Agricultural Digital Forecast System
0
International Workshop on the Content, Communication and Use of Weather and Climate Products and Services for
Sustainable AgricultureMay 17-22, 2009
Kyu Rang KIM, Wee Soo KANG1, and Eun Woo PARK1
National Institute of Meteorological Research,Korea Meteorological Administration
1Seoul National University
Agricultural Grid Weather Information System based on Digital Weather Forecast in Korea and its Application to Rice Blast Disease Warning
Agricultural Digital Forecast System
1
OutlineOutline
I. Conventional Ag-Met Information Services in Korea
II. Introduction to Digital Weather Forecast
III. Current Development of Application Services
based on Digital Weather Forecast
IV. Outcome and Further Development
Agricultural Digital Forecast System
2
I. Conventional Ag-Met Information Services
Agricultural Digital Forecast System
3
I. Conventional Ag-Met Information Services
• Currently Available AgroMet Information from KMA
• Chemical Spray Favorableness Index
• Agricultural Facility Warning Index
• Mostly Region-based information
• Higher Resolution Services are Required
• Due to complex terrain and land use/cover, spatial variation of
AgroMet conditions is very large
• Farmers want their own field-specific data
• Automated weather stations (AWS) were installed to monitor Ag-
Met variables in such highly variable fields
Agricultural Digital Forecast System
To meet the site specificity…
AWS in agricultural fieldAir TemperatureRelative HumiditySoil TemperatureRainfallSolar RadiationWind Speed/DirectionLeaf WetnessSoil Humidity
Installed in agriculturalfield as needed by farmers/extension services
* KMA installed AWS (>600) generally on roof-top
4
I. Conventional Ag-Met Information Services
Agricultural Digital Forecast System
Examples of AWS basedAgroMet Services
Rice Paddy (Rice Blast)= very successful
Apple OrchardFarmers provide/share significant information on orchard management
Pear Orchard, etc
Problems in using AWShigh costdifficult maintenancelimited resolutionno real forecasting
5
I. Conventional Ag-Met Information Services
Agricultural Digital Forecast System
Examples of AWS basedAgroMet Services
Rice Paddy (Rice Blast)= very successful
Apple OrchardFarmers provide/share significant information on orchard management
Pear Orchard, etc
Problems in using AWShigh costdifficult maintenancelimited resolutionno real forecasting
6
I. Conventional Ag-Met Information Services
Agricultural Digital Forecast System
Examples of AWS basedAgroMet Services
Rice Paddy (Rice Blast)= very successful
Apple OrchardFarmers provide/share significant information on orchard management
Pear Orchard, etc
Problems in using AWShigh costdifficult maintenancelimited resolutionno real forecasting
7
I. Conventional Ag-Met Information Services
Agricultural Digital Forecast System
Examples of AWS basedAgroMet Services
Rice Paddy (Rice Blast)= very successful
Apple OrchardFarmers provide/share significant information on orchard management
Pear Orchard, etc
Problems in using AWShigh costdifficult maintenancelimited resolutionno real forecasting
8
I. Conventional Ag-Met Information Services
Agricultural Digital Forecast System
Examples of AWS basedAgroMet Services
Rice Paddy (Rice Blast)= very successful
Apple OrchardFarmers provide/share significant information on orchard management
Pear Orchard, etc
Problems in using AWShigh costdifficult maintenancelimited resolutionno real forecasting
9
I. Conventional Ag-Met Information Services
Agricultural Digital Forecast System
Examples of AWS basedAgroMet Services
Rice Paddy (Rice Blast)= very successful
Apple OrchardFarmers provide/share significant information on orchard management
Pear Orchard, etc
Problems in using AWShigh costdifficult maintenancelimited resolutionno real forecasting
10
I. Conventional Ag-Met Information Services
Agricultural Digital Forecast System
Examples of AWS basedAgroMet Services
Rice Paddy (Rice Blast)= very successful
