dymecs the evolution of thunderstorms in the met office unified model kirsty hanley robin hogan john...
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DYMECSThe evolution of thunderstorms in the Met Office Unified Model
Kirsty HanleyRobin HoganJohn NicolRobert PlantThorwald Stein
Emilie CarterCarol HalliwellHumphrey LeanAndrew Macallan
Mal ClarkeAlan DooDarcy Ladd
Thorwald Stein ([email protected])
Convection-permitting models (e.g. UKV) struggle with timing and characteristics of convective storms
Nimrod UKV
Original slide from Kirsty Hanley
• Model storms too regular (circular and smooth)• Not enough small storms (smaller than 40 km2)• Model storms have typical evolution (not enough variability)
• Track rainfall features in Nimrod data and UKV surface precipitation
• Analyse bulk storm statistics (area, mean rainfall)
• Use tracking information for real-time tracking with Chilbolton
• Study storm height evolution• Derive vertical velocities from
RHI scans through convective cores
How to evaluate thunderstorms
Tracking storms
Tracking storms
• At T+1, compare image with previous time step
• Use TITAN overlap method to check for storm movement:
K1(T) L1(T+1)
U
B= (K1,L1)
IfOV(K1,L1) = A(B)/A(K1) + A(B)/A(L1) > threshold (e.g. 0.6)ThenL1(T+1) is same storm as K1(T)
Tracking storms• L1 gets a new label (no overlap)• L2 gets a new label (OV(K1,L2) <
threshold)• L3 gets label of K1 (OV(K1,L3) >
OV(K1,L4)) and defined as “parent”
• L4 gets a new label, but defined as “child”
• L5 gets label of K2 (OV(K2,L5) > OV(K3,L5)) and property “accreted K2, K3”
K1
L1
L2
L3
L4
K2
K3
L5
K4
Tracking storms• What if K4 were fast-moving?
– Use velocity information…
• Taking velocity as displacement of centroid brings trouble for breaking/merging events.
• Use FFT method to track displacement between rainfall images at larger scale
K1
L1
L2
L3
L4
K2
K3
L5
K4
Storm statistics – 07-20 UTC
Maximum rain-rate Storm area
Original slide from Kirsty Hanley
Too few weakly precipitating storms
Too low maximum rain rate?Hail? Errors in Nimrod?
Too few small storms (less than 40 km2)
• Bin storms by area – 10 storms per bin• Compare bin-averaged storm area with area-averaged rain-
rate of each bin.
Nimrod UKV
Storm statistics – 04-20 UTC
Original slide from Kirsty Hanley
Small but intense storms Small storms with weak precip
Sensitivity studies – total precipitation
3D Smagorinsky Prognostic graupel
KK autoconversion Rhcrit = 0.99
Original slide from Kirsty Hanley
Observations (Nimrod)
Normalised timeNormalised time
… and mean rainfall
Model (UKV 3Z)
Obtain mean evolution of area
Combine area and rainfallfor mean storm life cycle
Observations (Nimrod)
Normalised mean rainfallNormalised mean rainfall
Model (UKV 3Z)
Modelled storms stay too long at peak area
Modelled storms all have peak rainfallat the same time
Tracking storms
… with Chilbolton
• Per storm, store:– Area– Azimuth– Range– [u,v]– Centroid– Bounding box– Et cetera...
• Local rainfall maxima within storm (core, cell)for vertical profiles
50
100
150
200
Prioritizing Storms
Too near: Miss tops of storm ortakes too long to finish RHI
Too far: Miss low-level precipitationand coarser resolution
Distance from radarRainfall
Prioritizing StormsSweet spot for RHI scans
• Scan scheduler:– Read nimrod scene– Prioritize storms– Issue radar commands
• Scan strategy:– 4 RHI scans through
each core in (clockwise-most) storm 1
– PPI volume scan (10 PPIs) through storm 1
– Repeat for next storm (anti-clockwise)
– Finish with low-level PPI back to 1
50
100
150
200
Prioritizing Storms
Tracking storm “1504”
Storm tracked in Nimrod data over 3-hour period shows growth of surface rainfall area as well as intensification in mean rainfall.
Tracking storm “1504”
Analysis of Chilbolton volume scans shows increase in height as area remains constant.Occurrence of 40dBZ coincides with higher mean rainfall in Nimrod data.
Tracking storm “1504”
Analysis of Chilbolton volume scans shows increase in height as area remains constant.Occurrence of 40dBZ coincides with higher mean rainfall in Nimrod data.
Tracking storm “1504”
Analysis of Chilbolton volume scans shows increase in height as area remains constant.Occurrence of 40dBZ coincides with higher mean rainfall in Nimrod data.
Tracking storm “1504”
Storm properties can be linked to different stages in life cycle.Attempt similar approach in hourly model cloud fields by forward modelling reflectivities.
UM Storm for same case
Growth
Stable
Intensification
20 dBZ height statisticsUKV (model) Chilbolton (obs)
Vertical velocities
Red towards radar Blue away from radar
Original slide from Robin Hogan
Estimation of vertical velocities from continuity
Vertical cross-sections (RHIs) are typically made at low elevations(e.g. < 10°)
Radial velocities provide accurate estimate of the horizontal winds
Assume vertical winds are zero at the surface
Working upwards, changes in horizontal winds at a given level increment the vertical wind up to that point
Must account for density change with height
Original slide from John Nicol
Reflectivity (dBZ) Radial velocity (m/s)
Vertical velocity (m/s)Horizontal velocity (m/s)
2D wind field (m/s)
10:55 UTC26/08/2011
Sets of four vertical scans through a convective core can be used to track radial velocity features to retrieve vertical velocities.
Original slide from John Nicol
23/08 07/03 04/04
07/08 26/08 27/08 03/11
04/11 13/12
Strong convection w ≈1.7m/s
Moderate convection w ≈1.3m/s
Weak convection w ≈0.5m/s
14/12 26/01 03/03 10/04 11/04
Moderate convection w ≈0.9m/s
Simple categorisation by std. dev. of vertical velocities (dBZ>15)
Original slide from John Nicol
Assume that convergence at the lowest detectable level extends down to the surface
No divergence into plane from cross-radial windsLikely to be true for linear structures (e.g. fronts) orientated perpendicular to the radar scan but not for circular structures.
Vertical cross-section viewed from above
Radar
Are vertical velocities underestimated in cases such as this? By a factor two?
Assumptions
Original slide from John Nicol
Discussion• Small storms: Need to
go to higher resolution?• Detecting convergence
in dopplerized network