Post on 09-Dec-2021
© Crown Copyright 2013. Source: Met Office
Dale Barker
International Symposium on DA, LMU, Munich, 25 February 2014
Acknowledge: B. Macpherson, S. Ballard, C. Johnson, G. Dow, R. Marriott,
C. Piccolo.
Convective-Scale Data Assimilation Applications
© Crown Copyright 2013 Source: Met Office
© Crown Copyright 2013. Source: Met Office
UK Convective-Scale Weather Impacts
Boscastle: 16/08/2004
CNN
Luxair crash
06/11/2002 – 18 dead
BBC
Accident on M4
near Cardiff 10/1
2/2003
BBC
BBC
Fog, low cloud
Birmingham Tornado
13/07/2005
Damaging winds Flash floods
Sue Ballard
© Crown Copyright 2013. Source: Met Office
UK Convective-Scale Weather Impacts
– Not All About Convection!
Somerset, January 2014
© Crown Copyright 2013. Source: Met Office
Outline
1. The Era of Convective-Scale Probabilistic NWP
2. Observations For Convective-Scale NWP
3. Observation Impact Via Data Denial Experiments
4. Convective-Scale DA
a. Motivation/Challenges
b. Covariances
c. 4D-Var
d. Role Of Ensembles
5. The Way Forward
© Crown Copyright 2013. Source: Met Office
1. The Era Of Convective-Scale NWP
© Crown Copyright 2013. Source: Met Office
UKV and MOGREPS-UK 1.5km 70L (40km model top)
3DVAR (3 hourly) (->hourly 4DVAR)
36hr forecast
8 times per day
12-member EPS - 2.2km 4x/day 36h
Global and MOGREPS-G 17km 70L (80km model top) (17km from PS34)
Hybrid 4DVAR – 60km
66hr forecast twice/day
144hr forecast twice/day
12-member EPS - 33km 4x/day 72hr
Met Office Main NWP Models (2014-
15)
• Before 2011, cost of global NWP
>> convective-scale NWP.
• In 2013, costs are similar.
• Next HPC (2015-16) – cost of
high-res NWP > global NWP.
• Similar situation for people costs.
© Crown Copyright 2013. Source: Met Office
Convective-Scale NWP (UKV 1.5km)
The ‘Morpeth Flood’, 6 Sept 2008 •Prototype UK-V : 1.5 km L70 •No Data Assimilation
•Driven by 12 UTC 05 Sept 12 km Regional Model •Starting from T+3 15 UTC Regional Model
0600 UTC 6 Sept Model Simulated Imagery
18UTC 5 Sept => 15UTC 6 Sept
Model Precipitation Rate
18UTC 5 Sept => 15UTC 6 Sept
© Crown Copyright 2013. Source: Met Office
The ‘Morpeth Flood’, 6th Sept 2008
Forecast Validation
12 km L50
1.5 km L70
Observed Totals
Morpeth
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2. Observations For Convective-Scale
DA
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Observations Assimilated Into UKV
Model (September 2013)
* *
* *
* Subset of data assimilated only in UK model
© Crown Copyright 2013. Source: Met Office
UKV Observations (Day)
SEVIRI SONDES SURFACE
• Observations are ingested via data assimilation 8 times a day, 24/7.
© Crown Copyright 2013. Source: Met Office
UKV Observations (Night)
• Need alternative sources of data at night: UAVs, night sonde launches, WV lidar?
SEVIRI SONDES SURFACE
© Crown Copyright 2013. Source: Met Office
Novel Obs for Convective-Scale DA
TAMDAR: T, u/v, RH, icing, turbulence:
Lidar Water Vapour:
SEVERI Cloud Top Height:
Aerosol/visibility:
Radar (reflectivity, refractivit
y)
PV Cell (radiation):
http://wow.metoffice.gov.uk
© Crown Copyright 2013. Source: Met Office
3. Convective-Scale DA
Current Observation Impact Via Data
Denial Experiments
Gareth Dow
© Crown Copyright 2013. Source: Met Office
Convective-Scale NWP – Why Bother?
