Taking an advantage of innovations in science and technology to develop MHEWS

Transcription

1 Taking an advantage of innovations in science and technology to develop MHEWS Masashi Nagata Meteorological Research Institute, Tsukuba, Ibaraki, Japan Japan Meteorological Agency 1

2 Why heavy rainfalls are the main agenda in Japan? Geology: soft ground Meteorology: Two rainy seasons Synoptic scale: NWP works Topography: Mountainous & Steep terrain 10 minutes Local scale: Limited capability 2

3 Approaches to predict local heavy rain Forecast accuracy Modified Saito (2012) Limit of deterministic forecast Extrapolation QPE Observation and Analysis Short range forecast for precipitation (QPF) Mesoscale NWP model 2h 6hr 1day Forecast time

4 Approaches to predict local heavy rain Forecast accuracy Improved Observation and Analysis Limit of deterministic forecast Extrapolation Modified Saito (2012) Short range forecast for precipitation (QPF) Mesoscale NWP model 2h 6hr 1day Forecast time

5 Approaches to predict local heavy rain Forecast accuracy Improved Observation and Analysis Limit of deterministic forecast Extrapolation Modified Saito (2012) Improved Short range forecast for precipitation (QPF) Mesoscale NWP model 2h 6hr 1day Forecast time

6 Approaches to predict local heavy rain Forecast accuracy Improved Observation and Analysis Limit of deterministic forecast Extrapolation Modified Saito (2012) Improved Short range forecast for precipitation (QPF) Reduce the gap between nowcasting and NWP by advanced model and highresolution data assimilation 2h 6hr 1day Enhanced NWP model Cloud resolving model and data assimilation Forecast time

7 Rain-gauge Contributions to Early Warning System Observation & Analysis Precipitation Analysis Nowcasting Soil Water Index RADAR Echo intensity Composite map Echo intensity NWP Runoff Index Echo top height Echo top height Warnings and advisories

8 Japanese Version of WIGOS (Raingauges and Radars) Raingauges Radars (C-band) JMA Radars: 20 MLIT Radars: 26 JMA: 1,300 MLIT (Ministry of Land, Infrastructure, Transport and Tourism): 3,500 Local Governments (Prefectures): 5,800

9 Improvements Nowcasting of QPF 0.55 Score Improvement of JMA s QPF performance ( スコア ) 降水短時間予報の精度 (2-3 時間後 ) H19 H20 H21 H22 H23 H24 H25 H26 H27 H28 H29 Example of techniques: motion vector motion vector for heavy rainfall area Heavy rainfall area is moved based on a motion vector calculated separately from general rainfall area. Developing and decaying trends of heavy rainfall area are considered to calculate motion vectors. 9

10 Successful development of Mesoscale NWP Improvements Brown: model, Blue and Green: observation, Pink: data assimilation scheme 10

11 Hiroshima Sediment Disasters (20 Aug 2014) Surface Analysis 3-hour precipitation 21JST 19 Aug fatalities Max. 238mm Hiroshima Pref. 0 50km The downpour occurred 300 km to the south of the front

12 Forecasts with High-resolution NWP Model Analysis (3h total rainfall) Area afflicted by sediment disasters 18JST 19 Aug. Initial 19JST 19 Aug. Initial (00-03JST 20 August 2014) 20JST 19 Aug. Initial 21JST 19 Aug. Initial 22JST 19 Aug. Initial Differences in water vapor inflows may lead to the inconsistency in forecasts.

