high resolution modelling at zamg
TRANSCRIPT
High resolution modelling at ZAMG
Clemens Wastl
Content
• General overview on ZAMG NWP models
• Operational AROME model
• Nowcastingsystem AROME-RUC
data assimilation, nudging
• Ensemblesystem C-LAEF
blending, stochastic physics
• Other research areas:
• physics + diagnostics
Overview about available NWP at ZAMG
+ 00 + 12h + 1d + 3d + 15d + 6m
ECMWF EPS 1)
ECMWF seasonal forecast 1)
ALADIN-LAEF 1) / C-LAEF 1) EF
(EPS)
AROME-AUSTRIA 2)
ECMWF HRES 2)
…
ECMWF monthly forecast 1)
ALARO5-AUSTRIA 2) EF (EPS)
AROME-RUC 2)
INCA / EnINCA1)
1) probabilistic forecast system 2) deterministic forecast system
…
International cooperations
AROME = HARMONIE
Overview ZAMG NWP-models
deterministic probabilistic
ALARO
(4.8km, +72h, 4x / day
ALADIN – LAEF
(10.9km, +72h, 2x / day )
AROME
(2.5km, +60h, 8x / day)
C-LAEF (2.5km, +48, 2x /
day)
AROME – RUC
(1.2km, +12h, 24x /
day)
... operational ...
... under development...
ZAMG NWP INDEX 2005 - 2016
ALADIN ALARO ALARO 5km AROME
2018
e ZNI = weighted combination of MAE and RMSE from t, rh, mslp, glo, ff, dd, rr
q90
mean
q10
2005
Model generations at ZAMG
10 km 1 km5 km
ALADIN AROMEALARO
hydrostatic / non hydrostatic
enhanced model physics
convection parametrized / explicit
Non hydrostatic
enhanced model physics
convection explicit
enhanced interaction with ground
hydrostatic
convection parametrized
DeterministicProbabilistic = LAEF
MENT tasks & duties
Operations + Research:
AROME-RUC (1.2km) [test mode]
AROME-Aut (2.5 km) [operational]
C-LAEF (2.5km) [test mode]
ALARO5-Aut (4.8 km) [op. / no upgrades]
AROME Nowcasting (1km) [test mode]
LAEF (11 km) [op. / no upgrades]
Data assimilation (OI/EKF+3DVAR)
Resources and main areas:
• model physics and diagnostics (0.5 persons)
• ensemble prediction (1 person)
• data assimilation (1 persons) + surface assimilation (1 person)
• downstream application / operational tasks / verification (1.5 persons)
• + Master students / PHD students
Christoph Wittmann
Florian Meier
Phillip Scheffknecht
Florian Weidle
Clemens Wastl
Content
• General overview on ZAMG NWP models
• Operational AROME model
• Nowcastingsystem AROME-RUC
data assimilation, nudging
• Ensemblesystem C-LAEF
Ensemble-JK, stochastic physics
• Other Research areas:
• Physics + diagnostics
AROME
AROME (Applications of Research to Operations at Mesoscale):
+
Meso - NH physics NH Kernel ALADIN/ALARO)
(Laboratoire d'Aérologie, CNRM-GAME) (ALADIN Partners)
Convection resolving:
Horizontal resolution is high enough to resolve deep convection explicitly without
any convection parametrization – shallow convection is still parametrized.
Non-hydrostatic:
Hydrostatic approximation (vertical pressure gradient force = force of gravity) can
not be asssumed at this resolution - vertical momentum equation is fully solved.
