Academic literature on the topic 'TRMM rainfall data'

In this study rainfall is calculated from Tropical Rainfall Measuring Mission (TRMM) Version 6 (V6) 3B42 datasets and calibrated with reference to the observed daily rainfall by rain-gauge collected at 15 locations over Nepal during 1998-2007. In monthly, seasonal and annual scales TRMM estimated rainfalls follow the similar distribution of historical patterns obtained from the rain-gauge data. Rainfall is large in the Southern parts of the country, especially in the Central Nepal. Day-to-day rainfall comparison shows that TRMM derived trend is very similar to the observed data but TRMM usually underestimates rainfall on many days with some exceptions of overestimation on some days. The correlation coefficient of rainfalls between TRMM and rain-gauge data is obtained about 0.71. TRMM can measure about 65.39% of surface rainfall in Nepal. After using calibration factors obtained through regression expression the TRMM estimated rainfall over Nepal becomes about 99.91% of observed data. TRMM detection of rainy days is poor over Nepal; it can approximately detect, under-detect and over-detect by 19%, 72% and 9% of stations respectively. False alarm rate, probability of detection, threat score and skill score are calculated as 0.30, 0.68, 0.53 and 0.55 respectively. Finally, TRMM data can be utilized in measuring mountainous rainfall over Nepal but exact amount of rainfall has to be calculated with the help of adjustment factors obtained through calibration procedure. This preliminary work is the preparation of utilization of Global Precipitation Measurement (GPM) data to be commencing in 2013.

The construction of the Temef Dam in Oenino Village, Oenino District, and Konbaki Village, Polen District, South Central Timor Regency requires long and reliable rainfall data. To overcome the minimum data or the unavailability of automatic rainfall (ARR) and discharge data in the past decades, the use of Tropical Rainfall Measuring Mission (TRMM) satellite data is foreseen. The accuracy of TRMM data is obtained when the parameters of suitability and compatibility of TRMM are in a good agreement with the ARR. For the Temef watershed, there are six rainfall stations that were reviewed, namely Fatumnasi, Oeoh, Noelnoni, Polen, Nifukani, and Batinifukoko rainfall stations. Direct comparisons of rainfall data were conducted for 20 years (1998-2018) with temporal resolution on a monthly and daily basis. The results of the study show that the rainfall patterns in the TRMM data product (version 3B42V7) tend to be consistent with 3 rainfall stations in the Temef watershed namely Noelnoni, Fatumnasi, and Batinifukoko. A correlation coefficient of 0.505 – 0.813 was obtained from TRMM data calibration at monthly basis while a correction factor level of 0.0056 - 0.0129 was obtained for daily. The calibration on the annual maximum daily rainfall data resulted in a correction factor of 0.0298 - 0.2516. Monthly and daily TRMM data fit well with the data of 3 rainfall stations. However, the Noelnoni rainfall station showed poor results on the annual maximum daily rainfall.Keywords: TRMM data, ARR data, correction factor, correlation coefficient

Data collection based on satellite TRMM (Tropical Rainfall Measuring Mission) presents one of the good alternatives in estimating rainfall. TRMM technology can minimize manual rainfall recording errors and improve rainfall accuracy for hydrological analysis. The analysis method used in this research is divided into 3 (three) stages, namely Hydrology analysis, Statistical Analysis and Artificial Neural Network Analysis. From the results of TRMM JAXA analysis in the Temef Watershed Area of East Nusa Tenggara Province obtained TRMM JAXA satellite rainfall relationship to observation data shows rainfall patterns between the two data are interconnected but for cases with very high observation rainfall, TRMM rainfall data tends to be low. From statistical method analysis, the relationship between observation rainfall and TRMM JAXA rainfall obtained results with a "Very Strong" interpretation indicated by the results of 9 years calibration and 1 year validation where the selected equation is a polynomial equation (y=-0,0123x2 + 1,5553x + 20,222). Rain data correction results simulated with Debit data to see the relationship between rain and discharge that occurred, this analysis using Artificial Neural Network with Backpropagation method, the results showed a "Strong" interpretation where statistically the value of Nash-Sutcliffe Efficiency (NSE) 0.920, the coefficient value of correlation of field discharge and TRMM rainfall is 0,877 % and the relative error occurred is 2,62%

