Development of agricultural decision supporting tools using gridded

meteorological data embedded with weather forecast

Climate Change Adaptation Unit Climate Change Division

Institute for Agro-Environmental Sciences, NARO, Japan Hiroyuki Ohno (ohno@affrc.go.jp)

(December 5th, 2016)

Background In the past few years, record heat waves and heavy rain have occurred throughout Japan, which have seriously affected agricultural produce in some areas. In order to quickly grasp the degree and spread of weather damage, and to take measures to reduce them, we must always have the latest weather data that includes not only current and past data, but data of future predictions as well. Therefore, NARO have constructed a 1-km grid daily meteorological data delivery system.

The Agro-Meteorological Grid Square Data System (AMGSDS)

<-7日平均->

Monitoring Forecasting Climatic Normal

Today

45”(~1.1 km)

30

”(~0

.9 k

m)

The AMGSDS produces and serves daily agro-meteorological gridded data for Japan from 1980 to the current year.

Observed, forecasted, and climatic normal data are linked seamlessly.

Met. elements Past

Mean temperature 1980 ~

Maximum temperature 1980 ~

Minimum temperature 1980 ~

Precipitation 1980 ~

Sunshine duration 1980 ~

Global solar radiation 1980 ~

Relative humidity 2008 ~

Wind speed 2008 ~

Atmospheric radiation 2008 ~

Snow depth 2008 ~

Snow water equivalent 2008 ~

Newly fallen snow water equivalent 2008 ~

List of Products

Forecast

~26 days in the future

~26 days in the future

~26 days in the future

~26 days in the future

~9 days in the future

Under development

~9 days in the future

~9 days in the future

Under development

Under development

Under development

Under development

(Source: http://www.jma.go.jp/jma/kishou/know/amedas/kaisetsu.html)

Data source for the Past Data 1. The Automatic Meteorological Data Acquisition System

(AMeDAS) by the Japan Meteorological Agency (JMA) - 1,300 stations throughout Japan (approx. 1 / 20 km × 20 km)

Meteorological Offices – 156 locations (Including special

regional observation stations)

4-element observation stations – 686 locations (Rain /

Temperature / Wind / Daylight time)

3-element observation stations – 87 locations (Including 8

temporary locations) (Rain / Temperature / Wind)

Precipitation observation stations – 361 locations

(Including 5 temporary locations)

Snow depth observation stations – 312 locations

Chichijima and

Minamitorishima report 4

elements, Hahajima reports

rainfall (omitted in the figure).

＋

＝

Observed values by locations

Gridded deviation from normal

Gridded daily normal

－

Normal values by locations

＝

Past data (Temperature)

Processing Algorithm for the Past Data

Gridded daily normal

1. JMA’s Meso-Scale Model (MSM) - Regional model - Approx. 5 km grid - 1.5-day forecast

(fig. source: http://pfi.kishou.go.jp/material/nwp50th-ann.pdf)

Data sources for the forecast data

2. JMA’s Global Spectral Model (GSM) - Global model - Approx. 20 km grid - 9-day forecast

3. The Guidance for 1-month weather forecasts by JMA - Empirical forecast using 30 ensemble forecasts - Forecast for 156 points - 7-day mean anomaly - 4-week forecast

Y = ai Xi + B +

Z500, surface temperature, wind, precipitation, etc.

Regionally averaged meteorological values

ai Xi + B = Y

156 main stations

GSM for long range forecasts

Z500, surface temperature, wind, precipitation, etc.

10 ensemble means of 5 members

30 years of 5 members

GSM for long range forecasts

~

Past data (30-day average)

GMS data (20-km) 3ed partition GSM data (1-km)

Distribution of correction values

＋ ＝

－ ＝ Forecast data (Temperature)

Re-gridding

3ed partition GSM data (30-day average)

Processing Algorithm for the Forecast Data

Accuracy of the AMDSDS data

spring

summer

autumn

winter

Error of the past data by space interpolation Error of the forecast data for 1-day forecast

spring

summer

autumn

winter

Mean air temperature (C)

0%

10%

20%

30%

40%

50%

60%

0 5 10 15 20 25 30

誤差低減の効果

[%]

日数(n)

n日先の日値に対する効果

向こうn日間の平均値に対する効果

EF: error (R.M.S.E.) of mean air temperature of n days in the future

EC: the error when the normal (30-year average) value is used as the forecast

Effect of the forecast

n (days)

e= (

E C-E

F)/E

C (

%)

for the daily value of n days in the future

for the mean value from today to n days in the future

Atmospheric radiation is compared with observed values at Tateno (Ibaraki, Tsukuba), while the others are with observations at Yawara (Ibaraki, Tsukubamirai).

Performance of the Past Data A

MG

SD

S

Observation

(2010) (2010) (2010)

Pre

cip

itatio

n (

mm

/day)

(2010) (2010)

Daily

Mean R

ela

tive H

um

idity (

%)

Glo

bal S

ola

r R

adia

tio

n (

MJ/m

2/d

ay)

Tateno

Daily Mean Wind Speed (m/s)

(2010)

(2010)

Daily

Maxim

um

Air T

em

pera

ture

(deg C

)

Daily

Mean A

ir T

em

pera

ture

(deg C

)

Daily

Min

imum

Air T

em

pera

ture

(deg C

)

Observation

AM

GS

DS

Observation

Observation Observation Observation

Observation

Atmospheric Infrared Radiation (W/m-2)

Observation

Comparison with On-site Observations

AM

GS

DS

AM

GS

DS

AM

GS

DS

AM

GS

DS

AM

GS

DS

AM

GS

DS

OPeNDAP

HTTP Get

Gri

d D

ata

Ge

ne

rato

r

Dat

a D

eliv

ery

Se

rve

r

Data Delivery Service A data delivery server is equipped.

Arbitrary spatiotemporal subset can be delivered on demand.

MS-Excel Worksheet

GIS

Farming Support

Solutions

Programing Language

Set the latitude, longitude, and

year … acquire the data.

Crick the button to

…

You also get normal year values.

Tools for decision making (1) MS-Excel Application Worksheets

The program acquires the data and fills in the array variables.

You can create distribution figures or graphs based on the latest met. data.

Tools for decision making (2) Python Application Programs

Tools for decision making (3) Farming Support Solutions

Support Solution Server

Supporting Contents under Developing - Alerts for abnormally high/low temperature - Alerts for disease infection - Predicting crop development - Suggesting quantity of fertilizer to reduce heat damage - Suggesting suitable variety and farming period - Suggesting harvesting date - Suggesting date to apply insecticide / pesticide

Web API Server

Solution A

Solution B

Solution C

Tools for decision making (4) Web API Services

Supporting Contents under Developing - Alerts for abnormally high/low temperature - Alerts for disease infection - Predicting crop development - Suggesting quantity of fertilizer to reduce heat damage - Suggesting suitable variety and farming period - Suggesting harvesting date - Suggesting date to apply insecticide / pesticide

Summary

We have constructed a 1-km grid daily meteorological data delivery system (AMGSDS).

The Data have been updated with the latest monitoring and numerical forecasts and are seamlessly concatenated to those of climatic normal.

AMGSDS has a nationwide areal data domain and a temporal domain from 1980 through the current year.

Users can retrieve an arbitrary spatiotemporal subset of data from AMGSDS via the Internet.

The advantage of the forecast can be recognized more than 30 days in accumulation of daily mean temperature, while it vanishes within 6 days in the daily comparison.

We are developing agricultural decision supporting tools using AMGSDS and applying four methodologies.