452 NWP
2015
Major Steps in the Forecast
Process
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Data Collection
Quality Control
Data Assimilation
Model Integration
Post Processing of Model Forecasts
Human Interpretation (sometimes)
Product and graphics generation
Data Collection
• Weather is observed throughout the world and the
data is distributed in real time.
• Many types of data and networks, including:
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Surface observations from many sources
Radiosondes and radar profilers
Fixed and drifting buoys
Ship observations
Aircraft observations
Satellite soundings
Cloud and water vapor track winds
Radar and satellite imagery
Observation and Data Collection
Weather Satellites Are Now 99%
of the Data Assets Used for NWP
• Geostationary Satellites: Imagery,
soundings, cloud and water vapor winds
• Polar Orbiter Satellites: Imagery,
soundings, many wavelengths
• RO (GPS) satellites
• Scatterometers
• Active radars in space (GPM)
Quality Control
• Automated algorithms and manual intervention to
detect, correct, and remove errors in observed
data.
• Examples:
– Range check
– Buddy check
– Comparison to first guess fields from previous
model run
– Hydrostatic and vertical consistency checks for
soundings.
• A very important issue for a forecaster--sometimes
good data is rejected and vice versa.
3 March 1999: Forecast a snowstorm
… got a windstorm instead
Eta 48 hr SLP Forecast valid 00 UTC 3
March 1999
Pacific Analysis
At 4 PM
18 November
2003
Bad Observation
Forecaster Involvement
• A good forecast is on the lookout for NWP
systems rejecting bad data, particularly in
data sparse areas.
• Quality control systems can allow models to
go off to never never land.
• Less of a problem today due to satellite data
everywhere.
Objective Analysis/Data
Assimilation
• Observations are scattered in
three dimensions
• Numerical weather models
are generally solved on a
three-dimensional grid
• Need to interpolate
observations to grid points
and to ensure that the various
fields are consistent and
physically plausible (e.g.,
most of the atmosphere in
hydrostatic and gradient wind
balance).
Objective Analysis
• Interpolation of observational data to either
a grid (most often!) or some basis function
(e.g., spectral components)
• Typically iterative (done in several passes)
Objective Analysis/Data Assimilation
• Often starts with a “first guess”, usually the
gridded forecast from an earlier run (frequently a
run starting 6 hr earlier)
• This first guess is then modified by the
observations.
• Adjustments are made to insure proper balance.
• Often iterative
An early objective analysis
scheme is the Cressman scheme
3DVAR: 3D Variational Data
Assimilation
• Used by the National Weather Service
today for the GFS and NAM (called GSI)
• Tries to create an analysis that minimizes a
cost function dependent on the difference
between the analysis and (1) first guess and
(2) observations
• Does this at a single time.
3DVAR Covariances: Spreads
Error in Space
4DVAR: Four Dimension
Variational Data Assimilation
• Tries to optimize analyses at MULTIPLE
TIMES
• Tries to duplicate the observed evolution
over time as well as the situation at
initialization time.
• Uses the model itself as a data assimilation
too.
4DVAR Components
• Full non-linear model
• Tangent linear version of the full model
(linearized version of the forecast model)
• Adjoint of the tangent linear model -which
allows one to integrate the model
backwards. Tells sensitivity of final state to
the initial state.
4DVAR
• Typical runs the model back and forth
during an initialization period (6-12 hr),
roughly ten times.
• Substantial computational cost.
• Need to have adjoint and TL version of the
model.
• Currently used by ECMWF, CMC,
UKMET, and US Navy. NOT NCEP.
Many of the next generation data
assimilation approaches are
ensemble based
• Example: the Ensemble Kalman Filter
(EnKF)
Mesoscale Covariances
12 Z January 24, 2004
Camano Island Radar
|V950|-qr covariance
Surface Pressure Covariance
Land
Ocean
An Attractive Option: EnKF
Temperature observation
3DVAR
EnKF
Hybrid Data Assimilation:
Now Used in GFS
• Uses both 3DVAR and EnkF
• Uses EnkF covariances from GFS ensemble
in 3DVAR.
