Simulation of operational typhoon rainfall nowcasting using radar reflectivity combined with meteorological data

In this study, a practical typhoon effective rainfall nowcasting (TERN) model was developed for use in real-time forecasting. The TERN model was derived from a data-driven adaptive network-based fuzzy inference system (ANFIS). The model inputs include meteorological data and radar reflectivity data. The model simulation process begins with an online typhoon warning issued by the Central Weather Bureau (CWB) of Taiwan. It is then determined whether the typhoon approaches the study area according to the typhoon track predicted by the CWB. When a typhoon hits Taiwan, various data are received from sensor instruments, including the ground precipitation data, typhoon climatological data, and radar reflectivity factor by using Weather Surveillance Radar, 1988, Doppler (WSR-88D) products. The study site was Shihmen Catchment. A maximum of 10 typhoon events from 2000 to 2010 were collected. Regarding the model construction, the input combinations of the ground precipitations and reflectivity factors over the catchment functioned as optimal input variables. To verify the practicability of the ANFIS-based TERN model, Typhoon Krosa, which hit Taiwan in 2007, was simulated. The results demonstrated that the proposed methodology of real-time rainfall forecasts during typhoon warning periods yielded favorable performance levels, reliably predicting results regarding 1 h to 6 h forecasting horizons.

[1]  Richard Taylor.  Interpretation of the Correlation Coefficient: A Basic Review , 1990 .

[2]  Caijun Yue,et al.  Responses of precipitation to vertical wind shear, radiation, and ice clouds during the landfall of Typhoon Krosa (2007) , 2011 .

[3]  Jyh-Shing Roger Jang,et al.  Self-learning fuzzy controllers based on temporal backpropagation , 1992, IEEE Trans. Neural Networks.

[4]  H. Sauvageot.  Rainfall measurement by radar: a review , 1994 .

[5]  C. Tsay.  Orography Effects on the Structure of Typhoons: Analyses of Two Typhoons Crossing Taiwan , 1994 .

[6]  Fi-John Chang,et al.  Adaptive neuro-fuzzy inference system for prediction of water level in reservoir , 2006 .

[7]  Chih-Chiang Wei.  Improvement of Typhoon Precipitation Forecast Efficiency by Coupling SSM/I Microwave Data with Climatologic Characteristics and Precipitation , 2013 .

[8]  L. Chua,et al.  Influence of lag time on event-based rainfall–runoff modeling using the data driven approach , 2012 .

[9]  K. P. Sudheer,et al.  Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions , 2010, Environ. Model. Softw..

[10]  Günther Heinemann,et al.  Real-time areal precipitation determination from radar by means of statistical objective analysis , 2008 .

[11]  Wei-Chiang Hong,et al.  Rainfall forecasting by technological machine learning models , 2008, Appl. Math. Comput..

[12]  Konstantine P. Georgakakos.  Covariance propagation and updating in the context of real-time radar data assimilation by quantitative precipitation forecast models , 2000 .

[13]  Robin Chadwick,et al.  An Artificial Neural Network Approach to Multispectral Rainfall Estimation over Africa , 2012 .

[14]  Isabella Morlini,et al.  Artificial neural network estimation of rainfall intensity from radar observations , 2000 .

[15]  Pao-Shan Yu,et al.  Comparison of neural network architectures and inputs for radar rainfall adjustment for typhoon events , 2011 .

[16]  Kwok-Wing Chau,et al.  Prediction of rainfall time series using modular soft computingmethods , 2013, Eng. Appl. Artif. Intell..

[17]  Chih-Chiang Wei.  Wavelet Support Vector Machines for Forecasting Precipitation in Tropical Cyclones: Comparisons with GSVM, Regression, and MM5 , 2012 .

[18]  Stephen L. Chiu,et al.  Fuzzy Model Identification Based on Cluster Estimation , 1994, J. Intell. Fuzzy Syst..

[19]  J. Gourley,et al.  An Exploratory Multisensor Technique for Quantitative Estimation of Stratiform Rainfall , 2002 .

[20]  Cheng‐Ku Yu,et al.  Radar observations of intense orographic precipitation associated with typhoon Xangsane (2000) , 2008 .

[21]  Nien-Sheng Hsu,et al.  A multipurpose reservoir real-time operation model for flood control during typhoon invasion , 2007 .

[22]  Nien-Sheng Hsu,et al.  Intelligent real-time operation of a pumping station for an urban drainage system , 2013 .

[23]  Mu-Yen Chen,et al.  A hybrid ANFIS model for business failure prediction utilizing particle swarm optimization and subtractive clustering , 2013, Inf. Sci..

[24]  Chuntian Cheng,et al.  A comparison of performance of several artificial intelligence , 2009 .

[25]  Maria Mimikou,et al.  Flood Forecasting Based on Radar Rainfall Measurements , 1996 .

