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Reading, UK Parametrizations and data assimilation © ECMWF 2010 Marta JANISKOVÁ ECMWF email@example.com Parametrizations and data assimilation
Reading, UK Parametrizations and data assimilation © ECMWF 2010 Why is physics needed in data assimilation ? How the physics is applied in variational data assimilation system ? Which parametrization schemes are used at ECMWF ? What are the problems to be solved before using physics in data assimilation ? What is an impact of including the physical processes in assimilating model ? How the physics is used for assimilation of observations related to the physical processes ? Parametrization = description of physical processes in the model.
Reading, UK Parametrizations and data assimilation © ECMWF 2010 POSITION OF THE PROBLEM IMPORTANCE OF THE ASSIMILATING MODEL the better the assimilating model (4D-Var consistently using the information coming from the observations and the model) the better the analysis (and the subsequent forecast) the more sophisticated the model (4D-Var containing physical parametrizations) the more difficult the minimization (on-off processes, non-linearities) DEVELOPMENT OF A PHYSICAL PACKAGE FOR DATA ASSIMILATION = FINDING A TRADE-OFF BETWEEN: Simplicity and linearity (using simplified linear model is a basis of incremental variational approach) Realism
Reading, UK Parametrizations and data assimilation © ECMWF 2010 IMPORTANCE OF INCLUDING PHYSICS IN THE ASSIMILATING MODEL using an adiabatic linear model can be critical especially in the tropics, planetary boundary layer, stratosphere missing physical processes can increase the so-called spin-up/spin-down problem using the adjoint of various physical processes should provide: – initial atmospheric state more consistent with physical processes – better agreement between the model and data inclusion of such processes is a necessary step towards: – initialization of prognostic variables related to physical processes – the use of new (satellite) observations in data assimilation systems (rain, clouds, soil moisture, …)
Reading, UK Parametrizations and data assimilation © ECMWF 2010 STANDARD FORMULATION OF 4D-VAR the goal of 4D-Var is to define the atmospheric state x (t 0 ) such that the distance between the model trajectory and observations is minimum over a given time period [t 0, t n ] finding the model state at the initial time t 0 which minimizes a cost-function J : x i is the model state at time step t i such as: M is the nonlinear forecast model integrated between t 0 and t i H is the observation operator (model space observation space)
Reading, UK Parametrizations and data assimilation © ECMWF 2010 WHY AND WHERE ARE PHYSICAL PARAMETRIZATIONS NEEDED IN DATA ASSIMILATION? In 1D-Var, physical parametrizations can be needed in observation operator, H (no time evolution involved). In 4D-Var, physical parametrizations are involved in the observation operator, H, but also in the forecast model, M. Physical parametrizations are needed in data assimilation (DA): – to link the model state to the observed quantities, – to evolve the model state in time during the assimilation (trajectory, tangent-linear (TL) and adjoint (AD) computations in 4D-Var) Example: to assimilate reflectivity profiles, H must perform the conversion: Model state (T, q, u, v, P s ) Cloud and precipitation profiles Simulated reflectivity profile moist physics reflectivity model
Reading, UK Parametrizations and data assimilation © ECMWF 2010 OPERATIONAL 4D-VAR AT ECMWF – INCREMENTAL FORMULATION 4D-Var can be then approximated to the first order as minimizing: where is the innovation vector In incremental 4D-Var, the cost function is minimized in terms of increments: with the model state defined at any time t i as: tangent linear model Gradient of the cost function to be minimized: computed with the non-linear model at high resolution using full physics M computed with the tangent-linear model at low resolution using simplified physics M computed with a low resolution adjoint model using simplified physics M T Adjoint operators Tangent-linear operators
Reading, UK Parametrizations and data assimilation © ECMWF 2010 WHY SIMPLIFIED PHYSICS? One of the main assumptions in variational DA is that parametrizations and operators that describe atmospheric processes should be linear. otherwise, the use of the tangent-linear and adjoint approach is inappropriate and the analysis is suboptimal In practise, weak nonlinearities can be handled through successive trajectory updates (e.g., 3 outer loops in ECMWF 4D-Var) Physical parametrizations used in DA (TL and AD) are usually simplified versions of the original parametrizations employed in the forecast models: – to avoid nonlinearities (see further), – to keep the computational cost reasonable, – but they also need to be realistic enough ! ECMWF TL and AD models are coded using a manual line-by-line approach. Automatic AD coding softwares exist (but far from perfect and non-optimized).