Next Advance ENVAR
• Use temporal covariances to spread impact
of observations over TIME.
• Will go operational NEXT WEEK.
• Has some of the properties of 4DVAR
(adjusts model evolution)
Vertical Coordinates and
Nesting
Vertical Coordinate Systems
• Originally p and z: but they had a
problem…BC when the grid hit terrain!
• Then eta, sigma p and sigma z, theta
• Increasingly use of hybrids– e.g., sigmatheta
Sigma
Sigma-Theta
Nesting
Why Nesting?
• Could run a model over the whole globe,
but that would require large amounts of
computational resource, particularly if done
at high resolution.
• Alternative is to only use high resolution
where you need it…nesting is one approach.
• In nesting, a small higher resolution domain
is embedded with a larger, lower-resolution
domain.
Nesting
• Can be one-way or two way.
• In the future, there will be adaptive nests
that will put more resolution where it is
needed.
• And instead of rectangular grids, other
shapes can be used.
Next Generation Global Models
Under Development!
• Will use different geometries
MPAS: Hexagonal Shapes
MPAS
NOAA FIM Model
Model Integration: Numerical
Weather Prediction
• The initialization is used as the starting
point for the atmospheric simulation.
• Numerical models consist of the basic
dynamical equations (“primitive equations”)
and physical parameterizations.
“Primitive” Equations
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3 Equations of Motion: Newton’s Second Law
First Law of Thermodynamics
Conservation of mass
Perfect Gas Law
Conservation of water
With sufficient data for initialization and a
mean to integrate these equations, numerical
weather prediction is possible.
Example: Newton’s Second Law: F = ma
One Form
Physics Parameterizations
• We need physics parameterizations to
include key physical processes.
• Examples include radiation, cumulus
convection, cloud microphysics, boundary
layer physics, etc.
• Why? Primitive equations with lack the
necessary physics or lack sufficient
resolution to resolve key processes.
Parameterization
• Example: Cumulus Parameterization
• Most numerical models (grid spacing of 12km is the best available operationally)
cannot resolve convection (scales of a few
km or less).
• In parameterization, represent the effects of
sub-grid scale cumulus on the larger scales.
Numerical Weather Prediction
• A numerical model includes the primitive
equations, physics parameterization, and a way to
solve the equations (usually using finite
differences on a grid)
• Make use of powerful computers
• Keep in mind that a model with a horizontal grid
spacing is barely simulating phenomenon with a
scale four times the grid spacing. So a 12-km
model barely is getting 50 km scale features
correct.
Numerical Weather Prediction
• Most modeling systems are run four times a day
(00, 06, 12, 18 UTC), although some run twice a
day (00 and 12 UTC)
• The main numerical modeling centers in the U.S.
are:
– Environmental Modeling Center (EMC) at the National
Centers for Environmental Prediction (NCEP)--part of
the NWS. Located near Washington, DC.
– Fleet Numerical Meteorology and Oceanography
Center (FNMOC)-Monterey, CA
– Air Force Weather Agency (AFWA)-Offutt AFB,
Nebraska
Major U.S. Models
• Global Forecast System Model (GFS). Uses
spectral representation rather than grids in the
horizontal. Global, resolution equivalent to 13 km
grid model. Run out to 384 hr, four times per day.
• Weather Research and Forecasting Model
(WRF). Two versions: WRF-NMM and WRFARW(different ways of representing the
dynamics). WRF is a new mesoscale modeling
system system that is used by the NWS and the
university/research community. AFWA also uses
WRF. The NWS runs WRF-NMMB and WRFARW. WRF-NMMB is run at 12-km grid spacing,
four times a day to 84h. Also smaller 4-km nests.
