Toward All-Sky Assimilation for Microwave Radiances (Part II)

1. Toward All-Sky Assimilation for Microwave Radiances (Part II) 1,2Min-Jeong Kim and 1Emily Liu, 1Yanqiu Zhu, 1Daryl Kleist, 1Andrew Collard, and 1John Derber 1. NCEP/EMC 2. CIRA/Colorado State University

2. Outline • Introductions to our current all-sky radiance DA system and its current status were presented in the previous talk by Emily Liu. • This talk will focus on assessment of the system to diagnose issues and to build strategies to improve. • In addition, roles of GFS moisture physics schemes in all-sky radiance data assimilation and impacts of using them on GFS model forecasts will be presented. • Ongoing work and outstanding tasks will be addressed.

3. Experiments: #1 other instrument observations + Clear sky AMSU-A (operational GSI) #2 other instrument observations + Clear sky AMSU-A + cloudy sky AMSU-A(without moisture physics) #3 other instrument observations + Clear sky AMSU-A+ cloudy sky AMSU-A(with moisture physics) GFS Forecast Impact Experiments • Ensemble-3DVAR Hybrid GSI (operational) • Test resolution: T254 (Note: NCEP operational resolution is T582) • Test period: 07/01/2012 – 07/31/2012

4. GFS Forecast Impact Experiments SH Geopotential Height AC (500 hPa) • For now, assimilating cloud affected AMSU-A radiances making slight positive impacts on Southern Hemisphere. • Neutral impacts elsewhere.. Control (clear sky radiance DA) ECMWF obs. error model Estimate obs. Error from GFS O-F Control

5. Assessments (Part I) Clear sky AMSU-A (Operational GSI) vs. Clear+Cloudy Sky AMSU-A (No Moisture Physics Used.)

6. GFS Model 24 hr Forecast Errors: 500 hPa Geopotential Height CNTL: Clear sky DA TEST: Clear + Cloudy Sky (Cloud microphysics not used), Obs.err estimated from cycled results Allsky Error - ClearSky Error Regions with positive impacts (near sea ice edge..)

7. GFS Model 24hr Forecast Errors: 500 hPa Temperature CNTL: Clear sky DA TEST: Clear + Cloudy Sky (Cloud microphysics not used), Obs.err estimated from cycled results Allsky Error- ClearSky Error Regions with positive impacts (near sea ice edge)

8. Analysis Increment: 500hPa Temperature Clear Sky All Sky AllSky - ClearSky CNTL: Clear sky DA TEST: Clear + Cloudy Sky (Cloud microphysics not used), Obs.err estimated from cycled results Compared to results from operational GSI, all-sky radiance assimilation system increases atmospheric temperature near 60°S

9. Analysis Increment: Total Column Cloud Water All Sky Clear Sky AllSky - ClearSky CNTL: Clear sky DA TEST: Clear + Cloudy Sky (Cloud microphysics not used), Obs.err estimated from cycled results Compared to results from operational GSI, all-sky radiance assimilation system makes lots of clouds near 60°S

10. We are currently applying cloudy radiance data assimilation system in ocean surface only. • It seems like some parts of sea ice edge are defined as ocean in the current system. Therefore, we see observations with warm signal from sea ice are split into • increasing atmospheric temperatures • generating clouds

11. Assessments (Part II) Cloudy sky AMSU-A without moisture physics vs. Cloudy sky AMSU-A with moisture physics

12. Needs debugging Not yet developed TL/AD codes are completed and currently being tested. GFS Model Moisture Physics Shallow convection scheme Deep convection scheme Grid-scale condensation scheme Precipitation scheme Surface rain rates cw, T, q profiles 12

13. Role of GFS Moisture Physics in All-Sky MW Radiance DA Inner loop Moisture physics (TL,AD) CRTM Outer loop 1. Generating clouds even when we don’t have clouds in background.. 2. Ensuring balance between water vapor and clouds. (e.g. prevent generating clouds in dry environment.)

