agricultural grid weather information system based on ... · rice paddy (rice blast) = very...

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


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


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I. Conventional Ag-Met Information Services


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• 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


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



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

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Pear Orchard, etc


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Pear Orchard, etc


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Pear Orchard, etc


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Pear Orchard, etc


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Pear Orchard, etc


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Pear Orchard, etc


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Pear Orchard, etc


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Pear Orchard, etc


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Pear Orchard, etc


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Pear Orchard, etc


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Pear Orchard, etc


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Pear Orchard, etc


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Pear Orchard, etc


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Digital Forecast can provide detailed

forecast at higher resolution


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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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


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2. System Overview

Input Data & Processing

Information Delivery System

Disease Forecasting Model

Output Data



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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



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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



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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



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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



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6-1. Input Processing – Hourly Interpolation – Air Temp, RH


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)


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6-2. Input Processing – Hourly Interpolation – Probability of Precipitation


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)


(hourly)

Probability of precipitation for one hour: P1

Probability of precipitation forthree hours: P3

3

31

313

313

11

11

11

PP

PP

PP


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6-3. Input Processing – Hourly Interpolation – Amount of Precipitation


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


(hourly)


(hourly)

Cumulative Amountof Precipitation

(12-hourly)

Cumulative Amountof Precipitation

(12-hourly)

Precipitation Forecastfor the 12 hour period


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6-4. Input Processing – Hourly Interpolation – Leaf Wetness (Simple RH)


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



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CART모형



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


+4h +16h+10h+7h +13h

Leaf Wetness 1 hour


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•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)



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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


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• 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



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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



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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



1.5

2

2.5

3

3.5

4

4.5

5

0 10 20 30 40 50

RMSE

동네예보의예보자료시각과발표시각의차이 (hr)

1번지점

14번지점

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19번지점

21번지점

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43번지점

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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


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Forecast Hours


• 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


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Distance between the grid center and the AWS location

Mea

n R

MS

E fo

r eac

h st

atio

n


10

12

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16

18

20

0 10 20 30 40 50

RMSE


1번지점

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49번지점

51번지점

89번지점

90번지점

91번지점

9-3. Preliminary Evaluation Results: Relative Humidity


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• 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


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


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


0

0.1

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POD


1번지점14번지점15번지점19번지점21번지점23번지점25번지점26번지점28번지점29번지점43번지점44번지점45번지점46번지점49번지점51번지점89번지점90번지점91번지점

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FAR


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PC


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POFD


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89번지점

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91번지점

9-5. Preliminary Evaluation Results: Rainfall


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


0

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0 10 20 30 40 50

POD


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0

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FAR


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PC


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POFD


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90번지점

91번지점

9-6. Preliminary Evaluation Results: Leaf Wetness (Simple RH)


41Forecast 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


0

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0 10 20 30 40 50

POD


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FAR


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PC


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0 10 20 30 40 50

POFD


1번지점

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46번지점

49번지점

51번지점

89번지점

90번지점

91번지점

9-7. Preliminary Evaluation Results: Leaf Wetness (CART)


42Forecast 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


• 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


43


44


• 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


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Co-developers for Agricultural Digital Forecasting SystemWee Soo KANG and Eun Woo PARKSeoul National University

agricultural grid weather information system based on ... · rice paddy (rice blast) = very...

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