In this exercise, we will look at some methods used in making weather forecasts and will make forecasts of various weather elements for several locations. In weather forecasting, a meteorologist is attempting to predict how the weather will change during a specified period and what the weather conditions will be during the period of the forecast. Actually, making a forecast of weather conditions is quite easy. The difficulty arises when a forecaster wishes to be accurate. Of course, a forecast isn't much good if it does not accurately state the future weather conditions that will occur. Thus, meteorologists are continually attempting to improve the accuracy of forecasts and to accurately predict further into the future.

First, to make an accurate forecast, a meteorologist must first understand what processes are occurring in the atmosphere to produce the current weather at the location for which the meteorologist is forecasting. This is done by measuring certain elements (making observations) of the atmosphere; i.e., temperature, pressure, wind direction and speed, humidity, cloud cover, precipitation, etc. The more complete measurement coverage across the earth's surface and vertically through the atmosphere of the elements which affect the "weather" we experience, the better "picture" we have of the processes producing the weather we are currently experiencing. By observing the changes which take place to these elements over time and comparing the changing patterns with historical patterns, an understanding of expected weather conditions can be made.

If meteorologists can understand how the atmosphere changes over time in response to various factors; i.e., differences in warming across the earth's surface from solar radiation, radiational cooling at night, warming of the atmosphere due to latent heat release during condensation, etc., and can write mathematical equations to express these changes, then a useful tool becomes available to the forecaster - computer models - which can be constructed to express how the atmosphere is changing and will appear at some future time. The output from these models can be used as an aid to forecasters in preparing the forecasts.

Note: these computer models are by no means perfect and should never be relied on exclusively by forecasters when preparing forecasts. They are a tool and should be used in conjunction with the forecaster's understanding of the changing weather patterns, as determined from a close examination of measured weather data to determine if the actual weather conditions are changing in the manner that computer models are predicting that the weather conditions will change.

Major research and development effort is ongoing in improving all areas of the process, from development of better observational techniques (both surface systems, upper air systems, and satellite systems), development of forecasting techniques to be used by forecasters, to development of better mathematical equations and computer models, to procedures to communicate weather information to users in a timely and reliable manner.

One might think of the forecast preparation process to provide users (the public, industry, etc.) with needed information as depicted in the following figure. (1) Observations give the forecaster information about what is actually occurring in the atmosphere. The computer models also use these observations to provide information concerning possible future conditions. (2) The forecaster uses the latest observations and computer model information, a forecaster-machine mix, to develop (3) a forecast which is then distributed to users. Some attempts have been made to (4) control and modify the environment but more research needs to be done in this area before it ever becomes widely used.

There are several methods used in forecast preparation, depending on the time element involved and the weather element for which the forecast is needed.

Methods of Forecasting

Persistence Forecasting. Persistence forecasting is a prediction that the weather in the future will be the same as it currently is; that there will be no change to the weather conditions. Persistence forecasts are generally good only for short periods of a few hours and become less accurate as the time period lengthens. If it is raining now, a forecast that it is going to rain for another three hours would be a persistance forecast. In the tropics, especially at island stations, where day after day the weather is basically the same because the station is affected by the same air mass with no passages of fronts, a persistance forecast that tomorrow is going to be the same as today is usually quite accurate.

Steady-state or Trend Forecasting. In this method of forecasting, the forecaster is looking at the changes that are occurring in the weather systems; the fronts, air masses, high and low pressure systems, which are affecting the station. The forecast is based on the assumption that these changes will continue at the same rate they have been occurring. Thus, if a cold front is approaching the station at 20 miles per hour, then it will continue to move at 20 miles per hour in the same direction; so, the forecaster can determine the weather conditions based on the location of the front determined by extrapolating its position assuming its rate of movement doesn't change. Similarly, if a cold air mass is moving toward the station and temperatures at stations within the air mass are dropping at one Fahrenheit degree per hour, then temperatures at the station for which you are forecasting will drop at one Fahrenheit degree per hour. However, rarely will a front move at a consistent rate of motion for 24 hours or more. Thus, steady-state forecasting gives a good guide to follow at least for short periods of time. Attempting to use such a method for a forecast greater than 24 hours will usually prove inaccurate. For forecasts of a few minutes to several hours, the method has proven successful. The method called nowcasting which refers to forecasts for the next several hours, are often based on such steady-state techniques.

