Improvement of rainfall and flood forecasts by blending ensemble NWP rainfall with radar prediction considering orographic rainfall

Summary Many basins in Japan are characterized by steep mountainous regions, generating orographic rainfall events. Orographic rainfall may cause localized heavy rainfall to induce flash floods and sediment disasters. However, the accuracy of radar-based rainfall prediction was not enough because of the complex geographical pattern of the mountainous areas. In order to reduce damage due to localized heavy rainfall, characteristics of orographic rainfall must be identified into a short-term rainfall prediction procedure. The accuracy of radar-based rainfall prediction performs best for very short lead time, however the accuracy of radar prediction rapidly decreases with increasing lead times. At longer lead times, higher accuracy QPFs are produced by Numerical Weather Prediction (NWP) models, which solve the dynamics and physics of the atmosphere. This study proposes hybrid blending system of ensemble information from radar-based prediction and numerical weather prediction (NWP) to improve the accuracy of rainfall and flood forecasting. First, an improved radar image extrapolation method, which is comprised of the orographic rainfall identification and the error ensemble scheme, is introduced. Then, ensemble NWP outputs are updated based on mean bias of the error fields considering error structure. Finally, the improved radar-based prediction and updated NWP rainfall considering bias correction are blended dynamically with changing weight functions, which are computed from the expected skill of each radar prediction and updated NWP rainfall. The proposed method is verified temporally and spatially through a target event and is applied to the hybrid flood forecasting for updating with 1 h intervals. The newly proposed method shows sufficient reproducibility in peak discharge value, and could reduce the width of ensemble spread, which is expressed as the uncertainty, in the flood forecasting. Our study is carried out and verified using the largest flood event by typhoon ‘Talas’ of 2011 over the two catchments, which are Futatsuno (356.1 km2) and Nanairo (182.1 km2) dam catchments of Shingu river basin (2360 km2), which is located in the Kii peninsula, Japan.

[1]  A. Weerts,et al.  On noise specification in data assimilation schemes for improved flood forecasting using distributed hydrological models , 2013 .

[2]  T. Palmer,et al.  Stochastic representation of model uncertainties in the ECMWF ensemble prediction system , 2007 .

[3]  充晴 椎葉,et al.  Investigation of Short-Term Rainfall Prediction Method by a Translation Model , 1984 .

[4]  B. Golding.  Nimrod: a system for generating automated very short range forecasts , 1998 .

[5]  I. Zawadzki,et al.  Precipitation forecast skill of numerical weather prediction models and radar nowcasts , 2005 .

[6]  W. Collischonn,et al.  Ensemble streamflow forecasting experiments in a tropical basin: The São Francisco river case study , 2014 .

[7]  Quan J. Wang,et al.  A Review of Quantitative Precipitation Forecasts and Their Use in Short- to Medium-Range Streamflow Forecasting , 2011 .

[8]  Adrian E. Raftery,et al.  Weather Forecasting with Ensemble Methods , 2005, Science.

[9]  James D. Brown,et al.  Verification of temperature, precipitation, and streamflow forecasts from the NOAA/NWS Hydrologic Ensemble Forecast Service (HEFS): 2. Streamflow verification , 2014 .

[10]  M. Llasat,et al.  Improving QPF by blending techniques at the Meteorological Service of Catalonia , 2009 .

[11]  Jutta Thielen,et al.  Ensemble predictions and perceptions of risk, uncertainty, and error in flood forecasting , 2007 .

[12]  Kazuo Saito,et al.  The Operational JMA Nonhydrostatic Mesoscale Model , 2006 .

[13]  Sunmin Kim,et al.  ACCURACY IMPROVEMENT OF FLOOD FORECASTING USING PRE-PROCESSING OF ENSEMBLE NUMERICAL WEATHER PREDICTION RAINFALL FIELDS , 2014 .

[14]  Takuma Takasao,et al.  Incorporation of the effect of concentration of flow into the kinematic wave equations and its applications to runoff system lumping , 1988 .

[15]  James C. Bennett,et al.  A System for Continuous Hydrological Ensemble Forecasting (SCHEF) to lead times of 9 days , 2014 .

[16]  Ian Cluckie,et al.  Uncertainty analysis of hydrological ensemble forecasts in a distributed model utilising short-range rainfall prediction , 2006 .

[17]  Kaoru Takara,et al.  DEVELOPMENT OF STAGE-DISCHARGE RELATIONSHIP EQUATION INCORPORATIONG SATURATED-UNSATURATED FLOW MECHANISM , 2004 .

[18]  Yasuto Tachikawa,et al.  Ensemble flood forecasting with stochastic radar image extrapolation and a distributed hydrologic model , 2009 .

[19]  Chris G. Collier,et al.  GANDOLF: a system for generating automated nowcasts of convective precipitation , 2000 .

[20]  Brian Golding,et al.  Long lead time flood warnings: reality or fantasy? , 2009 .

[21]  Emmanuel Roulin,et al.  Skill of Medium-Range Hydrological Ensemble Predictions , 2005 .

[22]  E. Toth,et al.  Comparison of short-term rainfall prediction models for real-time flood forecasting , 2000 .

[23]  D. Seo,et al.  Ensemble prediction and data assimilation for operational hydrology , 2014 .

[24]  R. Buizza,et al.  A Comparison of the ECMWF, MSC, and NCEP Global Ensemble Prediction Systems , 2005 .

[25]  A. Seed,et al.  STEPS: A probabilistic precipitation forecasting scheme which merges an extrapolation nowcast with downscaled NWP , 2006 .