scale Observational Systems in Japan Meteorological Agency

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1 Meso scale scale Observational Systems in Japan Meteorological Agency WWRP Working Group on Mesoscale Weather Forecasting Research March 17, 2008 JMA Headquarters, Tokyo Kenji AKAEDA Observations Division, Observations Department JMA

2 Topics Weather related Natural Disasters in JAPAN Heavy Rain / Torrential Downpour causes Flood / Landslide Strong Wind by Typhoon Recent Gust Events by Tornados / Downbursts Meso scale scale Observational Systems in JMA Surface / Radar Network and Products Wind Profiler Network Terminal Doppler Lidar System

3 Number of Deaths & Missings Human Damage by Natural Disasters in Japan Heavy Rain / Strong Wind (Baiu Front, Typhoon) Gust (Tornado, Downburst, Gust front) Year

4 集中豪雨 torrential downpour Recent torrential downpour in JAPAN ( ) Number of heavy rain exceeds 50mm/h Flood July 2004 新潟福島豪雨 (Downpour in Niigata&Fukushima) Flash Flood Landslide Maximum 427mm (16.8inch) in 30 hour

5 台風 Typhoon Typhoon Tracks JAN NOV 2004 Violent Wind High Wave 10 Typhoons landed in Japan in 2004 Flood Tide Heavy Rain Number of Typhoon Total Approach landfall year record Maximum 1243mm (48.9inch) in 5 days

6 竜巻 Tornado in Japan Average Annual Number of Tornado : 17 Synoptic Situation : Typhoon, Cold front, Cyclone Monthly mean number ( ) Distribution of Tornado ( )

7 Tornado Damages Saroma Tornado November 7, 2006 F3, Length: 1.4km, Width: 300m Death: 9, Injured: 31 Destroyed House: 7 Damaged House: 7 Tamura(2007) Nobeoka Tornado September 17, 2006 F2, Length: 7.5km, Width: 300m Death: 3, Injured: 143 Destroyed House: 79 Damaged House: 348 Suzuki(2007)

8 Overview of the Meso scale scale Observational Systems in JMA Surface Observation Meteorological Satellite Upper air Observations Weather Radar Lightning Detection Observations By Vessel JMA Etc. Weather forecast Advisory Warning etc. Disaster managers TV stations private weather associations the nation

9 Surface Observations Meso scale Surface Observation Network AMeDAS Temperature 1hour rain Wind Weather Station AWS (Rain, T, Wind, Duration of Sunshine) AWS (Rain) Snow Depth Gauge Around 1300 rain gauges are operated by JMA. Moreover, other rain gauge data is being gathered and integrated with JMA data. Precipitation Oct 19th 10:00JST ~ 21st 09:00JST, 2004 Sunshine Typhoon TOKAGE by AMeDAS

10 JMA Radar Network : Doppler Weather Radar (11) : Conventional Weather Radar (9) : Terminal Doppler Weather Radar (8) Hakodate Kushiro Matsue Niigata Sendai 40N Fukuoka Murotomisaki Nagoya Tokyo Tanegashima Okinawa 20 Weather Radar (not terminal) data are all gathered to the central system located in JMA headquarters and the radars status are remotely monitored and controled by the central system. 30N 130E 140E 150E

11 Main specifications of JMA radar Conventional radar Doppler radar Frequency 5300 MHz band (C band) Transmitting Tube Magnetron Klystron Peak Power 250 kw Pulse Width PRF 2.5μs 260 Hz 2.5, 1.0 μs 260, 600/480, 940/752 Hz Antenna Diameter Detection Range Range Resolution Time interval of volume scan Observational EL range Number of ELs for a volume scan 4m (beam width around 1.0 deg) 400 km 500 m 10 min 0~30 deg (mountain radar use elevation angle below 0 deg) around km (reflectivity), 250 / 150 km (Doppler velocity) 500 m(reflectivity), 250 m (Doppler velocity)

12 Precipitation products by surface and radar networks Radar Radar network network Radar echo composite precipitation nowcast (up to 1 hour) Rain Rain gauge gauge network network Radar rain gauge composite Very short range precipitation forecast (up to 6 hours) Mesosocale Mesosocale Model Model (every (every 33 hour) hour)

13 3 Dimentional Radar Observation 3D Display Aug 2nd, 2002 Thunderstorm hit Metropolitan Area Horizontal : Resolution 1km 1km by Vis5D Processed Data H: 1km 1km V: 1km interval 15 layers Vertical : 1km interval 15 layers

14 Radar Data Analysis and Monitoring System (RaDAMoS) 3 Dimensional Data <How to utilize 3 D data for routine storm monitoring> Monitoring using Several index VIL:Vertically Integrated Liquid water TOP:Echo Top height ZMAX:Maximum Reflectivity In a column CM:Center of Mass VILD:VIL density RaDAMoS( プロトタイプ ) 表示例 Improve the Surveillance ability for Sever Local Weather ( Heavy rain, Gust, Lightning, etc.) Decide which storm is severe

15 Data quality control and scan strategy for operation of Doppler radars Strict data quality control with high performance is needed in real time for operational use of Doppler radars. Ground clutter rejection Doppler velocity unfolding second trip echo rejection Extension of observation range Optimal antenna scan strategy is necessary in accordance with topography around radars and structure and time evolution of weather targets.