Reading, UK Parametrizations and data assimilation © ECMWF 2010 Variational assimilation is based on the strong assumption that the analysis is performed in quasi-linear framework. However, in the case of physical processes, strong nonlinearities or thresholds can occur in the presence of discontinuous/non-differentiable processes (e.g. switches or thresholds in cloud water and precipitation formation, …) Without adequate treatment of most serious threshold processes, the TL approximation can turn to be useless. LINEARITY ISSUE finite difference (FD) TL integration u-wind increments fc t+12, ~700 hPa x 10 5
Reading, UK Parametrizations and data assimilation © ECMWF 2010 regularizations help to remove the most important threshold processes in physical parametrizations which can effect the range of validity of the tangent linear approximation after solving the threshold problems TL increments correspond well to finite differences u-wind increments fc t+12, ~700 hPa TL integration finite difference (FD) IMPORTANCE OF THE REGULARIZATION OF TL MODEL © ECMWF 2010
Reading, UK Parametrizations and data assimilation © ECMWF 2010 dy NL dy TL Potential source of problem (example of precipitation formation) dx
Reading, UK Parametrizations and data assimilation © ECMWF 2010 dx dy NL dy TL1 dy TL2 Possible solution, but …
Reading, UK Parametrizations and data assimilation © ECMWF 2010 dx1 dy NL dy TL2 … may just postpone the problem and influence the performance of NL scheme dy TL3 dx2
Reading, UK Parametrizations and data assimilation © ECMWF 2010 dx dy NL dy TL1 dy TL2 However, the better the model the smaller the increments
Reading, UK Parametrizations and data assimilation © ECMWF 2010 In NWP – a tendency to develop more and more sophisticated physical parametrizations they may contain more discontinuities For the perturbation model – more important to describe basic physical tendencies while avoiding the problem of discontinuities Level of simplifications and/or required complexity depends on: which level of improvement is expected (for different variables, vertical and horizontal resolution, …) which type of observations should be assimilated necessity to remove threshold processes Different ways of simplifications: development of simplified physics from simple parametrizations used in the past selecting only certain important parts of the code to be linearized
Reading, UK Parametrizations and data assimilation © ECMWF 2010 Regularization of vertical diffusion scheme: reduced perturbation of the exchange coefficients (Janisková et al., 1999): – original computation of Ri modified in order to modify/reduce f(Ri), or – reducing a derivative, f(Ri), by factor 10 in the central part (around the point of singularity ) -20. -10. 0. 10. 20. Ri number 10.0 0.0 f(Ri) - 10.0 - 20.0 Function of the Richardson number EXAMPLES OF REGULARIZATIONS (1) reduction of the time step to 10 seconds to guarantee stable time integrations of the associated TL model (Zhu and Kamachi, 2000) not possible in operational global models Exchange coefficients K are function of the Richardson number:
Reading, UK Parametrizations and data assimilation © ECMWF 2010 selective regularization of the exchange coefficients K based on the linearization error and a criterion for the numerical stability (Laroche et al., 2002) New operational ECMWF version: using reduction factor for perturbation of the exchange coefficients perturbation of the exchange coefficients neglected, K = 0 (Mahfouf, 1999) in the operational ECMWF version used to the middle of 2008 EXAMPLES OF REGULARIZATIONS (2)
Reading, UK Parametrizations and data assimilation © ECMWF 2010 ECMWF LINEARIZED PHYSICS (as operational in 4D-Var) Currently used in ECMWF operational 4D-Var minimizations (main simplifications with respect to the nonlinear versions are highlighted in red): Radiation: – TL and AD of longwave and shortwave radiation [ Janisková et al. 2002 ] – shortwave: only 2 spectral intervals (instead of 6 in nonlinear version) – longwave: called every 2 hours only. Vertical diffusion: – mixing in the surface and planetary boundary layers, – based on K-theory and Blackadar mixing length, – exchange coefficients based on Louis et al. , near surface, – Monin-Obukhov higher up, – mixed layer parameterization and PBL top entrainment recently added. – Perturbations of exchange coefficients are smoothed out. Gravity wave drag: [Mahfouf 1999] – subgrid-scale orographic effects [Lott and Miller 1997], – only low-level blocking part is used. No evolution of surface variables Dry processes:
Reading, UK Parametrizations and data assimilation © ECMWF 2010 ECMWF LINEARIZED PHYSICS (as operational in 4D-Var) Convection scheme: [Lopez and Moreau 2005] – mass-flux approach [Tiedtke 1989], – deep convection (CAPE closure) and shallow convection (q-convergence) – perturbations of all convective quantities included, – coupling with cloud scheme through detrainment of liquid water from updraught, – some perturbations (buoyancy, initial updraught vertical velocity) are reduced. Large-scale condensation scheme: [Tompkins and Janisková 2004] – based on a uniform PDF to describe subgrid-scale fluctuations of total water, – melting of snow included, – precipitation evaporation included, – reduction of cloud fraction perturbation and in autoconversion of cloud into rain. After solving the threshold problems clear advantage of the diabatic TL evolution of errors compared to the adiabatic evolution Moist processes:
Reading, UK Parametrizations and data assimilation © ECMWF 2010 VALIDATION OF THE LINEARIZED PARAMETRIZATION SCHEMES Non-linear model: Forecast runs with particular modified/simplified physical parametrization schemes Check that Jacobians (=sensitivities) with respect to input variables look reasonable (not too noisy in space and time) classical validation: TL - Taylor formula, AD - test of adjoint identity Tangent-linear (TL) and adjoint (AD) model: examination of the accuracy of the linearization: comparison between finite differences (FD) and tangent-linear (TL) integration Singular vectors: Computation of singular vectors to find out whether the new schemes do not produce spurious unstable modes.
Reading, UK Parametrizations and data assimilation © ECMWF 2010 Comparison: finite differences (FD) tangent-linear (TL) integration TANGENT-LINEAR DIAGNOSTICS
Reading, UK Parametrizations and data assimilation © ECMWF 2010 TL ADIAB – adiabatic TL model TL WSPHYS – TL model with the whole set of simplified physics (Mahfouf 1999) TL WSPHYS TL ADIAB FD Zonal wind increments at model level ~ 1000 hPa [ 24-hour integration]
Reading, UK Parametrizations and data assimilation © ECMWF 2010 Diagnostics: mean absolute errors: relative error TANGENT-LINEAR DIAGNOSTICS Comparison: finite differences (FD) tangent-linear (TL) integration
Reading, UK Parametrizations and data assimilation © ECMWF 2010 adiabsvd || vdif EXP - REF EXP relative improvement REF = ADIAB [%] X 10 20 30 40 50 60 80N 60N 40N 20N 0 20S 40S 60S 80S Temperature Impact of operational vertical diffusion scheme © ECMWF 2010
Reading, UK Parametrizations and data assimilation © ECMWF 2010 adiabsvd || vdif + gwd + radold + lsp + conv EXP - REF EXP relative improvement REF = ADIAB [%] X 10 20 30 40 50 60 80N 60N 40N 20N 0 20S 40S 60S 80S Temperature Impact of dry + moist physical processes (1 st used setup) © ECMWF 2010
Reading, UK Parametrizations and data assimilation © ECMWF 2010 16 adiabsvd || vdif + gwd + rad + cloud+conv current new new new cycle EXP - REF REF = ADIAB EXP 10 20 30 40 50 60 Temperature Impact of all physical processes (including improved schemes) relative improvement[%] X X X X 80N 60N 40N 20N 0 20S 40S 60S 80S adiab adiabsvd vdif oper_old oper_2007 © ECMWF 2010
Reading, UK Parametrizations and data assimilation © ECMWF 2010 Zonal wind EXP - REF Impact of all physical processes relative improvement [%] X X X X conv current new cycle adiab adiabsvd vdif oper_old oper_2007 adiab adiabsvd vdif oper_old oper_2007 EXP - REF 20 18 Specific humidity X X X conv current new cycle relative improvement[%]
Reading, UK Parametrizations and data assimilation © ECMWF 2010 IMPACT OF THE LINEARIZED PHYSICAL PROCESSES IN 4D-VAR (1) comparisons of the operational version of 4D-Var against the version without linearized physics included shows: – positive impact on analysis and forecast – reducing precipitation spin-up problem when using simplified physics in 4D-Var minimization Time evolution of total precipitation in the tropical belt [30S, 30N] averaged over 14 forecasts issued from 4D-Var assimilation
