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About 9,657 papers for “rainfall estimation”

Radar rainfall estimation

V. N. Bringi, V. Chandrasekar · Cambridge University Press eBooks · 2001

The detection and measurement of precipitation has been pursued since the beginnings of radar, and the early history has been summarized by Atlas and Ulbrich (1990). Joss and Waldvogel (1990) have reviewed the use of reflectivity at a single polarization to estimate rainfall. Dual-wavelength radars have been used to measure attenuation from which rainfall is estimated (Eccles and Mueller 1973). More recently, attenuation-based methods have been evaluated both theoretically and experimentally for airborne and spaceborne radars (for example, see Meneghini and Kozu 1990). This chapter focuses on dual-polarization radar methods to estimate rainfall (Seliga and Bringi 1976; 1978) based on the microphysical properties of rain discussed in Chapter 7.

Cited by 6

Rainfall Erosivity Estimation Using Daily Rainfall Amounts

Wen Zhang · 2002

Rainfall erosivity shows the potential ability of the soil loss caused by rainfall and it is very important for predicting soil loss quantitatively. A rainfall erosivity model using daily rainfall amounts to estimate half-month rainfall erosivity directly was established from data of 71 weather stations in China. The average coefficient of determination for all stations was 0. 718 and the average relative error estimating the annual average rainfall erosivity was 4.2% . Both parameters of α and β in the model were different in different regions. The parameter a was high correlative with parameter β and parameter β was related to rainfall characteristics, so both parameters could be estimated by using rainfall indexes. With the set of parameter values estimated by using rainfall indexs, the average coefficient of determination decreased to 0. 697 and the average relative error estimating the annual average rainfall erosivity increased to 17. 3% . The model worked very well in the regions where the rainfall was abundant and the result was on the low side when extreme storm erosivty was estimated. The daily rainfall erosivity model could be used to estimate the annual average rainfall erosivity and its seasonal distribution.

Cited by 221

Rainfall and its estimation

M. J. Manton · Australian meteorological magazine · 1993

The hydrologic cycle is the most important component of the global climate system. This is true on physical, biological and ecological grounds. Although water in the air makes up only 0.001 per cent of the total amount of water in the global system, it influences the behaviour of the atmosphere on scales ranging from microscopic to global. Precipitation is particularly significant to processes at the surface of the earth, and so there is a continuing effort to estimate rainfall on local, regional and global scales.

Cited by 0Open access

Rainfall Estimation and Attenuation Correction

V. Chandrasekar, Renzo Bechini · Cambridge University Press eBooks · 2023

A summary is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.

Cited by 0

Estimation of Rainfall Interstation Correlation

Emad Habib, Witold F. Krajewski, Grzegorz J. Ciach · Journal of Hydrometeorology · 2001

This study discusses questions of estimating correlation coefficient of point rainfall as observed at two measuring stations. The focus is on issues such as sensitivity to sample size, extreme rain events, and distribution of rainfall. The authors perform extensive analysis based on a two-point data-driven rainfall model that simulates the intermittence and extreme variability of rainfall using a bivariate mixed-lognormal distribution. The study examines the commonly used product–moment estimator along with an alternative transformation-based estimator. The results show a high level of bias and variance of the traditional correlation estimator, which are caused mostly by significant skewness levels that characterize rainfall observations. Application using data from a high-density cluster indicated the advantages of using the alternative estimator. The overall aim of the study is to draw the attention of researchers working with rainfall to some commonly disregarded issues when they seek accurate and reliable correlation information.