AROME
Horizontal
resolution
2.5km (600x432)
Vertical resolution 90 Levels
Runs / day 8 (00,03,..18,21 UTC)
Forecast Range 60h
Output-frequency 1h
Model time step 60sec
Coupling model IFS (lagged)
Coupling update 1h
Assimilation 3DVAR / OI
SCREEN
Addsurf
AROME - Technical details of operational run
927boundary data
927SOIL
SST-Austausch
BATOR
BATOR3D
OPLACE
REMSENS
CANARI OIMAIN
„first guess“
CCMA MINIMIZATION
integrationpost processingderived fields
products
ZAMG
observations
GRIBCONV
About 3h 45min after the initialization the output is available
for the forecasters and customers
**) not yet operationally used
Observation type Parameter assimilated Source
SYNOP+TAWES T2m,RH2m,U10m,V10m,f ZAMG+OPLACE
AMDAR (Flugzeug) U,V,T (+Q) ZAMG+OPLACE
GEOWIND (SAT-Winde) MSG3 U,V OPLACE
TEMP (Radiosonde) U,V,T,Q,f ZAMG+OPLACE
PILOT U,V ZAMG
WINDPROFILER **) U,V ECMWF MARSARCHIV/OPLACE
MSG3-SEVIRI WV-radiances OPLACE
NOAA16/18/19+MetOp-A-B
AMSU-A,-B,MHS,HIRS
radiances OPLACE
MetOp-A-B IASI radiances OPLACE
ASCAT wind U10m,V10m (25km) ZAMG/EUMETSAT
RADAR **) reflectivity / radial winds Austrocontrol/Remote Sensing
MODIS-Schneebedeckung snow yes / no ENVEO-CRYOLAND
SNOWGRID Schneemodell snowheight ZAMG
GNSS Daten **) ZTD (STD, Refraktivität), RO EPOSA, TU WIEN
3DVAR Assimilation: Observational data
AROME
AROME model is running on the HPC of ZAMG
HPE Apollo 8600 (=SGI ICE-XA)
192 nodes with 18-core SKL
96 GB RAM per node
2 frontend nodes (à 2x8 processors, 64 GB
RAM, ...)
Total: 3472 cores
OmniPath enhanced hypercube network
Lustre Filesystem with total capacity of
350TB
PBSpro scheduling system
The new HPE/SGI system replaced the old
SGI ICE-X in December 2017
Content
• General overview on ZAMG NWP models
• Operational AROME model
• Nowcastingsystem AROME-RUC
data assimilation, nudging
• Ensemblesystem C-LAEF
Ensemble-JK, stochastic physics
• Other Research areas:
• Physics + diagnostics
Problems in the application of AROME
Data availability:
• AROME-Aut is running 8 times per day – every 3 hours a new forecast
• Data delay ov about 3h 45min (00 UTC run is available at 03:45 UTC)
• Not suitable for short range and nowcasting approaches
• Potential (very high) of available observation data is not fully tapped
source: www.esa.int
Radar data (5min / 1km) Mode-S (4sec / each
aircraft)
GNSS (15min / 40 stations)
AROME-RUC (Rapid Update Cycle)
AROME-RUC: Rapid Update Cycle
12.06.2019AROMEIdea: fill gap between classical nowcasting systems and short range NWP
Hourly forecasts up to 12h with hourly 3D-Var and 25 min cutoff time available within
1h
• 900x576x90 GP 1.2km LBC+ soil from AROME-Aut
• additional observations (radar reflectivity, Doppler winds, MODE-S aircraft, national
SYNOP, national GNSS ZTD)
• additional initialisation: latent heat nudging +35min (Stephan 2008), FDDA nudging
(Liu et al. 2006) +30min (optional), cloud analysis (Brewster et al. 2003), IAU
(Brousseau)
INCA-nowcasting
AROME-RUC
AROME-OPER 2.5kmL90
AROME-RUC
Horizontal
resolution
1.2km (900x576)
Vertical resolution 90 Levels
Runs / day 24 (00,01,..23,24
UTC)
Forecast Range 12h
Output-frequency 15min
Model time step 30sec
Coupling model AROME (lagged)
Coupling update 1h
Assimilation 3DVAR / OI
Assimilation window -90 to +30min
LH Nudging INCA analysis (+5 to
+35min)
FDDA Nudging 10m Wind, T2m/RH2m
(+10 to +30min)
Running at the ZAMG HPC
AROME-RUC
time0
Last AROME-OPERLBC from
First guess -2h RUC
-1.5h-2h 0.5h
TEMP
AMDAR
MODE-S
GNSS
Satellite
SYNOP
reflectivity
Doppler w.