Apple OrchardFarmers provide/share significant information on orchard management
Pear Orchard, etc
Problems in using AWShigh costdifficult maintenancelimited resolutionno real forecasting
11
I. Conventional Ag-Met Information Services
Agricultural Digital Forecast System
Examples of AWS basedAgroMet Services
Rice Paddy (Rice Blast)= very successful
Apple OrchardFarmers provide/share significant information on orchard management
Pear Orchard, etc
Problems in using AWShigh costdifficult maintenancelimited resolutionno real forecasting
12
I. Conventional Ag-Met Information Services
Agricultural Digital Forecast System
Examples of AWS basedAgroMet Services
Rice Paddy (Rice Blast)= very successful
Apple OrchardFarmers provide/share significant information on orchard management
Pear Orchard, etc
Problems in using AWShigh costdifficult maintenancelimited resolutionno real forecasting
13
I. Conventional Ag-Met Information Services
Agricultural Digital Forecast System
Examples of AWS basedAgroMet Services
Rice Paddy (Rice Blast)= very successful
Apple OrchardFarmers provide/share significant information on orchard management
Pear Orchard, etc
Problems in using AWShigh costdifficult maintenancelimited resolutionno real forecasting
14
I. Conventional Ag-Met Information Services
Agricultural Digital Forecast System
Examples of AWS basedAgroMet Services
Rice Paddy (Rice Blast)= very successful
Apple OrchardFarmers provide/share significant information on orchard management
Pear Orchard, etc
Problems in using AWShigh costdifficult maintenancelimited resolutionno real forecasting
15
I. Conventional Ag-Met Information Services
Agricultural Digital Forecast System
Examples of AWS basedAgroMet Services
Rice Paddy (Rice Blast)= very successful
Apple OrchardFarmers provide/share significant information on orchard management
Pear Orchard, etc
Problems in using AWShigh costdifficult maintenancelimited resolutionno real forecasting
16
I. Conventional Ag-Met Information Services
Agricultural Digital Forecast System
Examples of AWS basedAgroMet Services
Rice Paddy (Rice Blast)= very successful
Apple OrchardFarmers provide/share significant information on orchard management
Pear Orchard, etc
Problems in using AWShigh costdifficult maintenancelimited resolutionno real forecasting
17
I. Conventional Ag-Met Information Services
Agricultural Digital Forecast System
Examples of AWS basedAgroMet Services
Rice Paddy (Rice Blast)= very successful
Apple OrchardFarmers provide/share significant information on orchard management
Pear Orchard, etc
Problems in using AWShigh costdifficult maintenancelimited resolutionno real forecasting
18
I. Conventional Ag-Met Information Services
Digital Forecast can provide detailed
forecast at higher resolution
Agricultural Digital Forecast System
Data from Digital Weather Forecast
• Operational since Oct. 2008• 5km, 3hourly forecast• Qualitative forecast• 48 hours of forecast length• 16 layers of forecast data• Horizontal domain grid size:
149(E-W) * 253(N-S) = 37,697
AIR TEMPERATURE (T3H) SKY CONDITION (SKY)
MINIMUM TEMP (TMN) WIND DIRECTION (WDD)
MAXIMUM TEMP (TMX) WIND SPEED (WDS)
PROBABILITY OF PRECIPITATION (POP)
SIG WAVE HEIGHT (WAV)
RELATIVE HUMIDITY (REH) ACC. PPTN IN 12-HOUR (R12)
TYPE OF PRECIPITATION (PTY)
ACC. SNOW IN 12-HOUR (S12)
ComponentsDigital Weather forecast
II. Digital Weather Forecast
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Agricultural Digital Forecast System
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A. Data Extraction from Digital Forecast for Agricultural Models• Extract three hourly 5km grid data
• Temporal interpolate to produce hourly 5km grid data
• Apply additional models to estimate leaf wetness, essential for disease forecast
B. Implementation of Application (Plant Disease Development) Models• Implementation and optimization of disease forecast and application models
• Map-based Internet interface for disease forecast and information
