Founded 2004
Funded by the Western Region IPM center
Workgroup Program
Ag Weather Net
Ag Weather Net
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
Ag Weather Net
Current Membership
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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
 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.
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.
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.
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
Ag Weather Net
Where are We Going?
Interactive Virtual Weather Station
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Where are we?
Virtual Weather Station 1.0
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12,500+ weather stations assimilated per day nationwide
• Hourly or better from MesoWest plus several
grower-run networks
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 finegrid DEM
http://pnwpest.org/wea/indextable.html
Ag Weather Net
1971-2000
Today’s
PRISM Climate
Anomalies
Ag Weather Net
Today’s Spatial
Estimates
Parameter-elevation
Regressions on
Independent
Slopes
Model
 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
http://www.prism.oregonstate.edu/
Ag Weather Net
Ag Weather Net
Rain Shadow: Mean Annual Precipitation
Oregon Cascades
Portland
Mt. Hood
Eugene
PRISM
Station
Weighting
Mt. Jefferson
100 in/yr
90 in/yr
Terrain
orientation
Sisters
Terrain
steepness
14 in/yr
Moisture Regime
Redmond
Elevation
10 in/yr
Bend
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Coastal Effects: 1971-00 July Maximum Temperature
Central California Coast
Sacramento
Stockton
San Francisco
Oakland
Preferred
°
34
Fremont
PRISM
Station
Weighting
Trajectories
San Jose
Coastal
Proximity
Santa Cruz
Elevation
° Hollister
°
27
20
Monterey
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Salinas
Inversion Layer
N
Willits
9°
Ukiah
17
Cloverdale
Lake Pilsbury.
°
Lakeport
10
16
°
°
PRISM
Station
Weighting
°
12
Topographic
Index
17
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°
Inversion Layer
Improving Resolution of Spatial Interpolation
4 km/pixel
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Improving Resolution of Spatial Interpolation
0.8 km/pixels
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Old 1961-1990
New 1971-2000
4 km
800 m
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
Ag Weather Net
100-km forecast
model grids
(GFS)
Station
observations
MtnRT
Basic
QC
0.8-km
PRISM allday climate
grid
IDW
Interp
0.8-km
current
weather grid
2-km 6-hrly
forecast grid
Mt1hr
interp
2-km 1-hrly
forecast grid
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Gaussian
filter
0.8-km
MtnRT
PRISM
12-hrly
forecast
grid
Where are we?
Virtual Weather Station 1.0
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Hop Powdery Mildew
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Powdery Mildew Risk Index
1. If >6 continuous hours > 30°C, then -20
2.
3.
4.
5.
points, else;
If > 2.5mm rain, then -10 points, else;
If >6 continuous hours > 30°C on previous
day, then no change in the index, else;
If at least six continuous hours between 1627°C, then +20 points, else;
If none of the above rules apply, then -10
points.
A 0 to 100 index0
Ag Weather Net
Comparison of actual vs estimated weather data in
calculating powdery mildew index
Ag Weather Net
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# OF
SPORES
X
# OF
INFECTIONS
NIGHT & A.M.
WEATHER
[ RAIN ]
+ HEAT
UNITS
WITHIN – PLANT
SPREAD
NEW
PUSTULES
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Realtime PRISM
Targ. Clim. PRISM
Ground Obs.
Forecasts
Static PRISM
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Estimation of missing data
Flatliner checks – repeating values
Compare extreme values with record highs and lows
Spatial consistency checks
Develop “bad boy” list
Ag Weather Net
N
C
N
C
Temperature gradient
Temperature gradient
Corvallis – Newport = 10.8C
Corvallis – Newport = 3.8C
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A warm, dry weather pattern
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One of several cold-weather patterns
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
Ag Weather Net
MtnRT Temperatures
+
PRISM
Targeted
Climatolo
gies
+
MtnRT
Winds
30-40-km
forecast model
grids (WRF)
Station
observations
MtnRT
Spatial
QC
4-km 3-hrly
forecast grid
Future
IDW
MtnRT- PRISM
Interp
0.8-km PRISM
all-day or
targeted
climate grid
PRISM
0.8-km
current
weather grid
Gaussian
filter
0.8-km
0.8-km
3-hrly forecast grid
Mt1hr
interp
3-hrly forecast grid
0.8-km
1-hrly forecast grid
0.8-km
Adjusted
Bias
1-hrly
forecast grid
0.8-km 1-hrly
forecast grid
Correct
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Public Weather Data
 Expected configuration
Ag Weather Net
Station placement not always optimal
Does sensor location matter?
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