The Operational  Data Assimilation System

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Data assimilation system. . The observations are used to correct errors in the short forecast from the previous analysis time.Every 12 hours we assimilate 7 ? 9,000,000 observations to correct the 80,000,000 variables that define the model's virtual atmosphere.This is done by a careful 4-dimensional interpolation in space and time of the available observations; this operation takes as much computer power as the 10-day forecast..

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The Operational Data Assimilation System

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1. The Operational Data Assimilation System Overview of the operational data assimilation cycle Computational issues Observations used by the ECMWF Assimilation System Multi-incremental 4D-Var Why is 4D-Var performing better than 3D-Var? Recent improvements of ECMWF’s Assimilation System Near future data assimilation implementations

2. Data assimilation system

3. The operational configuration at ECMWF Configuration: Deterministic model: T1279L91 (~16km) Outer loop of 4D-Var T1279L91 and inner loops T159/T255/T255 (~125km/80km/80km) EPS target resolution T639L62 (to 10 days) and T319L62 thereafter Wave model (25km and 36 directions) Implemented in operations 26 January 2010

6. The ECMWF operational schedule

7. Operational schedule Early delivery suite introduced June 2004

12. The observation operator

13. The variational method allows model radiances to be compared directly to observed radiances Enables use of advanced observation operators

14. Jb: Ensures that the background model fields are adjusted meteorologically consistently in the region close to the observation location

15. Jb: The Balance Operator ensures the height and wind field balance is retained

16. Observation minus model differences are computed at the observation time using the full forecast model at T1279 (16km) resolution 4D-Var finds the 12-hour forecast evolution that optimally fits the available observations. A linearized forecast model is used in the minimization process based on the adjoint method It does so by adjusting surface pressure, the upper-air fields of temperature, wind, specific humidity and ozone The analysis vector consists of 80,000,000 elements at T255 resolution (80km) 4D-Var implementation used at ECMWF The size of the control vector is approx 8M.The size of the control vector is approx 8M.

17. 4D-Var incremental formulation Courtier, Thépaut and Hollingsworth (1994)

18. Revision of operational 4D-Var algorithm Implementation: Inner/outer iteration algorithm Hessian pre-conditioning Conjug. Gradient minimisation Improved TL approximations Multi-incremental T159/T255/T255 These developments facilitate: Use of higher density data Higher resolution inner loop (T255) and outer loop (T1279) Enhanced use of (relatively costly) TL physics Cloud and rain assimilation

19. Multi-incremental quadratic 4D-Var at ECMWF

20. 4D-Var with three outer loop: efficient, accurate and allows non-linearity

26. Parallel performance is important. Scalability is not perfect for the 4D-Var analysis

27. Hurricane Lili. Surface scatterometer winds. An example how 4D-Var propagates information vertically

28. Hurricane Lili. Surface scatterometer winds. An example how 4D-Var propagates information vertically

29. Surface scatterometer wind information is propagated vertically and improve the analysis. Due to flow-dependent structure functions in 4D-Var

30. 4D-Var is using more a-synoptic data than 3D-Var

31. 4D-Var versus 3D-Var and Optimum Interpolation 4D-Var is comparing observations with background model fields at the correct time 4D-Var can use observations from frequently reporting stations The dynamics and physics of the forecast model in an integral part of 4D-Var, so observations are used in a meteorologically more consistent way 4D-Var combines observations at different times during the 4D-Var window in a way that reduces analysis error 4D-Var propagates information horizontally and vertically in a meteorologically more consistent way 4D-Var more complex: needs linearized perturbation forecast model and its adjoint to solve the cost function minimization problem efficiently

32. Recent revisions to the assimilation system Use of many new satellites and new instruments (will be presented by Peter Bauer tomorrow) Variational bias correction of satellite radiances Adaptive bias correction for radiosondes and SYNOP pressure data Increased resolution from T799/T95/T159/T255 to T1279/T159/T255/T255 More advanced Tangent Linear physics scheme in 4D-Var Improved handling of supersaturation in the humidity analysis Huber norm Variational Quality Control Weak-constraint 4D-Var accounting for model error Advanced diagnostic tools to understand impact of observations on analysis/forecast: Forecast Sensitivity to Observations

33. First version (SSM/I radiances) 2005; extended to SSMIS, TMI, AMSR-E in 2007 Direct 4D-Var radiance assimilation from March 2009 Main difficulties: inaccurate moist physics parameterizations (location/intensity), formulation of observation errors, bias correction, linearity.

34. Humidity analysis improvements New humidity background error model: takes into account how humidity errors are affected by temperature errors in cloudy conditions (statistical relationship) extension of the humidity errors to cater for supersaturated conditions (with respect to ice)

36. Q only applied in stratosphere. Cost function including model error and bias: Model error ? Model bias (cycled) ?b

37. Weak-constraint 4D-Var formulation captures part of model error biases in the stratosphere

39. Soil moisture assimilation using Ext. Kalman Filter Will be implemented in June 2010

40. Ensembles of data assimilations Is being implemented now, first for EPS later for 4D-Var Run an ensemble of analyses with randomly perturbed observations and SST fields, and form differences between pairs of analyses (and short-range forecast) fields. These differences will have the statistical characteristics of analysis (and short-range forecast) error.

41. Estimating Background Error Statistics from Ensembles of Data Assimilations (EDA) Run an ensemble of analyses with random observation and SST perturbations, plus stochastic model error representation. Form differences between pairs of background fields. These differences will have the statistical characteristics of background error (but twice the variance).

42. To estimate analysis uncertainty To improve the initial perturbations in the Ensemble Prediction To calculate static and seasonal background error statistics To estimate flow-dependent background error in 4D-Var - “errors-of-the-day” To improve QC decisions and improve the use of observations in 4D-Var Why implementing Ensemble Data Assimilation?

43. Ongoing developments in data assimilation at ECMWF EKF for soil moisture analysis EDA to provide flow-dependent background error information to 4D-Var Vertical resolution increase planned for later in 2010 (~140+ levels TBD) Long window 4D-Var: extend to 24 hour window, improve model error term Improved assimilation of cloud/aerosols/rain observations Flow-dependent data selection Account for observation error correlations Modularisation of the IFS and scalability of the assimilation system

44. Summary of today’s lecture Overview of the operational data assimilation cycle Computational issues Observations used by the ECMWF Assimilation System Multi-incremental 4D-Var Why is 4D-Var performing better than 3D-Var? Recent improvements of ECMWF’s Assimilation System Near future data assimilation implementations

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