1. On Improving GFS Forecast Skills in the Southern Hemisphere: Ideas and Preliminary Results Fanglin Yang Andrew Collard, Russ Treadon, John Derber NCEP-EMC Global Modeling Branch Bi-Weekly Briefing March 21, 2013 Acknowledgments: Many thanks are given to Yanqiu Zhu, Daryl Kleist, Shrivinas Moorthi and Xiujuan Su for helpful discussions.

2. Annual Mean 500hPa HGT Day-5 AC 2 Year The difference between NH and SH scores for ECMWF is much smaller than that for the GFS.

3. 3 Die-off Curves of HGT Anomaly Correlation, JJA 2012 NH 1000hPa NH 500hPa SH 1000hPa SH 500hPa In SH, the difference between GFS and ECM is larger near the surface than at 500hPa. In NH, the difference between GFS and ECM does not vary with height.

4. 4 JJA-2012 Temp Differences between GFS and ECMWF, Analyses GFS analysis is much warmer than ECMWF analysis in the lower troposphere at the Southern mid to high latitudes. The largest difference is found in the lower troposphere above the boundary layer. Zonal Mean Temperature 850 hPa Temp Austral Winter

5. 5 Dec-Feb 2012/13 Temp Differences between GFS and ECMWF, Analyses GFS analysis is much warmer than ECMWF analysis in the lower troposphere over the ocean near Antarctic. The largest difference is found in the lower troposphere above the boundary layer. Zonal Mean Temperature Austral Summer 850 hPa Temp

6. 6 GFS analysis is much warmer than ECMWF analysis. GFS forecast is much colder than GFS analysis. Indication: GFS data assimilation warms up the air; GFS forecast model itself tends to cool the air, and agrees more with ECMWF forecast. Austral Winter

7. 7 GFS analysis is much warmer than ECMWF analysis. GFS forecast is much colder than GFS analysis. Indication: GFS data assimilation warms up the air; GFS forecast model itself tends to cool the air, and agrees with ECMWF forecast. Austral Summer

8. 8 Findings: GFS analysis is one to two degrees warmer than ECMWF analysis in the Southern lower troposphere at middle to high latitudes in all seasons of the year. GFS forecasts are colder than GFS analyses in the same region. The difference between GFS and ECMWF forecasts is smaller than that between GFS and ECMWF analyses.

9. 9 Differences of 850hPa temperature between GFS 00Z-cycle analyses and guess. The latter are 18Z GDAS 6-hour forecasts. In general, the analyses are 0.1~0.5 degrees warmer than the guess in the Southern mid to high latitudes. JJA-2012DJF 2012/13 GFS A - G: 850hPa Temperature

10. 10 Cloud cover of the GFS is 10-20% less than that of ECMWF over the Southern mid to high latitudes. GFS and ECMWF Cloud Cover Differences

11. 11 Compared to ECMWF, GFS cloud cover is about 7% less in the NH and 13% in the SH. GFS has a fast spin-down of cloud cover in the first 24 to 48 hours in all regions. Mean Cloud Cover Cases of February 2013 NH SH Tropics

12. 12 No ECMWF cloud data available. GFS has a fast spin-down of cloud cover in the first 24 to 48 hours. Mean Cloud Cover Cases of July2012 NH SH Tropics

13. 13 The spin-down of GFS total cloud cover in the first 24 to 48 hours is primarily caused by a reduction in high clouds. Mean Cloud Cover, 01July2012 Low, Middle and High Clouds NH SH Tropics

14. 14 Findings: GFS analysis is warmer than guess (GDAS 6-hour forecast) in the troposphere over the Southern Hemisphere middle to high latitudes. GFS global cloud cover is about 10% less than ECMWF cloud cover. The deficit is the largest in the Southern Hemisphere (~13%). GFS cloud cover has a quick spin-down in the first 24 to 48 hours of forecast. The spin-down is primarily caused by the reduction of high clouds.

15. 15 Idealized Experiment: GFS T574, single case forecast with up to 1-deg initial temperature perturbation. Zonal Mean Temp Zonal Mean U Zonal Mean Z

16. 16 Sensitivity Experiment I: expgsi Double the observation errors in GSI for AMSU-A Channels 1-4 and 15 (and ATMS equivalents if nchanl=22) in the Southern Hemisphere between the latitudes of 40 o S and 80 o S. The model used for the experiment is GFS T382L64 with 3D-VAR GSI. A new control run at this configuration was made for clean comparison. Both runs were carried out on ZEUS. 1 June 2012 ~ 31 August 2012

17. 17 Single case, gsistat.gdas.2012080100 AMSU-A Data Assimilation Statistics it satellite instrument # read # keep # assim penalty cpen Control Run Expgsi Run o-g 03 rad n15 amsua 895950 125311 108525 24811. 0.22862 o-g 03 rad n18 amsua 1629735 165613 126525 43386. 0.34290 o-g 03 rad metop-a amsua 1314345 165024 135058 49814. 0.36883 o-g 03 rad aqua amsua 8715600 124012 61760 13635. 0.22077 o-g 03 rad n19 amsua 1524540 154933 119163 40043. 0.33604 o-g 03 rad n15 amsua 895950 125311 106160 23763. 0.22385 o-g 03 rad n18 amsua 1629735 165613 123295 41676. 0.33802 o-g 03 rad metop-a amsua 1314345 165024 131931 48303. 0.36612 o-g 03 rad aqua amsua 8715600 124012 61763 13553. 0.21943 o-g 03 rad n19 amsua 1524540 154933 116650 38850. 0.33305 551,031 539,799 2% less

18. 18 Analysis Difference in Temperature: expgsi - control Zonal Mean Temperature 850 hPa Temp By inflating the observation errors of AMSU-A channels over the Southern mid to high latitudes we reduced the analysis temperature increment by one to two degrees. This change reduced the analysis temprature difference between GFS and ECMWF.

