The 5.06 APPLICATIONS OF THE MCGILL ALGORITHM FOR PRECIPITATION NOWCASTING USING SEMI-LAGRANGIAN EXTRAPOLATION WITHIN THE ARPAV HYDROMET DECISION SUPPORT SYSTEM J. William 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 * Corresponding author address: J. William Conway, Weather Decision Technologies, Norman, Oklahoma, USA, 73069, 1. INTRODUCTION Weather Decision Technologies (WDT) has worked with McGill University to commercialize and operationalize the McGill Algorithm for Precipitation Nowcasting Using Semi-Lagrangian Extrapolation (MAPLE). This capability is now used extensively in WDT’s North American forecasting operations and was recently brought on-line in Italy. In 2004/2005 WDT, working in collaboration with Enterprise Electronics Cooperation (EEC), implemented a Hydromet Decision Support System (HDSS) in Teolo, Italy at the Weather Operations Center of the Agenzia Regionale per la Prevenzione e Protezione Ambientale del Veneto (ARPAV). The HDSS was engineered to directly assimilate base level data from the existing EEC Doppler weather radar, integrate it with, and process it within MAPLE in order to support multi-hour precipitation forecasting. The HDSS system ingests multiple data sources (surface, rain gauge, model, radar, satellite) and automatically runs a series of single and multi-radar algorithms to detect and predict short-term weather phenomena. The HDSS single radar applications include the Storm Cell Identification and Tracking (SCIT) algorithm (Johnson et al. 1998), the Hailswath Detection Algorithm, the Mesocyclone Detection Algorithm (MDA) (Stumpf et al. 1998), and the Tornado Detection Algorithm (TDA) (Mitchell et al. 1998). These single radar applications are enhanced versions of those algorithms that reside on the US WSR-88D network. The Hailswath Detection Algorithm was developed by WDT and is patterned after the Hail Detection Algorithm (Witt et al. 1998). The HDSS multi-radar applications include MAPLE (Germann and Zawadzki, 2002), the 3D Mosaic Algorithm (Zhang et al. 2004) and the Quantitative Precipitation Estimation Using Multiple Sensors (QPE-SUMS) algorithm (Gourley et al. 2001). The close integration of this suite of advanced algorithms results in a powerful and automated meteorological system for monitoring hazardous and severe weather, one which also provides reliable rainfall estimates and forecasts. The purpose of this paper is to discuss the application of MAPLE within HDSS, and its application to Quantitative Precipitation Forecasting.

Figure 1 shows an example of derived Maple vectors using Variational Echo Tracking first introduced by Laroche and Zawadzki (1994, 1995). Figure 1 was derived using a 1 hr history window. Once the vector field is derived reflectivity points are advected using the semi-Lagrangian method. Unlike cross correlation methods, this type of streamfunction approach allows for rotation to be preserved in the forecast fields. The scales of predictability are then used to determine maximum forecast persistence of reflectivity areas. Germann and Zawadzki (2002) examined 4 cases containing various scales of precipitation. Across the length of the events they integrated several 9 hr forecast windows and examined statistics for those time periods. During the variational echo tracking step they determined a decorrelation factor among the initial echoes. Statistics were calculated for POD and conditional mean absolute error (CMAE). CMAE was used as a direct measure of reflectivity error and thus QPE. For instance, a CMAE of 9 dBZ corresponds to a QPE error factor of up to 4 depending on the range of the reflectivity being examined. Overall, a POD of 60% and a CMAE of 7.6 dBZ over a 9 hour period were derived over the combined cases. This says that after 9 hours for the cases tested, the occurrence of precipitation at a specific location is correct 60% of the time with an error factor of 3.2 for derived rainrates. 2.MAPLE PROCESSING SPECIFICS Germann and Zawadzki (2001, 2002) have developed a unique application for forecasting reflectivity fields based on semi-Lagrangian extrapolation. The basic process involves three steps: 1) determination of the reflectivity field motion through variational echo tracking; 2) advection of the reflectivity field is performed using modified semi-Lagrangian scheme; 3) persistence forecasts are compared to actual data for the forecast times and calculations of the scale predictabilities are measured. The process is then repeated with using the decomposition according to the scale predictability. A source/sink term is also applied to take into account storm growth and decay.

