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Ensemble Kalman Filter in a boundary layer 1D numerical model

Ensemble Kalman Filter in a boundary layer 1D numerical model. Samuel Rémy and Thierry Bergot (Météo-France) Workshop on ensemble methods in meteorology and oceanography, Paris, 15th-16th of May 2008. Outline. Description of the model Diagnosis of background error variance

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Ensemble Kalman Filter in a boundary layer 1D numerical model

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  1. Ensemble Kalman Filter in a boundary layer 1D numerical model Samuel Rémy and Thierry Bergot (Météo-France) Workshop on ensemble methods in meteorology and oceanography, Paris, 15th-16th of May 2008

  2. Outline • Description of the model • Diagnosis of background error variance • Results in a near-fog situation • Ensemble Kalman Filter • Hybrid assimilation scheme • Results in a fog situation • Conclusion and future work

  3. COBEL + Forcings IC 1DVar ALADIN ALADIN + ISBA Guess COBEL Description of COBEL-ISBA • Main features of COBEL-ISBA (Météo-France, LA-UPS) • Coupling of an atmospheric model (COBEL) and of a surface-ground scheme (ISBA) • High vertical resolution (30 levels from 0.5m to 1360m, 20 under 200m) • 1DVar assimilation scheme with site-specific observations • Detailed physical parameterizations for fog modelling References : Bergot et al. (2005), Weather and Forecasting

  4. Site-specific observation system • COBEL-ISBA is in currently operational at the Paris-CdG airport to help forecast fog events • Specific observation system consists of : • 30 meter tower : temperature and humidity at 1,5,10 and 30m • Soil of temperature and water content measurement • Shortwave and longwave radiative fluxes at 2 and 45m • Weather station : 2m temperature and humidity, visibility and ceiling • To complete the observations, temperature and humidity profiles from the NWP ALADIN are used

  5. Radiative fluxes observations Input ALADIN Observations Final Analysis (T,Q,Ql) Guess (T,Q) 1DVar/EnKF/Hybrid Cloud init Initialization • Two parts : • Assimilation scheme produces profiles of T and Q • In case of clouds, the cloud init component estimates the thickness of the cloud layer then adjusts the Q profile to saturation within the cloud • Assimilation/simulation every hour • 8-hours simulations

  6. Current assimilation scheme • Uses the local observations to give profiles of T and Q with • Monovariate assimilation scheme • Two parts for R : • Error variance of the observations : no covariance • Error variance of the ALADIN profiles : non-zero covariance • 1DVAR : fixed values for B : • T variance 2 K² • Q variance 0.5 (g/kg)² • Correlation length 200m

  7. B diagnosis methods We used two methods : • Direct computation with an ensemble • « cross product » method Gives variance over a long period of time, in the observation space References : Desroziers et al., QJRMS, 2005

  8. (…) t-3h t-2h t-4h t-1h t Diagnosis ensemble • Ensemble composed of the 8 previous simulations • Important diurnal cycle Spread of T Spread of Q 1000m 200m COBEL Levels COBEL Levels 25m Time (UTC) Time (UTC)

  9. Background error variance-covariance • B matrix for T (mean over the 2004-2005 winter) estimate by the ensemble : • Important variation linked with the development of a mixed boundary layer during the day 3h 15h 1000m 200m COBEL levels COBEL levels 25m COBEL levels COBEL levels

  10. Ensemble Kalman Filter • « on the flow » estimate of B seems more adequate • Ensembles : 8, 16, 32 and 64 members have been tried • We choose 32 members • Ensembles obtained by observation perturbation • Perturbations follow a normal law with zero mean and observation error variance • Different variance for real observations and the ALADIN profiles used as observations • 0.1 K and 0.1 g/kg for real observations • 2K and 0.5 g/kg for ALADIN profiles • Perturbation on the other inputs of the model : • Geostrophic wind • Soil temperature and humidity • Advections References : Roquelaure and Bergot (2007), J. of Applied Met. And Clim.

