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

Self-organizing nonlinear output (SONO): A neural network suitable for cloud patch-based rainfall estimation at small scales

Accurate measurement of rainfall distribution at various spatial and temporal scales is crucial for hydrological modeling and water resources management. In the literature of satellite rainfall estimation, many efforts have been made to calibrate a statistical relationship (including threshold, linear, or nonlinear) between cloud infrared (IR) brightness temperatures and surface rain rates (RR). In this study, an automated neural network for cloud patch-based rainfall estimation, entitled self-organizing nonlinear output (SONO) model, is developed to account for the high variability of cloud-rainfall processes at geostationary scales (i.e., 4 km and every 30 min). Instead of calibrating only one IR-RR function for all clouds the SONO classifies varied cloud patches into different clusters and then searches a nonlinear IR-RR mapping function for each cluster. This designed feature enables SONO to generate various rain rates at a given brightness temperature and variable rain/no-rain IR thresholds for different cloud types, which overcomes the one-to-one mapping limitation of a single statistical IR-RR function for the full spectrum of cloud-rainfall conditions. In addition, the computational and modeling strengths of neural network enable SONO to cope with the nonlinearity of cloud-rainfall relationships by fusing multisource data sets. Evaluated at various temporal and spatial scales, SONO shows improvements of estimation accuracy, both in rain intensity and in detection of rain/no-rain pixels. Further examination of the SONO adaptability demonstrates its potentiality as an operational satellite rainfall estimation system that uses the passive microwave rainfall observations from low-orbiting satellites to adjust the IR-based rainfall estimates at the resolution of geostationary satellites. Copyright 2005 by the American Geophysical Union

Application of lightning data to satellite-based rainfall estimation

Information on lightning may improve rain estimates made from infrared images of a geostationary satellite. We address this proposition through a case from the Cooperative Huntsville Meteorological Experiment (COHMEX). During the afternoon and evening of 13 July 1986 waves of showers and thunderstorms developed over and near the lower Tennessee River Valley. For the shower and thunderstorm region within 200 km of the National Weather Service radar at Nashville, Tennessee, we measure cold-cloud area in a sequence of GOES infrared images covering all but the end of the shower and thunderstorm period. From observations of the NASA/Marshall direction-finding network in this small domain, we also count cloud-to-ground lightning flashes and, from scans of the Nashville radar, we calculate volume rain flux. Using a modified version of the Williams and Houze scheme, over an area within roughly 240 km of the radar (the large domain), we identify and track cold cloud systems. For these systems, over the large domain, we measure area and count flashes; over the small domain, we calculate volume rain flux. For a temperature threshold of 235K, peak cloud area over the small domain lags both peak rain flux and peak flash count by about four hours. At a threshold of 226K, the lag is about two hours. Flashes and flux are matched in phase. Over the large domain, nine storm systems occur. These range in size from 300 to 60,000 km(exp 2); in lifetime, from about 2 1/2 h to 6 h or more. Storm system area lags volume rain flux and flash count; nevertheless, it is linked with these variables. In essential respects the associations were the same when clouds were defined by a threshold of 226K. Tentatively, we conclude that flash counts complement infrared images in providing significant additional information on rain flux

An Adaptive Neural Network Scheme for Radar Rainfall Estimation from WSR-88D Observations

Recent research has shown that neural network techniques can be used successfully for ground rainfall estimation from radar measurements. The neural network is a nonparametric method for representing the rela-tionship between radar measurements and rainfall rate. The relationship is derived directly from a dataset consisting of radar measurements and rain gauge measurements. The effectiveness of the rainfall estimation by using neural networks can be influenced by many factors such as the representativeness and sufficiency of the training dataset, the generalization capability of the network to new data, season change, location change, and so on. In this paper, a novel scheme of adaptively updating the structure and parameters of the neural network for rainfall estimation is presented. This adaptive neural network scheme enables the network to account for any variability in the relationship between radar measurements and precipitation estimation and also to incorporate new information to the network without retraining the complete network from the beginning. This precipitation estimation scheme is a good compromise between the competing demands of accuracy and generalization. Data collected by a Weather Surveillance Radar—1988 Doppler (WSR-88D) and a rain gauge network were used to evaluate the performance of the adaptive network for rainfall estimation. It is shown that the adaptive network can estimate rainfall fairly accurately. The implementation of the adaptive network is very efficient and convenient for real-time rainfall estimation to be used with WSR-88D. 1

