Numerical Weather Prediction (NWP) Model Fundamentals: A...
Transcript of Numerical Weather Prediction (NWP) Model Fundamentals: A...
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Numerical Weather Prediction (NWP)
Model Fundamentals: A review(Plus 1/2 slide on climate models)
William R. Bua, UCAR/COMET
NCAR ISP Summer colloquium on African Weather and Climate
27 July 2011
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Outline
• What is the land-ocean-atmosphere system and
its connection to weather and climate?
• What is in an NWP system?
• What are the shortcomings of NWP models?
• Ensemble Forecast Systems: Mitgating the
shortcomings of NWP models
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The Land-Ocean-Atmosphere System
• Conservation of momentum,
heat, moisture
• Conservation of mass
• Hydrostatic approximation
• Dynamical equations are
coupled to
– The earth’s land/ocean surface
(friction/ turbulence, surface
evaporation/ evapotranspiration
and precipitation)
– Sub-grid scale physical/diabatic
processes (radiation, evaporation/
condensation, water phase
changes in precip processes,
cloud/radiation interaction, etc.)
Equations of Motion (Eulerian/Pressure coordinate form)
Simplified Equations
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The Land-Ocean-Atmosphere System
• Radiation processes– Incoming solar radiation
– Outgoing terrestrial radiation
• Microphysics– Condensation/evaporation/
sublimation
– Collision/coalescence, mixed phase processes, phase changes
• Convection (shallow *and* deep)
• Turbulent processes
• Land surface processes– Vegetation, soil moisture,
snow, surface energy balance and fluxes
Land and
topography
Precipitation
microphysicsConvection
Vegetation, soil moisture,
surface energy balance/fluxes
Shortwave
scattering
Incoming
shortwave
rad.
Reflection
Parameterized Land/Atmosphere Physical Processes
Longwave Radiation
Longwave Rad.
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Climate and Weather Prediction Models
General Circulation
(Climate) models
• Interested in climate details (means,
anomalies, standard deviations) at long
time scales
• Long, lower resolution runs
– Climate drift must be corrected
• Physical processes are simplified
• Slowly varying processes must be
accounted for
– A fully coupled system
– For multi-decadal climate change
• Interactive vegetation adapts to
changing climate
• Carbon cycle/slowly varying
atmospheric chemistry
Numerical Weather
Prediction (NWP) Models
• Interested in short time scales
and weather details
• Short, high resolution runs
– Climate drift not important,
especially for short range
• Physical processes are more
realistic (e.g. microphysics)
• Atmosphere/land coupling; slow
processes held fixed
– Fixed ocean (SSTs)/sea ice
– Fixed vegetation
– Fixed atmospheric composition/
greenhouse gases
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NWP MODELS: DYNAMICS
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NWP Models: Dynamics
• Horizontal coordinate
system
– Equations computed
either by
– Breaking down the
horizontal direction into
grid points and taking
differences from point to
point …. or
– Breaking down the large
scale flow into a series
of increasingly small
sine and cosine waves
and plugging those into
the equations to do the
calculations
…+
= Shortest wave
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NWP Models: Dynamics• Numerical problems
decrease with improved
horizontal resolution
– 2-point wave: poor
depiction, disperses without
advecting
– 7-point wave: better
depiction, disperses and
advects
– 20-point wave: well-depicted
and forecasted
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NWP Models: Dynamics
• Vertical coordinate
– Upper left: terrain-
following sigma
– Second: step-
mountain
– Third: hybrid sigma-
isentropic (theta)
– Fourth: hybrid sigma-
pressure (transition to
pressure complete at
about 100-hPa)
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NWP Model: Dynamics• Topography
– Only as good as the resolution of
the model
– Can choose representation of
topo in each grid box
• Envelope: valleys and passes
filled, blocking effect enhanced
• Silhouette: averages tallest
features, more valley details
• Mean: averages all features, trims
mtns, diminishes mtn blocking
– Standard deviation of topo in
grid box used for physical
processes
• Land/sea mask depends on
resolution also
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NWP Model: Non-hydrostatic Dynamics
• Add an equation for vertical accelerations (below)
• Use in high-res models (< about 5-10 km)
– Will result in mesoscale details of convective systems,
including outflow boundaries and cold pools
– Requires sophisticated physics, esp. for precipitation
– Costs more to run, usually small domain and short-range
forecast only
T-storms, mtn. waves ↑ for warm
moist air
relative to env.
weight of
precip. “pulling
on the air”
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1-km Simulated Radar Reflectivity
NSSL-WRFNCEP-WRF
Actual radar
valid at about
same time
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NWP MODELS: PHYSICS
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NWP Models: Radiation (SW)• Actual SW scatter/
reflection/ abspt.
btw. TOA and sfc.
