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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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Weather ForecastingChapter 13

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Methods of Forecasting

  • Weather forecasting can be done using many different techniques:

    • Folklore forecasts

    • Persistence

    • Climatology

    • Trend forecast

    • Analog forecasts

    • Numerical forecasting

    • Ensemble forecasting

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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!

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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)

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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

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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

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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

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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)

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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

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Numerical Weather Prediction Process

  • Step 1: Weather Observations

  • Step 2: Data Assimilation

  • Step 3: Forecast Model Integration

  • Step 4: Forecast Tweaking and


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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

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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

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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

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Fig. 13.9

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Fig. 13.10

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Fig. 13.11

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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

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Fig. 13.13

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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

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Fig. 13.21

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Fig. 13.15

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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

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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

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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

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