High-resolution Radar Rainfall Research Articles

A Neural Network-Based Hydrological Model for Very High-Resolution Forecasting Using Weather Radar Data

Many hydro-meteorological disasters in small and steep watersheds develop quickly and significantly impact human lives and infrastructures. High-resolution rainfall data and machine learning methods have been used as modeling frameworks to predict those events, such as flash floods. However, a critical question remains: How long must the rainfall input data be for an empirical-based hydrological forecast? The present article employed an artificial neural network (ANN)hydrological model to address this issue to predict river levels and investigate its dependency on antecedent rainfall conditions. The tests were performed using observed water level data and high-resolution weather radar rainfall estimation over a small watershed in the mountainous region of Rio de Janeiro, Brazil. As a result, the forecast water level time series only archived a successful performance (i.e., Nash–Sutcliffe model efficiency coefficient (NSE) > 0.6) when data inputs considered at least 2 h of accumulated rainfall, suggesting a strong physical association to the watershed time of concentration. Under extended periods of accumulated rainfall (>12 h), the framework reached considerably higher performance levels (i.e., NSE > 0.85), which may be related to the ability of the ANN to capture the subsurface response as well as past soil moisture states in the watershed. Additionally, we investigated the model’s robustness, considering different seeds for random number generating, and spacial applicability, looking at maps of weights.

Downscaling the Z-R relationship and bias correction solution for flash flood assessment in a data-scarce basin, Thailand.

Weather radar is a form of alternative indirect rainfall measurement for use in mitigating flash flood hazards. It is a challenging task to obtain accurate radar rainfall data without integration with automatic rain gauge networks. This paper investigated transformation equations to convert the calibrated daily Z-R relationship to the sub-hourly scale and proposed optional schemes for downscaling the daily bias adjustment factor into 15 min resolution scale to produce a high-resolution radar rainfall product for flash flood modelling. Radar reflectivity data from three radar stations in Thailand and their corresponding daily gauge rainfall data were used in the analysis. Two bias adjustment schemes (DMFB and DS_DMFB), accounting for the temporal variation, and one spatiotemporal scheme (SPTB_IDS) were used to generate three corresponding rainfall datasets for the unified river basin simulator (URBS) model to simulate flood hydrographs in the Tubma basin, Thailand. The results showed that combining the proposed 15-min Z-R scaling equation and the SPTB_IDS produced the most reliable radar rainfall amount leading to an increase in the accuracy of flood modelling with the lowest uncertainty. This indicated that the temporal downscaling solution together with spatial interpolation technique for sub-hourly radar rainfall assessment could benefit flash flood simulation in a data-scarce basin.

Long lead-time radar rainfall nowcasting method incorporating atmospheric conditions using long short-term memory networks

High-resolution radar rainfall data have great potential for rainfall predictions up to 6 h ahead (nowcasting); however, conventional extrapolation approaches based on in-built physical assumptions yield poor performance at longer lead times (3–6 h), which limits their operational utility. Moreover, atmospheric factors in radar estimate errors are often ignored. This study proposed a radar rainfall nowcasting method that attempts to achieve accurate nowcasting of 6 h using long short-term memory (LSTM) networks. Atmospheric conditions were considered to reduce radar estimate errors. To build radar nowcasting models based on LSTM networks (LSTM-RN), approximately 11 years of radar, gauge rainfall, and atmospheric data from the UK were obtained. Compared with the models built on optical flow (OF-RN) and random forest (RF-RN), LSTM-RN had the lowest root-mean-square errors (RMSE), highest correlation coefficients (COR), and mean bias errors closest to 0. Furthermore, LSTM-RN showed a growing advantage at longer lead times, with the RMSE decreasing by 17.99% and 7.17% compared with that of OF-RN and RF-RN, respectively. The results also revealed a strong relationship between LSTM-RN performance and weather conditions. This study provides an effective solution for nowcasting radar rainfall at long lead times, which enhances the forecast value and supports practical utility.

