Slide 1

Recent developments at ECMWF Working Group in Diagnostic: structure and role 1Carla Cardinali DAOS TORPEX meeting Exeter June 20111. Over-sight of collaborative projects Diagnosis of existing assimilation or forecasting problems Diagnosis of major new model cycles2. Strategic coordination of diagnostic developments Highlighting opportunities for new diagnostic tools of common interest Making existing diagnostic tools more widely usable Ensuring sufficient computing and storage resources for diagnostics3. Across-section communication of information and results Using a central diagnostics web page By coordination with the special topics of the OD/RD meeting With seminars on tools and collaborative projects

Trouble shooting: Spring scores investigation Diagnostics and investigation are underway to address the issue of a number of busts in Spring.

pilotaireptempamv

pilotaireptempamv

The largest degradation follow the amplification of the ridgeLand surface data assimilation evolution Patricia de Rosnay et alOptimum Interpolation (OI) Screen Level AnalysisDouville et al. (2000)Mahfouf et al. (2000)Soil moisture analysis based on Temperature and relative humidity analysisRevised snow analysisDrusch et al. (2004)Cressman snow depth analysis using SYNOP data improved by using NOAA / NSEDIS Snow cover extend data1999 2004 2009 2010/2011Recent developments/implementations:SEKF surface analysisUse of active microwave data: ASCAT soil moisture productUse of passive microwave SMOS Brightness Temperature productNew snow analysis and use of NOAA/NESDIS 4km snow cover product

Structure Surface Analysis:OI snow analysis and high resolution NESDIS data (4km)SEKF Soil Moisture analysisSimplified Extended Kalman Filter

METOP-ASCAT SMOS

De Rosnay et al., ECMWF NewsLetter, 2011Sabater et al., ECMWF NewsLetter, 20116EKF soil moisture analysis For each grid point, Analysed soil moisture state vector a:

a(t) = b(t) + K (y(t)-H b(t))

b Background soil moisture state vector Dimension Nx (=3, for the top three layers analysed), y Observation vector , Dimension Ny (=2 when T2m and Rh2m are used)H Jacobian matrix of the observation operator Estimated in finite differences (perturbed simulations) Dimension Nx raws, Ny columnsK Kalman gain matrix, fn of H and covariance matrix of background Bg (Nx . Nx) and observation R (Ny.Ny) errors.

Relevant Observations: Used in operations: Conventional observations (T2m, RH2m) Used in research: ASCAT Soil Moisture Under development: SMOS Brightness temperature

EKF corrects the trajectory of the Land Surface ModelECMWF Soil Moisture Analysis verificationAlbergel ClementValidated for several sites across Europe (Italy, France, Spain, Belgium)Validation results in France Dec 2008- Dec 2009Verification of ECMWF SM over the SMOSMANIA Network

Snow analysis

Snow analysis uses SYNOP snow depth data and NOAA/NESDIS IMS snow cover

2010implementation: New Snow analysis based on the Optimum Interpolation with Brasnett 1999 structure functions

A new IMS 4km snow cover product to replace the 24km product

Improved QC (monitoring, Blacklisting)

2011:- Assimilate additional snow dataFrom Sweden (New Report Type)

SYNOPdataNew Surface datacm9Direct 4D-Var assimilation of NCEP Stage IV rain data(Lopez 2011, MWR, in press)Ingredients:

Data: NCEP Stage IV radar + gauge precipitation product (4-km resol.). Data are averaged to model resolution prior to assimilation. Domain: eastern USA. 6-hourly accumulations are assimilated smoother & more linear. Ln(RR6h[mm/h]+1) transform (background departures closer to Gaussian).Screening: Obs rejected in regions with steep orography, surface snowfall or ducting. Only points that are rainy in both background and obs are assimilated. Fixed observation error: o=0.2 (in log-space). Variational bias correction implemented.ECMWF 2011

Short-range precipitation forecast is significantly improved..April-May 2009Sept-Oct 2009Equitable Threat ScoreFalse Alarm Rate12h-accumulated precipitation FC 00Z+12h (T511 L91)Direct 4D-Var assimilation of NCEP Stage IV rain dataECMWF 2011False Alarm RateEquitable Threat Score

NCEP Stage IV obs (mm/day)CTRL NCEP Stage IVNEW NCEP Stage IVImpact of NCEP Stage IV assimilation on 12h forecasts of precipitation.Sept-Oct 2009 average(CY35R2; T511 L91)ECMWF 2011 Mean bias and RMS error are reduced Impact on forecast scores for other parameters (Z, T, wind, RH):

- neutral or slightly positive impact on the global scale. - some hint of positive impact over Europe (days 4-5) and Asia (days 8-10). Direct 4D-Var assimilation of NCEP Stage IV rain dataECMWF 2011

RMSE South. Hemis. 500hPa windRMSE North. Hemis. 500hPa wind

RMSE Europe 500hPa temperatureRMSE Asia 850hPa TemperaturegoodForecast Root Mean Square Error changes due to direct 4D-Var assimilation of NCEP Stage IV rain data 1 April 6 June 2010, T1279 (~15 km global) L91The ECMWF EDA consists of 10 independent 4DVar assimilation cycles where the observations, boundary conditions and the model are perturbed according to their perceived uncertainties The EDA is run at reduced resolution (T399 outer loop, T95/T159 inner loop) to reduce computational costsA control member with no perturbations is also run at the same resolution of the EDAModel error is represented by the stochastic SPPT method

EDAMassimo Bonavita14EDAThe EDA system is designed to provide estimates of analysis and background uncertaintyThis has intrinsic value as an estimate of the quality of the deterministic analysis (i.e., Re-analysis applications, synoptic evaluation,)It improves the representation of initial uncertainties in the Ensemble Prediction System It is used to estimate state-dependent background error variances in the deterministic 4D-Var

15EDA

16EDA sample variances are now used to estimate flow-dependent background error variances in the deterministic analysisEDA sample variances need to be spectrally filtered and rescaled to reduce sampling errors and systematic errors The use of EDA variances in the deterministic analysis has two main effects:Increase the weight given to observations in areas of large uncertainty;Introduce a degree of flow-dependency in the analysis increments

EDASlide 17Forecast Products Users meeting17

Vorticity spread ml=78 (850hPa)EDARandomization methodSlide 18

Vorticity analysis increment at ml=78 18Slide 19Use of correlation information from the EDA in 4D-Var

EDA developments

EDA StDev of LNSPEDA Lscale of BG errors LNSP19