Weather and Climate Prediction Cliff Mass University of Washington
Outline • Evolution of numerical weather prediction • Application to regional climate and seasonal forecasting • Some ideas for climate.com
2007-2008 4-km UW MM5 System
But just as important as the computer revolution has been the weather data revolution, with satellites giving us three dimensional data over the entire planet
NOAA Polar Orbiter Weather Satellite
Satellite Sensors Provide Thousands of High Quality Vertical Soundings Daily over the Pacific
Impacts • The addition of massive amounts of new observations is causing a steady improvement in weather prediction • We are now starting to see frequent examples of forecast skill past one week: • Hurricane Sandy is only one example
Superstorm Sandy: well predicted over a week ahead of time ECMWF Forecast of Sea Level Pressure
Skill Improvements (ECMWF) Major improvements, mainly due to satellite data and improved models
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.
The Transition Numerical Weather Prediction is progressively transitioning to ensemble prediction and ensemble data assimilation
The Data Assimilation Revolution • The combing of observations and model output to provide a three-dimensional description of the atmosphere is called data assimilation. • Until recently the leading technology was 4DVAR, 4D Variational Data Assimilation. NWS has lagged in using this. • Ensemble-based data assimilation has many advantages and is increasingly being used. • Future convergence between ensemble prediction and data assimilation is probable.
The Technology of Regional NWP Can Be Used for Seasonal or Climate Prediction
Regional Dynamical Downscaling • For regional numerical weather prediction we can embed high resolution models within a coarse resolution global forecasts. • Can do the same thing for climate/seasonal prediction by simply replacing global weather forecasting models with global climate models (GCMs) or seasonal global prediction models (e.g., NOAA’s Climate Forecast System-CFS) • Just need the computer resources.
UW Regional Dynamical Downscaling • Have completed a number of 100-year regional climate simulations using the WRF model at 12-km grid spacing. • Driven by a half-dozen different climate models and emission scenarios.
Change in Snowpack from 1990 to 2090 -40% 0% +40%
Climate Simulations • Will be running with many more climate model driven simulations. • Now evaluating the use for monthly and seasonal prediction at high resolution using output from the NOAA CFS model, a coupled atmosphere/ocean modeling system. • Is there useful predictive skill at 1-9 months for mean quantities?