* UK Index = Forecast skill for surface weather: surface u/T, cloud fraction/amount, precip, visibility
• Global NWP improvements included in baseline above (~1-2%/yr).
• So 10% benefit of UKV represents > 5-10yrs lead over global model.
Percentage
benefit wrt
UK Index*
© Crown Copyright 2013. Source: Met Office
UKV Benefit Over Global Model:
Broken Down by Weather Variable
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UK Data Assimilation Impact
Studies (3hourly 3DVAR)
No. of Forec
asts
Dates Period
4x21=84 Mar 10th → Mar 31st Spring
4x38=152 Jan 3rd → Feb 10th Winter
4x44=176 Nov 1st → Dec 14th Autumn
4x40=160 Jul 1st → Aug 10th Summer
Forecasts to T+24 at 00Z, 06Z, 12Z & 18Z
Period picked at Random
Period picked due to specific (SCu) event
© Crown Copyright 2013. Source: Met Office
Verification by Element
Colours indicate best setup for element/period
Full DA(9) Partial DA(11) DownScaler(4)
Mar 2012
Jan 2012
Nov 2011
Jul 2011
Overall Wind Temp Cloud B
ase Heig
ht
Cloud A
mount
Precip Vis Period
Note: Some boxes are more significant than others Partial DA = Assimilate only those obs assimilated in global model as well
© Crown Copyright 2013. Source: Met Office
Impact of Global/CS-Scale DA
DA = Cycling Convective-Scale DA, DS = Downscaler (Global DA)
NoDA = No DA (forcing through LBCs)
• Most benefit through CS-scale DA (DA vs DS).
• High-res DA benefit is ~half of total benefit of high-res NWP (~10%)
NWP Range (UK Index): T+6 to T+36
© Crown Copyright 2013. Source: Met Office
Impact of Global/CS-Scale DA
DA = Cycling Convective-Scale DA, DS = Downscaler (Global DA)
NoDA = No DA (forcing through LBCs)
• Increased benefit of DA for very-short range (LBC impact less)
Nowcasting Range: T+6 to T+12
© Crown Copyright 2013. Source: Met Office
UK4 Observation Network
denial experiments (Autumn period)
Surface +2.9%
Upper Air (excluding aircraft)
+2.1%
Aircraft +2.0%
Radar +2.0%
Satellite +1.7%
“Extra” (all obs networks not in
global model)
+0.5%
• Conclude: All ob types adding benefit, mainly from ‘standard obs’ in high-res DA.
© Crown Copyright 2013. Source: Met Office
4. Convective-Scale DA
a. Motivation/Challenges
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• Flexibility/need to introduce observations
more frequently (e.g. sub-hourly).
• Improved fit to observations through
more accurate (higher resolution, less
biased) background forecast.
• Reduced obs representivity error.
• Additional, high-res ‘novel’ observations
available (e.g. radar reflectivity)
Why Convective-Scale Data
Assimilation?
© Crown Copyright 2013. Source: Met Office
• Limited predictability -> probabilistic NWP/DA.
• Fast processes (e.g. convection) require rapid
DA updates -> initialization/spin-up issues.
• Careful treatment of LBCs/large-scales.
• Highly nonlinear (non-Gaussian fcst errors).
• Need to add value to global DA/NWP.
• Many novel observation types, complex errors.
• Imperfect high-res NWP models – model error.
• Coupling with land, hydrological, ocean DA.
