Molly Smith Ryan Torn Kristen Corbosiero and Philip Pegion NWS Focal Points Steve DiRienzo and Mike Jurewicz Fall 2016 CSTAR Meeting 2 November 2016 Motivation Landfalling ID: 537394
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Ensemble variability in rainfall forecasts of Hurricane Irene (2011)
Molly Smith, Ryan Torn, Kristen Corbosiero, and Philip PegionNWS Focal Points: Steve DiRienzo and Mike Jurewicz Fall 2016 CSTAR Meeting2 November, 2016Slide2
Motivation
Landfalling TCs are often associated with widespread heavy precipitation, which can lead to devastating flood events. Slide3
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. Slide4
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?Slide5
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.Slide6
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 2013–2015.Slide7
GFS Ensemble Mean Precipitation
GFS Predicted Accumulations (mm)
Observed Accumulations (mm)
Observation source: EOLSlide8
GFS Ensemble Mean Precipitation
GFS Predicted Accumulations (mm)
Observed Accumulations (mm)
Observation source: EOLSlide9Slide10Slide11
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 2013–2015.Members were ranked by the amount of precipitation they brought to the Catskill region of New York during this time period. Slide12
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 2013–2015.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. Slide13
Why did some storms track father west?Slide14
Hypothesis: Differences in storm track are associated with differences in steering flow.Slide15
westerly anomalies
easterly anomalies
Composite Difference Plots
250-850 hPa Zonal Steering Flow at InitializationSlide16
westerly anomalies
easterly anomalies
Comparison of 10 wettest ensemble members and 10 driest
Composite Difference Plots
250-850 hPa Zonal Steering Flow at InitializationSlide17
westerly anomalies
easterly anomalies
Comparison of 10 wettest ensemble members and 10 driest
Vectors: Ensemble mean
Composite Difference Plots
250-850 hPa Zonal Steering Flow at InitializationSlide18
westerly anomalies
easterly anomalies
Comparison of 10 wettest ensemble members and 10 driest
Vectors: Ensemble mean
Colors: Standardized difference between wet and dry members
Warm colors: Wet members have greater value
Cold colors:
Wet members have
lesser value
Composite Difference Plots
250-850 hPa Zonal Steering Flow at InitializationSlide19
westerly anomalies
easterly anomalies
Comparison of 10 wettest ensemble members and 10 driest
Vectors: Ensemble mean
Colors: Standardized difference between wet and dry members
Warm colors: Wet members have greater value
Cold colors: Wet members have lesser value
Stippling: Significant at 95% level
Composite Difference Plots
250-850 hPa Zonal Steering Flow at InitializationSlide20
250-850 hPa Zonal Steering – 00hr
Easterly flow anomalies arise early in the wetter members.
westerly anomalies
easterly anomaliesSlide21
250-850 hPa Zonal Steering – 00hr
Easterly flow anomalies arise early in the wetter members.
westerly anomalies
easterly anomaliesSlide22
Potential vorticity (PV) anomalies are associated with wind flow anomalies. Is that a factor here?Slide23
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.More cyclonic PV
Less cyclonic PVSlide24
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.More cyclonic PV
Less cyclonic PVSlide25
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.More cyclonic PV
Less cyclonic PVSlide26
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.More cyclonic PV
Less cyclonic PVSlide27
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.More cyclonic PV
Less cyclonic PVSlide28
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.More cyclonic PV
Less cyclonic PVSlide29
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.More cyclonic PV
Less cyclonic PVSlide30
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.More cyclonic PV
Less cyclonic PVSlide31
With what mechanism did Irene slow the approaching trough?Slide32
In the wetter members, Irene experienced greater outflow on its western side. This outflow may have contributed to blocking the approaching trough.
250 hPa Divergent U – 15hrwesterly anomalieseasterly anomaliesSlide33
In the wetter members, Irene experienced greater outflow on its western side. This outflow may have contributed to blocking the approaching trough.
250 hPa Divergent U – 15hrwesterly anomalieseasterly anomaliesSlide34
In the wetter members, Irene experienced greater outflow on its western side. This outflow may have contributed to blocking the approaching trough.
250 hPa Divergent U – 15hrwesterly anomalieseasterly anomaliesSlide35
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.Slide36
Observed Accumulations (mm)
Observation source: EOL
WRF 3km Ensemble
Mean Precipitation
WRF 3km Predicted
Accumulations (mm)Slide37
Observed Accumulations (mm)
Observation source: EOL
WRF 3km Ensemble
Mean Precipitation
WRF 3km Predicted
Accumulations (mm)Slide38
WRF 3km Ensemble
Precipitation SpreadObserved Catskills Precipitation (170 mm)Slide39
Three hypotheses to explain variability:
Wetter members have stronger upslope forcing over the Catskills than drier members.Slide40
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.Slide41
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.Slide42
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.Slide43
Objective Clustering of Ensemble Members
Cluster 1: Low RainfallCluster 2: Eastern Cluster
Cluster
3: Western ClusterSlide44
Hypothesis 1: Upslope Wind Angle
Wetter, Western Cluster
Drier, Eastern Cluster
Forecast Hour: 39
900-hPa Winds
3-hourly PrecipitationSlide45
Hypothesis 1: Upslope Wind Angle
Wetter, Western Cluster
Drier, Eastern Cluster
Forecast Hour: 39
900-hPa Winds
3-hourly PrecipitationSlide46
Hypothesis 2: Moisture Convergence
Wetter, Western ClusterDrier, Eastern Cluster
Forecast Hour: 391000–700-hPa Mean Winds
Integrated Moisture Trans.Slide47
Hypothesis 3: Q-Vector Convergence
Wetter, Western ClusterForecast Hour: 39700-hPa Isotherms700-hPa Q-Vector Conv.Drier, Eastern ClusterSlide48
Wetter, Western Cluster
Drier, Eastern ClusterV10
• ▽Z
sSlide49
Wetter, Western Cluster
Drier, Eastern ClusterV10
• ▽Z
s
Rain initialed by upslope, but primarily forced by Q-vector conv.Slide50
Wetter, Western Cluster
Drier, Eastern ClusterV10
• ▽Z
s
There is strong upslope present initially, but very little moisture available for it to produce rain.Slide51
Wetter, Western Cluster
Drier, Eastern ClusterV10
• ▽Z
s
Although initial upslope is similar to the eastern cluster, it coincides with increased moisture, causing much greater rainfall.Slide52
Wetter, Western Cluster
Drier, Eastern ClusterV10
• ▽Z
s
Q-vector conv. replaces upslope forcing, maintaining high rain rates.Slide53
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.GFS ConclusionsSlide54
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.WRF ConclusionsSlide55
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.General ConclusionsSlide56
Questions?