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

2. Ag Weather Net

3. 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

4. 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

5.  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.

6. 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.

7. 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.

8. 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

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

10. Ag Weather Net

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

13. 12,500+ weather stations assimilated per day nationwide Hourly or better from MesoWest plus several grower-run networks

14. 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 http://pnwpest.org/wea/indextable.html

15. Ag Weather Net 1971-2000 PRISM Climate Today’s Spatial Estimates Today’s Anomalies

16. 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 http://www.prism.oregonstate.edu/

17. 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

18. Coastal Effects: 1971-00 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

19. 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

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

21. 0.8 km/pixels Improving Resolution of Spatial Interpolation Ag Weather Net

22. New 1971-2000 800 m Old 1961-1990 4 km

23. 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

24. 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

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

27. Hop Powdery Mildew

28. 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 16- 27°C, then +20 points, else; 5. If none of the above rules apply, then -10 points. A 0 to 100 index0

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

30. Ag Weather Net

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

32. Ag Weather Net

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

34. 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

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

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

37. 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

38. + + PRISM Targeted Climatolo gies MtnRT Temperatures MtnRT Winds

39. Ag Weather Net Future MtnRT- PRISM 30-40-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

40. Ag Weather Net Public Weather Data  Expected configuration

41. Station placement not always optimal

42. Ag Weather Net Does sensor location matter?