Data Assimilation for Very Short-Range Forecasting in COSMO
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Data Assimilation for Very Short-Range Forecasting in COSMO Christoph Schraff Deutscher Wetterdienst, Offenbach, Germany. operational : radar-derived precipitation rates by latent heat nudging in development : LETKF NWP for nowcasting : 2 examples. COSMO-DE : x = 2.8 km

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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


COSMO-DE: x = 2.8 km

(deep convection explicit,

shallow convection param.)

~ 2014 : x  2 km , LETKF

COSMO consortium /convection permitting COSMO configurations

Germany

Greece

Italy

Poland

Romania

Russia

Switzerland

operational

configurations :

x = 2.2 – 2.8 km


current COSMO DA:Observation Nudging

Method: Dynamic Relaxation against observations ( : model state vector)

G determines the characteristic

time scale for the relaxation

+ assimilates high-frequency obs

+ continuous analyzed state

 indirect obs  need retrievals

 limited background error

cross-covariances


  • Required: relation: precipitation rate  model variables

(observed) (info required by nudging)

precipitation  condensation  release of latent heat

  • Approach: modify latent heating rates such that the model responds

    by producing the observed precipitation rates

     Latent Heat Nudging (LHN)

current COSMO DA: use of radar-derivedprecipitation by Latent Heat Nudging (LHN)

  • Assumption: vertically integrated latent heat release  precipitation rate


LHN - temperature increment (in K/h)

Scaling factor :

Scaling factor :

current COSMO DA: Latent Heat Nudging , implementation

  • Assumption: vertically integrated latent heat release  precipitation rate

Vertical profiles:

cloud liquid water content (in g/kg) latent heat release (in K/h)


current COSMO DA: Latent Heat Nudging , general info

LHN: modify temperature (latent heating)

COSMO-DE: x = 2.8 km

(deep convection explicit,

shallow convection param.)

+ adjust specific humidity

to maintain relative humidity

  • 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

radar composite as used since June 2011:

16 D, 2 NL, 2 B, 9 F, 3 CH , 2 CZ stations


analysis

+ 1 h

+ 2 h

+ 3 h

+ 4 h

+ 5 h

+ 6 h

current COSMO DA: Latent Heat Nudging , impact study

x = 2.8 km , no convection parameterisation , LHN with humidity adjustment

1-hour sum of precipitation


current COSMO-DE DA: LHN, scale-dependent verification

15 June – 15 July 2009 , 0-UTC COSMO-DE forecast runs

FSS,30km

(11 grid pts.)

FSS,280km

(101 g.p.)

ETS,2.8km

threshold

0.1 mm/h

opr (LHN)

no LHN

2.0 mm/h

5 10 15 20

5 10 15 20

5 10 15 20

forecast lead time [h]


perturbations: LBC + IC + physics

perturb.

GME, IFS, GFS, GSM

future (km-scale) COSMO DA:strategy

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

 products (precip beyond warning threshold) used

by bench forecasters for lead times  3 hrs

 ensemble-based data assimilation component required


LETKF + physics (COSMO) :method

  • 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 are

      locally linear combinations of first guess ensemble members


  • Kalman gain / analysis increments not optimal,

    if deterministic background xB (strongly) deviates from ensemble mean background

Analysis for a deterministic forecast run :use Kalman Gain K of analysis mean

deterministic analysis

recently implemented

L : interpolation of analysis increments from grid of LETKF ensemble

to (possibly finer) grid of deterministic run

ensemble

deterministic


LETKF (km-scale COSMO) : ensemble system !scientific issues / refinement

  • 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 ?

  • non-linear aspects, convection initiation

    (outer loop , latent heat nudging ?)

  • technical aspects: efficiency, system robustness

     2014 (quasi-)operational


LETKF (km-scale COSMO) : ensemble system !some important observations at km scale

  • 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) : ensemble system !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 operator

      in parallel model environment

      Particular issue: localisation for (vertic. + horiz.) non-local obs

GPS stations (ZTD resp. IWV)


LETKF (km-scale COSMO) : ensemble system !some important observations at km scale

  • 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) : ensemble system !some important observations at km scale

NWC-SAF SEVIRI cloud products: example

cloud type CT

cloud top height CTH

fractional water clds

high semitransparent

very high clouds

high clouds

medium clouds

low clouds

very low clouds

cloud-free water

cloud-free land

undefined

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) : ensemble system !some important observations at km scale

  • 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


DWD nowcasting product with use of NWP : ensemble system !NowCastMIX , for storm prediction

  • 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 : ensemble system !NowCastMIX

example :

forecast for next 90 min.

thunderstorms with :

gusts Bft 7

gusts Bft 8-10

gusts Bft 8-10, hail, heavy rain

gusts Bft 8-10, hail, very heavy rain


study on blending ensemble system !probabilistic nowcasting & NWP (EPS)

Kober et al., 2011

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)

2300 UTC: radar obs

radar reflectivity

at initial time of ‘forecast’

nowcast of probability

valid for 14 July 2009, 2300 UTC


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

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

(calibration with reliability diagram statistics)


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

seamless probabilistic blending

additive combination

in probability space


Data Assimilation for very short-range forecasting in COSMO ensemble system !

thank you for your attention


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