– Blue vs. brown
lines
• RRTM model:
– UV (3 bands, 0.2-
0.4 μm)
– Visible (2 bands,
0.44 – 0.76 μm)
– Near IR (9 bands,
0.778 – 12.2 μm)
… 12.2
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NWP Models: Radiation (LW)
• Long (IR) wave radiation absorption/reemission in real
atmosphere (actual spectrum shown, with absorption
bands labeled with gaseous absorber)
– Many absorption lines in evidence
• RRTM scheme breaks LW spectrum into 16 bands for
calculations from about 4 μm to 400 μm wavelength
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NWP Models: Radiation and Clouds
• Real atmosphere
• Clouds reflect,
scatter, and absorb
SW radiation; some
SW reaches surface
• Clouds absorb and
reemit LW radiation
• Cloud layers, cloud
fraction, water phase
(liquid and/or ice), cloud
overlap all should be
addressed in NWP
models
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• Actual atmosphere
– Very small scales (mm - μm)
– Condensation/evaporation/sublimation
– Collision/coalescence (rain)
– Aggregation (snow, riming)
– Bergeron process (ice crystals grow
preferentially in mixed phase clouds)
– Fall rates depend on precip. type
• Models
– Bulk processes based on forecast T,
RH, vertical motion
– Precipitation sometimes assumed to
fall out instantaneously
NWP Models: Precip. Microphysics
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• Convection: Real atmosphere
– Conditional instability drives updrafts
(small scale, <1 km)
– Moisture condenses latent
heating, clds./precip.
– Downdrafts from precip. evap.
cooling and precip. drag
– End result: PBL cools/dries, free
atmosphere warms/moistens
• Conv. Param., NWP models
– Can’t resolve thunderstorms;
unresolved updrafts taken into acct.
– Impact on model variables estimated
• Convective trigger
• Vertical exch. of heat/moisture/
momentum at grid scale
– Shallow conv. treated separately
NWP Models: Convection
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NWP Models: Surface Processes
• Surface water balance
– Precipitation minus evaporation
as input
• Evaporation controlled by soil
moisture, vegetation, and local
weather conditions (wind, RH,
PAR)
• Surface energy balance
– Incoming minus outgoing
energy fluxes
– Sfc. water and energy balances
coupled via evaporation
0LESHLWGLWSWnet
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NWP Models: Turbulent Processes• Observed planetary boundary
layer from surface upward:– Contact and surface layers
– Mixed layer (day) or stable BL with
overlying residual layer (night)
– Capping inversion (night) or
entrainment zone (day)
• NWP version (sub-grid scale):– Contact layer: Fluxes depend on
wind, moisture, temperature forecasts
– Surface layer = constant flux layer
– Mixed and residual layer mixing
depends on wind shear, lapse rate,
diffusion coefficient
– PBL top • Found using forecast stability
• Moisture/momentum/heat exchange w/
free atmosphere modeled, sometimes w/
shallow convection
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• Free atmosphere sub-grid
scale mixing/turbulence
– Rate determined by lapse rate
and horizontal/ vertical wind
shear
– Aviation concerns where wind
shears are strong
• Typically near jet stream
• NWP
– Lapse rate and adjacent layer
and grid box wind shears used to
mix air
– Richardson number used as
proxy
NWP Models: Turbulent Processes
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• Mountain blocking and
gravity wave drag
– Depends on stability of flow
over topo, angle of wind
relative to topo, topo variability
– More stable: More blocking,
less gravity wave breaking
• NWP:
– Uses resolved topo height and
sub-grid scale topo standard
deviation
– Forecast stability partitions
flow between gravity wave
drag and mountain blocking
NWP Models: Turbulent Processes
Blocked flow around mtn.
