Site-Specific Weather Data for Disease Forecasting: Reality or Pipe Dream? Bob Seem Cornell University New York State Agricultural Experiment Station Geneva,

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Presentation transcript:

Site-Specific Weather Data for Disease Forecasting: Reality or Pipe Dream? Bob Seem Cornell University New York State Agricultural Experiment Station Geneva, NY Midwest Weather Group Meeting 25 July 2008

What is site-specific weather? Weather data and associated information determined for a specific location (lon-lat) or grid-based information at a resolution of ~1km 2

Why is site-specific weather important? Emerging alternative to regional- scale data Incorporates local physiographic features Economical alternative to automatic weather stations Natural link to disease forecasts and graphical representation of disease risk

Examples of site-specific weather implementation Application of mesoscale weather models Application of NWS numerical forecast models Application of statistical/interpolation schemes

Mesoscale Models MM5 North Amercian Meso (NAM, formerly Eta) MASS (LAWSS) WRF

Input Preprocessors Surface Data Climatic Data Raw Data f Models LAWSS SWEB DMCast Elevation, Landuse … NDVI, SST … Reanalysis, AWS … Temp, RH, Wind, Rad … Leaf wetness … Downy Mildew Risk … Output Processing Data Transformation Data Analysis Merging, Concatenation Mapping, Plotting …

Sample output - weather variables Variable Name Unit Surface Air TemperatureC Air Temperature at 2mC Skin TemperatureC Dew Point TemperatureC U Windm/s V Windm/s Altimeter Settingmb Sensible Heat FluxW/m**2 Latent Heat FluxW/m**2 Total Evaporationmm Total Shortwave RadiationMJ/m**2 Outgoing IR RadiationW/m**2 Cloud Coverfraction Mean RH SFC - 500MBpercent Total Precipitationmm Shallow Soil Layer Water Contentvol. fraction Cover Layer Water Contentm

Grid Horizontal Resolution # Grid points Domain Size (km 2 ) Hours / Domain # Domains for Finger Lakes A9km x 9km90 x B3km x 3km100 x C1km x 1km100 x D 333m x 333m 100 x (parallel) E 150m x 150m 100 x (parallel) Domain size and running hours

Grid A (9km resolution) B1B2B3 B4B5B6B7B8B9 Nested Domains

C1C2C3 C4C5C6C7C8C9 Grid A B5 Grid B5 (3km resolution) Nested Domains

D1D2D3 D4D5D6D7D8D9 Grid B5 C5 Grid C5 (1km resolution) Nested Domains

Grid C5 D2D3 D5D6 D1 D4D7D8D9 Nested Domains Grid D1-D9 (333m resolution)

Disease model – Downy mildew ABC DEF Lake Erie Seneca Lake Cayuga Lake

Location Criteria of daily comparison Agreement ratio between observed and simulated data during: the entire simulationnon-rainy days Geneva (42.9N, 77.0W) Wetness > /62 (53%)28/32 (88%) DMrisk > 0 41/62 (66%)28/32 (88%) Fredonia (42.7N, 78.9W) Wetness > /62 (63%)28/37 (76%) DMrisk > 0 36/62 (58%)27/37 (73%)

Weather Research and Forecast (WRF) Model A next-generation mesocale numerical weather prediction system designed to serve both operational forecasting and atmospheric research needs. It features multiple dynamical cores, a 3-dimensional variational data assimilation system, and a software architecture allowing for computational parallelism and system extensibility. WRF is suitable for a broad spectrum of applications across scales ranging from meters to thousands of kilometers.

USDA/ZedX Soybean Rust PIPE 24-Hour Precip and RH

USDA/ZedX Soybean Rust PIPE Spore Wet Deposition

USDA/ZedX Soybean Rust PIPE Soybean Development Stages

USDA/ZedX Soybean Rust PIPE Rust Development Stages

NWS numerical forecast models Resolution not as high as mesoscale models Convergence is occurring Products greatly increased and not focused just on aviation weather

NWS Graphical Weather Forecasts

Copyright(c) 1995 SkyBit, Inc. Phone: (800) E-Weather Forecast for: EXPERIMENT STATION-GENEVA, NY Forecast beginning : TUE May 16, 1995 May 16 May 17 HOUR (EDT) 8a 11a 2p 5p 8p 11p 2a 5a 8a 11a 2p 5p 8p 11p TEMP (F) "- SOIL TEMP (F) REL HUM (%) HR PRECIP(in).00/.00/.00/.05/.26/.62/.11/ 6HR PRECIP PROB(%) 1/ 0/ 14/ 48/ 84/ 64/ 46/ 3HR WETNESS (hrs) WIND DIR (pt) SW WSW W W SE SSE S S S SW SW W W WSW WIND SPEED (mph) CLOUD COVER SCT SCT BKN BKN OVC OVC OVC OVC OVC OVC OVC OVC BKN OVC 3HR RADIATION (ly) DRYING (key) SPRAYING (key) Daily products - E-Weather

Considerable advancements made over recent years (e.g., WRF) Computing power is greatly improved Flexible links for disease models (and precision ag in general) Need better input data (soils, soil moisture, vegetation cover) Some variables not reliable…yet Conclusions I

Need better biology How do we validate site-specific information –Validation sites vs total sites –Do management decisions change Don’t let infatuation overtake reality Conclusions II