Data Assimilation for Very Short-Range Forecasting in COSMO
This presentation is the property of its rightful owner.
Sponsored Links
1 / 24

Data Assimilation for Very Short-Range Forecasting in COSMO PowerPoint PPT Presentation


  • 98 Views
  • Uploaded on
  • Presentation posted in: General

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

Download Presentation

Data Assimilation for Very Short-Range Forecasting in COSMO

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


Data assimilation for very short range forecasting in cosmo

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


Data assimilation for very short range forecasting in cosmo

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


Data assimilation for very short range forecasting in cosmo

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


Data assimilation for very short range forecasting in cosmo

  • 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


Data assimilation for very short range forecasting in cosmo

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)


Data assimilation for very short range forecasting in cosmo

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


Data assimilation for very short range forecasting in cosmo

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


Data assimilation for very short range forecasting in cosmo

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]


Data assimilation for very short range forecasting in cosmo

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


Data assimilation for very short range forecasting in cosmo

LETKF (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


Data assimilation for very short range forecasting in cosmo

  • deterministic run must use same set of observations as the ensemble system !

  • 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


Data assimilation for very short range forecasting in cosmo

LETKF (km-scale COSMO) : 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


Data assimilation for very short range forecasting in cosmo

LETKF (km-scale COSMO) : 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


Data assimilation for very short range forecasting in cosmo

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 operator

      in parallel model environment

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

GPS stations (ZTD resp. IWV)


Data assimilation for very short range forecasting in cosmo

LETKF (km-scale COSMO) : 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


Data assimilation for very short range forecasting in cosmo

LETKF (km-scale COSMO) : 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 highclouds

highclouds

mediumclouds

lowclouds

very lowclouds

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 %


Data assimilation for very short range forecasting in cosmo

LETKF (km-scale COSMO) : 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


Data assimilation for very short range forecasting in cosmo

DWD nowcasting product with use of NWP : 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)


Data assimilation for very short range forecasting in cosmo

DWD nowcasting product with use of NWP : 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


Data assimilation for very short range forecasting in cosmo

study on blendingprobabilistic 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


Data assimilation for very short range forecasting in cosmo

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

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

(calibration with reliability diagram statistics)


Data assimilation for very short range forecasting in cosmo

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

Data Assimilation for very short-range forecasting in COSMO

thank you for your attention


  • Login