products are important for atmospheric and hydrological model data assimilation, forecasting, and climate diagnostic applications. This information also aids in the refinement of physical assumptions within algorithms by identifying geographical regions and seasons where existing algorithm physics may be incorrect or incomplete. Examination of relative errors between independent estimates derived from satellite microwave data is particularly important over regions with limited surface-based equipments for measuring rain rate such as the global oceans and tropical continents. In this context, analysis of microwave based satellite datasets from the Tropical Rainfall Measuring Mission (TRMM) enables to not only provide information regarding the inherent uncertainty within the current TRMM products, but also serves as an opportunity to prototype error characterization methodologies for the TRMM follow-on program, the Global Precipitation Measurement (GPM) . Most of the TRMM uncertainty evaluation studies focus on the accuracy of rainfall accumulated over time (e.g., season/year). Evaluation of instantaneous rainfall intensities from TRMM orbital data products is relatively rare. These instantaneous products are known to potentially cause large uncertainties during real time flood forecasting studies at the watershed scale. This is more so over land regions, where the highly varying land surface emissivity offers a myriad of complications, hindering accurate rainfall estimation. The error components of orbital data products also tend to interact nonlinearly with hydrologic modeling uncertainty. Keeping these in mind, the present thesis fosters the development of uncertainty analysis using instantaneous satellite orbital data products (latest version 7 of 1B11, 2A25, 2A23, 2B31, 2A12) derived from the passive and active microwave sensors onboard TRMM satellite, namely TRMM Microwave Imager (TMI) and precipitation radar (PR). The study utilizes 11 years of orbital data from 2002 to 2012 over the Indian subcontinent and examines the influence of various error sources on the convective and stratiform precipitation types. Two approaches are taken up to examine uncertainty. While the first approach analyses independent contribution of error from these orbital data products, the second approach examines their combined effect. Based on the first approach, analysis conducted over the land regions of Mahanadi basin, India investigates three sources of uncertainty in detail. These include 1) errors due to improper delineation of rainfall signature within microwave footprint (rain/no rain classification), 2) uncertainty offered by the transfer function linking rainfall with TMI low frequency channels and 3) sampling errors owing to the narrow swath and infrequent visits of TRMM sensors. The second approach is hinged on evaluating the performance of rainfall estimates from each of these orbital data products by accumulating them within a spatial domain and using error decomposition methodologies. Microwave radiometers have taken unprecedented satellite images of earth’s weather, proving to be a valuable tool for quantitative estimation of precipitation from space. However, as mentioned earlier, with the widespread acceptance of microwave based precipitation products, it has also been recognized that they contain large uncertainties. One such source of uncertainty is contributed by improper detection of rainfall signature within radiometer footprints. To date, the most-advanced passive microwave retrieval algorithms make use of databases constructed by cloud or numerical weather model simulations that associate calculated microwave brightness temperature to physically plausible sample rain events. Delineation of rainfall signature from microwave footprints, also known as rain/norain classification (RNC) is an essential step without which the succeeding retrieval technique (using the database) gets corrupted easily. Although tremendous advances have been made to catapult RNC algorithms from simple empirical relations formulated for computational expedience to elaborate computer intensive schemes which effectively discriminate rainfall, a number of challenges remain to be addressed. Most of the algorithms that are globally developed for land, ocean and coastal regions may not perform well for regional catchments of small areal extent. Motivated by this fact, the present work develops a regional rainfall detection algorithm based on scattering index methodology for the land regions of study area. Performance evaluation of this algorithm, developed using low frequency channels (of 19 GHz, 22 GHz), are statistically tested for individual case study events during 2011 and 2012 Indian summer monsoonal months. Contingency table statistics and performance diagram show superior performance of the algorithm for land regions of the study region with accurate rain detection observed in 95% of the case studies. However, an important limitation of this approach is comparatively poor detection of low intensity stratiform rainfall. The second source of uncertainty which is addressed by the present thesis, involves prediction of overland rainfall using TMI low frequency channels. Land, being a radiometrically warm and highly variable background, offers a myriad of complications for overland rain retrieval using microwave radiometer (like TMI). Hence, land rainfall algorithms of TRMM 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 TMI ocean algorithm). In the present study, sensitivity analysis is conducted using spearman rank correlation coefficient as the indicator, to estimate the best combination of TMI low frequency channels that are highly sensitive