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Fanglin Yang I.M. Systems Group, Inc. Environmental Modeling Center National Centers for Environmental Prediction AGU Fall Meeting December 15-19, 2014;

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Presentation on theme: "Fanglin Yang I.M. Systems Group, Inc. Environmental Modeling Center National Centers for Environmental Prediction AGU Fall Meeting December 15-19, 2014;"— Presentation transcript:

1 Fanglin Yang I.M. Systems Group, Inc. Environmental Modeling Center National Centers for Environmental Prediction AGU Fall Meeting December 15-19, 2014; San Francisco Sensitivity of NCEP GFS Forecast of Hurricane Sandy to Model Biases Acknowledgment: All EMC global branch members are acknowledged for their contribution to GFS upgrade. I’d like to thank Russ Treadon for running T1534 GFS experiments, Shrinivas Moorthi and Jongil Han for discussion of model physics, Jiayi Peng and the Ensemble Group for providing GEFS plots, and Hendrik L. Tolman, Mark Iredell and Bill Lapenta for helpful suggestions.

2 Outline 1.Real-time Sandy Forecasts by Major International NWP Models 2.Forecast Uncertainty and Predictability 3.Sandy Forecast by Experimental High-Resolution 13-km T1534 GFS 4.Summary and Discussion 2

3 Hurricane Sandy (Cat 3) FormedOctober 22, 2012 Landfall October 29 (became extratropical cyclone) DissipationOctober 31 Highest winds 115 mph (185 km/h) Lowest pressure 940 hPa Fatalities285 Damage >$68 billion (2 nd most costly hurricane in US history. Katrina the 1st) 3 Sandy Rainfall (David Roth, WPC) New Jersey Pier Sandy caused storm surge along the entire east coast of the United States. The highest storm surge measured in New York reached ft. >10” NCEP GFS FCST

4 1. Hurricane Sandy Forecasts by Major International NWP Models 4

5 Sandy Mean Track Errors by Deterministic NWP Models GFS ECMWF Forecast Hour GFS and ECMWF both made very good forecast for lead time shorter than 72 hours. GFS forecast was worse than ECMWF forecast for leads longer than 72 hours, but still better than a few other NWP models. 5 CMC

6 6 Days Before Landfall, Z Cycle GFS ECMWF ECMWF predicted almost 6 days in advance that Sandy would turn northwest and hit US east coast, while GFS predicted Sandy would keep move northeastward. 6 ECMWF predicted almost perfectly the landfall point. GFS forecast still moved far northeastward. 5 Days Before Landfall, Z Cycle GFS

7 4 Days before Landfall, Z Cycle ECMWF forecast was too close to the coast and shifted to the south. GFS forecast was still too far east and north. GFS ECMWF 7 3 Days before Landfall, Z Cycle ECMWF forecast was too far south, while GFS predicted a landfall in New York too far north. GFS ECMWF

8 2 days before Landfall, Z Cycle The solutions from all models started to converge, although ECMWF biased to the south and GFS biased to the north. 8 GFS ECMWF One Day before Landfall, Z Cycle All models predicted a landfall in New Jersey.

9 GFS and ECMWF Analyses for 00Z 29Oct2012 GFS and ECMWF 6-day Forecasts valid at 00Z 29Oct2012 GFS and ECMWF 6-day Forecasts valid at 00Z 29Oct2012 Upstream trough predicted by the GFS is too shallow compared to both analyses and ECMWF forecast. GFS northeast flow in front of the trough steered the storm off the coast. GFS ECM 9

10 10 Large difference in initial conditions between EMCWF and GFS was found over the central northern Pacific region. Needs to improve first guess (model) and analyses (data assimilation) in this region. Differences of 500-hPa Height between GFS and ECMWF

11 2. Forecast Uncertainty and Predictability 11

12 Ensemble Forecasts, 12Z22Oct2012 Cycle (7.5-d before land fall) NCEP para: T574L64 (33km) NCEP para: T574L64 (33km) ECMWF: T639L62 (32km) ECMWF: T639L62 (32km) Thick blue: ensemble mean Credit: Ensemble Group NCEP Opr: T254L42 (55km) NCEP Opr: T254L42 (55km) Both ECMWF and NCEP ensembles showed large track uncertainty in long- lead forecasts. The storm has limited long-lead predictability. Arguably, tracks predicted by a single high-resolution deterministic model can fall, by chance, anywhere within the envelop of the ensembles. Both ECMWF and NCEP ensembles showed large track uncertainty in long- lead forecasts. The storm has limited long-lead predictability. Arguably, tracks predicted by a single high-resolution deterministic model can fall, by chance, anywhere within the envelop of the ensembles.

