Presentation is loading. Please wait.

Presentation is loading. Please wait.

Development of Data Assimilation Systems for Short-Term Numerical Weather Prediction at JMA Tadashi Fujita (NPD JMA) Y. Honda, Y. Ikuta, J. Fukuda, Y.

Similar presentations


Presentation on theme: "Development of Data Assimilation Systems for Short-Term Numerical Weather Prediction at JMA Tadashi Fujita (NPD JMA) Y. Honda, Y. Ikuta, J. Fukuda, Y."— Presentation transcript:

1 Development of Data Assimilation Systems for Short-Term Numerical Weather Prediction at JMA Tadashi Fujita (NPD JMA) Y. Honda, Y. Ikuta, J. Fukuda, Y. Ishikawa, K. Yoshimoto

2 Contents 1. Meso-scale NWP system (MA: Meso-scale Analysis) 1-1. MA operational system 1-2. recent update 2. Local NWP system (LA: Local Analysis) 2-1. LA trial operation system 2-2. recent developments 3. Summary

3 Contents 1. Meso-scale NWP system (MA: Meso-scale Analysis) 1-1. MA operational system 1-2. recent update 2. Local NWP system (LA: Local Analysis) 2-1. LA trial operation system 2-2. recent developments 3. Summary

4 Meso-scale NWP System Forecast Model : Meso-Scale Model (MSM) based on JMA Nonhydrostatic Model (JMA-NHM) Data Assimilation System : Meso-scale Analysis (MA) based on Nonhydrostatic meso 4DVar-system (JNoVA) Horizontal resolution : 5km Domain: 3600*2880 km (721*577 grid points) Forecast term + 00,06,12,18Z => 15hours + 03,09,15,21Z => 33hours Forecast model Meso-Scale Model (MSM) Initial condition (atmosphere) Meso-scale Analysis (MA) Boundary condition 20km-GSM (Global Spectral Model) Domain Specifications

5 Objectives of Meso-scale NWP System Disaster Prevention – Prediction of severe weather such as heavy rainfall is one of the main targets for mitigation and reduction of damage to property and loss of life. – Input to short-range precipitation forecast system – Input to storm surge model Aviation Weather Forecast – Enrichment of the weather information for aviation safety – Terminal Area Forecast (TAF) Guidance and so on.

6 MA operational system 00UTC03UTC JNoVA 4DVar (inner model 15km JMA-NHM) FG (5km JMA-NHM) Outer model 5km JMA-NHM 33h forecast Obs. - FG Analysis increment 03UTC06UTC JNoVA 4DVar (inner model 15km JMA-NHM) FG (5km JMA-NHM) outer model 5km JMA-NHM 15h forecast Obs. - FG Analysis increment MA MSM MSM (5km JMA-NHM)

7 MA Coverage Maps of Observation Data

8 Coverage Maps of Observation Data Direct assimilation of satellite radiance data

9 9 9 Score of MSM Precipitation Forecast Verification Grid : 20km Square Verified Element: 1mm/3hr Verification Period :From Mar to Sep Threat Score 4DVar 20km GSM GPS Major revision of physical processes dx=10km =>5km Nonhydro 4DVar Nonhydro model Improvement of convective scheme satellite radiance temperature Radar reflectivity

10 Contents 1. Meso-scale NWP system (MA: Meso-scale Analysis) 1-1. MA operational system 1-2. recent update 2. Local NWP system (LA: Local Analysis) 2-1. LA trial operation system 2-2. recent developments 3. Summary

11 Assimilation of RH data retrieved from3D radar reflectivity => Improvement of humidity and precipitation forecast of MSM MSM 3h accumulated precipitation forecast 26 Jul UTC Use of 3D radar reflectivity data (started 9 Jun. 2011) First Guess (MSM) Ze from Radar simulator Ze obs. RH retrieval algorithm retrieved RH MSM (5km) Outer model (5km) inner model (15km) MA (cf. Meteo France method)

12 Contents 1. Meso-scale NWP system (MA: Meso-scale Analysis) 1-1. MA operational system 1-2. recent update 2. Local NWP system (LA: Local Analysis) 2-1. LA trial operation system 2-2. recent developments 3. Summary

13 Local NWP System Forecast Model : Local Forecast Model (LFM) JMA Nonhydrostatic Model (JMA-NHM) Data Assimilation System : Local Analysis (LA) JNoVA 3DVar Trial operation started in Nov operation planned in 2012 Horizontal resolution : 2km Forecast term + 9hours Forecast model Local Forecast Model (LFM) Initial condition (atmosphere) Local Analysis (LA) Boundary condition 5km-MSM Specifications Objectives Producing sophisticated disaster prevention and aviation weather information with high resolution NWP LA (5km 441x501) LFM (2km 551x801) domain used in trial operation