Towards understanding the impact of convective weather on aircraft arrival traffic at the Hong Kong International Airport

Convective weather (thunderstorm clusters with horizontal extents from tens to hundreds of kilometers, and vertically up to 10 - 15 kilometers in the troposphere) along aircraft flight trajectory is a key factor influencing air traffic management decisions and aviation safety. For an airport, aircraft arrival performance is typically more sensitive to weather conditions than departures. The occurrence of thunderstorms along or around flight routes may force the arriving aircraft to hold/vector in the terminal maneuvering area (TMA) following air traffic control (ATC) instructions, leading to additional fuel burn and delays. Such effects could cascade and bring further negative impacts to air traffic inside TMA. In this work, we perform a joint analysis of high-resolution radar rainfall distribution with 2-D aircraft flight trajectory data surrounding the Hong Kong International Airport (HKIA) to uncover the potential spatio-temporal impact of thunderstorms on air traffic. Results indicate that aircraft arrival traffic is sensitive to the location of convective weather in TMA, which also introduces a time lag effect on subsequent air traffic on the order of hours.

The Role of the Spatial Distribution of Radar Rainfall on Hydrological Modeling for an Urbanized River Basin in Japan

Recently, the use of gridded rainfall data with high spatial resolutions in hydrological applications has greatly increased. Various types of radar rainfall data with varying spatial resolutions are available in different countries worldwide. As a result of the variety in spatial resolutions of available radar rainfall data, the hydrological community faces the challenge of selecting radar rainfall data with an appropriate spatial resolution for hydrological applications. In this study, we consider the impact of the spatial resolution of radar rainfall on simulated river runoff to better understand the impact of radar resolution on hydrological applications. Very high-resolution polarimetric radar rainfall (XRAIN) data are used as input for the Hydrologic Engineering Center–Hydrologic Modeling System (HEC-HMS) to simulate runoff from the Tsurumi River Basin, Japan. A total of 20 independent rainfall events from 2012–2015 were selected and categorized into isolated/convective and widespread/stratiform events based on their distribution patterns. First, the hydrological model was established with basin and model parameters that were optimized for each individual rainfall event; then, the XRAIN data were rescaled at various spatial resolutions to be used as input for the model. Finally, we conducted a statistical analysis of the simulated results to determine the optimum spatial resolution for radar rainfall data used in hydrological modeling. Our results suggest that the hydrological response was more sensitive to isolated or convective rainfall data than it was to widespread rain events, which are best simulated at ≤1 km and ≤5 km, respectively; these results are applicable in all sub-basins of the Tsurumi River Basin, except at the river outlet.

Extreme rainfall from Hurricane Harvey (2017): Empirical intercomparisons of WRF simulations and polarimetric radar fields

We examine extreme rainfall from Hurricane Harvey (2017) based on empirical analyses from polarimetric radar observations as well as high-resolution model simulations using the Weather Research and Forecasting model (WRF). Spatial and temporal structures of extreme rainfall from Hurricane Harvey were characterized using a dense network of rain gauges and high-resolution radar rainfall fields. Numerical simulations using two different microphysical parameterizations, the WRF 6-class single-moment (WSM6) scheme and Morrison double-moment scheme, were employed, together with an additional simulation using the hail version of the Morrison microphysical scheme. Extreme rainfall from Hurricane Harvey is closely tied to the structure and evolution of outer rainbands. Intercomparisons of the simulated and observed polarimetric radar variables show contrasts and similarities of different microphysical schemes in representing critical microphysical processes for extreme rainfall. All three WRF simulations overestimate the frequency of larger rain drops, but exhibit comparable signatures of specific differential phase to observations. The WSM6 simulation shows strong convection that leads to the largest coverage of convective rainfall over outer rainbands of all three WRF simulations. We highlight the capabilities of atmospheric model simulations and improved quantitative rainfall estimates in characterizing key features of extreme rainfall from landfalling TCs as well as critical storm ingredients that produce them.

Does demand for subway ridership in Manhattan depend on the rainfall events?

The Northeast United States, particularly New York State has experienced an increase in extreme daily precipitation during the past 50 years. Recent events such as Hurricane Irene and Superstorm Sandy, have revealed vulnerability to the intense precipitation within the transportation sector. In the scale of New York City, where transit system is the most dominant mode of transportation and daily mobility of millions of passengers depends on it, any disruption in the transit service would result in gridlocks and massive delays. To assess the impacts of rainfall on the subway ridership, we merged high resolution radar rainfall and subway ridership data to conduct a detailed analysis for each of the 116 subway stations at the borough of Manhattan. The analysis is carried out on both hourly and daily resolution level, where a spatial-temporal Bayesian multi-level regression model is used to capture the underlying dependency between the parameters. The estimation results are obtained through Markov Chain Monte Carlo sampling method. The results for daily analysis indicate that during weekdays, transit ridership in the stations located in commercial zones are less sensitive to the rainfall compared to the ones in residential zones.

