Update on the Western Weather Work Group

Carla ThomasWestern IPM Center

Western Plant Diagnostic Network

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.

WWWG HistoryOur 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

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.

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.

WWW.USPEST.ORG/WEA

+ 16,000 weather stations

Daily Temperature Regime

Average TemperatureClose alignment between V2 and Std.

Over/ Under estimation of V2 2010 and 2011 at THILL

Max TemperatureV2 follows closely with Std 60 in most data sets.

Over estimation of V2 Max Temp consistent across season

Min TemperatureUnder prediction of Min Temperature is common in most Datasets.

Over estimation of Min Temp is common at THill.

Temperature means over the data sets.

Daily Temp variable Crop (State)

15 min vs. 60 min interval at an in-canopy placement

In-canopy placement vs. standard placement at 60

min intervals

In-canopy placement at 15 min intervals vs. standard

placement at 60 min intervals

Standard placement at 60 min intervals vs. V2

r

Mean Error (oC)

Mean absolute error (oC) r

Mean Error (oC)

Mean absolute error (oC) r

Mean Error (oC)

Mean absolute error (oC)

r

Mean Error (oC)

Mean absolute error (oC)

Maximum Hop (OR) 0.999 -0.21 0.21 0.998 0.42 0.49 0.998 0.21 0.41 0.993 0.59 0.84 Hop (WA) 0.999 -0.25 0.25 0.992 -0.98 0.98 0.991 -1.22 1.22 0.931 1.22 2.54 Grape (OR) 0.998 -0.18 0.27 0.993 -0.46 0.82 0.992 -0.73 1.02 0.929 0.17 1.54

Grape (WA) 0.999 -0.25 0.25 0.993 -1.42 1.43 0.993 -1.67 1.67 0.965 1.86 2.80 Cherry (WA) 0.935 1.61 2.20 Minimum Hop (OR) 0.999 0.13 0.13 0.993 -0.46 0.50 0.993 -0.33 0.41 0.958 0.92 1.11 Hop (WA) 0.998 0.24 0.24 0.987 -0.60 0.61 0.985 -0.36 0.51 0.812 1.48 2.85

Grape (OR) 0.998 0.12 0.12 0.995 0.19 0.34 0.993 0.31 0.41 0.863 0.09 1.56 Grape (WA) 0.998 0.26 0.26 0.998 0.34 0.41 0.996 0.60 0.63 0.889 1.72 2.41 Cherry (WA) 0.779 1.00 2.49 Average Hop (OR) 1.00 0.00 0.05 0.999 -0.04 0.18 0.999 -0.04 0.18 0.995 0.64 0.68

Hop (WA) 1.00 0.01 0.06 0.990 -0.80 0.80 0.990 -0.79 0.79 0.941 0.78 1.68 Grape (OR) 1.00 -0.00 0.06 0.998 -0.17 0.32 0.998 -0.17 0.32 0.955 0.15 1.09 Grape (WA) 1.00 0.00 0.06 0.998 -0.63 0.63 0.998 -0.63 0.63 0.968 0.88 1.33 Cherry (WA) 0.943 0.52 1.18

Lower R-values with Min TempTemp shows a

good correlation between v2 and

Std 60.

Monthly Mean Temp DataDaily

average temperature

Silv March 2010 0.999 -0.02 0.07 0.997 0.13 0.16 0.996 0.11 0.16

0.913 0.63 0.73

Silv April 2010 0.999 -0.01 0.06 0.995 0.00 0.18 0.993 -0.01 0.20

0.970 0.65 0.75

Silv May 2010 0.999 -0.02 0.08 0.996 -0.36 0.37 0.996 -0.38 0.38

0.984 0.84 0.84

Silv June 2010 0.999 0.01 0.06 0.992 -0.45 0.47 0.992 -0.45 0.47

0.990 0.70 0.70

Silv July 2010 0.999 -0.01 0.08 0.997 -0.38 0.44 0.997 -0.39 0.45

0.995 1.08 1.08

Hyslop March 2010 0.999 -0.01 0.06 0.996 -0.08 0.16 0.996 -0.08 0.15

0.984 0.32 0.40

Hyslop April 2010 0.999 0.01 0.07 0.995 -0.32 0.33 0.994 -0.30 0.33

0.992 0.50 0.50

Hyslop May 2010 0.999 -0.03 0.09 0.997 -0.71 0.71 0.997 -0.73 0.73

0.985 0.79 0.79

Hyslop June 2010 0.999 -0.02 0.09 0.965 -0.99 1.00 0.960 -1.01 1.03

0.987 0.65 0.65

Hyslop July 2010 0.999 -0.03 0.12 0.996 -1.27 1.27 0.996 -1.30 1.30

0.994 0.67 0.67

V2 and Actual Mean Temp showed a

strong correlation, but no trends from month to month.

Daily RH Regime

V2 Relative Humidity lags behind Actual across all seasons and sites.

Relative Humidity

Crop

Daily Max Relative Humidity Daily Min Relative Humidity

r Mean

difference

Mean absolute differenc

e r

Mean differen

ce

Mean absolute

difference Grass HYS 2010 0.7916 -6.47 6.57 0.8863 -1.97 4.06 Grass HYS 2011 0.7512 -6.52 6.62 0.8658 -2.39 4.08 Grass JCTY 2010 0.5395 -4.49 4.99 0.8475 -2.95 4.85 Grass JCTY 2011 0.6328 -6.90 6.92 0.9009 -3.59 4.82 Grass SILV 2010 0.5823 -3.29 4.56 0.8846 -2.39 5.05 Grass SILV 2011 0.5876 -1.67 3.75 0.7435 -5.01 7.70 Grass DTN 2011 0.7801 -4.28 4.38 0.8431 0.78 3.58 Grape ARSM 2010 0.8280 -2.19 4.19 0.8383 4.77 6.96 Grape Croft 2010 0.7230 -5.11 5.93

0.9216 1.98 3.99

Grape THill 2010 -0.0469 4.95 5.07 0.4922 16.00 24.66 Grape Wren 2010 0.7044 -4.55 4.63 0.8499 3.75 5.82

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.

