New PP Sat-Cloud: Assimilation of Satellite Data with Clouds and Over Land Reinhold, Christoph, Marc, Francesca, Piotr, Jerzy, Iulia, Michael, Vadim DWD, ARPA-SIM, IMGW, NMA, RHM COSMO General Meeting, Cracow September 2008

Summary: increase the use of satellite data for regional applications of the COSMO-model for Nudging and KENDA Subproject 1: SEVIRI (IR) data with clouds (and over land)‏ Subproject 2: AMSU-A (MW) data over land with estimation of surface emissivity Sat-Cloud: Assimilation of Satellite Data with Clouds and Over Land 1d-Var Project: use radiances (SEVIRI, IASI, AMSU-A)‏ over sea and for cloud free and precipitation-free situations, respectively Costs: 7.0 FTE (IR) FTE (MW) + Manag. over 3 years in total :

Sat-Cloud: Assimilation of Satellite Data with Clouds and Over Land Motivation: Subproject 1: SEVIRI with Clouds in general 90% of weather conditions are cloud-affected wealthy information on humidity meteorologically important situations simulation of cloudy radiances (forward operator) extremely demanding strong nonlinear dependence of clouds (and cloudy radiances)‏ on relative humidity However: NWC Cloud-Classification product: Classification of cloud types (low, medium, high, broken, etc.)‏ based on threshold techniques for SEVIRI Cosmo-model CLCT

Sat-Cloud: Assimilation of Satellite Data with Clouds and Over Land Idea: Subproject 1: SEVIRI with Clouds  derive cloud analysis based on NWC CCP (cloud classification product)‏ validate and improve CCP using  humidity observations (radiosondes, aircrafts),  ceilometers  IASI-radiances (cloud height from IASI)‏  NWP-data (clouds, relative humidity)‏  forecast errors and spatial AMSU-A satellite error covariances (derive similar RH/T profile for same cloud)‏  parameterise model RH depending CCP dependence of RH on clouds (contact cloud microphysics)‏ scale RH profile, adjoint operator from ECMWF use vertical resolution of improved CCP use resulting RH profile or CCP for KENDA  improved use of IASI-radiances using CCP to distribute information spatially (background errors, spreading of nudging weights, weights for KENDA)

Sat-Cloud: Assimilation of Satellite Data with Clouds and Over Land Motivation: Subproject 2: AMSU-A over land trophospheric channels have significant impact from surface surface emmisitiy over land difficult to estimate: surface moisture, vegetation, surface temperature, etc. less sensitive to clouds: 90% of data is useable as in clear sky conditions centres of COSMO-models located over land least influence of boundaries: potential for high impact in model However:

use AMSU-A window channels to estimate surface emissivity use surface emissivity to improved simulation of low peaking channels (i.e. forwared operator)‏ retrieve profiles over land use profiles or surface emissivities for KENDA Idea: Subproject 2: AMSU-A over land Sat-Cloud: Assimilation of Satellite Data with Clouds and Over Land Advantage: estimated surface emissivities are at the exact satellite fov and time other than using emissivity maps Contact: COLOBOC for surface conditions

Reinhold Hess, 7 Thank You for attention Athens, 2007

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) Sat-Cloud: Assimilation of Satellite Data with Clouds and Over Land Motivation: Subproject 1: SEVIRI with Clouds

Reinhold Hess, 9 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 observation given the respective errors Satellite Radiances – Developments at DWD for GME

Reinhold Hess, 10 Assimilation of satellite radiances with 1D-Var and Nudging Athens, 2007 mean sea level pressure & max. 10-m wind gustsvalid 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, 11 Assimilation of satellite radiances with 1D-Var and Nudging Athens, but more tuning and long term trials are required for operational application 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)‏ Still to be done:...

Reinhold Hess, 12 Athens, D-Var for LME – Assimilation of AMSU-A: Cloud and Rain detection Preparation of Data from IMGW Centre, Processing from Database

Reinhold Hess, 13 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)-y bias 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, 14 Reading, 2007 GME lat 30 to 60 deg, lon:-30 to 0 degCOSMO-EU: approx fovs approx 1200 obs/fovapprox obs/fov scanline biases AMSU/NOAA 18 (15 to 25 June 2007)‏

Reinhold Hess, 15 Reading, 2007 GME lat 30 to 60 deg, lon:-30 to 0 degCOSMO-EU: approx fovs approx 1200 obs/fovapprox obs/fov scanline biases AMSU/NOAA 18 (15 to 25 June 2007)‏ lapse rate?

Reinhold Hess, 16 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, 17 timeserie of bias corrected observations minus first guess Athens, 2007 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, 18 Athens, 2007 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

Reinhold Hess, 19 levels: 0.10, 0.29, 0.69, 1.42, 2.611, 4.407, 6.95, 10.37, hPa ECMWF profiles versus estimated profiles, top GME levels accuracy 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

Tuning of observation error covariance matrix R Estimation of satellite observation-error statisics in radiance space with simulations based on radiosondes intra-channel (vertical) correlations horizontal correlations

Tuning of background error covariance matrix B covariances with 500hPacorrelations with 500hPa vertical error stuctures derived from IFS blue: westerly winds red: stable high pressure B defines the scales that are 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 corrected o-b differences

Reinhold Hess, 23 Assimilation of satellite radiances with 1D-Var and Nudging Athens, but more tuning and long term trials are required for operational application Activities during last COSMO-year: Tuning of bias correction Use of IFS forecast above model top instead of climate first guess Tuning of observation error covarience matrix R Tuning of background error covariance matrix B Developments for IASI (cloud detection, bias correction, monitoring, tests)‏ Still to be done: Further tuning of 1D-Var and Nudging Thorough validation of Profiles Parallel Experiments Technical Implementation for operational application

Reinhold Hess, 24w 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 Athens, 2007

Reinhold Hess, 25 Athens, 2007 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 radiances small scales and humidity to be improved ->Number of observations sufficient for bias correction, but representativity is issue ->long way from individual testcases to operational application ->Climate first guess above model top has (negative) impact also for trophospheric channels ->...?

h UTCCMa CT CLCT

Reinhold Hess, 27 Athens, 2007

no thinning of 298 ATOVS30 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, 28 Athens, 2007

1D-Var for LME – Cloud and Rain detection Validation with radar data Microwave surface emissivity model: rain and cloud detection (Kelly & Bauer)‏ Validation with MSG imaging Darmstadt, 2007 Reinhold Hess, 29

Reinhold Hess, 30 Reading, 2007 courtesy: HIRLAM-DMI

Reinhold Hess, 31 Reading, 2007 courtesy: HIRLAM-DMI (Bjarne Amstrup)‏ Jan Feb