Challenges For Convective-Scale DA
© Crown Copyright 2013. Source: Met Office
4. Convective-Scale DA
b. Covariance Modelling
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Convective-Scale Climatological B
Training data (J. F. Caron)
NMC method
• From a three week cycle of the hourly SUK 1.5km (16/03/10 to 06/04/10) in 3DVAR mod
e using the UKV Covstats
• 75 Lagged forecast differences (6h-3h) with forecast pairs using the same LBCs
© Crown Copyright 2013. Source: Met Office
• Variances (Eigen Values)
From a set of 75 6h-3h forecast using same LBCs, every 6h (valid at 00, 06, 12 and 18 UTC)
• First 5 vertical modes
Model
Lev
el
Model
Lev
el
Model
Lev
el
Model
Lev
el
Eigen Vector
Eigen Vector Eigen Vector
Eigen Vector
Convective-Scale Climatological B
Training data (J. F. Caron)
© Crown Copyright 2013. Source: Met Office
• SOAR horizontal length scales in vertical mode space
From a set of 75 6h-3h forecast using same LBCs, every 6h (valid at 00, 06, 12 and 18 UTC)
UKV = 130 km*
UKV = 130 km*
UKV = 90 km*
* For every mode
UKV = 150 km*
UKV = 180 km*
Convective-Scale Climatological B
Training data (J. F. Caron)
© Crown Copyright 2013. Source: Met Office
• Degree of mass - rotational wind coupling
From a set of 75 6h-3h forecast using same LBCs, every 6h (valid at 00, 06, 12 and 18 UTC)
Convective-Scale Climatological B
Training data (J. F. Caron)
© Crown Copyright 2013. Source: Met Office
Response to the assimilation of a pseudo
innovation = 2 K and observation error = 1 K
Single pseudo-obs experiments
NDP CVT CovStats UKV CovStats
temperature at level 20 (~850 hPa)
theta at level 20 (~850 hPa) theta at level 20 (~850 hPa)
© Crown Copyright 2013. Source: Met Office
Partition of Error Covariance Between
Different Regimes
Brousseau (2009)
No Rain
Rain
© Crown Copyright 2012. Source: Met Office
Adaptive Mesh Transform
• Motivation: lntroduce flow-dependence analysis
response near strong temperature inversions in
presence of stratocumulus clouds (no UKV
ensemble so can’t use hybrid method yet).
• Static adaptive mesh methods concentrate grid
points where there is a rapid variation of the
atmospheric field.
• Transformation from the physical grid to the
computational grid is guided by a monitor function:
• Grid transformation introduced within VAR control
variable transform:
Computational mesh
Nominal physical mesh
Analysis Increment for
single q ob above Sc band
x Uv UpUaUvUhv
22 )(1 zcM
(Piccolo & Cullen, 2011: Q. J. R. Met. Soc., 137, 631-640)
© Crown Copyright 2013. Source: Met Office
Iterative Calculation of Monitor Function
M (background-state - 3h forecast)
UK4 domain: 3 Jan 2011 00z
M (After 10 iteration 3D-Var) M (After 2nd converged 3D-Var)
Adaptive vertical grid provides a small positive impact to the UK index:
Period Vis Precip Cloud amount
Cloud base
Temp Wind Overall
23 Dec 2010 – 3 Jan 2011 -2.56% 5.48% -1.05% 3.03% 0.22% -0.04% +0.25%
10 Aug 2010 - 20 Aug 2010 12.20% 0.00% 0.00% 4.17% 0.23% 0.10% +0.55%
© Crown Copyright 2013. Source: Met Office
4. Convective-Scale DA
c. 4DVar
© Crown Copyright 2013. Source: Met Office
4D Variational Data Assimilation (4D-Var)
(old forecast)
(new)
(initial condition for NWP)
Outer-Loop: Re-run nonlinear model, update QC/obs/forcing, re-minimize.
Quasi-Static Variational Assimilation (QSVA) = Outer-Loop with increasing K
© Crown Copyright 2013. Source: Met Office
Example Multi-Outer Loop 4DVAR
Choi et al (2013): WRFDA applied in 6km Korean domain
black: KMA, blue: USAF, red: ROKAF
Conventional obs + Doppler
winds from 14 Korean radars:
4DVAR
OUTER
© Crown Copyright 2013. Source: Met Office
Example QSVA 4DVAR
Cost-function minimization:
Choi et al (2013): WRFDA applied in 6km Korean domain
0_0 0_10
0_20 0_30
© Crown Copyright 2013. Source: Met Office
Example QSVA 4DVAR
Relative error of 4DVAR linear model:
Choi et al (2013): WRFDA applied in 6km Korean domain
• Outer-loop/QSVA effectively extend the range of the validity of the 4DVAR linear
model
© Crown Copyright 2013. Source: Met Office
Example QSVA 4DVAR
Impact of QSVA on 4DVAR computational cost:
Choi et al (2013): WRFDA applied in 6km Korean domain
• QSVA also effectively reduces the cost of outer loop 4DVAR
• Possible extensions: Multi-resolution,
Note large cost
because only
running on 8PEs!