Major U.S. Models
• MM5 (Penn. State/NCAR Mesoscale Model
Version 5). Has been the dominant model in the
research community. Run here at the UW (36, 12
and 4 km resolution).
• COAMPS (Navy). The Navy mesoscale
model..similar to MM5
• There are many others--you will hear more about
this in 452.
• Forecasters often have 6-10 different models to
look at. Such diversity can provide valuable
information.
Major International NWP Centers
• ECMWF: European Center for MediumRange Weather Forecasting. The Gold
standard. Their global model is considered
the best.
• UK Met Office: An excellent global model
similar to GFS
• Canadian Meteorological Center
• Other lesser centers
Global Forecast System (GFS) Model
• Previous called the Aviation (AVN) and Medium Range
Forecast (MRF) models.
• Spectral global model and 64 levels
• Relatively primitive microphysics.
• Sophisticated surface physics and radiation
• Run four times a day to 384 hr (16 days!).
• Major increase in skill during past decades derived from
using direct satellite radiance in the 3DVAR analysis
scheme and other satellite assets.
• 13 km grid spacing equivalent over the first 10 days of
the model forecast and 35 km from 10 to 16 days (384
hours). Thus, it now essential a global mesoscale model
GFS
• Vertical coordinates are hybrid
sigma/pressure… sigma at low levels to
pressure aloft.
Vertical coordinate comparison across North America
GFS Data Assimilation (GDAS)
• Has a later data cut-off time than the mesoscale
models…and thus can get a higher percentage of
data.
• Uses much more satellite assets..thus improve
global analysis and forecasts.
• Major gains in southern hemisphere
• Hybrid Data assimilation based on 3DVAR
(they call it GSI) and GFE ensemble (next slide)
• Every 6 hr.
GFS Hybrid Data Assimilation
GFS is not the only global model
and is not the best
Higher Resolution Operational
Models
Major U.S. High-Resolution Mesoscale
Models (all non-hydrostatic)
• WRF-ARW (developed at NCAR)
• NMM-B (developed at NCEP Environmental
Modeling Center)
• COAMPS (U.S. Navy)
• MM5 (NCAR, old, replaced by WRF)
• RAMS (Regional Atmospheric Modeling
System, Colorado State)
• ARPS (Advanced Regional Prediction System):
Oklahoma
Operational Mesoscale Model History in US
• Early: LFM, NGM (history)
• Eta (mainly history)
• MM5: Still used by some, but mainly phased
out
• NMM- Main NWS mesoscale model, updated
Eta model. Sometimes called WRF-NMM and
NAM.
• WRF-ARW: Heavily used by research and
some operational communities.
• NMM replaced by NMM-B
WRF and NMM
History of WRF model
• An attempt to create a national mesoscale
prediction system to be used by both operational
and research communities.
• A new, state-of-the-art model that has good
conservation characteristics (e.g., conservation of
mass) and good numerics (so not too much
numerical diffusion)
• A model that could parallelize well on many
processors and easy to modify.
• Plug-compatible physics to foster improvements
in model physics.
• Designed for grid spacings of 1-10 km
WRF Modeling System
WRF Software Infrastructure
Obs Data,
Analyses
Static
Initialization
3DVAR Data
Assimilation
Dynamic Cores
Mass Core
NMM Core
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Standard Physics Interface
Physics Packages
Post Processors,
Verification
Two WRF Cores
• ARW (Advanced Research WRF)
• developed at NCAR
• Non-hydrostatic Numerical Model (NMM) Core
developed at NCEP
• Both work under the WRF IO Infrastructure
NMM
ARW
The NCAR ARW Core Model:
(See: www.wrf-model.org)
 Terrain following vertical coordinate
 two-way nesting, any ratio
 Conserves mass, entropy and scalars using up to
6th order spatial differencing equ for fluxes. Very
good numerics, less implicit smoothing in
numerics.