C. Evaluation of the system• Accuracy assessment between AWS data- vs. digital forecast-based disease forecast
III. Development of Application Services
1. Procedures for Agricultural Digital Forecast
Agricultural Digital Forecast System
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2. System Overview
Input Data & Processing
Information Delivery System
Disease Forecasting Model
Output Data
III. Development of Application Services
Agricultural Digital Forecast System
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3. Data Storage System
- Stores weatherdata transferredfrom KMA
- Storesinterpolatedweather datacreated fromJPS sub-system
- Provides the datato the JPS andWSS sub-systems
III. Development of Application Services
Agricultural Digital Forecast System
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4. Job Process System- Executes three jobs hourly
1. Interpolate Data- Interpolate hourly mesh
weather data from theDigital Weather Forecast
2. Run Application Models- Calculate the rice blast
infection model
3. Render Maps- Converts the forecasting
data to map images
III. Development of Application Services
Agricultural Digital Forecast System
24
5. Web Service System- Interacts with users- Presents the data as maps
through web map interfacein a web site
Web Map Interface- Supports panning, zoom-in and
zoom-out of the maps- Shows disease forecasting
map layer overlaid onlayers of digital elevation anddistrict maps
III. Development of Application Services
Agricultural Digital Forecast System
25
6. Input Data Processing
최저기온
MINIMUM TEMP. (TMN)기온
AIR TEMPERATURE (T3H)
최고기온
MAXIMUM TEMP. (TMX)상대습도
REL. HUMIDITY (REH)
풍향
WIND DIRECTION (WDD)강수확률 PROBABILITY OF PPTN. (POP)
유의파고
SIG. WAVE HEIGHT (WAV)강수량
ACC. PPTN. IN 12-HOUR (R12)
강수형태
TYPE OF PPTN. (PTY)풍속
WIND SPEED (WDS)
하늘상태
SKY CONDITION (SKY)적설량
ACC. SNOW IN 12-HOUR (S12)
Digital Forecast (12)Digital Forecast (12)Step 1
Decode Digital Forecast
Step 2Hourly Interpolate
T3H, REH, POP, R12
Step 3Save 5km x 5km Grid
Weather Data
- Hourly interpolation of 48 hour
digital forecast data for T3H and
REH
- Hourly estimate of rainfall from
POP and R12
- Leaf wetness estimation from
T3H, RH, and WDS
Input for Disease Forecast
III. Development of Application Services
Agricultural Digital Forecast System
26
6-1. Input Processing – Hourly Interpolation – Air Temp, RH
III. Development of Application Services
Digital ForecastAir Temp(3-hourly)
Digital ForecastAir Temp(3-hourly)
Linear Interpolation
+1h +7h+4h
+1h +7h+4h+2h +3h +5h +6h
InterpolatedAir Temp(hourly)
InterpolatedAir Temp(hourly)
Agricultural Digital Forecast System
27
6-2. Input Processing – Hourly Interpolation – Probability of Precipitation
III. Development of Application Services
Digital ForecastProbability ofPrecipitation
(3-hourly)
Digital ForecastProbability ofPrecipitation
(3-hourly)
HourlyEstimation
+1h +7h+4h
+1h +7h+4h+2h +3h +5h +6h
Estimated Probability of Precipitation
(hourly)
Estimated Probability of Precipitation
(hourly)
Probability of precipitation for one hour: P1
Probability of precipitation forthree hours: P3
3
31
313
313
11
11
11
PP
PP
PP
Agricultural Digital Forecast System
28
6-3. Input Processing – Hourly Interpolation – Amount of Precipitation
III. Development of Application Services
Rainfall Estimation
+1h +13h+7h
Estimated Precipitation(hourly)
Estimated Precipitation(hourly)
+4h +10h
+1h +13h+7h+4h +10h
+
+1h +13h+7h+4h +10h
Using the Hourly Probability ofPrecipitation as Weights
The 12-hourly Precipitation is Distributed to Each Hour
Estimated Probability of Precipitation
(hourly)
Estimated Probability of Precipitation
(hourly)
Cumulative Amountof Precipitation
(12-hourly)
Cumulative Amountof Precipitation
(12-hourly)
Precipitation Forecastfor the 12 hour period
Agricultural Digital Forecast System
29
6-4. Input Processing – Hourly Interpolation – Leaf Wetness (Simple RH)
III. Development of Application Services
Hourly InterpolatedRelative Humidity
Hourly InterpolatedRelative Humidity
HourlyEstimation
+4h +16h+10h+7h +13h
