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
Introduction
Background
• 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
2
Introduction
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!)
3
Enviro-weather Automated
Weather Network
July 2014
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.
Introduction
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
* Automated Surface Observing System
Introduction
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
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?
7
Methodology
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
Observation Sites
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
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Results (hourly)
RTMA analysis: Overview
Temperature, Dewpoint, Relative humidity
RMSE
BIAS
RMSE
BIAS
RMSE
BIAS
6-station median
10
Results (hourly)
RTMA analysis: Bias histograms
Relative humidity bias (%)
11
Results (hourly)
Stage II vs IV precipitation
ASOS
Larger percent correct
(False alarm)
(Miss)
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Results (hourly)
Stage II vs IV precipitation
EW*
Larger percent correct
(False alarm)
(Miss)
* warm season (1 Apr-30 Sep) only
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Results (daily)
Max & Min T, Growing Degree Days
RMSE
BIAS
6-station median
RMSE
BIAS
RMSE
BIAS
*Baskerville-Emin method
Base 10 C
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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
Apple scab primary infection
(as applied
model
at Enviro-weather)
• 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
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Results (apple scab)
Apple scab wetting periods
5-year period
ASOS
ASOS
EW
*
EW
6-station median: ANL-OBS
RTMA5
STAGEIV
ASOS
-2
5.5
EW
-14.5
6.5
6-station median: ANL-OBS
RTMA5
STAGEIV
ASOS
2.70
3.01
EW
2.32
2.28
* Mean event duration
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Results (apple scab)
Apple scab infection events
5-year period
ASOS
EW
6-station median: ANL-OBS
ASOS
EW
ASOS
*
EW
RTMA5
STAGEIV
7.50
9.00
1-2 more per year
4.50
11.00
6-station median: ANL-OBS
RTMA5
STAGEIV
ASOS
2.76
2.56
EW
1.42
1.18
* Mean event duration
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Results (apple scab)
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-toyear variability (not shown)
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Conclusions
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
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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
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
Questions?
www.enviroweather.msu.edu
22
Gridded Forecasts
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)
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Gridded Forecasts
NDFD: Growing Degree Days*
00 UTC forecast
*Baskerville-Emin method
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Backup slides
Results (hourly)
RTMA analysis: Bias histograms
2 m temperature (K)
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Results (hourly)
RTMA analysis: Bias histograms
2 m dewpoint temperature (K)
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T bias: Diurnal trends
6-station median
TD bias: Diurnal trends
6-station median
RH bias: Diurnal trends
6-station median
Applescab: Infection Severity
(Percentage of total infection hours)
Applescab: Infection Severity
(Percentage of total infection hours)
Codling moth: Difference in # of days
to milestones
Accumulated GDD: 2009 vs. 2011
ST2/ST4: Performance measures
ASOS
EW
37
A word about RTMA 2.5 km…
ASOS
6-station median
38