3.Quantitative Precipitation Forecasting using MAPLE In the United States WDT runs MAPLE operationally on national reflectivity composites created from WSR- 88D Level II data. Applied to these data are precipitation type forecasts derived from the Weather Research and Forecast (WRF) model. For this purpose the WRF model is run every hour at WDT’s Central Processing Facility and produces output at 15 min time-steps out to 6 hrs. A WDT algorithm uses the WRF output to derive a precipitation type mask at the same 15 min time-steps. MAPLE forecasts are also run across the US out to 4 hrs at 15 min time-steps. Each MAPLE forecast has precipitation typing applied to it. Based on the precipitation type, whether convective or stratiform precipitation is present, and geographical location, variable Z-R and Z-S relations are applied at each grid point across the MAPLE domain. Total forecast precipitation accumulations are then derived at each grid point. These forecasts are updated every 15 minutes. The application of MAPLE at ARPAV is conducted over a high-resolution latitude/longitude grid of 0.01 deg spacing. Currently one radar at Teolo (LIZT in Fig. 3) is ingest and processed with MAPLE. A second radar (LIZL northeast of LIZT in Fig. 3) is expected to be implemented in the future. Figure 3 shows the Teolo radar domain and the blockage pattern. To insure a full radar scan for MAPLE usage a hybrid low-level reflectivity field in polar coordinates is created. This hybrid scan integrates data from elevations that do not experience blockage. In Figure 3 the yellow areas are where data are taken from the 2 nd elevation and orange areas represent 3 rd elevation scan data. Composite reflectivity is derived from the 3D Mosaic algorithm. MAPLE Variational Echo Tracking is performed on this composite field and the resultant vectors are in turn used to extrapolate the hybrid reflectivity field. A Z-R relationship specific to the Veneto Region is then applied to the field. Figure 4 shows an example of predicted precipitation accumulation over a 2 hr forecast period. Preliminary examination show that the precipitation values predicted (QPF) over this time period qualitatively agree with independent QPE values valid for the forecast period derived from QPE-SUMS. 4. Future Work MAPLE has been implemented at ARPAV only for a short period of time. The collection of many cases and data analysis needs to be performed to derive Z-R and Z-S relationships applicable to the Veneto Region. A relatively dense raingauge network exists which will be used for verification studies. Further, data from a mesoscale local area model are expected to soon be available which can be applied to perform precipitation typing, which will further expand the utility of MAPLE. The conference presentation will discuss further advances in analysis up to that point.

5. References Germann, U. and I. Zawasdski, 2002: Scale-dependence of the predictability of precipitation from continental radar images. Part I: Discription of methodology. Mon. Wea. Rev., 130, Germann, U. and I. Zawasdski, 2003: Scale-dependence of the predictability of precipitation from continental radar images. Part II: Probability forecasts. J. Appl. Meteor., submitted. Johnson, J.T., P.L. MacKeen, A. Witt, E.D. Mitchell, G.J. Stumpf, M.D. Eilts, and K.W. Thomas 1998: The Storm Cell Identification and Tracking Algorithm: An Enhanced WSR-88D Algorithm. Weather and Forecasting, 13, Laroche, S. and I. Zawadzki, 1994: A variational analysis method for retrieval of three-dimensional wind field from single- Doppler radar data. J. Atmos. Sci., 51, Laroche, S. and I. Zawadzki, 1995: Retrievals of horizontal winds from single-Doppler clear-air data by methods of cross correlation and variational analysis. J. Atmos. Ocean Technol., 12, Mitchell, E.D., S.V. Vasiloff, G.J. Stumpf, A.Witt, M.D. Eilts, J.T. Johnson, and K.W. Thomas, 1998: The National Severe Storms Laboratory Tornado Detection Algorithm. Weather and Forecasting, 13, Stumpf, G.J., A. Witt, M., E.D. Mitchell, P.L. Spencer, J.T. Johnson, M.D. Eilts, K.W. Thomas, and D.W. Burgess, 1998: The National Severe Storms Laboratory Mesocyclone Detection Algorithm. Weather and Forecasting, 13, Witt, A., M.D. Eilts, G.J. Stumpf, J.T. Johnson, E.D. Mitchell, and K.W. Thomas, 1998: An Enhanced Hail Detection Algorithm for the WSR-88D. Weather and Forecasting, 13,

Figure 1. Composite radar image overlain with corresponding echo motion field derived from Variational Echo Tracking procedure (from Germann and Zawadzki 2001).

Figure 2. MAPLE domain over Veneto Region of Northern Italy showing radar hybrid scan used for MAPLE processing. Yellow (orange) represents areas where data are taken from the 2 nd (3 rd ) elevaton angles.

Figure 3. Example of forecast MAPLE precipitation accumulation over Veneto Region for a 2 hr period. Maximum amounts (dark green) are approximately 65 mm.