Gravity wave-inducing flow over mtn.
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NWP MODELS: DATA
ASSIMILATION
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NWP Models: Data Assimilation (DA)
• Procedure:
– Start with short-range
forecast (1st guess)
and observations
– QC obs., combine
w/short-range forecast
– Weight fcst. and obs.
based on typical error
– Create new analysis
• Analysis minimizes
total error from all
sources
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NWP Models: Data Assimilation
• Advantages
– Uses short-range fcst. as 1st guess
• Short-range fcst. is usually good
– Analysis consistent with what model
can fcst. (no unrepresentative obs.!)
– Error characteristics “known” for
each observation type and 1st guess
• Limitations
– 1st guess error not flow-dependent
(or not flow-dependent enough)
– Errors usually assumed symmetric
around error location (unrealistic
where there are gradients)
– 1st guess not always good
– NWP models cannot correctly
forecast all high impact phenomena
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NWP MODELS: POST-
PROCESSING FORECAST DATA
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NWP Models: Model-Derived
Products
• Post-processing model-resolution data to
another grid resolution
• Statistical guidance
• Model assessment tools– Verification (will be covered in more detail later)
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NWP Models: Model-Derived Products
• Horizontal conversion
– Grid-point vs. spectral
• Raw data (either from native
grid-space or spectral space)
intermediate grid
• Derive parameters, then …
• Vertical conversion
– From native vertical coordinate
to standard output levels
• Derive Parameters, then …
• Horiz. interpolation to
dissemination grids
• Station data is taken from
native grid
– Interpolate to station or use
nearest grid point or grid column
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• Advantages of post-processed grids
– Can remove unneeded detail through averaging or
other smoothing
– Smaller, easier to send than native grid data
– Availability of derived products (e.g. stability indices,
tropopause data, freezing level)
• Limitations
– For some fields, degradation of data (e.g. static
stability diagrams like Skew-T may not be accurate)
or loss of detail (e.g. precipitation in regions of
rugged terrain)
NWP Models: Model-Derived Products
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• Stat. post-processing/MOS
– Relate NWP vars. to obs. wx.
via stepwise linear regression
(pt.-by-pt. or grouped by region)
• Requires sufficient model data to
get stable statistics
– Find variable that best-
minimizes residual fcst. error
– Stepwise, find each variable that
best-minimizes remaining error
– Stop when additional vars. do
not improve fcst.
– Apply to future forecasts
NWP Models: Model-Derived Products
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NWP Models: Model-Derived Products
• Statistical post-processing
– Model Output Statistics (MOS) used in S Africa for seasonal
forecasting
• Used in conjunction with regional climate models (RCM) nested
within a long-range forecast from general circulation model (GCM)
• Statistical post-processing (Landman et al., 2009) outperforms RCMs
nested in GCMs
– Not aware (yet) of MOS used in Africa for medium-range
forecast guidance
• Main use for MOS in America is in the short- to medium range
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NCEP OPERATIONAL GLOBAL
FORECAST SYSTEM (GFS)
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GFS and GEFS Dynamics
• Equations of motion (advection, continuity)
calculated in spectral space (sines and cosines)
– Exact mathematics for however many wavelengths
are calculated
– Truncation error from limiting the minimum
wavelength for calculations
– Operational T574 (~30 km) through 192 hours,
T190 (~ 90 km) from 192-384 hours
– Ensemble at T190 through 384 hours (~ 90 km)
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GFS Dynamics
• Vertical coordinate– Sigma-pressure (σ-p)
hybrid
– Levels placed as at right
• Advantage of hybrid (σ-p):– Sigma levels tilted too
much above 500-hPa; adverse for pressure gradient force calc.