[26]  A. K. Lohani,et al.  Hydrological time series modeling: A comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques , 2012 .

[27]  Chris Kidd,et al.  Satellite Rainfall Estimation Using a Combined Pasive Microwave and Infrared Algorithm. , 2003 .

[28]  Chih-Pei Chang,et al.  Effects of Terrain on the Surface Structure of Typhoons over Taiwan , 1993 .

[29]  Holger R. Maier,et al.  Input determination for neural network models in water resources applications. Part 1—background and methodology , 2005 .

[30]  Mark DeMaria,et al.  Evaluation of GFDL and Simple Statistical Model Rainfall Forecasts for U.S. Landfalling Tropical Storms , 2007 .

[31]  Dong-Jun Seo,et al.  The WSR-88D rainfall algorithm , 1998 .

[32]  Witold F. Krajewski,et al.  Radar rainfall estimation for flash flood forecasting in small urban watersheds , 2007 .

[33]  Gwo-Fong Lin,et al.  A hybrid neural network model for typhoon-rainfall forecasting , 2009 .

[34]  E. Anagnostou,et al.  Overland Precipitation Estimation from TRMM Passive Microwave Observations , 2001 .

[35]  Mansour Talebizadeh,et al.  Uncertainty analysis for the forecast of lake level fluctuations using ensembles of ANN and ANFIS models , 2011, Expert Syst. Appl..

[36]  Jinsheng Roan,et al.  Retrievals for the Rainfall Rate over Land Using Special Sensor Microwave Imager Data during Tropical Cyclones: Comparisons of Scattering Index, Regression, and Support Vector Regression , 2012 .

[37]  K. Chau,et al.  Prediction of rainfall time series using modular artificial neural networks coupled with data-preprocessing techniques , 2010 .

[38]  Tsung-Yu Lee,et al.  Linking typhoon tracks and spatial rainfall patterns for improving flood lead time predictions over a mesoscale mountainous watershed , 2012 .

[39]  Kun Shan Chen,et al.  Satellite and ground observations of the evolution of Typhoon Herb near Taiwan , 2001 .

[40]  J. Marshall,et al.  THE DISTRIBUTION OF RAINDROPS WITH SIZE , 1948 .

[41]  Hidde Leijnse,et al.  Identification and uncertainty estimation of vertical reflectivity profiles using a Lagrangian approach to support quantitative precipitation measurements by weather radar , 2013 .

[42]  Emmanouil N. Anagnostou,et al.  On the use of real‐time radar rainfall estimates for flood prediction in mountainous basins , 2000 .

[43]  Chris Kidd,et al.  Rainfall Estimation from a Combination of TRMM Precipitation Radar and GOES Multispectral Satellite Imagery through the Use of an Artificial Neural Network , 2000 .

[44]  P. W. Li,et al.  Short-range quantitative precipitation forecasting in Hong Kong , 2004 .

[45]  Yen-Ming Chiang,et al.  Dynamic ANN for precipitation estimation and forecasting from radar observations , 2007 .

[46]  Theodore B. Trafalis,et al.  Data mining techniques for improved WSR-88D rainfall estimation , 2002 .

[47]  Lloyd H.C. Chua,et al.  Runoff forecasting using a Takagi-Sugeno neuro-fuzzy model with online learning , 2013 .

[48]  M. Erol Keskin,et al.  Adaptive neural-based fuzzy inference system (ANFIS) approach for modelling hydrological time series , 2006 .

[49]  K. C. Tripathi,et al.  Prediction of Indian summer monsoon rainfall using Niño indices: A neural network approach , 2011 .

[50]  C. Shu,et al.  Regional flood frequency analysis at ungauged sites using the adaptive neuro-fuzzy inference system , 2008 .

[51]  Somia A. Asklany,et al.  Rainfall events prediction using rule-based fuzzy inference system , 2011 .

[52]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[53]  Vahid Nourani,et al.  A geomorphology-based ANFIS model for multi-station modeling of rainfall–runoff process , 2013 .

[54]  Witold F. Krajewski,et al.  Radar hydrology: rainfall estimation. , 2002 .

[55]  Qilong Min,et al.  Dynamic response of microwave land surface properties to precipitation in Amazon rainforest , 2013 .

[56]  Giovanni Emilio Perona,et al.  Accuracy of rainfall estimates by two radars in the same Alpine environment using gage adjustment , 2001 .

[57]  Cheng-shang Lee,et al.  A Climatology Model for Forecasting Typhoon Rainfall in Taiwan , 2006 .

[58]  Jay P. Breidenbach,et al.  Real-Time Correction of Spatially Nonuniform Bias in Radar Rainfall Data Using Rain Gauge Measurements , 2002 .

[59]  Partitioning the distribution function of radar reflectivity in convective storms using maximum likelihood method , 2013 .