to near surface rainfall rate (NSR) from PR. Results indicate that, the TMI channel combinations not only contain information about rainfall wherein liquid water drops are the dominant hydrometeors, but also aids in surface noise reduction over a predominantly vegetative land surface background. Further, the variations of rainfall signature in these channel combinations were seldom assessed properly due to their inherent uncertainties and highly non linear relationship with rainfall. Copula theory is a powerful tool to characterize dependency between complex hydrological variables as well as aid in uncertainty modeling by ensemble generation. Hence, this work proposes a regional model using Archimedean copulas, to study dependency of TMI channel combinations with respect to precipitation, over the land regions of Mahanadi basin, India, using version 7 orbital data from TMI and PR. Studies conducted for different rainfall regimes over the study area show suitability of Clayton and Gumbel copula for modeling convective and stratiform rainfall types for majority of the intraseasonal months. Further, large ensembles of TMI Tb (from the highly sensitive TMI channel combination) were generated conditional on various quantiles (25th, 50th, 75th, 95th) of both convective and stratiform rainfall types. Comparatively greater ambiguity was observed in modeling extreme values of 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 superior performance of the proposed copula based technique. Another persistent source of uncertainty inherent in low earth orbiting satellites like TRMM arise due to sampling errors of non negligible proportions owing to the narrow swath of satellite sensors coupled with a lack of continuous coverage due to infrequent satellite visits. This study investigates sampling uncertainty of seasonal rainfall estimates from PR, based on 11 years of PR 2A25 data product over the Indian subcontinent. A statistical bootstrap technique is employed to estimate the relative sampling errors using the PR data themselves. Results verify power law scaling characteristics of relative sampling errors with respect to space time scale of measurement. Sampling uncertainty estimates for mean seasonal rainfall was found to exhibit seasonal variations. To give a practical demonstration of the implications of bootstrap technique, PR relative sampling errors over the sub tropical river basin of Mahanadi, India were examined. Results revealed that bootstrap technique incurred relative sampling errors of <30% (for 20 grid), <35% (for 10 grid), <40% (for 0.50 grid) and <50% (for 0.250 grid). With respect to rainfall type, overall sampling uncertainty was found to be dominated by sampling uncertainty due to stratiform rainfall over the basin. In order to study the effect of sampling type on relative sampling uncertainty, the study compares the resulting error estimates with those obtained from latin hypercube sampling. Based on this study, it may be concluded that bootstrap approach can be successfully used for ascertaining relative sampling errors offered by TRMM-like satellites over gauged or ungauged basins lacking in in-situ validation data. One of the important goals of TRMM Ground Validation Program has been to estimate the random and systematic uncertainty associated with TRMM rainfall estimates. Disentangling uncertainty in seasonal rainfall offered by independent observations of TMI and PR enables to identify errors and inconsistencies in the measurements by these instruments. Motivated by this thought, the present work examines the spatial error structure of daily precipitation derived from the version 7 TRMM instantaneous orbital data products through comparison with the APHRODITE data over a subtropical region namely Mahanadi river basin of the Indian subcontinent for the seasonal rainfall of 6 years from June 2002 to September 2007. The instantaneous products examined include TMI and PR data products of 2A12, 2A25 and 2B31 (combined data from PR and TMI). The spatial distribution of uncertainty from these data products was quantified based on the performance metrics derived from the contingency table. For the seasonal daily precipitation over 10x10 grids, the data product of 2A12 showed greater skill in detecting and quantifying the volume of rainfall when compared with 2A25 and 2B31 data products. Error characterization using various error models revealed that random errors from multiplicative error models were homoscedastic and that they better represented rainfall estimates from 2A12 algorithm. Error decomposition technique, performed to disentangle systematic and random errors, testified that the multiplicative error model representing rainfall from 2A12 algorithm, successfully estimated a greater percentage of systematic error than 2A25 or 2B31 algorithms. Results indicate that even though the radiometer derived 2A12 is known to suffer from many sources of uncertainties, spatial and temporal analysis over the case study region testifies that the 2A12 rainfall estimates are in a very good agreement with the reference estimates for the data period considered. These findings clearly document that proper characterization of error structure offered by TMI and PR has wider implications in decision making, prior to incorporating the resulting orbital products for basin scale hydrologic modeling. The current missions of GPM envision a constellation of microwave sensors that can provide instantaneous products with a relatively negligible sampling error at daily or higher time scales. This study due to its simplicity and physical approach offers the ideal basis for future improvements in uncertainty modeling in precipitation.