1. Update on the Western Weather Work Group Carla Thomas Western IPM Center Western Plant Diagnostic Network

2. Western Weather Work Group David Gent, Walt Mahafee, Bill Pfender OSU/ARS Chris Daly, OSU/Prism Paul Jepson, Len Coop OSU/IPPC Gary Grove, Dennis Johnson, Gerrit Hoogenboom, WSU Carla Thomas, Doug Gubler, Joyce Strand, Neil McRoberts, UCD Alan Fox, Fox Weather Emeritus-Fran Pierce WSU, George Taylor OSU Western Weather Work Group –WIPMC Funding 2005-2010 Mission: 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.

3. WWWG History Our vision is to develop access to a backbone network of physical stations while creating "virtual stations" that are based on advanced, validated interpolation of measured variables and model outputs. RIPM Proof of Concept funding NRI-errors/uncertainties of inputs and outputs of models AFRI-improvement of interpolation, forecasts, assessments PIPE-infrastructure support for existing and emerging systems NPDN-Biosecurity applications/distributed systems

4. WWWG Research Objectives Interpolation of specific variables at necessary time and space resolutions. Development and use of appropriate forecast methods. Techniques to estimate difficult-to-measure variables from other measured variables need to be developed or refined, and validated. Development of standardized modeling structures for specific types of pathogens to improve availability of disease models. Quantification of uncertainties associated with the various data and computations so that a level of confidence could be placed on output and communicated to users.

5. WWWG Operational Objectives Development of networks of weather stations. Data acquisition, quality control, storage, archival, and delivery. Focus on needs in accounting for and dealing with missing data. Delivery of pest management applications. Training. Outreach. Evaluation of overall effectiveness.

6. WWW.USPEST.ORG/WEA + 16,000 weather stations

7. Daily Temperature Regime

8. Average Temperature Close alignment between V2 and Std. Over/ Under estimation of V2 2010 and 2011 at THILL

9. Max Temperature V2 follows closely with Std 60 in most data sets. Over estimation of V2 Max Temp consistent across season

10. Min Temperature Under prediction of Min Temperature is common in most Datasets. Over estimation of Min Temp is common at THill.

11. Temperature means over the data sets. Lower R- values with Min Temp Temp shows a good correlation between v2 and Std 60.

12. Monthly Mean Temp Data V2 and Actual Mean Temp showed a strong correlation, but no trends from month to month.

13. Daily RH Regime V2 Relative Humidity lags behind Actual across all seasons and sites.

14. Relative Humidity V2 and Std 60, typically do not correlate well for Max RH. Typically, V2 and Std 60 correlate well for Min RH, but could be better.

15. Min Relative Humidity V2 Min RH usually closely aligns with Std 60. At THill, V2 Min RH varies widely.

16. Dew Point Temp Max and Mean Dew Point Temp, correlate well between V2 and Std 60. The exception is THill. Min Dew Point Temp shows a weaker correlation between V2 and Std 60.

17. Daily Leaf Wetness

18. Hours of Leaf Wetness

19. Precipitation V2 overestimates Precipitation across sites. V2 usually is accurate on predicting rain events when they do occur.

20. HPM Graphs

21. Downy Mildew Graphs V2 Downy infection risk values typically overestimate Actual infection values.

22. Grape Powdery Mildew V2 underestimates GPM risk values under 100, and like HPM, occasionally shows early infection

23. Rust Models

24. Interface to Disease Maps via MyPest Page - http://uspest.org/risk/models This project was supported by the Agriculture and Food Research Initiative Competitive Grants Program No. 2010-85605-2054 from the National Institute of Food and Agriculture.

25. Gridded Disease Maps

26. Gridded data Example 2: PRISM data for Precipitation compared to Precip/Disease Maps Interface PRISM Data - 2 Regions IPPC Interface & V2 data– Willamette Valley Comparable data for a rainfall event Grape bunch rot disease grid and overlay on Google map This project was supported by the Agriculture and Food Research Initiative Competitive Grants Program No. 2010-85605-2054 from the National Institute of Food and Agriculture.

