Assimilation of Satellite Radiances into Assimilation of Satellite Radiances into LM with 1D-Var and NudgingLM with 1D-Var and Nudging
Reinhold, Christoph, Francesca, Blazej,Piotr, Iulia, Michael, Vadim
DWD, ARPA-SIM, IMGW, NMA, RHM COSMO General Meeting, Cracow
15-19 September 2008
- plenary session -
COSMO-Project:Assimilation of satellite radiances with 1D-Var and Nudging
1DVAR + Nudging = Nudgevari.e. RETRIEVE temperature and humidity profiles and then nudge them as “pseudo”-observations
Goals of Project:
• Assimilate radiances (SEVIRI, ATOVS, AIRS/IASI) in COSMO-EU
• Explore the use of nonlinear observation operators with Nudging
• Explore the use of retrievals for regional models
Variational use of Satellite Radiances
Principle:
• use model first guess (temperature and humidity profiles)• simulate radiances from first guess (radiative transfer computation)• adjust profiles until observed and simulated radiances match - inversion by minimisation - optimal merge of information
defined by observation and background errors- keep vertical structure of model
Observation of NOAA 17, HIRS 8 (window channel) Simulation based on 3-hour GME forecast
Example: ATOVS of NOAA 15-18, METOP-A: 40 Channels (15 microwave, 19 infrared, 1 visible)
AMSU-A Temperature Weighting Functions
Reinhold Hess, 4
Assimilation of satellite radiances with 1D-Var and Nudging
mean sea level pressure & max. 10-m wind gusts valid for 20 March 2007 , 0 UTC
m/s
+ 48 h, REF (no 1DVAR) analysis
+ 48 h, 1DVAR-THIN3 + 48 h, 1DVAR-THIN2
AMSU-A:
Status: Slightly positive impact both for AMSU-A and SEVIRI...
Reinhold Hess, 5
Assimilation of satellite radiances with 1D-Var and Nudging
Athens, 2007
...but more tuning and long term trials are requiredfor operational application
Still to be done:...
Activities during last COSMO-year:• Preparation of AMSU-Data from IMGW Centre, Processing from Database • Tuning of bias correction• Use of IFS forecast above model top instead of climate first guess• Tuning of observation error covariance matrix R• Tuning of background error covariance matrix B• Developments for IASI (cloud detection, bias correction, monitoring, tests)
Reinhold Hess, 6
Assimilation of satellite radiances with 1D-Var and Nudging
...but more tuning and long term trials are requiredfor operational application
Still to be done:• Thorough validation of Profiles• Further tuning of Nudging• Parallel Experiments, long term studies
Activities during last COSMO-year:• Preparation of AMSU-Data from IMGW Centre, Processing from Database • Tuning of bias correction• Use of IFS forecast above model top instead of climate first guess• Tuning of observation error covariance matrix R• Tuning of background error covariance matrix B• Developments for IASI (cloud detection, bias correction, monitoring, tests)
Reinhold Hess, 7
Cost Funktion
Bias Correction for limited area model COSMO-EU
bias correction in two steps:• remove scan line dependent bias
considered in H, however residual errors• remove air mass dependent bias
systematic errors related to• air mass temperature• air mass humidity• surface conditions
modeled with predictors• observed AMSU-4(5) and -9• simulated AMSU-4 and 9• model values, e.g. geop. thick, IWV, SST
Variational Assimilation requires bias free observation increments H(x)-ybias from observation y, first guess x and radiative transfer H (RTTOV)
theoretical study (Gaussian error analysis):• two weeks of data is long enough for significant statistics sample size• predictors are highly correlated – chose representative synoptical and seasonal conditions
Reinhold Hess, 8
GME lat 30 to 60 deg, lon:-30 to 0 deg COSMO-EU: approx 1200-1500 fovs
approx 1200 obs/fov approx 1000-1500 obs/fov
scanline biases AMSU/NOAA 18 (15 to 25 June 2007)
Reinhold Hess, 9
timeserie of bias corrected observations minus first guess
AMSU-A channels 4-11, NOAA-16, ERA 40 stratosphere
stable in the troposphere, however large variations for high sounding channels=> use of channels AMSU-A 5-7 only
Reinhold Hess, 10
timeserie of bias corrected observations minus first guess
AMSU-A channels 4-11, NOAA-16, IFS stratosphere
stable in the troposphere, small variations for high sounding channels=> use of channels AMSU-A 5-9
Tuning of observation error covariance matrix R