 NCAR physics package (converted from MM5 and
Eta), NOAH unified land-surface model, NCEP
physics adapted too
NWS
1
NMM —The
NAM RUN
• Run every six hours over N. American and adjacent
ocean
• Run to 84 hours at 12-km grid spacing.
• Uses the Grid-Point Statistical Interpolation (GSI)
data assimilation system (3DVAR)
• Start with GDAS (GFS analysis) as initial first guess
at t-12 hour (the start of the analysis cycle)
• Runs an intermittent data assimilation cycle every
three hours until the initialization time.
1-Non-hydrostatic mesoscale model, NAM: North American
Mesoscale run
NMM-B
• Hybrid sigma-pressure vertical coordinate
• 60 levels
• Betts-Miller-Janjic convective
parameterization scheme
• Mellor-Yamada-Janji boundary layer
scheme
NMM-B Details
• One-way nested forecasts computed concurrently with the
12-km NMM-B parent run for
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CONUS (4 km to 60 hours)
Alaska (6 km to 60 hours)
Hawaii (3 km to 60 hours)
Puerto Rico (3 km to 60 hours)
For fire weather, moveable 1.33-km CONUS and 1.5-km Alaska nests are also run
concurrently (to 36 hours).
• A change in horizontal grid from Arakawa-E to ArakawaB grid, which speeds up computations without degrading
the forecast
September 2011
NAM-B Upgrade
New NAM
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NEMS based NMMB
B-grid replaces E-grid
Parent remains 12 km to 84 hr
Four Fixed Nests Run to 60 hr
– 4 km CONUS nest
– 6 km Alaska nest
– 3 km HI & PR nests
• Single placeable 1.33km or 1.5 km
FireWeather/IMET/DHS run to 36hr
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NMMB 4-km Conus
NAM
• Generally less skillful than GFS, even over
U.S.
• Generally inferior to WRF-ARW at same
resolution (more diffusion and smoothing,
worse numerics)
Navy COAMPS (Coupled Ocean/Atmosphere Mesoscale
Prediction System)
• Sigma-Z
• Atmosphere
And Ocean
Accessing NWP Models
• The department web site (go to weather loops or
weather discussion) provides easy access to many
model forecasts.
• The NCEP web site is good place to start for NWS
models.
http://www.nco.ncep.noaa.gov/pmb/nwprod/analysis/
• The Department Regional Prediction Page gets to the
department regional modeling output.
http://www.atmos.washington.edu/mm5rt/
A Palette of Models
• Forecasters thus have a palette of model forecasts.
• They vary by:
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Region simulated
Resolution
Model Physics
Data used in the assimilation/initialization process
• The diversity of models can be a very useful tool
to a forecaster.
Rapid Refresh NWP
The Main Tool For Nowcasting
RUC: AKA-Rapid Refresh
• A major issue is how to assimilate and use the
rapidly increasing array of off-time or continuous
observations (not a 00 and 12 UTC world
anymore!