– σ-p reduces this problem considerably
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GFS Dynamics• The physics grid
– Sub-grid scale physical process calculations done at grid points and transformed into “spectral space”
– Grid is 0.31 -0.38 resolution over southern Africa domain for operational, about 0.9 -1.1resolution for ensemble GFS
• Topography– T574 topo at right
• Highest point resolved is in Lesotho (2725-m)
– T190 topo next
• Highest points in Kenya and Lesotho (2096-m)
– Land-sea mask
• T190 loses islands, some lakes, shoreline resolution
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GFS Precipitation and Clouds
• Precipitation and clouds– “Grid-scale precipitation”
• Simple microphysical processes are modeled (“simple cloud”)
• Precipitation hydrometeors NOT tracked; fall out instantaneously
• Cloud water (in both liquid and solid phases) is tracked and used to determine radiative qualities
– Convective scheme• Simplified Arakawa-Schubert
(SAS)
• Physically realistic, includes observed convective processes
T382
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GFS Vegetation Type and Fraction
• Vegetation type and greenness fraction– Required to tap sub-
surface soil moisture• 13 types
• Climatological seasonal cycle for green vegetation fraction
• Vegetation canopy can retain up to 2-mm of water and drip-through is modeled
– Greenness fraction from climatology
• If excessive drought or wetness, may result in surface energy balance problems
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Veg fraction Jan-Apr
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Veg fraction, May-Aug.
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Veg fraction, Sep.-Dec.
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GFS Soil Model• Soil moisture model
– Surface layer (0-10cm)
– Root zone layer 1 (10-40cm)
– Root zone layer 2 (40-100 cm)
– Deep soil layer (100-200 cm)
– Diffusion and gravitation act sub-sfc
water, movement depends on soil
type (9 soil types)
• Soil thermal model
– Additional layer (200-800 cm) with
deep soil temp (~avg annual
temperature) constant (bottom
boundary condition)
– Diffusion of heat through layers with
top boundary condition provided by
surface (skin) temperature
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GFS Radiation
• Short wave (Chou, 1990,
1992)
– Predicted ozone (O3),
water vapor (H2O)
– Prescribed CO2
– Prescribed O2
– Aerosols
• RRTM long wave
– CO2, H2O, O3, CH4, N2O,
CCl4, chloro-
fluorocarbons
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GFS Radiation and Clouds
• Cloud radiative properties
depend on water phase (liquid
or solid), cloud water mixing
ratio
• Cloud fraction dependence
– For grid-scale clouds, cloud
water mixing ratio and RH
– For convective cloud, convective
precipitation amount
• Clouds are overlapped
randomly
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GFS surface layer
• Transport of heat and moisture in surface layer (treated as 1st model layer) depends on vertical gradients and winds
• Surface roughness affects the wind speed and depends on vegetation type
• Gradient of pot temp, q, wind determines sensible, latent heat fluxes, momentum flux
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GFS Planetary Boundary Layer and
Free Atmosphere Turbulence
• A “non-local scheme”
• PBL top set to where Bulk
Richardson number Ri is
first > 0.5
• Vertical diffusion coeff. fit
to flux at PBL top and
surface, which
determines the diffusion
rate through the PBL
• In free atmosphere, local
wind shear and stability
determine turbulent
vertical transports
Ri >0.5
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GFS: Data Assimilation System
• Gridpoint statistical interpolation system (GSI)
– 6-hour cycle
– 6-hour forecast is background (1st guess) for new analysis
– Observations weighted by relative accuracy then GSI
minimizes error taking all obs into acct.