13 3. High-Resolution (13km) T1534 GFS With Data Assimilation 13 T1534 Semi-Lagrangian GFS will become the operation model in January 2015 (see Glenn White’s presentation)

14 Composite Tracks for Sandy 18L, 2012 (T574 vs. T1534) T574T1534 All cycles from through are included. T1534 GFS forecasts are centered more toward the observed best track (black line) than did the T574 GFS Credit: Vijay Tallapragada 14

15 22Oct2012, 7 days before landfall 15 12Z 18Z T1534 GFS showed early sign of tuning toward northwestward better 15

16 23Oct2012, 6 days before landfall 16 00Z06Z 12Z18Z T1534 showed little or no improvement over T574 GFS Neutral

17 24Oct2012, 5 days before landfall 17 00Z 12Z 06Z 18Z better worseNeutral worse 17

18 25Oct2012, 4 days before landfall 18 00Z 12Z 06Z 18Z better worse Neutral neutral

19 26Oct2012, 3 days before landfall 19 00Z 12Z 06Z 18Z better All four cycles are improved. T1534 GFS tracks are almost perfect ! 19

20 Mean Track and Intensity Errors October 2012, 4 cycles/day 20 T1534 GFS T574 GFS Track Intensity Overall, the forecast of hurricane Sandy’s track is improved in the experimental T1534 semi-lag GFS in comparison with the 2012 operational T574 Eulerian GFS. The improvement is found mostly for short-lead forecasts within 72 hours. Long-lead 4 to 7-day forecasts showed improvement for certain cycles. There are still large cycle-to-cycle variations. 20

21 21 Is the (moderate) improvement in Sandy forecast by the 13-km GFS a result of a better model? Have the model biases been reduced?

22 22 Fit to Rawinsonde Observations, Temperature [20N-80N] Averaged for all 00Z-cycle forecasts for October 22-31, 2012 Fit to Rawinsonde Observations, Temperature [20N-80N] Averaged for all 00Z-cycle forecasts for October 22-31, 2012 New GFS Compared to the 2012 operational GFS (27km), the new GFS (13km) greatly reduced warm biases in the troposphere.

23 23 Fit to Rawinsonde Observations, Wind Speed [20N-80N] Averaged for all 00Z-cycle forecasts for October 22-31, 2012 Fit to Rawinsonde Observations, Wind Speed [20N-80N] Averaged for all 00Z-cycle forecasts for October 22-31, 2012 New GFS Compared to the 2012 operational GFS (27km), the new GFS (13km) reduced wind bias in the troposphere. Winds become stronger. For comprehensive evaluations:

24 24 Why does the 13-km GFS forecast of Sandy tracks have large cycle-to-cycle variations ? Aside from predictability, is there any other causes ?

25 25 No improvement New GFS Both the 2012 and new GFS analyses showed large difference from ECMWF analysis in the central North Pacific 13-km New GFS 2012 GFS 2012 GFS 500-hPa HGT Analysis, GFS vs ECMWF, 00Z24Oct2014 – a bad case

26 New GFS 2012 GFS 26 Significant improvement The 2012 GFS analysis showed large difference from ECMWF analysis in Central North Pacific The new GFS analysis became much closer to ECMWF analysis in this region The 2012 GFS analysis showed large difference from ECMWF analysis in Central North Pacific The new GFS analysis became much closer to ECMWF analysis in this region 13-km New GFS 2012 GFS 500-hPa HGT Analysis, GFS vs ECMWF, 06Z24Oct2014 – A good case

27 Summary and Discussion Operational GFS and ECMWF both made good high-resolution deterministic forecast for lead time shorter than 72 hours. GFS forecast was worse than ECMWF forecast for lead time longer than 72 hours. The mid-latitude trough predicted by GFS 6 days in advance was much shallower than analyses and ECMWF forecast. Large uncertainty was found in both NCEP and ECMWF long-lead ensemble forecasts, indicating hurricane Sandy has inherently limited long-lead predictability. The 13-km T1534 Semi-Lagrangian GFS, fully cycled with improved data assimilation, further reduced track errors within 72 hours. Long-lead 4 to 7- day forecasts had large cycle-to-cycle variation, which is likely due to limited predictability and the quality of analyses. The improvement in Sandy forecast by the 13-km GFS is a result of both better model and better data assimilation. Increasing model resolution alone is not sufficient (demonstrated by a set of sensitivity experiments which are not shown here). 27

28 Backup slides 28

29 Is the improvement all due to changes in model resolution and physics? Sensitivity Experiments Without Data Assimilation All runs used the same T574 operational GFS initial condition 29

30 Sensitivity to Model Resolution and Dynamics 30 prsandy878: operational GFS, rerun at T878 Eulerian resolution 9~15km) prsandy1534: T1534 semi-lag GFS (~13km) prsandy1534b: T1534 semi-lag GFS, radiation dt=450s instead of 3600s prsandy1534c: T1534 semi-lag GFS, use obs SST instead of relaxed SSTs Obs Ops GFS T878 GFS T1534 SL-GFS obs SST Simply increasing model resolution without improving initial conditions did not improve track forecast !