14 LA trial operation system 3DVAR (5km) LF1 (5km) 3DVAR LF1 3DVAR LFM (2km) MSM (in operation) 3DVAR JMA-NHM 1h forecast, dx=5km Boundary Condition First Guess hydrometeors Analysis LF1 MSM (in operation) LA Rapid update cycle (RUC) 3DVAR FT=0FT=-1FT=-2FT=-3 FT=3

15 LA Coverage Maps of Observation Data Surface stations (temperature and wind) Doppler radar (radial velocity) Aviation(temperature and horizontal wind) Ground-based GPS (total column water vapor) Wind Profiler (horizontal wind)

16 LFM precipitation forecast precipitation related to heated land in the afternoon (16 Aug UTC 1h precipitation) Observation LFM (FT=3)

17 Contents 1. Meso-scale NWP system (MA: Meso-scale Analysis) 1-1. MA operational system 1-2. recent update 2. Local NWP system (LA: Local Analysis) 2-1. LA system in trial operation 2-2. recent development 3. Summary

18 (i) Use of radar reflectivity observation Simulate radar reflectivity from LF1 (JMA-NHM forecast) => estimate RH from reflectivity => assimilate RH in 3DVAR LF1 3DVAR Radar simulator retrieval Radar obs. Ze Rain, snow, graupel RH RH - reflectivity Database

19 3h accumulated precipitation ( FT=3 ) ControlTestObservation FT=0 Total column water vapor (Test-Control) (i) Use of radar reflectivity observation

20 (ii) Vertical Coordinate of Control Variable Control(=Trial Operation) : z*-coordinate Influence of topography remains strong up to high altitudes ground model top Slowly shift to z-coordinate Rapidly shift to z-coordinate z*-coordinate CV new coordinate Test : New coordinate follow terrain near the surface => rapidly shift to z-coordinate aloft

21 Vertical Cross Section of T increment ControlTest Reasonably limits the influence of topography within the lower troposphere. (ii) Vertical Coordinate of Control Variable

22 Control: ground potential temperature is fixed ⇒ excessive temperature increment in the lower troposphere ground 1.5m 20m 0m surface the lowest model level PT Obs excessive increment Test : extend the control variable to include ground potential temperature 1.5m 20m 0m Analyze ground PT fixed ⇒ Analyze ground PT to mitigate excessive increment (iii) Extension of Control Variables

23 ControlTest Vertical cross section of temperature analysis increment Mitigate excessive temperature increment in the lower troposphere (iii) Extension of Control Variables

24 (iv) Incremental Analysis Updates Obs. MSM LFM Gradually add increment 3DVar Gradually add 3DVar increment in the assimilation window => enhance balance of the analysis 30min. (cf. Bloom et al. 1996, Clayton 2003, Lee et al. 2006, etc.)

25 (iv) Incremental Analysis Updates Gradually add 3DVar increment in the assimilation window => enhance balance of the analysis Obs. MSM 5km JMA-NHM Actual implementation in test experiment 3DVar 30min.

26 Domain averaged Ps tendency Qc summed over (limited) domain Control Test (iv) Incremental Analysis Updates (Test with 5km forecast) Control Test FT=0 FT=-3h FT=6h Rapid update cycle 5km forecast FT=0 FT=6h 5km forecast update of B.C.

27 Test by single surface T observation (T increment on the lowest model level) Control Test Terrain between grid points is used to modify horizontal background error correlation (steep => damp fast) Implemented using coordinate transformation + recursive filter (v) Terrain-Adjusted Background Error Correlation

28 Summary JMA operates Meso-scale NWP system aimed at disaster prevention and aviation weather information services. Steady improvement of MSM forecast has been attained from various improvements of the system, including recent introduction of radar reflectivity data (retrieved RH) in MA. Trial operation of Local NWP system is currently underway, toward the operational run scheduled in Various development of LA is underway to improve the system. introduction of new observation, including radar reflectivity data new CV vertical coordinate ground PT analysis IAU terrain-adjusted background error correlation

29


Download ppt "Development of Data Assimilation Systems for Short-Term Numerical Weather Prediction at JMA Tadashi Fujita (NPD JMA) Y. Honda, Y. Ikuta, J. Fukuda, Y."

Similar presentations


Ads by Google