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Numerical Weather Prediction models at FMI

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Presentation on theme: "Numerical Weather Prediction models at FMI"— Presentation transcript:

1 Numerical Weather Prediction models at FMI
Sami Niemelä FMI 2014

2 Outline (and atmospheric models in general)
An overview to Numerical Weather Prediction (NWP) (and atmospheric models in general) An introduction to data assimilation process in NWP An introduction to mesoscale NWP-model Operational NWP-models at FMI 2

3 boundaries Atmospheric models FMI HIRLAM HARMONIE Climate models
e.g. ECHAM5. EC-EARTH Weather models Composition models e.g. SILAM ECMWF – IFS UM (UK) Arpege (France) GME/ICON (Germany) GFS (USA) GEM (Canada) JMA (Japan) Global models Limited area models (LAM) HIRLAM HARMONIE WRF Aladin UM Cosmo boundaries FMI HIRLAM HARMONIE LAM vs. Global better spatial & temporal resolution more frequent use of observations more focused solutions are possible

4

5 Climate

6 Climate NWP/global

7 Climate NWP/global NWP/ LAM

8 Components of NWP-system
Observations First guess (+3h...+6h) Data assimilation Initial state Forecast model Forecasts

9 DATA ASSIMILATION

10 Data assimilation Cost function in 3D-VAR: where - General principle -
First guess contribution Observation contribution where Background state Background error covariances Observation error covariances Observation operator. Fig: ECMWF

11 - Observation and background error estimates -
Data assimilation - Observation and background error estimates - Temperature error standard deviations TEMP BACKGROUND AIREP

12 “Missing observation is better than bad observation”
Data assimilation - Observation and background error estimates - Temperature error standard deviations Observation quality control TEMP BACKGROUND AIREP BACKGROUND OBSERVATION “Missing observation is better than bad observation”

13 Data assimilation - Observation impact -
Single temperature obs : dT = +1 K

14 Data assimilation - Observation impact - Temperature increment
Wind increment (east-west) Wind increment (north-south)

15 FORECAST MODEL

16 - Conservation equations in general -
Mesoscale NWP-model - Conservation equations in general - Mass Momentum +SV Heat Water (humidity,clouds,rain etc.) Gaseous and aerosol materials p = ρRT

17 - Conservation equations in general -
Mesoscale NWP-model - Conservation equations in general - Mass Momentum +SV Heat Water (humidity,clouds,rain etc.) Gaseous and aerosol materials p = ρRT Processes and equations can be divided to adiabatic part (dynamics) physical parameterizations (sub-grid part) → Model can not resolve features of a size < 6 – 8 * grid size !!!

18 HARMONIE NWP-model - Adiabatic part - +SV
Non-hydrostatic, fully compressible Euler eqs. Semi implicit time stepping Semi-Lagrangian advection scheme Spectral model: SI solver Spectral horizontal diffusion Horizontal derivatives Mass based terrain-following hybrid vertical coordinate. HARMONIE is designed to be used with grid size 3 km or less! +SV p = ρRT, TKE = Adv. + Shear ± Buoy. ± sub-grid

19 3D prognostic variables
HARMONIE NWP-model - Adiabatic part - Non-hydrostatic, fully compressible Euler eqs. Semi implicit time stepping Semi-Lagrangian advection scheme Spectral model: SI solver Spectral horizontal diffusion Horizontal derivatives Mass based terrain-following hybrid vertical coordinate. HARMONIE is designed to be used with grid size 3 km or less! +SV TKE = Adv. + Shear ± Buoy. ± sub-grid 3D prognostic variables temperature horizontal wind (u and v) specific humidity cloud water ice crystals rain snow graupel turbulent kinetic energy (TKE) 2 special NH-terms: pressure and vertical divergence (and 2D prognostic surface pressure)

20 - Physical parameterizations -
HARMONIE NWP-model - Physical parameterizations - Radiation Surface Shallow convection Turbulence Microphysics +SV p = ρRT, TKE = Adv. + Shear ± Buoy. ± sub-grid

21 - Physical parameterizations -
HARMONIE NWP-model - Physical parameterizations - Radiation Surface Shallow convection Turbulence Microphysics +SV TKE = Adv. + Shear ± Buoy. ± sub-grid Radiation scheme SW → 6 spectral bands LW → 16 spectral bands Effective radius of liquid/ice particle parameterized Climatological cloud condensate nuclei concentration Climatological distribution of ozone and aerosols Computationally expensive → Radiative tendencies updated every 15th time step

22 Surface scheme: SURFEX
HARMONIE NWP-model - Physical parameterizations - Radiation Surface Shallow convection Turbulence Microphysics +SV Surface scheme: SURFEX TKE = Adv. + Shear ± Buoy. ± sub-grid 4 tiles within a grid box Nature (forest, low vegetation etc.) Town Sea Inland waters (lakes, rivers) Physiography from ECOCLIMAP database Surface fluxes of heat, moisture and momentum! 1D-turbulence method used to diagnose T2m, q2m, V10m SURFEX can be used in offline mode.