  11. Simulated observations • To avoid model error : better understanding of the impact of the assimilation scheme on the initial profiles and forecast • To have access to a « truth » • To have access to observations not avalaible in reality (ie liquid water content, top of cloud cover, T and Q above 30m, …) • Better evalutation of the model • Possibility of adding components to the local observation system (sodar, …) • To be able to create observations for the situations we wish to study • Observations are produced by adding a perturbation on a reference run.

  12. Simulated observations • Two 15-days situation were produced • A situation with mostly clear skies and shallow fogs at the end of the period, to study fog formation and false alarm situations (NEAR-FOG) • A situation with frequent and thick fogs, to study the cloud init and the dissipation of fog (FOG) • Simulations every hours => 360 simulations for each situation NEAR-FOG FOG

  13. Covariance filtering • Time filter : • Spatial filter : Schur product with a correlation length of 200m • B matrix for T, NEAR-FOG situation, mean over 360 simulations COBEL levels COBEL levels COBEL levels COBEL levels No filtering Spatial filtering Spatial & time filtering

  14. Adaptative covariance inflation • Increase the spread of the ensemble, as a function of : • Distance between the mean of the ensemble and the observation • Observation error variance • Ensemble spread • Applied sequentially for each observations • This method works only with observations with zero covariance (ie not with ALADIN profiles) • Applied separately for T and Q References : Anderson, Tellus, 2007

  15. Adaptative covariance inflation • Inflation factor for T and Q • Larger during the day : • Ensemble spread is generally smaller then • Larger for T than for Q • Smaller difference between the perturbation added to produce the ensemble and the observation error variance Day 1 Day 2 Day 3 Day 4 Day 1 Day 2 Day 3 Day 4 References : Anderson, Tellus, 2007

  16. Results for NEAR-FOG Q T • Estimates of B for T and Q, mean over the 360 simulations • Smaller variances at 15h • Covariances relatively greater (vs variances) at 15h 3h COBEL levels COBEL levels COBEL levels COBEL levels 15h

  17. Results for NEAR-FOG • Initial profiles have less impact during the day, when the atmosphere is neutral/slightly unstable • The mean of the perturbations have more impact than the perturbations themselves (hence the value of B diagnosed with the ensemble of 8 previous simulations) Day 1, 14h Day 2, 3h Init Truth T+1 Obs

  18. Results for NEAR-FOG • Results on temperature and specific humidity RMSE and bias, as compared with 1DVAR • Mean over the 360 simulations • Better for T than for Q, especially for the bias • Analyzed Q is worse than 1DVAR at 6am because a single case : cloud top was estimated much higher than 1DVAR (and truth). T Q

  19. Fog forecasting for NEAR-FOG • NEAR-FOG : 42 half-hours of « observed » LVP • Scores on LVP forecast against observation (ie Hit Rate, False Alarm Rate) not very significant : not enough cases • Statistics on the onset and liftoff of fog events • Airports want to know the beginning and end of fog/low cloud events • At Paris-CdG, Low Visibility Procedures (LVP) if • Visibility < 600m And/or • Ceiling < 60m

  20. Fog forecasting for NEAR-FOG • Frequency histograms for onset and lift-off of fog events • Mean and Stdev computed without false alarms • Much more standard deviation on onset than on burnoff • False alarms less frequent with EnKF • Onset less biased with EnKF Mean 3 Stdev 18 Mean –34 Stdev 41 1DVAR Mean -17 Stdev 44 Mean 6 Stdev 25 EnKF

  21. 3h 15h COBEL levels for T COBEL levels for T COBEL levels for Q COBEL levels for Q Multivariate EnKF • Correlation matrix between T and Q, estimate from the 8 previous simulations ensemble, mean over the 2004-2005 winter • Not to be neglected, especially during the night • Work in progress

  22. Hybrid scheme for NEAR-FOG • Hybrid scheme : the B matrixes used in the ensemble are fixed • The B matrix used in the reference run is computed with the ensemble, as for EnKF • Same ensemble as for EnKF (32 members) • Same vertical and time filtering of covariances • Same adaptative inflation algorithm • Values of the inflation factor for T and Q are a bit smaller

