Stochastic Physics developments for the
Met Office ensemble prediction system
Richard Swinbank, Warren Tennant, Anne McCabe and Claudio Sanchez
WWOSC
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
• The Met Office Global and Regional Ensemble Prediction System
(MOGREPS) is designed to quantify the risks associated with highimpact weather and uncertainties in details of forecasts.
Global
33km grid
Up to 7 days
00, 06, 12, 18 UTC
UK
2.2km grid
Up to 36hr
03, 09, 15, 21 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
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Met Office runs (24 members)
ensemble
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 coarsegraining methodology
applied to a cloudresolving 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.
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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
Transition forecast.
4 x 4 km
4 x 4 km
2.2 x 4 km
zone
2.2 x
2.2 km
4 x 2.2 km
4 x 2.2 km
• 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 4 km
4 x 4 km
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
BL
Parameter
Description
lam_meta
Combines parameters par_mezcla and lambda_min to
modify neutral / asymptotic mixing length
Replaces
par_mezcla &
lambda_min
BL
g0_rp
Added to Ri_crit
Range
0.2 / 1 / 3
par_mezcla -> lam_meta par_mezcla
lambda_min -> lam_meta lambda_min
Used to calculate stability functions and critical
Richardson number
5 / 10 / 40
Ri_crit -> 10 Ri_crit / g0_rp
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
MP
RH_crit
Threshold of relative humidity for cloud formation (level
3)
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
0.6 / 1 / 1.4
0.90 / 0.92 / 0.94
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
Forecast probability of visibility less than 1km
Observations
No RP
scheme
With RP scheme
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
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.
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Thank-you
any questions…?
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