On the importance of land surface emissivity to assimilate low level humidity and temperature observations over land

Transcription

1 CNRM / GAME F. KARBOU 2nd Workshop on Remote Sensing and Modeling of Surface Properties Slide 1 On the importance of land surface emissivity to assimilate low level humidity and temperature observations over land F. Karbou In collaboration with: F. Rabier, J-P. Lafore, E. Gerard, J-L. Redelsperger, O. Bock CNRM-GAME, Météo-France & CNRS IGN

2 CNRM / GAME F. KARBOU 2nd Workshop on Remote Sensing and Modeling of Surface Properties Slide 2 1- Introduction a) AMSU-A & AMSU-B Indirect vertical measurements of Temperature & Humidity: AMSU-A AMSU-B

3 CNRM / GAME F. KARBOU 2nd Workshop on Remote Sensing and Modeling of Surface Properties Slide 3 1- Introduction a) AMSU-A & AMSU-B Indirect vertical measurements of Temperature & Humidity: AMSU-A AMSU-B

4 CNRM / GAME F. KARBOU 2nd Workshop on Remote Sensing and Modeling of Surface Properties Slide 4 1- Introduction a) AMSU-A & AMSU-B Indirect vertical measurements of Temperature & Humidity: ( 10km ) AMSU-A, Ch7, Temperature

5 CNRM / GAME F. KARBOU 2nd Workshop on Remote Sensing and Modeling of Surface Properties Slide 5 1- Introduction b) Land surface emissivity Land surface emissivity : «dynamical land emissivity parameterization» operational since July 2008 (Karbou et ( 2006 al. Land emissivity is computed from selected surface channels (AMSU-A ch3 (50 GHz) (( GHz and from AMSU-B ch1 (89 Emissivity is dynamically updated for each atmo. & surface situations ( 2004 al. Interfaced with RTTOV (Eyre 1991; Saunders et al. 1999; Matricardi et Large improvement of RTTOV performances (bias, std, correlations)

6 CNRM / GAME F. KARBOU 2nd Workshop on Remote Sensing and Modeling of Surface Properties Slide 6 1- Introduction b) Land surface emissivity «dynamical land emissivity parameterization» operational since July 2008 (Karbou et ( 2006 al. Correlations between Obs and RTTOV Sim., AMSU-A ch4, August 2006 CTL

7 CNRM / GAME F. KARBOU 2nd Workshop on Remote Sensing and Modeling of Surface Properties Slide 7 1- Introduction b) Land surface emissivity «dynamical land emissivity parameterization» operational since July 2008 (Karbou et ( 2006 al. Correlations between Obs and RTTOV Sim., AMSU-A ch4, August 2006 CTL CTL + dynamical emis.

8 CNRM / GAME F. KARBOU 2nd Workshop on Remote Sensing and Modeling of Surface Properties Slide 8 1- Introduction b) Land surface emissivity First assimilation experiments to study the impact of assimilating surface sensitive observations AMSU-A AMSU-B 4 experiments, 2month period (summer 2006), different configurations for selecting AMSU surface sensitive channels

9 CNRM / GAME F. KARBOU 2nd Workshop on Remote Sensing and Modeling of Surface Properties Slide 9 2- Assimilation experiments a) Observation statistics : better HIRS, SSM/I, RS ( GHz Fg-departures (obs-guess), AMSU-B channel 5 (183.31±7 CTL EXP: CTL + AMB2, AMB5 AMA4

10 CNRM / GAME F. KARBOU 2nd Workshop on Remote Sensing and Modeling of Surface Properties Slide Assimilation experiments b) Forecast scores Scores geopotential height / Radiosondes, 48h N20 AUS-NZ CTL --- BIAS RMSE EXP --- BIAS RMSE S20 TROPIQ

11 CNRM / GAME F. KARBOU 2nd Workshop on Remote Sensing and Modeling of Surface Properties Slide Assimilation experiments b) Forecast scores 24h Differences of geopotential forecast errors with respect to ECMWF analyses (CTL-EXP), 200hPa, 1month Smaller errors in EXP

12 CNRM / GAME F. KARBOU 2nd Workshop on Remote Sensing and Modeling of Surface Properties Slide Assimilation experiments b) Forecast scores 24h Differences of geopotential forecast errors with respect to ECMWF analyses (CTL-EXP), 200hPa, 1month 48h Smaller errors in EXP Smaller errors in EXP

13 CNRM / GAME F. KARBOU 2nd Workshop on Remote Sensing and Modeling of Surface Properties Slide Assimilation experiments b) Forecast scores 24h Differences of geopotential forecast errors with respect to ECMWF analyses (CTL-EXP), 200hPa, 1month 48h Smaller errors in EXP 72h Smaller errors in EXP

14 CNRM / GAME F. KARBOU 2nd Workshop on Remote Sensing and Modeling of Surface Properties Slide Assimilation experiments c) Impact on humidity analysis EXP-CTL CTL

15 CNRM / GAME F. KARBOU 2nd Workshop on Remote Sensing and Modeling of Surface Properties Slide Assimilation experiments c) Impact on humidity analysis TCWV Daily time series at OUAG

16 CNRM / GAME F. KARBOU 2nd Workshop on Remote Sensing and Modeling of Surface Properties Slide Assimilation experiments c) Impact on humidity analysis TCWV diurnal cycle at TOMB, 45 days

17 CNRM / GAME F. KARBOU 2nd Workshop on Remote Sensing and Modeling of Surface Properties Slide 17 Conclusion & perspectives A good representation of land surface emissivity motivated assimilation studies to assimilate ( blacklisted low level humidity & temperature observations (usually The assimilation of these channels: Positive impact in scores % radiosondes : all domains Positive impact in scores % ECMWF analysis (500hPa, 200hPa): all domains Large impact on humidity analysis (& temp., wind) over the Tropics: low to mid-levels TCWV Change evaluated against independent GPS measurements Change in OLR and rain forecasts in better agreement with independent data Emissivity is one issue but surface temperature is as important as emissivity (see E. Gerard's ( talk ( Forecasting More results in Karbou et al. 2009a-b (revised for Weather and

18 CNRM / GAME F. KARBOU 2nd Workshop on Remote Sensing and Modeling of Surface Properties Slide 18 Conclusion & perspectives Next steps: More in depth studies to better understand the humidity change in the Tropics (in ( ECMWF ) collaboration with P. Bauer Observation System Experiments (OSE): MERIS (TCWV) versus AMSU-B MERIS-CTL AMSUB-CTL