Estimate Rainfall Fields Research Articles

The NEFOCAST System for Detection and Estimation of Rainfall Fields by the Opportunistic Use of Broadcast Satellite Signals

In this paper, we present results from the NEFOCAST Project, funded by the Tuscany Region, aiming at detecting and estimating rainfall fields from the opportunistic use of the rain-induced excess attenuation incurred in the downlink channel by a commercial DVB satellite signal. The attenuation is estimated by reverse-engineering the effects of the various propagation phenomena affecting the received signal, among which, in first place, the perturbations factors affecting geostationary orbits, such as the gravitational attraction from the moon and the sun and the inhomogeneity in Earth mass distribution and, second, the small-scale irregularities in the atmospheric refractive index, causing rapid fluctuations in signal amplitude. The latter impairments, in particular, even if periodically counteracted by correction maneuvers, may give rise to significant departures of the actual satellite position from the nominal orbit. A further problem to deal with is the daily and seasonal random fluctuation of the rain height and altitude/size of the associated melting layer. All of the above issues lead to nonnegligible random deviations from the dry nominal downlink attenuation, that can be misinterpreted as rain events. In this paper, we show how to counteract these issues by employing two differentially configured Kalman filters designed to track slow and fast changes of the received signal-to-noise ratio, so that the rain events can be reliably detected and the relevant rainfall rate estimated.

An Assessment on Geographically Weighted Regression-Based Merging Method of Satellite and Gauge Rainfall

Satellite-derived and gauge rainfall data are incompatible spatial data. Although great efforts have been devoted to their combination recently, it is still a complicated issue to be addressed. In this study, the performance of merging satellite and gauge rainfall analyses is examined over a humid region in Southeast China. Using satellite rainfall from TRMM 3B43V7 and ground rain gauge measurements, a geographically weighted regression (GWR) based statistical merging algorithm was proposed to continuously produce monthly rainfall fields at 1-km resolution during the period 2003-2009. Results indicate that the benefits of this rainfall merging approach were remarkable only when the gauge density is lower than one gauge per 1,500 km2, suggesting the information provided by TRMM 3B43V7 is more useful for estimating rainfall fields when the ground measurements are rather sparse.

An improved statistical approach to merge satellite rainfall estimates and raingauge data

Deriving high quality daily rainfall estimates are required not only for successful hydrological modelling but also for its application in ungauged basins. At present, there are two resources to estimate rainfall fields: raingauge stations and remote sensing devices (such as satellites and radar). The observations obtained from raingauges are accurate and reliable, but are usually limited to interpolate daily rainfall as input into hydrological models by spatial coverage. Hence, the areal precipitation estimates constructed solely by raingauges exhibit a great deal of uncertainty where the raingauge network is sparse, especially for local convective events. Satellite-based gridded precipitation estimates are produced at high temporal (3 h) and reasonably high spatial resolution (0.25° × 0.25°). However, the accuracy of these surfaces is lower than using ground based observations. The main aim of this paper is to present a novel method of generating a synthesis of a discrete set of point raingauge observations and a satellite derived rainfall product. This method stems from some ideas of data assimilation and focuses on the difference between gridded and point data. A nonparametric kernel smoothing method is employed in this merging strategy with emphasis on discontinuity correction and spatial interpolation adapting for sparse design. A cross-validation study was undertaken to blend observations from the Australian raingauge network and satellite derived TRMM Multisatellite Precipitation Analysis (TMPA) 3B42. In order to test the proposed method, several methods including ordinary kriging and cokriging were trialled. The comparison showed that the bias results produced by the proposed method were far superior to the other methods trialled and other error statistics such as root mean square error were comparable or better (although the kriging methods tended to perform better in densely gauged areas). In particular, the bias of the proposed method was reduced consistently over the whole country, whilst the kriging methods trialled produced relatively large biases across the country leading to near overall zero bias. In addition to improving quantitative efficiency, the estimated rainfall field showed good visual performance in the presence of sparse gauge stations.

Time-dependent <i>Z-R</i> relationships for estimating rainfall fields from radar measurements

Abstract. The operational use of weather radars has become a widespread and useful tool for estimating rainfall fields. The radar-gauge adjustment is a commonly adopted technique which allows one to reduce bias and dispersion between radar rainfall estimates and the corresponding ground measurements provided by rain gauges. This paper investigates a new methodology for estimating radar-based rainfall fields by recalibrating at each time step the reflectivity-rainfall rate (Z-R) relationship on the basis of ground measurements provided by a rain gauge network. The power-law equation for converting reflectivity measurements into rainfall rates is readjusted at each time step, by calibrating its parameters using hourly Z-R pairs collected in the proximity of the considered time step. Calibration windows with duration between 1 and 24 h are used for estimating the parameters of the Z-R relationship. A case study pertaining to 19 rainfall events occurred in the north-western Italy is considered, in an area located within 25 km from the radar site, with available measurements of rainfall rate at the ground and radar reflectivity aloft. Results obtained with the proposed method are compared to those of three other literature methods. Applications are described for a posteriori evaluation of rainfall fields and for real-time estimation. Results suggest that the use of a calibration window of 2–5 h yields the best performances, with improvements that reach the 28% of the standard error obtained by using the most accurate fixed (climatological) Z-R relationship.

