September 2005WSN05, Toulouse, France Applications of the McGill Algorithm for Precipitation Nowcasing Using Semi- Lagrangian Extrapolation (MAPLE) within the ARPAV HydoMet Decision Support System Bill Conway 1, Dr Gabriele Formentini 2, Chip Barrere 1, Dr Luciano Lago 2 1 Weather Decision Technologies, Norman, Oklahoma, USA 2 Environmental Protection and Prevention Agency Veneto Region, Centro Meteorological, Teolo, Italy

September 2005WSN05, Toulouse, France Teolo radar viewed from weather station

September 2005WSN05, Toulouse, France View of Teolo towards Venice from weather station

September 2005WSN05, Toulouse, France Presentation Background   WDT has worked with ARPAV in Italy to provide a HydroMet Decision Support System (HDSS)   HDSS contains technologies developed by WDT, the National Severe Storms Laboratory, McGill University of Montreal, Canada, and the Oklahoma Climate Survey   HDSS integrates numerous data sources and contains algorithms that provide the following functionality:   Storm centroid tracking, analysis, and prediction   Storm area tracking and prediction (MAPLE)   Rainfall prediction using MAPLE   Hail detection and prediction   Circulation prediction and detection   Quantitative Precipitation Estimation Using Multiple Sensors   Web based display – WxScope   three dimensional workstation display – 3D Sigma   This paper concentrates on application of the MAPLE algorithm and its applications with the ARPAV HDSS to reflectivity forecasting and rainfall prediction

September 2005WSN05, Toulouse, France HDSS Web Page Example

September 2005WSN05, Toulouse, France MAPLE - Briefly   Developed at McGill University, Montreal, Canada by Zawadski and Germann over a period of several years   Provides forecasts of reflectivity out to 8 hours depending on scale predictablity   Uses prior image history to forecast reflectivity out to 8 hrs in advance using stream function analysis   Determines the changing scale of predictability using past images compared to current image though wavelet analysis   Filters non-predictable scales from the T=0 analysis   Deduces stream functions for predictable scales and uses those stream functions to forecast radar reflectivity location and intensity   Current research includes integration of numerical model data for applications towards storm growth and decay   WDT has developed software to run MAPLE in real-time for commercial applications and also to provide radar based QPF

September 2005WSN05, Toulouse, France Example Vector Derivation

September 2005WSN05, Toulouse, France 1.5h >8h 5.5h C a n a d a Gulf of Mexico Scale predictability determined by comparison of previous forecasts with current images. Scales are removed in the forecast after exceeding their derived “predictability” flag Example of Scale Predicability

September 2005WSN05, Toulouse, France 4 hr Precip Type Forecast Example 4 hr Precip Type Forecast

September 2005WSN05, Toulouse, France Hybrid Scanning Grey – data from 1 st elevation Yellow – data from 2 nd elevation Orange – data from 3 rd elevation

September 2005WSN05, Toulouse, France Hybrid Scan Example

September 2005WSN05, Toulouse, France MAPLE Applications to QPF  Uses output from QPE-SUMS as “T0” input for MAPLE  Applies a Z-R or Z-S relationship to each 5 min MAPLE time step based on surface temperature and whether stratiform or convective  Will apply a bias correction at each time step based on QPESUMS radar to gauge correction*  Accumulates total rainfall forecasts at each grid point across the MAPLE domain

September 2005WSN05, Toulouse, France Objective Analysis of Rain Gauge Data

September 2005WSN05, Toulouse, France Example of variable Z-R/Z-S Relationships

September 2005WSN05, Toulouse, France 1 Hr MAPLE Hybrid Forecast

September 2005WSN05, Toulouse, France MAPLE 2 hr Accumulation

September 2005WSN05, Toulouse, France Future Work  McGill continuing to work on model integration and storm growth/decay for MAPLE improvements  Correct Italian data for beam blockage  Integrate further radars from Italy network as they become available  Develop software for real-time statistical analysis of MAPLE performance  Optimize the Z-R and Z-S relationships used in the northern Italy region  Use basin delineation and flash flood guidance with MAPLE QPF results to provide a Flash Flood Prediction Algorithm Merci! Grazie! Thanks! Ciao!