16 Doppler velocity products and its Utilization Data Assimilation for Meso scale Numerical Forecast Model 3 D Doppler Velocity Improve model prediction of heavy rainfall Hourly Wind Analysis Wind Profiler ACARS Meso model Wind vector distribution Calculation using VVP algorithm Meso cyclone detection Monitor wind field Monitor potential of Tornado Doppler Velocity by TDWR Low level Wind Shear Detection Doppler Lidar

17 Example of automatic meso cyclone detection Radar Reflectivity 1h before tornado Series of detected meso cyclones 30min after tornado Doppler Velocity Direction from Radar August 8, 2003

18 Automatic meso cyclone detection and Tornado July 10, 2002 September 29, 2004 Position of Tornado January 21, hour before Tornado 0 30min. After Tornado Preliminary investigation (based on 26 Tornado cases) Around 40% of Tornados accompanies meso cyclone Around 70% of Tornados including their surroundings accompanies meso cyclone

19 Combination of several method to issue Hazardous Wind Watch Radar Observation NWP Detection of meso cyclone Echo Intensity Hazardous Wind Index Combination Hazardous Wind Watch Severe Weather Potential (EHI)

20 Wind Profiler Network and Data Acquisition System (WINDAS) JMA started operation of wind profiler network in April 2001 after making several experiments for operation. Upper air Observation network over Japan Radiosonde Stations (18) JMA Wind Profilers (31) NICT Wind Profilers (2) 40N L band Wind Profiler Frequency: MHz Peak Power: 1.8kW Beam Width: 4deg Vertical Resolution: 100, 200, 300, 600m 30N 130E 500 km (on 35N) 140E NICT:National Institute of Information & Communication Technology

21 Wind Profilers in WINDAS Standard Type (21) Radome Type (9) Doubled Clutter Fence Type (1) Control Center (JMA Headquarters in Tokyo)

22 Seasonal change of height coverage Height coverage of wind profiler mainly depends on the amount of water vapor in the lower atmosphere and shows prominent seasonal variation. Height (km) Total mean Non precipitation Under precipitation APR MAY JUNE JULY AUG SEP OCT NOV DEC JAN FEB MAR

23 Impact of Profiler Data to the Mesoscale Model Forecast of a severe rainstorm is well improved by including wind profiler data in the Mesoscale Model using the 4D Variational data assimilation system. (a) 3hr Forecast of MSM without Profiler Data (b) 3hr Forecast of MSM with Profiler Data (c) Composite map of radars and rain gauges Rawinsonde Profiler 100km Total Rain Amount for 3 hours (mm) Forecast Winds at 850hPa without Profiler with Profiler

24 Wind analysis using MSM, Wind Profilers, Doppler radars and ACARS Winds obtained from the Mesoscale Model are adjusted with data from wind profilers, Doppler radars and ACARS. Winds analyzed on 10km grids are provided every hour for weather forecasts and aviation weather services. Track of Typhoon 16 on 30 August 2004

25 Contamination from Migrating Birds Migrating birds have made most significant error in the wind measurements. Profiler Wind Raionsonde Wind Intense migrating bird echoes observed on 15 October 2001 at Muroran profiler.

26 Contamination from Migrating Birds Data contaminated by migrating birds have been removed by monitoring timevariation of signal power between coherent integration and incoherent integration. Doppler Spectrum every 0.4 second 1 minute mean spectrum after rejection migratingbird echoes

27 Quality Control of wind profiler data Data of wind profilers used in real time at operational weather services have to be kept in high quality. Various types of quality control are adopted at each stage of signal processing and data processing in WINDAS. Wind speed Height error point Quadratic surface check Time

28 Terminal Doppler Lidar Wave Length:2.0μm(IR) Pulse Width:400ns Pulse Repetition Frequency:500Hz Diameter of Telescope :10cm Weight:2700kg Test Operation from Dec m

29 Detection Range 10km Tokyo International (Haneda) Airport Doppler Lidar

30 PIREP Date Time Phenomenon Altitude Position Airplane 12/27 02:43 WS 10KT GAIN 200FT ON FNA RWY34L B747 02:41UTC 02:43UTC 02:45UTC Detection Range of Doppler Lidar 02:40UTC 02:50UTC

31 Turbulence in Strong South-westerly Wind Doppler Velocity(EL=0.7deg) Velocity Width(EL=0.7deg)

32 LLWS Detection for All weather Condition Doppler Radar 空港気象ドップラーレーダー Doppler Lidar 空港気象ドップラーライダー Precipitating Condition Non precipitating Condition All weather Condition

33 LLWS in Precipitating and Non-precipitating Conditions Doppler Lidar Doppler Radar Doppler Velocity(EL=0.7deg)

34 Recent Advancement of Meso scale scale Observational Systems in JMA Operation of terminal Doppler radar since 1996 Operation of lightning detection system (SAFIR) since 2000 Operation of wind profiler network since 2001 and incorporated into 4 D data assimilation system since 2002 Start of automated radiosonde observation in 2003 Upgrade from conventional weather radar to Doppler radar since 2006 Test operation of terminal Doppler lidar since 2006 Experiments of operational use of GPS meteorology

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