14. Role of GFS Moisture Physics in All-Sky MW Radiance DA • All-sky AMSU-A included. • Setting no clouds in the background. • Including “moisture physics(mp)” in the inner loop. •  Clouds are generated using the information from observations (wv cw) 14

15. Without Moisture physics With Moisture physics 620hPa CW increment Water Vapor increment RH >80 % in analysis 15

16. GFS Model 24 hr Forecast Errors: 500 hPa Temperature CNTL: Clear + Cloudy Sky (Cloud microphysics not used) TEST: Clear + Cloudy Sky (Cloud microphysics used) Error(MoistPhy)- Error(Without MoistPhy) Currently, including moisture physics increase temperature forecast errors especially near the Tropics.

17. Analysis Increment: Total Column Cloud Water CNTL: Clear + Cloudy Sky (Cloud microphysics not used) TEST: Clear + Cloudy Sky (Cloud microphysics used) Without MoistPhy With MoistPhy • Clouds are generated less when using • moisture physics.. • Good to see that near the sea ice edge • But maybe making not enough clouds near Tropics .. ? • Increasing water vapor instead of clouds near Tropics ..?? With MoistPhy - Without MoistPhy 17

18. Analysis Increment: Total Column Water Vapor CNTL: Clear + Cloudy Sky (Cloud microphysics not used) TEST: Clear + Cloudy Sky (Cloud microphysics used) With MoistPhy Without MoistPhy Using moisture physics in the optimization increases water vapor near Tropics With MoistPhy - Without MoistPhy 18

19. Summary • We starts to see our all-sky microwave radiance system makes slight positive impacts on Southern Hemisphere. • We need to improve the way discriminate sea ice from ocean surface for all-sky radiance assimilation • Moisture physics degrading results especially near the Tropics for now. It shows the tendency evaporating clouds maybe too much. Further investigation is ongoing. 19

20. Ongoing & Future Plans 20 20 20 20 20

21. Systematic Bias Increasing with CLWP

22. How to deal with precipitation we couldn’t removed from QC (1) Revisit QC to remove moderate/lightly precipitating observations (2) Enhance radiance bias correction scheme for all-sky microwave radiance assimilation  See Yanqiu Zhu’s presentation tomorrow (3) Include precipitation in the background fields

23. Revisit Quality Control for cloud affected radiance data in “non-precipitating” sky Evaluating QC using Observed (retrieved) TMI surface rainrates assimilated not assimilated Detecting “heavy” rain with scattering Missing“moderate/light” rain without scattering factch6 > 1.0 : precipitating in operational GSI

24. GFS moisture physics Shallow convection scheme Deep convection scheme Grid-scale condensation scheme Brad Ferrier kindly helped us calculate the rain and snow mixing ratio profiles using GFS large-scale precipitation scheme Precipitation scheme Surface rain rates cw,t, q profiles How to deal with precipitation we couldn’t removed from QC (1) Revisit QC to remove moderate/lightly precipitating observations (2) Enhance radiance bias correction scheme for all-sky microwave radiance assimilation  See Yanqiu Zhu’s presentation tomorrow (3) Include precipitation in the background fields

25. Moisture Control Variables Cloud water error statistics show “non-Gaussian” distribution • Available options for moisture control variables in GSI • cw – Currently being used • rhtot – Needs work for Ensemble side • cw/σcw – Set up in GSI. Needs to build background error covariance for global analysis

26. Thank you ! Any comments or questions?

27. BACKUP SLIDES

28. Quality Control Cloud affected radiance DA • 1. Screening Precipitating sky: • factch6(>1) for all surface type : screening ch1-6, 15 • Based on scattering index (sval) and channel 6 O-F • 2. Screening thick cloudy sky: • factch4(>0.5) for all surface type: screening ch1-5, 15 • Based retrieved cloud liquid water path and channel 4 O-F • 3. Sensitivity of Tsim to Surface emissivity for ocean surface • 4. Topography : inflating error for ch 6 (z>2km) or ch 7(z>4km) • 5. Transmittance • 6. Inflated near Tropics ( 0.75 at 0°, and 1 at 25°N & 25°S)

29. Observation Error Varying with AMSU-A Scan Angle Scan angle dependence

30. AMSU-A Cloudy Radiance Data Remove precipitation & thick clouds QC in “operational” GSI QC in all-sky GSI Remove precipitation KEEP thick clouds METOP-A CH 2 TBs Hurricane Sandy (10/28, 00Z, 2012)