• Background for analysis is assumed to be good quality, typically has
the heaviest weighting
• All obs moved to the analysis time for assimilation
• All obs are quality controlled before assimilation
– Balance constraint makes analysis internally consistent
between mass and wind
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CANADIAN GLOBAL
ENVIRONMENTAL MULTISCALE
MODEL (GEM)
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Canadian Global Environmental
Model (GEM)• Equations of motion
(advection, continuity)
calculated on a grid
– Truncation error from grid
length limitations
– 800x600 points
• 33x33 km at 49°N
• 33x50 km at equator
– Run at 00 and 12 UTC (to 240
and 144 hours, respectively)
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GEM Vertical Coordinate
• Hybrid vertical coordinate
– Flatter surface less
PGF error
• 80 vertical levels
– Model top at 0.1 hPa
• Best resolution in PBL,
tropopause/jet-stream
level and in stratosphere
– Improves assimilation of
satellite radiances
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GEM Topography
• Uses “mean orography” (average over grid
box)
– Data from U.S. Geological Survey 30” data
set
• Parameterizations related to topography
– Gravity wave effects on flow
– Mountain blocking
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GEM Physics
T382
• Precipitation and clouds– “Grid-scale precipitation”
• Simple microphysical processes are modeled (“simple cloud”)
• Precipitation hydrometeors NOT tracked; fall out instantaneously
• Cloud water (liquid and solid phases) tracked and used for radiation parameterization
– Convective scheme• Deep
– Kain – Fritsch conv. scheme
• Shallow
– Kuo-Transient
• Physically realistic, estimates observed convective processes
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GEM Vegetation Type and Fraction
• Interactive Soil-Biosphere-Atmosphere (ISBA)
– Vegetation derived from USGS vegetation type data
set
• 24 vegetation types
• Canopy water immediately available for evaporation
• Each type has unique evapotranspiration parameters
– Can have mixed land-water-sea ice-glacial ice grid
boxes; each has its own unique surface energy
balance
• Energy fluxes are area-weighted average
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GEM Vegetation Type and
Fraction
• All vegetation types in each grid box
accounted for
– Parameters are averaged for all types that
appear in grid box
– Land surface heat and moisture fluxes are
predicted from these *averaged* parameters
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GEM Soil Model
• Soil is divided up into clay and
sand fractions
– Clay strongly holds onto water
– Sand is more porous
• For moisture, two layers
– Surface layer 10-cm thick directly
evaporates
– Deep layer is accessed by
vegetation roots
• For temperature, two levels
– Surface skin level
– Deep soil level
Surface layer
Eva
po
tran
sp
iratio
n
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GEM Radiation Schemes
• New implementation in 2009
– Long- and shortwave radiation schemes
• K-distribution technique (Li and Barker 2005)
based on line-by-line calculations (accurate and
fast!)
– Cloud-radiation interaction
• Cloud water content in each model layer predicted,
phase diagnosed
• Optical depth of layer determined by clear air
radiatively active gases + cloud liquid/ice content
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GEM Planetary Boundary Layer
and Free Atmosphere Turbulence
• Vertical diffusion of heat, energy, and moisture by turbulence in PBL– Diffusion based on amt of turbulent kinetic energy in each layer and
…
– The distance a representative parcel from the layer can travel up and down before buoyancy stops its vertical motion (including distance from the ground)
– Includes buoyancy due to lapse rate, vertical wind shear (mechanical turbulence) and moist processes
• Non-topographic gravity waves accounted for in areas of convection, instabilities, and where geostrophic adjustment is occurring
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GEM Data Assimilation System
• Atmosphere
– 4-D VAR (x,y,z *and* time)
• No longer a simple snapshot of the atmospheric
conditions
• Now a time evolution of atmospheric conditions
during the assimilation done in “batches”
– Land surface
• Optimal interpolation of skin temperature and soil
moisture based on analyzed 1.5-m RH and air
temperature
– Not actual soil moisture data, but makes soil moisture
and skin temp consistent with screen temp and RH at
time of day when PBL is well-mixed
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GEM Data Assimilation SystemObservations Used
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MODEL SHORTCOMINGS:
ERROR IN NWP MODELS
The rationale for Ensemble Forecast Systems (EFS)
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Initial Conditions
• Initial condition (IC)
uncertainty
– Atmosphere is a chaotic system
with multiple flow regimes
– Lorenz (1963): Sensitive
dependence to ICs
• Varies based on atmospheric flow
• NWP models and IC
uncertainty
– Example: 500-hPa height
• Initial differences about 10-
20 meters
• Sensitive dependence to ICs
leads to large errors (150+
meters) by 96-h
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Model-specific Sources of Error
• Model uncertainty
– Dynamics truncation
error (because calculated
on grid, or up to “N” waves
in spectral models)
– Flows that cannot be handled well by the GFS
• Tight gradients
• Sharply curved flow
• Blocking and cut-off flows
Grid point truncation error
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Model-specific Sources of Error
• Physics– Convective
parameterization
– Topography (Orographic precipitation? Errors of representativeness for locales in areas of rough terrain?)