Rainfall is a difficult parameter to measure, due to large spatial and temporal variations. Lack of data availability, data incompletely, less spreading of station, less observer, and manual data entry are other problems for rainfall predicting. To encourage these problems rainfall satellite can be used, because it has high temporal and spatial resolution, widely coverage, near real-time and fast accessibility. This research was conducted in the upper Kapuas River Basin, West Kalimantan, to determine how TRMM satellite-derived rainfall compares with ground-measured values and the possibility of using it to complement ground-measured rainfall. The statistical analyses and correction factor development for TRMM data are conducted to validate and correct the TRMM data on eleven sub basin in Kapuas River basin. Validation showed high correlation between TRMM and gauge data. Validation shows a high correlation and lowest RMSE between TRMM and gauge data in the sub basin adjacent to the gauge station (r= 0.76-0.8, RMSE 0,84-0,92). The results of the analysis also show that after correction, the corrected TRMM data errors were reduced for the eleven rainfall

Abstract. The lack of rainfall data in Brazil, and, in particular, in Rio Grande do Sul State (RS), hinders the understanding of the spatial and temporal distribution of rainfall, especially in the case of the more complex extreme events. In this context, rainfall's estimation from remote sensors is seen as alternative to the scarcity of rainfall gauges. However, as they are indirect measures, such estimates needs validation. This paper aims to verify the applicability of the Tropical Rainfall Measuring Mission (TRMM) satellite information for extreme rainfall determination in RS. The analysis was accomplished at different temporal scales that ranged from 5 min to daily rainfall while spatial distribution of rainfall was investigated by means of regionalization. An initial test verified TRMM rainfall estimative against measured rainfall at gauges for 1998–2013 period considering different durations and return periods (RP). Results indicated that, for the RP of 2, 5, 10 and 15 years, TRMM overestimated on average 24.7% daily rainfall. As TRMM minimum time-steps is 3 h, in order to verify shorter duration rainfall, the TRMM data were adapted to fit Bell's (1969) generalized IDF formula (based on the existence of similarity between the mechanisms of extreme rainfall events as they are associated to convective cells). Bell`s equation error against measured precipitation was around 5–10%, which varied based on location, RP and duration while the coupled BELL+TRMM error was around 10–35%. However, errors were regionally distributed, allowing a correction to be implemented that reduced by half these values. These findings in turn permitted the use of TRMM+Bell estimates to improve the understanding of spatiotemporal distribution of extreme hydrological rainfall events.

Abstract. Planning of drought relief and floods in developing countries is greatly hampered by lack of a sufficiently dense network of weather station measuring precipitation. In this paper we test the utility of three satellite products to augment the ground based precipitation measurement to provide improved spatial estimates of rainfall. The three products are: Tropical Rainfall Measuring Mission (TRMM) product (3B42), Multi-Sensor Precipitation Estimate-Geostationary (MPEG) and Climate Forecast System Reanalysis (CFSR). The accuracy of three products is tested in the Lake Tana Basin in Ethiopia where in 2010 38 weather stations were available with a full record of daily precipitation amounts. Daily grid satellite based rainfall estimates were compared to: (1) point observed ground rainfall (2) areal rainfall in the major river sub-basins of Lake Tana. The result shows that, the MPEG and CFSR satellite provided most accurate rainfall estimates. On the average for 38 stations 78 and 86% of the observed rainfall variation is explained by MPEG and CFSR data respectively while TRIMM explained only 17% of the variation. Similarly, the areal comparison indicated a better performance for both MPEG and CFSR data in capturing the pattern and amount of rainfall. MPEG and CFSR have also a lower RMSE compared to the TRMM satellite rainfall. The Bias indicated that, the MPEG is consistent in underestimating the observed rainfall while the TRMM and CFSR were not consistent; they overestimated for some and underestimated for the others.