Reading, UK Parametrizations and data assimilation © ECMWF 2010 1-DAY FORECAST ERROR OF 500 hPa GEOPOTENTIAL HEIGHT OPER (very simple radiation) vs. NEWRAD (new linearized radiation) (27/08/2001 12h t+24) A1: FC_OPER – ANAL_OPER A2: FC_NEWRAD – ANAL_OPER A2 – A1 75.4 -30.3 63.8 -23.3 -17.9 -10.0 IMPACT OF THE LINEARIZED PHYSICAL PROCESSES IN 4D-VAR (2) impact of new linearized radiation
Reading, UK Parametrizations and data assimilation © ECMWF 2010 June 2005 – March 2009 – 1D+4D-Var assimilation of SSM/I brightness temperatures (TBs) in regions affected by rain and clouds. (Bauer et al. 2006 a, b) Since March 2009 – active all-sky 4D-Var assimilation of microwave imagers AIRS – Advanced Infrared Sounder ARM – Atmospheric Radiation Measurement programme GPS – Global Positioning System SSM/I – Special Infrared Sounder Operational: Experimental: 1D-Var assimilation of cloud-related ARM observations. (Janisková et al. 2002) surface downward LW radiation, total column water vapour, cloud liquid water path Investigation of the capability of 4D-Var systems to assimilate cloud-affected satellite infrared radiances – using cloudy AIRS TBs. (Chevallier et al. 2004) 1D-Var assimilation of precipitation radar data. (Benedetti and Lopez 2003) 1D-Var assimilation of cloud radar reflectivity – retrieved from 35 GHz radar at ARM site. (Benedetti and Janisková 2004) 2D-Var assimilation of ARM observations affected by clouds & precipitation – using microwave TBs, cloud radar reflectivity, rain-gauge and GPS TCWV. (Lopez et al. 2006) 4D-Var assimilation of cloud optical depth from MODIS. (Benedetti & Janisková 2007) 1D+4D-Var assimilation of NCEP Stage IV hourly precipitation data over USA – combined radar + rain gauge observations. (Lopez and Bauer 2007) 1D-Var of cloud radar reflectivity from CloudSat (QuARL project) Reading, UK Assimilation of rain and cloud related observations at ECMWF
Reading, UK Parametrizations and data assimilation © ECMWF 2010 Impact of the direct 4D-Var assimilation of SSM/I all-skies TBs on the relative change in 5-day forecast RMS errors (zonal means). Period: 22 August 2007 – 30 September 2007 Wind Speed 4D-Var assimilation of SSM/I rainy brightness temperatures (Geer, Bauer et al.) 0.10.050 -0.1 forecast is better forecast is worse Relative Humidity
Reading, UK Parametrizations and data assimilation © ECMWF 2010 B = background error covariance matrix R = observation and representativeness error covariance matrix H = nonlinear observation operator (model space observation space) (physical parametrization schemes, microwave radiative transfer model, reflectivity model, …) For a given observation y o, 1D-Var searches for the model state x=(T,q v ) that minimizes the cost function: Background term Observation term 1D-Var assimilation of observations related to the physical processes The minimization requires an estimation of the gradient of the cost function: The operator H T can be obtained: – explicitly (Jacobian matrix) – using the adjoint technique
Reading, UK Parametrizations and data assimilation © ECMWF 2010 4D-Var 1D-Var moist physics + radiative transfer background T,q v Observed rainfall rates Retrieval algorithm (2A12,2A25) 1D-Var on TBs or reflectivities1D-Var on TMI or PR rain rates Observations interpolated on models T511 Gaussian grid TMI TBs or TRMM-PR reflectivities TCWV obs =TCWV bg + z q v 1D-Var+4D-Var assimilation of observations related to precipitation TRMM – Tropical Rainfall Measuring Mission TMI – TRMM Microwave Imager PR – Precipitation Radar
Reading, UK Parametrizations and data assimilation © ECMWF 2010 Background PATER obs 1D-Var/RR PATER 1D-Var/TB Tropical Cyclone Zoe (26 December 2002 @1200 UTC) 1D-Var on TMI Rain Rates / Brightness Temperatures Surface rainfall rates (mm h -1 ) 1D-Var on TMI data (Lopez and Moreau, 2003)
Reading, UK Parametrizations and data assimilation © ECMWF 2010 2A25 RainBackground Rain1D-Var Analysed Rain 2A25 Reflect.Background Reflect.1D-Var Analysed Reflect. 1D-Var on TRMM/ Precipitation Radar data (Benedetti and Lopez, 2003) Tropical Cyclone Zoe (26 December 2002 @1200 UTC) Vertical cross-section of rain rates (top, mm h -1 ) and reflectivities (bottom, dBZ): observed (left), background (middle), and analysed (right). Black isolines on right panels = 1D-Var specific humidity increments.
