The Experts below are selected from a list of 10932 Experts worldwide ranked by ideXlab platform
Wangchun Woo - One of the best experts on this subject based on the ideXlab platform.
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Operational Application of Optical Flow Techniques to Radar-Based Rainfall Nowcasting
Atmosphere, 2017Co-Authors: Wang-chun Woo, Wai-kin Wong, Wangchun Woo, Waikin WongAbstract:Hong Kong Observatory has been operating an in-house developed rainfall Nowcasting system called “Short-range Warning of Intense Rainstorms in Localized Systems (SWIRLS)” to support rainstorm warning and rainfall Nowcasting services. A crucial step in rainfall Nowcasting is the tracking of radar echoes to generate motion fields for extrapolation of rainfall areas in the following few hours. SWIRLS adopted a correlation-based method in its first operational version in 1999, which was subsequently replaced by optical flow algorithm in 2010 and further enhanced in 2013. The latest optical flow algorithm employs a transformation function to enhance a selected range of reflectivity for feature tracking. It also adopts variational optical flow computation that takes advantage of the Horn–Schunck approach and the Lucas–Kanade method. This paper details the three radar echo tracking algorithms, examines their performances in several significant rainstorm cases and summaries verification results of multi-year performances. The limitations of the current approach are discussed. Developments underway along with future research areas are also presented
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deep learning for precipitation Nowcasting a benchmark and a new model
Neural Information Processing Systems, 2017Co-Authors: Xingjian Shi, Waikin Wong, Hao Wang, Dityan Yeung, Zhihan Gao, Leonard Lausen, Wangchun WooAbstract:With the goal of making high-resolution forecasts of regional rainfall, precipitation Nowcasting has become an important and fundamental technology underlying various public services ranging from rainstorm warnings to flight safety. Recently, the Convolutional LSTM (ConvLSTM) model has been shown to outperform traditional optical flow based methods for precipitation Nowcasting, suggesting that deep learning models have a huge potential for solving the problem. However, the convolutional recurrence structure in ConvLSTM-based models is location-invariant while natural motion and transformation (e.g., rotation) are location-variant in general. Furthermore, since deep-learning-based precipitation Nowcasting is a newly emerging area, clear evaluation protocols have not yet been established. To address these problems, we propose both a new model and a benchmark for precipitation Nowcasting. Specifically, we go beyond ConvLSTM and propose the Trajectory GRU (TrajGRU) model that can actively learn the location-variant structure for recurrent connections. Besides, we provide a benchmark that includes a real-world large-scale dataset from the Hong Kong Observatory, a new training loss, and a comprehensive evaluation protocol to facilitate future research and gauge the state of the art.
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convolutional lstm network a machine learning approach for precipitation Nowcasting
Neural Information Processing Systems, 2015Co-Authors: Xingjian Shi, Waikin Wong, Zhourong Chen, Hao Wang, Dityan Yeung, Wangchun WooAbstract:The goal of precipitation Nowcasting is to predict the future rainfall intensity in a local region over a relatively short period of time. Very few previous studies have examined this crucial and challenging weather forecasting problem from the machine learning perspective. In this paper, we formulate precipitation Nowcasting as a spatiotemporal sequence forecasting problem in which both the input and the prediction target are spatiotemporal sequences. By extending the fully connected LSTM (FC-LSTM) to have convolutional structures in both the input-to-state and state-to-state transitions, we propose the convolutional LSTM (ConvLSTM) and use it to build an end-to-end trainable model for the precipitation Nowcasting problem. Experiments show that our ConvLSTM network captures spatiotemporal correlations better and consistently outperforms FC-LSTM and the state-of-the-art operational ROVER algorithm for precipitation Nowcasting.
Marc Berenguer - One of the best experts on this subject based on the ideXlab platform.
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Hydrological application of radar rainfall Nowcasting in the Netherlands.
Environment International, 2020Co-Authors: Danny Heuvelink, Marc Berenguer, C.c. Brauer, Remko UijlenhoetAbstract:Accurate and robust short-term rainfall forecasts (nowcasts) are useful in operational flood forecasting. However, the high temporal and spatial variability of rainfall fields make rainfall Nowcasting a challenging endeavour. To cope with this variability, Nowcasting techniques based on weather radar imagery have been proposed. Here, we employ radar rainfall Nowcasting for discharge predictions in three lowland catchments in the Netherlands, with surface areas ranging from 6.5 to 957 km2. Deterministic (Lagrangian persistence) and probabilistic (SBMcast) Nowcasting techniques are used to produce short-term rainfall forecasts (up to a few hours ahead), which are used as input for the hydrological model WALRUS. Rainfall forecasts were found to deteriorate with increasing lead time, often due to underestimation. Discharge could be forecasted 25–170 min earlier than without rainfall Nowcasting, with the best performance for the largest catchment. When accounting for catchment response time, the best (but most variable) relative performance was found for the smallest catchment. Probabilistic Nowcasting effectively accounted for the uncertainty associated with rainfall and discharge forecasts. The uncertainty in rainfall forecasts was found to be largest for the smaller catchments. The uncertainty in how much earlier the discharge could be forecasted (the gain in lead time) ranged from 15 to 50 min.