Daily and monthly rainfall data series are necessary data for planning purposes in Civil Engineering and other fields. Incomplete rainfall data often occurs, so that rainfall data must be estimated based on rainfall data from several other nearby locations. The addition of rainfall data can lead to inaccurate planning. Rainfall data used for planning in the civil engineering sector is usually taken from the BMKG station. This data is taken from the rainfall station above the ground. Besides, that can also produce rainfall data from TRMM. Can take rainfall data from TRMM at all locations according to a coordinate of location. This rainfall data denotes an average rainfall taken from the satellite approximately 250 meters above the ground surface. An equation will be obtained by comparing the daily and monthly rainfall data from the two data sources. Based on TRMM rainfall, we can use the equation to estimate ground rainfall in a location. In this study, daily rainfall, monthly rainfall, the spectrum of daily and monthly rainfall data from BMKG are compared with rainfall and the spectrum of daily and monthly rainfall data from TRMM. The analysis results show that the monthly rainfall data from TRMM and BMKG correlate better than daily rainfall data.

This paper identifies the climatic regions in Indonesia based on the rainfall pattern similarity using TRMM data. Indonesia is a tropical climate region with three main climate clusters, i.e. monsoonal, anti-monsoonal and semi-monsoonal. The clusters were formed by examining rainfall observation datasets recorded at a number of stations over Indonesia with coarse spatial resolution. Clustering based on higher resolution datasets is needed to characterize the rainfall pattern over remote areas with no stations. TRMM provides a high resolution gridded dataset. A statistical test has been applied to evaluate the significance of TRMM bias, and it indicated that the TRMM based satellite precipitation product is a reasonable choice to be used as an input to cluster regions in Indonesia based on the similarity of rainfall patterns. The clustering by Euclidean distance revealed that Indonesia can be grouped into three significantly different rainfall patterns. Compared to the existing references, there have been regions where the rainfall pattern has been shifted. The results in this research thus update the previously defined climate regions in Indonesia.

Kerala is the gateway of the Indian southwest monsoon. The Tropical Rainfall Measurement Mission (TRMM) rainfall data is an efficient approach to rainfall measurement. This study explores the temporal variability in rainfall and trends over Kerala from 1998-2019 using TRMM data and observatory data procured from India Meteorological Department (IMD). Direct comparison with observatory data at various time scales proved the reliability of the TRMM data (monthly, seasonal and annual). The temporal rainfall converted by averaging the data on an annual, monthly and seasonal time scale, and the results have confirmed that the rainfall estimated based on satellite data is dependable. The station wise comparison of rainfall in monsoon season provides satisfactory results. However, estimation of rainfall in mountainous areas is challenging task using the TRMM. In the basins of humid tropical regions, TRMM data can be a valuable source of rainfall data for water resource management and monitoring with some vigilance. In Kerala, the study found an insignificant increase in the southwest monsoon and winter season rainfall during last two decades. The rainfall over Kerala showed uncertainty in the distribution of monthly, seasonal and yearly time scales. This study provides a preview of recent weather patterns that would enable us to make better decisions and improve public policy against climate change.

Abstract. Spatial rainfall is a key input to Distributed Hydrological Models, which is the most important limitation for the accuracy of hydrological models. Model performance and uncertainty could increase when rain gauge is sparse. Satellite-based precipitation products would be an alternative to ground-based rainfall estimates in present and the foreseeable future, however, it is necessary to evaluate the products before further implication. The objective of this paper is to provide assessments of: (a) the Tropical Rainfall Measuring Mission (TRMM) rainfall product using gauge data, (b) the TRMM rainfall as forcing data for hydrological simulation, and (c) the role of satellite data in calculating water balance and water management. TRMM rainfall data show reasonable performances at monthly and annual scales, fitting well with surface observation-based histogram of precipitation. Satisfactory performances in monthly runoff simulation (NS = 0.50 ~ 0.70, R2 = 0.73 ~ 0.85) observed in our study indicate that the TRMM rainfall data have potential applications in driving hydrological model, water balance analysis, and basin water resource management in developing countries or remote locations, where precipitation gauges are scare.