– Surface energy balance considerations
• Soil moisture
• Climatological vegetation fraction (does not vary based on climate anomalies)
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Model-specific Sources of Error
• Data assimilation systems
– Bad 1st guess (the 6-
hour forecast)
– Extreme excursions from
balance constraint (data
might be right, but will be
rejected)
– Lack of good data
– Time interpolation of data
– Coarseness of some data
(e.g. satellite radiances in
the vertical)
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Question:
• What kind of an NWP system could we
design to show us the impacts of:
– NWP model uncertainty/imperfections
– Initial condition uncertainty/imperfections
– The predictability of the current atmospheric
flow regime (given that the atmosphere is
chaotic)?
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ENSEMBLE FORECAST SYSTEMS:
MITIGATING EFFECTS OF FORECAST
UNCERTAINTY
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Terminology for Ensembles
• Ensemble Forecast Systems (EFS)
• Familiar EFSs
– National Centers for Environmental Prediction (NCEP, U.S.) :
• Global Ensemble forecast system (GEFS)
– Canadian Meteorological Center (CMC)• Canadian ensemble forecast system (CEFS)
– North American Ensemble Forecast System (NAEFS) GEFS + CEFS
– European Center for Medium-Range Forecasts (ECMWF)
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Terminology for Ensembles
• Ensemble member– One from among a full set of ensemble forecasts
• Ensemble control– The ensemble member run from the control initial conditions
• Ensemble perturbation– Initial condition and forecast differing from the control initial
condition and forecast
• Post-processing– Development of meaningful EPS products from the raw
ensemble output using statistical methods (we’ll cover some of those more in depth in this lecture)
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EFS: Architecture
• Goal: have as many plausible forecast outcomes as
possible
– IC uncertainty: choose ICs to
• Maximize forecast spread
• Minimize ensemble mean error (center perturbations on IC control, use
GOOD NWP models!)
– Model diversity to account for model imperfections/uncertainty
• Dynamical formulation differences
• Vary parameters in a physical parameterization, use different physical
parameterizations in one model, or use multiple models with different
parameterizations
• EFS usually 2-3 times coarser than high-res. deterministic
model in horizontal and vertical
– Computational constraints
– Higher resolution competes with wanting many forecast
possibilities
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EFS and Initial Conditions (ICs)
• Methods
– Bred vectors (NCEP)
• Find fastest-growing errors by
perturbing ICs and using
differences to “breed”
perturbations
– Singular vectors (ECMWF)
• Statistical method to find fastest-
growing errors
– Use EFS to determine 1st guess
flow-dependent uncertainty
(Ensemble Kalman Filter or
EnKF) and makes EFS
perturbations (multitasking)
• Directly links DA system and EFS
• Can be part of a hybrid 3D- or
4D-VAR DA system
ANL
P1 forecast
P4 forecastP3 forecast
P2 forecast
t=t0 t=t2t=t1
Rescaling
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EFS and Dynamical Core
• Where EFS has diversity in dynamics
– Use different formulation for dynamical
equations (e.g. spectral versus grid point,
change grid point configuration, etc.)
– Use different numerical methods for
calculations (e.g. parcel-following semi-
Lagrangian versus fixed point Eulerian)
– Use different parameters for calculations (e.g.
vertical diffusion)
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EFS and Physical Parameterizations
• Use different parameterizations (e.g. convection as at right)
• Tweak parameters within a parameterization (e.g. change vegetation type or vegetation resistance in a single soil model)
• Add stochastic (random) noise to time tendencies of temperature, moisture, winds from physical parameterizations
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EFS: The final product
• EFS samples the probability distribution of forecast outcomes
• Statistical analysis is necessary to post-process the large volumes of data produced by EFS and describe the probability distributions
Initial condition
probability distribution
7-day forecast
probability distribution
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GEFS, CEFS, AND NAEFS
ARCHITECTURES
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Model GFS (current)
Initial uncertainty ETBV1
Model uncertainty Stochastic physics2
Tropical storm Relocation of model
vortex to analysis
Daily frequency 00,06,12 and 18UTC
Hi-res control
(GFS)
T574L64
Low-res control
(ensemble control)
T190L28
00, 06, 12 and 18UTC
Perturbed members 20 for each cycle
Forecast length 384
Implemented 2010
GEFS Configuration
1 Ensemble Transform Bred
Vectors (with rescaling)
2 Random perturbation of
tendencies from physical
parameterizations every 6 hours
NCEP plans to increase GEFS
resolution to T254 (~55 km) for
the first 192 hours in NH spring
2012.