Reading, UK Parametrizations and data assimilation © ECMWF 2010 Background term Observation term For a given observation y o, 1D-Var searches for the model state x=(T,q v ) that minimizes the cost function: 1D-Var assimilation of cloud related observations (1) H(x): moist physics or + radar/lidar radiative model (+ radiation scheme) x _b : Background T,q 1D-Var (analyzed T, q) Y: retrieved cloud parameters (level-2 products) or backscatter cross-sections, reflectivities (level-1 products)
Reading, UK Parametrizations and data assimilation © ECMWF 2010 1D-Var assimilation of cloud related observations (2) (QuARL project) OBS – CloudSat (94 GHz radar) FG AN – 1D-Var of cloud reflectivity Cloud reflectivity [dBZ] – 23/01/2007 over Pacific QuARL – Quantitative Assessment of Operation.Value of Space-Borne Radar and Lidar Measurements of Cloud and Aerosol Profiles
Reading, UK Parametrizations and data assimilation © ECMWF 2010 1D-Var assimilation of cloud related observations (3) (QuARL project) Comparison of the first guess and analysis against cloud optical depth FG departure AN-refl departure AN-reflopt departure BIAS - reflectivity FG departure AN-refl departure AN-reflopt departure STD.DEV. - reflectivity Reading, UK Bias and standard deviation of first-guess vs. analysis departures for reflectivity height [km] profile number
Reading, UK Parametrizations and data assimilation © ECMWF 2010 Experimental 4D-Var assimilation of cloud optical depth from MODIS (1) (Benedetti and Janisková, 2008) Period: 2006040500 - 2006042000 Assimilation of cloud optical depth at 0.55 m from MODIS – fit to observations Scatter-plot of OBS versus FG Scatter-plot of OBS versus ANA BIAS = 0.22 RMS = 0.57 corr. coef. = 0.480 BIAS = 0.21 RMS = 0.51 corr. coef. = 0.672 Positive impact on the distribution of the ice water content, particularly in the Tropics. Impact on 10-day forecast positive for upper level temperature in the Tropics and neutral for the model wind. ECMWF 4D-Var is approaching a level of the technical maturity necessary for global assimilation of cloud related observations. Conclusions: MODIS – Moderate Resolution Imaging Spectroradiometer © ECMWF 2010
Reading, UK Parametrizations and data assimilation © ECMWF 2010 MLS retrievals: Ice Water Content at 215 hPa ECMWF model – CNTRL run CNTRL run – MLS obs ECMWF model – EXP run EXP run – MLS obs MLS = Microwave Limb Sounder Courtesy of F. Li, Jet Propulsory Laboratory, CA, USA IWC [mg/m 3 ] Experimental 4D-Var assimilation of cloud optical depth from MODIS (2) Comparison with independent cloud observations
Reading, UK Parametrizations and data assimilation © ECMWF 2010 Physical parametrizations become important components in current variational data assimilation systems as they are needed: to link the model state to the observation quantities to evolve the model state in time during the assimilation Positive impact from including linearized physical parametrization schemes into the assimilating model has been demonstrated. However, there are several problems with including physics in adjoint models: – development and thorough validation require substantial resources – computational cost may be very high – non-linearities and discontinuities in physical processes must be treated with care Constraints and requirements when developing new simplified parametrizations for data assimilation: – find a compromise between realism, linearity and computational cost – evaluation in terms of Jacobians (not too noisy in space and time) – systematic validation against observations – comparison to the non-linear version used in forecast mode (trajectory) – numerical tests of tangent-linear and adjoint codes for small perturbations – validity of the linear hypothesis for perturbations with larger size (typical of analysis increments) GENERAL CONCLUSIONS © ECMWF 2010
