Supplemental Topic Weather Analysis and Forecasting

Background Weather analysis –Using maps, charts, tables, or any other data to determine what is happening or what happened –Presumably precise and accurate Weather forecasting –Predicting what may occur using analyses, observations, computer models, education, & experience –Considered imperfect Must know composition of atmosphere Atmospheric conditions constantly changing Complicated further by complex terrain, limited understanding of atmosphere, operating costs, disagreement among forecast models, etc.

Use of Weather Maps in Analysis Two types of maps –1. Surface –2. Upper-air Surface and upper-levels of atmosphere interact constantly at a wide range of scales Meteorologists/climatologists must be able to keep both surface and upper-air in mind when performing analyses and making forecasts

Surface Maps General representation of pressure patterns and fronts (isobars, pressure centers, precip, etc.) Can determine what happened by looking at consecutive maps Can infer what is occurring using what learned to this point Station models – symbols that show conditions at a location at observation time

Station Model

Upper-air Maps Measurements for location of launch plotted on thermodynamic chart for use in forecast Maps produced for “standard levels” – 1000mb, 850mb, 700mb, 500mb, 300mb, 250mb, 200mb Provide height of pressure level, temp, dewpoint (or dewpoint depression), wind speed and direction, and (at 300, 250, 200mb), location of jet stream Analyze for troughs, ridges, advection of warm/cool air, divergence/convergence, etc.

Thermodynamic Chart

Examples of Upper-air Maps 850mb 700mb 500mb 300mb

Other Analysis Tools Satellite –Generally use three types of imagery: visible, infrared, water vapor –Also sounder data Radar –Pulses of energy sent out by radar, hit object (hopefully precip.), energy returned to radar reveals distance and number/size of particles –Useful for detecting precipitation, fronts, tornadoes, wind direction, and even masses of insects or flocks of birds

Forecasting Procedures Analysis: just discussed Prediction –Models use data to solve equations that estimate behavior of atmosphere – Equations solved at various intervals and use previous results in subsequent forecast – why should this concern a forecaster? (chaos) Post-processing –Production of maps from model output –Usefulness of model data varies by location, time of year, conditions, and forecaster ability –Model output statistics (MOS) try to take local factors, such as topography, into account

Forecasting Methods Climatological –Based on long-term averages –Accuracy and precision vary by location Persistence –Based on trends: current conditions will continue –Usually reliable for short periods Analog –Based on records: what happened in past under similar conditions? –Could lead to poor forecast – why? Numerical –Based on output from computer models/programs –Must rely on accuracy of model given initial condition

Three Types of Forecasts Quantitative –Predicts specific value –Examples: precipitation amount, temperature Qualitative –Predicts class or category –Examples: type of precipitation, partly cloudy Probability –Chance of event occurring (in percent) –Likelihood of rain

Three Types of Forecasts TONIGHT...PARTLY CLOUDY... SCATTERED SHOWERS AND THUNDERSTORMS MAINLY IN THE EVENING. LOWS 68 TO 78. WEST WIND 5 TO 10 MPH BECOMING SOUTHEAST AROUND 5 MPH AFTER MIDNIGHT. CHANCE OF PRECIPITATION 30 PERCENT.

Forecast Periods “Nowcasts” –What’s happening now and what’s expected in next few hours –Most detailed, usually most accurate Short-term –Covers from 3 to 72 hours –Fairly detailed, accuracy tends to decrease a bit Medium range –72 hrs to 7-10 days –Utilize ensemble forecasts –Detail and accuracy decrease, esp. after 7 days Long range –Performed by Climate Prediction Center (in US) –Ranges from end of medium range to weeks, months, or longer –Made of period (not individual days), probability usually low, skill varies by location, time of year, and variable forecasted –Use climatology, numerical models, statistics, and subjective judgment

Assessing Forecasts/Post-processing Need to determine usefulness of data and forecasts, justify expenditures, and predict importance of changes Quality vs. value –Quality: is forecast right? : Example: did it rain in Tucson as predicted? –Value: does forecast perform desired goals? : Example – did hurricane warning work? Accuracy vs. skill –Accuracy: how close are predicted and actual conditions? –Skill: is method used the best choice?

Limitations on Forecasting Computing power Understanding of atmosphere Funding/expenses Chaos Model error Stubborn and/or old-school forecasters Human error Coverage of instruments for measurements/observations