+4h +16h+10h+7h +13h
Hourly EstimatedPrecipitation
Hourly EstimatedPrecipitation
+
+4h +16h+10h+7h +13h
IF Rain ≥ 0.1mm OR RH > 95% THENWet = 1
ELSEWet = 0
ENDIF
RH 95%
Rainfall 0.1mm
Leaf Wetness 1 hour
RH: Relative HumidityRain: RainfallWet: Leaf Wetness Period
Hourly EstimatedLeaf Wetness Period
Hourly EstimatedLeaf Wetness Period
Agricultural Digital Forecast System
30
Hourly InterpolatedRelative Humidity
Hourly InterpolatedRelative Humidity
CART모형
Hourly EstimatedLeaf Wetness Period
Hourly EstimatedLeaf Wetness Period
Hourly Data(19:00-09:00, w/o rainfall)
Group 5Dew
Group 3No Dew
Group 2Dew
Group 1No Dew
DPD < 2 °CDPD ≥ 2 °C
DPT < 16.3 °CDPT ≥ 16.3 °C
WS ≥ 0.6 m/s WS < 0.6 m/s
Group 4No Dew
DPD < 1.4 °C DPD ≥ 1.4 °C
Hourly InterpolatedAir Temperature
Hourly InterpolatedAir Temperature
Hourly Dew Point Temperature (DPT)Hourly Dew Point
Temperature (DPT)
Hourly InterpolatedWind Speed (WS)
Hourly InterpolatedWind Speed (WS)
Hourly Dew Point Depression (DPD)Hourly Dew Point Depression (DPD)
+(Yun et al., 1998)
6-5. Input Processing – Hourly Interpolation – Leaf WetnessCART (Classification and Regression Tree) model
III. Development of Application Services
+4h +16h+10h+7h +13h
Leaf Wetness 1 hour
Agricultural Digital Forecast System
31
•Rice Blast Forecast Model as a AgroMet Application Model- Rice is the most important staple food in Korea (ca. 1,000,000 ha)- Extension services use AWS-based rice blast warning system- Input Variables: Hourly Temperature, Leaf Wetness, Rainfall- Infection Risk (Yoshino, 1979) (Forecasted Wet Hours) - (Base Wet Hours Determined by Temp) ≥ 4hr Mean Air Temperature during the previous 5 days = 20~25℃ Rainfall ≤ 4mm/hr
- Daily Infection Risk and Warnings Levels Observed daily infection risk hours at 16 locations during ‘98-’03 Max. daily infection risk hours were determined as 14 hours Four levels of daily infection risk hours (R) were determined from
0, 3, and 7 hours, which are 0, 40, and 80 percentileof yearly total infection risk hours, respectively
7. Application Model (Disease Development Model for Rice Blast)
III. Development of Application Services
Agricultural Digital Forecast System
32
Level
Daily Infection
Risk (hours)
Infection Probability Warning
1 R = 0 None “Zero”
2 0 < R < 3 Low “Low”
3 3 ≤ R < 7 Higher “Mid”
4 R ≥ 7 Highest “High”
Fig. Yearly cumulative frequencies of daily total hours of rice blast infection,observed at sixteen locations in Korea during 1998-2003
Daily Infection Risk (hours)
Year
ly C
umul
ativ
e In
fect
ion
Ris
k (h
ours
)III. Development of Application Services
7-1. Application Model (Disease Development Model for Rice Blast)- Warning Level Determination from Climatic Data
Agricultural Digital Forecast System
33
• Disease Model Run Hours: +4h ~ +27h (24h Period)• Disease Model Output: Disease Infection Warning Hours (0~24h) or Warning Levels (4)
Disease ModelRun Hours
Forecast Hours isLess than 24 Hours-> insufficient Data
for the Disease Model
7-2. Application Model – Data Requirement by the Disease Modeland Forecast Hours by the Digital Forecast
III. Development of Application Services
Agricultural Digital Forecast System
34
8. Web-based Disease Forecasting System- Shows the
disease warningforecasts (hours)estimated for the upcoming24 hour period
- Infection risk hoursare also show asthe four warning levels:Zero, Low, Intermediate, and High
- Current target area: Gyeonggi province
III. Development of Application Services
Agricultural Digital Forecast System
35
9. Evaluation of the Forecasts: Preliminary Results and Plans in 2009
경기도농업기술원 AWS (화성기산동)
AgMetAWS
in Rice Paddies
작물과학원남양h험소 AWS (장안면) h흥h농업기술센터 AWS (장현동) 평택h농업기술센터 AWS (오성면)안성h농업기술센터 AWS (보개면)용인h농업기술센터 AWS (원삼면)이천h농업기술센터 AWS (호법면)여주군농업기술센터 AWS (여주읍)광주h농업기술센터 AWS (추월면)양평군농업기술센터 AWS (양평읍)가평군농업기술센터 AWS (가평읍)포천h농업기술센터 AWS (산북면)연천군제2농업연구소 AWS (연천읍)양주h농업기술센터 AWS (광적면)파주h농업기술센터 AWS (월롱면)김포h농업기술센터 AWS (통진면)
• AgroMet AWS in Gyeonggi Province
- 19 AWSs in rice paddies and upland fields are available.
