1. Towards Utilizing All-Sky Microwave Radiance Data in GEOS-5 Atmospheric Data Assimilation System Development of Observing System Simulation Experiments for all-sky radiance data assimilation at NASA GMAO Min-Jeong Kim NASA GMAO/GESTAR Collaborators: Ricardo Todling, Will McCarty, Ron Gelaro, Ron Errico, Nikki Prive, Jong Kim, Dan Holdaway, Jianjun Jin, and Wei Gu

2. 2 GEOS-5 Moisture Analysis: Too wet? Too dry? Where?

3. 3 Including Lower Peaking AMSU-A Channels Helped? Not Assimilating Channels 1, 2, & 3Assimilating Channels 1, 2, & 3 Only CLEAR SKY DATA used

4. 4 Including Lower Peaking AMSU-A Channels Helped? Assimilating Channels 1, 2, & 3Not Assimilating Channels 1, 2, & 3 Only CLEAR SKY DATA used

5. Observation errors assigned for Clear Sky Condition Why microwave surface channels don’t make any significant influence..? N15 AMSU-A Channel Error (K) 13.0 22.0 3 40.6 50.3 60.23 70.25 80.275 90.34 100.4 110.6 121.0 131.5 142.0 153.0 (1)Assigned observation errors are too large? Are we too cautious? (2)Bias correction process absorb the information? (3)… ??

6. 6 AMSU-A surface channels in clear sky condition with all other observations currently being assimilated in GMAO were used. Estimated observation were calculated using the method in Desroziers et al. 2005. The observation errors assigned in GSI satellite obs error table seem to be largely inflated especially for AMSU-A channels 1, 2, and 3. Comparisons of “used” and “estimated” observation errors Observation error currently being used in GSI Observation error estimated AMSU-A channels Observation error (K) AMSU-A channels N18 AMSU-A, Clear sky

7. 7 3DVAR GSI (operational. Which will be updated to 3D-Var Hybrid this summer.) Horizontal resolution: 0.5 degree Control: Observation error being used operationally in NCEP for AMSU-A channels1, 2, and 3 Experiment: Reduced observation error for AMSU-A Channels 1, 2, and 3 Experiment Setup Observation error currently being used in GSI (Control) Observation error estimated Reduced observation error (Experiment) Observation error (K) AMSU-A channels

8. 8 Preliminary Results AMSU-A lower atmosphere peaking channels are trying to push water vapor fields toward right direction. It has tendency to reduce moisture fields middle troposphere and increase moisture in lower troposphere near the tropics.

9. 9 Developing All-Sky MW Radiance Data Assimilation Components Observation operator : Including 3D cloud liquid and ice water Quality control: keeping cloud affected radiance data while screening out observation with scattering signals from precipitation Bias correction: Remove cloud liquid water path from bias correction predictors Moisture control variables: (1) q, ql, and qi (2) q, cw(ql+qi) Background error: NMC method (soon will be updated with 3D-var hybrid) Observation error: Started with very simple. Clear sky condition (same as operational), Cloudy sky: Constants estimated from O-F standard deviation

10. Developing All-Sky MW Radiance Data Assimilation Components Cloud Control variables & Background Errors

11. Developing All-Sky MW Radiance Data Assimilation Components Cloud Control variables & Background Errors

12. Developing All-Sky MW Radiance Data Assimilation Components Cloud Control variables & Background Errors

13. GEOS-5 Background clouds (Vertically integrated cloud water) Observed clouds (retrieved cloud liquid water path) Cloud Analysis Increments (Preliminary results from current development for all-sky MW radiance data assimilation)

14. Cloud control variable: cwCloud control variable: ql & qi Cloud Analysis Increments (Preliminary results from current developments made to GSI)

15. Single point AMSU-A Observations Cloud Analysis Increments (Preliminary results from current developments made to GSI) @850hPa

16. Δql (when ql/qi control variable) Δqi ( when ql/qi control variable) Δql (when cw control variable) Δqi (when cw control variable) Cloud Analysis Increments

