Use of a stochastic precipitation nowcast scheme for fluvial flood forecasting and warning

Abstract In collaboration with the Bureau of Meteorology (Melbourne, Australia), the Met Office(Joint Centre for Hydro–Meteorological Research, UK) has developed a stochastic precipi-tation nowcast scheme, designed to model and predict the PDF of surface rain rate and rainaccumulation in space and time. Here we demonstrate the range of probabilistic productsgenerated by the scheme, and their potential applications for fluvial flood forecasting andwarning.With the aid of a hydrological model (the PDM), we consider the use of ensemblesof predicted catchment rain accumulation in evaluating the range of possible river flowresponses from a given catchment. When employed in conjunction with a catchment specific,cost-based decision-making model, we highlight the value of PDFs of forecast catchmentrainfall accumulation and river flow as an aid to objective decision making within theflood warning process.  Crown Copyright 2005. Reproduced with the permission ofHer Majesty’s Stationery Office. Published by John Wiley & Sons, Ltd.Keywords: precipitation; nowcasting; probability; stochastic; hydrology; decision-making

[1]  R. J. Moore,et al.  Real-Time Flood Forecasting Systems: Perspectives and Prospects , 1999 .

[2]  Roman Krzysztofowicz,et al.  The case for probabilistic forecasting in hydrology , 2001 .

[3]  Robert J. Moore,et al.  Risk, Reliability, Uncertainty, and Robustness of Water Resource Systems: Aspects of uncertainty, reliability, and risk in flood forecasting systems incorporating weather radar , 2002 .

[4]  Isztar Zawadzki,et al.  Forecasting of hourly accumulations of precipitation by optimal extrapolation of radar maps , 1994 .

[5]  Neill E. Bowler,et al.  Development of a precipitation nowcasting algorithm based upon optical flow techniques , 2004 .

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

[7]  R. Moore.  The probability-distributed principle and runoff production at point and basin scales , 1985 .

[8]  Scalar multifractal radar observer's problem , 1996 .

[9]  S. Mecklenburg,et al.  Improving the nowcasting of precipitation in an Alpine region with an enhanced radar echo tracking algorithm. , 2000 .

[10]  I. Zawadzki,et al.  Scale-Dependence of the Predictability of Precipitation from Continental Radar Images. Part I: Description of the Methodology , 2002 .

[11]  Howard Raiffa,et al.  Applied Statistical Decision Theory. , 1961 .

[12]  W Schmid,et al.  Short-term risk forecasts of heavy rainfall. , 2002, Water science and technology : a journal of the International Association on Water Pollution Research.

[13]  Roman Krzysztofowicz,et al.  Risk, Reliability, Uncertainty, and Robustness of Water Resource Systems: Probabilistic hydrometeorological forecasting , 2002 .

[14]  Tage Andersson,et al.  A Model for Probability Nowcasts of Accumulated Precipitation Using Radar , 1991 .

[15]  René Laprise,et al.  Predictability of Precipitation Patterns: An Operational Approach , 1994 .

[16]  W. Krajewski,et al.  A large-sample investigation of statistical procedures for radar-based short-term quantitative precipitation forecasting , 2000 .