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Watching your rear Neil I. Fox with help from Elizabeth Hatter and Liz Heiberg Dept. Soil, Environmental and Atmospheric Science University of Missouri.

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Presentation on theme: "Watching your rear Neil I. Fox with help from Elizabeth Hatter and Liz Heiberg Dept. Soil, Environmental and Atmospheric Science University of Missouri."— Presentation transcript:

1 Watching your rear Neil I. Fox with help from Elizabeth Hatter and Liz Heiberg Dept. Soil, Environmental and Atmospheric Science University of Missouri - Columbia

2 The importance of rear edge propagation velocity in flash flood forecasting  Why worry about your rear  How your rear moves compared to your middle  Using knowledge of your rear operationally  Stop the rear jokes

3 Why worry about your rear?  Current nowcasting tools (e.g., SCIT tracks, TITAN) concentrate on the motion of storm ‘center’  Excellent for Severe Weather warning  Flash flood forecasting: Interested in total duration of precipitation  Event management / Emergency services like to know end time

4 Storm velocity  Current storm tracks tell you when stuff will arrive  In (flash) flood forecasting the characterization of storm velocity (stationarity) is the least well parameterized factor.

5 This study looked at  A comparison of WATADS generated SCIT storm cell velocities and diagnosed rear edge motion vectors  Hypothesis The rear edge of a storm tends to move more slowly than the center (therefore durations of storms will be underestimated)The rear edge of a storm tends to move more slowly than the center (therefore durations of storms will be underestimated)

6 This study then looked at  The use of three measures of storm velocity as indicators of flash flood potential 1/v c1/v c 1/v r1/v r (v c -v r )/v c v r(v c -v r )/v c v r  The last of these is defined as the ‘Storm Duration Factor’

7 Difference between v c & v r

8 Storm duration factor (SDF) Duration (D) over a point at distance x : Rainfall accumulation (R a ) at x assuming steady-state rainfall rate R:

9 Data  Data was taken from a number of cases where (flash) flooding occurred  Level II data, analyzed using WATADS  A range of storm types, locations and situations  Not all storm cells observed caused flooding

10 Case studies  KLSX 05/07/2000  KEAX 08/18/2002  KHTX 05/05/2003  KEWX 06/28/2002

11 Analysis  Centroid velocities found using the NSSL algorithms (SCIT) fastest cell if there’s a choicefastest cell if there’s a choice  Rear edge velocities found by locating position from tracing centroid vector backward until Z falls below threshold  Positions for scans 15 mins apart used to calculate velocities (to reduce errors)

12 Rear edge location

13 Analysis  The three measures were plotted against rainfall accumulations for the subsequent 60 minutesfor the subsequent 60 minutes 25 km, 50 km and 75 km ahead of storm center location25 km, 50 km and 75 km ahead of storm center location

14 Comparison of v c & v r

15 Velocities  70 data points  Mean v c = 27.4 ± 7.7 m s -1  Mean v r = 13.0 ± 5.9 m s -1

16 1/v c & precip accumulation

17 1/v r & precip accumulation

18 SDF & precip accumulation

19 Results  All correlation coefficients are horrible  If you squint you can kind of see what you want to see  More work required!!

20 Extensions  More analysis  Breakdown by storm type  Better selection of points of interest areas of interest?areas of interest?  Incorporation of rainfall rate?


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