Transcript Data assimilation for nowcasting potential and limits of a

Data assimilation for nowcasting
potential and limits of a 3D variational approach
How to use the newer, better NWP models to help
nowcasting applications
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the AROME system: status and plans
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3DVar vs other techniques
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The concepts of balance & control
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Redefining the NWC/NWP boundary
Example of kilometric-scale NWP model:
AROME
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a new mesoscale convection-permitting NWP system built from
ECMWF's IFS, Europe's ALADIN, and France's Meso-NH models
Efficient spectral, semi-Lagrangian, semi-implicit NH compressible
numerics to allow fast real-time production
Reasonably sophisticated physics: prognostic TKE turbulence, 5species prognostic cloud microphysics, RRTM/FM radiation, tiled
surface scheme with soil, vegetation, lakes, sea, snow, town energy
balance, high-quality physiographies
With own data assimilation using radar, satellite, in situ operational
observations
1-way nesting in 10-km ALADIN data assimilation, itself nested in
20-km ARPEGE global 4DVar assimilation
Impact of NWP model resolution:
10km vs 2.5km, fields of low-level wind
(blue) and T (red) on the model grids
(different wind scaling in each figure)
Arome MCS simulation (04-08-94 15 to 18 UTC)
2,5 km / dt=15s / domain 144 * 144 / analysis Diagpack + Humidity bogus
Arome-2.5km 9h-range fog dissipation forecast
Meteosat visible image
The AROME data assimilation
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derived from ECMWF 4D-Var, plus mesoscale features
3DVar algorithm with FGAT (first guess at appropriate time)
allowing 1-min time resolution, with 1-hour cycling
Multivariate non-separable Jb structure functions derived from
ensemble statistics
Variational relaxation of large scales to coupling model
Use of automated screen-level obs network (T, Td, wind) with
variational control of PBL stability
Direct multivariate assimilation of geostationary IR radiances in
clear air (control of tropospheric humidity)
(planned) 1D cloud bogussing, starting with nowcasts of convective
clouds (ISIS/RDT software)
(planned) Direct multivariate assimilation of radial Doppler winds
from radars, and 1D radar precipitation bogussing
Impact study :
Dyn. Adapt.
Raingauges
Precipitation forecast
2004/07/18 12UTC
RR P12 – P6
3DVar
3DVar with SEVIRI
Objective score impact of 10km assimilation
vs. range (rmse and bias)
(pink=ARPEGE 4DVar dynamical adpation,
blue=ALADIN LAM 3DVar)
RR
RH2m
AROME real-time forecast on 21 June 2005
radar composite
15 TU
AROME fc
AROME fc started
dynamical adaptation
from mesoscale assimilation
AROME status & plans
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2.5km forecast model runs daily since May 2005 on 500km domain
with 1-minute timestep
Excellent performance on wind, low-level temperature and
convective weather
Quality is situation-dependent: long routine verification is needed
Assimilation runs at lower 10km resolution so far with very positive
impact on 0-12h forecast ranges wrt. dynamical adaptation
main target: 6-hourly 36-h NWP forecasts over France
(1000kmx1000km) in less than 30 minutes, in 2008 + hourly very
short-range forecasts
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priority on relocatable nowcasting applications in 2009-2010
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see presentations by G Jaubert, V Ducrocq, O Caumont, T Bergot
3DVar vs other techniques
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3DVar is complex software, but numerically cheap i.e. quick
(unpreconditioned ALADIN 3DVar converges in 50 iterations i.e.
about 5 minutes)
4DVar would take at least 10 times more computing, delaying
forecasts by tens of minutes: serious handicap for short-range NWP
short-window 4DVar works well for Doppler wind processing
Kalman filter can beat 3DVar in theory without the timeliness
penalty (heavy computations are done out of the critical path) but not
as mature yet for operations
3DVar physical foundation makes it nicely extensible to new
observation types (e.g. the ever-changing satellites)
future algorithm: probably a 3DVar basis mixed with short-window
4DVar + an ensemble KF focused on sensitive phenomena
Usable observations for convective systems assimilation
Concepts of balance & control
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3DVar smoothing functions & multivariate relationships must be specified a
priori by a « background Jb term » forecast error model:
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either you have observations of the phenomena that drive the
prediction: e.g. PBL humidity and convergence lines for convection
initiation --- the choice of DA algorithm will not matter
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or, you have indirect observations and you need to spatialize them
using likely Jb multivariate structures: local, weather-dependent
balance properties, to retrieve the driving phenomenon
It is often better to observe causes than effects (e.g.: ground precip)
Automatic model feedbacks & static Jbs work better for large scales
(geostrophism, Ekman pumping...) than mesoscales (PBL tops, orography,
3D convective & frontal structures)
Two competing strategies at mesoscale:
– « automatic » balance estimation: 4DVar and (ensemble)KF
– « ad hoc » spatialization: object bogussing from image processing
Perspective:
From NWP to Nowcasting
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challenge 1: refresh NWP forecasts faster than forecast error growth
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will require ad hoc structuring of NWP production systems (Rapid
Update Cycle, decentralized computing or superfast telecoms)
challenge 2: produce short-term direct forecasts of observables and end
user products
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simulation of satellite, radar etc. output at high resolution & quality
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human monitoring tool to intercept/correct poor model output
challenge 3: intelligent probabilistic post-processing of hard-to-model
weather elements e.g. storm risk areas vs. exact Cb cell location
How can we help humans to cope with
increasing data volumes
of irregular quality ?