1 / 25

Takuya KAWABATA , Yoshinori SHOJI, Hiromu SEKO and Kazuo SAITO Meteorological Research Institute

Assimilation Experiment of a Local Heavy Rainfall Event with a Cloud-Resolving 4D-Var Assimilation System. Takuya KAWABATA , Yoshinori SHOJI, Hiromu SEKO and Kazuo SAITO Meteorological Research Institute Japan Meteorological Agency. Outline. Motivation

acton
Download Presentation

Takuya KAWABATA , Yoshinori SHOJI, Hiromu SEKO and Kazuo SAITO Meteorological Research Institute

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Assimilation Experiment of a Local Heavy Rainfall Eventwith a Cloud-Resolving 4D-Var Assimilation System Takuya KAWABATA, Yoshinori SHOJI, Hiromu SEKO and Kazuo SAITO Meteorological Research Institute Japan Meteorological Agency

  2. Outline • Motivation • A Cloud-resolving assimilation system (NHM-4DVAR) • Radar reflectivity and radial wind • -> Poster presentation • GPS slant delay • Assimilation Experimentof Toshima rainfall event

  3. Cloud resolving assimilation system Horizontal resolution : 0.5-2 km Target : meso-γ scale(e.g. cumulonimbi) Observation : Radar data, GPS data • Small horizontal scale • Large temporal variance • Large difficulty on forecast • Assimilation system, observation • High resolution • High frequency

  4. High resolution and frequent observation(low level convergence and water vapor) ＧＰＳ satellite Almost cumulonimbi are initiated by the low level convergence with moist air. Convection Rain drops Moist air Radar GPSreceiver Doppler radial wind data : Air flow GPS data : Moist air

  5. GEONET(GPS Earth Observation Network System) • Installed by Geographical Survey Institute • Number: 1,200 points • Density: 20 km^2 http://terras.gsi.go.jp/gps/gps-based_control_station.html

  6. NHM-4DVAR • Model • Forward model : NHM (JMA operational meso scale • nonhydrostatic model) • Adjoint model：Dynamical coreCloud microphysical process (warm rain)Control variables • Wind(u, v, w), surface pressure, potential temperature, nonhydrostatic pressure, total water (water vapor +cloud water), relative rain water, pseudo relative humidity • Observation • Doppler radial wind, radar reflectivity, GPS precipitable water vapor,GPS slant delay, wind profiler, surface wind, surface temperature, surface pressure, RASS • Horizontal resolution • 2km

  7. GPSobservation Zenith delay Assumption of horizontal homogeneity mapping Precipitable water vapor High resolution • Slant delay • Without assumption of homogeneity • Information for vertical, horizontal distribution of water vapor • Impact to the high resolution assimilation system Slant delay measure delay by atmosphere and water vapor

  8. Water vapor derived by ground based GPS • Precipitable water vapor (PWV):vertically accumulated water vapor • 　　　・Only water vapor • 　　　・Only zenith direction • Zenith delay (ZD): vertically accumulated delay of radio waves • 　　　・water vapor, pressure, temperature • 　　　・Only zenith direction • Slant delay (SD): accumulated delay on the path of radio waves • 　　　・water vapor, pressure, temperature • 　　　・slant direction to GPS satellites easy to assimilate many information Are slant delay data advantageous to PWV?

  9. Observation operator Wet Dry (a) Refractive index 100km exponential reduction Pd: Partial pressure of dry air,Pv : Partial pressure of water vapor, T: Temperature, K1, K2, K3：Constants Model Top (b) Integrate along the pass of radio wave =Obs G : Distance between the GPS satellite and the receiver. S : Real length of propagation root of radio wave. Assumption: S = G (c) Assumption Amount of delay becomes ZERO at the height of 100 km over the model top with exponential reduction.

  10. Amount of delay on each grid in the model domain Cross points indicate GPS stations. The directions of lines indicate one to the GPS satellites. Warm color:large delay -> humid, high pressure. -> low level Cold color: small delay -> dry, low pressure -> high level

  11. Toshima heavy rainfall（2008.08.05） Assimilation area 1101 JST 1130 JST 1200 JST 1230 JST 1258 JST 250 km • Five drainage workers at construction site were killed by an abrupt freshet. • Theamount of rainfall observed by Toshima observation site of the bureau of sewage of Tokyo was 57.5mm during 1-hour from 1153 JST.

  12. Assimilation experiment with 10-minute window 1300 2008.08.05 1100 1110 1120 1130 1200 1430 JST forecast Observational Data GPS slant delay (10 min)->Case GPS-SD GPS precipitable water vapor (10 min)->Case GPS-PWV Doppler radial wind (1 min) Radar reflectivity (1 min) Surface wind & temperature (10 min)

  13. Forecast results ( Radar reflectivity) OBS GPS-PWV 1200JST 1230JST 1300JST 1330JST 1400JST 1430JST GPS-SD 1140JST 20km At 1140 JST, Convection appear at the same point of the observation in both cases. At 1200 JST, the convective cell in case GPS-SD is vanished, because of lock of water vapor. At 1330 JST, both convection develop and move northward, but they are smaller than the OBS. At 1430 JST, both convection develop as the same size.

  14. Forecast results(Precipitable water vapor) GPS-PWV GPS-SD OBS 1150JST 1350JST The pattern of distribution of PWV is reproduced in both case. Quantitatively, high amount ofPWV on Kanto plain is well represented in case GPS-PWV, but not in case GPS-SD.

  15. Water vapor and horizontal wind(at 351 m) 1130JST 1150JST GPS-SD GPS-PWV • Because of the north-easterly wind, there is convergence around the initiating point. • There is high amount area of water vapor on north of the initiating point. • In case GPS-SD, the amount of water vapor may be insufficient.

  16. Water vapor and horizontal wind(1130 JST, 1140m)Case: GPS-PWV • There is southerly wind field at 1140 m height. • Because of this field, the convection moves northerly.

  17. Summary • Develop the observation operator for the GPS slant delay data. • Assimilation experiments on Toshima heavy rainfall event were conducted. • Both cases, GPS-SD and GPS-PWV, were reproduced the convection induced the Toshima heavy rainfall. • There may be nonlinearity in the GPS-SD assimilation. • The convection initiated and developed by the convergence with humid air at low level, moved northerly by southerly wind at high level.

  18. Future plans • Check the slant delay observations. • More linear operator of the slant delay assimilation. • How about the zenith delay?

  19. Departure (Obs - Guess) • Approximately Gaussian distribution • Without bias • Large contribution of dry part • Small variance of wet part Red : Total Green ： Dry Blue : Wet Ave.-0.003 m Var. 0.034 m • QC • Observation under 10 m • Departure under 1 m. • Under the 50 m differential between the height of observation site and one of the model grid.

  20. Assimilation result ( Radar reflectivity) SLT PWV OBS

  21. Specifications of NHM-4DVAR

  22. Operational assimilation system Horizontal resolution : over 10 km Target : meso-α, β scale Observation : conventional data, satellite data Large scale convergence by meso-α, β front Updraft by large scale orography

  23. Warm Rain Process Condensation Water vapor Evaporation Evaporation Cloud Water Rain Water auto conversion Accretion Precipitation

  24. Cloud resolving assimilation system Horizontal resolution : 0.5-2 km Target : meso-γ scale(e.g. cumulonimbi) Observation : Radar data, GPS data • Small horizontal scale • Large temporal variance • Large difficulty on forecast • Assimilation system, observation • High resolution • High frequency without mountain without front

More Related