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Hydrological validation of a radar-based Nowcasting technique
Journal of Hydrometeorology, 2005Co-Authors: Marc Berenguer, Carles Corral, Rafael Sánchez-diezma, Daniel Sempere-torresAbstract:Nowcasting precipitation is a key element in the anticipation of floods in warning systems. In this framework, weather radars are very useful because of the high resolution of their measurements both in time and space. The aim of this study is to assess the performance of a recently proposed Nowcasting technique (S-PROG) from a hydrological point of view in a Mediterranean environment. S-PROG is based on the advection of weather radar fields according to the motion field derived with an algorithm based on tracking radar echoes by correlation (TREC), and it has the ability of filtering out the most unpredictable scales of these fields as the forecasting time increases. Validation of this Nowcasting technique was done from two different perspectives: (i) comparing forecasted precipitation fields against radar measurements, and (ii) by means of a distributed rainfall runoff model, comparing hydrographs simulated with a hydrological model using rainfall fields forecasted by S-PROG against hydrographs generated with the model using the entire series of radar measurements. In both cases, results obtained by a simpler Nowcasting technique are used as a reference to evaluate improvements. Validation showed that precipitation fields forecasted with S-PROG seem to be better than fields forecasted using simpler techniques. Additionally, hydrological validation led the authors to point out that the use of radar-based Nowcasting techniques allows the anticipation window in which flow estimates are forecasted with enough quality to be sensibly extended.
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hydrological validation of a radar based Nowcasting technique
Journal of Hydrometeorology, 2005Co-Authors: Marc Berenguer, Carles Corral, Rafael Sanchezdiezma, Daniel SemperetorresAbstract:Abstract Nowcasting precipitation is a key element in the anticipation of floods in warning systems. In this framework, weather radars are very useful because of the high resolution of their measurements both in time and space. The aim of this study is to assess the performance of a recently proposed Nowcasting technique (S-PROG) from a hydrological point of view in a Mediterranean environment. S-PROG is based on the advection of weather radar fields according to the motion field derived with an algorithm based on tracking radar echoes by correlation (TREC), and it has the ability of filtering out the most unpredictable scales of these fields as the forecasting time increases. Validation of this Nowcasting technique was done from two different perspectives: (i) comparing forecasted precipitation fields against radar measurements, and (ii) by means of a distributed rainfall runoff model, comparing hydrographs simulated with a hydrological model using rainfall fields forecasted by S-PROG against hydrographs...
Ross N Hoffman - One of the best experts on this subject based on the ideXlab platform.
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error propagation of radar rainfall Nowcasting fields through a fully distributed flood forecasting model
Journal of Applied Meteorology and Climatology, 2007Co-Authors: Enrique R Vivoni, Dara Entekhabi, Ross N HoffmanAbstract:Abstract This study presents a first attempt to address the propagation of radar rainfall Nowcasting errors to flood forecasts in the context of distributed hydrological simulations over a range of catchment sizes or scales. Rainfall forecasts with high spatiotemporal resolution generated from observed radar fields are used as forcing to a fully distributed hydrologic model to issue flood forecasts in a set of nested subbasins. Radar Nowcasting introduces errors into the rainfall field evolution that result from spatial and temporal changes of storm features that are not captured in the forecast algorithm. The accuracy of radar rainfall and flood forecasts relative to observed radar precipitation fields and calibrated flood simulations is assessed. The study quantifies how increases in Nowcasting errors with lead time result in higher flood forecast errors at the basin outlet. For small, interior basins, rainfall forecast errors can be simultaneously amplified or dampened in different flood forecast locat...