19. 19 JJA-2012 Temperature Differences between GFS and ECMWF Analyses GFS analysis is much warmer than ECMWF analysis in the lower troposphere over the ocean near Antarctic. The largest difference is found in the lower troposphere above the boundary layer. Zonal Mean Temperature 850 hPa Temp Austral Winter

20. 20 GFS Forecast Skills : SH 500-hPa Height Anomaly Correlation http://www.emc.ncep.noaa.gov/gmb/wx24fy/wgne/prexpgsi/ Day-5 AC increased by ~0.01Increases of AC are significant in the first 5 days, except for day 4. No significant changes were found in NH scores, see

21. 21 GFS Forecast Skills : SH Wind RMSE http://www.emc.ncep.noaa.gov/gmb/wx24fy/wgne/prexpgsi/ Significantly reduced SH wind RMS at all levels and at almost all forecast length. No significant impact was found in NH and the tropics.

22. 22

23. 23 GFS Forecast Skills : Temperature RMSE SH Tropics NH Significantly reduced Temperature RMS in SH. Neutral impact in NH and the tropics.

24. 24 Neutral impact on Atlantic hurricane track errors Reduced hurricane track errors in the Eastern Pacific

25. 25 Sensitivity Experiment II: expcld Reduce the auto-conversion rate (cr) of ice to snow by 25% to increase the cover of high clouds. The model used for the experiments is GFS T382L64 with 3D- VAR GSI. So far the experiment has only been run for about two weeks (June 2012). Where m is ice mixing ratio, and m io is minimum/threshold ice mixing ratio.

26. 26 EXPCLD increased high cloud cover by 5.5%, especially in the tropics and SH mid-high latitudes. Middle and low clouds were not affected. However, there is still a quick spin- down of cloud cover in the first 24 to 48 hours, especially in the tropics, in both the CNTL and EXPCLD runs. Global Mean Tropical Mean

27. 27 SH 500hPa HGT AC Neutral to slight positive impact on SH 500hPa HGT AC. Increased SH temperature bias. Too few samples. Need to have more forecast cases. SH Temp Fit-to-Obs

28. 28 Summary 1.This study investigated the causes for GFS’s relatively low forecast scores in the Southern Hemisphere. 2.It is found that GFS analysis is one to two degrees warmer than ECMWF analysis in the SH lower troposphere middle to high latitudes. A sensitivity experiment was conducted with doubled observational errors for AMSU-A channels 1-4 and 15 in this region. The model used is GFS T382 with 3D-VAR GSI. This test was able to effectively reduced the warm bias. Most forecast skill scores in the SH are significantly improved. 3.It is also found that GFS has less cloud than ECMWF, especially in the SH. GFS high clouds have a quick spin-down in the first 24 to 48 hours of forecasts. A sensitivity experiment was conducted with a reduced conversion ratio of ice to snow. Results from very limited forecast samples showed that the scores in the SH were either neutral or slightly degraded. The spin-down of high clouds still exists.

29. 29 What’s next 1.The sensitivity experiment we conducted using doubled AMSU-A observational errors showed the potential of improving GFS scores in the SH. More scientificly sound approaches should be explored. We may repeat this sensitivity experiment using T574 GFS with GSI hybrid EnKF 3DVAR. 2.Andrew Collard is testing new ideas. Yanqiu Zhu is experimenting a new bias correction method, which also showed certain improvement of temperature analysis in the SH. 3.The impact of GFS cloud cover on SH scores needs more investigation. The cause of the spin-down of GFS high clouds needs to be understood.

30. 30 Extra slides

31. Annual Mean 500hPa HGT Day-5 AC Difference 31 Year The difference between NH and SH scores for ECMWF is much smaller than that for the GFS.

32. 32 JJA 2012Dec2012 - Jan2013 NHSHNHSH ECMWF0.7990.8200.8330.829 GFS0.7710.7600.8260.783 ECMWF - GFS 0.0280.0600.0070.046 500-hPa HGT Day-6 Anomaly Correlation After the hybrid-EnKF GSI implementation in May 2012, GFS was greatly improved. It was almost caught up with the ECMWF in the NH for 2012/13 winter. However, GFS still lags ECMWF by almost 5 points in the SH.

33. 33 Die-off Curves of HGT Anomaly Correlation, Dec-Jan 2012/13 NH 1000hPa NH 500hPa SH 1000hPa SH 500hPa the difference between GFS and ECM is larger in the SH than in the NH