Open the image dalmeteogram.gif in the Atmo 202 folder.

This meteogram shows the measured air temperature at the Dallas/Fort Worth airport.

Assume that this meteogram is showing measured weather information for today. (It is actually data from 1996.) Assume also that it is 8PM at night, tonight, (0200Z - We will use a 6-hour time difference) on the image and you must make a forecast for the minimum temperature tomorrow. The minimum temperature typically occurs between 1100 and 1200Z (05AM and 06AM in Texas) assuming no major changes in the air mass across the station. Using the steady-state method (trend), determine what the minimum temperature should be at 1200Z.

Now, consider how to make a forecast for the maximum temperature for tomorrow from this observational information. Remember, you want to be accurate. This may be somewhat more difficult since you have no trend to follow.

Make a forecast for the maximum temperature for tomorrow and explain what method you used, either persistence or steady-state (trend) and how you used this technique to arrive at the maximum temperature value you chose.

Record your answers on your answer sheet.

Close the meteorogram.

Open the image, UStempAnal.gif in the Atmo 202 folder.

This map shows an analysis of surface temperatures for 1500 UTC for Wednesday, January 31, 1996.

What is the temperature at Oklahoma City (about the middle of Oklahoma)?

Suppose that the air mass causing this temperature at Oklahoma City was moving directly south at a rate of 150 miles in 24 hours.

What would the temperature be in College Station at 1500 UTC on Thursday, February 01, 1996? Hint: The distance, north to south, across Kansas is equal to 200 miles.

Record your answers on your answer sheet.

The temperature you obtained for College Station is probably not going to be the actual temperature College Station receives for several reasons.

The air mass will probably not continue to move at the rate predicted for the full 24-hour period.
As the air descends from the elevation of Oklahoma City (397 meters) to College Station (104.24 meters) it will experience adiabatic warming at a rate of 1oC per 100 meters, or almost 3oC; and,
If the land at the more southerly latitude of College Station is warmer than at Oklahoma City, the air mass will be warmed as it progresses southward.
These type of considerations must be made by a forecaster when preparing a forecast.

Analogue method. This technique utilizes the fact that existing weather patterns on weather charts which resemble previous weather patterns on previous weather charts should produce the same type of weather elements, or phenomena, that the previous patterns produced. These previous patterns can then be used as a guide for making forecasts of weather elements. For example, if from previous weather maps it is seen that an intense ridge at 500-hPa during the warm months over the west coast produces a surface, high-pressure center located over the Nevada-Utah regions and this pattern produced strong Santa Ana winds along the west coast of the United States, then when a forecaster sees a pattern developing which has a strong ridge at 500 hPa developing near the west coast, a forecast of strong, easterly winds at coastal stations will likely be accurate. Similarly, such a pattern in the cold months tends to direct those polar and arctic air masses located in central Canada toward the southeast and brings cold outbreaks of air across the central and eastern part of the United States.

This analogue method, or pattern recognition - the ability of a forecaster to recognize weather patterns which will produce certain weather phenomena - is a vital skill a forecaster needs to be able to prepare accurate forecasts. Sometimes, these patterns can be used to predict the conditions for several days in advance, although the predictions of specific conditions; how strong the Santa Ana winds will be or how cold the temperature will be, are often not adequately predicted by such techniques.

Climatological Forecast. This method uses such guidelines as the average value of weather elements for a region, the maximum and minimum values of weather elements, the most or least time of occurrence of certain weather phenomena, etc. to make a prediction of the value of those weather elements for some future period. It is based on the assumption that the specific element value will not be significantly different than the values of that element from previous observations. As an example, if you were making a forecast as to whether College Station would have snow for Christmas, then, knowing that during the 30-year climatology record for College Station only a trace of snow was ever recorded, a forecast of no snow for Christmas next year would probably be quite accurate. Similarly, a forecast of no snow for Christmas in the year 2010 would also probably be quite accurate. Climatology can be used as a guide for making both short-term, hours to days ahead, and long-term forecasts, such as 30-day and 90-day forecasts or longer.

Open this image.

