1. Potential Benefits and Challenges of Integrating Gridded Weather Data in IPM Applications: A Preliminary Assessment in Michigan Michael T. Kiefer and Jeffrey A. Andresen Michigan State University, Department of Geography, East Lansing, MI

2. Why use gridded weather analyses? High-spatial and temporal resolution representation of near-surface weather conditions A variety of intended uses creation and verification of gridded forecasts coastal zone and fire management dispersion modeling for the transport of hazardous materials aviation and surface transportation management impact studies of climate change on the regional scale. Increasing use in agricultural sector Background 2 Introduction

3. Motivation Uses for gridded analyses in agriculture Fill in gaps between weather stations Proxy for an observation if point observation is missing Improve situational awareness (e.g., contoured maps of temperature depicting frontal boundary) Specific application: Enviro-weather (EW) automated weather network. 79 automated weather stations (and growing!) Introduction 3

4. Enviro-weather Automated Weather Network Interactive information system linking real-time weather data, forecasts, and biological and other process-based models for assistance in operational decision-making and risk management associated with Michigan’s agriculture and natural resource industries. July 2014

5. Gridded Datasets Real Time Mesoscale Analysis (RTMA) – Generated at the National Centers for Environmental Prediction (NCEP), a division of the National Weather Service (NWS) – First guess (i.e., background): 1-hr forecast from Rapid Update Cycle (RUC) / Rapid Refresh (RAP) models – Large number of observations assimilated (ASOS*, mesonet, satellite wind, etc.) – Includes precipitation analysis (Stage II) – Grid spacing: 2.5 km (5 km recently phased out) – Temporal frequency: hourly Introduction * Automated Surface Observing System

6. Gridded Datasets Stage IV precipitation analysis (aka MPE) – 1-hour precipitation estimates from NWS Doppler radar combined with rain gauge observations (~3000 more gauges than Stage II) – Regional analyses generated at individual river forecast centers (RFCs), sent to NCEP, and merged – Manual quality control performed at each RFC – Grid spacing: 4 km – Temporal frequency: hourly, but manual QC process and transmittal to NCEP delays availability (i.e., not real-time). 6- and 24-hour analyses also available. Introduction

7. Study Questions How do nearest-grid-point RTMA temperature, dewpoint and relative humidity (derived) differ from point observations? Are precipitation differences smaller with Stage IV than Stage II? If so, how much smaller? Overall, are differences larger at EW stations than ASOS stations? If so, how much larger? How do differences impact the output of plant pest and disease models? Introduction 7

8. Study Parameters Five years (1 Aug 2008 – 31 Jul 2013) 12 stations (6 ASOS, 6 EW) Variables extracted at nearest grid point – Temperature, dewpoint, wind speed, wind direction, hourly precipitation Gross error check used to reject obviously erroneous observations Timescales: hourly, daily, diurnal, seasonal 8 Methodology

9. Observation Sites 9 ASOS network KLAN: Lansing KGRR: Grand Rapids KDTW: Detroit Metro KTVC: Traverse City KAPN: Alpena KIMT: Iron Mountain EW network EITH: Ithaca ESAN: Sandusky ECOL: Coldwater EENT: Entrican EARL: Arlene ESTE: Stephenson Methodology

10. RTMA analysis: Overview Results (hourly) 10 Temperature, Dewpoint, Relative humidity 6-station median RMSE BIAS RMSE BIASRMSEBIAS

11. RTMA analysis: Bias histograms 11 Results (hourly) Relative humidity bias (%)

12. Stage II vs IV precipitation 12 Results (hourly) ASOS (False alarm) (Miss) Larger percent correct

13. Stage II vs IV precipitation 13 Results (hourly) EW* * warm season (1 Apr-30 Sep) only (False alarm) (Miss) Larger percent correct

14. Max & Min T, Growing Degree Days Results (daily) 14 Base 10 C *Baskerville-Emin method 6-station median RMSEBIASRMSEBIASRMSEBIAS

