Progress report and planned activities THORPEX working group on Data Assimilation and Observing Strategies

Transcription

1 Progress report and planned activities THORPEX working group on Data Assimilation and Observing Strategies Pierre Gauthier (UQAM, Canada,Co-chair) Florence Rabier (Météo-France and CNRS, France, Co-chair) Carla Cardinali (ECMWF, Int) Ron Gelaro (GMAO, USA) Ko Koizumi (JMA, Japan) Rolf Langland (NRL, USA) Andrew Lorenc (Met Office, UK) Peter Steinle (CAWCR, Australia) Mickael Tsyrulnikov (HRCR, Russia)

2 Outline General progress AMMA Intercomparison experiment and T-PARC T Report on WMO Data Impact Workshop Plans

3 Report on work performed Impact of observations Guidance for observation campaigns and the configuration of the Global Observing system Assessment of the value of targeted observations papers by Cardinali, Kelly and Buizza Evaluation of observation impact with different systems A-TreC, Papers by Petersen, Fourrié, Langland, Weissman and Cardinali AMMA IPY (talk by TE Nordeng) Proposed intercomparison experiment in the context of T-PARC Improving the use of satellite data Use of sensitivity information to do adaptive data selection (papers by ( QJ Dando et al, Interest group for data assimilation daos-ig@lists.cmc.ec.gc.ca Promote activities (. EMS Various conferences (AMS/EUMETSAT, Paper published in NPG in January 2008.

4 AMMA A few results obtained in the AMMA-THORPEX WG, much more to come in JL Redelsperger s talk tomorrow

5 The ECMWF AMMA reanalysis Anna Agustí-Panareda, Carla Cardinali, Jean-Philippe Lafore, Period: 1 May 30 September 2006 Resolution: T511 (~40 km), L91 Extra data used: sonde profiles of wind, temperature and humidity extracted from the AMMA database IFS cycle with improved physics: CY32r3 (Bechtold et al., ECMWF ( pp. Newsletter No. 114, Winter 2007/08, AMMA radiosonde humidity bias correction (Agustí-Panareda et ( Q.J.R.Meteorol.Soc al. 2008, submitted to Agusti-Panareda et al

6 Radiosonde RH Bias correction Well-documented dry bias for Vaisala sonde types (e.g. Wang et al., 2002, Nuret et al., 2008). Motivation: In West Africa many radiosondes are located within a region of strong low-level moisture gradient and there is lack of ppn in the short-range forecast over Sahel. Can be used in data impact studies of enhanced AMMA radiosonde network, AMMA reanalysis experiment and water budget studies within the AMMA project. Based on the ECMWF operational RS bias correction implemented in CY32r3. Main differences between AMMA and OPER. RS RH bias correction: Takes into account the dependence of bias on the observed RH values, which is very important in the Sahel because of its pronounced seasonal cycle. Agusti-Panareda et al

7 Radiosonde (RS) RH Bias correction: RESULTS Comparison with GPS TCWV RS-GPS: BIAS UNCORRECTED RS CORRECTED RS Olivier Bock Agusti-Panareda et al

8 Impact of radiosonde bias correction: RESULTS Mean total daily PPN FC (T+42-T+18) [mm/day] 1 to 31 Aug 2006, 12 UTC RSBIAS CORRECTION CNTRL OBS: RFE 2.0 (NOAA ( CPC CNTRL RSBIAS CORRECTION Agusti-Panareda et al

9 CNTR: data available on GTS AMMA: additional RS profiles AMMABC: with ECMWF special bias correction PreAMMA: with a 2005 RS network Similar results obtained at Météo-France: monthly averaged RR better with AMMA data, and with bias correction Faccani et al

10 DFS= degrees of freedom for signal DFS =Tr (δh(x( a )/ δy) Calculated for each station, averaged August 2006 More influence Large impact of additional AMMA data Faccani et al

