An Introduction to Forecast Models

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An Introduction to Forecast Models
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Outline

• Important Considerations Atmospheric Science,
  Physical Processes.
  
• Weather Forecasting and Creating a Forecast
  Model.
  
• Model Construction and Resolution.
• Initialization and Model Run.
• Verification.
• Basics to Model Viewing, Time and Types of Data.
• Model Types Operational, Model Output
  Statistics, Ensembles.
  
• Forecast Ranges Short-Range, Medium-Range,
  Long-Range.
  
• Model Access (sources of data).
• http//geocities.com/quincyq03/0207PPT.ppt

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Why are models important to weather forecasting?

• Weather is governed by laws of physics that are
  present in space, our atmosphere and at the
  Earths surface.
  
• Equations have been derived and theorized to
  explain weather.
  
• These equations are often very complex and the
  linear aspect does not sufficiently describe our
  atmosphere.
  
• Calculus/differential equations are necessary for
  calculations.
  
• These calculations take far too long to solve by
  hand and there are many variables that must be
  considered.
  
• Forecast models have helped drastically improve
  forecasting skill, accuracy and verification.

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Atmospheric Science

• The study of Meteorology and forecasting is
  complex.
  
• There are many processes taking place, in
  addition to many variables that affect weather
  patterns and events.
  
• Global circulation causes radiative forcings,
  that due to the earths shape, creates wind and
  thermal gradients.
  
• Daytime and nighttime due to sunrise and sunset
  also affect diurnal variances in wind and
  temperatures.
  
• The shape of the earths orbit around the sun
  also creates seasons and variability in weather
  conditions, since there are times when parts of
  the earths surface are closer or farther away
  from the sun.

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Atmospheric Science

• Solar heating and radiation from the earth also
  take place.
  
• Uneven surface heating due to many factors,
  including terrain and bodies of water, creates a
  further imbalance.
  
• Drag and turbulence also play a role in the
  imbalance.
  
• Storm systems and air masses are advected
  (transported) but are constantly evolving as
  well.
  
• Storm systems spin up, develop, mature and
  weaken, while air masses are constantly modifying
  and moving.
  
• Convergence and divergence of storm systems and
  air masses takes placethere is a LOT going on!
  

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Processes and Variables to Consider

• Even the previous slides only begin to graze the
  surface, as there is much more that drives
  weather!
  
• Introductory Meteorology courses aim to introduce
  the student to just some of the basic
  fundamentals of Meteorology! Further courses,
  especially Dynamics and Thermodynamics get into
  more detailed and mathematical analyses of these
  processes.
• The previous processes and variables all have
  calculated and/or theoretical equations to
  describe them.
  
• Assumptions are made and without absolute
  knowledge, the equations are often approximate.
  
• With that said, scientists have come to fairly
  accurate equations and methods of forecasting.
  

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(From Holtons An Introduction to Dynamic
Meteorology) This image will be referred to aga
in later in the presentation.
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Weather Forecasting

• Weather forecasting, up into the early 20th
  century, was largely very poor and often
  controversial.
  
• Early forecasts were done by hand and were often
  fairly inaccurate, even within the short-range.
  
• Some forecasts, such as short-term ones of
  pressure and temperature were decent, but
  scientists were beginning to realize that weather
  forecasting would improve with time, research and
  technological advances.
• Weather prediction, through numerical calculation
  would be that next step. Numerical Weather
  Prediction
  

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Creating a Forecast Model

• Numerical Weather Prediction (NWP) was first
  proposed by Lewis Fry Richardson in 1920.
  

"Richardson's Forecast Factory"
Richardson knew that the amount of data that
would need to be processed would be enormous, to
create forecasts with accuracy and practical
value. Today, computers are used to handle all of
the information needed.
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Creating a Forecast Model

• Networks of upper-air observation were introduced
  in the 1940s, allowing for tracking atmospheric
  data.
  
• Equations of atmospheric motion were studied and
  simplified, and by 1950, the first NWP
  experiments began.
  