• Leaf wetness and other weather elements (Preliminary Evaluation)
• Disease forecast based on Digital Weather Forecast (Evaluation Plan in 2009)
- Disease development will also be monitored and compared by plant pathologists.
Disease Forecast based onDigital Weather Forecast
DIW-basedvs.
AgMet AWS-basedweather data
III. Development of Application Services
Agricultural Digital Forecast System
1.5
2
2.5
3
3.5
4
4.5
5
0 10 20 30 40 50
RMSE
동네예보의예보자료시각과발표시각의차이 (hr)
1번지점
14번지점
15번지점
19번지점
21번지점
23번지점
25번지점
26번지점
28번지점
29번지점
43번지점
44번지점
45번지점
46번지점
49번지점
51번지점
89번지점
90번지점
91번지점
• RMSE between the Hourly Digital Forecast and the AWS Observation• RMSE increased with Forecast Hours; Interpolated Temperature had higher RMSE than the 3-hourly Forecast
9-1. Preliminary Evaluation Results: Air Temperature
III. Development of Application Services
36
Forecast Hours
Agricultural Digital Forecast System
• Relationship between mean RMSE for each station and distance between the grid center and the AWS location• Forecast accuracy was independent of distance and elevation difference between the AWS and the grid center
=> Source of Error: Variation in Vegetation and Land Use/Cover
2
2.5
3
3.5
4
4.5
0 500 1000 1500 2000 2500 3000 3500
관
측
지
점
별
평
균
RMSE
관측지점과동네예보격자의중심점사이의거리 (m)
R = ‐0.061
9-2. Preliminary Evaluation Results: Air Temperature
III. Development of Application Services
37
Distance between the grid center and the AWS location
Mea
n R
MS
E fo
r eac
h st
atio
n
Agricultural Digital Forecast System
10
12
14
16
18
20
0 10 20 30 40 50
RMSE
동네예보의예보자료시각과발표시각의차이 (hr)
1번지점
14번지점
15번지점
19번지점
21번지점
23번지점
25번지점
26번지점
28번지점
29번지점
43번지점
44번지점
45번지점
46번지점
49번지점
51번지점
89번지점
90번지점
91번지점
9-3. Preliminary Evaluation Results: Relative Humidity
III. Development of Application Services
38
• RMSE between the Hourly Digital Forecast and the AWS Observation• RMSE increased with Forecast Hours; Interpolated RH had higher RMSE than the 3-hourly Forecast
Forecast Hours
Agricultural Digital Forecast System
12
13
14
15
16
17
18
19
0 500 1000 1500 2000 2500 3000 3500
관
측
지
점
별
평
균
RMSE
관측지점과동네예보격자의중심점사이의거리 (m)
R = ‐0.004
9-4. Preliminary Evaluation Results: Relative Humidity
III. Development of Application Services
39
• Relationship between mean RMSE for each station and distance between the grid center and the AWS location• Forecast accuracy was independent of distance and elevation difference between the AWS and the grid center
=> Source of Error: Variation in Vegetation and Land Use/Cover
Distance between the grid center and the AWS location
Mea
n R
MS
E fo
r eac
h st
atio
n
Agricultural Digital Forecast System
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 10 20 30 40 50
POD
동네예보의예보자료시각과발표시각의차이 (hr)
1번지점14번지점15번지점19번지점21번지점23번지점25번지점26번지점28번지점29번지점43번지점44번지점45번지점46번지점49번지점51번지점89번지점90번지점91번지점
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 10 20 30 40 50
FAR
동네예보의예보자료시각과발표시각의차이 (hr)
1번지점
14번지점
15번지점
19번지점
21번지점
23번지점
25번지점
26번지점
28번지점
29번지점
43번지점
44번지점
45번지점
46번지점
49번지점
51번지점
89번지점
90번지점
91번지점
0
0.1
0.2
0.3
0.4
0.5
0.6
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0.8
0.9
1
0 10 20 30 40 50
PC
동네예보의예보자료시각과발표시각의차이 (hr)
1번지점
14번지점
15번지점
19번지점
21번지점
23번지점
25번지점
26번지점
28번지점
29번지점
43번지점
44번지점
45번지점
46번지점
49번지점
51번지점
89번지점
90번지점
91번지점
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 10 20 30 40 50
POFD
동네예보의예보자료시각과발표시각의차이 (hr)
1번지점
14번지점
15번지점
19번지점
21번지점
23번지점
25번지점
26번지점
28번지점
29번지점
43번지점
44번지점
45번지점
46번지점
49번지점
51번지점
89번지점
90번지점
91번지점
