Bias adjustment of infrared-based rainfall estimation using Passive Microwave satellite rainfall data

This study explores using Passive Microwave (PMW) rainfall estimation for spatial and temporal adjustment of Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System(PERSIANN-CCS). The PERSIANN-CCS algorithm collects information from infrared images to estimate rainfall. PERSIANN-CCS is one of the algorithms used in the IntegratedMultisatellite Retrievals for GPM (Global Precipitation Mission) estimation for the time period PMW rainfall estimations are limited or not available. Continued improvement of PERSIANN-CCS will support Integrated Multisatellite Retrievals for GPM for current as well as retrospective estimations of global precipitation. This study takes advantage of the high spatial and temporal resolution of GEO-based PERSIANN-CCS estimation and the more effective, but lower sample frequency, PMW estimation. The Probability Matching Method (PMM) was used to adjust the rainfall distribution of GEO-based PERSIANN-CCS toward that of PMW rainfall estimation. The results show that a significant improvement of global PERSIANN-CCS rainfall estimation is obtained

Estimating rainfall and water balance over the Okavango River Basin for hydrological applications

A historical database for use in rainfall-runoff modeling of the Okavango River Basin in Southwest Africa is presented. The work has relevance for similar data-sparse regions. The parameters of main concern are rainfall and catchment water balance which are key variables for subsequent studies of the hydrological impacts of development and climate change. Rainfall estimates are based on a combination of in-situ gauges and satellite sources. Rain gauge measurements are most extensive from 1955 to 1972, after which they are drastically reduced due to the Angolan civil war. The sensitivity of the rainfall fields to spatial interpolation techniques and the density of gauges was evaluated. Satellite based rainfall estimates for the basin are developed for the period from 1991 onwards, based on the Tropical Rainfall Measuring Mission (TRMM) and Special Sensor Microwave Imager (SSM/I) data sets. The consistency between the gauges and satellite estimates was considered. A methodology was developed to allow calibration of the rainfall-runoff hydrological model against rain gauge data from 1960-1972, with the prerequisite that the model should be driven by satellite derived rainfall products for the 1990s onwards. With the rain gauge data, addition of a single rainfall station (Longa) in regions where stations earlier were lacking was more important than the chosen interpolation method. Comparison of satellite and gauge rainfall outside the basin indicated that the satellite overestimates rainfall by 20%. A non-linear correction was derived used by fitting the rainfall frequency characteristics to those of the historical rainfall data. This satellite rainfall dataset was found satisfactory when using the Pitman rainfall-runoff model (Hughes et al., this issue). Intensive monitoring in the region is recommended to increase accuracy of the comprehensive satellite rainfall estimate calibration procedur

Hydrologic data for urban studies in the Austin, Texas, metropolitan area, 1981

This technical report includes maps of ground-water data-collection sites, rainfall for specific storms, storm rainfall-runoff for the 1981 water year, monthly water-level measurements of observation wells, water quality data, peak discharge, and daily rainfall summaries for specific gages.Waller Creek Working Grou

NON-PARAMETRIC STATISTICAL APPROACH TO CORRECT SATELLITE RAINFALL DATA IN NEAR-REAL-TIME FOR RAIN BASED FLOOD NOWCASTING

Floods resulting from intense rainfall are one of the most disastrous hazards in many regions of the world since they contribute greatly to personal injury and to property damage mainly as a result of their ability to strike with little warning. The possibility to give an alert about a flooding situation at least a few hours before helps greatly to reduce the damage. Therefore, scores of flood forecasting systems have been developed during the past few years mainly at country level and regional level. Flood forecasting systems based only on traditional methods such as return period of flooding situations or extreme rainfall events have failed on most occasions to forecast flooding situations accurately because of changes on territory in recent years by extensive infrastructure development, increased frequency of extreme rainfall events over recent decades, etc. Nowadays, flood nowcasting systems or early warning systems which run on real- time precipitation data are becoming more popular as they give reliable forecasts compared to traditional flood forecasting systems. However, these kinds of systems are often limited to developed countries as they need well distributed gauging station networks or sophisticated surface-based radar systems to collect real-time precipitation data. In most of the developing countries and in some developed countries also, precipitation data from available sparse gauging stations are inadequate for developing representative aerial samples needed by such systems. As satellites are able to provide a global coverage with a continuous temporal availability, currently the possibility of using satellite-based rainfall estimates in flood nowcasting systems is being highly investigated. To contribute to the world's requirement for flood early warning systems, ITHACA developed a global scale flood nowcasting system that runs on near-real-time satellite rainfall estimates. The system was developed in cooperation with United Nations World Food Programme (WFP), to support the preparedness phase of the WFP like humanitarian assistance agencies, mainly in less developed countries. The concept behind this early warning system is identifying critical rainfall events for each hydrological basin on the earth with past rainfall data and using them to identify floodable rainfall events with real time rainfall data. The individuation of critical rainfall events was done with a hydrological analysis using 3B42 rainfall data which is the most accurate product of Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) dataset. These critical events have been stored in a database and when a rainfall event is found in real-time which is similar or exceeds the event in the database an alert is issued for the basin area. The most accurate product of TMPA (3B42) is derived by applying bias adjustments to real time rainfall estimates using rain gauge data, thus it is available for end-users 10-15 days after each calendar month. The real time product of TMPA (3B42RT) is released approximately 9 hours after real-time and lacks of such kind of bias adjustments using rain gauge data as rain gauge data are not available in real time. Therefore, to have reliable alerts it is very important to reduce the uncertainty of 3B42RT product before using it in the early warning system. For this purpose, a statistical approach was proposed to make near real- time bias adjustments for the near real time product of TMPA (3B42RT). In this approach the relationship between the bias adjusted rainfall data product (3B42) and the real-time rainfall data product (3B42RT) was analyzed on the basis of drainage basins for the period from January 2003 to December 2007, and correction factors were developed for each basin worldwide to perform near real-time bias adjusted product estimation from the real-time rainfall data product (3B42RT). The accuracy of the product was analyzed by comparing with gauge rainfall data from Bangladesh and it was found that the uncertainty of the product is less even than the most accurate product of TMPA dataset (3B42