Multi-hydro hydrological modelling of a complex peri-urban catchment with storage basins comparing C-band and X-band radar rainfall data

ABSTRACTThe spread of impervious surfaces in urban areas combined with the rise in the intensity of rainfall events as a result of climate change has led to dangerous increases in storm water flows. This paper discusses a new implementation of the fully distributed hydrological model Multi-Hydro (developed at École des Ponts ParisTech), when operating storage basins, and its ability to deal with high-resolution radar rainfall data. The peri-urban area of Massy (south of Paris, France) was selected as a case study for having six of these drainage facilities, used extensively in flood control. Two radar rainfall datasets with different spatiotemporal resolutions were used: Météo-France’s PANTHER rainfall product (C-band) and ENPC’s X-band DPSRI. The rainfall spatiotemporal variability was analysed statistically using Universal Multifractals (UM). Finally, to validate the application, the water level simulations were compared with local measurements in the Cora storage basin located next to the catchment’s single outlet.

Spatial Analysis of High-Resolution Radar Rainfall and Citizen-Reported Flash Flood Data in Ultra-Urban New York City

New York City (NYC) is an ultra-urban region, with over 50% impervious cover and buried stream channels. Traditional flood studies rely on the presence of stream gages to detect flood stage and discharge, but these methods cannot be used in ultra-urban areas. Here we create a high-resolution radar rainfall dataset for NYC and utilize citizen and expert reports of flooding throughout the city to study flash flooding in NYC. Results indicate that interactions between the urban area and land–sea boundary have an important impact on the spatial variability of both heavy rainfall and flooding, sometimes in contrast to results obtained for other cities. Top days of daily and hourly rainfall exhibit a rainfall maximum over the city center and an extended region of higher rainfall downwind of the city. The mechanism for flooding appears to vary across the city, with high groundwater tables influencing more coastal areas and high rain rates or large rain volumes influencing more inland areas. There is also a strong relationship between sewer type and flood frequency, with fewer floods observed in combined sewer areas. Flooding is driven by maximum one-hour to one-day rainfall, which is often substantially less rain than observed for the city-wide daily maximum.

Spatial Characterization of Flood Magnitudes over the Drainage Network of the Delaware River Basin

Abstract A framework to characterize the distribution of flood magnitudes over large river networks is developed using the Delaware River basin in the northeastern United States as a principal study region. Flood magnitudes are characterized by the flood index, which is defined as the ratio of the flood peak for a flood event to the historical 10-yr flood magnitude. Event flood peaks are computed continuously over the drainage network using a distributed hydrologic model, CUENCAS, with high-resolution radar rainfall fields as the principal forcing. The historical 10-yr flood is calculated based on scaling relationships between the 10-yr flood and drainage area. Summary statistics for characterizing the probability distribution and spatial correlation of flood magnitudes over the drainage network are developed based on the flood index. This framework is applied to four flood events in the Delaware River basin that reflect the principal flood-generating mechanisms in the eastern United States: landfalling tropical cyclones (Hurricane Ivan in September 2004 and Hurricane Irene in August 2011), late winter/early spring extratropical systems (April 2005), and warm season convective systems (June 2006). The framework can be utilized to characterize the spatial distribution of floods, most notably for floods caused by landfalling tropical cyclones, which play an important role in controlling the upper tail of flood peak magnitudes over much of the eastern United States.

Urban pluvial flood prediction: a case study evaluating radar rainfall nowcasts and numerical weather prediction models as model inputs.

Flooding produced by high-intensive local rainfall and drainage system capacity exceedance can have severe impacts in cities. In order to prepare cities for these types of flood events - especially in the future climate - it is valuable to be able to simulate these events numerically, both historically and in real-time. There is a rather untested potential in real-time prediction of urban floods. In this paper, radar data observations with different spatial and temporal resolution, radar nowcasts of 0-2 h leadtime, and numerical weather models with leadtimes up to 24 h are used as inputs to an integrated flood and drainage systems model in order to investigate the relative difference between different inputs in predicting future floods. The system is tested on the small town of Lystrup in Denmark, which was flooded in 2012 and 2014. Results show it is possible to generate detailed flood maps in real-time with high resolution radar rainfall data, but rather limited forecast performance in predicting floods with leadtimes more than half an hour.

