Weather Data Few Fields Deal With Such

Weather Data

Few Fields Deal With Such Quantities of Information • Climatological Data • Observational Data • Model Output

Data Collection • Weather is observed throughout the world and the data is distributed in real time. • Many types of data and networks, including: – – – – Surface observations from many sources Radiosondes and radar profilers Fixed and drifting buoys Ship observations Aircraft observations Satellite soundings Cloud and water vapor track winds Radar and satellite imagery

Data Collection • Satellite data is now the dominant data source (perhaps 90%)—we are talking hundreds of terabytes per day • Huge increases in the numbers of surface stations and aircraft reports.

Data Quality Control • Automated algorithms and manual intervention to detect, correct, and remove errors in observed data.

Pacific Analysis At 4 PM 18 November 2003 Bad Observation

Observation and Data Collection

Radiosonde

ASOS: Automated Surface Observing System: Backbone Observing System in the U. S.

Observing Networks at the Surface 3000 -4000 observations per hour over WA and OR

ACARS: Aircraft Observations Generally on wide-body aircraft Aircraft Communications Addressing and Reporting System

Weather Satellites Give Us Much More than Pretty Pictures • We start with imagery in several wavelengths: – Visible – Infrared – Water vapor (wavelengths where we see the water vapor distribution) • Plus the ability to get winds from tracking clouds/water vapor, vertical soundings, and winds based on ocean waves

Better than Star Trek!

Each wavelength gives us information

Cloud and Water Vapor Track Winds Based on Geostationary Weather Satellites

Quick. Scat Satellite Bounces microwaves off the ocean surface Capillary waves dependent on wind speed and directon

Camano Island Weather Radar

Numerical Weather Prediction

Objective Analysis/Data Assimilation • Numerical weather models are generally solved on a three-dimensional grid • Observations are scattered in three dimensions • Need to interpolate observations to grid points and to insure that the various fields are consistent and physically plausible (e. g. , most of the atmosphere in hydrostatic and gradient wind balance).

Objective Analysis/Data Assimilation • Often starts with a “first guess”, usually the gridded forecast from an earlier run (frequently a run starting 6 hr earlier) • This first guess is then modified by the observations. • Adjustments are made to insure proper physical balance. • Objective Analysis/Data Assimilation produces what is known as the model initialization, the starting point of the numerical simulation.

Model Integration: Numerical Weather Prediction • The initialization is used as the starting point for the atmospheric simulation. • Numerical models consist of the basic dynamical equations (“primitive equations”) and physical parameterizations.

“Primitive” Equations • • • 3 Equations of Motion: Newton’s Second Law First Law of Thermodynamics Conservation of mass Perfect Gas Law Conservation of water With sufficient data for initialization and a mean to integrate these equations, numerical weather prediction is possible. Example: Newton’s Second Law: F = ma

Simplified form of the primitive equations

Numerical Weather Prediction • A numerical model includes the primitive equations, physics parameterization, and a way to solve the equations (usually using finite differences on a grid) • Makes use of powerful computers • Keep in mind that a model with a certain horizontal grid spacing is barely simulating phenomenon with a scale four times the grid spacing. So a 12 -km model barely is getting 50 km scale features correct.

Forecast Skill Improvement National Weather Service Forecast Error Better Year

NGM, 80 km, 1995

2001: Eta Model, 22 km

2007 -2008 12 -km UW MM 5 Real-time 12 -km WRF-ARW and WRF-NMM are similar December 3, 2007 0000 UTC Initial 12 -h forecast 3 -hr precip.

2007 -2008 4 -km MM 5 Real-time

Surface Temperature-12 km

Temperature-1. 3 km

Many Models • The National Weather Service runs several models. • So do other weather services around the world. • So do regional groups like the UW. • HUGE amounts of output! (each run can easily produce 100 s of GB.

More Models Yet • In a real sense, the way we have been forecasting is 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 More Fundamental Problem • Thus, there is fundamental uncertainty in weather forecasts that can not be ignored. • 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 • Possible to do now with the vastly greater computation resources that are available.

The Thanksgiving Forecast 2001 42 h forecast (valid Thu 10 AM) SLP and winds 1: cent Verification - Reveals high uncertainty in storm track and intensity - Indicates low probability of Puget Sound wind event 2: eta 5: ngps 8: eta* 11: ngps* 3: ukmo 6: cmcg 9: ukmo* 12: cmcg* 4: tcwb 7: avn 10: tcwb* 13: avn*

Ensemble-Based Probabilistic Products

Probability Density Functions Everywhere

PROBCAST: www. probcast. com

The National Weather Service Data Interaction Forecaster at the Seattle National Weather Service Office

AWIPS

Problems in Communication

Icons are not effective in providing probabilities

And a “slight” chance of freezing drizzle reminds one of a trip to Antarctica

Commercial sector is no better

A great deal of research and development is required to develop effective approaches for communicating probabilistic forecasts which will not overwhelm people and allow them to get value out of them.

Traditional Approaches of Weather Information Dissemination/Display Are Incapable of Delivering the Specificity and Volume of Data Typical TV weathercasters have only 2 -4 minutes!

Many of us worried about this problem in the 90’s but now the solution is literally at hand

There are now HUNDREDS of weather apps for smartphones…and the best are yet to come!

The End