Radar data from cold air outbreak during Constrain

1. Radar data from cold air outbreak during Constrain Kirsty McBeath, Paul Field

2. Introduction • Looking at cases of cold air outbreak in the Northwest approaches • 4 flights during Constrain examined these conditions during January 2010 • Radar data from these cases used for comparisons with UKV model • This data is from a case on January 31st 2010 which coincides with flight b507 of the BAe-146 research aircraft MODIS 31st Jan 2010

3. Overview of Radar Observations

4. 1.91 ±0.36km 150km Composite radar data • Data available every 5 minutes for 24 hour period • Scans performed at a range of elevation angles: 0.5°, 1.0°, 1.5°, 2.5° • 4 scan angles intercept cells at different distances from radar • Data from 4 scan angles combined to produce one dataset which captures all cells • 0.5°: 75-150km • 1.0°: 54-85km • 1.5°: 32-64km • 2.5°: 30-42km

5. Model data

6. Reflectivity values for UM computed using model microphysics data, this reduces processing done on radar data and removes assumptions used when converting reflectivity to rain-rate Model Radar

7. Changes made to model • Shear dominated boundary layer • Local Richardson number used as indicator of shear dominating convection: if so then boundary layer diagnoses stratocumulus topped boundary layer (see Bodas-Salcedo et al. 2011) • Reducing ice nucleation temperature (Tnuc=-18°C) • Changing the primary hetrogemeous ice nucleation temperature from -10°C to -18°C. This inhibits ice production until the boundary layer top approaches 4km • Reducing autoconversion efficiency (AcE = 0.1) • The autoconversion efficiency is usually set to 0.55 (using the Cotton formulation of autoconversion), this is reduced to 0.1 to reduce the transfer if cloud water to precipitation

8. Changes made to model • No ice • All ice processes switched off by setting Tnuc=-50°C and converting any existing ice to liquid • Field PSD • Snow representation changed from standard exponential (Wilson and Ballard 1999) to representation of Field et al. (2007) • 3D Smagorinsky • Vertical mixing done explicitly using 3D Smagorinsky approach rather than boundary layer scheme

9. Model variations

10. Cluster analysis

11. Cluster Analysis 10dBz (~4mm/day) threshold used to select regions of precipitation in both datasets

12. Cell Size Radar mean size = 10.82±0.26km dimsq (AcE=0.1) and dimsi (Sh. Dom. B.L. and AcE=0.1) produce mean sizes within 1s of radar mean

13. Cell Size with lifetime Radar RMSE (from std dev) = 0.617 km dimsi fails to capture growth/decay of cells very well dimsz captures cell growth/decay quite well (has low RMSE value)

14. Cell lifetime Radar mean lifetime = 69±3mins dimsu (Sh. Dom. B.L., Tnuc=-18°C, AcE=0.1 & Field P.S.D.)has mean lifetime within 1s of radar Other runs with all do worse than control run for mean cell lifetime values

15. Cell reflectivity Radar mean reflectivity = 16.9±1.4 dBz dimsh (ctrl), dimsq (AcE=0.1) and dimsz (Sh. Dom. B.L. and Tnuc=-18°C) produce mean reflectivity within 1s of radar mean None of the variation runs produce mean cell reflectivity values closer to the radar mean than the control run

16. Cell reflectivity with lifetime Radar RMSE (from std dev) = 1.42 dBz Runs which do well for mean reflectivity, also do well when examining reflectivity with cell lifetime dimsq and dimsz both out-perform control when looking at RMSE over cell lifetime

17. dimsq(AcE = 0.1) and dimsz(Sh. Dom. B.L. and Tnuc= -18°C) out-perform the control run over 3 variables dimsk (Sh. Dom. B.L.), dimsn (3D Smag.) and dimsw (Tnuc= -18°C) all perform worse than the control run across all variables examined here * Within 1s of radar mean and better than control † Within 1s of radar mean but worse than control

18. Questions