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Introduction to Weather Forecasting Cliff Mass Department of Atmospheric Sciences University of Washington The Stone Age Prior to approximately 1955, weather forecasting was basically a subjective art, and not very skillful.

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introduction to weather forecasting

Introduction to Weather Forecasting

Cliff Mass

Department of Atmospheric Sciences

University of Washington

the stone age
The Stone Age
  • Prior to approximately 1955, weather forecasting was basically a subjective art, and not very skillful.
  • The technology of forecasting was basically subjective extrapolation of weather systems, in the latter years using the upper level flow (the jet stream).
  • Local weather details—which really weren’t understood-- were added subjectively.




the development of numerical weather prediction nwp
The Development of Numerical Weather Prediction (NWP)

Vilhelm Bjerknes in his landmark paper of 1904 suggested that NWP--objective weather prediction-- was possible.

  • A closed set of equations existed that could predict the future atmosphere
  • But it wasn’t practical then because there was no reasonable way to do the computations and a sufficient 3-D description of the atmosphere did not exist.
numerical weather prediction
Numerical Weather Prediction

One such equation is Newton’s Second Law:

F = ma

Force = mass x acceleration

Mass is the amount of matter

Acceleration is how velocity changes with time

Force is a push or pull on some object (e.g., gravitational force, pressure forces, friction)

Using observations we can determine the mass and forces, and thus can calculate the acceleration--giving the future

nwp becomes possible
NWP Becomes Possible
  • By the 1940’s an extensive upper air network was in place, plus many more surface observations. Thus, a reasonable 3-D description of the atmosphere was possible.
  • By the mid to late 1940’s, digital programmable computers were becoming available…the first..the ENIAC
1955 1965 the advent of modern forecasting
1955-1965: The Advent of Modern Forecasting
  • Numerical weather prediction became the cornerstone.
  • New observing technologies also had a huge impact:
    • Weather satellites
    • Weather radar





weather prediction steps
Weather Prediction Steps
  • Data collection and quality control
  • Data assimilation: creating a physically realistic 3-D description of the atmosphere called the initialization.
  • Model integration. Solving the equations to produce future 3D descriptions of the atmosphere
  • Model output post-processing using statistical methods
  • Dissemination and communication


Using a wide range of weather observations we can create a three-dimensional description of the atmosphere…

numerical weather prediction13
Numerical Weather Prediction
  • The observations are interpolated to a 3-D grid where they are integrated into the future using a computer model--the collection of equations and a method for solving them.
  • As computer speed increased, the number of grid points could be increased.
  • More (and thus) closer grid points means we can simulate (forecast) smaller and smaller scale features. We call this improved resolution.
model postprocessing in the u s model output statistics mos
Model Postprocessing in the U.S.: Model Output Statistics (MOS)
  • Main post-processing approach used by the National Weather Service
  • Based on linear regression: Y=a0 + a1X1 + a2X2+ a3X3 + …
  • MOS is available for many parameters and time and greatly improves the quality of most model predictions.
major improvement
Major Improvement

Weather forecasting skill has substantially improved over the last 50 years. Really.

forecast skill improvement
Forecast Skill Improvement

National Weather Service






Why Large Improvement in Weather Forecast Skill?

  • As computers became faster, were able to solve the equations at higher resolution
  • Improved physics
  • New observational assets allowed a better initialization
a more basic problem
A More Basic Problem
  • There is fundamental uncertainty in weather forecasts that can not be ignored.
  • This uncertainty has a number of causes:
    • Uncertainty in initialization
    • Uncertainty in model physics
    • Uncertainties in how we solve the equations
    • Insufficient resolution to properly model atmospheric features.
the atmospheric is chaotic
The Atmospheric is Chaotic
  • The work of Lorenz (1963, 1965, 1968) demonstrated that the atmosphere is a chaotic system, in which small differences in the initialization…well within observational error… can have large impacts on the forecasts, particularly for longer forecasts.
  • Not unlike a pinball game….
probabilistic prediction
Probabilistic Prediction
  • Thus, forecasts must be provided in a probabilistic framework, not the deterministic single answer approach that has dominated weather prediction during the last century.
  • Interestingly…the first public forecasts were probabilistic
ol probs
“Ol Probs”

Cleveland Abbe (“Ol’ Probabilities”), who led the establishment of a weather forecasting division within the U.S. Army Signal Corps.

Produced the first known communication of a weather probability to users and the public in 1869.

Professor Cleveland Abbe, who issued the first public

“Weather Synopsis and Probabilities” on February 19, 1871

ensemble prediction
Ensemble Prediction
  • The most prevalent approach for producing probabilistic forecasts and uncertainty information…ensemble prediction.
  • Instead of making one forecast…make many…each with a slightly different initialization or varied model physics.
  • Possible to do now with the vastly greater computation resources that are now available.


The Thanksgiving Forecast 2001

42h forecast (valid Thu 10AM)

SLP and winds

  • Reveals high uncertainty in storm track and intensity
  • Indicates low probability of Puget Sound wind event

1: cent

5: ngps

11: ngps*

8: eta*

2: eta

3: ukmo

6: cmcg

9: ukmo*

12: cmcg*

4: tcwb

7: avn

13: avn*

10: tcwb*


Ensemble Prediction

  • Can use ensembles to provide a new generation of products that give the probabilities that some weather feature will occur.
  • Can also predict forecast skill.
    • It appears that when forecasts are similar, forecast skill is higher.
    • When forecasts differ greatly, forecast skill is less.
ensemble post processing
Ensemble Post-Processing
  • To get the maximum benefits from ensembles, post-processing is needed, such as:
    • Correction for systematic bias
    • Optimal weighting of the various ensemble members--e.g., Bayesian Model Averaging
the uw muri project
The UW-MURI Project
  • Possibility the most advanced ensemble/postprocessing system in the world has been developed at the UW
  • Includes UW Atmospheric Sciences, Statistics, Psychology, and Applied Physics Lab
  • Remaining talks will describe some of the research and development completed by this effort.

Providing forecast uncertainty information is good….

But you can have too much of a good thing…