9-5. Preliminary Evaluation Results: Rainfall
III. Development of Application Services
40Forecast Hours
Pro
babi
lity
of D
etec
tion
Pro
porti
on o
f Cor
rect
Pro
babi
lity
of F
alse
Det
ectio
nFa
lse
Ala
rm R
ate
Agricultural Digital Forecast System
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 10 20 30 40 50
POD
동네예보의예보자료시각과발표시각의차이 (hr)
1번지점
14번지점
15번지점
19번지점
21번지점
23번지점
25번지점
26번지점
28번지점
29번지점
43번지점
44번지점
45번지점
46번지점
49번지점
51번지점
89번지점
90번지점
91번지점
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 10 20 30 40 50
FAR
동네예보의예보자료시각과발표시각의차이 (hr)
1번지점
14번지점
15번지점
19번지점
21번지점
23번지점
25번지점
26번지점
28번지점
29번지점
43번지점
44번지점
45번지점
46번지점
49번지점
51번지점
89번지점
90번지점
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동네예보의예보자료시각과발표시각의차이 (hr)
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9-6. Preliminary Evaluation Results: Leaf Wetness (Simple RH)
III. Development of Application Services
41Forecast Hours
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Det
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rm R
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Agricultural Digital Forecast System
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동네예보의예보자료시각과발표시각의차이 (hr)
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동네예보의예보자료시각과발표시각의차이 (hr)
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PC
동네예보의예보자료시각과발표시각의차이 (hr)
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동네예보의예보자료시각과발표시각의차이 (hr)
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9-7. Preliminary Evaluation Results: Leaf Wetness (CART)
III. Development of Application Services
42Forecast Hours
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etec
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f Cor
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Det
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nFa
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rm R
ate
Agricultural Digital Forecast System
• Source of errors for Air Temp. and RH are attributed to variations in vegetation
and land use/cover of the surrounding area
• Amount of hourly precipitation had high False Alarm Rate of 0.8 (overestimation
due to the limit of estimating hourly probability of rain)
• Simple RH leaf wetness model overestimates wetness
(Proportion of Correct 50%, False Alarm Rate 60%)
• CART leaf wetness model underestimates wetness
(Proportion of Correct 60%, False Alarm Rate 35%)
** Rice blast warning is highly sensitive to leaf wetness
• Precipitation and leaf wetness models need improvement.
• Detailed variations in vegetation and land use/cover should be more precisely
considered for agricultural meteorological services.
9-8. Preliminary Evaluation Results: Summary
III. Development of Application Services
43
Agricultural Digital Forecast System
44
IV. Outcome and Further Development
• Providing weather-driven disease risk information, which can also be used in everyday agriculture
• Supporting scientific decision on disease and pest control, which will lead to environment-friendly agriculture
• Pioneering the application of Digital Weather Forecast to various fields in agricultural meteorology
• More AgroMet friendly NWP models are needed to…• Resolve land use differences at higher resolution (~100m)• Incorporate AgroMet models easily• Assimilate AWS, satellite and radar data
Agricultural Digital Forecast System
45
IV. Outcome and Further Development
Co-developers for Agricultural Digital Forecasting SystemWee Soo KANG and Eun Woo PARKSeoul National University