Ag Weather Net Founded 2004 Funded by the Western Region IPM center Workgroup Program

Ag Weather Net

What brought us together?  All have worked to develop IPM tools based on weather data  All reached the same conclusion  The benefits of crop, pest, and disease forecasting models are only realized if we have high quality weather and forecast data at sufficient spatial resolution.  Prior collaborations among most members

Current Membership  Len Coop, OSU &IPPC: Pest modeling, GIS interpolation & delivery  Chris Daly, OSU &PRISM Group: Spatial climate analysis  Alan Fox, Fox Weather, LLC: Ag. weather modeling & forecasting  Dave Gent, USDA-ARS NFSPRC: Epidemiology (hops)  Gary Grove, WSU: Director WA AgWeatherNet & Epidemiology  Doug Gubler, UC Davis: Epidemiology and extension (fruits & nuts)  David Hannaway, OSU: Forage Crops and Extension  Paul Jepson, OSU IPPC: Director IPPC; IPM and biosecurity  Dennis Johnson, WSU: Epidemiology and extension (potatoes)  Walt Mahaffee, USDA-ARS: Epidemiology (small fruit & nursery)  Bill Pfender, USDA-ARS NFSPRC: Epidemiology (grass seed)  Joyce Strand, UC IPM: Ag. meteorology and information systems  Carla Thomas, UC Davis & NPDN: Epidemiology, biosecurity & IPM Ag Weather Net

 To develop a science-based system that provides principles and procedures to access, synthesize, distribute, and use weather and climate data products to improve crop management decision-making abilities through the delivery of weather based information.

Ag Weather Net Group Philosophy/Principles  IPM is local and personal.  Any regional or national approach must be based on a consortium of local IPM efforts linked together with some aspects coordinated or provided at a regional or national level.  Facilitating the availability of weather and climate based information will require public and private enterprises.  Benefits of competition and innovation must be balanced against development of general standards that may suppress innovation.

Ag Weather Net Group Philosophy/Principles  Seek contributions and collaborations from outside the group  Long term sustainability of a system will depend on expanding partnerships to include forestry, urban planning, recreational settings, transportation, etc.

Ag Weather Net Why is the group succeeding?  Diverse expertise  Subject matter and career point  First established common ground then developed direction  Constant change in who are the dominant leaders of the group  Various forms of communication that occur regularly  More than one meeting a year

Ag Weather Net Where are We Going? Interactive Virtual Weather Station

Ag Weather Net

Where are we? Virtual Weather Station 1.0 Ag Weather Net

12,500+ weather stations assimilated per day nationwide Hourly or better from MesoWest plus several grower-run networks

Ag Weather Net Deriving Weather from Climate Climatologically-Aided Interpolation (CAI) Climatology as first guess field Near real-time station data used to modify first guess field Downscaling Weather maps at higher spatial resolution than climatology Calculate local elevation regressions using fine- grid DEM

Ag Weather Net PRISM Climate Today’s Spatial Estimates Today’s Anomalies

Ag Weather Net  Generates gridded estimates of climatic parameters  Moving-window regression of climate vs. elevation for each grid cell  Uses nearby station observations  Spatial climate knowledge base weights stations in the regression function by their physiographic similarity to the target grid cell Ag Weather Net

Ag Weather Net Rain Shadow: Mean Annual Precipitation Oregon Cascades Portland Eugene Sisters Redmond Bend Mt. Hood Mt. Jefferson Three Sisters N 14 in/yr 90 in/yr 100 in/yr PRISM Station Weighting Terrain orientation Terrain steepness Moisture Regime Elevation 10 in/yr Ag Weather Net

Coastal Effects: July Maximum Temperature Central California Coast Monterey San Francisco San Jose Santa Cruz Hollister Salinas Stockton Sacramento Pacific Ocean Fremont N 34 ° 20 ° 27 ° Oakland Preferred Trajectories PRISM Station Weighting Coastal Proximity Elevation Inversion Layer Ag Weather Net