This image is a Climatic Outlook chart produced by the National Climatic Center. These charts provide expected conditions for up to a year in advance of the time the chart was produced. Each chart normally covers a three month period. This chart shows the outlook for temperature. The colors and the isopleth lines indicate the probability of occurrence for the mean temperatures for locations within the region to be above or below as indicated. Thus, a station on a 40 isopleth line in a region labeled (A) would have a 40% chance that the mean temperatures for the months indicated would be above the climatological mean for that station. One could then estimate the probability for stations located between the isopleth lines.

The table below relates the probabilities to colors used on the image.

Problem 3.
At College Station during the months indicated on the image you just opened, are the mean temperatures expected to be above - (A), below - (B), the same as - normal - (N), or an equal chances for (A), (B), or (N) to occur - (EC) as indicated by the National Weather Service outlook map. Also, indicate the probability indicated for College Station. You must estimate a probability value. Do not give a range of probabilities.

Answer the same question for Lubbock, Texas.

Record your answers on the answer sheet.

Open this image.

This chart is the Climatic Outlook for precipitation for the same 3-month period as shown by the temperature outlook. The colors and isopleth lines indicate the probability for the total precipitation during the 3-month period to be above - (A), below - (B), normal - (N), or an equal chance that (A), (B), or (N) will occur. The probabilities are displayed in the same manner as for the temperature outlook.

Problem 4.
At College Station during the months indicated on the image you just opened, is the average precipitation expected to be above (A), below (B), the same as - normal (N), or an equal chance for (A), (B), or (N) to occur - (EC) as indicated by the National Weather Service outlook map. If precipitation is expected to be above or below normal, indicate the probability as was done with the temperature outlook. Estimate a probability value. Do not give a range of probabilities.

Answer the same question for Lubbock, Texas.

Record your answers on the answer sheet.

Below is a table giving the climatological conditions for College Station and Lubbock which has been determined from past observations at these two stations.

Temperature (F)JanFeb MarAprMayJun JulAugSepOct NovDec
Mean Maximum60.665.5 72.678.885.391.795.6 96.290.982.070.962.8
Mean Minimum39.843.4 50.556.965.371.573.6 73.268.559.049.141.5
Mean Monthly50.254.5 61.667.975.381.684.6 84.779.770.560.052.2
JanFeb MarAprMayJun JulAugSepOct NovDec
Mean 3.322.38 2.843.205.053.791.92 2.633.914.223.183.23
Record Monthly Maximum15.69.828.0312.514.712.63 7.0612.612.1318.779.7312.66
Record Monthly Minimum0.100.010.29 0.080.11T0.00T
Temperature (F)JanFeb MarAprMayJun JulAugSepOct NovDec
Mean Maximum51.957.866.274.7 82.890.091.990.083.4 74.461.653.2
Mean Minimum24.428.936.245.4 55.664.167.766.058.4 47.034.526.1
Mean Monthly38.143.351.260.0 60.748.139.7
JanFeb MarAprMayJun JulAugSepOct NovDec
Mean 0.500.71 0.761.292.312.982.13 2.362.571.700.710.67
Record Monthly Maximum4.052.513.233.487.807.95 7.208.856.9010.802.672.24
Record Monthly Minimum0.00tracetrace 0.040.10tracetrace0.05 trace0.000.00trace

Based on the above climatology table, answer the following question.

Problem 5.
What is the sum of the monthly mean precipitation that College Station normally receives during the same months used in problems 3 and 4?

Answer the same question for Lubbock, Texas.

Record your answers on the answer sheet.

Climatology and El Niño, La Niña

In the ATMO 201 class, we learned about El Niño and La Niña. El Niño initially referred to a weak, warm current appearing annually around Christmas time along the coast of Ecuador and Peru and lasting only a few weeks to a month or more. Every three to seven years, an El Niño may last for many months, having significant economic and atmospheric consequences worldwide. In contrast, La Niña refers to an anomaly of unusually cold sea surface temperatures found in the eastern tropical Pacific. La Niña occurs roughly half as often as El Niño.

By studying the climatology of stations at the time of an El Niño or La Niña, we can see a teleconnection between these events and changes in weather patterns worldwide. In the United States, during an El Niño event, some locations may experience large extremes from the normal temperature or precipitation patterns while other areas show very little if any change from normal climatology values. The maps referenced in the following questions show the regions in the United States where extreme temperatures (abnormally high or low) and extreme precipitation (abnormally high or low) can be expected during December to February in an El Niño event.

