© crown copyright met office stochastic physics developments for the met office ensemble prediction...
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© Crown copyright Met Office
Stochastic Physics developments for the Met Office ensemble prediction systemRichard Swinbank, Warren Tennant, Anne McCabe and Claudio SanchezWWOSC
August 2014
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Contents
• Introduction to MOGREPS
• Stochastic Physics in MOGREPS-G
• Stochastic Kinetic Energy Backscatter
• MOGREPS-UK developments
• Revised Random Parameters scheme
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MOGREPS overview
UK2.2km gridUp to 36hr03, 09, 15, 21 UTC
Global33km gridUp to 7 days00, 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.
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).
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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.
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)
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)
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
2.2 x 4 km
4 x 2.2 km 4 x 2.2 km
4 x 4 km 4 x 4 km
4 x 4 km 4 x 4 km
2.2 x 2.2 km
Transition zone
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
Random Parameters for MOGREPS-UK
Scheme Parameter Description Range
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
BL A_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 charnock Sea surface roughness 0.01 / 0.018 / 0.026
BL g_1 Used to calculate cloud top diffusion coefficient 0.5 / 0.85 / 1.5
MP m_ci Parameter controlling ice-fall speed 0.6 / 1 / 1.4
MP RH_crit Threshold of relative humidity for cloud formation (level 3)
0.90 / 0.92 / 0.94
MP nd_min Droplet number concentration near the surface 20 / 75 / 100
MP x1_r Controls shape of rain particle size distribution 0.07 / 0.22 / 0.52
MP ec_auto Controls auto-conversion of cloud water to rain 0.01 / 0.055 / 0.6
Sensitivity of visibility to parameters
• Visibility forecasts for 02UTC on 12th Dec 2012 (data time 00 UTC 11th Dec)
Standard parameters Minimum A_1 Minimum nd_min
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.
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
Impact on fog probability
No RP scheme With RP scheme
Forecast probability of visibility less than 1km
Observations
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
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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.
Thank-you
any questions…?
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