The climatology of overshooting convection in tropical cyclones (TCs) is examined using Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR). The percentage of TC convective systems with overshooting convection is highest over the North Indian Ocean basin, while the northwest Pacific basin contains the highest population of both TC convective systems and convection with overshooting tops. Convective systems in the inner core region are more capable of penetrating 14 km and the associated overshooting convection are featured with much stronger overshooting properties compared with those in the inner rainband and outer rainband regions. In the inner core region of TCs, convection associated with precipitating systems of higher intensity and intensification rates has a larger probability of containing overshooting tops. To identify the relative importance of shallow/moderate versus deep/very deep convection in the rapid intensification (RI) of TCs, four types of precipitation-convection are defined based on the 20 dBZ radar echo height (Z20dBZ). Distributions of four types of precipitation-convection, and their contributions to total volumetric rain and total latent heating are quantified. It is shown that RI is closely associated with increased and widespread shallow precipitation around the storm center, while moderately deep and very deep convection (or overshooting convection) does not increase until in the middle of RI. This is further confirmed by the study of rainfall and convection evolution with respect to the timeline of RI events. Statistically, the onset of RI follows a significant increase in the areal coverage of rainfall, shallow precipitation, and cyan of 37 GHz color composites upshear-left, which in turn could be used as potential parameters to forecast RI. Very deep convection is most frequent 12-24 hours before RI onset and concentrates upshear-left, but it quickly decreases in the following 24 hours. The percent occurrence of very deep convection is less than 1% for RI storms. The tilt of vortex is large prior to, and near the RI onset, but rapidly decreases in the middle of RI, suggesting that the vertical alignment is a result instead of a trigger of RI.

There is being observed high variability in the spatial and temporal rainfall patterns under changing climate, enhancing both the intensity and frequency of the natural disasters like floods. Pakistan, a country which is highly prone to climate change, is recently facing the challenges of both flooding and severe water shortage as the surface water storage capacity is too limited to cope with heavy flows during rainy months. Thus, an effective and timely predication and management of high flows is a dire need to address both flooding and long term water shortage issues. The work of this thesis was aimed at developing and evaluating different open source data based methodologies for floods detection and analysis in Pakistan. Specifically, the research work was conducted for developing and evaluating a hydrologic model being able to run in real time based on satellite rainfall data, as well as to perform flood hazard mapping by analyzing seasonality of flooded areas using MODIS classification approach. In the first phase, TRMM monthly rainfall data (TMPA 3B43) was evaluated for Pakistan by comparison with rain gauge data, as well as by further focusing on its analysis and evaluation for different time periods and climatic zones of Pakistan. In the next phase, TRMM rainfall data and other open source datasets like digital soil map and global land cover map were utilized to develop and evaluate an event-based hydrologic model using HEC-HMS, which may be able to be run in real time for predicting peak flows due to any extreme rainfall event. Finally, to broaden the study canvas from a river catchment to the whole country scale, MODIS automated water bodies classification approach with MODIS daily surface reflectance products was utilized to develop a historical archive of reference water bodies and perform seasonal analysis of flooded areas for Pakistan. The approach was found well capable for its application for floods detection in plain areas of Pakistan. The open source data based hydrologic modeling approach devised in this study can be helpful for conducting similar rainfall-runoff modeling studies for the other river catchments and predicting peak flows at a river catchment scale, particularly in mountainous topography. Similarly, the outcomes of MODIS classification analysis regarding reference and seasonal water and flood hazard maps may be helpful for planning any management interventions in the flood prone areas of Pakistan.