Min Relative Humidity

V2 Min RH usually closely aligns with Std 60.

At THill, V2 Min RH varies widely.

Dew Point Temp

Crop

Daily Max Dew Point Temp Daily Min Dew Point Temp

Daily Mean Dew Point Temp

r Mean

difference

Mean absolute

difference r

Mean differen

ce

Mean absolute

difference

r

Mean differen

ce

Mean absolute

difference Grape ARSM 2010 0.8497 1.83 2.13 0.7970 3.40 3.45 0.9055 2.19 2.21 Grape Croft 2010 0.9176 1.55 1.68 0.7655 4.33 4.34 0.9048 2.62 2.62 Grape THill 2010 0.3907 13.09 13.11 0.7191 3.06 3.46 0.7329 6.00 6.04 Grape Wren 2010 0.8413 2.53 2.56 0.8628 4.10 4.10 0.9242 3.05 3.05 Grape ArSm 2011 0.8545 1.46 1.97 0.7510 4.64 4.76 0.8690 2.58 2.80 Grape THill 2011 0.5312 4.91 4.92 0.7648 3.94 4.12 0.8943 3.69 3.69 Grape BPP 2011 0.9454 0.65 1.06 0.8501 4.31 4.32 0.9634 2.18 2.22

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.

Daily Leaf Wetness

Hours of Leaf Wetness

Precipitation

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

HPM Graphs

Downy Mildew GraphsV2 Downy infection risk values typically overestimate Actual infection values.

Grape Powdery Mildew

V2 underestimates GPM risk values under 100, and like HPM, occasionally shows early infection

Rust Models

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.

Gridded Disease Maps

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 eventGrape bunch rotdisease grid andoverlay on Googlemap

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.

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.

Rust Models

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

My virtual stationValidated forecasts

Western Weather Work Group Current Work

Supporting existing and emerging systems through distributed resources

Spotted Wing Drosophila Overwintering Mortality

Area wide IPM coddling moth

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

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

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

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). 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.

Next Steps

• Expand Impact and Adoption Assessment

• Expand Infrastructure Support through signature programs

• PRIME• LAMP

Thank you!

Monthly Min Temp DataDaily

minimum temperature

Silv March 2010 0.998 0.12 0.12 0.990 0.50 0.50 0.986 0.63 0.63

0.882 1.15 1.25

Silv April 2010 0.991 0.22 0.22 0.992 0.54 0.54 0.985 0.75 0.75

0.817 0.84 1.19

Silv May 2010 1.000 0.09 0.09 0.993 0.61 0.61 0.992 0.69 0.69

0.704 1.41 1.43

Silv June 2010 0.993 0.26 0.26 0.988 0.54 0.54 0.980 0.80 0.80

0.670 0.95 1.11

Silv July 2010 0.999 0.23 0.23 0.993 0.91 0.91 0.987 1.14 1.14

0.358 2.05 2.15

Hyslop March 2010 0.997 0.15 0.15 0.992 0.43 0.44 0.990 0.58 0.59

0.936 0.45 0.74

Hyslop April 2010 0.997 0.13 0.13 0.993 0.44 0.44 0.992 0.57 0.57

0.856 0.77 0.90

Hyslop May 2010 0.996 0.13 0.13 0.995 0.34 0.37 0.992 0.47 0.48

0.653 1.36 1.47

Hyslop June 2010 0.996 0.15 0.15 0.941 0.77 0.91 0.940 0.91 0.99

0.694 0.75 1.06

Hyslop July 2010 0.997 0.19 0.19 0.993 1.14 1.14 0.990 1.33 1.33

0.457 1.17 1.91

V2 Monthly Min Temp data showed

a decline in the correlation with

Std 60 from Spring to Summer. It

appears to occur in May.

Rust ModelsV2 is unpredictable in its over and underestimation of Infection Values.

Management Recommendations for Hop Powdery Mildew.

Site Year Weather inputb Mean error in index Mean absolute error in index No. of fungicide

applicationsa Application

interval (days) Oregon Hop 105 2010 In-canopy 15 min 9 9.7 V2 -4.19 8.17 10 8.7 105 2011 In-canopy 15 min 10 8.7 V2 -1.51 2.47 10 8.7 205 2010 In-canopy 15 min 9 9.7 V2 -2.63 9.95 10 8.7 205 2011 In-canopy 15 min 10 8.7 V2 -1.56 5.22 10 8.7 305 2010 In-canopy 15 min 9 9.7 V2 -9.19 11.99 9 9.7 305 2011 In-canopy 15 min 10 8.7 V2 0.43 2.26 10 8.7 405 2010 In-canopy 15 min 10 8.7 V2 -2.26 8.49 9 9.7 405 2011 In-canopy 15 min 10 8.7 V2 -2.15 2.58 10 8.7 Mean In-canopy 15 min 9.6 9.1 V2 -2.88 6.39 9.8 9.0 Washington Hop H35 2010 In-canopy 15 min 11 7.9 V2 12.55 17.99 11 7.9 H35 2011 In-canopy 15 min 12 7.3 V2 -3.33 9.56 12 7.3 Mean In-canopy 15 min 11.5 7.6 V2 4.61 13.78 11.5 7.6

V2 typically called for one

more spray than the

Canopy 15. The spray interval was usually

shorter for the V2.