Nowcasting precipitation — a proposal for a way forward

Abstract There is little doubt that hybrid mesoscale model and image extrapolation systems will eventually become widespread for the nowcasting of precipitation for hydrology and aviation safety. Arguably the final forecast output that is required for these applications needs to be probabilistic in nature, therefore appropriate forecast schemes should statistically account for both the chaotic and intermittent nature of the intrinsic fields. In principle, the detailed knowledge that we have about the high-order statistics of rain patterns should also allow improved extrapolation schemes for nowcasting. A well-known technique that preserves space–time statistics is presented as an example of this. It is argued that the relative scoring of forecast systems is highly space–time resolution dependent, and that even when the same meteorological event is being considered this resolution dependency must be taken into account.

[1]  A. Bellon,et al.  The use of digital weather radar records for short‐term precipitation forecasting , 1974 .

[2]  I. D. Cluckie,et al.  Modelling large urban drainage systems in real time , 1999 .

[3]  A. N. Kolmogorov.  Equations of turbulent motion in an incompressible fluid , 1941 .

[4]  K. Wilk,et al.  SEVERE THUNDERSTORM RADAR ECHO MOTION AND RELATED WEATHER EVENTS . HAZARDOUS TO AVIATION OPERATIONS , 1972 .

[5]  S. Businger,et al.  Nowcasting for Space Shuttle Landings at Kennedy Space Center, Florida , 1996 .

[6]  D. J. Hall,et al.  Experiments In Automatic Cloud Tracking Using SMS-GOES Data , 1977 .

[7]  G. Drufuca,et al.  Radar-Derived Statistics on the Structure of Precipitation Patterns , 1977 .

[8]  Konstantine P. Georgakakos,et al.  Operational Rainfall Prediction on Meso‐γ Scales for Hydrologic Applications , 1996 .

[9]  E. Epstein,et al.  Stochastic dynamic prediction1 , 1969 .

[10]  Tim N. Palmer,et al.  THE MONTE CARLO FORECAST , 1990 .

[11]  Robert K. Crane,et al.  Space‐time structure of rain rate fields , 1990 .

[12]  K. Georgakakos,et al.  A two‐dimensional stochastic‐dynamical quantitative precipitation forecasting model , 1990 .

[13]  M. Leibowitz.  National security and scientific training. , 1984, Science.

[14]  A. Bellon,et al.  The Evaluation of Two Years of Real-Time Operation of a Short-Term Precipitation Forecasting Procedure (SHARP). , 1978 .

[15]  Merab Menabde,et al.  Multiaffine random field model of rainfall , 1999 .

[16]  A. Seed,et al.  Characterisation and Simulation of the Multiscaling Properties of the Energy-Containing Scales of Horizontal Surface-Layer Winds , 1999 .

[17]  S. Lovejoy,et al.  Combining Satellite and Radar Data for the Short-Range Forecasting of Precipitation , 1980 .

[18]  A. Kolmogorov.  A refinement of previous hypotheses concerning the local structure of turbulence in a viscous incompressible fluid at high Reynolds number , 1962, Journal of Fluid Mechanics.

[19]  Allan H. Murphy,et al.  Probability and Statistics in Meteorology: a Review of Some Recent Developments , 1972 .

[20]  Jun Du,et al.  Short-Range Ensemble Forecasting of Quantitative Precipitation , 1997 .

[21]  D. Schertzer,et al.  Discrete angle radiative transfer: 2. Renormalization approach for homogeneous and fractal clouds , 1990 .

[22]  Anthony B. Davis,et al.  Multifractal characterizations of nonstationarity and intermittency in geophysical fields: Observed, retrieved, or simulated , 1994 .

[23]  K. Browning,et al.  Persistence and orographic modulation of mesoscale precipitation areas in a potentially unstable warm sector , 1979 .

[24]  M Nelkin,et al.  In What Sense Is Turbulence an Unsolved Problem? , 1992, Science.

[25]  G. Austin,et al.  An evaluation of extrapolation techniques for the short‐term prediction of rain amounts , 1981 .

[26]  A. Bellon,et al.  The accuracy of short-term radar rainfall forecasts , 1984 .

[27]  A. Kolmogorov.  The local structure of turbulence in incompressible viscous fluid for very large Reynolds numbers , 1991, Proceedings of the Royal Society of London. Series A: Mathematical and Physical Sciences.

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

[29]  Shaun Lovejoy,et al.  Causal space‐time multifractal processes: Predictability and forecasting of rain fields , 1996 .

[30]  Merab Menabde,et al.  Multifractal characterization of rain fields with a strong orographic influence , 1996 .

[31]  E. Epstein,et al.  Stochastic dynamic prediction , 1969 .

[32]  Rafael L. Bras,et al.  Multivariate short-term rainfall prediction , 1980 .

[33]  I. Zawadzki.  Statistical Properties of Precipitation Patterns , 1973 .

[34]  K. Browning,et al.  On the Forecasting of Frontal Rain Using a Weather Radar Network , 1982 .