1. The DYMECS project A statistical approach for the evaluation of convective storms in high-resolution models
Thorwald Stein, Robin Hogan, John Nicol, Robert Plant, Peter Clark
University of Reading
Kirsty Hanley, Carol Halliwell, Humphrey Lean

2. Finer-scale structures are represented at higher resolutions – but which is a more faithful physical representation?
1500m model
200m model
clouds
clouds
rainfall
rainfall

3. We can estimate storm morphology with radar!
40 days of convective weather in over southern England. Scanned over 1,000 storms with Chilbolton 3-GHz radar. 25-m dish allows for high-resolution observations (~300m).
Animation by Robin Hogan

4. DYMECS: Improve understanding of representation of convective storms in models
Track storms in Met Office rainfall radar data and model surface rainfall rate for storm-size distribution and life-cycle analysis.
Reconstruct 3D storm volumes from Chilbolton scans of tracked storms and relate to storm volumes in model reconstructed from simulated radar reflectivity.
Retrieve updraft strength and width from Doppler velocities in Chilbolton RHI scans using mass-continuity approach and compare with model updraft statistics in storms.

5. Model setup: One-way nested simulations of the Met Office Unified Model (UM).
Grid lengths of 1500m (Met Office operational forecast model, UKV) at 70 levels; 500m, 200m, and 100m (1750 by 1500) at 140 levels.
Single-moment, bulk microphysics. Prognostic cloud ice and liquid and rain, prognostic graupel optional (now operational). Forward model radar reflectivities for like-with-like evaluation.
500m, 200m, and 100m simulations run with 3D Smagorinski subgrid turbulence scheme.
log k
log E(k)
param
explicit
UM default cs=0.2 (so 40m mixing length at 200m). Vary mixing length to test sensitivity of convective storm representation (300m, 100m, 40m).

6. 1. Rainfall feature characteristics
The DYMECS project: A statistical approach for the evaluation of convective storms in high-resolution models
1. Rainfall feature characteristics

7. Shower case Deep case 500-m model best 200-m model best
1.5-km model best
200-m model best
Hanley et al. (QJRMS, 2015)

8. Shower case Deep case
By decreasing Smagorinsky mixing length, we can produce storm morphologies similar to a higher resolution simulation
Hanley et al. (QJRMS, 2015)

9. 1500m model Radar 200m model 1500m: Large storms are too intense
Rainfall rate
Rainfall rate
Storm diameter
200m model
Storm diameter
Rainfall rate
200m:
Small storms are too intense
Storm diameter
Hanley et al. (QJRMS, 2014)

10. The DYMECS project: A statistical approach for the evaluation of convective storms in high-resolution models
2. 3D Storm morphology

11. Storm 3D volumes (25 August 2012)
Radar
1500m
500m
Intense small storms?
High ice water content in deep storms?
200m
dBZ
100m
Stein et al. (MWR, 2014)
Stein et al. (BAMS, 2015)

12. What is the cloud-ice hydrometeor distribution above heavy precipitation?
2D-histogram of reflectivity versus height for profiles with dBZ near surface (10 mm/hr).
Lines show 25th, 50th and 75th reflectivity percentile for a given height.
200-m
Observations
Reflectivity distribution with height:
Model is more likely to have reflectivities above 30-dBZ for a given height than observations.
Model also generates high rainfall rates from low cloud tops too frequently.
Stress use of method to test microphysics
Stein et al. (MWR, 2014)

13. The DYMECS project: A statistical approach for the evaluation of convective storms in high-resolution models
3. Updraft statistics

14. How to retrieve updrafts from single Doppler radar
Estimate
Truth
Estimate vertical velocity from vertical profiles of radial velocity, assuming zero divergence across plane.
Match quantiles of model 2D estimates to model true vertical velocities – for many updrafts!
Develop scaling relationship between estimate and truth based on simulations.
Nicol et al. (QJRMS, 2015)

15. 1.5-km model mass flux order of magnitude too large
Mean vertical velocity as function of radius from centre of updrafts. Set to zero where: w<1m/s
OR where values start increasing again (“primary profile”)
Stein et al. (BAMS, 2015)
Nicol et al. (QJRMS, 2015)

16. Simulations do not represent multiple updrafts in a single storm
Observations
1500m
500m
200m
100m
Primary (monotonic) cloud widths proxy for updraft width
Nicol et al. (QJRMS, 2015)

17. Simulations do not represent multiple updrafts in a single storm
Observations:
Individual primary (monotonic) reflectivity profiles are joined up by larger cloud region with Z>20dBZ.
Z>20dBZ
200m and 100m simulations:
Individual primary (monotonic) reflectivity profiles remain isolated cells even when monotonicity is relaxed.

18. The DYMECS radar data are available through the BADC.
Future of the DYMECS framework
“Convection-permitting” O(1km) simulations only permit the largest storms, not the ordinary mid-latitude thunderstorm.
For adequate thunderstorm representation, we need <500m grid length: enter the “turbulent grey zone”…
What controls the size of thunderstorms? (Matt Feist, PhD candidate)
Multi-doppler radar observations to better constrain updraft retrievals (MC3E, future campaigns).
Multi-radar tracking to enable frequent sampling of developing storms for process-based analysis (we cannot rely on LES alone…).
Multi-frequency radar for better IWC constraints (use Doppler spectra when possible? Kollias et al.)
New operational lightning forecast – so thunderstorm representation and graupel are important! (Reading PhD project)
The DYMECS radar data are available through the BADC.