11 Impact of assimilating low-level level humidity observations over land on the African Monsoon during AMMA Karbou et al Assimilation of MW observations over land New methods for estimating the land surface emissivity (Karbou et al. 2006) operational at Météo-France since July First trials: assimilate low-level level humidity observations from AMSU-B B over land (still ( rejected Control Experiment Density of assimilated AMSU-B Ch5 during August

12 Impact of assimilating low-level level humidity observations over land on the African Monsoon during AMMA Impact on analyses and forecasts ( SSM/I Improvement of the fit to humidity observations (HIRS, Objective scores% radiosondes: significantly positive Large impact on the analyses of humidity, temperature, wind (surface to 500 hpa) over tropics implying big changes in the Monsoon flux Moistening of the Atmo. In EXP Drying of the atmos. In EXP From 01/08/2006 to 14/09/2006 Karbou et al

13 Impact of assimilating low-level level humidity observations over land on the African Monsoon during AMMA 45 days, 00h Temperature differences at 950 hpa, 45 days, 00h

14 Case studies evaluated locally 600hPA relative humidity analysis on 25 thjuly 2006 at 1800 Conventional data + Satellite CSR data Conventional data only Mumba et al

15 Summary of AMMA results Humidity bias correction over AMMA region is beneficial Significant impact of additional AMMA RS data on the analysis and on RR Using more satellite data over land also has a large impact in the Tropics More results to come in a special issue Weather and Forecasting

16 Intercomparison of sensitivity to observations in the context of the THORPEX Pacific-Asia regional campaign (T-PARC) Contributions from Carla Cardinali (ECMWF), Ron Gelaro (GMAO), Rolf Langland (NRL), Pat Harr (NPS), Florence Rabier and Gérald Desroziers (Météo-France) Stéphane Laroche and Simon Pellerin (Environment Canada)

17 The observation impact intercomparison experiment Baseline experiment Common set of observations assimilated by all centres Assimilation and model configurations Metrics to measure the impact of observations Selection of period Winter phase of T-PARC: December 2008 to February 2009 Period selected: January 2007 observations available were closer to what would be available during T-PARC

18 Observations assimilated by NRL, GMAO and ECMWF (also at Météo-France and Environment Canada) Radiosondes Dropsondes Land surface stations (all data except winds and humidity) Ship surface (winds( and p s ) Aircraft (all data except humidity) AMV from geostationary satellites (no rapid-scan winds) MODIS winds AMSU-A A radiances QuikScat

19 Comparison of the characteristics of the systems NRL GMAO ECMWF Analysis T239L30 3D-Var 0.5ºx0.67 x0.67ºl72 6-h h 3D-Var T255L60 12-h h 4D- Var Forecasts T239L30 spectral 0.5ºx0.67 x0.67º L72 Finite Volume model T255L60 spectral

20 Adjoint of Assimilation Equation Baker and Daley 2000 (QJRMS) 20 Sensitivity to Observations: J y = [ + ] T 1 HPbH R HPb T K J x a Adjoint of forecast model produces sensitivity to x a Sensitivity to Background: J J T J = H x x y b a

21 Observation Impact Methodology (Langland and Baker, 2004) 21 OBSERVATIONS ASSIMILATED e 30 e 24 00UTC + 24h Observations move the model state from the background trajectory to the new analysis trajectory The difference in forecast error norms, 24 30, is due to the combined impact of all observations assimilated at 00UTC e e

22 Evaluation Evaluation of the impact of of the impact of observations observations Measure Measure of the of the reduction reduction in in forecast forecast error error ( ) ( ) T t t b b a a b a T t t t b t b T t t t a t a b a b a J J t J t J e + = + = + = + = = = 0 0 0, 2 1, 2 1, 2 1 x x x x x x x x x x x x Evaluation Evaluation at at the initial time the initial time ( ) + = b b T b a a T a T b b a J J, e x L x L K H x y

