Post on 18-Dec-2015
Something Has Changed
Before 1990 the National Weather Service got virtually every major storm wrong, even the
day before.
After 1990, they gave good warnings for nearly all.
Numerical Weather Prediction
• The basic idea is that if you can determine the current state of the atmosphere (known as the initialization) , you can predict the future using the equations that describe the physics of the atmosphere.
• These equations can be solved on a three-dimensional grid.
Numerical Weather Prediction• Numerical weather prediction is limited by the
available computer resources.
• As computer speed increases, the number of grid points can be increased.
• More (and thus) closer grid points means we can simulate (forecast) smaller scale features.
National WeatherService WeatherPrediction Computer
But just as important has been the weather data revolution, with
satellites giving us three dimension data over the entire
planet
We are now starting to see frequent examples of forecast
skill past one week:
Hurricane Sandy is only one example
Forecast Skill Will Continue to Extend Further in Time…with
limits (about 2 weeks)
• More satellite assets will provide a far better description of the atmosphere.
• Better models and higher resolution
• Better data assimilation: how we use the observations to produce an initialization for our models.
Increasing Resolution and Better Models Will Not Be Enough
The Next Major Revolution in Numerical Weather Prediction
Will Come Elsewhere
A Fundamental Problem• The way we have been forecasting
has been essentially flawed.
• 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….
A Fundamental Problem
• Similarly, uncertainty in our model physics (e.g., clouds and precipitation processes) also produces uncertainty in forecasts.
• Thus, all forecasts have some uncertainty.
• The uncertainty generally increases in time.
Forecast Probabilistically
• We should be using probabilities for all our forecasts or at least providing the range of possibilities.
• There is an approach to handling this issue that is being explored by the forecasting community…ensemble forecasts
Ensemble Prediction
• Instead of making one forecast…make many…each with a slightly different initialization or different model physics.
• Possible to do this now with the vastly greater computation resources that are available.
Ensemble Prediction
•Can use ensembles to give the probabilities that some weather feature will occur.
• Ensemble mean is more accurate than any individual member.
•Can also predict forecast skill!•When forecasts are similar, forecast skill is generally higher.•When forecasts differ greatly, forecast skill is less.
Prediction!
• The meteorological profession is rapidly gaining the ability to produce high-resolution probabilistic weather forecasts AND analyses.
• Probabilistic forecasts and analyses will be available for a wide range of weather parameters.
AMS Nowcasting Definition
A description of current weather and a short-term forecast varying from minutes to a few hours; typically shorter than most operational short-range forecasts.
American Meteorological Society’s Glossary of Weather and Climate
During the past decade or so the geographical and temporal detail
the weather profession can provide has greatly increased.
• High resolution forecasting, NWS forecasts on a 2.5 km grid, radar data, satellite imagery, huge numbers of surface stations, and now probabilistic prediction!
Example:The Pacific Northwest
Based on 72 different networks
3000-4000 observations per hour over WA and OR
Traditional Approaches of Weather Information Dissemination Are Incapable of Delivering the Specificity and Detail
Meteorologists Can Provide
Typical TV weathercasters have only 2.5 minutes!
Smartphones are Ideal for Weather Data Delivery!
• Lots of bandwidth
• They know where they are, so forecast information can be tailored to the user
• Substantial computational capacity.