  23. Hybrid scheme for NEAR-FOG Q T • Estimates of B for T and Q, mean over the 360 simulations • Important decrease at 3h as compared with EnKF • Smaller decrease at 15h 3h COBEL levels COBEL levels COBEL levels COBEL levels 15h

  24. Hybrid scheme for NEAR-FOG • Results on temperature and specific humidity RMSE and bias, as compared with 1DVAR • Mean over the 360 simulations • A little bit better than EnKF for temperature • RMSE as a function of forecast time • Bias • Not much change for specific humidity • Small improvement for RMSE as a function of forecast time T Q

  25. Hybrid scheme for NEAR-FOG • Frequency histograms for onset and burnoff of fog events • More standard deviation on the onset for HYBRID Mean 5 Stdev 23 Mean –18 Stdev 53 HYBRID Mean -17 Stdev 44 Mean 6 Stdev 25 EnKF

  26. Conclusion for NEAR-FOG • Diurnal for B with EnKF and HYBRID : more realistic • EnKF and HYBRID better than 1DVAR after 3-4 hours of forecast time • Hybrid is a slightly better than EnKF for RMSE and bias • EnKF improves the biais for the onset of fog • For the burnoff, the NEAR-FOG case is not adequate : shallow fogs dissipate very quickly after sunrise • The burnoff will be studied with the FOG case

  27. Results for FOG Q T • Estimates of B for T and Q, mean over the 360 simulations • As compared with NEAR-FOG • Smaller covariances at 3h • Larger T covariance at 15h 3h COBEL levels COBEL levels COBEL levels COBEL levels 15h

  28. Results for FOG • Results on temperature and specific humidity RMSE and bias, as compared with 1DVAR • Mean over the 360 simulations • Degradation for EnKF as compared with 1DVAR • HYBRID (not shown) : reduced degradation T Q

  29. Fog forecasting for FOG Mean –16 Stdev 70 Mean –6 Stdev 86 • Frequency histograms for onset and burnoff of fog events • Onset : same as NEAR-FOG, EnKF and HYBRID forecast onset time later • Burnoff : negative bias is reduced with EnKF and HYBRID 1DVAR Mean 4 Stdev 89 Mean -4 Stdev 63 EnKF Mean 8 Stdev 91 Mean -1 Stdev 67 HYBRID

  30. Fog forecasting for FOG • Hit Rate and pseudo False Alarm Ratio for LVP events (half-hour forecasted vs observed) over the 360 simulations • Function of forecast time • HR : differences mainly during the first 4 hours of simulation • pFAR : differences mainly during the last 3 hours of simulation 1DVAR Mean HR 0.83 Mean pFAR 0.12 Hit rate Pseudo FAR Forecast time Forecast time Mean HR 0.83 Mean pFAR 0.14 Mean HR 0.83 Mean pFAR 0.14 EnKF HYBRID

  31. Conclusion for FOG • Degradation of analyzed and forecasted RMSE and bias, probably due to cloud init • Small improvement for the forecast of the burnoff time of fog events • Not much change for HR and pFAR • Need to improve EnKF and HYBRID in the presence of liquid water

  32. EnKF with real observations • Hit Rate and pseudo False Alarm Ratio for LVP events (half-hour forecasted vs observed) over the winter 2004-2005 (2200 simulations • EnKF is an interesting alternative to 1DVAR 1DVAR Hit rate Pseudo FAR Mean HR 0.62 Mean pFAR 0.5 Forecast time Forecast time Mean HR 0.6 Mean pFAR 0.46 Mean HR 0.6 Mean pFAR 0.48 EnKF HYBRID

  33. Future work • Multivariate (T,Q) EnKF • Problem in the presence of liquid water (FOG case) • Take in account the influence of liquid water on T and Q • Estimate of covariance between T and Ql, mean over winter 2004-2005 : 15h 3h COBEL levels for T COBEL levels for T COBEL levels for Ql COBEL levels for Ql

  34. Future work • Run EnKF and HYBRID with a different local observation system : • 10m mast (instead of 30m) • No mast • No radiative fluxes observations • No soil temperature and water content observation • Addition of a sodar • Continue work on real cases

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