Ukiah CloverdaleLakeport Willits Clear Lake Pacific Ocean Lake Pilsbury. N PRISM Station Weighting Topographic Index Inversion Layer 12 ° 17 ° 9°9° 16 ° 10 ° 17 ° Ag Weather Net

Improving Resolution of Spatial Interpolation 4 km/pixel Ag Weather Net

0.8 km/pixels Improving Resolution of Spatial Interpolation Ag Weather Net

New m Old km

Ag Weather Net MtnRT – Fox Weather, LLC  Directly downscales coarse-grid forecast model output  Local prediction for Rain/Temp/RH/LW, wind, at 2 km  Well-developed, operational, out to 5 days for OR, WA, CA  Predicts inversion heights and nocturnal cold layers  Spatially accounts for terrain and Coastal effects PRISM Forecast System – OSU PRISM Group  Modifies a long-term climatology with forecast model output  Uses CAI (“climatological fingerprint”)  In early stages of development  Experimental operation for temp only, 24-hr forecasts  0.8 km resolution for NW OR

Ag Weather Net MtnRTPRISM 100-km forecast model grids (GFS) 0.8-km PRISM all- day climate grid 0.8-km current weather grid Station observations 0.8-km 12-hrly forecast grid 0.8-km 12-hrly forecast grid Gaussian filter IDW Interp IDW Interp MtnRT 2-km 6-hrly forecast grid Mt1hr interp Basic QC 2-km 1-hrly forecast grid

Ag Weather Net Where are we? Virtual Weather Station 1.0 Ag Weather Net

Hop Powdery Mildew

Ag Weather Net Powdery Mildew Risk Index 1. If >6 continuous hours > 30°C, then -20 points, else; 2. If > 2.5mm rain, then -10 points, else; 3. If >6 continuous hours > 30°C on previous day, then no change in the index, else; 4. If at least six continuous hours between °C, then +20 points, else; 5. If none of the above rules apply, then -10 points. A 0 to 100 index0

Ag Weather Net Comparison of actual vs estimated weather data in calculating powdery mildew index

Ag Weather Net

# OF SPORES X NIGHT & A.M. WEATHER # OF INFECTIONS + HEAT UNITS NEW PUSTULES [ RAIN ] WITHIN – PLANT SPREAD

Ag Weather Net

Ground Obs. Forecasts Realtime PRISM Targ. Clim. PRISM Static PRISM

Ag Weather Net  Estimation of missing data  Flatliner checks – repeating values  Compare extreme values with record highs and lows  Spatial consistency checks  Develop “bad boy” list

Ag Weather Net Temperature gradient Corvallis – Newport = 3.8C Temperature gradient Corvallis – Newport = 10.8C C N C N

Ag Weather Net One of several cold-weather patternsA warm, dry weather pattern

Ag Weather Net Weather Research and Forecast Model WRF  Next generation meso-scale forecast model (after MM5)  Developed by NCAR, NOAA, Air Force, et al.  Operational at 37-km for western US at Fox Weather  Beta testing for use with MtnRT  More accurate forecasts than 100-km GFS, especially in coastal areas

+ + PRISM Targeted Climatolo gies MtnRT Temperatures MtnRT Winds

Ag Weather Net Future MtnRT- PRISM km forecast model grids (WRF) 0.8-km PRISM all-day or targeted climate grid 0.8-km current weather grid Station observations 0.8-km 3-hrly forecast grid 0.8-km 3-hrly forecast grid Gaussian filter PRISM MtnRT 4-km 3-hrly forecast grid Mt1hr interp Spatial QC 0.8-km 1-hrly forecast grid 0.8-km 1-hrly forecast grid 0.8-km 3-hrly forecast grid 0.8-km 3-hrly forecast grid IDW Interp IDW Interp Bias Correct Bias Correct Adjusted 0.8-km 1-hrly forecast grid Adjusted 0.8-km 1-hrly forecast grid 0.8-km 1-hrly forecast grid 0.8-km 1-hrly forecast grid

Ag Weather Net Public Weather Data  Expected configuration

Station placement not always optimal

Ag Weather Net Does sensor location matter?