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[email protected] Assimilation for Very Short-Range Forecasting in COSMOWMO WS on Use of NWP for Nowcasting, Boulder, 24 – 26 Oct. 2011 1
Data Assimilation for Very Short-Range Forecasting in COSMO
Christoph SchraffDeutscher Wetterdienst, Offenbach, Germany
• operational : radar-derived precipitation rates by latent heat nudging
• in development : LETKF
• NWP for nowcasting : 2 examples
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[email protected] Assimilation for Very Short-Range Forecasting in COSMOWMO WS on Use of NWP for Nowcasting, Boulder, 24 – 26 Oct. 2011 2
GermanyGreece
ItalyPoland
RomaniaRussia
Switzerland
operational configurations :
x = 2.2 – 2.8 km
COSMO-DE: x = 2.8 km (deep convection explicit, shallow convection param.)
~ 2014 : x 2 km , LETKF
COSMO consortium /convection permitting COSMO configurations
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)(
),(),,(),(obsk
kkk txWGtxFtxt
Method: Dynamic Relaxation against observations ( : model state vector)
G determines the characteristictime scale for the relaxation
current COSMO DA:Observation Nudging
+ assimilates high-frequency obs
+ continuous analyzed state
indirect obs need retrievals
limited background errorcross-covariances
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• Assumption: vertically integrated latent heat release precipitation rate
• Approach: modify latent heating rates such that the model responds by producing the observed precipitation rates Latent Heat Nudging (LHN)
LHNnudging t
T
t
TTF
t
T
)(
mo
obsLHLHN RR
RRwithTT 1
• Required: relation: precipitation rate model variables (observed) (info required by nudging)
precipitation condensation release of latent heat
current COSMO DA: use of radar-derivedprecipitation by Latent Heat Nudging (LHN)
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[email protected] Assimilation for Very Short-Range Forecasting in COSMOWMO WS on Use of NWP for Nowcasting, Boulder, 24 – 26 Oct. 2011 5
modobs RRRR
LHN - temperature increment (in K/h)
Scaling factor :
mod
obs
RR
RR
modobs RRRR
Scaling factor :
mod
obs
RR
RR
Vertical profiles: cloud liquid water content (in g/kg) latent heat release (in K/h)
current COSMO DA: Latent Heat Nudging , implementation
• Assumption: vertically integrated latent heat release precipitation rate
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[email protected] Assimilation for Very Short-Range Forecasting in COSMOWMO WS on Use of NWP for Nowcasting, Boulder, 24 – 26 Oct. 2011 6
LHN: modify temperature (latent heating)
+ adjust specific humidity to maintain relative humidity
COSMO-DE: x = 2.8 km (deep convection explicit, shallow convection param.)
radar composite as used since June 2011:16 D, 2 NL, 2 B, 9 F, 3 CH , 2 CZ stations
current COSMO DA: Latent Heat Nudging , general info
• computationally efficient, applicable to complex microphysics
• composite of precip rates every 5 min
• adjustment applied locally in areas with precipitation, not in environment strong, but short-lived positive impact
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[email protected] Assimilation for Very Short-Range Forecasting in COSMOWMO WS on Use of NWP for Nowcasting, Boulder, 24 – 26 Oct. 2011 7
analysis+ 1 h+ 2 h+ 3 h+ 4 h+ 5 h
x = 2.8 km , no convection parameterisation , LHN with humidity adjustment
+ 6 h
1-hour sum of precipitation
current COSMO DA: Latent Heat Nudging , impact study
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[email protected] Assimilation for Very Short-Range Forecasting in COSMOWMO WS on Use of NWP for Nowcasting, Boulder, 24 – 26 Oct. 2011 8
15 June – 15 July 2009 , 0-UTC COSMO-DE forecast runs
threshold0.1 mm/h
opr (LHN) no LHN
FSS, 280 km (101 g.p.)
FSS, 30 km (11 grid pts.)
ETS, 2.8
km
2.0 mm/h
5 10 15 20 forecast lead time [h]
5 10 15 20 5 10 15 20
current COSMO-DE DA: LHN, scale-dependent verification
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future (km-scale) COSMO DA:strategy
ensemble-based data assimilation component required
convection-permitting NWP: after ‘few’ hours, a forecast of convection is a long-term forecast
deliver probabilistic (pdf) rather than deterministic forecast
need ensemble forecast and data assimilation system
forecast component: COSMO-DE EPS pre-operational
perturbations: LBC + IC + physics
GME, IFS, GFS, GSM
perturb.
products (precip beyond warning threshold) used by bench forecasters for lead times 3 hrs
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[email protected] Assimilation for Very Short-Range Forecasting in COSMOWMO WS on Use of NWP for Nowcasting, Boulder, 24 – 26 Oct. 2011 10
• COSMO priority project KENDA (Km-scale ENsemble-based Data Assimilation)
• implementation following Hunt et al., 2007
• basic idea: do analysis in the space of the ensemble perturbations
– computationally efficient, but also restricts corrections to subspace spanned by the ensemble
– explicit localization (doing separate analysis at every grid point, select only obs in vicinity)
– analysis ensemble members arelocally linear combinations of first guess ensemble members
LETKF (COSMO) :method
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[email protected] Assimilation for Very Short-Range Forecasting in COSMOWMO WS on Use of NWP for Nowcasting, Boulder, 24 – 26 Oct. 2011 11
11)(
RYPXK,xyKxx O Tbaw
bBBA LH
Analysis for a deterministic forecast run :use Kalman Gain K of analysis mean
bTbaw k YRY I P 1)1(
L : interpolation of analysis increments from grid of LETKF ensemble to (possibly finer) grid of deterministic run
deterministic
1
,,
RY
PXTb
aw
b
ensemble
deterministic analysis recently implemented
• Kalman gain / analysis increments not optimal, if deterministic background xB (strongly) deviates from ensemble mean background
• deterministic run must use same set of observations as the ensemble system !