23 - Physical parameterizations -
HARMONIE NWP-model - Physical parameterizations - Radiation Surface Shallow convection Turbulence Microphysics +SV TKE = Adv. + Shear ± Buoy. ± sub-grid Shallow convection The effect shallow cumuli and dry thermals Conservative variables: liquid potential temperature total water content (vapour+cloud water/ice) Represents small updrafts within model column: → counter-gradient mixing In HARMONIE, deep convection assumed to be resolved explicitly! Fig. from Soares et al. (2004)

24 - Physical parameterizations -
HARMONIE NWP-model - Physical parameterizations - Radiation Surface Shallow convection Turbulence Microphysics +SV TKE = Adv. + Shear ± Buoy. ± sub-grid Turbulence scheme Prognostic TKE equation with a diagnostic mixing length Exchange coefs. for momentum, heat and moisture → turbulent fluxes Conservative variables: liquid potential temperature total water content (vapour+cloud water/ice) TKE is advected by Semi-Lagrangian scheme.

25 - Physical parameterizations -
HARMONIE NWP-model - Physical parameterizations - Radiation Surface Shallow convection Turbulence Microphysics +SV TKE = Adv. + Shear ± Buoy. ± sub-grid Microphysics scheme Water vapour and 5 water condensates as prognostic: Cloud water/ice, rain, snow, graupel Diameter spectrum assumptions: cw/ci → generalized gamma law Precip. → exponential law (e.g. Marshall-Palmer) Terminal velocity linked to particle diameter via power law Hydrometeors advected by Semi-Lagrangian scheme

26 OPERATIONAL NWP AT FMI

27 HIRLAM-B programme LAM consortia in Europe
ALADIN (13) COSMO (6) UK (1) High Resolution Limited Area Model 10 countries development and operative use FMI ”Lead Center for RCR” (reference Hirlam in operative use) ECMWF European Centre for Medium-Range Weather Forecasts global forecast : boundaries for member country LAM's Hirlam ver. 1.0 v.1990

28 Hirlam + Aladin = HARMONIE (1-3 km meso model)
LAM consortia in Europe HIRLAM-B programme HIRLAM (10) ALADIN (13) COSMO (6) UK (1) High Resolution Limited Area Model 10 countries development and operative use FMI ”Lead Center for RCR” (reference Hirlam in operative use) ECMWF European Centre for Medium-Range Weather Forecasts global forecast : boundaries for member country LAM's Hirlam ver. 1.0 v.1990 2006 Hirlam-A 2011 Hirlam-B Hirlam + Aladin = HARMONIE (1-3 km meso model) Focus on HARMONIE

29 Development of computer resource
GFlops = operations/second

30 History of operational NWP at FMI

31 Operational NWP-models at FMI
- Since Jan HIRLAM – V74 Hydrostatic primitive eq. model Grid size: 7.5 km Vertical levels: 65 20 in lowest 1 km Forecast 4 times/d +54h Time step: 240 s 4D-VAR data assimilation “Last HIRLAM” 1030 x 816 720 x 800 HARMONIE – aro38h1 Non-hydrostatic, Euler eq. model Grid size: 2.5 km Vertical levels: 65 20 in lowest 1 km Forecast 8 times/d +54h Time step: 60 s 3D-VAR data assimilation ECMWF boundaries

32 Observation coverage maps
Observations in HIRLAM Surface observations SYNOP : international obs. network SHIP : ships BUOY : buoys (DRIBU) Upper air observations TEMP : radio soundings PILOT : manual soundings AIREP AMDAR : aircraft ACARS ATOVS : satellite measurements AMSU-A AMSU-B and MHS SYNOP TEMP AIREP

33 Satellite observations in HIRLAM
AMSU-A observations in RCR Observation instruments/channels: AMSU-A (noaa-15, noaa-16), 10ch AMSU-A (noaa-18), 10ch AMSU-B (noaa-16), 5ch MHS (noaa-18), 5ch Radiance data is bringing 3D temperature and humidity information to the analysis: Over sea areas Over arctic sea ice

34 Observations in HARMONIE
Observation maps Observations in HARMONIE SYNOP Surface observations SYNOP : Ps,T2m,RH2m,snow SHIP : ps DRIBU : ps SST : ECMWF-analysis Upper air observations TEMP : T,q,u,v sounding AMDAR : T,u,v aircrafts TEMP AMDAR

35 The effect of the high resolution
- HIRLAM vs. HARMONIE - HIRLAM HARMONIE Radar observation

36 The effect of the high resolution
- Severe thunder case 31 Jul HARMONIE Precipitation intensity Lightning intensity Radar observation

37 Downstream applications
SILAM / Pollen : long-range transport of chemical and biological substances Road model : road weather for traffic warnings Oceanographic model : Baltic Sea currents Wave model : Baltic Sea waves Sea ice model NWP model data available free via FMI Open Data Road condition Dry snow Icy Partly icy Frost Wet snow Wet Damp Dry

38 Long-term verification scores: HIRLAM
Temperature – 2m Wind speed –10m

39 THANK YOU!

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