• The first forecast computer model was created and
  it used the Barotropic Equation of Atmospheric
  motion to create 500hPa height forecasts. (hPa
  millibar(mb))
• Those forecasts out to 24 hours were
  significantly more accurate than any previous
  forecasts, but aside from the scientists, were
  not very practical or easy to understand.

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Creating a Forecast Model

• Further atmospheric equations were simplified and
  entered into forecast models. (numerous complex
  equations)
  
• Computer technology evolved and allowed for more
  complex and accurate equations to be used over
  time.
  
• As further research was done in the 1960s and
  1970s, physicists and meteorologists were able to
  create more realistic, detailed and useful model
  forecasts.
• With time, computer models have become and are
  becoming more able to process enormous amounts of
  data, which are necessary to create accurate
  forecasts.

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Model Construction

• The atmosphere is three-dimensional, the Earth is
  spherical and the surface is uneven.
  
• Significant amounts of data must be input into
  forecast models to account for the variables to
  be considered.
  
• Weather balloons are used for upper-air
  observation and information from many vertical
  levels are derived.
  
• These observations are taken across the world at
  12-hour intervals and cover both land and bodies
  of water.

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Model Construction

• Other data is also obtained from satellites,
  radar, ground/soil observations, the oceans and
  elsewhere.
  
• Supercomputers are used to take all of this
  information in and from equations and theories,
  compute forecasts.
  

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Model Construction (continued)

• Think of a rectangular chunk of the world.
• There is are east-west and north-south
  directions, but also an upward direction of
  altitude.
  
• Computer models need three-dimensional coverage,
  so data at points across the earth surface (n,
  s, e, w) are input, but information is also added
  for specific heights above the ground.

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Model Construction (continued)

• Each grid point represents the average value for
  the air (volume) surrounding it, which can be
  considered an air parcel.
  
• The grid points make up cubes that cover the
  earths surface, as well as extensions upward
  into the atmosphere to be 3-dimensional.
  
• Atmospheric variables are represented through
  these grid points and since there is a finite
  number of them, finite difference approximations
  must be used to calculate variances.

• Grid Points are equally spaced
• locations that used to forecast the
• weather through models.

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Model Construction (continued)

• A grid cell is the 3-dimensional cube that the
  grid cells combine to create.

• The greater the number of grid points in a
  model, the smaller the grid cells become.
  
• Smaller grid cells leads to greater model
  resolution and more detailed forecasts.

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G R I D
C E L L S
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Model Resolution

• The resolution of a model, is determined by the
  distance that separates the grid points.
  
• For example, if the distance between two grid
  points on a model is 15km, the model resolution
  is 15km.
  
• With research and technological capabilities,
  model resolution continues to improve.
  
• The Nested Grid Model (NGM) has not been updated
  recently and is one of the lower resolution
  models today.
  
• Mesoscale models, such as the MM5 and WRF/NAM
  have some of the highest resolutions.

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Model Resolution

• Model resolution does not necessarily mean more
  accurate forecasts
  
• In some ways, the lower resolution models give
  the best representation of the general weather
  patterns.
  
• For more detailed forecasts, however, the lower
  resolution models tend to miss out on details,
  especially in the boundary layer (lower levels
  and surface).
• All models have their uses and it depends on the
  forecaster viewing them and the particular
  weather event or pattern to determine which are
  best to use.

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Model Initialization

• The models take in observations, and through
  finite difference approximations, create a model
  initialization.
  
• This initialization is a 00hr forecast that
  represents the current data that the model run
  will forecast from.
  
• Depending on the model resolution and other
  parameters, that will determine what the 00hr
  data looks like.
  
• Initializations tend to be similar between models
  at a given time, with only minor variations.
  
• However, as will become more evident in
  subsequent discussions, sometimes bad data or
  poor initializations can lead to lower quality
  forecasts from that model run.