3D-VAR
12h-forecast
INCA-
LHN
TAWES-Nudging
IAU
Nowcasting componentsclassic DA
AROME-RUC – Influence of observation data
RADAR
Air craft data
Surface stationsSatellite data
Wetter balloons
AROME-RUC: Assimilation of Mode-S data
AROME-RUC: Latent Heat Nudging
•Method of Jones & Macpherson 1997 for 2D-RADAR-Product (UM,
COSMO-Modell, WRF)
∆𝜃𝐿𝐻𝑁 = ∆𝜃𝑝ℎ𝑦𝑠𝑅𝑅𝑜𝑏𝑠−𝑅𝑅𝑚𝑜𝑑𝑒𝑙
𝑅𝑅𝑚𝑜𝑑𝑒𝑙(Jones & Macpherson)
Advantages of Nudging:
• Only 2D-precipitation from INCA (analysis + forecasts), no full 3D
radar data
• Much faster, very short computational time
• It is applied during the first time of model integration (AROME
RUC: 25 - 45 min)
• 4D-assimilation: observations of different times can be considdered
(difference to 3D-VAR)
Correction of latent heating by observed precipitation
AROME-RUC: Example for LH Nudging
AROME+3D-VAR-RADAR
AROME+3D-VAR-RADAR
+LHN Version1
AROME+3D-VAR-RADAR
+LHN Version2
INCA: Analyse
AROME-RUC: FDDA nudging
𝜕𝑢
𝜕𝑡=
𝜕𝑢
𝜕𝑡𝑝ℎ𝑦𝑠+ 𝐺
σ𝑖𝑤²𝑥𝑦𝑖(𝑢𝑖𝑜𝑏𝑠 − 𝑢𝑚𝑜𝑑𝑒𝑙)
σ𝑖𝑤𝑥𝑦𝑖
𝜕𝑢
𝜕𝑡=
𝜕𝑢
𝜕𝑡𝑝ℎ𝑦𝑠+ 𝐺
σ𝑖𝑤²𝑥𝑦𝑖𝑢𝑖𝑜𝑏𝑠
σ𝑖𝑤𝑥𝑦𝑖− 𝐺
σ𝑖𝑤²𝑥𝑦𝑖 𝑢𝑚𝑜𝑑𝑒𝑙)
σ𝑖𝑤𝑥𝑦𝑖
𝐷𝐼𝑆𝑇𝐴𝑁𝐶𝐸′ = 𝐷𝐼𝑆𝑇𝐴𝑁𝐶𝐸 + 𝑅|𝑝𝑠𝑂𝐵𝑆 − 𝑝𝑠𝐺𝑃|
𝑑𝑧𝑡ℎ𝑟𝑒𝑠 = 75ℎ𝑃𝑎
𝑤𝑥𝑦 =𝑅20.752 − 𝑍𝐷𝐼𝑆𝑇𝐴𝑁𝐶𝐸′2
𝑅20.752 + 𝑍𝐷𝐼𝑆𝑇𝐴𝑁𝐶𝐸′2 (𝑝𝑠𝐺𝑃
500ℎ𝑃𝑎+ 1)
Observations at: +10 / 20 / 30min
R=20km
G=0.02
(namelist switches)
10m wind observations; Liu et al.
2006
Content
• General overview on ZAMG NWP models
• Operational AROME model
• Nowcastingsystem AROME-RUC
data assimilation, nudging
• Ensemblesystem C-LAEF
Ensemble-JK, stochastic physics
• Other Research areas:
• Physics + diagnostics
Problems in the application of AROME
High variability between consecutive runs (depending on weather/season)
Example: RMSE of wind speed forecast
situation of weak pressure gradient westerly/northwesterly flow
Problem in this case: different observation data
Problems in the application of AROME
Some reasons for strong variability between consecutive runs:
• Different number of observation data and impact factor
• 8 AROME runs per day, 2 consecutive are based on the same boundary
conditions from ECMWF
• High spatial/temporal resolution – high sensitivy of model to changes in
the intial state
Some reasons for bad/wrong forecasts:
i. Initialisation state (analysis) in the model
ii. Formulation and design of the forecasting model (physics, dynamics)
iii. Spatial/temporal resolution of model not sufficient (topography,
physiography)
iv. Problems coming from coupling with the boundary conditions of global
modelConsideration of such uncertainties in the forecast C-LAEF
(AROME EPS)
EPS – Ensemble Prediction System
• Ensemble prediction systems are designed to assess the uncertainties in
NWP
• Ensemble system is based on several forecasts with different
settings/perturbations
• It‘s getting more and more popular at the national weather services
• Global EPS (only a few: GFS, ECMWF, GSM, ICON, …)
• With increased computer power – convection permitting LAM EPS systems
Ensemble
data assimilation,
Singular vectors,
