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© Crown copyright Met Office Stochastic Physics developments for the Met Office ensemble prediction system Richard Swinbank, Warren Tennant, Anne McCabe.

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Presentation on theme: "© Crown copyright Met Office Stochastic Physics developments for the Met Office ensemble prediction system Richard Swinbank, Warren Tennant, Anne McCabe."— Presentation transcript:

1 © Crown copyright Met Office Stochastic Physics developments for the Met Office ensemble prediction system Richard Swinbank, Warren Tennant, Anne McCabe and Claudio Sanchez WWOSC August 2014

2 © Crown copyright Met Office Contents Introduction to MOGREPS Stochastic Physics in MOGREPS-G Stochastic Kinetic Energy Backscatter MOGREPS-UK developments Revised Random Parameters scheme

3 © Crown copyright Met Office MOGREPS overview UK 2.2km grid Up to 36hr 03, 09, 15, 21 UTC Global 33km grid Up to 7 days 00, 06, 12, 18 UTC Uncertainties in the prediction are represented using ETKF for (global) initial condition perturbations Stochastic physics 12 members of each ensemble are run every 6 hours Many probabilistic forecast products are based on a lagged pair of ensemble runs (24 members) The Met Office Global and Regional Ensemble Prediction System (MOGREPS) is designed to quantify the risks associated with high- impact weather and uncertainties in details of forecasts.

4 Stochastic physics schemes used by MOGREPS-G Random Parameters (RP): Knowledge uncertainty in values of physics parameters (entrainment rate, fallspeed, gravity-wave drag coefficient etc) Parameters vary during the forecast to sample uncertainty in the model evolution No convective parameters are currently included Stochastic Kinetic Energy Backscatter (SKEB): Injects wind increments proportional to the SQRT of diagnosed kinetic energy dissipation from semi-lagrangian advection and missing sources from deep convection Plan to include Stochastic Perturbation Tendency (SPT) (replacing RP) and SKEB in future standard Global Atmosphere model physics (GA7). © Crown copyright Met Office

5 SKEB random forcing pattern and wind increments Power spectrum: g(n)  {20;60} (was {5;60}) Deduced using coarse- graining methodology applied to a cloud- resolving model to give the power in a single mode as  (n) = n -1.27 This random forcing pattern modulates the diagnosed energy dissipation so energy is injected at selected scales.

6 © Crown copyright Met Office SKEB Changes in ENDGame ENDGame dynamical core is more active than New Dynamics SKEB energy injection had to be turned down to compensate Energy input wave numbers: 5-60 -> 20-60 Numerical dissipation component halved

7 Biharmonic SKEB The current version of SKEB uses a “Smagorinsky” formula to model numerical diffusion. A new version of SKEB uses “biharmonic” diffusion – closer to behaviour of semi-Lagrangian advection. Comparison of Dnum at approx. 10km (1-day average)

8 Biharmonic SKEB The Smagorinsky version mainly targets jets, but is excessive at high latitudes The Biharmonic version also maximises around jets, but is more evenly distributed with latitude. Comparison of zonal-mean Dnum (3-day average)

9 © Crown copyright Met Office Surface Perturbations Surface fields in each ensemble member had identical initial conditions, so to improve surface spread we trialled two schemes: SST perturbations are generated with a random pattern of prescribed spatial structure (implemented) SMC perturbations are formed through a simple breeding method (future) Both schemes help to address the under-dispersive T2m forecasts.

10 MOGREPS-UK MOGREPS-UK is currently just a downscaler of the MOGREPS-G ensemble forecast. Initial & boundary conditions from global forecast. Model physics as 1.5km UKV with no stochastic physics 4 cycles per day, 12 members to T+36. 2.2 x 4 km 4 x 2.2 km 4 x 4 km 2.2 x 2.2 km Transition zone

11 Random Parameters in MOGREPS-UK A first step to representing the uncertainties in convective-scale forecasts Motivation: to better represent uncertainties in low cloud and visibility Based on MOGREPS-G version but: Targeting appropriate BL / microphysics parameters, following advice from APP Combining associated parameters so that they vary together. Improved algorithm for time variation of parameters

12 Random Parameters for MOGREPS-UK SchemeParameterDescriptionRange BL lam_meta Replaces par_mezcla & lambda_min Combines parameters par_mezcla and lambda_min to modify neutral / asymptotic mixing length par_mezcla -> lam_meta par_mezcla lambda_min -> lam_meta lambda_min 0.2 / 1 / 3 BL g0_rp Added to Ri_crit Used to calculate stability functions and critical Richardson number Ri_crit -> 10 Ri_crit / g0_rp 5 / 10 / 40 BLA_1 Added to a_ent_shr Used in entrainment rate calculation and now included in a_ent_shr 0.1 / 0.23 / 0.4 BL charnockSea surface roughness0.01 / 0.018 / 0.026 BL g_1Used to calculate cloud top diffusion coefficient0.5 / 0.85 / 1.5 MP m_ciParameter controlling ice-fall speed0.6 / 1 / 1.4 MPRH_critThreshold of relative humidity for cloud formation (level 3) 0.90 / 0.92 / 0.94 MP nd_minDroplet number concentration near the surface20 / 75 / 100 MP x1_rControls shape of rain particle size distribution0.07 / 0.22 / 0.52 MP ec_autoControls auto-conversion of cloud water to rain0.01 / 0.055 / 0.6

13 Sensitivity of visibility to parameters Visibility forecasts for 02UTC on 12 th Dec 2012 (data time 00 UTC 11 th Dec) Standard parametersMinimum A_1Minimum nd_min

14 Time variation of parameters Each parameter value is applied for whole domain, but is varied in time Apply frequent, but small, parameter changes (AR1 process) Range defined by 3 values: minimum, nominal, maximum. Parameters are equally likely to be in each half of the range. Parameters no longer “stick” at min or max values.

15 Increased variability of fog The new MP and BL parameters lead to a wider range of low-visibility points, compared with no RP scheme. Number of points with visibility < 1km, for each member

16 Impact on fog probability No RP schemeWith RP scheme Forecast probability of visibility less than 1km Observations

17 MOGREPS-UK plans Short-term Use UKV analysis combined with perturbations from MOGREPS-G. First phase of stochastic physics – version of “random parameters” scheme suited for MOGREPS-UK. Longer term – (on new HPC) Hourly UK ensemble; combine several runs to make larger lagged ensemble Higher resolution (horizontal and vertical) Convective-scale ensemble data assimilation (needing much larger ensemble for DA cycling). Consider possible KE backscatter scheme for MOGREPS-UK

18 © Crown copyright Met Office Summary MOGREPS is designed to quantify uncertainties in the forecast – with a focus on the short-range and UK Current MOGREPS-G schemes are Stochastic Kinetic Energy Backscatter & Random Parameters Plan to introduce bi-harmonic SKEB, and include SPT scheme in standard Global Atmosphere Physics A new version of Random Parameters has been developed for MOGREPS-UK, with promising results.

19 Thank-you any questions…? © Crown copyright Met Office

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