A Comparison of Rainfall Estimation Techniques

This study compares two techniques that have been developed for rainfall and streamflow estimation with the aim of identifying strengths and weaknesses of each. The first technique utilises thin plate smoothing splines to develop rainfall surfaces for the catchment, which are then, in conjunction with daily point-wise rainfall data used to determine areal catchment estimates. The second technique develops a regression-based model relating elevation to total annual rainfall in order to scale rainfall for daily mean catchment rainfall estimates. Both approaches are compared in common catchments in the upper Murrumbidgee catchment. The comparison includes using the data from each of the approaches as input to a rainfall-runoff model and by comparison of the quality of modelled results to observed streamflow. The strengths, weaknesses and use for catchment managers in decision making are identified. The study results revealed that where rain station spatial density and data quality are high, both regression and the spline method perform equally as well in estimating long term rainfall trends. In conclusion, catchment managers could apply the simple regression technique over the sophisticated spline method to achieve the comparable results. This is particularly useful where an efficient yet simple method is required for assessing streamflow within similar catchments

Pengaruh Penerapan Quality Control Data Radar Terhadap Akurasi Estimasi Curah Hujan Di Wilayah Pontianak Dan Sekitarnya

Weather radar operational have a limitation that leads to a quality reduction of radar rainfall estimation, such as interference at Pontianak’s weather radar. The application of 3D Clutter Map and Data Processing (3DCDP),  Brightband Echo Correction (BBC), and Z–Based Attenuation Correction (ZATC) which include in post–processing QC is the common procedure of radar limitation mitigation. This procedure applied to data of Pontianak weather radar over 1 January 2019 – 31 December 2019. The QC implemetation effect observed qualitatively and quantitatively by compare radar’s product before and after QC implementation. Data of radar rainfall estimation compared with rainfall observation data from AWS/ARG, and validated with RMSE and ME values. The comparison of the weather radar image shows that the interference and clutter around the weather radar is reduced. According to 19 validation value on the 10 cases of chosen rain, 18 value shows QC’s increased the accuracy of radar rainfall estimation, though insignificant and still underestimate

COMPARATIVE TEST OF SEVERAL RAINFALL ESTIMATION METHODS USING HIMAWARI-8 DATA

Indonesian society needs information on potential hydrometeorological disasters, therefore the development of rainfall estimation methods becomes an important research activities to support disaster risk reduction. Central Kalimantan were selected as research location for comparative test of rainfall estimation methods based on Himawari-8 IR1 (11μm) data, because it has area with cloud cover fairly intensive throughout the year. Some rainfall estimation methods tested in this research are AE, CST, CSTM, IMSRA. Non Linear Relation, and Non Linear Inversion. Each of these methods tends to have a weakness in the value of accuracy, so this research aims to determine the most accurate method to be applied in Palangkaraya (27 meters above sea level) city and Muratewe (60 meters above sea level) district in Central Kalimantan. The experiment was conducted during the period of highest rainfall in January and February 2016 by converting the temperature data cloud tops (IR1) into a precipitation with AE, CST, CSTM, IMSRA, Non Linear Relation and Non Linear Inversion method. Based on the results of quantitative analysis, it was known that IMSRA was the best method which can be applied in rainfall estimation in Muarateweh’s and Palangka Raya’s winter period. The Accuracy of all estimation methods decreased when it was applied in Palangka Raya at afternoon and in Muarateweh at night until early morning. The estimation method with the lowest score was the AE with an average MSE value > 90 and the best estimation method was IMSRA with MSE value <12

Estimation of physical variables from multichannel remotely sensed imagery using a neural network: Application to rainfall estimation

Satellite-based remotely sensed data have the potential to provide hydrologically relevant information about spatially and temporally varying physical variables. A methodology for estimating such variables from multichannel remotely sensed data is presented; the approach is based on a modified counterpropagation neural network (MCPN) and is both effective and efficient at building complex nonlinear input-output function mappings from large amounts of data. An application to high-resolution estimation of the spatial and temporal variation of surface rainfall using geostationary satellite infrared and visible imagery is presented. Test results also indicate that spatially and temporally sparse ground-based observations can be assimilated via an adaptive implementation of the MCPN method, thereby allowing on-line improvement of the estimates

A technique to obtain a multiparameter radar rainfall algorithm using the probability matching procedure

The natural cumulative distributions of rainfall observed by a network of rain gauges and a multiparameter radar are matched to derive multiparameter radar algorithms for rainfall estimation. Conventional usage of multiparameter radar measurements for rainfall estimation has been associated with tracking the variability of the raindrop size distribution. The use of multiparameter radar measurements in a statistical framework to estimate rainfall is presented in this paper. The techniques developed in this paper are applied to the radar and rain gauge measurement of rainfall observed in central Florida and central Italy. Conventional pointwise estimates of rainfall are also compared. The probability matching procedure, when applied to the radar and surface measurements, shows that multiparameter radar algorithms can match the probability distribution function better than the reflectivity-based algorithms, thereby indicating the potential of multiparameter radar measurements for statistical approach to rainfall estimation. It is also shown that the multiparameter radar algorithm derived matching the cumulative distribution function of rainfall provides more accurate estimates of rainfall on the ground in comparison to any conventional reflectivity-based algorithm