Estimation of satellite observation-error statistics• in radiance space• with simulations based on radiosondes• intra-channel (vertical) correlations• horizontal correlations
Tuning of background error covariance matrix B
covariances with 500hPa correlations with 500hPa
vertical error structures derived from IFSblue: westerly windsred: stable high pressure
B defines the scales thatare to be corrected
Idea: define B according to cloud classification
SAF-NWC software for MSG1 and MSG2
situation dependent
scale dependent
flow dependent
Developments for IASI: 8641 IR-channels (started in July 2007)
• cloud detection NWP-SAF McNally• bias correction (generalisation of bias correction predictors)• upgrade to RTTOV-9• monitoring (tartan/dns-plots)• tests studies started
Analysis difference 500 hPa temperature [K]after 24 hours of assimilation
Time series (dna, tartan) of bias correctedo-b differences
Reinhold Hess, 14
ww
COSMO Priority Project: Assimilation of Satellite Radiances with 1DVAR and Nudging
Status of Developments September 2008 technical implementation ready (ATOVS/SEVIRI/AIRS/IASI) basic monitoring of radiances (day by day basis) basic set up, case studies available neutral to slightly positive results stratospheric background with IFS forecasts tuning of bias correction, R, B
Use of 1D-Var developments available for other activities:• GPS tomography• Radar reflectivities
To be done: more nudging coefficients/thinning of observations required long term evaluation positive results
Reinhold Hess, 15
Assimilation of satellite radiances with 1D-Var and Nudging
Lessons learned:
->Boundary values have a paramount impact on forecast quality,better use of observations in the centre of the models,quality of parameterisations
->Large scales hardly to be improved with radiancessmall scales and humidity to be improved
->Number of observations sufficient for bias correction,but representativity is issue
->Climate first guess above model top has (negative) impact alsofor trophospheric channels
->Assimilation of clouds/humidity required
Reinhold Hess, 16
Thank You for attention
Reinhold Hess, 17 Reading, 2007
GME lat 30 to 60 deg, lon:-30 to 0 deg COSMO-EU: approx 1200-1500 fovs
approx 1200 obs/fov approx 1000-1500 obs/fov
scanline biases AMSU/NOAA 18 (15 to 25 June 2007)
lapse rate?
Reinhold Hess, 18
timeserie of bias corrected observations minus first guess
AMSU-A channels 4-11, NOAA-18, ERA 40 stratosphere
stable in the troposphere, however large variations for high sounding channels=> use of channels AMSU-A 5-7 only
Athens, 2007
Reinhold Hess, 19 levels: 0.10, 0.29, 0.69, 1.42, 2.611, 4.407, 6.95, 10.37, 14.81 hPa
ECMWF profiles versus estimated profiles, top GME levelsaccuracy about 5K for lower levels, but ECMWF may have errors in stratosphere too
linear regression of top RTTOV levels from stratospheric channels(other choice: use IFS forecasts as stratospheric first guess)
use of climatological values (ERA40) seems not sufficient
provide first guess values above model top (COSMO-EU: 30hpa)
Athens, 2007
Cooperation with Vietnam:Application of 1D-Var and 3D-Var with HRM
Reinhold Hess, 20 Athens, 2007
no thinning of 298 ATOVS 30 ATOVS by old thinning (3) 30 ATOVS, correl. scale 70%
40 ATOVS by thinning (3) 82 ATOVS by thinning (2) 82 ATOVS, correl. scale 70%
T-‘analysis increments’ from ATOVS, after 1 timestep (sat only), k = 20
Reinhold Hess, 21 Athens, 2007
1D-Var for LME – Cloud and Rain detection
Validation withradar data
Microwave surface emissivity model: rain and cloud detection (Kelly & Bauer)
ValidationwithMSG imaging
Darmstadt, 2007Reinhold Hess, 22
Reinhold Hess, 23 Reading, 2007
courtesy: HIRLAM-DMI
Reinhold Hess, 24 Reading, 2007courtesy: HIRLAM-DMI (Bjarne Amstrup)
Jan - 2003 - Feb
Reinhold Hess, 25
1D-Var (compute each vertical profile individually):minimise cost functional
temperature and humidity profile
first guess and error covariance matrix
observations (several channels) and error covariance matrix
radiation transfer operator
The condition gives:
analysed profile and analysis error covariance matrix
,
,
,
The analysis is the mathematically optimal combination of first guess and observationgiven the respective errors
Satellite Radiances – Developments at DWD for GME
Reinhold Hess, 26 Athens, 2007
1D-Var for LME – Assimilation of AMSU-A: Cloud and Rain detection
