Short-term Rainfall Forecasts Research Articles

이류모델을 활용한 초단시간 강우예측의 적용성 평가

The frequency and size of typhoon and local severe rainfall are increasing due to the climate change and the damage also increasing from typhoon and severe rainfall. The flood forecasting and warning system to reduce the damage from typhoon and severe rainfall needs forecasted rainfall using radar data and short-term rainfall forecasting model. For this reason, this study examined the applicability of short-term rainfall forecast using translation model with weather radar data to point out that the utilization of flood forecasting in Korea. This study estimated the radar rainfall using Least-square fitting method and estimated rainfall was used as initial field of translation model. The translation model have verified accuracy of forecasted radar rainfall through the comparison of forecasted radar rainfall and observed rainfall quantitatively and qualitatively. Almost case studies showed that accuracy is over 0.6 within 4 hours leading time and mean of correlation coefficient is over 0.5 within 1 hours leading time in Kwanak and Jindo radar site. And, as the increasing the leading time, the forecast accuracy of precipitation decreased. The results of the calculated Mean Area Precipitation (MAP) showed forecast rainfall tend to be underestimated than observed rainfall but the correlation coefficient more than 0.5. Therefore it showed that translation model could be accurately predicted the rainfall relatively. The present results indicate that possibility of translation model application of Korea just within 2 hours leading forecasted rainfall.

The Application of Bayesian Neural Network in Rainfall Forecasting

The process of Rainfall Forecasting very complex and highly nonlinear and exhibits both temporal and spatial variability’s, In this article, a Rainfall Forecasting model using the Bayesian neural networks (BNN) is proposed for Rainfall Forecasting. The study uses the data from a coastal forest catchment. This article studies the accuracy of the short-term rainfall forecast obtained by BNN time-series analysis techniques and using antecedent rainfall depths and stream flow as the input information. The verification results from the proposed model indicate that the approach of BNN Rainfall Forecasting model presented in this paper shows a reasonable agreement in Rainfall Forecasting modeling with high accuracy.

Towards a definition of a real-time forecasting network for rainfall induced shallow landslides

Abstract. PREVIEW is an European Commission FP6 Integrated Project with the aim of developing, at an European level, innovative geo-information services for atmospheric, geophysical and man-made risks. Within this framework, the Landslides Platform Service 2 (forecasting of shallow rapid slope movements) has developed an integrated procedure for the forecasting and warning of distributed shallow landsliding to be used for civil protection purposes. The Service consists of an automated end-to-end forecasting chain which uses data from a probabilistic downscaled short-term rainfall forecast, soil saturation estimates and meteorological radar outputs. The above data are entered into a hydro-geological model that makes use of an infinite slope approach to calculate the distributed Factor of Safety over the entire basin. All outputs, and much of the input data, are shown on a WebGIS system so that end-users can interactively access and download data. A distinctive feature of the service is the use of an innovative soil depth model for predicting the distributed thickness of the regolith cover within the basin, which is one of the most important parameters controlling shallow landslide triggering. The service was developed in a pilot test site in NE Italy, the Armea basin. Validation makes use of two rainfall events: one that occurred in 2000 and a smaller, more recent event (2006) that caused fewer landslides. Rainfall data have been used to compute a distributed factor-of-safety map that has been overlaid onto the landslide inventory. Instead of a traditional validation approach based on the number count of correctly identified landslides, we carried out an alternative procedure based on the landslides area that gave outcomes which, for this preliminary stage of the research, can be considered promising.