Lagrangian Analyses of Rainfall Structure and Evolution for Organized Thunderstorm Systems in the Urban Corridor of the Northeastern United States

AbstractIn this study, a climatology of the structure and evolution of rainfall for organized thunderstorm systems in the urban corridor of the northeastern United States is developed. These storm systems are major agents of flash flooding for urban regions of the northeastern United States and, more generally, for the United States east of the Rocky Mountains. The analyses are motivated by problems that center on characterizing flash flood hazards. The authors focus on spatial heterogeneities of rainfall associated with urbanization in a region of complex landscape including mountainous terrain and land–water boundaries along the geometrically complex coastline of the New York City–New Jersey metropolitan region. The sample of storms selected for investigation consists of the 50 days from April to September 2001–09 with the largest cloud-to-ground lightning flash density derived from National Lightning Detection Network (NLDN) observations over the study region. Storm-tracking analyses of 3D radar reflectivity fields are performed for the 50 storm days and used to develop a Lagrangian climatology of storm structure and evolution for the study region. Rainfall analyses for the 50 storm days are based on high-resolution (1 km, 15 min) bias-corrected radar rainfall fields developed from the Hydro-NEXRAD system. The analyses suggest that complex terrain and land–water boundaries have large impacts on Lagrangian storm properties. Areas of increased heavy rainfall and lightning flash density over New York City were identified. The authors found evidence for changing storm structure as thunderstorms pass over New York City, but little evidence that thunderstorms split as they approach New York City.

Correcting for wind drift in high resolution radar rainfall products: a feasibility study

Summary Increasing demands from emergency responders for accurate flood prediction, particularly in cities, have motivated consistent increases in the resolution of urban drainage models. Such models are now primarily limited by the accuracy and resolution of the initialising rainfall field. Surface rainfall estimates from radar, traditionally derived at scales of order 1 km, are now requested at grid lengths of 100 m to drive improvements in the outputs of these models. Deriving radar precipitation products on grids at the sub-kilometre scale introduces new requirements for the processing of reflectivity measurements into surface rainfall rates. A major source of uncertainty is the physical distance between the radar measurement and the surface onto which precipitation falls. Whilst adjustments to account for inhomogeneity in the vertical reflectivity profile have been extensively investigated, the effects of horizontal displacement have not. This paper discusses the issue of wind drift, first by outlining the need for correction, and then by evaluating the corrections available for impact at the required scale. One correction is detailed and its sensitivity evaluated with respect to the assumptions necessary in its derivation. These sensitivities are verified by trials on the Met Office operational radar processing system, where errors on wind drift displacement estimates are shown to be of order 1 km or more. This is significantly greater than the grid length desired by hydrological users. The paper therefore concludes by suggesting further research necessary to ensure the accuracy of radar precipitation estimates at sub-kilometre resolution.

On the sensitivity of urban hydrodynamic modelling to rainfall spatial and temporal resolution

Abstract. Cities are increasingly vulnerable to floods generated by intense rainfall, because of urbanisation of flood-prone areas and ongoing urban densification. Accurate information of convective storm characteristics at high spatial and temporal resolution is a crucial input for urban hydrological models to be able to simulate fast runoff processes and enhance flood prediction in cities. In this paper, a detailed study of the sensitivity of urban hydrodynamic response to high resolution radar rainfall was conducted. Rainfall rates derived from X-band dual polarimetric weather radar were used as input into a detailed hydrodynamic sewer model for an urban catchment in the city of Rotterdam, the Netherlands. The aim was to characterise how the effect of space and time aggregation on rainfall structure affects hydrodynamic modelling of urban catchments, for resolutions ranging from 100 to 2000 m and from 1 to 10 min. Dimensionless parameters were derived to compare results between different storm conditions and to describe the effect of rainfall spatial resolution in relation to storm characteristics and hydrodynamic model properties: rainfall sampling number (rainfall resolution vs. storm size), catchment sampling number (rainfall resolution vs. catchment size), runoff and sewer sampling number (rainfall resolution vs. runoff and sewer model resolution respectively). Results show that for rainfall resolution lower than half the catchment size, rainfall volumes mean and standard deviations decrease as a result of smoothing of rainfall gradients. Moreover, deviations in maximum water depths, from 10 to 30% depending on the storm, occurred for rainfall resolution close to storm size, as a result of rainfall aggregation. Model results also showed that modelled runoff peaks are more sensitive to rainfall resolution than maximum in-sewer water depths as flow routing has a damping effect on in-sewer water level variations. Temporal resolution aggregation of rainfall inputs led to increase in de-correlation lengths and resulted in time shift in modelled flow peaks by several minutes. Sensitivity to temporal resolution of rainfall inputs was low compared to spatial resolution, for the storms analysed in this study.