These plots show regions in the continental United States where, for temperature, there has been a greater likelihood of an extreme cold or warm season than one would expect by climatology, or for precipitation, a greater likelihood of an extreme wet or dry season than one would expect by climatology during an El Niño (or La Niña) event. They do not show the expected value of temperature or precipitation during the season and in fact, it's possible that the composite or average value is of a different sign than the expected risk. The numbers plotted indicate the percent increase over what would be expected normally. Open the images indicated and answer the following questions.

Open the image djf-te.gif in the Atmo 202 folder.

Problem 6.
Considering temperature, indicate in what parts of the country there is a strong El Niño teleconnection to the temperatures that probably would be expected during a winter El Niño event by recording at least three states in the region that show temperatures would be much warmer than normal and at least three states that show temperatures would be much colder than normal in at least part of the state.

Record your answers on the answer sheet.

Open the image djf-pe.gif in the Atmo 202 folder.

Problem 7.
Considering precipitation, indicate in what parts of the country there is a strong El Niño teleconnection to the precipitation that probably would be expected during a winter El Niño event by recording at least three states in the region that show precipitation amounts would be much wetter than normal and at least three states that show precipitation amounts would be much dryer than normal in at least part of the state.

Record your answers on the answer sheet.

Open the image djf-tl.gif in the Atmo 202 folder.

Problem 8.
Considering temperature, describe the changes that can be expected across Texas during a wintertime La Niña episode. What other part of the country can expect extremes of temperature change and would that region be colder or warmer than normal?

Record your answers on the answer sheet.

Open the image djf-pl.gif in the Atmo 202 folder.

Problem 9.
Considering precipitation, describe the changes that can be expected across Texas during a wintertime La Niña episode. What other part of the country can expect the most extreme precipitation changes and would that region experience less than normal or greater than normal precipitation?

Record your answers on the answer sheet.

Problem 10.
Considering all of the images concerning El Niño and La Niña, record two states in which there is not an apparent teleconnection to either El Niño or La Niña.

Record your answers on the answer sheet.

Numerical Weather Prediction.

Numerical Weather Prediction involves using mathematical equations which describe the processes occurring in the atmosphere that causes changes to weather elements; such as, temperature, pressure, wind speed, wind direction, moisture content, etc., those elements used to describe the state of the atmosphere. Typically, this "state of the atmosphere," or "picture" is defined by the value of weather at many different locations, called grid pints, not only at ground or sea level, but also vertically in the atmosphere (troposphere and lower stratosphere). The horizontal distance between these grid points is different for different models and the number of levels from sea level up to the lower stratosphere differs for different models. Once weather observations are made and the value of the measured weather elements are entered into the program, the computer can then solve the equations to determine new values of the weather elements for some period in the future; for example, ten minutes past the time the observation measurements were made. The computer then uses these new values to determine subsequent values at each grid point ten minutes later. This procedure continues until values have been determined for 12, 24, 36, 48 hours, and for some models even longer, into the future. The computer then prepares prognostic charts based on these calculated values, analyzes the data and determines locations of fronts, pressure centers, highs and lows on upper air charts, etc. The charts can then be printed or displayed on computer terminals for forecasters to use in preparing forecasts.

Prognostic maps produced by numerical methods are only as good as the equations defining the processes, (and for some processes, no equations exist), the accuracy and coverage of the observational data used by the models, the techniques used to develop the model and the ability of the computer itself to accurately run the model.

It is important for forecasters to not rely solely on model output, but rather to use them as another tool in preparing their forecasts.

Some of the models in current use are the NCEP North American Mesoscale Weather Research & Forecasting (NAM WRF) Model, the Global Forecast System (GFS) Model, the Global Forecast System Extended Range (GFSX) Model, Rapid Update Cycle (RUC) Model, the European Center for Medium range Weather Forecasting (ECMWF) forecast model.

The Rapid Update Cycle model prepares prognostic charts out to only 12 hours, whereas other models may produce prognostic charts out to 240 hours (10 days).

To make a forecast using these various prognostic maps, a meteorologist must decide which of the maps is closest to being correct.

