1 / 24

F. Rabier, C. Faccani, N. Fourrié, F. Karbou, J-P Lafore, P. Moll, M. Nuret, J-L Redelsperger

Data assimilation experiments for AMMA, using radiosonde observations and satellite observations over land. F. Rabier, C. Faccani, N. Fourrié, F. Karbou, J-P Lafore, P. Moll, M. Nuret, J-L Redelsperger Météo-France and CNRS, Toulouse, France A. Agusti-Panareda ECMWF, Reading F. Hdidou

signa
Download Presentation

F. Rabier, C. Faccani, N. Fourrié, F. Karbou, J-P Lafore, P. Moll, M. Nuret, J-L Redelsperger

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Data assimilation experiments for AMMA, using radiosonde observations and satellite observations over land F. Rabier, C. Faccani, N. Fourrié, F. Karbou, J-P Lafore, P. Moll, M. Nuret, J-L Redelsperger Météo-France and CNRS, Toulouse, France A. Agusti-Panareda ECMWF, Reading F. Hdidou Direction de la Météorologie Nationale, Morocco O. Bock IGN, France

  2. AMMA: The African Monsoon Multidisciplinary Analysis Better understand the mechanisms of the African monsoon and prevent dramatic situations (Redelsperger et al, 2006) Enhanced observations over West Africa in 2006 In particular, major effort to enhance the radiosonde network (Parker et al, 2008)

  3. Impact of using the AMMA radiosonde dataset • New radiosonde stations • Enhanced time sampling • AMMA database: additional data which were not received in real time + enhanced vertical resolution • Bias correction for RH developed at ECMWF (Agusti-Panareda et al) • Data impact studies With various datasets, With and without RH bias correction Number of soundings provided on GTS in 2006 and 2005 Period: 15 July- 15 September, 0 and 12 UTC

  4. Impact on mean TCWV CNTR: data from GTS AMMA: from the AMMA database AMMABC: AMMA + bias correction PreAMMA: with a 2005 network NOAMMA: No Radiosonde data

  5. Validation of Total Column Water Vapour analyses: Comparison with GPS data at Tombouctou NO AMMA CNTR: data from GTS AMMA: from the AMMA database AMMABC: AMMA + bias correction PreAMMA: with a 2005 network NOAMMA: No Radiosonde data GPS: Observations AMMABC Observations Very poor performance of NO AMMA Best performance of AMMABC

  6. Impact on quantitative prediction of precipitation over Africa CNTR: data from GTS AMMA: from the AMMA database AMMABC: AMMA + bias correction PreAMMA: with a 2005 network NOAMMA: No Radiosonde data Higher scores for AMMABC Lowest scores for NO AMMA

  7. Downstream impact • Impact on geopotential at 500hPa, averaged over 45 days • 48hr forecasts: AMMABC vs PREAMMA

  8. 3 day range: AMMABC vs PREAMMA

  9. Fit to European RadiosondesScores at the 3 day range, PREAMMA versus AMMABC

  10. Energy source (1) Upwelling radiation Signal attenuated by theatmosphere (2) Downwelling radiation (3) Surface emission Surface (emissivity, temperature) Assimilating low-level humidity observations over land Microwave observations over land High emissivity (~1.0) Only channels that are the least sensitive to the surface are currently assimilated Remaining large uncertainties on land emissivity and skin temperature Top of Atmosphere Assimilation of MW observations over land New methods for estimating the land surface emissivity (Karbou et al. 2006) operational at Météo-France since July 2008. Karbou et al, 2009

  11. Impact of assimilating low-level humidity observations over land on the African Monsoon during AMMA • Improved emissivity parametrisation • Better simulation by the Radiative Transfer Model of the low-level peaking channels • Possibility to assimilate more channels • Experiments performed during AMMA in 2006 Control Experiment Density of assimilated AMSU-B Ch5 during August 2006

  12. Assimilation of humidity observations over land Assimilation of AMSU-B Ch2 (150 GHz) & Ch 5 (183±7 GHz) over land, 45 days TCWV (EXP) - TCWV (CTL) TCWV (CTL) Karbou et al, 2009

  13. Summary of AMMA results • Humidity bias correction (from ECMWF) over the AMMA region is beneficial • Significant positive impact of additional AMMA RS data on the humidity analysis and on precipitation over Africa • Positive downstream impact over Europe • Using more satellite data over land also has a large positive impact in the Tropics • Results in a AMMA special issue Weather and Forecasting

  14. 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

  15. Radiosonde (RS) RH Bias correction: RESULTSComparison with GPS TCWVRS-GPS: BIAS UNCORRECTED RS CORRECTED RS Olivier Bock Agusti-Panareda et al

  16. 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

  17. The ECMWF AMMA reanalysisAnna 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 Newsletter No. 114, Winter 2007/08, pp. 29-38)‏ • AMMA radiosonde humidity bias correction (Agustí-Panareda et al. 2008, submitted to Q.J.R.Meteorol.Soc)‏ Agusti-Panareda et al

  18. DFS= degrees of freedom for signalDFS =Tr (δH(xa)/ δy)Calculated for each station, averaged 1-15 August 2006Large impact of additional AMMA data More influence Faccani et al

  19. Assimilation of humidity observations over land Objective scores %radiosondes geopotential, 24-hr fcst, 34 cases, PRESSURE (hPa) CTL --- BIAS/TEMP __ EQM/TEMP EXP --- BIAS/TEMP __ EQM/TEMP PRESSURE (hPa) PRESSURE (hPa)

  20. The effect of land surface emissivity EXPERIMENT OPER Correlations between observations and RTTOV simulations AMSU-A ch4, August 2006 First step towards the assimilation of surface sensitive observations over land

  21. The effect of land surface emissivity A land emissivity parameterisation at Météo-France Number of assimilated ch7 AMSU-A data (Temperature 10 km) , August 2006 OPER EXPERIMENT Change of land emissivity

  22. Impact of emissivity on simulations Simulations from CTL Time series of global correlations between observations and RTTOV simulations over land : AMSU-B ch2 (150 GHz), August 2006 Simulations from EXP

  23. Impact on monthly mean precipitation over Africa Similar results obtained at ECMWF Monthly averaged RR better with bias correction AMMABC: AMMA + bias correction PreAMMA: with a 2005 network NOAMMA: No Radiosonde data CPC: Observations Very poor performance of NO AMMA Best performance of AMMABC Faccani et al, 2009

More Related