numerical weather prediction (nwp) model fundamentals: a ... · numerical weather prediction (nwp)...

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

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

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

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.

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

NWP MODELS: DYNAMICS

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

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

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)

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

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”

1-km Simulated Radar Reflectivity

NSSL-WRFNCEP-WRF

Actual radar

valid at about

same time

NWP MODELS: PHYSICS

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

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

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

• 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

• 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

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

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

• 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

• 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.

NWP MODELS: DATA

ASSIMILATION

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

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

NWP MODELS: POST-

PROCESSING FORECAST DATA

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)

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

• 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

• 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

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

NCEP OPERATIONAL GLOBAL

FORECAST SYSTEM (GFS)

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)

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

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

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

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

Veg fraction Jan-Apr

Veg fraction, May-Aug.

Veg fraction, Sep.-Dec.

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

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

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

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

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

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

CANADIAN GLOBAL

ENVIRONMENTAL MULTISCALE

MODEL (GEM)

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)

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

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

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

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

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

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

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

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

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

GEM Data Assimilation SystemObservations Used

MODEL SHORTCOMINGS:

ERROR IN NWP MODELS

The rationale for Ensemble Forecast Systems (EFS)

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

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

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)

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)

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)?

ENSEMBLE FORECAST SYSTEMS:

MITIGATING EFFECTS OF FORECAST

UNCERTAINTY

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)

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)

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

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

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)

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

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

GEFS, CEFS, AND NAEFS

ARCHITECTURES

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.

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

CEFS Physics Diversity (all use GEM

dynamical core)

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

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

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

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 ….

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

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)

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: Bill.Bua@noaa.gov