31 T574L64 (~23km): Sensitivity to SST and Physics 31 prsandy574b: use observed SST instead of relaxed SSTs prsandy574c: use RAS convection scheme Using observed SST and switching convection from SAS to RAS did not improve track forecast ! Using real-time SST slightly improved track forecast. Obs Ops GFS Obs SST RAS

32 Intensity Errors for Hurricane Sandy 32 Intensity errors from operational HWRF were the lowest compared to other numerical models, and were 20-40% better than the official forecasts [ Credit: NCEP HWRF Group]

33 Sandy Track Errors by GFS and GEFS Track error(NM) Forecast hours Cases GFS GEFS Ensemble mean track is better than the deterministic GFS track, especially for longer forecast lead hours. Credit: NCEP Global Ensemble Group, Special thanks to Jiayi Peng 33

34 6 days Before Landfall, Z Cycle GFS ECMWF ECMWF predicted almost 6 days in advance that Sandy would turn northwest and hit US east coast, while GFS predicted Sandy would keep move northeastward. 34 GEFS mean track is similar to the deterministic GFS track, moving the storm northeastward. A few ensemble members followed the observed track. Deterministic Ensemble GEFS

35 5 Days before Landfall, Z Cycle GFS ECMWF ECMWF predicted almost perfectly the landfall point. GFS forecast still moved far northeastward. 35 GEFS mean track is slightly better than the deterministic GFS track, but still biased towards northeast. More members started to turn northwestward. Deterministic Ensemble GEFS

36 4 Days before Landfall, Z Cycle ECMWF forecast was too close to the coast and shifted to the south. GFS forecast was still too far east and north. GFS ECMWF 36 GEFS mean track was better than the deterministic GFS track. Deterministic Ensemble GEFS

37 3 Days before Landfall, Z Cycle ECMWF and CMC both made very good forecasts. GFS predicted a landfall in New York, too far north. 37 GEFS was slightly better than GFS, although both predicted a landfall in New York, too far north. GFS ECMWF Deterministic Ensemble GEFS

38 2 days before Landfall, Z Cycle The solutions from all models started to converge, although ECMWF biased to the south and GFS biased to the north. 38 GFS ECMWF Deterministic Ensemble GEFS Solutions from all members also started to converge. GEFS mean track was better than the deterministic GFS track.

39 One Day before Landfall, Z Cycle All models predicted a landfall in New Jersey. 39 GEFS was slightly worse than the GFS, biased toward the north. GFS ECMWF Deterministic Ensemble GEFS

40 Impact of 06 and 18UTC Radiosondes on GFS Forecasts of Hurricane Sandy 40

41 Experimental Design Radiosondes were launched at 06Z and 18Z at all CONUS stations during the period 18Z 25 Oct 2012 through 12Z 30 Oct 2012 The supplemental radiosondes were assimilated in the operational GDAS/GFS 06 and 18Z cycles A data denial experiment was carried out using the operational configuration of the NCEP GDAS/GFS: – Excluding the 06 and 18Z CONUS radiosonde data – Initialized using the operational GFS initial conditions at 18Z 25 Oct 2012 – Last cycle executed was 12Z 30Oct2012 – Fully cycled with data assimilation 41

42 Supplemental Data Ingested by GDAS/GFS Total Count: GFS 615 GDAS 617 By Kate Howard 42

43 43 Supplementary Radiosondes data made no significant impact on mean track error statistics

44 44 Without the supplementary Radiosondes intensity forecast appeared to be slightly worse

45 Forecasts of 24-hr rainfall accumulation ending at 12Z 30Oct2012 from different cycles and forecasts of tracks from the corresponding cycles 45

46 cycle 46 Without the supplementary radiosondes, the experiment prsandy has even larger track errors than the operational GFS. The extra data did help the forecast !

47 cycle 47 The extra data did help the forecast !

48 cycle 48 The extra radiosondes did not help much.

49 cycle 49 The extra radiosondes did not make any impact

50 cycle 50 The extra radiosondes did not improve the forecast.

51 Supplementary radiosondes were launched at 06Z and 18Z at all CONUS stations during the period 18Z 25 Oct 2012 through 12Z 30 Oct The results of these experiments did not show a statistically significant impact of supplementary radiosondes on the quality of the model forecasts. The first few forecasts were slightly improved by the addition of the radiosondes, but after that, both degradation and improvement were noted, giving the overall neutral results. This lack of a significant impact from the additional radiosondes may have resulted from several factors, including: 1. the additional radiosondes were only launched once agreement was beginning to occur among the forecasts from different models, 2. the mid-latitude blocking system was already well established in the assimilation system, 3. the location of the additional radiosondes (upstream of the storm). 51


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