1. ITSC-12 Cloud processing in IASI context Lydie Lavanant Météo-France, Centre de Météorologie Spatiale, BP 147, 22300 Lannion Cedex France Purpose: Retrieval of cloud information and atmospheric profiles in cloudy conditions Steps: » Test case description » CO 2 -slicing method » Avhrr cloud description in IASI fov » IASI channel selection in cloudy conditions » Preliminary results of profile retrieval in cloudy conditions 01/03/02

2. Test Case Global IASI orbit simulation. Feb. 1996 13000 situations with: simulated IASI cloudy and noisy spectra 0.25 cm-1 (R. Rizzi model) Colocated atmospheric profiles: NWP analyses in T, Q, O on RTIASI levels Cloud description (cover, CLWV, CIWV) on 31 NWP levels Dataset provided by Eumetsat (ISSWG)

3. CO 2 slicing method Ref: Menzel and Stewart 1983, Smith and Frey 1990 [(Rclr – Rmeas) k / (Rclr – Rmeas) ref ] – [N  k (Rclr - Rcld) k / N  ref (Rclr - Rcld) ref ] = f pc Rmeas: measured radiance Rclr: clear radiance computed from the colocated forecast Rcld: black-body radiance at the cloud level n k= channel from 690 cm-1 to 810 cm-1 Ref= reference channel = 899.75 cm-1 For each channel k: cloud pressure = pressure which minimises equation P_co2 =  (p_co2(k) w 2 (k)) / Sw 2 W =  f pc /  lnp N  = (Rclr – Rmeas) ref / (Rclr - Rcld) ref Assumption: one thin cloud layer Rejections: (Rclr – Rmeas) < sqrt(2)*radiometric noise N  < 0

4. Preliminary results of CO2 method using CDS cloudy spectra 100-20023158.4102.4 200-30035847.1103.3 300-400396-9.7132.7 400-500658-31.8131.7 500-600802-51.3120.2 600-700913-97.2112.3 700-8001057-126.6125.9 800-900793-147.6135.8 900-10001534-129.9164.0 1000-105072-51.471.4

5. Method: Adapt RTIASI for implementing RTTOV7 cloudy routines developed by F. Chevallier and al. (2001) Simulate cloudy noisy IASI spectra Rmeas for all CDS situations using: » NPW profiles (T,H2O,.., CC, CLWV, CIWV) » radiometric noise Compute clear noisy radiances Rclr for the same fov using: » RTIASI clear » noisy NWP profiles (apply forecast errors) Apply CO2-slicing method

6. Examples of IASI cloudy spectra

7. Variation with the number of channels 1 cloud layer N  > 0.3

8. 1 cloud layer RTIASI cloudy + noise Profile= analysis 24 channels. resolution:5cm-1 Variation with emissivity

9. Several cloud layers RTIASI cloudy + noise Profile= analysis

10. Variation with emissivity 1 cloud layer RTIASI cloudy Profile=forecast

11. P_Co2,  _Co2 1 cloud layer RTIASI cloudy + noise Profile=forecast  = 0.2 - 0.25

12. Cloud top pressure CDS dataset cloud pressure CO2 retrieved cloud pressure

13. AVHRR Cloud mask in IASI fov Operational routine for HIRS fov (inside AAPP) » Based on a threshold technique applied. every AVHRR pixel in sounder fov. to various combinations of channels »Combinations of channels depend on:. geographical location of the pixel. solar illumination and viewing geometry »Thresholds computed in-line with:. constant values from experience. tabulated functions defined off-line through RTTOV simulations on climatological data-set. TWVC retrieved from colocated AMSU-A Current products: » percentage clear AVHRR in FOV » surface temperature from AVHRR split-window » black body cloud coverage in FOV » cloud top temperature for the black body layer » clear/cloudy flag for each AVHRR pixel Next version: »Ts, Tcld, Cloud type for each Avhrr pixel »-> number of clouds

14. AVHRR Cloud mask in IASI fov

15. AVHRR Cloud mask in IASI fov Validation over Europe correctly detectedCloudy targetsCloud free targets sea ; day 1774 (99.8%)584 (87.8%) sea ; glint269 (98.8%)72 (89%) sea ; twilight59 ( 98.3%)12 (90%) land ; day995 (99.5%)638 (80.9%) land ; twilight27 (100%)11 (67.4%) 7007 targets of 5x5 AVHRR pixels Noaa12, 14, 15 for 3 years 38 cloud types Mask comparison with visual analysis of satellite imagery by CMS nephanalysts March 2001 Clear landClear seaCloudy land Cv>50 Cloudy sea Cv>50 N93710313655 Bias (K)0.020.640.490.15 Std (K)0.990.390.980.65 Comparison of satellite obs. and Hirs 8 RTTOV6 Tbs using: * NWP profile, * AVHRR clear cover +Ts, * AVHRR black-body cloud cover +Tn

16. Channels selection and retrieval in clear conditions on CDS Rodgers DFS selection Guess error matrix = forecast Use a mean profile for mid-latitude conditions the 300 most informative channels Clear situations nbsit= 187 (1/10)

17. Channels selection above the cloud Select channels from the 300 most informative channels in clear conditions Ex: for p_cloud=850 hPa. uncontaminated channels above the cloud top level: about 65% channels selected. cloud contaminated channels with (Tbobs – Tbgucld) < 0.3K : about 85% channels selected

18. Profile retrieval in cloudy conditions. CDS dataset un-contaminated channels above the cloud 600 < p_cloud < 700 Nbsit= 132 (1/3) 700 < p_cloud < 800 Nbsit= 146 (1/4) 800 < p_cloud < 900 Nbsit= 138 (1/7) 900 < p_cloud < 1000 Nbsit= 166 (1/5)

19. with un- contaminated channels above cloud: 1DVar in clear conditions Profile retrieval in cloudy conditions with cloud information as control variable all P_cld > 800hPa 1807 situations (15% of situations) P_cld  _cld Cloud control variables: ln(p_cld),  cld Cloud guess: CO2 p_cld and  cld selected channels: Tbobs – Tbgucld < 0.3K -> more than 80% channels selected before 1d_var after 1d_var all selected channels: 1DVar cloudy forecast as background

20. Summary: Create IASI cloudy spectra using NWP analyses (T,H2o,.., CC, CLWV, CIWV) Use CO2 method to determine the cloud top pressure and emissivity Retrieve temperature profile in cloudy condition with CO2 cloud parameters as guess validate on CDS dataset Future: Consolidate the results on recent NWP data (with cloud profile information on 60 levels) -> package » add the water vapor profile » Combine IASI, AVHRR and AMSU information Validate on AIRS observations Adapt the method to the IASI stand-alone package Test a cloud-clearing method (J. Joiner)