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Example HPC Model Discussion

• MODEL DIAGNOSTIC DISCUSSION
• NWS HYDROMETEOROLOGICAL PREDICTION CENTER CAMP
  SPRINGS MD
  
• 1230 PM EST MON FEB 04 2008
• VALID FEB 04/1200 UTC THRU FEB 08/0000 UTC
• MODEL INITIALIZATION...
• ...SEE NOUS42 KWNO ADMNFD FOR STATUS OF UPPER AIR
  INGEST...
  
• MODEL INITIALIZATION ERRORS DO NOT APPEAR TO HAVE
  A SIGNIFICANT IMPACT ON THE FCST.
  
• ...TROF PUSHING INTO THE W D1-2 AND THRU THE
  CNTRL CONUS D3... THE NAM IS TOO COLD WITH H85
  TEMPS OVER WRN TX AND THE ADJACENT RIO GRANDE VLY
  BY UP TO 6 C. THE GFS HAD SIMILAR ISSUES...BUT
  NOT TO THE DEGREE OF THE NAM...WITH TEMPS OFF BY
  APPROX 2-3 C IN THE SAME LCNS. THE NAM AND GFS
  MAY BE TOO COLD WITH THE H85 TEMPS AHEAD OF THE
  TROF OVER THE SRN PLAINS/LWR MS VLY INTO THE
  FORECAST PD AS A RESULT. TO THE N...THE NAM...AND
  TO A LESSER EXTENT THE GFS...DID NOT DO A GOOD
  JOB INITIALIZING THE STRENGTH OF THE H85 TEMP
  GRADIENT OVER NERN CAN/ERN AK...WITH THE NAM AND
  GFS UP TO 3-4 C TOO WARM WITH THE COLD POOL OVER
  THE YUKON AND UP TO 4 C TOO COLD OVER NRN
  B.C./ALBERTA. SOME OF THIS AIRMASS IS XPCTD TO
  PHASE WITH THE TROF OVER CONUS LATER IN THE PD.
  THE NAM AND GFS MAY NOT BE ACCURATELY DEPICTING
  THESE AIRMASSES AND THE TEMP GRADIENT IN THE
  FORECAST PD.

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Model Run

• Once an initialization takes place, models then
  use the initialization data and equations to
  extrapolate a forecast.
  
• Models need a method for time-stepping.
  (extrapolation)
  
• Explicit Time Differencing involves the a
  predicted value to be determined at a given point
  for time step S1, from the previous time step,
  S.
• Implicit Time Differencing is more complicated
  and includes the steps of S1 and S-1.
  
• The latter system is more complicated and takes
  up much more computer power, but has many
  advantages.

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Model Run

• As a model continues a forecast from the starting
  time, 00(hr) and continue out from there. (6hr,
  12hr, etc)
  
• Models are run out under their limitations and
  setups, until the process is adjusted or ends.
  
• For example, with the Global Forecasting System
  (GFS), the model resolution is downscaled after
  180 hours.
  
• Model accuracy tends to decrease over time.
• Since approximations are used at initialization
  and throughout the model run, such a decrease in
  accuracy with time can be expected. Also, the
  model is forecast potential scenarios, so that
  must be considered as well.

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Model Verification
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Model Run Delay

• Models have so much data to ingest and work with,
  that there is typically a long delay for their
  forecasts.
  
• Depending on the model, this delay may be
  anywhere from around 1 hour to even 6 or more
  hours!
  
• For those of you who do view some model output,
  this explains why you have to wait a few hours
  after the initialization time to view the data!

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Basics to Model Viewing

• The time scale that the vast majority of the
  models use is Universal Time (UTC)
  
• During Standard Time (fall/winter), UTC is 5
  hours ahead of Eastern Standard Time.
  
• During Daylight Savings Time, UTC is only 4 hours
  ahead of Eastern Savings Time.
  
• UTC is also known as Zulu (z) time.

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Main Types of Models

• Operational models
• The ones that are most common numerous.
• Model Output Statistics (MOS)
• Adjusted operational model runs, based on
  statistics.
  
• Ensembles
• Several members with slight variations.

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The Operational Model

• This model is the standard run for most models.
• The NWS Forecast Discussions will often refer to
  these runs as the OP run.
  