23 Sensitivity with respect to analysis Configuration of the measure of forecast error Departure with respect to a verifying analysis (each centre uses its own) Dry adjoint model 24h (third order) sensitivity gradient (LB04), dry forecast error norm, from surface to 150hPa Forecast Sensitivity to Observation impact at 0,6,12,18 (3D-Var or 4D-Var 6h) or 00, 12 (4D-Var 12 h)

24 Total observation impact at 00 UTC NAVDAS 24h Ob Impact Jan z+06z Ships SatWind SSMIspd RaobDsnd Qscat Windsat MODIS LandSfc Aircraft AMSUA

25 Impact per observation NAVDAS 24h Impact Per Ob Jan z+06z Ships SatWind SSMIspd Raobs Qscat Windsat MODIS LandSfc Aircraft AMSUA ECMWF 24 h Impact per Obs Jan UTC Ships SatWind RaobDsnd Qscat MODIS LandSfc Aircra ft AMSU-A

26 WMO Workshop on impact studies Geneva, May 2008 Organized by the OPAG-IOS Participation of the DAOS Cardinali, Gauthier, Gelaro, Koizumi, Langland, Rabier, Steinle Preliminary results from the intercomparison experiment were presented by Cardinali, Gelaro and Langland DAOS-WG to provide input on the design of the global observing system Recommendation at the workshop to encourage the use the adjoint based method to get a more detailed assessment of the impact of observations Nice complement to OSEs

27 Combined Use of ADJ and OSEs (Gelaro et al., 2008) ADJ applied to various OSE members to examine how the mix of observations influences their impacts Removal of AMSUA results in large increase in AIRS (and other) impacts Removal of AIRS results in significant increase in AMSUA impact Removal of raobs results in significant increase in AMSUA, aircraft and other impacts (but not AIRS)

28 Combined Use of ADJ and OSEs (Gelaro et al., 2008) ADJ applied to various OSE members to examine how the mix of observations influences their impacts Removal of AMSUA results in large increase in AIRS impact in tropics Removal of wind observations results in significant decrease in AIRS impact in tropics (in fact, AIRS degrades forecast without satwinds!)

29 Summary and conclusion Value of data deployed during T-PARC Experiment aims at capturing the different stages of Tropical cyclones from their genesis to their migration into northern latitudes Value of data over the Pacific for the short to medium-range forecasts over Asia and North America Meteorological high-impact events in Asia and North America Data assimilation objectives Assess the impact of observations on deterministic and probabilistic forecasts Targeting techniques and adaptive satellite data assimilation Large sets of data will be made available that could be used to better use satellite data in those situations

30 PROJECTS RELATED TO THORPEX DAOS WG 1. Continue work on AMMA and IPY 2. Observation Impact Inter-comparison Study Rolf Langland, Ron Gelaro, Carla Cardinali, and Simon Pellerin (Environment Canada) Deliverable: journal paper in NRL: Re-analysis and evaluation of targeted and regular observations collected during TCS-08 (Aug-Sep 2008) and winter T-PARC (Jan-Feb 2009). Methods: OSEs and adjoint-based observation impact. 4. Comparison of systematic differences in analyses produced by operational forecast centers Differences are much larger in areas where analyses are based primarily on satellite observations. 5. Targeting methods for medium-range forecasts.

31 T-PARC driftsonde (Sept. 18, 2008)

32 PROJECTS RELATED TO THORPEX DAOS WG 1. Observation Impact Inter-comparison Study in collaboration with Ron Gelaro, Carla Cardinali, and Simon Pellerin. Deliverable: journal paper in Re-analysis and evaluation of targeted and regular observations collected during TCS-08 (Aug-Sep 2007) and winter T-PARC (Jan- Feb 2008). Methods: OSEs and adjoint-based observation impact. 3. Comparison of systematic differences in analyses produced by operational forecast centers. Differences are much larger in areas where analyses are based primarily on satellite observations. 4. Targeting methods for medium-range forecasts. Rolf Langland NRL-Marine Meteorology Division