15. Plant disease and pest models Fire blight – Inputs: Degree days, degree hours, 24-hr mean and maximum temperature (also need information on wetting event or trauma) Codling moth – Input: Degree day Apple scab (primary infection model) – Inputs: Degree day, precipitation, 1-hr mean temperature, mean RH, leaf wetness proportion 15

16. Apple scab primary infection model Fungus (Venturia inaequalis) Rain of at least 0.01” needed to soak overwintering leaves and release ascospores Wetting period begins with 0.01”+ – may be extended with additional rain, RH >= 90% (dew), or leaf wetness proportion >= 25% (r/d) – Progress to infection a function of temperature – Dry period of less than 8 hours stalls progress to infection but does not eliminate risk 16 (as applied at Enviro-weather)

17. Apple scab wetting periods Results (apple scab) 17 RTMA5STAGEIV ASOS 2.703.01 EW 2.322.28 RTMA5STAGEIV ASOS -25.5 EW -14.56.5 6-station median: ANL-OBS ASOSEW ASOSEW * Mean event duration * 5-year period

18. Apple scab infection events Results (apple scab) 18 RTMA5STAGEIV ASOS 2.762.56 EW 1.421.18 RTMA5STAGEIV ASOS 7.509.00 EW 4.5011.00 6-station median: ANL-OBS ASOSEW ASOSEW 5-year period * Mean event duration * 1-2 more per year

19. Apple scab: Interpretation Wetting period count sensitive to choice of Stage II or Stage IV. Duration less sensitive. (Number of wetting periods is a function of precipitation only) Infection events (number and duration) sensitive to choice of Stage II/IV, especially sensitive to RTMA temperature & RH errors Considerable station-to-station and year-to- year variability (not shown) 19 Results (apple scab)

20. Gridded Analysis Summary Gridded analyses have promise as a source of weather data for IPM applications in Michigan However, we must proceed with caution: Disease models with multiple weather inputs pose a challenge for RTMA/STAGEIV; also: long-duration degree day accumulations (aggregate errors) Considerable station-to-station variation in errors Errors generally larger at EW sites than ASOS sites Temperature/dewpoint analysis suggests that bias correction has promise, but would need to be site-specific Conclusions 20

21. Current/Future Directions Develop gridded leaf wetness duration proxy Work toward integration of: – mesonet observations with gridded analyses – historical climate data with gridded analyses and forecasts Look at additional IPM applications to further evaluate applicability of gridded data – Special focus: assess feasibility of using gridded precipitation analyses and forecasts in IPM applications Explore spatial variability of gridded product error 21

22. Acknowledgements Enviro-weather supported by MI Project GREEEN, MI AgBioResearch, MSU Extension, external grants, corporate/individual sponsorships, and grower contributions Special thanks go to Tracy Aichele for assistance with plant disease/pest models 22 www.enviroweather.msu.edu Questions?

23. NDFD evaluation National Digital Forecast Database (NDFD) – consists of gridded forecasts of sensible weather elements (e.g., cloud cover, maximum temperature) – seamless mosaic of digital forecasts from NWS field offices working in collaboration with the National Centers for Environmental Prediction (NCEP) – 7 Days: Day 1-3 forecasts (updated hourly) and day 4-7 forecasts (updated four times per day) 23 Gridded Forecasts

24. NDFD: Growing Degree Days* 24 00 UTC forecast *Baskerville-Emin method Gridded Forecasts

25. Backup slides

26. RTMA analysis: Bias histograms 26 Results (hourly) 2 m temperature (K)

27. RTMA analysis: Bias histograms 27 Results (hourly) 2 m dewpoint temperature (K)

28. T bias: Diurnal trends 6-station median

29. TD bias: Diurnal trends 6-station median

30. RH bias: Diurnal trends 6-station median

33. Applescab: Infection Severity (Percentage of total infection hours)

35. Codling moth: Difference in # of days to milestones

36. Accumulated GDD: 2009 vs. 2011

37. ST2/ST4: Performance measures 37 ASOSEW

38. A word about RTMA 2.5 km… 38 ASOS 6-station median