Reading, UK Parametrizations and data assimilation © ECMWF 2010 REFERENCES Bauer, P., Lopez, P., Benedetti, A., Salmond, D. and Moreau, E., 2006a: Implementation of 1D+4D-Var assimilation of precipitation affected microwave radiances at ECMWF. Part I: 1D-Va. Quart. J. Roy. Meteor. Soc., 132, 2277-2306 Bauer, P., Lopez, P., Salmond, D., Benedetti, A., Saarinen, S. and Bonazzola, M., 2006b: Implementation of 1D+4D-Var assimilation of precipitation affected microwave radiances at ECMWF. Part II: 4D-Var. Quart. J. Roy. Soc., 132, 2307-2332 Benedetti, A. and Janisková, M., 2004: Advances in cloud assimilation at ECMWF using ARM radar data. Extended abstract for ICCP, Bologna 2004 Benedetti, A. and Janisková, M., 2008: Assimilation of MODIS cloud optical depths in the ECMWF model. Mon. Wea. Rev., 136, 1727-1746 Errico, R.M., 1997: What is an adjoint model. Bulletin of American Met. Soc., 78, 2577-2591 Fillion, L. and Errico, R., 1997: Variational assimilation of precipitation data using moist convective parametrization schemes: A 1D-Var study. Mon. Wea. Rev., 125, 2917-2942 Fillion, L. and Mahfouf, J.-F., 2000: Coupling of moist-convective and stratiform precipitation processes for variational data assimilation. Mon. Wea. Rev., 128, 109-124 Klinker, E., Rabier, F., Kelly, G. and Mahfouf, J.-F., 2000: The ECMWF operational implementation of four-dimensional variational assimilation. Part III: Experimental results and diagnostics with operational configuration. Quart. J. Roy. Meteor. Soc., 126, 1191-1215 Chevallier, F., Bauer, P., Mahfouf, J.-F. and Morcrette, J.-J., 2002: Variational retrieval of cloud cover and cloud condensate from ATOVS data. Quart. J. Roy. Meteor. Soc., 128, 2511-2526 Chevallier, F., Lopez, P., Tompkins, A.M., Janisková, M. and Moreau, E., 2004: The capability of 4D-Var systems to assimilate cloud_affected satellite infrared radiances. Quart. J. Roy. Meteor. Soc., 130, 917-932 Janisková, M., Thépaut, J.-N. and Geleyn, J.-F., 1999: Simplified and regular physical parametrizations for incremental four- dimensional variational assimilation. Mon. Wea. Rev., 127, 26-45 Janisková, M., Mahfouf, J.-F., Morcrette, J.-J. and Chevallier, F., 2002: Linearized radiation and cloud schemes in the ECMWF model: Development and evaluation. Quart. J. Roy. Meteor. Soc.,128, 1505-1527
Reading, UK Parametrizations and data assimilation © ECMWF 2010 Janisková, M., Mahfouf, J.-F. and Morcrette, J.-J., 2002: Preliminary studies on the variational assimilation of cloud-radiation observations. Quart. J. Roy. Meteor. Soc., 128, 2713-2736 Janisková, M., 2004: Impact of EarthCARE products on Numerical Weather Prediction. ESA Contract Report, 59 pp. Laroche, S., Tanguay, M. and Delage, Y., 2002: Linearization of a simplified planetary boundary layer parametrization. Mon. Wea. Rev., 130, 2074-2087 Lopez, P. and Moreau, E., 2005: A convection scheme for data assimilation purposes: Description and initial test. Quart. J. Roy.. Meteor. Soc., 131, 409-436 Lopez., P., Benedetti, A., Bauer, P., Janisková, M. and Köhler, M.., 2006: Experimental 2D-Var assimilation of ARM cloud and precipitation observations. Quart. J. Roy. Meteor. Soc., 132, 1325-1347 Lopez, P., 2007: Cloud and precipitation parametrizations in modeling and in variational data assimilation. A review. J. Atmos. Sc., 64, 3770-3788 Lopez, P. and Bauer, P., 2007: 1D+4D-Var assimilation of NCEP Stage IV radar and gauge hourly precipitation data at ECMWF. Mon. Wea. Rev., 135, 2506-2524 Mahfouf, J.