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Model GEM (current)
Initial uncertainty EnKF1
Model uncertainty Multiple physical
parameterizations2
Tropical storm Relocation of model
vortex to analysis
Daily frequency 00 and 12UTC
Hi-res control
(GEM)
33-km, 80 levels
Low-res control
(ensemble control)
~100-km, 28 levels
00 and 12 UTC
Perturbed members 20 for each cycle
Forecast length 384
Implemented 2009
CEFS Configuration
1 Ensemble Kalman Filter (from
data assimilation system)
2 Random perturbation of
tendencies from physical
parameterizations every 6 hours
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CEFS Physics Diversity (all use GEM
dynamical core)
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Summary (1)
• To forecast weather and climate
– Model the land-ocean-atmosphere-(and
cryosphere (ice)) system
– NWP models are used for the short-to-
medium range
– Climate models (a.k.a. general circulation
models or GCMs) use the same basic
formulation …
• … but deal with longer time scales, so ocean and
sea (and for century-long global change runs, even
land) ice should be considered variable, and
coupled to the atmosphere and
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Summary (2)
• Deterministic NWP models include
– Dynamics
• Fcst. resolvable motions with equations (e.g. advection)
– Physics
• “Parameterize” unresolved physical processes through
estimating their impact on forecast (e.g. convection)
– Analysis/data assimilation systems determine the
initial conditions from which to start the forecast
– Post-processing
• Write out forecast data to be assessed
• Relate model data to verification based on statistics
• Compute diagnostics to assess possible high-impact
events
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Summary (3)
• The U.S. NCEP Global Forecast System is a global
spectral model
– ~ 30-km equivalent grid point resolution and 64 levels
– 3D-VAR snapshot, obs data moved to analysis time
– Runs to 15 days, 4x per day
– Full model physics over land, but (for now) …
– ~ Fixed SST anomalies, sea ice can change in thickness
• Met. Service of Canada Global Environmental
Multiscale (GEM) model
– 33-km gridpoint model with 80 levels
– 4D-VAR, obs data assimilated at obs time by forecast model
– Runs to 10 days at 00 UTC, 6 days at 12 UTC
– Full model physics over land, but fixed SST and sea ice
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Summary (4)
• Sources of forecast error
– Chaotic nature of the atmosphere (“sensitive
dependence on initial conditions”, Lorenz 1963)
– Data assimilation errors (i.e. initial condition
uncertainty) lead to growing forecast errors and
ultimately very different forecasts
– Model imperfections
• Dynamics: Numerical approximations, truncation error
• Physics: Estimate of impact of unresolved processes
• No way to get a perfect single forecast in the
foreseeable future, which leaves us with ….
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Summary (5)
• EFSs to leverage IC uncertainty, NWP
imperfections
– “Perturbed” ICs based on forecast sensitivity,
increases range of forecast solutions
• Good to link NWP analysis system to the EFS
– NWP model imperfections addressed by
• Using different models
• Using different physical parameterizations within the
same model
• Modifying parameters in physical parameterizations
• Adding random noise to calculated impact from physical
parameterizations on the forecast variables
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For more information …
• MetEd NWP training websitehttps://www.meted.ucar.edu/training_detail.php
Click on topics, choose Numerical Modeling (NWP)
• Course 1 (NWP basics)
– Info on how NWP and EFS work
– Info on how specific models work, including specific EFS
– Introduction to specific new forecast tools
• Course 2
– Using NWP in the Forecast Process (applications to
operations)
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The NWP Training Team
• An “Army” of One at present
– Liaison between U.S. Environmental Modeling
Center’s NWP model development staff and
operational meteorologists
– Developing lessons and other training on
NWP models in operational context
– E-mail: [email protected]