13 Water vapor inflow and its vertical cross-section メソ解析から作成 Water vapor amount at 500m height Back-building mechanism: No strong synoptic-scale forcing Vertical section of RH (132E) Heavy Rain 00JST 20 Aug Kyushu Shikoku South North afflicted area(hiroshima) (g/kg) Water vapor inflow occurred mainly in the lowest layer

14 GNSS maritime PWV and Lidar observation of moisture The ship-board Global Navigation Satellite System promises future applications of the maritime precipitable water vapor observation. mm/h 左 )2013 年 6 月 25 日 8 時 (JST) の解析雨量分布右 ) : メソ解析に利用された国土地理院 GNSS PWV 分布 : 全球解析に利用された衛星マイクロ波によるPWV 分布 : 凌風丸搭載 GNSSによるPWV(6 月 21 日 9 時 ~6 月 25 日 8 時 ) mm Left: Radar analysis Right: PWV derived from :Ground based GNSS, : Satellite Microwave, Ship-board GNSS Comparison between radio sonde observation and GNSS maritime PWV 右 ) 凌風丸の高層ゾンデ観測によるPWVとの比較散布図 (2012 年 7 月 ~2014 年 10 月 ) 赤 )00UT( バイアス-0.25mm, RMS 3.36mm) 青 )12UT( バイアス-2.54mm, RMS 3.44mm) Lidar observation of moisture in the case of a cold front passage H27 年度より, 東大大気海洋研の協力により, 新青丸を用いたリアルタイム解析実験を開始する予定 現状では船舶から大量の観測データを地上に配信する必要があり, 通信費用が課題 準天頂衛星が 4 機体制となる 2018 年より, 船舶上で解析が可能となり, データ通信量は劇的に減らすことが可能

15 Improvement of Temporal Resolutions 2 Sep 2011 (Severe Tropical Storm Talas) 5 min interval 2.5 min interval by Himawari-8/9 30 min interval

16 Phased-Array Radar

17 Phased-Array Radar Installation Flat panel antenna The state-of-the-art radar will be Installed at Meteorological Research Institute in Tsukuba city Used for research activities In operation in early summer m

18 3-D Cumulonimbus Observation Radar reflectivity Doppler speed Vault (Adachi et al. 2014)

19 Damaging Tornado in Ibaraki Prefecture (6 May 2012) One of the most destructive tornadoes ever in Japan Damage swath; 17km length 500m width One person died and 37 injured More than 1000 houses damaged 気象研究所

20 Damaging Tornado in Ibaraki Prefecture (6 May 2012) Lower part of cumulonimbus The tornado left a devastation in 茨城県常総市からつくば市に被害をもたらした竜巻 2012 年 5 月 6 日午後 0 時 5 its wake from Jousoucity to 0 分頃つくば市平沢 ( Tsukuba つくば市の住民撮影 共同通信社提供 city at 0:05pm, 6 May ) 引用 : 気象庁現地災害調査速報平成 24 年 5 月 6 日に茨城県常総市からつくば Photo: A resident in Tsukuba city 市にかけて発生した突風について courtesy of Kyodo News Funnel cloud swirled sediments

21 2012/5/6 Tsukuba Tornadoes MP radar observation at MRI (Yokota et al., 2014) Doppler velocity (m s -1 ) 12:30 JST, elevation 1.0 Rain (g m -3 ) estimated by reflectivity and MP information 3 peaks corresponding to tornadoes MRI MP radar data: courtesy by Meteorological Sattelite and Observation System Research Department of MRI

22 2012/5/6 Tsukuba Tornadoes Surface observations by dense AWS networks Winds (m s -1 ) Temperature (K) RH (%) 12:30 JST Shear line is detected (Yokota et al., 2014) A part of AWS data, courtesy by NTT Docomo

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24 Summary Observation, Analysis and Nowcastingare as important as NWP. National version of WIGOS works well in rainfall observations. Nowcastinghas higher performance in predicting heavy rainfalls up to at least two hours. There is still room for improvement of nowcasting. Water vapor information is essential for improving local scale heavy rainfall predictions with mesoscale NWP High-resolution observation of water vapor High-frequency observation of water vapor Very-high-resolution mesoscale models can give a new perspective of simulating small-scale severe phenomena by assimilating enhanced observation data. 24