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.

With increasing computational power, simulations of regional climate are now becoming possible on convective-resolving grids, thus eliminating the need for a convective parameterization. In the present study, a series of seasonal calculations using the Weather Research and Forecasting (WRF) model are computed at 4-km grid spacing, which reasonably resolves most convective systems. Simulations are computed for both the DJF and MAM seasons as averaged over 2005-2008, with a model domain covering the majority of the Amazon Basin and the adjacent South American coastline. Precipitation statistics are computed and compared to satellite rainfall retrieval data from the 13-year Tropical Rainfall Measuring Mission (TRMM) record. For comparison, a set of companion simulations with 12-km grid spacing are also computed, using the Kain-Fritsch convective parameterization. As compared to the 12-km runs, the 4-km simulations show significant improvement in the overall mean rain rate, the rain rate probability distributions, and the diurnal evolution and timing of precipitation. Both the 4-km and 12-km cases capture the coastal propagating signal and the interior basin-wide diurnal oscillation; however, the 4-km case shows better timing and evolution statistics. Compared to TRMM, the 4-km case rains too infrequently, but is more likely to produce rain events at high rain rates, thus resulting in a similar overall average rain rate. Overall, the present calculations show significant promise for computing regional rainfall patterns on convective-resolving grids.

AbstractThis chapter investigates extreme rainfall events that caused flood during summer months of June–September 2010–2014. The aim is to determine the impact of horizontal moisture flux divergence (HMFD) and vertical wind shear on forecasting extreme rainfall events over Nigeria. Wind divergence and convective available potential energy (CAPE) were also examined to ascertain their threshold values during the events. The data used include rainfall observation from 40 synoptic stations across Nigeria, reanalyzed datasets from ECMWF at 0.125° × 0.125° resolution and the Tropical Rainfall Measuring Mission (TRMM) dataset at resolution of 0.25° × 0.25°. The ECMWF datasets for the selected days were employed to derive the moisture flux divergence, wind shear, and wind convergence. The derived meteorological parameters and the CAPE were spatially analyzed and superimposed on the precipitation obtained from the satellite data. The mean moisture flux and CAPE for some northern Nigerian stations were also plotted for 3 days prior to and 3 days after the storm. The result showed that HMFD and CAPE increased few days before the storm and peak on the day of the storms, and then declined afterwards. HMFD values above 1.0 × 10−6 g kg−1 s−1 is capable of producing substantial amount of rainfall mostly above 50 mm while wind shear has a much weaker impact on higher rainfall amount than moisture availability. CAPE above 1000 Jkg−1 and 1500 Jk−1 are favorable for convection over the southern and northern Nigeria, respectively. The study recommends quantitative analysis of moisture flux as a valuable short-term severe storm predictor and should be considered in the prediction of extreme rainfall.

AbstractThis chapter investigates extreme rainfall events that caused flood during summer months of June–September 2010–2014. The aim is to determine the impact of horizontal moisture flux divergence (HMFD) and vertical wind shear on forecasting extreme rainfall events over Nigeria. Wind divergence and convective available potential energy (CAPE) were also examined to ascertain their threshold values during the events. The data used include rainfall observation from 40 synoptic stations across Nigeria, reanalyzed datasets from ECMWF at 0.125° × 0.125° resolution and the Tropical Rainfall Measuring Mission (TRMM) dataset at resolution of 0.25° × 0.25°. The ECMWF datasets for the selected days were employed to derive the moisture flux divergence, wind shear, and wind convergence. The derived meteorological parameters and the CAPE were spatially analyzed and superimposed on the precipitation obtained from the satellite data. The mean moisture flux and CAPE for some northern Nigerian stations were also plotted for 3 days prior to and 3 days after the storm. The result showed that HMFD and CAPE increased few days before the storm and peak on the day of the storms, and then declined afterwards. HMFD values above 1.0 × 10−6 g kg−1 s−1 is capable of producing substantial amount of rainfall mostly above 50 mm while wind shear has a much weaker impact on higher rainfall amount than moisture availability. CAPE above 1000 Jkg−1 and 1500 Jk−1 are favorable for convection over the southern and northern Nigeria, respectively. The study recommends quantitative analysis of moisture flux as a valuable short-term severe storm predictor and should be considered in the prediction of extreme rainfall.

