Assimilation of weather radar observations at the UK Met Office David Simonin 6th WMO workshop on the Impact of Various Observing Systems on NWP 10-13 May, 2016

Acknowledgements Met-Office radar team Lee Hawkness-Smith (Reflectivity) Bruce Macpherson and Gareth Dow (UK4) Jo Waller (Correlated observation error) John Nicol (Refractivity)

Content: Motivation UK radar network and observations Impact on forecasts Doppler wind Doppler wind + LHN of rain rate Direct assimilation of reflectivity How to increase positive impact QC Observation operator Observation Error Conclusion

Motivation The development of operational convective-scale NWP (e.g. NWP-based nowcasting system) models that represent convection explicitly require the development of high resolution data assimilation systems to provide initial conditions at the appropriate scale. Radar data are the “perfect” candidate for assimilation: High resolution High repetition time Large area coverage

Radar network and observations

UK Radar Network 18 radars currently in the network PPI Scan Introduction 18 radars currently in the network PPI Scan 5 elevations between 0˚ and 9˚ 1˚ Ray, 75/300/600 m Gate Volume scan available every 5 minutes Format: ODIM HDF5 (OPERA) One weather radar ~10 million obs. per hour reflectivity, refractivity and Doppler wind

Two modes of acquisition UK Radar Network Two modes of acquisition Long Pulse acquisition 600m / 250km Short Pulse acquisition 75m / 100km Reflectivity Doppler Wind Phases LHN of rain rate Doppler wind Operationally assimilated Direct assimilation Doppler wind with correlated obs error Refractivity change Active research

Impact on forecast

Impact on forecast Doppler Radial wind NDP domain (1.5km) 3DVAR, Hourly cycle, nested in UKV. 70 Levels LBCs updated every 30mins; refreshed every 6 hours Super-observations, 1 volumes scan (analysis time) Example: T+2 rain rate

Impact on forecast Doppler Radial wind Aggregated RMSE – Wind profiler (u-comp.) Aggregated RMSE – Doppler wind

Aggregated FSS using hourly rain accumulation Impact on forecast Doppler Radial wind Aggregated FSS using hourly rain accumulation

Doppler Radial wind + LHN of rain rate Impact on forecast Doppler Radial wind + LHN of rain rate UK4 (4km) model LBCs provided by NEA (global from 2012) 3DVAR, 3 hourly Impact UK index (1 month) - Doppler wind and LHN rain rate Weighted Basket of Indices / Combo of ETS & RMS scores Vis Precip Cloud Cover Cloud Base Height Temp Wind Overall -0.006 +0.237 +0.022 +0.018 +0.048 +0.091 +1.64% Courtesy of Bruce Macpherson and Gareth Dow

Reflectivity Observations Impact on forecast Reflectivity Observations Direct assimilation of reflectivity observation 4Dvar hourly cycle / UKV (No outer-loop) 3 volumes scans (0, 15, 30 minutes) Both dry and wet observations are used. Radar control Experiment In the case of spurious or excessive precipitation, the reflectivity assimilation scheme is beneficial (but the effect is small). Overall, there is a strong dry bias. Assimilation dominated by dry observations. Reduce weight More thinning Scheme affected by PF model limitations. introducing outer-loop The key issue to be addressed before reflectivity assimilation can be considered a candidate to replace latent heat nudging is the dry bias of the assimilation scheme. The dry bias may simply be due to the fact that the vast majority of reflectivity observations are dry, and thus the overall weight of the dry observations exceeds that of observations of precipitation. Thus Var may find a better fit to the reflectivity observations by reducing precipitation everywhere than by fitting the relatively small fraction of observations of precipitation. Generating cloud where none previously exists is not possible in a linear model such as the PF model. The use of multiple outerloops in Var would help reduce this problem, as the full non-linear forecast model is able to generate cloud Ideas to investigate for addressing this issue include: thinning the dry observations more than rainy observations, assigning a larger observation error to dry observations than rainy observations, using an asymmetrical Huber norm, where a larger weight is allowed for positive innovations than for negative innovations Courtesy of Lee Hawkness-Smith

How to increase positive impact

Impact from Quality control Example: Doppler radial wind gross error Radar failure Sea clutter Unfolding As John show in the opening presentation of this workshop, Quality Control for satellite level 1/2 observation is key to the impact. The same is try for Radar data. Here is an example of problem that could occur when acquiring Doppler radial wind.

Impact from Quality control Improvement in acquisition Improvement in acquisition (dual polarisation) enables a better classification. Old QC New QC Sea clutter black listed Courtesy of Nawal Husnoo

Impact from Quality control Retrieving Changes in Refractivity Refractivity change retrieval depends on the assumption that a change in the phase results only from a change in the refractive index. But it can result also from the motion of the target.  We need good quality clutter [A] [B] [C] [D] Refractivity change derived using a Quality index  The quality index is based on the average coherence of the target and is used as a weight in the retrieval. Refractivity change derived without the Quality index 

Impact from Quality control Using additional parameters Make use of extra parameters such as the spectral width. Low spectral width: Smooth flow High spectral width: Turbulent flow Noise contamination Doppler radial wind Doppler Spectral width Improve the Quality Control Refine the observation error Assign weight to the super-observation etc...

Impact from the Observation Operator The observation operator provides the link between the model variables and the observations. It can be multi-variant and represent transformation(s) to go from model space into observation space. Will influence: O-B and O-A (fit to obs.) Representativeness error In practice: How complex should it be? Beam propagation, Beam broadening Beam attenuation (due to hydrometeors), Hydrometeor fall speed (for radial velocities) Should we use a retrieval? Radar derived Refractivity’s changes retrieved from change in phase over dt and dx.  The retrieval will introduce some correlated observation error  Should we try to assimilate directly changes in phase? It is a lot harder!

Impact from the Observation Operator Example: Doppler radial wind Original Observation Operator O-B RMSE over 1 month Original H New H Introducing Beam Broadening Reflectivity weighting (bright band) Beam broadening to represent the cross section. Improvements visible in the O-B Reduced VAR’s performance: Detrimental to the other wind observations.

Impact from the Observation Error R = correlated error + uncorrelated error Observation errors arise from four main sources: Instrument error Representativity error Observation operator error Observation pre-processing Super-Observation: It will reduce data volume and uncorrelated observation errors. It will not remove the correlated part of the error. Apply some thinning: It could be used to reduce the effect of the correlated error. Too much thinning could remove some convective-scale information.

Impact from the Observation Error Example: Doppler radial wind Methods to derive R: Desroziers diagnostic (rely on O-B and O-A)  retrieve full R Desroziers diagnostic: σo2 is function of altitude. (Not gate size) σo2 and correlations function of H and processing Influence of H on R Large reduction in length scale at far range (78km) Slight reduction at near range (18km) Operational H (triangles) Improved H (diamonds). Horizontal correlations larger than expected Tests will start in the coming weeks. Courtesy of Joanne Waller

Conclusion: Convective-scale NWP has benefited from the assimilation of weather radar observations. However, we still have a long journey ahead before we can make the most of it.

Conclusion: Convective scale NWP model are increasing in size:  There is now a need for well design data exchange program. Standardisation (meta data and additional variables) Common file format OPERA (a program from EUMETNET) is a good example. Some important areas of research: Direct reflectivity assimilation  replacement to LHN Refractivity assimilation  will provide surface humidity information Use of non-diagonal observation error matrix Keep improving the observation quality

Thank you! Questions?