Weather and Climate Prediction

Cliff MassUniversity of Washington

Outline

• Evolution of numerical weather prediction• Application to regional climate and seasonal

forecasting• Some ideas for climate.com

The Resolution Revolution in Numerical Modeling

NGM, 80 km,1995

1995

2007-2008

4-km UWMM5 System

2013 WRF Model at 1.3 km

Last Week

WRF, 1.3 km

1.33 km resolution temperature

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

Example: The Pacific Data Void No Longer Exists

Cloud Track Winds

Better than Star Trek!

NOAA PolarOrbiter WeatherSatellite

Satellite Sensors Provide Thousands of High Quality Vertical Soundings Daily over the Pacific

Cosmic GPS Satellites Provide More Soundings!

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

Observed 180 hr (7.5 days)

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.

This is Ridiculous!

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 Winter Surface Air Temperatures (F)

-40% 0% +40%

Change in Snowpack from 1990 to 2090

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?

Some Ideas

Providing Useful Climate InformationBased on Historical Records

• One of the greatest deficiencies of the climate community and the NOAA/NWS.

• There is a huge amount historical climate data available (station data, reanalysis information) but it is difficult or impossible for folks to get the actionable information they need.

Some Climate Questions• When is the best time for wedding in Seattle?• What is the windiest time of the day in Tucson?• When are the high temperatures in Rome

between 60 and 70?• I want to take a vacation the second week in

March. Where will temperatures between 70 and 80 with less than a 30% chance of rain, within a 7 hr flight?

• What is the climatological last day of freezing temperatures at my house?

Climate Information Today

• Pre-generated tables and graphics.

National Climatic Data Center (NCDC)

God Help You if You are a Layman Looking for Climate Information at the NCDC Site

Low Hanging Fruit

• Secure U.S. and International Climate/Historical Weather Data (available from NCAR, NCDC, and others for minimal costs). I assume climate.com already has it.

• Put into a relational data base.• Build an interface/inquiry engine using natural

language queries if possible.

Climate Apps: The Surface Has Been Barely Scratched

GardenKeeper

• Using calibrated radar-based precipitation data, tells you when watering is necessary at your location (considering water demands of your plants and evapotranspiration based on recent weather)

• Warns when freezing conditions are imminent during the winter.

• Tells you when you can plant seeds and young plants in the spring

GardenKeeper

Custom Automated Pinpoint Forecasts

The Idea

• The owner of a vineyard wants accurate forecasts that considers the microclimate of his property.

• An owner of a private airport wants forecasts tailored exactly to his airfield.

• The harbormaster of a yacht club wants accurate forecasts at his location.

There is a way.

• They contact climate.com for pinpoint forecasting service.

• Working with the client, weather instrumentation is installed at the exact locations of interest, with the data retrieved via wifi, cell phone, or wired connection.

• As soon as several weeks of data are available, statistical postprocessing is applied to operational models (e.g., GFS, NAM) to provide an optimal forecast at the observation location.

There is a way

• The longer the observations are in place the better the statistical postprocessing.

• Could use linear regression, extended logistical regression, or other approaches.

• Forecast biases could be radically reduced at such sites.

• Could use ensembles or analog methods to give probabilistic predictions.

The End