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[email protected] Assimilation for Very Short-Range Forecasting in COSMOWMO WS on Use of NWP for Nowcasting, Boulder, 24 – 26 Oct. 2011 12
• ensemble size Nens = 32 40
• covariance inflation (adaptive multiplicative, additive)
• localisation (multi-scale data assimilation, successive LETKF steps with different obs / localisation ? adaptive , dep. on obs density ? )
• update frequency at ? 3 hr RUC 1 hr at 15 min !
non-linearity vs. noise / lack of spread / 4D property ?
• perturbed lateral BC (ICON hybrid VAR-EnKF / EPS) noise control ?
LETKF (km-scale COSMO) : scientific issues / refinement
• non-linear aspects, convection initiation (outer loop , latent heat nudging ?)
• technical aspects: efficiency, system robustness
2014 (quasi-)operational
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• radar : direct 3-D radial velocity & 3-D reflectivity (start summer 2010)
develop sufficiently accurate and efficient observation operators, soon available
Particular issues for use in LETKF: obs error variances and correlations,superobbing, thinning,localisation
LETKF (km-scale COSMO) : some important observations at km scale
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[email protected] Assimilation for Very Short-Range Forecasting in COSMOWMO WS on Use of NWP for Nowcasting, Boulder, 24 – 26 Oct. 2011 14
LETKF (km-scale COSMO) : some important observations at km scale
• ground-based GPS slant path delay (start Jan. 2012)
– direct use in LETKF, or tomography
– implement non-local obs operatorin parallel model environment
Particular issue: localisation for (vertic. + horiz.) non-local obs
GPS stations (ZTD resp. IWV)
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• cloud information based on satellite and conventional data (start March 2011)
– derive incomplete analysis of cloud top + cloud base, using conventional obs (synop, radiosonde, ceilometer) and NWC-SAF cloud products from METEOSAT SEVIRI
use obs increments of cloud or cloud top / base height or derived humidity
LETKF (km-scale COSMO) : some important observations at km scale
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fractional water clds
high semitransparent
very high clouds
high clouds
medium clouds
low clouds
very low clouds
cloud-free water
cloud-free land
undefined
cloud type CT cloud top height CTH
NWC-SAF SEVIRI cloud products: example
COSMO: cloud water qc > 0 , or cloud ice qi > 5 .10-5 kg/kg clc = 100 %subgrid-scale clouds clc = f(RH; shallow convection; qi , qi,sgs) < 100 %
LETKF (km-scale COSMO) : some important observations at km scale
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[email protected] Assimilation for Very Short-Range Forecasting in COSMOWMO WS on Use of NWP for Nowcasting, Boulder, 24 – 26 Oct. 2011 17
• cloud information based on satellite and conventional data (start March 2011)
– derive incomplete analysis of cloud top + cloud base, using conventional obs (synop, radiosonde, ceilometer) and NWC-SAF cloud products from Meteosat SEVIRI
use obs increments of cloud or cloud top / base height or derived humidity
– use SEVIRI brightness temperature directly in LETKF in cloudy (+ cloud-free) conditions (in view of improving the horizontal extent of cloud / cloud top height)
– compare approaches
Particular issues: non-linear observation operators, non-Gaussian distribution of observation increments
LETKF (km-scale COSMO) : some important observations at km scale
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• displacement forecast: weighted mean using data from
– KONRAD: radar-derived detection of storm cells + displacement vectors
– CellMOS: displacement forecast based on radar / lightning data
– RADVOR-OP: radar-derived forecast of precip + displacement
– COSMO-DE: upper-air wind (?)
• storm category using fuzzy logics
– gust: COSMO-DE V-max (700 – 950 hPa) , displacement
– rain: radar + fuzzy set based on KONRAD cell categ. ,COSMO-DE PW , radar VIL
– hail: radar VIL, KONRAD
– lightning (yes / no)
DWD nowcasting product with use of NWP :
NowCastMIX , for storm prediction
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example :
forecast for next 90 min.
DWD nowcasting product with use of NWP :
NowCastMIX
thunderstorms with :gusts Bft 7gusts Bft 7gusts Bft 8-10gusts Bft 8-10gusts Bft 8-10, hail, heavy raingusts Bft 8-10, hail, heavy raingusts Bft 8-10, hail, very heavy raingusts Bft 8-10, hail, very heavy rain
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study on blendingprobabilistic nowcasting & NWP (EPS)
Kober et al., 2011
radar reflectivityat initial time of ‘forecast’
probability of reflectivity > threshold (19 dBZ)
nowcasting: by neighbourhood method (area grows at 1 km / minute, 240 km) + displacement (pyramidal optical flow technique, Keil and Craig, 2007)
nowcast of probabilityvalid for 14 July 2009, 2300 UTC
2300 UTC: radar obs
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Kober et al., 2011: blendingprobabilistic nowcasting & NWP (EPS)
NWP probability: COSMO-DE-EPS N(Z>thr) / Nens (fraction method)
(calibration with reliability diagram statistics)
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Kober et al., 2011: blendingprobabilistic nowcasting & NWP (EPS)
seamless probabilistic blending
additive combination in probability space
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Data Assimilation for very short-range forecasting in COSMO
thank you for your attention