-F., 1999: Influence of physical processes on the tangent-linear approximation. Tellus, 51A, 147-166 Marécal, V. and Mahfouf, J.-F., 2000: Variational retrieval of temperature and humidity profiles from TRMM precipitation data. Mon. Wea. Rev., 128, 3853-3866 Marécal, V. and Mahfouf, J.-F., 2002: Four-dimensional variational assimilation of total column water vapour in rainy areas. Mon. Wea. Rev., 130, 43-58 Marécal, V. and Mahfouf, J.-F., 2003: Experiments on 4D-Var assimilation of rainfall data using an incremental formulation. Quart. J. Roy. Meteor. Soc., 129, 3137-3160 Moreau, E., Lopez, P., Bauer, P., Tompkins, A.M., Janisková, M. And Chevallier, F., 2004: Variational retrieval of temperature and humidity profiles using rain rates versus microwave brightness temperatures. Quart. J. Roy. Meteor. Soc., 130, 827-852 Tompkins, A.M. and Janisková, M., 2004: A cloud scheme for data assimilation: Description and initial tests. Quart. J. Roy. Meteor. Soc., 130, 2495-2518 Zhu, J. and Kamachi, M., 2000: The role of the time step size in numerical stability of tangent linear models. Mon. Wea. Rev., 128, 1562-1572
Reading, UK Parametrizations and data assimilation © ECMWF 2012 Marta JANISKOVÁ ECMWF Parametrizations and data assimilation.
Reading Parametrizations and data assimilation Marta JANISKOVÁ ECMWF
Parameterizations in Data Assimilation Philippe Lopez Physical Aspects Section, Research Department, ECMWF (Room 113) ECMWF Training Course May 2010.
Parametrizations in Data Assimilation ECMWF Training Course May 2012 Philippe Lopez Physical Aspects Section, Research Department, ECMWF (Room 113)
EWGLAM Oct Some recent developments in the ECMWF model Mariano Hortal ECMWF Thanks to: A. Beljars (physics), E. Holm (humidity analysis)
Slide 1 IPWG, Beijing, October 2008 Slide 1 Assimilation of rain and cloud-affected microwave radiances at ECMWF Alan Geer, Peter Bauer, Philippe.
The 2nd International Workshop on GPM Ground Validation TAIPEI, Taiwan, September 2005 GV for ECMWF's Data Assimilation Research Peter
USE OF PRECIPITATION INFORMATION FROM SPACEBORNE RADAR FOR VERIFICATION AND ASSIMILATION IN THE ECMWF MODEL A.Benedetti, P. Lopez, E. Moreau, P. Bauer,
1 The GEMS production systems and retrospective reanalysis Adrian Simmons.
Hou/JTST Exploring new pathways in precipitation assimilation Arthur Hou and Sara Zhang NASA Goddard Space Flight Center Symposium on the 50 th.
Polly Smith, Alison Fowler, Amos Lawless School of Mathematical and Physical Sciences, University of Reading Exploring coupled data assimilation using.
R. Forbes, 17 Nov 09 ECMWF Clouds and Radiation University of Reading ECMWF Cloud and Radiation Parametrization: Recent Activities Richard Forbes, Maike.
Mesoscale Assimilation of Rain-Affected Observations Clark Amerault National Research Council Postdoctoral Associate - Naval Research Laboratory, Monterey,
Training course: boundary layer; surface layer Parametrization of surface fluxes: Outline Surface layer (Monin Obukhov) similarity Surface fluxes: Alternative.
TRMM Tropical Rainfall Measurement (Mission). Why TRMM? n Tropical Rainfall Measuring Mission (TRMM) is a joint US-Japan study initiated in 1997 to study.
Robin Hogan, Richard Allan, Nicky Chalmers, Thorwald Stein, Julien Delanoë University of Reading How accurate are the radiative properties of ice clouds.
