UK Met Office snow data assimilation Samantha Pullen GCW Snow Watch Team – Second Session, 13-14 June 2016, Columbus, OH.

Contents Met Office NWP systems Land surface model Land surface analysis from satellite NH snow analysis for global NWP Snow analysis for UK NWP Plans for the future Discussion items Requirements for NWP Issues, gaps, GCW priorities

Unified model (UM) coupled with JULES land surface model Met Office NWP systems Global 17 km, 70 levels, forecast range to 6 days Main DA hybrid incremental 4D VAR Land surface DA – NH snow analysis, EKF soil moisture and soil temp, from screen errors in RH and T and ASCAT soil wetness. UK Variable resolution 4 km down to 1.5 km, 70 levels, forecast range to 36 hours Main DA incremental 3D VAR No land surface DA as yet Unified model (UM) coupled with JULES land surface model © Crown copyright Met Office

Land surface model: JULES Multi-layer snow module (Essery) From Best et al., 2011 (doi:10.5194/gmd-4-677-2011) Tiled – sub-grid heterogeneity – fluxes for each surface type: 5 Plant functional types: Broadleaf trees Needleleaf trees C3 grass C4 grass Shrubs Plus: Urban Inland water Bare soil Land ice Prognostic snow variables: Snow depth Snowpack bulk density Number snow layers Within layers: Thickness Ice mass Liquid mass Temperature Density Grain size Operational UKV, global implementation winter 2016. © Crown copyright Met Office

Land Surface Analysis Remote Sensing Observations Climatologies Data Assimilation Increasing obs processing complexity LAI, Albedo SST, Sea Ice* Snow Soil Moisture & Temp * From OSTIA Reconfiguration to different domains Develop Land Surface DA for different domains © Crown copyright Met Office

Gridbox Fractional Cover (Obs) 06 UTC Analysis Daily NH snow analysis NESDIS IMS 4km Relative resolutions Gridbox Fractional Cover (Obs) Relating fc and depth + Time delay check T+6 from previous cycle Snow amount (kgm-2) Using T+6 from previous day Remove snow © Crown copyright Met Office

UK snow forecasting The UK does not experience regular widespread snowfall except in the Highlands of Scotland Tends to be transient, often wet, shallow, multiple snowfall/melt cycles in one season. Low frequency, but high impact event – accurate analyses and forecasts of snowfall and lying snow extremely important Currently no snow observations assimilated in UK model (UKV) December 2010 Comparison of model vs observed (SYNOP) snow depth shows considerable scatter – potential for improvement to freely-evolving snow amount

Snow DA for the UK NWP system In development…. Data source Snow depth values Ground-based obs of snow depth, and state of ground (snow or no snow) from synoptic network SD where reported 0 m SD from snow-free state of ground reports 0 m SD from snow-free pixels 0.05 m SD from snow-covered pixels where model snow-free Satellite-derived snow cover from H-SAF (MSG-SEVIRI) daily product Model first-guess SD Optimal Interpolation Snow depth analysis

H-SAF and UKV comparisons with SYNOP H-SAF vs SYNOP agreement well over 90% most of period Often low coincidence of SYNOP with classified pixels - beware UKV vs SYNOP agreement high but not as high as H-SAF Large reduction in agreement rate 17-19th (both comparisons). Rapidly changing snow cover, timing of obs relative to falling snow, model evolution. SYNOP too sparse for detailed validation of snow edge. H-SAF vs SYNOP UKV vs SYNOP Overall results H-SAF closer to ground truth than UKV Where H-SAF and UKV differ, can infer that UKV errors proportionally greater than H-SAF errors on average Assimilation of H-SAF into UKV will add value (Repeated using common set of SYNOP for direct comparison) Ra Ro Ru H-SAF vs UKV 80.82 6.16 13.05 H-SAF vs SYNOP 89.38 (89.10) 0.64 (0.33) 9.98 (10.57) UKV vs SYNOP 82.65 (85.64) 4.86 (3.83) 12.49 (10.53)

17th December 2010 UKV underestimated snow Some (very) preliminary results… 17th December 2010 UKV underestimated snow Prototype system developed to test assimilation of: Snow depths from SYNOP reports Snow cover from H-SAF Separately at first – can explore sensitivities, ob errors, correlation length scales, spatial densities etc… © Crown copyright Met Office

Using SYNOP snow depth observations Bg snow amount Using SYNOP snow depth observations Snow depths where snow present Zero depths diagnosed from state of ground reports Analysis increments Analysis BGE correlation length scale 5.5 km - variable Limits horizontal influence of obs to ~ 50 km Obs error SD 0.04 m Bg error SD 0.03 m © Crown copyright Met Office

Using H-SAF snow cover observations Diagnosed snow depth obs (0.05 m) All usable pixels Bg snow amount Using H-SAF snow cover observations 0.05 m snow depth where H-SAF snow cover and UKV snow-free Zero depths diagnosed from snow-free H-SAF pixels Analysis increments Analysis BGE correlation length scale 5.5 km - variable Limits horizontal influence of obs to ~ 50 km Obs error SD 0.08 m Bg error SD 0.03 m © Crown copyright Met Office

Longer term... Station snow depth assimilation in global model + dense national networks now available on GTS + zero snow reports – better snow line Use of complementary satellite observations Wet snow extent from SAR (SEN3APP project) Snow water equivalent (SWE) from passive microwave (AMSR-2 v2?) Novel ob sources WOW (crowd sourced) GPS receivers EKF/EnKF? Microwave radiances with snow emission model? Met Office may adopt LIS (NASA) Hard for any single (remote-sensed) snow dataset to fulfil requirements for NWP assimilation – best approach may be to exploit the best features of a number of products to use in a complementary way. © Crown copyright Met Office

Coverage - depends on model domain, global/NH common Requirements for NWP Continuity - operational robustness, long-term security to justify development work, succession of satellite sources... Temporal resolution - daily sufficient for snow change timescales. Complementary data sources can have lower frequency Level of derivation – preferably not assimilation products themselves, e.g. contain some model information (not consistent), contain ground-based obs (not suitable if model already assimilates) Coverage - depends on model domain, global/NH common Cloud cover - how extensively does it affect product? High temporal sampling can mitigate to some extent. Multi-sensor approach can allow gap-filling. Is it the only data source? Errors - well-defined and documented, quality flags disseminated with product. SC 15-20%, SWE 10mm. Has to improve forecast/analysis to be used. Availability in near-real-time - daily product within half a day, 6-hourly within 3 hours Spatial resolution - guided by model resolution, doesn’t have to match. Higher resolution allows fractional cover calculation on model grid. Too low, representativity issues.

Issues, gaps, GCM priorities (from NWP perspective) Satellite-derived SWE (nrt, global) Improved uncertainty – WMO OSCAR Requirements – 20 mm min. forest Observations of density, grain size Snow cover – complementary data sources (forest, clouds) Highly derived, combined data sources less useful for NWP Ground-based snow depth Improved exchange of dense national network data Routine reporting of zero snow (mandated) Support for operationally robust, long-term datasets (succession planning between instruments) © Crown copyright Met Office

Questions? Thank you