Cited by 141

Rainfall Estimation Over Tropical Oceans

C. Prabhakara, Maria Cadeddu, R. Meneghini et al. · NASA Technical Reports Server (NASA) · 1997

Multichannel dual polarization microwave radiometer SSM/I observations over oceans do not contain sufficient information to differentiate quantitatively the rain from other hydrometeors on a scale comparable to the radiometer field of view (approx. 30 km). For this reason we have developed a method to retrieve average rain rate over a mesoscale grid box of approx. 300 x 300 sq km area over the TOGA COARE region where simultaneous radiometer and radar observations are available for four months (Nov. 92 to Feb. 93). The rain area in the grid box, inferred from the scattering depression due to hydrometeors in the 85 Ghz brightness temperature, constitutes a key parameter in this method. Then the spectral and polarization information contained in all the channels of the SSM/I is utilized to deduce a second parameter. This is the ratio S/E of scattering index S, and emission index E calculated from the SSM/I data. The rain rate retrieved from this method over the mesoscale area can reproduce the radar observed rain rate with a correlation coefficient of about 0.85. Furthermore monthly total rainfall estimated from this method over that area has an average error of about 15%.

Cited by 0

Satellite rainfall estimation products (SRPs)

Mandela C M HOUNGNIBO · Zenodo (CERN European Organization for Nuclear Research) · 2023

This dataset is used as part of our study entitled: Validation of high-resolution satellite precipitation products over West Africa for rainfall monitoring and early warning. The data that makes up the set are:<br> - ARC2<br> - RFE2<br> - CMORPHv1.0<br> - IMERGv06-FINAL<br> - IMERGv06-Late<br> -MSWEP2<br> - PERSIANN-CCSCDR<br> - PERSIANN-DIRNow<br> - TAMSATv3.1

Cited by 0Open access

Microwave Rainfall Estimation over Coasts

J. McCollum, Ralph Ferraro · Journal of Atmospheric and Oceanic Technology · 2005

Abstract The microwave coastal rain identification procedure that has been used by NASA for over 10 yr, and also more recently by NOAA, for different instruments beginning with the Special Sensor Microwave Imager (SSM/I), is updated for use with Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and Advanced Microwave Scanning Radiometer (AMSR)-[Earth Observing System (EOS)] E microwave data. Since the development of the SSM/I algorithm, a wealth of both space-based and ground-based radar-rainfall estimates have become available, and here some of these data are used with collocated TMI and AMSR-E data to improve the estimation of coastal rain areas from microwave data. Two major improvements are made. The first involves finding the conditions where positive rain rates should be estimated rather than leaving the areas without estimates as in the previous algorithm. The second is a modification to the final step of the rain identification method; previously, a straight brightness temperature cutoff was used, but this is modified to a polarization-corrected temperature criterion. These modifications are made for the TRMM version 6 product release and the third (1 September) release of AMSR-E products to the public, both in 2004. The modifications are slightly different for each of these two sensors.

Cited by 50

Rainfall estimation with opportunistic sensors

Graf, Maximilian ca. 20./21. Jh. · 2024

Precipitation can impact human security significantly, and its accurate estimation in time and space is vital for various applications, including water management decisions and flash flood forecasting. Traditional observation systems like rain gauges, weather radars, and satellite instruments have individual limitations in capturing precipitation accurately and are not available in all regions of the world. Opportunistic rainfall sensors (OS) like commercial microwave links (CMLs) and personal weather stations (PWS) can provide additional rainfall information, and their numbers have surpassed the ones from traditional sensors. However, dealing with the heterogeneous quality of OS-derived precipitation information remains a scientific problem and limits their use in hydrologic applications. To address this gap, this thesis aims to develop methods for quality control, processing, and spatial reconstruction of OS data. First, data from 4000 CMLs in Germany, which are part of the cellular backhaul network, were used to derive rainfall information based on the attenuation of their microwave signal. CML processing methods were developed and optimized, leading to rainfall estimates with good quality when compared to a rain gauge-adjusted weather radar product used as a reference. To improve CML processing further, a novel method for the crucial step of rain event detection was developed using a convolutional neural network. This method improved the rainfall estimation significantly by reducing falsely classified rainfall by over 50%. With a similar performance for new CMLs and time periods, the method proved its ability to generalize to previously unseen data. CMLs may experience a total loss of signal due to high attenuation during heavy rainfall. These so-called blackouts were investigated using three years of CML and 20 years of weather radar data. Overall, only around one percent of rainfall was missed due to blackouts in the CML data. However, blackouts have to be considered in applications using CML rainfall estimates, as this one percent consisted of the most intense events. Surprisingly, longer CMLs had more blackouts, despite having a higher dynamic range to compensate for more attenuation that is caused by their length. PWS are another source of opportunistically sensed rainfall information. Data from around 20,000 PWS were evaluated individually and in combination with CML and rain gauge data in Germany. Filtering and interpolation methods were developed for these datasets, and the resulting rainfall maps were evaluated against three reference datasets covering different spatial and temporal scales. The OS-based products performed similarly well as operational radar products of the DWD, especially on local and regional scales with hourly resolution, and surpassed the quality of products using conventional rain gauges. In conclusion, this thesis demonstrates the development and evaluation of methods for filtering, processing, and combining CML and PWS data. The evaluation of the OS-based rainfall estimates proves that a quality similar to that of operational rainfall products can be achieved.

