Very-Short-Range Forecast of Precipitation in Japan World Weather Research Program Symposium on Nowcasting and Very Short Range Forecasting Toulouse France,

Very-Short-Range Forecast of Precipitation in Japan

World Weather Research Program Symposium on Nowcasting and Very Short Range Forecasting

Toulouse France, 5-9 September 2005

SUGIURA Iori

Japan Meteorological Agency

Products of Very-Short-Range Forecast of Precipitation in JapanBasic Products:

1. Radar-AMeDAS precipitation (R/A) and2. Very Short Range Forecast of Precipitation (KOUTAN)

Derivative Products:3. Soil Water Index (SWI) and4. RunOff Index (ROI)

Basic Products

1. Radar-AMeDAS Precipitation (R/A)

· Estimated 1 hour precipitation based on Radar echo intensity data and rain gauge observation data· Used as basic precipitation analysis data in Japan · Covering whole over Japan with 2.5km grid· Operated at half an hour intervals

Overview

N

Basic Products 1. Radar-AMeDAS Precipitation (R/A)

Weather Radar Observation

JMA Radar observation network

· consists on 20 sites of ordinary weather Radar.· operated at 10 minute intervals· observe echo intensity (1km grid) and echo top height (2.5km grid) with 19 elevation volume scan· Z = 200R1.6 is applied as “Z-R relationship”

Radar echo intensity data are spatially detailed for wide area. But in general, they are not suitable for being considered as quantitative precipitation.


Raingauge observation

AMeDAS raingauge network of JMA

Raingauges of local governments and MLIT*

AMeDAS* raingauges# of stations more than 1300average spacing 17km by 17kmnon-AMeDAS raingauges# of stations more than 5000average spacing various

Raingauge observation is accurate, but it is only point local data.

AMeDAS*: Automated Meteorological Data Acquisition System

MLIT*: Ministry of Land, Infrastructure and Transport

By using both radar and raingauge data we can analyze accurate and spatially detailed precipitation!


Precipitation Analysis

FF1(x,y)= F(x,y)= Fa{1+F{1+Fx・・H(x,y)H(x,y)22}}

Assumed following relationship among analyzed rain R, hourly-mean echo intensity E and factor F:

R(x,y,t) = F(x,y,t)E(x,y,t)If F is calculated, we can get R.

1st step:For each radar, decide Fa and Fx in following equation:

here H is radar beam height

Fa and Fx are decided so that they meets the following 2 principles.

(1) precipitation amount for a given area should be consistent between all adjacent Radars(2) calibrated hourly-mean echo intensity agrees with raingauge data on average


Basic concept of calculating calibration factor


Calibrated precipitation (last)

14JST 21 September 1999

hourly mean of radar echo intensity (start)

2nd step:The factors F1(x,y) are tuned for each grid in order to represent local variability with comparing calibrated hourly-mean echo intensity and raingauge precipitation.


Final step:Composite calibrated hourly-mean echo intensity data of all radars. R/A is obtained.

14JST 21 September 1999

N




Basic Products

2. VSRF of Precipitation (KOUTAN)

· Forecast of 1 hour precipitation up to 6 hours ahead. · Used to issue warnings and advisories related to heavy rain in JMA. · Covering whole over Japan with 5.0km grid· Operated at half an hour intervals· Calculated by merging extrapolation of calibrated radar echo intensity (EX6) and precipitation prediction from Meso-Scale numerical Model (MSM).

Overview

Basic Products 2. VSRF of Precipitation (KOUTAN)

movement of

precipitation before 1

hour

now

After 1 After 1 hour,hour,

it will be it will be

movement vector

Assuming speed and direction of the movement will be the same as they were an hour ago, precipitation area is moved up to 6 hours ahead.

In this time, precipitation area will be intensified or decayed by orographic effect.

Basic Concept of extrapolation (EX6)

With comparing precipitation distribution of now and before, the movement of precipitation before 1 hour is obtained.


Quality of forecasts (accuracy X resolution) as a function of forecast time (partly from Browning, 1980)

Extrapolation method

persistence

MSM

Merging method

0 151296 18

Qu

ality

of

fore

cast

Forecast time(hour)

3

Accuracy of EX6 is good for up to 3 forecast hours, but it decreases drastically with forecast time. In other hand, quality of MSM does not change much with forecast time.

Merging method

If EX6 and MSM are merged with appropriate ratio, good accuracy is obtained over the forecasting period. This is the merging method.


Example of Merged Forecast: MRG(EX6+MSM)

R/A 0300

MSM Fcst 0300

R/A 2100

EX6 Ft6 0300

2100UTC 06 - 0300UTC 07 August 2003

MRG Fcst 0300




Derivative Products

3. Soil Water Index (SWI)

· Index for predicting occurrence of landslide disasters caused by heavy rain. · calculated for each 5 by 5 km grid every half an hour.· archived for the last 10 years in order to compare with current SWI and judge high potential of landslide.· If current SWI of some area is the highest value in the archive, JMA shall judge that probability of occurrence of landslide is the highest for the last 10 years for the area.

Overview

Derivative Products 3. Soil Water Index (SWI)

Soil water index for every 5 by 5 km areaAbout 16,000meshes in Japan

Occurrence of landslides is closely related to soil water index.

Soil water index

Comparison of current water content in soil with past records

　

Soil water index Calculation

Rain

Permeation

Storage

First tank

Second tank

Third tank

Storage

Storage

Water content in soil isestimated by "totalprecipitation" exclusiveof "volume run off intorivers" and "volumepermeated into soildownward.

The Soil Water Indexequals to the totalstorage volume of 3serial tanks.

Surfacerunoff

Undergroundwater runoff

Storage insurface layerpermeationrunoff

Advisories/warnings for heavy rainHow high the potential for landslides

is for the last ten years

SWI Archives for 5 by 5 km & damage reports for the　 last 10 years

Radar AMeDas precipitation

& VSRF precipitation forecasts

Calculating SWI

calculated by 3 Serial tank model

Derivative Products 3. Soil Water Index (SWI)

0

10000

20000

30000

40000

50000

60000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

the last ten years archives ranking

５９％

Relationship land-slide disasters and the ten years archives ranking in 1991-2000 per local governments




Derivative Products

4. RunOff Index (ROI)

· Index closely related to runoff amount for each grid which contains rivers. · ROI agrees with river water level better than precipitation.· Now under researching relationship between ROI and occurrence of flush flood in detail

Overview

Derivative Products 4. RunOff Index (ROI)

Precipitation

Water Level

RunOff Index

Time

Flow amount is calculated using tan

k model with the slope of land, type

of soil and land use (urbanization) b

eing provided Radar-AMeDAS preci

pitation.

Flow speed is calculated with flow amount, slope and shape of cross section of the river

Flow amount is calculated based on runoff and flow amount from upstream

RunOff Index agrees with water level better than precipitation.It has more direct relationship with disasters.

Precipitation in a basin does not agree with the water level of a river.

Basic concept of ROI

Summary

· Basic products of very-short-range forecast of precipitation in Japan are precipitation analysis and forecast based on radar and raingauge observations.

· JMA has developed derivative products for predicting occurrence of disasters related to heavy rain. These products are more closely related to disasters than precipitation itself.

END

Thank you

Documents

Very-Short-Range Forecast of Precipitation in Japan World Weather Research Program Symposium on Nowcasting and Very Short Range Forecasting Toulouse France,