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Weather Forecasting Chapter 13. Methods of Forecasting. Weather forecasting can be done using many different techniques: Folklore forecasts Persistence Climatology Trend forecast Analog forecasts Numerical forecasting Ensemble forecasting. Folklore Forecasts.

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Weather Forecasting Chapter 13

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methods of forecasting
Methods of Forecasting
  • Weather forecasting can be done using many different techniques:
    • Folklore forecasts
    • Persistence
    • Climatology
    • Trend forecast
    • Analog forecasts
    • Numerical forecasting
    • Ensemble forecasting
folklore forecasts
Folklore Forecasts
  • Developed over time, often in the form of rhymes
  • Some are OK, others are quite bad
  • Famous example: “Red sky at night, sailor’s delight; Red sky at morning, sailor take warning.”
  • Groundhog Day is an excellent example of a terrible forecast!
persistence forecast
Persistence Forecast
  • Quite simply, the weather we are having now will be the weather we have later
  • Accuracy will depend largely on the weather patterns and your location
    • Don’t use this if frequent changes in the weather are common (like Chicago)
    • Works best in the tropics
    • Works OK if the weather pattern is “blocked” (not much change in the weather possible for days or weeks)
climatology forecast
Climatology Forecast
  • The long-term average weather conditions are used to predict the weather for a given day
  • While weather does change a lot, climatology can be accurate fairly often
  • Obviously won’t work well if one is facing record-setting weather conditions
trend forecast
Trend Forecast
  • The weather will change, but assumes that the weather-changing patterns will continue at the present rate
  • For example, one can forecast the arrival of a cold front using its present speed
  • Works best if the time period is short
  • “Nowcasting” – forecasting for a brief period in the future (several hours)
  • Accuracy drops as time increases
analog forecast
Analog Forecast
  • Basic premise: History repeats itself
  • Find examples of the past that match the current conditions
  • Then forecast whatever happened the next day in the historical case
  • Requires many years of weather maps and fast ways to compare them
weather types
Weather Types
  • Categorizing the weather into various “personalities”
  • Many examples are heard in the news:
    • “Nor-Easter”: Snow storm moving up the Atlantic coast, bringing heavy snow and strong winds to the East coast
    • “Alberta Clipper”: Fast-moving storm that usually just drops an inch or two of light snow
    • “Panhandle Hooker”: Storm that develops in the lee of the Rocky Mountains near the TX/OK panhandles, then curves towards the northeast later (usually gives us our big snow days)
numerical forecasting
Numerical Forecasting
  • Forecasting the weather using computers requires two basic ingredients to produce accurate forecasts:
    • Initial Conditions: The current conditions of the atmosphere over a wide area
    • Primitive Equations: Mathematical equations that can be solved by computer to forecast into time
  • Numerical modeling is the technique of approximating tough real-world problems with numbers
  • The numerical formulas used are called a model
  • This is impossible to use without supercomputers
numerical weather prediction process
Numerical Weather Prediction Process
  • Step 1: Weather Observations
  • Step 2: Data Assimilation
  • Step 3: Forecast Model Integration
  • Step 4: Forecast Tweaking and


weather observations
Weather Observations
  • A numerical forecast is only as accurate as the observations that go into the forecast at the beginning
  • Garbage in = Garbage out
  • Surface weather observations, radiosonde data, and satellites
  • Vast and continuous data-collection process is overseen by the World Meteorological Organization (WMO)
  • In the US, the National Centers for Environmental Prediction (NCEP) gets the data and runs the computer models
grids and gridpoints
Grids and Gridpoints
  • Models work by dividing the atmosphere up into three-dimensional boxes called grids
  • The point in the middle is called the gridpoint
  • All the mathematical equations are solved at each gridpoint
  • Then, the model is stepped forward in time and the equations are run again
  • The distance between one point and another is called grid spacing
  • Models that use this technique are called gridpoint models
data assimilation initialization
Data Assimilation & Initialization
  • Weather observations now must be inserted into each gridpoint
  • Because an actual observation probably doesn’t exist at every gridpoint, interpolation (fitting the data) must be performed
  • Data initialization: Filtering the data to remove noise and to make the data more smooth
  • The chore of interpolating and initializing the data is called data assimilation
forecast model integration
Forecast Model Integration
  • Millions of calculations are performed at a given time
  • Integration – solving equations in space and time, then using the result to further calculate changes later on
  • To forecast 24 hours globally, it takes 1 trillion calculations
  • To get the results back quickly, supercomputers must be used
  • The physical distance between the gridpoints is very important
  • The smaller the grid spacing, the easier it is for the model to observe and predict small phenomena
  • Resolution – the grid spacing of a model (fine vs. coarse)
  • The more resolution, the more calculations must be performed in each time step
numerical models today
Numerical Models Today
  • Various numerical models are used today
  • Each model has a different resolution, different ways to handle things it can not see or predict, and different equations used in the computations
  • Therefore, models often differ with each other
  • Forecast “tweaking” and broadcasting of the forecast makes use of many different models, knowing their biases, to put a forecast together
what makes bad forecasts
What Makes Bad Forecasts?
  • Imperfect data
  • Compromises between gridspacing and speed of calculations
  • Improper parameterizations – crude approximations or “fudges” of actual phenomena that the model can’t resolve
  • Chaos – Sensitivity to initial conditions will drive a forecast model to react very differently with very subtle changes
ensemble forecasts
Ensemble Forecasts
  • Confidence in a numerical model’s output can be gained by looking at ensemble forecasts
  • First, the forecast is calculated by the model
  • Then, the model is run again by changing some variables only slightly
  • This is done over and over again, changing the initial values every time
  • If the model still produces the same forecast, then the outcome can be judged as fairly certain
  • If the model produces widely different forecasts after each run, then a particular outcome is in doubt
  • “Spaghetti plots” = ensemble forecast
  • All output is displayed on the same image. If the output varies widely, a lot of lines will be all over the place
current results
Current Results
  • Numerical weather prediction is getting better – the “Storm of the Century” in 1993 was handled well
  • There are still some bad forecasts too, however
  • There is a two-week limit in numerical forecasting before chaos sets in
  • 36-hour forecasts are quite accurate
  • 72-hour forecasts are as good as 36-hour forecasts were back in the mid-1970s