Cited by 0Open access

Design Rainfall Estimation and Changes

2018

Khaled Haddad is a researcher in water engineering at the University of Western Sydney and has 10 years’ experience in this Ÿeld. His research is focused on regional œood frequency analysis with a particular emphasis on uncertainty in regional œood estimation. He has published over 50 refereed papers and research reports in various aspects of statistical hydrology. Khaled is also heavily involved in the current revision of the “Australian Rainfall and Runo£: A Guide to Flow Estimation.”

Cited by 0

Rainfall estimation from satellite images

Diane Verna Ingraham · Open Collections · 2010

The design, management and operation (as well as the associated costs) of major water resource projects are directly related to the assessment of the anticipated volumes of runoff to be handled by the project. In remote or sparsely gauged regions it is often very difficult to determine these volumes due to a lack of data. The meteorological satellites, and in particular, the Geostationary Operational Environmental Satellites (GOES) can provide good areal coverage of the Earth and its weather systems potentially every half hour day and night. Since the first meteorological satellite images were transmitted, many attempts have been made to estimate rainfall using the images to identify specific cloud characteristics and correlating these with expected rainfall. However, these methods have been limited to convective rainfall in the tropics or near tropics. A method is presented here for estimating half-hourly rainfall which relates the vertical updraft velocities and hence, the moisture flux into the cloud, to the rate of vertical and horizontal growth of the cloud top as revealed in the GOES infrared images. The method performs well in estimating rainfall from widespread frontal systems common over British Columbia. A number of computational difficulties which arose during the research were resolved. One was to ascertain cloud top temperature contours for those GOES infrared images which were not enhanced. This involved the use of a video camera-special effects generator-video monitor system. The second was one of bookkeeping to "keep track of" the individual cloud cells. This was taken care of through the use of computer routines which directed the input and output of data, accounted for the growth and movement of the storm cells over the region and interpolated for rainfall at those locations which fell between adjacent precipitation contours. The method was used to estimate rainfall for a number of test storms occurring over British Columbia. The results were remarkably successful although there were some local inadequacies. An updating -procedure was developed in which the satellite estimated values of rainfall were improved by taking into consideration the information provided by concurrent rainfall observations. Furthermore, the parameters of the updating model (determined for gauged -locations) can be used to update rainfall estimates for ungauged locations. In the light of present raingauge installation and operating costs and the limitations of radar in mountainous areas, the satellite rainfall estimation procedure provides an economical operational supplement to existing conventional precipitation data collection.

Cited by 1Open access

Rainfall estimation from telecommunication network

Janer Cifre, Sebastià · RECERCAT (Consorci de Serveis Universitaris de Catalunya)

In Sahel, droughts and floods are a threat to the economy and the population. Rain Cell Africa is a development project with the purpose of measuring rainfall and anticipating risks related to excess (floods) or deficit (drought) of precipitation, based on mobile networks. Drops of water affect the radio signal exchange between two antennas. The power transmitted is attenuated and the drop of signal could be related to the integrated rain intensity. However, to estimate all these rain attenuations accurately, a depth analysis of signal?s behaviour is required. In this work, an exhaustive statistical analysis of signals from a few links has been done in order to have a reference of their behaviour. It has been characterized all signal variations during dry days and it has been compared with rain attenuations. Through these comparisons, it has been able to reduce some of these variations without reducing rain attenuation.