Evaluation of RadVil, a Radar-Based Very Short-Term Rainfall Forecasting Model

Abstract A very short-term rainfall forecast model is tested on actual radar data. This model, called RadVil, takes advantages of voluminal radar data through vertically integrated liquid (VIL) water content measurements. The model is tested on a dataset collected during the intensive observation period of the Mesoscale Alpine Program (MAP). Five rain events have been studied during this experiment. The results confirm the interest of VIL for quantitative precipitation forecasting at very short lead time. The evaluation is carried out in qualitative and quantitative ways according to Nash and correlation criteria on forecasting times ranging from 10 to 90 min and spatial scales from 4 to 169 km2. It attempts to be consistent with the hydrological requirements concerning the rainfall forecasting, for instance, by taking account of the relation between the catchments' size, their response time, and the required forecasting time. Several versions of RadVil corresponding to several VIL measurement strategies have been tested. Improvements offered by RadVil depend on meteorological situations. They are related to the spatial and temporal evolution of the VIL field structure and the validity of the models assumptions. Finally, a relationship between the temporal structure of VIL fields and forecast quality is established.

Neural networks and non-parametric methods for improving real-time flood forecasting through conceptual hydrological models

Abstract. Time-series analysis techniques for improving the real-time flood forecasts issued by a deterministic lumped rainfall-runoff model are presented. Such techniques are applied for forecasting the short-term future rainfall to be used as real-time input in a rainfall-runoff model and for updating the discharge predictions provided by the model. Along with traditional linear stochastic models, both stationary (ARMA) and non-stationary (ARIMA), the application of non-linear time-series models is proposed such as Artificial Neural Networks (ANNs) and the ‘nearest-neighbours’ method, which is a non-parametric regression methodology. For both rainfall forecasting and discharge updating, the implementation of each time-series technique is investigated and the forecasting schemes which perform best are identified. The performances of the models are then compared and the improvement in the efficiency of the discharge forecasts achievable is demonstrated when i) short-term rainfall forecasting is performed, ii) the discharge is updated and iii) both rainfall forecasting and discharge updating are performed in cascade. The proposed techniques, especially those based on ANNs, allow a remarkable improvement in the discharge forecast, compared with the use of heuristic rainfall prediction approaches or the not-updated discharge forecasts given by the deterministic rainfall-runoff model alone. Keywords: real-time flood forecasting, precipitation prediction, discharge updating, time-series analysis techniques

Short period forecasting of catchment-scale precipitation. Part II: a water-balance storm model for short-term rainfall and flood forecasting

Abstract. A simple two-dimensional rainfall model, based on advection and conservation of mass in a vertical cloud column, is investigated for use in short-term rainfall and flood forecasting at the catchment scale under UK conditions. The model is capable of assimilating weather radar, satellite infra-red and surface weather observations, together with forecasts from a mesoscale numerical weather prediction model, to obtain frequently updated forecasts of rainfall fields. Such data assimilation helps compensate for the simplified model dynamics and, taken together, provides a practical real-time forecasting scheme for catchment scale applications. Various ways are explored for using information from a numerical weather prediction model (16.8 km grid) within the higher resolution model (5 km grid). A number of model variants is considered, ranging from simple persistence and advection methods used as a baseline, to different forms of the dynamic rainfall model. Model performance is assessed using data from the Wardon Hill radar in Dorset for two convective events, on 10 June 1993 and 16 July 1995, when thunderstorms occurred over southern Britain. The results show that (i) a simple advection-type forecast may be improved upon by using multiscan radar data in place of data from the lowest scan, and (ii) advected, steady-state predictions from the dynamic model, using "inferred updraughts", provides the best performance overall. Updraught velocity is inferred at the forecast origin from the last two radar fields, using the mass-balance equation and associated data and is held constant over the forecast period. This inference model proves superior to the buoyancy parameterisation of updraught employed in the original formulation. A selection of the different rainfall forecasts is used as input to a catchment flow forecasting model, the IH PDM (Probability Distributed Moisture) model, to assess their effect on flow forecast accuracy for the 135 km2 Brue catchment in Somerset. Keywords: rainfall forecasting, flood forecasting, weather radar, satellite, storm model

Comparison of short-term rainfall prediction models for real-time flood forecasting