Ensemble Kalman Filter
Multimodel
Multi-physics
Stochastic physics
Blending of LBC
C-LAEF: Convection permitting – Limited Area
Ensemble Forecasting
• Ensemble-
data assimilation (EDA)
• Ensemble-
data assimilation of
surface variables (sEDA)
• Ensemble-JK
Initial conditions +
error
• Stochastic physics:
Combination of tendency
and parameter
perturbation scheme
• Coupling with
ECMWF-ENS
• Ensemble-JK
Lateral boundary +
conditions errorModel error
Uncertainties representation
in C-LAEF
C-LAEF
Ensemble size 16+1
Horizontal
resolution
2.5 km
Vertical resolution 90 Levels
Runs/Day 4 (00, 06, 12, 18
UTC)
Forecast range +48h (00, 12 UTC)
+6h (06, 18 UTC)
Output-Frequency 1h
Model time step 60s
Coupling-Model ECMWF-EPS
(lagged)
Coupling-Update 3h
Assimilation 3DVAR / OI
Perturbation Surface, 3DVAR,
LBC, Model
Running at the ECMWF HPC
in Reading/Bologna
Convection permitting Limited Area Ensemble Forecasting
C-LAEF: IC and LBC perturbations
JK blending method developed by Guidard and Fischer (2008)
Integration of uncertainty from global EPS directly to C-LAEF 3D-Var
Combination of large scale (global EPS) with small scale (C-LAEF)
perturbations
Consistency between IC und LBC perturbations in C-LAEFCost function (3DVar)
Cost function in Jk blending method:
Keresturi et al., 2019: Improving initial condition perturbations in a convection-permitting
ensemble prediction system , Q J R Meteorol Soc. Accepted Author Manuscript. doi:10.1002/qj.3473
C-LAEF: Physics schemes
Radiation scheme
Shallow convection scheme
Turbulence scheme
Microphysics scheme
δ𝑇1δ𝑡
δ𝑇2δ𝑡
,δ𝑄2
δ𝑡,δ𝑈2, 𝑉2
δ𝑡
δ𝑇3δ𝑡
,δ𝑄3
δ𝑡,δ𝑈3, 𝑉3
δ𝑡
δ𝑇4δ𝑡
,δ𝑄4
δ𝑡
Way how tendencies of different physics
schemes are processed in AROME / C-
LAEF
𝑑𝑇
𝑑𝑡=
𝑖=1
4δ𝑇𝑖δ𝑡
,𝑑𝑄
𝑑𝑡=
𝑖=1
4δ𝑄𝑖
δ𝑡, 𝑒𝑡𝑐.
C-LAEF: Stochastic perturbation of total model
tendencies
SPPT (ECMWF)Standard SPPT: Perturbation of total model tendencies (Buizza et al., 1999; Palmer et al., 2009)
𝑑𝑇
𝑑𝑡=
𝑖=1
4δ𝑇𝑖δ𝑡
𝑑𝑇′
𝑑𝑡=𝑑𝑇
𝑑𝑡∗ (1 + P)
𝑑𝑄
𝑑𝑡=
𝑖=1
4δ𝑄𝑖
δ𝑡
P …. stochastic pattern
𝑑𝑄′
𝑑𝑡=𝑑𝑄
𝑑𝑡∗ 1 + P … . .
Constraints:
• Energy conservation
• Tapering function ß
• Physical consistency
• Assumes same level of uncertainty
for all parametrisations
P = P ∗ β
Radiation scheme
δ𝑇1δ𝑡
∗ (1 + 𝑃1)
Shallow convection scheme
Turbulence scheme
Microphysics scheme
C-LAEF: Stochastic perturbation of partial model tendencies:
pSPPT (ZAMG)
δ𝑇2δ𝑡
∗ 1 + 𝑃2 ,δ𝑄2
δ𝑡∗ 1 + P2 , etc.
δ𝑇3δ𝑡
∗ 1 + 𝑃3 ,δ𝑄3
δ𝑡∗ 1 + P3 , etc.
δ𝑇4δ𝑡
∗ 1 + 𝑃4 ,δ𝑄4
δ𝑡∗ 1 + P4 , etc.
In pSPPT the partial tendencies of T, Q, U,
V are perturbed directly after each
parametrization
Influence on subsequent schemes
Different perturbations are applied to the
physics schemes
In C-LAEF we need 4 different perturbation
patterns with different temporal and
horizontal scales
Wastl et al., 2019: Independent perturbations for
physics parametrization tendencies in a convection-
permitting ensemble (pSPPT), Geosci. Model Dev.,
12, 261-273.