• The GFS, NAM, ECMWF, GGEM, etc all have an
  operational model the one used the most.
  

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Model Output Statistics (MOS)

• This model is based off of its operational
  counterpart, but has many adjustments to it.
  
• MOS forecasts are used to fine-tune and adjust
  the operational output.
  
• MOS will output more detailed information, than
  what can generally be derived from the OP run.
  
• Statistical analyses are used to further adjust
  the forecasts for individual stations, seasons,
  etc.

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More on MOS

• The most common example of a MOS model would be
  the GFS MOS (MAV and MEX).

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MOS Wrap-up
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Ensemble Forecasts

• Ensembles are created from the operational models
  in two different ways
  
• Different physical parameterizations
• Various initial conditions.
• Physical Parameterizations have to do with how
  models develop clouds, transfer energy, handle
  the boundary layer, etc.
  
• Initial Conditions are the conditions that the
  models initialize from.
  

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The Two Ways

• Why different physical parameterizations?
• Under some situations, the operational model may
  be more biased towards a certain, eventual
  outcome.
  
• By viewing comparing runs from different
  physical parameterizations, they can be analyzed
  better.
  
• Why alter the initial conditions?
• Consider a model runthe initialization is almost
  always off very slightly with all of the
  variables.
  
• The model approximates initial conditions, so
  there is always some error across the board.

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Physical Parameterizations

• The approximation of unresolved processes in
  terms of resolved variables is referred to as
  parameterization.Holton 474
  
• Physical parameterizations in forecast models are
  very difficult, complex and aim to increase
  forecast accuracy.
  
• The atmospheric (including boundary layer and
  surface) processes must be approximated and
  simulated.
  
• Aside from Ensembles, other models also have
  various physical parameterization schemes.
  
• This explains, in part, why some models seem to
  handle certain weather patterns or events
  differently.

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Models have various schemes of evaluating and
forecasting these processes.
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Physical Parameterizations

• The operational model run has set
  parameterizations, but each ensemble member may
  have slight variations.
  
• The method of explaining/forecasting the
  processes is already approximate, so minor
  adjustments are added.
  
• Each ensemble member will have slight variations,
  so as the model runs out, further forecast
  variations will arise.
  
• Depending on the situation and the individual
  member, different scenarios will unfold.
  
• Forecasters can compare and assess the ensemble
  members, to evaluate the operational run and also
  see what kind of spread and mean forecast is
  forecasted.

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Altering Initial Conditions

• The Butterfly Effect can be considered.
• Instead of a butterfly slightly flapping its
  wings to throw off a forecast, slight deviations
  from the model initializations will cause a
  similar chain reaction.
• This chain reaction explains why models are not
  perfect.
  
• As a forecast model generates longer and longer
  forecasts, each proceeding interval has less and
  less accurate information to extrapolate from.
  
• Although the initial conditions are important,
  there are often many minor errors in a given
  initialization.

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Altering Initial Conditions

• Ensemble models that use this method, take the
  initial conditions and adjust them slightly in
  several ways.
  
• There are many ensemble members that now have
  slightly different initializations to work with.
  
• The ensembles then extrapolate their forecasts
  and commonalities can be noticed Ensemble
  Mean.
  
• By viewing all of the different ensembles, we can
  evaluate the operational run and determine if the
  run looks accurate, or if it is more likely to be
  an outlier.
• However, a difficulty with the ensembles is that
  sometimes the mean is far off from the actual
  verification and outliers may verify.
  Occasionally, the ensemble spread may not even
  cover the actual scenario!

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Forecast Ranges

• We will consider the short-range forecasts to
  include generally forecasts to go out to 72 hours
  or less.
  
• The medium-range forecasts will be from 3 to 7
  days.
  
• Long-range forecasts include those beyond 7
  days.
  
• Different models have various forecast ranges.

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Short-Range Forecasts

• Short-range forecasts tend to focus on the exact
  details, such as temperature gradients,
  precipitation, and mesoscale phenomena.
  
