CHAPTER 14 WEATHER FORECASTING CHAPTER 14 WEATHER FORECASTING

 In addition to moisture, instability, and lifting, we need strong wind shear  At low levels, southerly winds bringing warm, moist air into the area  Aloft, advection of dry air adds to instability  Upper-level divergence leads to low-level upward motion Example from book

 Temperatures (high/low/average/changes)  Precipitation (will it rain or snow, and how much?)  Wind (speed and direction)  Cloud cover  Severe/hazardous weather (tornadoes, hurricanes, floods, etc.)  Fire weather  Marine weather (forecasts for ships at sea)  Pollution/smog/air quality  Much more…

 Short-term forecasts (aka “nowcasts”): minutes to a couple hours in advance ◦ What time will the severe storm affect my area and how intense will it be?  Short-range forecasts: 6 to 60 hours in advance ◦ How likely is it to rain tomorrow?  Mid-range forecasts: 3-10 days in advance ◦ Will it be warm or cold next weekend?  Climate predictions: months or seasons in advance ◦ Will we have above normal or below normal precipitation for the next ski season?

 National Weather Service ◦ 122 weather forecast offices (WFOs) located around the country ◦ NWS issues a variety of forecasts, warnings, and other products for a local area ◦ We are covered by the WFO in League City (south of Houston) ◦ ◦ /hgx/ /hgx/

 National Centers for Environmental Prediction ◦ NCEP headquarters collects observations and runs numerical models to make forecasts ◦ Storm Prediction Center, Norman, OK (Severe thunderstorm and tornado watches) ◦ National Hurricane Center/Tropical Prediction Center, Miami, FL (official hurricane forecasts, watches and warnings) ◦ Hydrometeorological Prediction Center, Camp Springs, MD (precipitation and flood forecasts) ◦ Climate Prediction Center, Camp Springs, MD (seasonal outlooks, El Niño predictions)  Private Companies ◦ TV and radio stations, The Weather Channel, AccuWeather, WeatherNews, many others

 Depends on the user/customer ◦ For the general public, getting the high/low within a few degrees is probably good enough ◦ For an energy company, an error of a few degrees can be very costly ◦ Transportation departments need very accurate snowfall forecasts: will plows need to be deployed? Should highways be closed? ◦ Aviation industry needs to know cloud cover, where storms are moving, turbulence, etc.  Human forecasters need to provide skill ◦ Anyone could forecast “80 and sunny” every day of the year in LA and be pretty close 90% of the time ◦ It’s the other 10% where a skilled forecaster earns his/her salary

 Before computers, forecasts were mainly limited to predicting the movement of existing weather (“It snowed yesterday in Minnesota, so it will snow today in Wisconsin”)  Pattern recognition was (and still is) used by experienced forecasters – after looking at the weather every day, you gain an “instinct” for certain weather patterns  Ingredients-based forecasting ◦ Will there be moisture, instability, and lift?  “Rules of thumb” ◦ If air temperature is 14°C colder than water temperature, lake effect snow is possible ◦ For high temperature (in summer): take forecast 850-mb temperature at 0000 UTC, add 15°C

 North American Mesoscale (NAM, previously called Eta): Primary model used for forecasting in the U.S. ◦ Run every 6 hours (00, 06, 12, 18 UTC) out to 84 hours, 12-km horizontal grid spacing  Global Forecast System (GFS): Covers the entire globe ◦ Run every 6 hours, 35-km horizontal grid spacing to 180 hours, 70-km grid to 384 hours (16 days)  Rapid Update Cycle (RUC) ◦ Run every hour out to 12 hours – for short-term forecasts  Mesoscale models: Weather Research and Forecasting (WRF), Regional Atmospheric Modeling System (RAMS), Mesoscale Model version 5 (MM5) ◦ Run by forecast offices, universities, etc., on a regional basis  Climate Prediction Models

 (National Center for Atmospheric Research)  /analysis/ (National Centers for Environmental Prediction) /analysis/  (if on campus network; this site is still under construction!) But, interpret this information with caution until you have a good sense for how it works – for official forecasts and warnings, use weather.gov (they’re experts in interpreting numerical forecasts!)

A super-outbreak, right during your 201 Class…

Fig. 14.1, p. 415

Table 12.4, p. 345

Table 12.5, p. 346

Gets a little shaky for a bit around 5:45 while he adjusts camera: Photos of the Damage:

 El Niño, La Niña and Southern Oscillation ◦ ENSO is a combined atmosphere and ocean phenomenon.  Originally, referred to unusually warm waters off of NW coast of S. America.  Reverse of pressure difference between Tahiti and Darwin is SO, Walker Cell reversal  This was found to be part of a large system which also includes shift of convective max, changes in the upper ocean structure and currents.  All together - ENSO ◦ Weather all over planet impacted - teleconnections  Asia  Australia  South America  Caribbean  North America

Fig , p. 418

Fig , p. 419

Fig , p. 420

Fig , p. 421

ov/products/analysis_monit oring/lanina/enso_evolution- status-fcsts-web.pdf

Fig , p. 422

Fig , p. 421 Also, Atlantic hurricane season “modulation”. El Niño years tend to be less active seasons, La Niña enhanced.

 The other oscillations ◦ Pacific Decadal Oscillation (PDO)  Similar to ENSO, cycles, W. Coast impacts ◦ North Atlantic Oscillation (NAO)  Large impact on European weather, some on E. U.S.  Affects tropical storm tracks ◦ Arctic (AO)  Close cousin of NAO

Fig , p. 423

Fig , p. 424

Fig , p. 425