Cited by 0Open access

Rainfall Seasonality and Estimation Errors of Area-Average Rainfall

Chulsang Yoo · Journal of Korea Water Resources Association · 2002

본 연구에서는 강수의 계절성에 따라 면적평균강수의 추정오차가 어떻게 달라지는지를 평가하였다. 공간상관을 고려하는 경우와 고려하지 않는 경우 모두를 다루었으며, 각각의 경우에 대해 추정오차의 변화를 살펴보았다. 유사한 경우로서 계절성을 무시하고 누가시간을 증가시켜 추정오차가 어떻게 변하는지도 살펴보았다. 본 연구는 금강유역에 적용하였으며 30년 이상의 일 강수 기록을 가진 28개 지점의 자료를 이용하였다. 본 연구의 결과를 정리하면 다음과 같다: (1) 월 단위의 면적평균강수량에 대한 추정오차는 대체로 강수량에 비례하여 나타나며, 따라서 강한 계절성을 나타낸다. 그러나 이를 평균 강수량으로 나눈 상대오차는 1월과 12월을 제외하면 대략 5 - 8% 정도로 유사한 값을 보인다. (2) 연 강수량에 대한 추정오차는 연강수량의 3% 정도인 것으로 나타났다. (3) 그러나, 강수량이 아닌 강수량의 표준편차를 기준으로 삼는 경우 면적평균강우의 추정오차는 월 단위 및 년 단위에서 동일하게 표준편차의 11% 정도로 계산된다. (4) 마지막으로, 공간상관을 고려하지 않는 경우의 추정오차는 고려하는 경우의 2배 정도까지 커짐을 확인할 수 있었다. This study evaluates the variation of estimation error of area-average rainfall due to rainfall seasonality. Both the cases considering and not considering the spatial correlation are compared to derive the characteristics of estimation error. Similar cases with different accumulation time without considering the rainfall seasonality are also investigated. This study was applied to the Geum-river basin with total 28 rain gauge measurements haying more than 30 years of daily rainfall measurements. As results of the study we found that: (1) The absolute estimation error of monthly area-average rainfall show strong seasonality like the total rainfall amount. However, the relative estimation error normalized by its mean was estimated to have similar values about 5 to 8% except January and December. (2) The relative estimation error of annual area-average rainfall estimated was found to have the estimation error about 3% of its annual mean. (3) However, the relative estimation error normalized by the standard deviation remains almost the same for both monthly and annual rainfall amounts, which was estimated about 11% of its standard deviation. (4) Finally, the estimation error without considering the spatial correlation was found to become almost twice the estimation error with considering the spatial correlation.

Cited by 0Open access

Intensity estimation after detection for accumulated rainfall estimation

Taeer Weiss, Tirza Routtenberg, Jonatan Ostrometzky et al. · Frontiers in Signal Processing · 2024

This work focuses on optimizing the estimation of accumulated rain from measurements of the attenuation level of signals from commercial microwave links (CMLs). The process of accumulated rain estimation is usually based on estimation after detection, where it is first determined whether there is rain for a specific period, and then the accumulated rain at the detected rainy period is estimated. Naturally, errors in detection affect the accuracy of the consequent accumulated rain estimation. Traditionally, the detection and the estimation steps are designed independently. The detection threshold is arbitrarily set at the lowest level that would be declared as rain, without considering its effect on the accuracy of the accumulated rain estimation. This study applies a novel method that sets a detection threshold to optimize estimation after detection and apply it for accumulated rain estimation. It is based on optimizing a post-detection estimation risk function that incorporates both the estimation and detection-related errors; this essentially takes into consideration the coupling of the detection and the estimation stages and thus optimizes the overall accumulated rainfall estimation. The proposed approach is applied to actual CML attenuation measurements taken from a cellular network in Gothenburg, Sweden. This demonstrates that the proposed method achieves better accuracy for accumulated rain estimation compared with the detection threshold being set independently.

Cited by 2Open access