1. Ensemble variability in rainfall forecasts of Hurricane Irene (2011)
Molly Smith, Ryan Torn,
Kristen Corbosiero, and Philip Pegion
NWS Focal Points: Steve DiRienzo and Mike Jurewicz
Fall 2016 CSTAR Meeting
2 November, 2016

2. Motivation
Landfalling TCs are often associated with widespread heavy precipitation, which can lead to devastating flood events.

3. Motivation
Landfalling TCs are often associated with widespread heavy precipitation, which can lead to devastating flood events.
Relatively few studies have explored the sensitivity of precipitation forecasts for such events to model initial conditions.

4. Motivation
Landfalling TCs are often associated with widespread heavy precipitation, which can lead to devastating flood events.
Relatively few studies have explored the sensitivity of precipitation forecasts for such events to model initial conditions.
What modulates precipitation variability in ensemble modeling of heavy rainfall events?

5. Motivation
Landfalling TCs are often associated with widespread heavy precipitation, which can lead to devastating flood events.
Relatively few studies have explored the sensitivity of precipitation forecasts for such events to model initial conditions.
What modulates precipitation variability in ensemble forecasts of heavy rainfall events?
This work uses Hurricane Irene (2011) as a case study.
Irene featured catastrophic inland flooding caused by widespread rainfall totals of 4–7 inches.
The Catskill region of New York received up to 12 inches.

6. Methods
An 80-member GFS ensemble was initialized at 0000 UTC 27 August 2011 and run for 48 hours, until 0000 UTC 29 August.
These GFS ensemble forecasts were created with the operational version of the GFS in use from 2013–

7. GFS Ensemble Mean Precipitation
GFS Predicted Accumulations (mm)
Observed Accumulations (mm)
Mention time period
Observation source: EOL

8. GFS Ensemble Mean Precipitation
GFS Predicted Accumulations (mm)
Observed Accumulations (mm)
Mention time period
Observation source: EOL

11. Methods
An 80-member GFS ensemble was initialized at 0000 UTC 27 August 2011 and run for 48 hours, until 0000 UTC 29 August.
These GFS ensemble forecasts were created with the operational version of the GFS in use from 2013–
Members were ranked by the amount of precipitation they brought to the Catskill region of New York during this time period.

12. Methods
An 80-member GFS ensemble was initialized at 0000 UTC 27 August 2011 and run for 48 hours, until 0000 UTC 29 August.
These GFS ensemble forecasts were created with the operational version of the GFS in use from 2013–
Members were ranked by the amount of precipitation they brought to the Catskill region of New York during this time period.
The synoptic characteristics of the ten wettest ensemble members were then compared to those of the ten driest, to see if any large-scale patterns were behind the differences in forecasted precipitation.

13. Why did some storms track father west?
Emphasize that this is a hypothesis to test

14. Hypothesis: Differences in storm track are associated with differences in steering flow.
Emphasize that this is a hypothesis to test

15. Composite Difference Plots
hPa Zonal Steering Flow at Initialization
easterly anomalies
westerly anomalies

16. Composite Difference Plots
hPa Zonal Steering Flow at Initialization
Comparison of 10 wettest ensemble members and 10 driest
easterly anomalies
westerly anomalies

17. Composite Difference Plots
hPa Zonal Steering Flow at Initialization
Comparison of 10 wettest ensemble members and 10 driest
Vectors: Ensemble mean
easterly anomalies
westerly anomalies

18. Composite Difference Plots
hPa Zonal Steering Flow at Initialization
Colors: Standardized difference between wet and dry members
Warm colors: Wet members have greater value
Cold colors: Wet members have lesser value
Comparison of 10 wettest ensemble members and 10 driest
Vectors: Ensemble mean
easterly anomalies
westerly anomalies

19. Composite Difference Plots
hPa Zonal Steering Flow at Initialization
Colors: Standardized difference between wet and dry members
Warm colors: Wet members have greater value
Cold colors: Wet members have lesser value
Comparison of 10 wettest ensemble members and 10 driest
Vectors: Ensemble mean
Stippling: Significant at 95% level
easterly anomalies
westerly anomalies

20. 250-850 hPa Zonal Steering – 00hr
Easterly flow anomalies arise early in the wetter members.
easterly anomalies
westerly anomalies

21. 250-850 hPa Zonal Steering – 00hr
Easterly flow anomalies arise early in the wetter members.
easterly anomalies
westerly anomalies

22. Potential vorticity (PV) anomalies are associated with wind flow anomalies. Is that a factor here?

23. 350K PV – 00hr
Wetter members are initialized with cyclonic PV anomalies to the south and west of Irene. These anomalies help to steer Irene inland.
Less cyclonic PV
More cyclonic PV

24. 350K PV – 00hr
Wetter members are initialized with cyclonic PV anomalies to the south and west of Irene. These anomalies help to steer Irene inland.
Less cyclonic PV
More cyclonic PV

25. 350K PV – 00hr
Wetter members are initialized with cyclonic PV anomalies to the south and west of Irene. These anomalies help to steer Irene inland.
Less cyclonic PV
More cyclonic PV

26. 350K PV – 06hr
Wetter members are initialized with cyclonic PV anomalies to the south and west of Irene. These anomalies help to steer Irene inland.
Less cyclonic PV
More cyclonic PV

27. 350K PV – 18hr
Wetter members are initialized with cyclonic PV anomalies to the south and west of Irene. These anomalies help to steer Irene inland.
Less cyclonic PV
More cyclonic PV

