Cyberinfrastructure ChallengesTo Regional

Weather Forecasting for Disease Warning

Systems

Kathleen M. Baker Western Michigan University

Midwest Weather Working GroupOctober 7, 2009

Synoptic weather forecasting and web-based information delivery systems for managing crop disease risk in multiple regions of the U.S.

RAMP #2008-02925

SouthDakotaStateUniversity

Project Overview

Comparison of techniques for forecasting ANN at point locations vs spatial grids Variability across regions / diseases Cyberinfrastructure options Test cases:

Potato late blight ---- MI Fusarium head blight of barley --- MN/SD/ND Leaf spot of peanut --- GA, FL

Focus today: MI-PLB, August 2008

GFS MEX MOS Alphanumeric Message

KAZO GFSX MOS GUIDANCE 12/05/2008 0000 UTC FHR 24| 36 48| 60 72| 84 96|108 120|132 144|156 168|180 192 WED 05| THU 06| FRI 07| SAT 08| SUN 09| MON 10| TUE 11| WED 12 X/N 26| 14 29| 25 35| 22 32| 28 41| 28 36| 22 36| 25 36 TMP 20| 17 26| 28 30| 24 30| 33 35| 30 30| 24 31| 28 32 DPT 16| 13 18| 23 26| 19 24| 29 31| 26 24| 20 24| 23 24 CLD OV| CL PC| OV PC| OV OV| OV OV| OV PC| OV OV| CL PC WND 11| 7 9| 11 11| 8 6| 13 11| 11 11| 10 13| 15 9 P12 36| 1 4| 63 21| 16 16| 61 55| 42 30| 25 32| 29 24 P24 | 8| 76| 29| 64| 51| 41| 46 Q12 0| 0 0| 1 0| 0 0| 4 3| 2 0| 0 | Q24 | 0| 1| 0| 3| 2| |

 Normals May June July August Sept.

Cooler than mean MonthHiLo MonthHiLo MonthHiLo LatLong NoSpatial

Warmer than mean MonthHiLo NoSpatial MonthHiLo LatLong LatLong

ANN Schematic

The Total 5 Year Accuracy

• Mean of forecasts – 81%• Mean of stations– 79%• Median – 79%• SD – 3%

0.5 0.6 0.7 0.8 0.9 1.0Accuracy

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Total 5 year accuracies

Continued ANN work

How do we create these models in the most efficient way? Randomization between years Addition of climatic normals Necessary data archive

How does accuracy compare to more advanced options?

Cyberinfrastructure “a research environment that supports

integration of geographically distributed computing and information processing services to enable data-intensive collaborative science enterprises”

TeraGrid a petaflop of computing capability more than 30 petabytes of online & archival data

storage rapid access and retrieval over high-performance

networks

LEAD Cyberinfrastructure

Linked Environments for Atmospheric Discovery (LEAD) Portal – $11 mil

Democratization of forecasting

WRF outputs Hourly grids Varying resolution

LEAD Workflow

Forecast Variable Outputs

Integration with GIS Workflows

Potato Late Blight Risk Output

MI-PLB Accuracy Assessment LEAD WRF Neural Net

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

Total Accuracy

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Total Accuracy of ULCD stations (August 2008)

0.5 0.6 0.7 0.8 0.9 1.0

TotalAccuracy

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Total Accuracy of neural network ULCD stations (August 2008)

ACCURACY AUG 2008

AVG MED MIN MAX SD

71% 76% 16% 95% 17%

ACCURACY AUG 2008

AVG MED MIN MAX SD

72% 72% 55% 87% 6%

24hr LEAD

Accuracy MI-PLB

August 08

Challenges

Variability in accuracyOptimization of spatial resolutionStablization of system Limited datasetVariable selection

2m vs canopy conditions

Aug 08 – PLB Risk2m LEAD Canopy LEAD

Discussion

Gridded WRF forecasts have many advantages over ANN point based model Increased accuracy at critical locations Increased accuracy at critical times Increased spatial resolution Potential to vary spatial resolution Potential to vary height of measurements Expanded variable set Run time flexibility

Continuing work…

Compare accuracy and variability for different crops in different regions

Compare ANN interpolation from points to spatially gridded forecasts Is the investment worth it?

Conclusion

The high quality foundation of data provided by cyberinfrastructure projects such as LEAD has the potential to truly transform agricultural decision support

Cloud computing possibilities…

Proposed Organization of Crop Disease Risk Forecasting System

Model workflow

Early Warning

Reports

Modelingservices

Analysis

ForecastsAccess to local,

regional and global meteorological

forecasts

Access to local phys-geographic data, incl. data from other CI

projects

Forcings

monitoringof crop

data

Crop Observatories

ValidationAssemble

modelValidatio

ndata

Data conversion services

(e.g. projection)

Event detection and alert services

workflows

Neural Network, Excel, GIS, …

Funding:USDA CSREES FQPA RAMP 2008-02925Co-PI’s: Joel Paz, University of Georgia, Ag & Biological EngineeringJeffrey Stein, South Dakota State University, Plant BiologyWilliam Kirk, Michigan State University, Plant Pathology Phillip Wharton, University of Idaho, Plant PathologyDennis Todey, South Dakota State Univ, Ag & Biosystems EngineeringLinked Environments for Atmospheric Discovery (LEAD):Kelvin Droegemeier, University of Oklahoma; Beth Plale, Suresh Marru, Felix Terkhorn, Indiana UniversityResearch Assistants:Magdalena Wisniewska; Jason Smith; Cassandra Hoch; Doug Rivet;Susan Benston, Steve Schultz