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 Japan Basic Products: 1. Radar-AMeDAS precipitation (R/A) and 2. Very Short Range Forecast of Precipitation (KOUTAN) Derivative Products: 3. Soil Water Index (SWI) and 4. 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 = 200R 1.6 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.

Basic Products 1. Radar-AMeDAS Precipitation (R/A) Raingauge observation AMeDAS raingauge network of JMA Raingauges of local governments and MLIT* AMeDAS* raingauges # of stations more than 1300 average spacing 17km by 17km non-AMeDAS raingauges # of stations more than 5000 average 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!

Basic Products 1. Radar-AMeDAS Precipitation (R/A) Precipitation Analysis F(x,y)= F{1+F ・ H(x,y) 2 } F 1 (x,y)= F a {1+F x ・ H(x,y) 2 } 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 F a and F x in following equation: here H is radar beam height F a and F x 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 Products 1. Radar-AMeDAS Precipitation (R/A) Basic concept of calculating calibration factor

Basic Products 1. Radar-AMeDAS Precipitation (R/A) Calibrated precipitation (last) 14JST 21 September 1999 hourly mean of radar echo intensity (start) 2nd step: The factors F 1 (x,y) are tuned for each grid in order to represent local variability with comparing calibrated hourly-mean echo intensity and raingauge precipitation.

Basic Products 1. Radar-AMeDAS Precipitation (R/A) Final step: Composite calibrated hourly-mean echo intensity data of all radars. R/A is obtained. 14JST 21 September 1999 N

Products of Very-Short-Range Forecast of Precipitation in Japan Basic Products: 1. Radar-AMeDAS precipitation (R/A) and 2. Very Short Range Forecast of Precipitation (KOUTAN) Derivative Products: 3. Soil Water Index (SWI) and 4. RunOff Index (ROI)

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 hour, After 1 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.

Basic Products 2. VSRF of Precipitation (KOUTAN) Quality of forecasts (accuracy X resolution) as a function of forecast time (partly from Browning, 1980) Extrapolation method persisten ce MSM Merging method Quality of forecast 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.

Basic Products 2. VSRF of Precipitation (KOUTAN) Example of Merged Forecast: MRG(EX6+MSM) R/A 0300 MSM Fcst 0300 R/A 2100 EX6 Ft UTC UTC 07 August 2003 MRG Fcst 0300

Products of Very-Short-Range Forecast of Precipitation in Japan Basic Products: 1. Radar-AMeDAS precipitation (R/A) and 2. Very Short Range Forecast of Precipitation (KOUTAN) Derivative Products: 3. Soil Water Index (SWI) and 4. RunOff Index (ROI)

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 area About 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 Water content in soil is estimated by "total precipitation" exclusive of "volume run off into rivers" and "volume permeated into soil downward. The Soil Water Index equals to the total storage volume of 3 serial tanks. Surface runoff Underground water runoff Storage in surface layer permeation runoff Advisories/warnings for heavy rain How 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) the last ten years archives ranking ５９ ％ Relationship land-slide disasters and the ten years archives ranking in per local governments

Products of Very-Short-Range Forecast of Precipitation in Japan Basic Products: 1. Radar-AMeDAS precipitation (R/A) and 2. Very Short Range Forecast of Precipitation (KOUTAN) Derivative Products: 3. Soil Water Index (SWI) and 4. RunOff Index (ROI)

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 tank model with the slope of land, type of soil and land use (urbanization) being provided Radar-AMeDAS precipitation. 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.

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