Analysis of global oceanic rainfall from microwave data

A Global Rainfall Atlas was prepared from Nimbus 5 ESMR data. The Atlas includes global oceanic rainfall maps based on weekly, monthly and seasonal averages, complete through the end of 1975. Similar maps for 1973 and 1974 were studied. They reveal several previously unknown areas of enhanced rainfall and preliminary data on interannual variability of oceanic rainfall

Stochastic Rainfall-runoff Model with Explicit Soil Moisture Dynamics

Stream runoff is perhaps the most poorly represented process in ecohydrological stochastic soil moisture models. Here we present a rainfall-runoff model with a new stochastic description of runoff linked to soil moisture dynamics. We describe the rainfall-runoff system as the joint probability density function (PDF) of rainfall, soil moisture and runoff forced by random, instantaneous jumps of rainfall. We develop a master equation for the soil moisture PDF that accounts explicitly for a general state-dependent rainfall-runoff transformation. This framework is then used to derive the joint rainfall-runoff and soil moisture-runoff PDFs. Runoff is initiated by a soil moisture threshold and a linear progressive partitioning of rainfall based on the soil moisture status. We explore the dependence of the PDFs on the rainfall occurrence PDF (homogeneous or state-dependent Poisson process) and the rainfall magnitude PDF (exponential or mixed-exponential distribution). We calibrate the model to 63 years of rainfall and runoff data from the Upper Little Tennessee watershed (USA) and show how the new model can reproduce the measured runoff PDF

Diurnal variability of tropical rainfall retrieved from combined GOES and TRMM satellite information

Recent progress in satellite remote-sensing techniques for precipitation estimation, along with more accurate tropical rainfall measurements from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and precipitation radar (PR) instruments, have made it possible to monitor tropical rainfall diurnal patterns and their intensities from satellite information. One year (August 1998-July 1999) of tropical rainfall estimates from the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) systems were used to produce monthly means of rainfall diurnal cycles at hourly and 1° × 1° scales over a domain (30°S-30°N, 80°E-10°W) from the Americas across the Pacific Ocean to Australia and eastern Asia. The results demonstrate pronounced diurnal variability of tropical rainfall intensity at synoptic and regional scales. Seasonal signals of diurnal rainfall are presented over the large domain of the tropical Pacific Ocean, especially over the ITCZ and South Pacific convergence zone (SPCZ) and neighboring continents. The regional patterns of tropical rainfall diurnal cycles are specified in the Amazon, Mexico, the Caribbean Sea, Calcutta, Bay of Bengal, Malaysia, and northern Australia. Limited validations for the results include comparisons of 1) the PERSIANN-derived diurnal cycle of rainfall at Rondonia, Brazil, with that derived from the Tropical Ocean Global Atmosphere Coupled Ocean-Atmosphere Response Experiment (TOGA COARE) radar data; 2) the PERSIANN diurnal cycle of rainfall over the western Pacific Ocean with that derived from the data of the optical rain gauges mounted on the TOGA-moored buoys: and 3) the monthly accumulations of rainfall samples from the orbital TMI and PR surface rainfall with the accumulations of concurrent PERSIANN estimates. These comparisons indicate that the PERSIANN-derived diurnal patterns at the selected resolutions produce estimates that are similar in magnitude and phase

Statistical analysis of the equivalent design rainfall

Statistical analyses of rainfall data are used for the design of sewerage systems and pump-stations, for the evaluation of the duration and the frequency of overflow in runoff detention facilities, for the determination of the critical influence on a municipal wastewater-treatment plant or for the protection of watercourses from storm-water runoff (e.g., from highways). The basic data in this calculation are the intensity and the duration of a rainstorm. Different procedures used in the analysis of Equivalent Design Rainfall (EDR) in Slovenia and abroad are described. The stochastic model used is presented in more detail because of its applicability for the determination of the probability of the occurrence of partial rainfalls of higher frequencies and the determination of the lower limit of rainfall evaluation. Computation procedures and the results of the evaluation of rainfall data according to the stochastic model are presented for Ljubljana

Multifractal analyses of daily rainfall time series in Pearl River basin of China

The multifractal properties of daily rainfall time series at the stations in Pearl River basin of China over periods of up to 45 years are examined using the universal multifractal approach based on the multiplicative cascade model and the multifractal detrended fluctuation analysis (MF-DFA). The results from these two kinds of multifractal analyses show that the daily rainfall time series in this basin have multifractal behavior in two different time scale ranges. It is found that the empirical multifractal moment function $K(q)$ of the daily rainfall time series can be fitted very well by the universal mulitifractal model (UMM). The estimated values of the conservation parameter $H$ from UMM for these daily rainfall data are close to zero indicating that they correspond to conserved fields. After removing the seasonal trend in the rainfall data, the estimated values of the exponent $h(2)$ from MF-DFA indicate that the daily rainfall time series in Pearl River basin exhibit no long-term correlations. It is also found that $K(2)$ and elevation series are negatively correlated. It shows a relationship between topography and rainfall variability.Comment: 16 pages, 7 figures, 1 table, accepted by Physica