• These forecasts tend to originate from models
  with higher resolution.
  
• When forecasting severe weather or precipitation
  types, these are useful for specifics.
  
• However, a forecaster must also consider that
  models are not flawless and real-time data should
  also be considered.

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Examples of SR Models

• WRF/NAM Forecasts out to 84 hours.
• SREF Short-Range Ensemble Forecasts!
• RUC Rapid updates for out to 12 hours.
• NGM Lower resolution, but out to 48 hours.
• MAV MOS 72 hour forecasts.
• RGEM Regional GEM out to 48 hours.

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SREF for 2m temps in the NE
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Examples of MR/LR Models

• GFS Forecasts out to 180 hours and 384 hours.
• ECMWF Euro Centre for MR WX Forecasts!
• UKMET UK model that forecasts for the MR.
• GGEM Known as the Canadian (MR).
• NOGAPS Developed by the US Navy (MR).
• Ensembles from various models to mainly MR.
• CFS Climate Forecast System (LR).

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GFS Long-Range Forecast
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Model Access

• There is a substantial amount of model data that
  can be accessed for free online.
  
• Some models are restricted, but most arent.
• NCEP http//www.nco.ncep.noaa.gov/pmb/nwprod/anal
  ysis/
  
• This site is commonly used and has several
  American models.
  
• E-Wall http//www.meteo.psu.edu/gadomski/ewall.h
  tml
  
• This site has a lot of various models, from SR to
  MR, American to Foreign and is a personal
  favorite of mine!
  
• Attend next weeks session on Online Weather
  Resources for many more links and information!

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INCOMPLETE Works Cited

• http//www.tpub.com/content/aerographer/14269/css/
  14269_75.htm
  
• http//en.mimi.hu/meteorology/
• http//www.booty.org.uk/booty.weather/metinfo/mode
  ls/NWP_basics.htm
  
• http//www.ncep.noaa.gov/nwp50/Presentations/Tue_0
  6_15_04/Session_1/Lynch_NWP50.pdf
  
• Information has been paraphrased or has been
  included through previous experience and
  knowledge by Quincy Vagell, unless noted through
  footnote.
• Any and all slides and information within them
  can be shared and used, though proper referencing
  is necessary.
  
• For more information, details or to ask any
  questions you may have, contact Quincy Vagell.

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Additional Considerations

• The next three slides were not presented, but
  have been left in the PowerPoint.
  
• The first two discuss some of the variables and
  processes considered by the models.
  
• The third and final slide shows a view of how one
  particular model processes data and creates
  forecasts.
  

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Analytical Meteorology

• MTR 175, Introduction to Analytical Meteorology
  is a course that focuses on physical concepts and
  elementary problems in Meteorology. Processes and
  variables that must be considered with modeling
  include
• Radiation The transfer of energy. cooling,
  heating, etc
  
• Advection Transport from one location to
  another.
  
• Conduction Heat transfer through physical
  objects.
  
• Turbulence Irregular fluctuation in
  (atmospheric) flow.
  
• Drag Frictional force between the air and the
  ground.
  
• Latent heat Heat transfer through change of
  state.
  
• Saturation Deals with moisture content.
• Stability Deals with resistance to vertical
  movement.
  
• Buoyancy The ability of an object to rise or
  sink.
  

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Analytical Meteorology

• Precipitation Formation, size, intensity,
  velocity type.
  
• Gradient The rate of change of a quantity. (P,
  T, etc)
  
• Development and dissipation. (fronto- and
  cyclo-genesis)
  
• Divergence Outflow/separation of a physical
  quantity.
  
• Convergence Inflow/coming together of a
  quantity.
  
• Tendency - Trend. (pressure, temperature, etc)
• Orographics How land and water affect weather.
• Vorticity A measure of rotation. (ie. Air
  parcel)
  
• Condensation The change from vapor to a
  liquid.
  
• Evaporation The change from liquid to a vapor.
• Random probability Must be considered, since it
  is impossible to create perfectly precise
  forecasts.

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Data Assimilation