28. 350K PV – 18hr
Wetter members are initialized with cyclonic PV anomalies to the south and west of Irene. These anomalies help to steer Irene inland.
Less cyclonic PV
More cyclonic PV

29. 350K PV – 18hr
Wetter members are initialized with cyclonic PV anomalies to the south and west of Irene. These anomalies help to steer Irene inland.
Less cyclonic PV
More cyclonic PV

30. 350K PV – 36hr
Wetter members are initialized with cyclonic PV anomalies to the south and west of Irene. These anomalies help to steer Irene inland.
Less cyclonic PV
More cyclonic PV

31. With what mechanism did Irene slow the approaching trough?

32. 250 hPa Divergent U – 15hr
In the wetter members, Irene experienced greater outflow on its western side. This outflow may have contributed to blocking the approaching trough.
Talk about mean first
easterly anomalies
westerly anomalies

33. 250 hPa Divergent U – 15hr
In the wetter members, Irene experienced greater outflow on its western side. This outflow may have contributed to blocking the approaching trough.
Talk about mean first
easterly anomalies
westerly anomalies

34. 250 hPa Divergent U – 15hr
In the wetter members, Irene experienced greater outflow on its western side. This outflow may have contributed to blocking the approaching trough.
Talk about mean first
easterly anomalies
westerly anomalies

35. Downscaling
However, the GFS is less than ideal for modeling terrain and mesoscale processes.
Our 0.5 degree GFS output was downscaled to 3 km using WRF, with physics comparable to those employed in HRRR.

36. WRF 3km Ensemble Mean Precipitation
WRF 3km Predicted Accumulations (mm)
Observed Accumulations (mm)
Mention time period
Observation source: EOL

37. WRF 3km Ensemble Mean Precipitation
WRF 3km Predicted Accumulations (mm)
Observed Accumulations (mm)
Mention time period
Observation source: EOL

38. WRF 3km Ensemble Precipitation Spread
Observed Catskills Precipitation (170 mm)

39. Three hypotheses to explain variability:
Wetter members have stronger upslope forcing over the Catskills than drier members.

40. Three hypotheses to explain variability:
Wetter members have stronger upslope forcing over the Catskills than drier members.
Wetter members have greater moisture convergence over the Catskills than drier members.

41. Three hypotheses to explain variability:
Wetter members have stronger upslope forcing over the Catskills than drier members.
Wetter members have greater moisture convergence over the Catskills than drier members.
Wetter members have stronger Q-vector convergence over the Catskills than drier members.

42. Objective Clustering of Ensemble Members
Used k-means algorithm to sort the ensemble members into three clusters, based on the 39-h horizontal distribution of precipitation over the domain 41.5–43.5 N, 73–76.5 W.
39-h was the interval of maximum precipitation rates over the Catskills.
Three clusters is enough to accurately portray the variability between members.

43. Objective Clustering of Ensemble Members
Cluster 3: Western Cluster
Cluster 2: Eastern Cluster
Cluster 1: Low Rainfall

44. Hypothesis 1: Upslope Wind Angle
Forecast Hour: 39
900-hPa Winds
3-hourly Precipitation
Wetter, Western Cluster
Drier, Eastern Cluster

45. Hypothesis 1: Upslope Wind Angle
Forecast Hour: 39
900-hPa Winds
3-hourly Precipitation
Wetter, Western Cluster
Drier, Eastern Cluster

46. Hypothesis 2: Moisture Convergence
Forecast Hour: 39
1000–700-hPa Mean Winds
Integrated Moisture Trans.
Wetter, Western Cluster
Drier, Eastern Cluster

47. Hypothesis 3: Q-Vector Convergence
Forecast Hour: 39
700-hPa Isotherms
700-hPa Q-Vector Conv.
Wetter, Western Cluster
Drier, Eastern Cluster

48. Wetter, Western Cluster
Drier, Eastern Cluster
V10 • ▽Zs

49. Wetter, Western Cluster
Drier, Eastern Cluster
Rain initialed by upslope, but primarily forced by Q-vector conv.
V10 • ▽Zs

50. Wetter, Western Cluster
Drier, Eastern Cluster
There is strong upslope present initially, but very little moisture available for it to produce rain.
V10 • ▽Zs

51. Wetter, Western Cluster
Drier, Eastern Cluster
Although initial upslope is similar to the eastern cluster, it coincides with increased moisture, causing much greater rainfall.
V10 • ▽Zs

52. Wetter, Western Cluster
Drier, Eastern Cluster
Q-vector conv. replaces upslope forcing, maintaining high rain rates.
V10 • ▽Zs

53. GFS Conclusions
Precipitation differences in the GFS ensemble are largely due to differences in Irene’s position.
Analysis errors associated with a PV anomaly to the SW of Irene appear to be related to track differences.
Initial steering flow anomalies set up a feedback loop with an upstream trough, causing the pattern to amplify.

54. WRF Conclusions
When downscaled to 3 km with WRF, precipitation variability in the 80- member ensemble is driven by factors other than storm track (although storm track does play a role).
The WRF ensemble mean does a very good job forecasting precipitation amounts over the Catskills, due to its superior terrain resolution and ability to simulate mesoscale processes.
Precipitation in the wetter WRF members appears to be driven by a combination of terrain effects, synoptic forcing, and high available moisture, while precipitation in the drier WRF members appears to be driven primarily by synoptic forcing.

55. General Conclusions
A small east-west deviation in a member’s storm track can have a huge effect on the amount of rain received by a particular location.
Clustering ensemble members into specific forecast scenarios can reveal more information than just using the ensemble mean and standard deviation.

56. Questions?