Jean-Francois Geleyn, Jean-Francois Mahfouf

Slides:



Advertisements
Similar presentations
Assimilation of radar data - research plan
Advertisements

Parametrization of surface fluxes: Outline
Tuning and Validation of Ocean Mixed Layer Models David Acreman.
Evaluation of HARMONIE using a single column model in the KNMI
1D Dice Experiment at Meteo-France and LES preliminary result E. Bazile, I. Beau, F. Couvreux and P. Le Moigne (CNRM/GAME) DICE Workshop Exeter October.
The use of the NWCSAF High Resolution Wind product in the mesoscale AROME model at the Hungarian Meteorological Service Máté Mile, Mária Putsay and Márta.
1 14tn ALADIN Workshop - Innsbruck Testing the new ACDRAG scheme in the ALPIA environment A tricky ping-ponggame Bart Catry Ghent University Belgium F.
1 ATOVS and SSM/I assimilation at the Met Office Stephen English, Dave Jones, Andrew Smith, Fiona Hilton and Keith Whyte.
Some Innovative Applications and Approaches Using Nudging Four Dimensional Data Assimilation: Lili Lei and David R. Stauffer Dept. of Meteorology, Penn.
Brian Ancell, Cliff Mass, Gregory J. Hakim University of Washington
Experimenting with the LETKF in a dispersion model coupled with the Lorenz 96 model Author: Félix Carrasco, PhD Student at University of Buenos Aires,
LAMEPS Development and Plan of ALADIN-LACE Yong Wang et al. Speaker: Harald Seidl ZAMG, Austria Thanks: Météo France, LACE, NCEP.
Overview of the present HIRLAM surface assimilation Mainly taken from: HIRLAM Technical Report No. 58.
Ensemble-variational sea ice data assimilation Anna Shlyaeva, Mark Buehner, Alain Caya, Data Assimilation and Satellite Meteorology Research Jean-Francois.
Advances in the use of observations in the ALADIN/HU 3D-Var system Roger RANDRIAMAMPIANINA, Regina SZOTÁK and Gabriella Csima Hungarian Meteorological.
The ELDAS system implementation and output production at Météo-France Gianpaolo BALSAMO, François BOUYSSEL, Jöel NOILHAN ELDAS 2nd progress meetin – INM.
WWOSC 2014, Aug 16 – 21, Montreal 1 Impact of initial ensemble perturbations provided by convective-scale ensemble data assimilation in the COSMO-DE model.
Verification and Case Studies for Urban Effects in HIRLAM Numerical Weather Forecasting A. Baklanov, A. Mahura, C. Petersen, N.W. Nielsen, B. Amstrup Danish.
The data assimilation system implementation and output for validation Gianpaolo BALSAMO, François BOUYSSEL, Jöel NOILHAN ELDAS Data Coordination meetin.
Evaluation and Application of Air Quality Model System in Shanghai Qian Wang 1, Qingyan Fu 1, Yufei Zou 1, Yanmin Huang 1, Huxiong Cui 1, Junming Zhao.
A canopy model of mean winds through urban areas O. COCEAL and S. E. BELCHER University of Reading, UK.
Data assimilation and forecasting the weather (!) Eugenia Kalnay and many friends University of Maryland.
Development and Testing of a Regional GSI-Based EnKF-Hybrid System for the Rapid Refresh Configuration Yujie Pan 1, Kefeng Zhu 1, Ming Xue 1,2, Xuguang.
Weather forecasting by computer Michael Revell NIWA
Deutscher Wetterdienst Vertical localization issues in LETKF Breogan Gomez, Andreas Rhodin, Hendrik Reich.
Ensemble Kalman Filter in a boundary layer 1D numerical model Samuel Rémy and Thierry Bergot (Météo-France) Workshop on ensemble methods in meteorology.
Ensemble assimilation & prediction at Météo-France Loïk Berre & Laurent Descamps.
Page 1© Crown copyright 2004 SRNWP Lead Centre Report on Data Assimilation 2005 for EWGLAM/SRNWP Annual Meeting October 2005, Ljubljana, Slovenia.
1 Large Eddy Simulation of Stable Boundary Layers with a prognostic subgrid TKE equation 8 th Annual Meeting of the EMS, Amsterdam, 2008 Stephan R. de.
WP 3: DATA ASSIMILATION SMHI/FMI Status report 3rd CARPE DIEM meeting, University of Essex, Colchester, 9-10 January 2003 Structure SMHI/FMI plans for.
Assess Observation Impacts in the Hybrid GSI-EnKF Data Assimilation Systems for NCEP Global Forecast System Model Through OSE and Ensemble Based Observation.