The first part of this chapter presents a description of the GATE rain rate data (Polyak and North, 1995), its two-dimensional spectral and correlation characteristics, and multivariate models. Such descriptions have made it possible to show the concentration of significant power along the frequency axis in the spatial-temporal spectra; to detect a diurnal cycle (a range of variation of which is about 3.4 to 5.4 mm/hr); to study the anisotropy (as the result of the distinction between the north-south and east-west transport of rain) of spatial rain rate fields; to evaluate the scales of the distinction between second-moment estimates associated with ground and satellite samples; to determine the appropriate spatial and temporal scales of the simple linear stochastic models fitted to averaged rain rate fields; and to evaluate the mean advection velocity of the rain rate fluctuations. The second part of this chapter (adapted from Polyak et al., 1994) is mainly devoted to the diffusion of rainfall (from PRE-STORM experiment) by associating the multivariate autoregressive model parameters and the diffusion equation coefficients. This analysis led to the use of rain data to estimate rain advection velocity as well as other coefficients of the diffusion equation of the corresponding field. The results obtained can be used in the ground truth problem for TRMM (Tropical Rainfall Measuring Mission) satellite observations, for comparison with corresponding estimates of other sources of data (TOGA-COARE, or simulated by physical, models), for generating multiple rain samples of any size, and in some other areas of rain data analysis and modeling. For many years, the GATE data base has served as the richest and most accurate source of rain observations. Dozens of articles presenting the results of the GATE rain rate data analysis and modeling have been published, and more continue to be released. Recently, a new, valuable set of rain data was produced as a result of the TOGA-COARE experiment. In a few years, it will be possible to obtain satellite (TRMM) rain information, and a rain statistical description will be needed in the analysis of the observations obtained on an irregular spatial and temporal grid.

The presence of accurate and spatiotemporal data is of utmost importance in hydrological studies for river basins. However, limited ground-measured rainfall data restrict the accuracy of these analyses. Data scarcities can often be seen not only in many developing countries but also in the developed world. Therefore, much attention is given to alternative techniques to accomplish the data requirement. Precipitation data extraction from satellite precipitation products is one of the frequently used techniques in the absence of ground-measured rainfall data. The Mahaweli River Basin (MRB) is the largest river basin in Sri Lanka and it covers 1/6th of the total land area of the country. Mahaweli River is the heart of the country and the water of it is being used for many activities, including hydropower development, water supply, irrigation, etc. Therefore, analyzing rainfall trends of MRB is interesting and worthwhile for many stakeholders of the river basin. Therefore, this research investigates the suitability of Satellite Rainfall Products (SRP’s) as an alternative for Rain Gauge measured data in the MRB by performing trend analysis between the two datasets. Six precipitation products, namely PERSIANN, PERSIANNCCS, PERSIANN-CDR, GPM IMERG V06, TRMM-3B42 V7, TRMM-3B42RT V7 were extracted for 10-35 years for 14 locations of the MRB spatially distributed in the three climatic zones of the catchment. Non-parametric tests, including the Mann-Kendall test and Sen’s slope estimator tests, were used to detect the possible rainfall trends in precipitation products. Significant increasing trends were observed for both ground-measured and SRP’s in the annual scale while mixed results were observed in monthly and seasonal scales. The trends from ground-measured rainfall and SRP’s were compared and the suitability of SRP’s as an alternative technique was stated. KEYWORDS: ground-measured rainfall data, Mahaweli River Basin, rainfall trends, satellite precipitation products, PERSIANN, IMERG, TRMM