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extending the predictability of hydrometeorological flood events using radar rainfall Nowcasting
Journal of Hydrometeorology, 2006Co-Authors: Enrique R Vivoni, Dara Entekhabi, Rafael L Bras, V Y Ivanov, Matthew P Van Horne, Christopher Grassotti, Ross N HoffmanAbstract:Abstract The predictability of hydrometeorological flood events is investigated through the combined use of radar Nowcasting and distributed hydrologic modeling. Nowcasting of radar-derived rainfall fields can extend the lead time for issuing flood and flash flood forecasts based on a physically based hydrologic model that explicitly accounts for spatial variations in topography, surface characteristics, and meteorological forcing. Through comparisons to discharge observations at multiple gauges (at the basin outlet and interior points), flood predictability is assessed as a function of forecast lead time, catchment scale, and rainfall spatial variability in a simulated real-time operation. The forecast experiments are carried out at temporal and spatial scales relevant for operational hydrologic forecasting. Two modes for temporal coupling of the radar Nowcasting and distributed hydrologic models (interpolation and extended-lead forecasting) are proposed and evaluated for flood events within a set of nes...
Daniel Semperetorres - One of the best experts on this subject based on the ideXlab platform.
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hydrological validation of a radar based Nowcasting technique
Journal of Hydrometeorology, 2005Co-Authors: Marc Berenguer, Carles Corral, Rafael Sanchezdiezma, Daniel SemperetorresAbstract:Abstract Nowcasting precipitation is a key element in the anticipation of floods in warning systems. In this framework, weather radars are very useful because of the high resolution of their measurements both in time and space. The aim of this study is to assess the performance of a recently proposed Nowcasting technique (S-PROG) from a hydrological point of view in a Mediterranean environment. S-PROG is based on the advection of weather radar fields according to the motion field derived with an algorithm based on tracking radar echoes by correlation (TREC), and it has the ability of filtering out the most unpredictable scales of these fields as the forecasting time increases. Validation of this Nowcasting technique was done from two different perspectives: (i) comparing forecasted precipitation fields against radar measurements, and (ii) by means of a distributed rainfall runoff model, comparing hydrographs simulated with a hydrological model using rainfall fields forecasted by S-PROG against hydrographs...
Lei Han - One of the best experts on this subject based on the ideXlab platform.
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a machine learning Nowcasting method based on real time reanalysis data
Journal of Geophysical Research, 2017Co-Authors: Juanzhen Sun, Lei Han, Wei Zhang, Yuanyuan Xiu, Hailei Feng, Yinjing LinAbstract:Despite marked progress over the past several decades, convective storm Nowcasting remains a challenge because most Nowcasting systems are based on linear extrapolation of radar reflectivity without much consideration for other meteorological fields. The variational Doppler radar analysis system (VDRAS) is an advanced convective-scale analysis system capable of providing analysis of 3-D wind, temperature, and humidity by assimilating Doppler radar observations. Although potentially useful, it is still an open question as to how to use these fields to improve Nowcasting. In this study, we present results from our first attempt at developing a Support Vector Machine (SVM) Box-based Nowcasting (SBOW) method under the machine learning framework using VDRAS analysis data. The key design points of SBOW are as follows: 1) The study domain is divided into many position-fixed small boxes and the Nowcasting problem is transformed into one question, i.e., will a radar echo > 35 dBZ appear in a box in 30 minutes? 2) Box-based temporal and spatial features, which include time trends and surrounding environmental information, are constructed; and 3) The box-based constructed features are used to first train the SVM classifier, and then the trained classifier is used to make predictions. Compared with complicated and expensive expert systems, the above design of SBOW allows the system to be small, compact, straightforward, and easy to maintain and expand at low cost. The experimental results show that, although no complicated tracking algorithm is used, SBOW can predict the storm movement trend and storm growth with reasonable skill.
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A stochastic method for convective storm identification, tracking and Nowcasting
Progress in Natural Science, 2008Co-Authors: Lei Han, Guang Yang, Hongqing Wang, Yongguang Zheng, Yingjing LinAbstract:Abstract The convective storm identification, tracking and Nowcasting method is one of the important Nowcasting methodologies against severe convective weather. In severe convective cases, such as storm shape or rapid velocity changes, existing methods are apt to provide unsatisfied storm identification, tracking and Nowcasting results. To overcome these difficulties, this paper proposes a novel approach to identify, track and short-term forecast (nowcast) of convective storms. A mathematical morphology-based storm identification method is adopted which can identify storm cells accurately in a cluster of storms. As for the difficult tracking problem, sequential Monte Carlo (SMC) method is utilized to simplify the tracking process. It is not only inherently suitable for handling complicated splits and mergers, but also capable of handling the case of storm-missing detection. In order to provide more accurate forecast of a storm position, this method takes the advantages of the cross-correlation method. The qualitative and quantitative evaluations show the efficiency and robustness of the proposed approach.