Introduction to data assimilation in meteorology Pierre Brousseau, Ludovic Auger ATMO 08,Alghero, september 2008.
Robin Hogan Anthony Illingworth, Sarah Kew, Jean- Jacques Morcrette, Itumeleng Kgololo, Joe Daron, Anna Townsend Quantifying sub-grid cloud structure and.
DYMECS: Dynamical and Microphysical Evolution of Convective Storms (NERC Standard Grant) University of Reading: Robin Hogan, Bob Plant, Thorwald Stein,
1 Numerical Weather Prediction Parameterization of diabatic processes Convection III The ECMWF convection scheme Christian Jakob and Peter Bechtold.
Page 1 of 26 A PV control variable Ross Bannister* Mike Cullen *Data Assimilation Research Centre, Univ. Reading, UK Met Office, Exeter, UK.
Robin Hogan Julien Delanoe University of Reading Remote sensing of ice clouds from space.
1 00/XXXX © Crown copyright Carol Roadnight, Peter Clark Met Office, JCMM Halliwell Representing convection in convective scale NWP models : An idealised.
A step toward operational use of AMSR-E horizontal polarized radiance in JMA global data assimilation system Masahiro Kazumori Numerical Prediction Division.
3rd Workshop of the International Precipitation Working Group (IPWG) Melbourne, Australia, 23-27/10/ day forecasts for Melbourne, initialized on.
Sensitivity Studies (1): Motivation Theoretical background. Sensitivity of the Lorenz model. Thomas Jung ECMWF, Reading, UK
Parametrization of PBL outer layer Martin Köhler Overview of models Bulk models local K-closure K-profile closure TKE closure.
Rossana Dragani Using and evaluating PROMOTE services at ECMWF PROMOTE User Meeting Nice, 16 March 2009.
Assimilation Algorithms: Tangent Linear and Adjoint models Yannick Trémolet ECMWF Data Assimilation Training Course March 2006.
Robin Hogan Ewan OConnor Anthony Illingworth Department of Meteorology, University of Reading UK PDFs of humidity and cloud water content from Raman lidar.
Modern Era Retrospective-analysis for Research and Applications: Introduction to NASA’s Modern Era Retrospective-analysis for Research and Applications:
Data assimilation for validation of climate modeling systems Pierre Gauthier Department of Earth and Atmospheric Sciences Université du Québec à Montréal.
Anthony Illingworth, + Robin Hogan, Ewan OConnor, U of Reading, UK and the CloudNET team (F, D, NL, S, Su). Reading: 19 Feb 08 – Meeting with Met office.
Ewan OConnor, Robin Hogan, Anthony Illingworth, Nicolas Gaussiat Radar/lidar observations of boundary layer clouds.
ECMWF CO 2 Data Assimilation at ECMWF Richard Engelen European Centre for Medium-Range Weather Forecasts Reading, United Kingdom Many thanks to Phil Watts,
KMD Consortium for Small-Scale Modeling (COSMO) Strengths and Weaknesses Vincent N. Sakwa RSMC, Nairobi.
© Crown copyright 2006Page 1 CFMIP II sensitivity experiments Mark Webb (Met Office Hadley Centre) Johannes Quaas (MPI) Tomoo Ogura (NIES) With thanks.
Integrated Profiling at the AMF Kerstin Ebell 1, Ulrich Löhnert 1, Susanne Crewell 1, Dave Turner 2 1 Institute for Geophysics and Meteorology, University.
Preliminary results from assimilation of GPS radio occultation data in WRF using an ensemble filter H. Liu, J. Anderson, B. Kuo, C. Snyder, A. Caya IMAGe.
UCLA Vector Radiative Transfer Models for Application to Satellite Data Assimilation K. N. Liou, S. C. Ou, Y. Takano and Q. Yue Department of Atmospheric.
Recent Evidence for Reduced Climate Sensitivity Roy W. Spencer, Ph.D Principal Research Scientist The University of Alabama In Huntsville March 4, 2008.
Slide 1 The Wave Model ECMWF, Reading, UK. Slide 2The Wave Model (ECWAM) Lecture notes can be reached at: - News & Events - Training.
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A thermodynamic model for estimating sea and lake ice thickness with optical satellite data Student presentation for GGS656 Sanmei Li April 17, 2012.
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