© crown copyright met office stochastic physics developments for the met office ensemble prediction...

© Crown copyright Met Office

Stochastic Physics developments for the Met Office ensemble prediction systemRichard Swinbank, Warren Tennant, Anne McCabe and Claudio SanchezWWOSC

August 2014

© Crown copyright Met Office

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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