Problem 11.
Open the gfsx_pres_24h.gif image and the nam_pres_24h.gif image in the Atmo 202 folder. These maps show a 24-hour forecast of sea-level pressure, (solid lines), 6-hour accumulation of precipitation, (shaded areas), and the 1000-500 mb thickness, (dashed lines). Pick two differences between these images. Explain what those differences are. You do not have to explain why there are those differences.

Record your answers on the answer sheet.

Problem 12.
Look at the gfs_pres_24h.gif in the Atmo 202. This map show a 24-hour forecast of sea-level pressure, (solid lines), 6-hour accumulation of precipitation, (shaded areas), and the 1000-500 mb thicknesss, (dashed lines). Remember that wind blows clockwise (anticyclonically) about high pressure centers and counter-clockwise (cyclonically) about low pressure centers.

Look at the 24-hour sea-level pressure forecast. From what direction should the winds be blowing across Texas A&M at the valid time of this map?

Record your answers on the answer sheet.

Problem 13.
Look at this this site. This site provides links to several maps generated by the Rapid Update Cycle (RUC) model. Under the Forecast Time(s) column, check the box labeled loop all times. Then choose the link titled: MSLP/Winds. Notice the movement of the High and Low pressure centers and the changing wind directions during the next 12 hours shown on the maps.

Go BACK to the RUC model page and again, make certain the loop all times box is checked. Now select the link titled: Clouds all levels. Notice at the bottom of the image that the colors indicate the composition of the clouds; whether they are of liquid water droplets, supercooled droplets, or ice crystals.

Is the model forecasting for: no clouds, for the total amount of clouds to increase, decrease, or stay the same at College Station during the next 12 hours?

Record your answers on the answer sheet.

Model Output Statistics. Model Output Statistics (MOS) is an objective weather forecasting technique which consists of determining a statistical relationship between the element being forecast and values calculated by a numerical model. For example, the probability of precipitation occurring at a location during a 12-hour period may be based on the season of the year, the sea level pressure value at that location, the surface, 700-mb and 500-mb temperature values calculated at the location, the relative humidity values calculated at levels from the surface to 200 hPa, or the difference between the actual mixing ratio and the saturation mixing ratio at various levels. These are typical element values which may be used in an equation to determine the probability of precipitation at a location. The equation used for determining cloud amount may use a completely different set of element values. Bulletins containing the MOS forecasts are computer generated for various stations in the United States and are a tool used by forecasters in preparation of their local forecasts. Since the MOS forecasts are generated from statistical considerations using values generated from computer models, and thus can only be as accurate as the models, the forecaster should use them only as a guide and should modify the MOS information as necessary for local conditions.

Model Output Statistics can be presented in a table format or in the form of a meteogram, with which you should be familiar. In a MOS meteogram, the computer model generated weather element values are projected into the future, rather than displaying past observational data as was the example in exercise 2.

Problem 14.
Using these Model Output Statistics (MOS) meteograms in the Atmo 202 folder, prepare forecasts of the indicated elements for the following cities for the days and times indicated. Since you do not have observational data or climatological data for these locations, you cannot be expected to determine whether actual conditions are changing in the same manner that the computer models are expecting the conditions to change. Thus, you cannot be expected to modify the MOS data to produce a more accurate forecast. Rather, simply use the information that MOS is presenting.

For your forecasts, make certain you are looking only at values between midnight to midnight local time. We will assume that there is a 6-hour time difference between the times UTC as indicated on the charts and the local time for all the stations indicated. You will have to subtract 6 hours from the UTC time indicated to get the local time for the station.

Record your answers on the answer sheet.

at noon
at noon
at noon
of base
of lowest cloud layer
(total for 24 hours)
Dallas TX
_______ _______ _____ _____ ______ ______ _______ _______
San Antonio TX
_______ _______ _____ _____ ______ ______ _______ _______
Lubbock TX
_______ _______ _____ _____ ______ ______ _______ _______
Seattle WA
_______ _______ _____ _____ ______ ______ _______ _______
International Falls MN
_______ _______ _____ _____ ______ ______ _______ _______

This concludes the exercise. Please close netscape and sign off from the computer.

Copyright © 1996-2007 Texas A&M University, Texas A&M Meteorology Department and Marion Alcorn.