17. Smaller obs error Larger obs error Pressure (hPa) kg/kg Sensitivity to Observation Error Single point AMSU-A observations test Cloud liquid water (ql) increment

18. Sensitivity to Observation Error Single point AMSU-A observations test Smaller obs error Larger obs error Pressure (hPa) kg/kg Pressure (hPa) K Larger obs error Smaller obs error q incrementTv increment

19. What would happen if we had additional outer Loops ? Single point AMSU-A observations test 2 outer loops 3 outer loops 4 outer loops Pressure (hPa) kg/kg Cloud liquid water (ql) increment

20. What would happen if we had additional outer Loops ? Single point AMSU-A observations test Pressure (hPa) K kg/kg Tv increment q increment No significant difference in using 2, 3, and 4 outer loops

21. 21 Experiment Setup 3DVAR GSI (operational. Which will be updated to 3D-Var Hybrid this summer.) Horizontal resolution: 0.5 degree Observation operator : CRTM version 2.1.3 Test period: 06/01/2013 – 07/31/2013 CNTLEXP Observations All observations being assimilated in the operational system + AMSU-A Channels 1, 2, and 3 All observations being assimilated in the operational system + AMSU-A channels 1, 2, and 3 + AMSU-A Cloudy Radiance Data Observation operatorNot simulate cloudy radianceSimulate cloudy radiance Moisture state variableqq, ql, qi Moisture control variableqq, ql, qi Background errorRelative humidityRelative humidity, ql, qi Bias correction predictor Const., tlap, tlap2, scan angle, cloud liquid water path Const., tlap, tlap2, scan angle Observation errorErrors being used operationally Clear sky data: Errors operationally Cloudy sky data: Observation errors estimated and then inflated with STD of O-F.

22. 22 Comparisons of Analysis Results

23. 23 Comparisons of Analysis Results

24. 24 Comparisons of “used” and “estimated” observation errors AMSU-A radiance data in all- sky condition with all other observations currently being assimilated in GMAO were used. Estimated observation errors were calculated using the method in Desroziers et al. 2005. The observation errors currently assigned for all-sky AMSU-A data for our experiments seem to be too large especially for AMSU-A channels 1, 2, and 3. Observation error currently being used in All-sky radiance MW experiments Observation error estimated AMSU-A channels N18 AMSU-A, All-sky Observation error (K)

25. Histogram for O-F for All-Sky MW Radiance DA Gaussian Fit

26. Huber norm Observation Error Model for All-Sky MW Radiance DA

27. Goal: Including moisture physics during the analysis process to improve the balance among moisture variables. For example, (1) Preventing the analysis system from making clouds in dry atmosphere and making it moisten atmosphere instead.. (2) When background is almost saturated, generating clouds instead of making atmosphere supersaturated Utilizing moisture physics TL/AD in GSI

28. 28 Utilizing Linearized GEOS-5 Microphysics Models  Developed by Dan Holdaway (NASA GMAO)  Utilizing 4DVAR setting in GSI except applying for 1 time step using moisture physics parts only without using dynamics part of model TL/AD J(δx) = δx T B -1 δx + ∑ n (H n M n δx o -d n ) T R n -1 (H n M n δx o -d n ) In “4DVar” setting, M n (Δt) = TM c T * If M n = I : 3D-Var M c is cubed grid model, T is grid transformation operator M = TM dyn M phys T* set as Identity matrix ( I) in this test 4D-Var:

29. Utilizing Linearized GEOS-5 Microphysics Models Results from Initial tests in 2 ˚ resolution, 02/16/2014 00Z analysis All-Sky AMSU-A Data Assimilated

30. 30 OSSE Developments for All-Sky Microwave Radiance DA To evaluate and tune up present and proposed techniques for all- sky microwave radiance data assimilation by exploiting known truth. To understand what contribution cloud/precipitation affected microwave radiance data assimilation can add to analysis Collaborators: Ron Errico and Nikke Prive (NASA GMAO)

