Assimilation of Radar Reflectivity in
high resolution NWP
Lee Hawkness-Smith
MetOffice@Reading
RMetS High Resolution Data Assimilation 19 April 2013
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Data Assimilation Group,
MetOffice@Reading
• Collaboration with Universityy of Reading
g
• Research theme: Use of novel observations for
nowcasting and NWP prediction of convective
or urban-scale weather
• See our posters!
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Overview
• Weather radar reflectivity observations
• What are they and what do they tell us?
• How many of them are there?
• What quality issues are there?
• Why assimilate radar reflectivity observations?
• Assimilation methods
• The Met Office approach
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Radar reflectivity
y observations
What are they and what do they tell us?
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How many
y of them are there?
18 operational radars in the British Isles
U tto 5 scans every 5 minutes
Up
i t outt tto 250 kkm
Millions of observations in just minutes!
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What q
quality
y issues are there?
wet radome
-NOISEP rti l beamf ll ng
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Why assimilate them?
Nowcasting: forecast hazardous weather
and precipitation quantitatively and promptly
~ to T+6 within 15 minutes of data time
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Nowcasting techniques
Extrapolation
 Quick and simple technique
 What about orographic enhancement,
mesoscale dynamics,
dynamics etc.
etc ?
NWP
 More physically realistic modelling of the evolution
of weather events
 Requires
equ es high
g spat
spatial
a a
and
d te
temporal
po a resolution
eso ut o
modelling and data assimilation, and therefore
rapid collection, processing and dissemination of
g data volumes – expensive!
p
large
Merged
• Current compromise solution
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Data assimilation techniques
q
used for radar reflectivity
•
Latent heat nudging
Rescale model latent heat profiles by the ratio of
observation / model p
precipitation
p
ratios
•
1D-Var+3D/4D-Var
Use a 1D variational method to generate temperature and
humidityy increments for assimilation in 3D or 4D-Var
•
Incremental 4D-Var
Minimise the differences between the model and observations
g a simplified,
p
, linear model
byy iterating
•
Full-fields 4D-Var
Iterate a full non-linear forecast model in the minimisation – very
expensive!
p
•
Ensemble methods
Use ensemble members to represent background error
covariances
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Latent heat nudging
• P
Proven to
t be
b effective
ff ti att improving
i
i precipitation
i it ti iin fi
firstt ffew
hours of forecast
• Latent heat increments may not be dynamically consistent with
analysis
• Relationship between surface precipitation and latent heat
release in model column may break down at high resolution
• Does not use full 3D information from volume scans, only
derived surface rainrates
• Deriving surface rainrates leads to further errors
• Must maintain another system
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1D-Var+3D/4D-Var
1D
Var 3D/4D Var (indirect)
• Avoid handling the non-linearities of radar reflectivity
observations in the full 3D/4D-VAR
• Double use of background information may reduce impact of
observations and reinforce incorrect features in the model
• Information loss as observations treated in a column
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4D-Var
4D
Var (direct)
•
No a priori diagnostic adjustment of moisture or heating rate
•
Directly assimilate all observations together in a consistent framework
•
(Aim for) consistency in microphysics
•
It has worked well for satellite radiances
•
Requires a simple (for convergence) yet physically reasonable adjoint
model
d l – challenging
h ll
i ffor precipitation
i it ti processes
•
How do we assimilate observations when the background has zero
rain?
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Ensemble methods
• For pure ensemble methods, no linearisation or
adjoint required
• More suited for massively parallel computing
• Limited ensemble size due to computational
p
constraints means localization required
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The Met Office Approach
• Two methods developed:
• 1D-VAR+3D/4D-VAR indirect assimilation (Nicolas Gaussiat)
• Direct 4D-VAR assimilation (Lee Hawkness-Smith, Cristina
Charlton Perez)
Charlton-Perez)
• Make of use of mature Met Office VAR system
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Direct assimilation of radar
reflectivity in Met Office 4D-Var
• Use Nowcasting Demonstration Project (NDP) system
• Southern UK domain
• 1.5 km fixed resolution UM, 3 km PF model grid
• Hourly cycling
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NWP p
production process
p
(from a radar data assimilation
scientist point of view)
Other obs
RadarNet
Observation
Processing
System
High quality
radar data
Processed obs,
model equivalent
at obs locations
Previous
Unified Model
run
Model dump
MetDB
Variational
assimilation
(VAR)
Increments
Unified Model
Forecast run
From Radar to customer ASAP
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My focus
Post
processing
Customers
OPS: Processing
g and QC
Q with
respect to model
Nicolas Gaussiat
•
Filter obs using quality flags: clutter, speckle, partial beam blockage
•
Forward model from UM rainrate + increment:
• Corrections for beam bending and earth curvature
• Gaseous attenuation is simulated
• No beam integration, attenuation from rain
or forward modelling of ice yet
•
Calculate observation – background (O-B) values and use in quality control
•
Superobbing of O-B and thinning (to a scale the model can represent)
•
Output statistics of OPS runs to improve error specification and monitor radar
performance
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VAR: forward model and adjoint
j
links increments to total water to
Zobs
b - Zmodel
d l
•
PF model:
Added rainrate on each model level as a field
Autoconversion-like term from ((diagnostic)
g
) cloud water,, rain falls out in single
g
timestep (no evaporation)
•
Radar Reflectivity
y operator:
p
Same operator as used in OPS
Z [dBZ] = 10 log10(ZR + ZI) Currently only assimilate rain obs
ZR = 180R7/4.67
Rain component: UM + PF increment
Linearisation issues – may be better to assimilate something that scales with qr...
Minimisation assumes Gaussian error statistics(!) → transform variables
Accumulation may help
Currently just use obs where model simulated reflectivity > radar noise level
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Research areas for 4D-VAR direct assimilation
•
High
Hi
h quality
lit observations
b
ti
required
i d – collaboration
ll b ti b
between
t
U
University
i
it off
Reading, Met Office Observations R&D and Met Office Data Assimilation
•
Control variables – hydrometeor control variables required or let
hydrometeors spin up?
•
Need a good covariance model – challenging at convective scale
•
Optimum length of time window - balance between allowing evolution of
background covariances and timescale of validity of linearity assumptions
•
How to handle large innovations, including zero rain
•
Representation of ice microphysics
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Case study:
y 09Z 5 April
p 2011
Radar obs
Background
derived
model
Latent
4D-Var
heat
Reflectivity
nudging
Assimilation
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