This study compares the accuracy of the short-term rainfall forecasts obtained with time-series analysis techniques, using past rainfall depths as the only input information. The techniques proposed here are linear stochastic auto-regressive moving-average (ARMA) models, artificial neural networks (ANN) and the non-parametric nearest-neighbours method. The rainfall forecasts obtained using the considered methods are then routed through a lumped, conceptual, rainfall–runoff model, thus implementing a coupled rainfall–runoff forecasting procedure for a case study on the Apennines mountains, Italy. The study analyses and compares the relative advantages and limitations of each time-series analysis technique, used for issuing rainfall forecasts for lead-times varying from 1 to 6 h. The results also indicate how the considered time-series analysis techniques, and especially those based on the use of ANN, provide a significant improvement in the flood forecasting accuracy in comparison to the use of simple rainfall prediction approaches of heuristic type, which are often applied in hydrological practice.

A study of optimal model lag and spatial inputs to artificial neural network for rainfall forecasting

Artificial neural networks (ANNs), which emulate the parallel distributed processing of the human nervous system, have proven to be very successful in dealing with complicated problems, such as function approximation and pattern recognition. Due to their powerful capability and functionality, ANNs provide an alternative approach for many engineering problems that are difficult to solve by conventional approaches. Rainfall forecasting has been a difficult subject in hydrology due to the complexity of the physical processes involved and the variability of rainfall in space and time. In this study, ANNs were adopted to forecast short-term rainfall for an urban catchment. The ANNs were trained to recognise historical rainfall patterns as recorded from a number of gauges in the study catchment for reproduction of relevant patterns for new rainstorm events. The primary objective of this paper is to investigate the effect of temporal and spatial information on short-term rainfall forecasting. To achieve this aim, a comparison test on the forecast accuracy was made among the ANNs configured with different orders of lag and different numbers of spatial inputs. In developing the ANNs with alternative configurations, the ANNs were trained to an optimal level to achieve good generalisation of data. It was found in this study that the ANNs provided the most accurate predictions when an optimum number of spatial inputs was included into the network, and that the network with lower lag consistently produced better performance.

Interfacing an enhanced radar echo tracking algorithm with a rainfall-runoff model for real-time flood forecasting

The problem of combining short-term rainfall forecasts with rainfall-runoff models for use within a real-time flow forecasting system is addressed. A tracking algorithm called COTREC (COntinuity of TREC vectors) is used to provide quantitative rainfall estimates for input to the PDM (Probability Distributed Model) rainfall-runoff model. In general, improved rainfall forecasts feed through to enhance the quality of the flood forecast. Applying COTREC-derived (advection) rainfall forecasts slightly improved the flood forecast compared to persistence. Including a linear extrapolation of growth areas did not lead to more precise flood forecasts.

Development of a recurrent Sigma-Pi neural network rainfall forecasting system in Hong Kong

At the moment, weather forecasting is still an art — the experience and intuition of forecasters play a significant role in determining the quality of forecasting. This paper describes the development of a new approach to rainfall forecasting using neural networks. It deals with the extraction of information from radar images and an evaluation of past rain gauge records to provide shortterm rainfall forecasting. All of the meteorological data were provided by the Royal Observatory of Hong Kong (ROHK). Preprocessing procedures were essential for this neural network rainfall forecasting. The forecast of the rainfall was performed every half an hour so that a storm warning signal can be delivered to the public in advance. The network architecture is based on a recurrent Sigma-Pi network. The results are very promising, and this neural-based rainfall forecasting system is capable of providing a rain storm warning signal to the Hong Kong public one hour ahead.

SHORT-TERM RAINFALL FORECASTING BY RADAR DATA

Rainfall is the most important and direct agent that causes flood disaster. It is therefore the flood forecasting essentially depends on forecasting of rainfall. With radar rainfall data remotely observed by the Foundation of River & Basin Integrated Communications, i. e. FRICS, a new methodology is developed for accomplishing short-term rainfall forecasting, wherein two main components are included:(1) settlement of rainfall vector movement with modified correlation method and Fuzzy rule, and (2) determination of spatial and temporal distribution of rainfall intensity using neural network (NN) approach. Reasonable results have been derived with a high accuracy through rainfall data on a real time basis.