Slide 1

Ensemble Kalman Filter in a boundary layer 1D numerical model Samuel Rmy and Thierry Bergot (Mto-France) Workshop on ensemble methods in meteorology and oceanography, Paris, 15th-16th of May 2008 Slide 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 Slide 3 Description of COBEL-ISBA Main features of COBEL-ISBA (Mto-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 1DVar ALADIN COBEL ISBA + + ALADIN ICForcings Guess COBEL References : Bergot et al. (2005), Weather and Forecasting Slide 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 Slide 5 Initialization Final Analysis (T,Q,Ql) 1DVar/EnKF/Hybrid Cloud init Guess (T,Q) Input ObservationsALADINRadiative fluxes observations 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 Slide 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 K Q variance 0.5 (g/kg) Correlation length 200m Slide 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 Slide 8 Diagnosis ensemble Ensemble composed of the 8 previous simulations Important diurnal cycle t-1h t-2ht-3h t-4h t () COBEL Levels Time (UTC) Spread of QSpread of T 200m 25m 1000m Slide 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 COBEL levels 15h COBEL levels 25m 200m 1000m Slide 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. Slide 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. Slide 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-FOGFOG Slide 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 No filteringSpatial filteringSpatial & time filtering Slide 14 Adaptative covariance inflation References : Anderson, Tellus, 2007 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 Slide 15 Adaptative covariance inflation References : Anderson, Tellus, 2007 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 1Day 2Day 3Day 4 Day 3Day 2Day 1 Slide 16 Results for NEAR-FOG Estimates of B for T and Q, mean over the 360 simulations Smaller variances at 15h Covariances relatively greater (vs variances) at 15h 3h 15h T Q COBEL levels Slide 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) Init Truth T+1 Obs Day 1, 14hDay 2, 3h Slide 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 Slide 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 Slide 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 1DVAR EnKF Mean 34 Stdev 41 Mean 3 Stdev 18 Mean -17 Stdev 44 Mean 6 Stdev 25 Slide 21 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 3h COBEL levels for T 15h COBEL levels for T COBEL levels for Q Slide 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 Slide 23 Hybrid scheme for NEAR-FOG 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 15h T Q COBEL levels Slide 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 Slide 25 Hybrid scheme for NEAR-FOG Frequency histograms for onset and burnoff of fog events More standard deviation on the onset for HYBRID HYBRID EnKF Mean 18 Stdev 53 Mean 5 Stdev 23 Mean -17 Stdev 44 Mean 6 Stdev 25 Slide 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 Slide 27 Results for FOG 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 15h T Q COBEL levels Slide 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 Slide 29 Fog forecasting for FOG 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 HYBRID EnKF 1DVAR Mean 6 Stdev 86 Mean 16 Stdev 70 Mean 4 Stdev 89 Mean -4 Stdev 63 Mean 8 Stdev 91 Mean -1 Stdev 67 Slide 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 EnKFHYBRID Forecast time Hit rate Pseudo FAR Mean HR 0.83 Mean pFAR 0.12 Mean HR 0.83 Mean pFAR 0.14 Mean HR 0.83 Mean pFAR 0.14 Slide 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 Slide 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 EnKFHYBRID Forecast time Hit rate Pseudo FAR Mean HR 0.62 Mean pFAR 0.5 Mean HR 0.6 Mean pFAR 0.46 Mean HR 0.6 Mean pFAR 0.48 Slide 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 : 3h COBEL levels for T 15h COBEL levels for T COBEL levels for Ql Slide 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