Analyses of the temporal and spatial structures of heavy rainfall from a catalog of high-resolution radar rainfall fields

In this paper, we develop a storm catalog of heavy rainfall events for a region centered on the Milwaukee, Wisconsin WSR-88D (Weather Surveillance Radar — 1988 Doppler) radar. The study region includes portions of southern Wisconsin, northern Illinois and Lake Michigan. The long-term objective of this study is to develop rainfall frequency analysis methods based on a storm catalog of major rain events. The specific objectives of this study are to develop a long-term catalog of high-resolution radar rainfall fields and characterize key features of the space–time variability of rainfall. The research questions that underlie these objectives are: 1) What are the spatial heterogeneities of rainfall over the study region for major flood-producing storm systems? 2) What are the key elements of storm evolution that control the scale-dependent properties of extreme rainfall? The storm catalog contains a record of the 50 “largest” storm days during the 1996–2011 observation period. We show that mean rainfall for the 50 largest storm days exhibits pronounced spatial heterogeneity with a broad maximum in western Wisconsin and a minimum in the eastern portion of the study region over Lake Michigan. We also show that there is a narrow line of maximum mean rainfall extending from west to east along the Wisconsin–Illinois border. This feature is tied to a maximum in the probability of daily rainfall exceeding 100mm. There are characteristic elements to the storm life cycle of heavy rainfall days that relate to size, structure and evolution of heavy rainfall. Extreme rainfall is also linked with severe weather (tornados, large hail and damaging wind). The diurnal cycle of rainfall for heavy rain days is characterized by an early peak in the largest rainfall rates, an afternoon–evening peak in rain area exceeding 25mmh−1 and development of a large stratiform rain area during the night and early morning.

Bias adjustment and advection interpolation of long-term high resolution radar rainfall series

Summary It is generally acknowledged that in order to apply radar rainfall data for hydrological proposes adjustment against ground observations are crucial. Traditionally, radar reflectivity is transformed into rainfall rates applying a fixed reflectivity – rainfall rate relationship even though this is known to depend on the changing drop size distribution of the specific rain. This creates a transient bias between the radar rainfall and the ground observations due to seasonal changes of the drop size distribution as well as other atmospheric effects and effects related to the radar observational technology. In this study different bias adjustment techniques is investigated, developing a complete 10-year dataset (2002–2012) of high spatio-temporal resolution radar rainfall based on a radar observations from a single C-band radar from Denmark. Results show that hourly adjustment mean field bias adjustment outperform daily mean field bias adjustment with regards to estimation of rainfall totals and peak rain rates. Furthermore, it is demonstrated that radar rainfall estimates can be improved significantly by implementation of a novel advection interpolation technique.

Critical Examination of Area Reduction Factors

AbstractArea reduction factors (ARFs), which are used to convert estimates of extreme point rainfall to estimates of extreme area-averaged rainfall, are central to conventional flood risk assessment. Errors in the estimation of ARFs can result in large errors in subsequent estimates of design rainfall and discharge. This paper presents a critical examination of commonly used ARFs, particularly those from the U.S. Weather Bureau TP-29, demonstrating that they do not adequately represent the true properties of extreme rainfall. This lack of representativeness is due mainly to formulations that mix rainfall observations from different storms and different storm types. Storm catalogs developed from a 10-year high-resolution radar rainfall data are used set to estimate storm-centered ARFs for Charlotte, North Carolina. Storms are classified as either tropical or nontropical to demonstrate that storm type strongly influences spatial rainfall structure. While there appears to be some relationship between ARF str...

