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Weather Forecasting Chapter 13PowerPoint Presentation

Weather Forecasting Chapter 13

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### Weather ForecastingChapter 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

- 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

- 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

- 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

- 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

- 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

- 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

- 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

- Step 1: Weather Observations
- Step 2: Data Assimilation
- Step 3: Forecast Model Integration
- Step 4: Forecast Tweaking and
Broadcasting

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

- 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

- 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

- 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

- 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?

- 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

- 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

- 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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