Very-Short-Range Forecast of Precipitation in Japan World Weather

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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-AMe. DAS precipitation (R/A) and 2. Very Short Range Forecast of Precipitation (KOUTAN) Derivative Products: 3. Soil Water Index (SWI) and 4. Run. Off Index (ROI)

Basic Products 1. Radar-AMe. DAS Precipitation (R/A) Overview · 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. 5 km grid · Operated at half an hour intervals N

Basic Products 1. Radar-AMe. DAS Precipitation (R/A) Weather Radar Observation · consists on 20 sites of ordinary weather Radar. · operated at 10 minute intervals · observe echo intensity (1 km grid) and echo top height (2. 5 km grid) with 19 elevation volume scan · Z = 200 R 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. JMA Radar observation network

Basic Products 1. Radar-AMe. DAS Precipitation (R/A) Raingauge observation AMe. DAS* raingauges # of stations more than 1300 average spacing 17 km by 17 km non-AMe. DAS raingauges # of stations more than 5000 AMe. DAS raingauge network of JMA average spacing various Raingauge observation is accurate, but it is only point local data. By using both radar and raingauge data we can analyze accurate and spatially detailed precipitation! AMe. DAS*: Automated Meteorological Data Acquisition System MLIT*: Ministry of Land, Infrastructure and Transport Raingauges of local governments and MLIT*

Basic Products 1. Radar-AMe. DAS Precipitation (R/A) Precipitation Analysis 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. 1 st step: For each radar, decide Fa and Fx in following equation: F 1(x, y)= Fa{1+Fx・H(x, y)2} 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 Products 1. Radar-AMe. DAS Precipitation (R/A) Basic concept of calculating calibration factor

1. Radar-AMe. DAS Precipitation (R/A) Basic Products 2 nd 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. 14 JST 21 September 1999 hourly mean of radar echo intensity (start) Calibrated precipitation (last)

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

Basic Products 2. VSRF of Precipitation (KOUTAN) Overview · 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. 0 km grid · Operated at half an hour intervals · Calculated by merging extrapolation of calibrated radar echo intensity (EX 6) and precipitation prediction from Meso-Scale numerical Model (MSM).

Basic Products 2. VSRF of Precipitation (KOUTAN) Basic Concept of extrapolation (EX 6) With comparing precipitation distribution of now and before, the movement of precipitation before 1 hour is obtained. 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. After 1 hour, it will be movement vector now movement of precipitation before 1 hour

2. VSRF of Precipitation (KOUTAN) Basic Products Merging method Quality of forecast Accuracy of EX 6 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. If EX 6 and MSM are merged with appropriate ratio, good accuracy is obtained over the forecasting period. Extrapolation This is the merging method Merging method 0 3 6 persisten ce 9 MSM 12 15 18 Forecast time(hour) Quality of forecasts (accuracy X resolution) as a function of forecast time (partly from Browning, 1980)

Basic Products 2. VSRF of Precipitation (KOUTAN) Example of Merged Forecast: MRG(EX 6+MSM) 2100 UTC 06 - 0300 UTC 07 August 2003 R/A 2100 MSM Fcst 0300 EX 6 Ft 6 0300 R/A 0300 MRG Fcst 0300

Derivative Products 3. Soil Water Index (SWI) Overview · 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.

3. Soil Water Index (SWI) Derivative Products Calculating SWI Soil water index for every 5 by 5 km area About 16, 000 meshes in Japan Occurrence of landslides is closely related to soil water index. SWI Archives for 5 by 5 km & damage reports for the　last 10 years Radar AMe. Das precipitation & VSRF precipitation forecasts Soil water index Calculation Soil water index Rain Comparison of current water content in soil with past records Storage Advisories/warnings for heavy rain How high the potential for landslides is for the last ten years First tank Surface runoff Second tank Storage in surface layer permeation runoff 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. Third tank Storage Permeation Underground water runoff calculated by 3 Serial tank model

3. Soil Water Index (SWI) Derivative Products Relationship land-slide disasters and the ten years archives ranking in 1991 -2000 per local governments 60000 50000 ５９％ 40000 30000 20000 10000 0 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

Derivative Products 4. Run. Off Index (ROI) Overview · 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

4. Run. Off Index (ROI) Derivative Products Basic concept of ROI Flow amount is calculated using tank model with the slope of land, type of soil and land use (urbanization) being provided Radar-AMe. DAS precipitation. Precipitation in a basin does not agree with the water level of a river. 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 Run. Off Index Water Level Time Run. Off Index agrees with water level better than precipitation. It has more direct relationship with disasters.

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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