Estimating the frequency of extreme rainfall using weather radar and stochastic storm transposition

Spatial and temporal variability in extreme rainfall, and its interactions with land cover and the drainage network, is an important driver of flood response. “Design storms,” which are commonly used for flood risk assessment, however, are assumed to be uniform in space and either uniform or highly idealized in time. The impacts of these and other commonly-made assumptions are rarely considered, and their impacts on flood risk estimates are poorly understood. This study presents an alternate framework for rainfall frequency analysis that couples stochastic storm transposition (SST) with “storm catalogs” developed from a ten-year high-resolution (15-min, 1-km2) radar rainfall dataset for the region surrounding Charlotte, North Carolina, USA. The SST procedure involves spatial and temporal resampling from these storm catalogs to reconstruct the regional climatology of extreme rainfall. SST-based intensity–duration–frequency (IDF) estimates are driven by the spatial and temporal rainfall variability from weather radar observations, are tailored specifically to the chosen watershed, and do not require simplifying assumptions of storm structure. We are able to use the SST procedure to reproduce IDF estimates from conventional methods for four urban watersheds in Charlotte. We demonstrate that extreme rainfall can vary substantially in time and in space, with potentially important flood risk implications that cannot be assessed using conventional techniques. SST coupled with high-resolution radar rainfall fields represents a useful alternative to conventional design storms for flood risk assessment, the full advantages of which can be realized when the concept is extended to flood frequency analysis using a distributed hydrologic model.

Spatial moments of catchment rainfall: rainfall spatial organisation, basin morphology, and flood response

Abstract. This paper describes a set of spatial rainfall statistics (termed "spatial moments of catchment rainfall") quantifying the dependence existing between spatial rainfall organisation, basin morphology and runoff response. These statistics describe the spatial rainfall organisation in terms of concentration and dispersion statistics as a function of the distance measured along the flow routing coordinate. The introduction of these statistics permits derivation of a simple relationship for the quantification of catchment-scale storm velocity. The concept of the catchment-scale storm velocity takes into account the role of relative catchment orientation and morphology with respect to storm motion and kinematics. The paper illustrates the derivation of the statistics from an analytical framework recently proposed in literature and explains the conceptual meaning of the statistics by applying them to five extreme flash floods occurred in various European regions in the period 2002–2007. High resolution radar rainfall fields and a distributed hydrologic model are employed to examine how effective are these statistics in describing the degree of spatial rainfall organisation which is important for runoff modelling. This is obtained by quantifying the effects of neglecting the spatial rainfall variability on flood modelling, with a focus on runoff timing. The size of the study catchments ranges between 36 to 982 km2. The analysis reported here shows that the spatial moments of catchment rainfall can be effectively employed to isolate and describe the features of rainfall spatial organization which have significant impact on runoff simulation. These statistics provide useful information on what space-time scales rainfall has to be monitored, given certain catchment and flood characteristics, and what are the effects of space-time aggregation on flood response modeling.

Use of radar rainfall estimates and forecasts to prevent flash flood in real time by using a road inundation warning system

Important damages occur in small headwater catchments when they are hit by severe storms with complex spatio-temporal structure, sometimes resulting in flash floods. As these catchments are mostly not covered by sensor networks, it is difficult to forecast these floods. This is particularly true for road submersions, representing major concerns for flood event managers. The use of Quantitative Precipitation Estimates and Forecasts (QPE/QPF) especially based on radar measurements could particularly be adequate to evaluate rainfall-induced risks. Although their characteristic time and space scales would make them suitable for flash flood modelling, the impact of their uncertainties remain uncertain and have to be evaluated. The Gard region (France) has been chosen as case study. This area is frequently affected by severe flash floods, and an application devoted to the road network has also been recently developed for the North part of this region. This warning system combines distributed hydro-meteorological modelling and susceptibility analysis to provide warnings of road inundations. The warning system has been tested on the specific storm of the 29–30 September 2007. During this event, around 200 mm dropped on the South part of the Gard and many roads were submerged. Radar-based QPE and QPF have been used to forecast the exact location of road submersions and the results have been compared to the effective road submersions actually occurred during the event as listed by the emergency services. Used on an area it has not been calibrated, the results confirm that the road submersion warning system represents a promising tool for anticipating and quantifying the consequences of storm events at ground. It rates the submersion risk with an acceptable level of accuracy and demonstrates also the quality of high spatial and temporal resolution radar rainfall data in real time, and the possibility to use them despite their uncertainties. However because of the quality of rainfall forecasts falls drastically with time, it is not often sufficient to provide valuable information for lead times exceeding 1 h.