27. Gridded data Example 1: PRISM and IPPC V2 data for temperature with example statistical comparison PRISM Data - 4 Regions – ca. 72 hr lag IPPC V2 data – 3 of 4 regions ca. 12 hr lag This project was supported by the Agriculture and Food Research Initiative Competitive Grants Program No. 2010-85605-2054 from the National Institute of Food and Agriculture.

28. Rust Models These are two examples of how the V2 predicts the latent period in the Rust model when there are differences.

30. My virtual station Validated forecasts

32. Western Weather Work Group Current Work Supporting existing and emerging systems through distributed resources

33. Spotted Wing Drosophila Overwintering Mortality

34. Area wide IPM coddling moth

36. Use of Western Weather Workgroup-developed degree- day and phenology models is increasing nationwide

37. Publications Gent, D. H., De Wolf, E. D, and Pethybridge, S. J. 2011. Perceptions of risk, risk aversion, and barriers to adoption of decision support systems and IPM: An Introduction. Phytopathology 101:640-643. Pfender, W. F., Gent, D. H., Mahaffee, W. F., Coop, L. B., and Fox, A. D. 2011. Decision aids for multiple-decision disease management as affected by weather input errors. Phytopathology 101:644-653. Gent, D. H., Mahaffee, W. F., McRoberts, N., and Pfender, W. F. 2013. The use and role of predictive systems in disease management. Annual Review of Phytopathology. In press. Pfender, W. F., Gent, D. H., and Mahaffee, W. F. 2012. Sensitivity of disease management decision aids to temperature input errors associated with out-of-canopy and reduced time-resolution measurements. Plant Disease 96:726-736 https://www.facebook.com/pages/Northwest-Hop-Information- Network/147514331928522 https://www.facebook.com/pages/Northwest-Hop-Information- Network/147514331928522

38. 1. Montana State - "SPUD" potato weather network (ingest station data; deliver disease models) - MSU contact Nina Zidack 2. WSU - deliver 6.5 day Fox Weather LLC/IPPC hourly weather forecasts - WSU contact Gerrit Hoogenboom 3. WSU - provide 1st incidence of potato late blight Google maps for Columbia Basin - WSU contact Dennis A. Johnson 4. UC Davis - ingest station data from multiple weather networks (incl. PESTCAST, ADCON, and METOS) and link to multiple disease models and provide virtual weather networks and data for supported wine grape growers - UC Davis contacts Doug Gubler, Brianna McGuire; UC Cooperative Extension contact Lynn Wunderlich 5. UC Davis - providing virtual weather station networks for grape IPM (same as preceeding contacts) 6. UC Davis - developed and add new phenology models for western flower thrips and Asian citrus Psyllid (Asian citrus Psyllid work supported by a SCRI grant). UC Davis contact: Neil McRoberts

39. 7. APHIS/PPQ/CPHST/Ft. Collins and Aurora, CO (numerous other interested parties) - add several models to uspest.org over past several years, including: Brown Marmorated Stink Bug, European grapevine moth, pine shoot beetle, light brown apple moth, Cereal leaf beetle, Gypsy moth, emerald ash borer. Supplied daily-updated degree-day grids since 2008 (currently used for backup).uspest.org 8. Wyoming - developed and added a model for Bauer Spring wheat, Contact Wyoming Extension Service (Sandra Frost) 9. All states - added a new Google maps based interface to run degree-day models, greatly improving accessibility to our currently supported 80 models, for all US states, especially those underserved and without statewide weather networks or models. 10. All states - developed a "web services" interface so that any model and weather station in our system can be specified and run from remote web pages, such as a county Extension website. A version of this feature is being used by UC Davis for grape IPM. 11. All states - developed "virtual weather data" and implemented to fill-in missing or flagged-as-suspicious weather data for all stations in our database. 12. All states - developed modified leaf wetness estimations allowing disease risk models to be run from weather stations that do not have leaf wetness sensors.

40. Next Steps Expand Impact and Adoption Assessment Expand Infrastructure Support through signature programs PRIME LAMP

41. Thank you!

42. Monthly Min Temp Data V2 Monthly Min Temp data showed a decline in the correlation with Std 60 from Spring to Summer. It appears to occur in May.

43. Rust Models V2 is unpredictable in its over and underestimation of Infection Values.

44. Management Recommendations for Hop Powdery Mildew. V2 typically called for one more spray than the Canopy 15. The spray interval was usually shorter for the V2.