C-LAEF: Stochastic perturbation of partial model
tendencies:
pSPPT (ZAMG) Shallow
convection
Turbulenc
e Microphysics
Radiatio
n
• Energy conservation
• Tapering function ß
(turbulence)
• Assumes same level of uncertainty
for all parametrisations
• Physical consistency
Stochastic perturbation of model tendencies showed promising results,
especially pSPPT, still some restrictions
Stochastic perturbation of key parameters (SPP, Ollinaho et
al., 2017) at process level in the turbulence scheme (see table)
Hybrid system (HSPP): Combination of pSPPT with parameter
perturbation in turbulence
C-LAEF: Stochastic physics in C-LAEF: Hybrid system (HSPP)
Parameter Range Description
XLINI 0 – 0.1 Minimum BL89 mixing length
XCTD 0.98 – 1.2 Constant for dissipation of potential
temperature and mixing ratio
XCTP 2.325 – 4.65 Constant for temperature-vapor pressure
correlation
XCEP 1.055 – 4.0 Constant for wind-pressure correlation
XCED 0.7 – 0.85 Constant for dissipation of total kinetic energy
(TKE)
XALPSBL 3.75 – 4.65 Value related to the TKE universal function
within the surface boundary layer
Parameters in the
turbulence scheme
which are
stochastically
perturbed.
αi′ = exp(P) ∗ α𝑖
• Energy conservation
• Tapering function
• Combination with surface
EDAWastl et al., 2019b: A hybrid stochastically perturbed
parametrization scheme in a convection-permitting
ensemble, Mon. Wea. Rev. 147, 2217-2230
C-LAEF: Results of summer test period
Ensemble spread (solid) and RMSE (dashed) for surface parameters in
July 2016.
RMSE is given as difference to the reference run.
C-LAEF: Results of summer test period
CRPS for temperature and wind speed at 500 hPa and 850 hPa in July 2016.
C-LAEF: Precipitation verification
Ensemble spread (solid) and RMSE (dashed) for precipitation in July 2016
(left) and January 2017 (right). RMSE is given as difference to the
reference run.
MEAN
Prob > 10
Prob > 20
Prob > 30
C-LAEF results, thunderstorm, 2018 04 16
C-LAEF results, 24h precipitation, 2018 10 27
C-LAEF suite on ecflow
Content
• General overview on ZAMG NWP models
• Operational AROME model
• Nowcastingsystem AROME-RUC
data assimilation, nudging
• Ensemblesystem C-LAEF
Ensemble-JK, stochastic physics
• Other Research areas:
• Physics + diagnostics
Actual/recent work areas
Physics / Diagnostics:
• Orographic effects in
radiation scheme
• Extended diagnostics
(clouds, visibility, ceiling /
LVP, …)
• convective diagnostics
(lightning
parameterization)
• (Micro)physics
(testing/tuning)
above: Low stratus over
Austria (SAT + AROME)
left: Simulated lightning
density + observed lightnings
Evaluation of AROME lightning condition
Questions to answer:
In particular for situation when precipitation is forecasted AND observed …
• …is there a benefit of using AROME lightning forecasts for automatic
products with respect to current methods (estimated via showalter
and/or CAPE)?
• Is … there a benefit of using AROME lightning forecasts with respect to
MOS?
FSS lightning AROME FSS CAPE(AROME) FSS show(AROME)
… answer is YES in both cases
(probabilistic) visibility for AROME
New parametrization:
reference parameterizations:
Orographic Raditation: Motivation
Temperature verification showed a
strong positive BIAS at some
stations in small Alpine valleys
This BIAS occurred predominantly
in the morning after sunrise
Investigation showed that radiation
shadowing is responsible for this
behaviour
Sunshine in the model; orographic
shadowing in reality
In the atmospheric radiation
parameterization of numerical
models each gridsquare is
assumed flat and effectively
homogeneous
BIAS, MAE and RMSE for AROME (blue)
and ALARO (red) at the valley stations of
Mallnitz and Obervellach for summer 2013.
Orographic Raditation: Method
Method is based on a paper of Müller and Scherer, 2005: „A Grid- and Subgrid-Scale
Radation Parametization of Topographic Effects for Mesoscale Weather Forecast Models.“
(Monthly Weather Review, 133)
It has already been implemented into HIRLAM (Senkova et al., 2007: „Parameterization
of orographic effects on surface radiation in HIRLAM“, Tellus 59A)
Slope angle and direction, relief shadow influence the short wave radiation budget
sky view factor influnces both long and short wave radiation budget
For parameterization of the orographic effects on radiation, slopes and local
horizon in different directions are required
SWLW
Clear sky conditions, LSL = .T.
warmer slopes (directed to sun), colder slopes in the shadow