• Want very good analyses and very good shortterm forecasts (1-3-6 hr)
• The RUC/RR ingests and assimilates data hourly,
and then makes short-term forecasts
• Uses the WRF model…which uses a hybrid
sigma/isentropic vertical coordinate
• Resolution: Rapid Refresh: 13 km and 50 levels,
High Resolution Rapid Refresh (3 km)
Rapid Refresh and HRRR
NOAA hourly updated models
13km Rapid
Refresh (RAP)
(mesoscale)
Version 2 – scheduled
NCEP implementation
Q2 (currently 28 Jan)
3km HRRR
(storm-scale)
High-Resolution
Rapid Refresh
RAP
HRRR
Scheduled NCEP
Implementation Q3 2014
NCEP Production Suite Review
Rapid Refresh / HRRR
3-4 December 2013
87
RAPv2 Prediction System Overview
• Hourly updated mesoscale analyses / forecasts
• WRF-ARW model (Grell-3 cumulus param, Thompson
microphysics, RUC-Smirnova land-surface, MYNN PBL
scheme)
• GSI hybrid analysis using 80-member global ensemble
• 13-km, 50 levels, 24 cycles/day – each run out to 18 hours
• 6-hour catch-up “partial” cycle run twice per day from GFS
• Output grids: 13, 20, and 40 km CONUS, 32 km full domain,
11 km Alaska, 16 km Puerto Rico
• Use and downstream dependencies
• Used by SPC, AWC, WPC, NWS FOs, FAA, energy industry,
and others for short-range forecasts and hourly analyses
• Downscaled RAP serves as first guess for RTMA
• RAP serves as initial condition for SREF members
• RAP will be used to initialize Hi-Res Rapid Refresh (HRRR)
Observations Used
Rapid Refresh
Hourly Update Cycle
Partial cycle atmospheric fields –
introduce GFS information 2x/day
Cycle hydrometeors
Fully cycle all land-sfc fields
(soil temp, moisture, snow)
1-hr
1-hr
1-hr
fcst
fcst
fcst
Back- Analysi
groun s
d
Fields
3DVA
3DVA
Fields
R
R
Obs
Obs
11
12
13
Time
(UTC)
Hourly Observations
RAP 2013 N.
Amer
Rawinsonde (T,V,RH)
120
Profiler – NOAA Network (V)
21
Profiler – 915 MHz (V, Tv)
25
Radar – VAD (V)
125
Radar reflectivity - CONUS
1km
Lightning (proxy reflectivity)
NLDN, GLD360
Aircraft (V,T)
2-15K
Aircraft - WVSS (RH)
0-800
Surface/METAR
(T,Td,V,ps,cloud, vis, wx)
2200- 2500
Buoys/ships (V, ps)
200-400
GOES AMVs (V)
2000- 4000
AMSU/HIRS/MHS radiances
Used
GOES cloud-top press/temp
13km
GPS – Precipitable water
260
RAPv2 Hybrid Data Assimilation
13 km
RAP
Cycle
14z
13z
ESRL/GSD RAP 2013
Uses GFS 80-member ensemble
Available four times per day
valid at 03z, 09z, 15z, 21z
15z
80-member GFS EnKF
Ensemble forecast valid at
15Z (9-hr fcst from 6Z)
GSI
Hybrid
GSI
Hybrid
GSI
Hybrid
GSI HM
Anx
GSI HM
Anx
GSI HM
Anx
Digital
Filter
18 hr fcst
Digital
Filter
18 hr fcst
Digital
Filter
18 hr fcst
Rapid Refresh: 13 km and larger
domain
High-Resolution Rapid Refresh:
3 km, 1 hr, smaller domain
RTMA
(Real Time Mesoscale Analysis System)
NWS New Mesoscale Analysis
System for verifying model output
and human forecasts.
Real-Time Mesoscale Analysis
RTMA
• Downscales a short-term forecast to fineresolution terrain and coastlines and then uses
observations to produce a fine-resolution analysis.
• Performs a 2-dimensional variational analysis
(2d-var) using current surface observations,
including mesonets, and scatterometer winds over
water, using short-term forecast as first guess.
• Provides estimates of the spatially-varying
magnitude of analysis errors
• Also includes hourly Stage II precipitation
estimates and Effective Cloud Amount, a GOES
derived product
• Either a 5-km or 2.5 km analysis.
RTMA
• The RTMA depends on a short-term model
forecast for a first guess, thus the RTMA is
affected by the quality of the model's
analysis/forecast system
• CONUS first guess is downscaled from a 1hour RR forecast.
• Because the RTMA uses mesonet data, which
is of highly variable quality due to variations
in sensor siting and sensor maintenance,
observation quality control strongly affects the
analysis.
Why does NWS want this?
• Gridded verification of their gridded
forecasts (NDFD)
• Serve as a mesoscale Analysis of Record
(AOR)
• For mesoscale forecasting and studies.
TX 2 m Temperature Analysis
105