31. 31 Nature Run: (1) ECMWF Nature Run: Free-running “forecast” from 2006 model T511L91 reduced linear Gaussian grid (approx 35km). SST and sea ice cover is real analysis for that period. Three-dimensional cloud liquid water and cloud ice water fields are available. However, Three-dimensional precipitation fields are not available. (2) GEOS-5 Nature Run: High spatial(7km) and temporal (30min) results. Three dimensional cloud liquid water, cloud ice water, rain, and snow fields are included. Assimilation system: NCEP/GMAO GSI (3DVAR) and GMAO GEOS-5 model. Resolution 0.5 x 0.625 degree grid, 72 levels. 3DVAR will be updated with 3DVAR-Hybrid in the summer 2014. Observation data: Conventional, GPSRO, SATWND, IASI, AIRS, AMSU-A, HIRS4, MHS + cloud and precipitation affected MW radiance OSSE Developments for All-Sky Microwave Radiance DA

32. 32 Validation of OSSE Setup Square root of zonal mean of temporal variance of analysis minus background fields for June 21- June 30, 2005. Clear sky radiance data of AMSU-A surface channels were additionally assimilated. T, OSSE q, OSSEU, OSSE T, Real q, RealU, Real Latitude P (hPa) K K kg/kg m/s

33. 33 Cloudy simulations were made with the CRTM using ECMWF Nature Run for AMSU-A and MHS. GEOS-5 new Nature Run which has high spatial and temporal resolution and has 3D cloud and precipitation fields will used as well in future. Avoiding the issues with using the same Radiative trasfer model in simulation and assimilation, using RTTOV in simulations is one of near future plans. Real June 19, 2013 00Z OSSE June 19, 2005 00Z Cloud liquid water analysis increments. Projected on AMSU-A NOAA-18 observed locations. “All-sky” AMSU-A data were assimilated. OSSE for All-Sky Microwave Radiance Data Assimilation Validation of OSSE Setup for All-Sky MW Radiance Data

34. Future Work  Work towards making lower peaking channels contribute good things on analysis. Examine and test with updated clear sky observation error(tnoise_clear) for MW lower peaking channels Revisit QC and look into bias correction behaviors Through OSSE, understanding information and impacts those surface channels can bring to analysis and find strategies to improve the current methods to assimilate them.  Further examine impacts of using moisture TL/AD models on analysis increments through experiments.  Continue to develop OSSE for all-sky microwave radiance data assimilation to evaluate and tune up present and proposed techniques for all-sky microwave radiance data assimilation by exploiting known truth. Better understand the impacts of observation error and moisture background error models on analysis. How important to assume correct observation error model or background error model ? Different moisture control variables  Currently testing and running experiments with all-sky AMSU-A and MHS radiance data. We are getting these all-sky microwave radiance data assimilation components ready so that we can assimilate microwave imager data from NASA GPM Microwave Imager(GMI) and AMSR2 in near future.

35. 35 Backup slides

36. 36 Simulated observations (made with OSSE simulation package developed by Ron Errico et al.) 1.All observations created using bilinear interpolation horizontally, log-linear interpolation vertically, linear interpolation in time. 2. Radiance observations created using CRTM version 2.1.3 3. No used of NR snow coverage 4. Locations for all “conventional” observations given by corresponding real ones, except no drift for RAOBS 5. SATWNDS not associated with trackable features in NR. Simulated observation errors (made with OSSE simulation package developed by Ron Errico et al.) 1.Some representativeness error implicitly present 2. Gaussian noise added to all observations 3.AIRS errors correlated between channels 4. Observation errors for SATWND and non-AIRS radiances horizontally correlated (using isotropic, Gaussian shapes) 5. Conventional soundings and SATWND observational errors vertically correlated (Gaussian shaped in log-p coordinate) 6. Tuning parameters are error standard deviations, fractions of variances for correlated components, vertical and horizontal scales OSSE Developments for All-Sky Microwave Radiance DA