레이더 정량강우와 연계한 홍수유출 및 범람해석 시스템 확립 II. Cokriging을 이용한 2차원 정량강우 산정

수문모형의 많은 입력자료 가운데 강우 관측자료가 모의결과에 가장 주요한 원인을 가진다. 과거부터 수문모형은 유역에서의 평균강우량 자료를 한 지점에서의 강우량 자료에 의존해 왔다. 그러나, 지점에서 측정된 강우량 자료를 유역을 대표하는 평균강우량 산정에 이용할 경우 부적절한 경우가 발생할 수 있으며, 특히 그 계측망이 조밀하지 못할 경우 더욱 그러하다. 레이더의 경우 보다 넓은 유역 전체를 관측하기 때문에 유역에서의 평균강우량을 제공함에 있어 좋은 관측장비라 할 수 있다. 레이더를 이용하여 강우를 관측할 경우 직접적인 강우관측이 아니기 때문에 QPE에서 몇몇 한계가 있을 수 있으나, 강우자료의 공간변화도에 대한 좋은 정보를 제공해 준다. 또한 지상의 강우관측소에서의 강우측정자료는 지점에서의 강우량에 대한 정확한 정보를 제공해 준다. 따라서, 본 연구의 목적은 지상의 강우관측소에서 관측된 강우량 자료와 레이더를 이용하여 관측된 자료, 즉 두 가지 관측자료를 Ordinary cokriging 보간을 이용하여 통합함으로써 개선된 강우량 추정방법을 개발한다. Among various input data to hydrologic models, rainfall measurements arguably have the most critical influence on the performance of hydrologic model. Traditionally, hydrologic models have relied on point gauge measurements to provide the area-averaged rainfall information. However, rainfall estimates from gauges become inadequate due to their poor representation of areal rainfall, especially in situations with sparse gauge network. Alternatively, radar that covers much larger areas has become an attractive instrument for providing area- averaged precipitation information. Despite of the limitation of the QPE(Quantitative Precipitation Estimation) using radar, we can get the better information of spatial variability of rainfall fields. Also, rain-gauges give us the better quantitative information of rainfall field. Therefore, in this study, we developed improved methodologies tu estimate rainfall fields using an ordinary cokriging technique which optimally merges radar reflectivity data into rain-gauges data.

Integration of weather radar data into a raster GIS framework for improved flood estimation

AbstractWeather radar data were used to estimate rainfall fields at 2‐km resolution for a large flood event in 1999 in south‐east Queensland, Australia, and subsequently integrated with a raster‐based hydrologic model (RAMS) for runoff generation and flow routing. Gauge‐based and radar‐based temporal storm patterns are quite similar for the storm event. Agreement between gauge‐based and radar‐based event rainfall totals is not as good as that for spatially averaged intensities. Correlation between gauge‐based and radar‐based rainfall measurements is not sensitive to the exponent value in the Z–R relationship for the event tested. The hydrologic model with 4 parameters for the entire 13 600‐km2 catchment works very well when calibrated against the measured hydrograph. An overall model efficiency of 0.61 is achieved with respect to predicted peak discharge for 17 validation sites in the catchment. RAMS compares favourably with URBS, a rainfall‐runoff model using gauge‐based measurements currently in use for flood forecasting purposes. Copyright © 2005 Royal Meteorological Society

On the use of NDVI for estimating rainfall fields in the Kalahari of Botswana

This article presents the results of an experiment to estimate rainfall from the normalized difference vegetation index (NDVI). The study was carried out in the semi-arid region of south and southern Botswana, principally the Kalahari desert, where few rainfall-reporting stations exist. A statistical regression between NDVI and rainfall is developed for four areas with diverse soil conditions. These are used to estimate rainfall at an independent station or group of stations. The estimates reliably capture both the seasonal cycle and inter-annual variability. These regressions are used to produce a map of mean annual rainfall for the period 1982 to 1993, based on NDVI data, for the region 22 to 27°S and 20 to 26°E. The estimates are verified using all data available in the Meteorological Services' archive for these years.

Simulation in order to choose a fitting method for extreme rainfall data

It is possible to estimate the relationship between rain magnitude and frequency of occurence by observing maximum annual data with the parametric methods of fitting any of various theorical distributions or with the nonparametric method which does not require a distributional assumption. In this article through some simulations, five methods are considered. The nonparametric method of Adamowski using the kernel function always gives an ideal distribution, but it is not possible to extrapolate the rainfall values of large return-periods. The comparison of the methods by simulation shows the clear superiority of the nonparametric method of Villasenor to estimate rainfall fields.

A method for delineating and estimating rainfall fields

A geostatistical model has been defined to deal with intermittent rainfall fields. The intermittency is represented by a binary random function, while, inside the rainy areas, the rainfall variability is rendered by an intrinsic random function. A rain gage data set from the Cevennes region (France) is used to infer the model. The inner variability is independent of the intermittency, thereby allowing a simple estimation relying on the separate kriging of these two components. Comparison of the rainfall assessments made by a classic global kriging and the proposed method shows its clear superiority for delineating and estimating rainfall fields.

A method for delineating and estimating rainfall fields

A method for delineating and estimating rainfall fields