Denis Blinov, G. Rivin, M. Nikitin, A. Bundel, A. Kirsanov, I. Rozinkina Data assimilation system based on nudging for COSMO-Ru7/So2 1COSMO GM Sibiu, 2.
ALADIN Workshop 2004, Innsbruck 1 Subjective evaluation of different versions of ALADIN/HU Helga Tóth, HMS.
AROME/ALADIN discussions, Prague Data assimilation and variational algorithms in ALADIN 11-12th April, Data assimilation and variational algorithms.
10 th COSMO General Meeting, Krakow, September 2008 Recent work on pressure bias problem Lucio TORRISI Italian Met. Service CNMCA – Pratica di Mare.
11th EMS & 10th ECAM Berlin, Deutschland The influence of the new ECMWF Ensemble Prediction System resolution on wind power forecast accuracy and uncertainty.
NCAR April 1 st 2003 Mesoscale and Microscale Meteorology Data Assimilation in AMPS Dale Barker S. Rizvi, and M. Duda MMM Division, NCAR
Page 1 Developments in regional DA Oct 2007 © Crown copyright 2007 Mark Naylor, Bruce Macpherson, Richard Renshaw, Gareth Dow Data Assimilation and Ensembles,
Consideration of Representativeness Error In an OSSE Framework Before deciding specifically how observations are to be simulated, how instrument and representativeness.
1 3D-Var assimilation of CHAMP measurements at the Met Office Sean Healy, Adrian Jupp and Christian Marquardt.
CWB Midterm Review 2011 Forecast Applications Branch NOAA ESRL/GSD.
Global vs mesoscale ATOVS assimilation at the Met Office Global Large obs error (4 K) NESDIS 1B radiances NOAA-15 & 16 HIRS and AMSU thinned to 154 km.
ALADIN 3DVAR at the Hungarian Meteorological Service 1 _____________________________________________________________________________________ 27th EWGLAM.
Verification of wind gust forecasts Javier Calvo and Gema Morales HIRMAM /ALADIN ASM Utrecht May 11-15, 2009.
Study of a bi-dimensional variational assimilation for soil moisture initialization in the mesoscale NWP model ALADIN Gianpaolo BALSAMO, François BOUYSSEL,
E. Bazile, C. Soci, F. Besson & ?? UERRA meeting Exeter, March 2014 Surface Re-Analysis and Uncertainties for.
A revised formulation of the COSMO surface-to-atmosphere transfer scheme Matthias Raschendorfer COSMO Offenbach 2009 Matthias Raschendorfer.
EWGLAM/SRNWP, Athens, 28 Sept – 1 Oct 2009 Developments in data assimilation for LACE Gergely Bölöni, Edit Adamcsek, László Szabó, Alena Trojáková, Xin.
Soil analysis scheme for AROME within SURFEX
Recent data assimilation activities at the Hungarian Meteorological Service Gergely Bölöni, Edit Adamcsek, Alena Trojáková, László Szabó ALADIN WS /
ASCAT soil moisture data assimilation with SURFEX
Progress in development of HARMONIE 3D-Var and 4D-Var Contributions from Magnus Lindskog, Roger Randriamampianina, Ulf Andrae, Ole Vignes, Carlos Geijo,
Met Office Ensemble System Current Status and Plans
met.no – SMHI experiences with ALARO
“Consolidation of the Surface-to-Atmosphere Transfer-scheme: ConSAT
Overview of AROME status and plans
Recent changes in the ALADIN operational suite
Weak constraint 4D-Var at ECMWF
S.Alessandrini, S.Sperati, G.Decimi,
A New Scheme for Chaotic-Attractor-Theory Oriented Data Assimilation
Retuning the turbulent gust component in the COSMO model
Vertical localization issues in LETKF
A coupled ensemble data assimilation system for seasonal prediction
FSOI adapted for used with 4D-EnVar
Statistical Evaluation of the Response of Intensity
Integration of NCAR DART-EnKF to NCAR-ATEC multi-model, multi-physics and mutli- perturbantion ensemble-RTFDDA (E-4DWX) forecasting system Linlin Pan 1,
Impact of Assimilating AMSU-A Radiances on forecasts of 2008 Atlantic TCs Initialized with a limited-area EnKF Zhiquan Liu, Craig Schwartz, Chris Snyder,
Operational Aladin-Belgium
Project Team: Mark Buehner Cecilien Charette Bin He Peter Houtekamer
Latest Results on Variational Soil Moisture Initialisation
Presentation transcript:

Jean-Francois Geleyn, Jean-Francois Mahfouf New interpolation formula in stable situation for the calculation of diagnostic fields at measurement height László Kullmann with supervision of Jean-Francois Geleyn, Jean-Francois Mahfouf

ALADIN/HIRLAM WORKSHOP Outline Description and of the current diagnostic formulas Paulson, Geleyn88, Canopy New interpolation formula determination of the new formula comparison with Geleyn88 and Canopy Application in DA Use the new formula as the TL/AD version of Canopy Conclusion 12-15 May 2009 ALADIN/HIRLAM WORKSHOP

Description of current diagnostic schemes in SURFEX Paulson scheme fixed function, extrapolation from lowest model level  Ts inconsistency Geleyn88 scheme fixed function with one parameter, interpolation between surface, lowest model level  underestimation of T2m in stable situation Canopy scheme (V. Masson) 6 SBL levels between lowest model level & surface, solving prognostic equation (1d) with LS forcing + turbulence + drag  T2m is prognostic (TL/AD problem) 12-15 May 2009 ALADIN/HIRLAM WORKSHOP

New interpolation formula neutral stable/unstable use prescribed  function (like in Paulson scheme) use the interpolation technique (JFG88) () contains  parameter  determine  from s(zN) 12-15 May 2009 ALADIN/HIRLAM WORKSHOP

New interpolation formula Geleyn88 function: Gratchev et al. function (proposed for stable condition): where ah=5, ch=3   too complicated... simplified function (ch=2): 12-15 May 2009 ALADIN/HIRLAM WORKSHOP

New interpolation formula (2) The Gratchev formula for wind is too complicated  use original formula which already gives good results New formula has a tuning parameter (ah)  determines the shape of the profile Determination of ah by fitting to observations  big variance: ah  [2,20]  use the proposed value ah=5 ah z [m] T [K] 12-15 May 2009 ALADIN/HIRLAM WORKSHOP

Comparison of new/old formula T2m RMSE T2m BIAS RH2m BIAS RH2m RMSE AROME forecast 04/12/2007-24/12/2007 only nature tile Canopy Geleyn88 New form. New formula gives similar results as Canopy 12-15 May 2009 ALADIN/HIRLAM WORKSHOP

ALADIN/HIRLAM WORKSHOP Tested in ALADIN (by Alena Trojakova) RMSE BIAS ah =  ah = 5 ah = 3 RMSE BIAS 12-15 May 2009 ALADIN/HIRLAM WORKSHOP

Testing the TL/AD of Canopy Check if new formula is able to describe the TL of Canopy Canopy scheme is prognostic  change in TN does not cause immediate change in T2m Checking the speed of relaxation Offline Surfex run random perturbation of forcing fields keep forcing fields constant for relaxation period surface fields are kept constant Compare TL of new formula with the change in Canopy field: T(t2-1)-T(t1-1) determine ah by fitting to Canopy profile 12-15 May 2009 ALADIN/HIRLAM WORKSHOP

ALADIN/HIRLAM WORKSHOP T2m RH2m wind 10m ah: fitted ah: fitted ah: fitted ah= 1 ah= 1 ah= 1 ah=  ah=  ah=  T2m New 12-15 May 2009 T2m Canopy ALADIN/HIRLAM WORKSHOP RH2m Canopy U10m Canopy

ALADIN/HIRLAM WORKSHOP AROME 3dVar test Comparing results using TL/AD of old (G88) and new formula 6h assimilation cycling Observations: TEMP, AIREP, AMSU-A, AMSU-B, SYNOP (T2, RH2, U10) direct obs. operator is the Canopy scheme (from guess) Ensemble B matrix Small differences on average, disappears after few hours 12-15 May 2009 ALADIN/HIRLAM WORKSHOP

ALADIN/HIRLAM WORKSHOP time period: 05/12/2007 – 22/12/2007 bias T2m rmse T2m ah =  ah = 5 ah = 1 bias RH2m rmse RH2m 12-15 May 2009 ALADIN/HIRLAM WORKSHOP

ALADIN/HIRLAM WORKSHOP Conclusion Determination of new interpolation formula The new formula gives similar result as Canopy or Paulson in stable case TL of new formula approximates well the TL of Canopy Using TL/AD of new formula in assimilation has small impact compared to the old one 12-15 May 2009 ALADIN/HIRLAM WORKSHOP