data assimilation for very short-range forecasting in cosmo

[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


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


• 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)


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


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


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


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


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


• 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


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 !


• 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


• 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



• 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)


• 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



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 %



• 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



• 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


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


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


Kober et al., 2011: blendingprobabilistic nowcasting & NWP (EPS)

NWP probability: COSMO-DE-EPS N(Z>thr) / Nens (fraction method)

(calibration with reliability diagram statistics)


Kober et al., 2011: blendingprobabilistic nowcasting & NWP (EPS)

seamless probabilistic blending

additive combination in probability space


Data Assimilation for very short-range forecasting in COSMO

thank you for your attention

data assimilation for very short-range forecasting in cosmo

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