© Crown Copyright 2013. Source: Met Office
• 1.5 km NWP-based nowcasting system
• Southern UK only (May 2012 – April 2013)
• Hourly cycling 4DVAR (UKV=3hourly 3DVAR)
• Tested Improved (CVT) covariances
• No outer-loop/QSVA/reflectivity assimilation.
Nowcasting Demonstration Project (NDP) Sue Ballard
© Crown Copyright 2013. Source: Met Office
Sue Ballard
Verification of hourly precipitation forecasts against radar
Same validity time, Available at same time to forecasters
NDP better than older UKV forecast at all ranges
NDP better than STEPS extrapolation/merged nowcast from T+2
Fraction Skill Score (Roberts and Lean) for 1.0mm/h/40km square Against Forecast Range
NWP-Nowcasting: Precipitation Skill
July 2012 August 2012
Next stage: UK-wide implementation of hourly 4DVAR in 2015-2016.
© Crown Copyright 2013. Source: Met Office
4. Convective-Scale DA
d. Role Of Ensembles
© Crown Copyright 2013. Source: Met Office
• Are we able to predict convection?
• Crook (1996) found strong
sensitivity to surface conditions.
• Perturbations <= ob/model error
can make difference between
convection and blue skies.
• Solutions:
• Better/more observations.
• Improve models/DA.
• Probabilistic forecasting.
Predictability Of Convection
Integrated Rainwater Max. Vertical Vel.
Crook (1996)
© Crown Copyright 2013. Source: Met Office
MOGREPS-UK
• Initial implementation of
MOGREPS-UK:
downscaler of short-range
(T+3) 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
© Crown Copyright 2013. Source: Met Office
Met Office Convective-Scale Ensemble (MOGREPS-UK: 12 members, 2.2km resolution)
Ensemble Mean Visibility Ensemble Probability (vis<1km)
© Crown Copyright 2013. Source: Met Office
Impact of Re-Centring MOGREPS-UK
On Convective-Scale Analysis
• Current downscaling of global ensemble:
xUK=R(xG)
• Test re-centring the MOGREPS-G perturbations
around the UKV
(1.5km) 3DVAR analysis:
xUK=xa+R(xG)-R(xG0)
Christine Johnson
© Crown Copyright 2013. Source: Met Office
Bias
mslp
T2
Perturbed
Downscaled Perturbed has:
• Improvement for mslp
• No change for temperature
Christine Johnson
© Crown Copyright 2013. Source: Met Office
mslp
T2
RMSE
and spread
Perturbed has:
• lower error and spread
• faster growth
Perturbed
Downscaled
wind
RMSE ens mean
RMSE control
Spread
RMSE ensemble mean
RMSE control
Spread
RMSE control
RMSE ensemble mean
Spread
© Crown Copyright 2013. Source: Met Office
Ensemble-Based KM-Scale DA:
Point Reflectivity Covariances
Shading : Full Fields Line Contours : Error Correlations
Tong and Xue, 2005
© Crown Copyright 2013. Source: Met Office
CS-Scale DA: The Way Forward
1. Short-term:
• Continue to implement upgrades to observation network/NWP model in
current, operational DA (3DVAR).
• Further develop VAR covariances (e.g. large-scale treatment).
• Active contribution to optimal design of UK observing network.
• Tropical, convective-scale NWP-Nowcasting in Singapore.
2. Medium-term (on next HPC from 2015, underway):
• Develop/implement convective-scale 4DVAR (~2015) (subject to
performance assessment!).
• Expand MOGREPS-UK ensemble ready for CS-scale EnDA.
• Possibly test hybrid 4D-Var?
3. Long-term (>3 years away, but starting now)
• Consider ensemble-based alternatives to 4DVAR (J Flowerdew).
• Software framework for Met Office next-generation ‘LFRic’ model.
© Crown Copyright 2013. Source: Met Office
Thanks.
Any Questions?