Recent changes to UM microphysics
and forthcoming new functionality
Paul Field, Jonathan Wilkinson, Kalli Furtado, Ben Shipway, Adrian
Hill, Florent Malavelle
Convection in Met Office models at high resolution 13 June 2013
© Crown copyright Met Office
UM Microphysics:
• Based on Wilson and Ballard (1999) with modifications.
• Single moment, bulk representation. Mass-mixing ratios for each
category.
• Particle size distribution, particle fall speeds and transfers between
categories (e.g. melting, condensation) diagnosed each time step.
Cloud
Liquid
qv (T+0)
qcl (T+0)
qcl (T+1)
Cloud Ice /
Snow
qcf (T+0)
qcf (T+1)
qr (T+1)
qg (T+1)
Vapour
Original
Scheme
New for high
resolution
Rain
qr (T+0)
New Jan 2013
Graupel
qg (T+0)
qv (T+1)
Boundary Layer
Clouds
• Improved drizzle and
fog package (Wilkinson
et al 2013, QJRMS).
• New particle size
distribution (Abel and
Boutle, 2012, QJRMS).
Thompson
Scheme
Wilkinson et al (2013)
• Moving towards an
improved liquid to rain
autoconversion (Boutle
and Abel, 2012, ACP),
Marshallbut not yet
Palmer
implemented.
(Old UM)
Abel and Boutle (2012)
Introducing a new Generic
Ice PSD
• Microphysics changes
• Change to a more realistic ice particle size distribution:
• Data covers wider temperature and IWC range
• Better quality data (Anti-shatter filtering)
• Change to a fallspeed that is within the range of available
data
• Radiation changes
• New ice optical properties which have
• Same PSD as microphysics
• Same mass-diameter relation as microphysics
• Aims:
• Improve high cloud
• Tie parameterisations to better observations
Houze et al 1979
Field et al. 2007
Comparison of PSDs
• Insitu data from
Constrain, 2010
• Black:
• current global
model PSD
• Green:
• Proposed change
Fallspeed parameterisation
• Black data from
Mitchell 1996
• Purple: Global
• Solid = snow
• Dashed = ice
• Green: UKV PS32
• Proposed change
10 year validation against ‘GA5’:
Ice water content
215mb
600mb
Control
Experiment
Differences
Prognostic Graupel and Lightning
Forecasting
• Compare UM graupel
scheme to others using
Kinematic driver model
(KiD; Shipway and Hill
2012, QJRMS)
• Trial: Ottery St Mary
hailstorm in a 1 km
model.
Old
New
• Use graupel mass to
predict lightning
(currently using
McCaul et al 2009,
Weather and
Forecasting)
Graupel water path
A DYMECS lightning case:
1300-1900 UTC on 07/08/11
UKV
Sferics
600kmx300
dx=1km
Testing the parameterization of updraft velocity standard
deviation ( W ) in NWP
Run UM-UKV at x,y=1,5km horizontal resolution
over the ASTEX domain [800*1000]
Vertical velocity [m/s]
< W>L using 3 NN [m/s]
new < W>L [m/s]
Apply contribution of sub-grid variability
<
W > = 0,038 m/s
<
<
W > = 0,199 m/s
W > increases by a factor ~ 5.2
Application to UKV: cold air outbreakcase
Testing the parameterization of updraft velocity standard
deviation ( W ) in NWP
Run UM-UKV at x,y=1km horizontal resolution
over the CONSTRAIN domain [750*1500]
LES@250m
Apply contribution of sub-grid variability
<
<
<
W > = 0,825 m/s
W > increases by a factor ~ 2.7
W > = 0,202 m/s
<
W > = 0,550 m/s
© Crown copyright Met Office
DX=500M
DX=200M
DX=100M
DX=1000M
+ SGS
<
w> =
0.257
<
w> =
<
w> =
0.402
<
w> =
<
w> =
0.600
<
w> =
0.713
<
w> =
0.712
<
w> =
0.754
Questions??