Study the Science Behind Weather
Learn to explain the causes of weather conditions, and some of the tools
and signs to predict weather patterns that influence us.
Understanding Climate is Critical for:
- Farming
- Horticulture
- Environmental Management
- Energy Management
- Building and Construction
- Human Health and Well-being
Lesson Structure
There are 2 lessons in this course:
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Nature and Scope of Climatology
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Terminology, meteorology, climatology, understanding the effects of climate and weather upon life, introduction to weather predicting
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Weather Science Foundations
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Solar radiation, temperature, humidity, precipitation, cloud formation
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Circulation patterns
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Atmospheric pressure, temperature and pressure, latitudinal circulation, air masses, winds, Coriolis effects, frontal systems, southern oscillation, ocean gyres
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Climate Classifications and Patterns
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Types of climates – microclimates, climate classification models and systems, atmospheric radiation
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Atmospheric Dynamics
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Basic atmospheric characteristics and variables; vertical structure of the atmosphere, types of precipitation e.g. fog, mist, hail, sleet, frost, physics of clouds and precipitation processes, cloud dynamics, thunderstorms, lightning, cyclones, tornadoes, aerosols - dust clouds, wildfire
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Climate Changes
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Factors that cause or influence climate change, natural causes e.g. earth’s orbit & alignment, sun’s energy, oceanic circulation, magnetic fields, volcanic activities, asteroids & comets, man made or anthropogenic influences e.g. deforestation, agriculture, gas emissions, other pollution, climate change events - glaciation and ice loss, flora and fauna, rise and fall of sea levels, permafrost, ozone depletion, greenhouse effects, global warming
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Applications of Climate Science
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Methods and techniques of weather forecasting, modern forecasting approaches, numerical weather prediction (NWP), statistical techniques, long and short range forecasting, understanding forecasting models, general circulation model GCM, coupled models, collection & application of forecast data, satellite, radar, NASAs tropical rainfall measuring mission (TRMM), verification methods, methods of standard verification
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Climate Science Management Project
Aims
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Demonstrate awareness of the nature and scope of climatology.
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Explain the relationship between different atmospheric variables and their effect on the earth.
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Explain the effects of high and low atmospheric pressure systems, and their relation to wind effects and oceanic processes.
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Distinguish between the different climate types, and explain different influences on each climate type.
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Describe the atmospheric influences upon the earth and the relationship between the formation and movement of clouds with different weather patterns.
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Describe the main factors that can affect climate change.
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Explain events which follow man made climate change activities and natural causes of climate change.
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Describe methods for forecasting changes in the behaviour of the earth and atmosphere over time.
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Explain methods of application and verification.
Forecasting Weather
There are a number of different methods for forecasting weather. Forecasting weather is basically predicting future changes in the earth’s atmosphere. Weather analysis is measuring the current atmospheric conditions. As a general rule it is often prudent to consider the wider picture before honing in on specific areas.
The choice of forecasting technique may depend upon the type of information available and the level of forecast accuracy required. Successful forecasting depends upon:
- The skill of the forecaster in interpreting the findings
- Knowledge of local weather and climate
- The experience of the forecaster
- The strength of the model used
Of course, forecasts are also provided for end-users and so have to be made in a form which they can readily understand. Once data is available it is often presented using weather maps which depict the status of the atmosphere by including information about temperature, humidity, atmospheric pressure and airflow.
How Do You Know Your Predictions are Valid?
Forecast verification is a means of testing the validity of the forecast. This is done by comparing the forecast to what actually happened. It is important to verify forecasts in order to:
- Check quality and whether forecasts are getting better
- Identify where improvements to techniques can be made
- Compare different forecast systems
It is widely agreed that the best forecasts are those which reflect what happened and this is considered to be the forecast ‘quality’. A number of researchers have broken quality down into further elements of which accuracy and skill (which is a measure of accuracy relative to the forecast type) are most important.
Forecasts are compared to what is often referred to as ‘truth’ data. This is recorded from actual observations provided by satellite cloud data, temperature measurements, rain gauge measurements, and so forth. Of course, even these measurements are prone to some degree of error such as sampling error, measurement errors, or analysis errors. Nevertheless, these observational errors are often ignored. The verification data is still valuable where the error is small or there isn’t much data to use as it still gives a good indication of forecast accuracy.
Naturally, the validity of verification techniques is higher when there is sufficient observational data to compare with and the quality of the data is strong. The results of verification analyses are often displayed within some confidence limit to acknowledge a degree of possible error.
Sometimes the reliability of samples is increased by pooling data. A larger pool of data implies more accurate comparison data, but a more accurate method is to put data into homogeneous groups e.g. on the basis of geographical region or the season.
Methods of Standard Verification
There are a number of different methods which can be used to verify results.
1) Eyeball Method
This is the traditional technique which relies on comparing the actual observations with the forecast data to note agreements and discrepancies. This method is useful for comparing data on just a few forecasts as it can be quite time-consuming. The problem is that it is prone to observer biases and personal interpretation and does not employ any quantitative techniques.
2) Dichotomous Methods
These are based on two premises: yes, an event will happen, or no an event will not happen. They are often used for forecasting snow, rain or storms. A contingency table is then drawn up to show the various permutations.
From this table, a weather event which is forecast to occur and which is observed to actually occur is a ‘hit’. An event which is forecast not to occur and does not occur is a correct negative, and so forth. A perfect forecast would only include hits and correct negatives but, of course, this is unlikely to occur.
Once data has been added to a contingency table different types of errors can easily be seen. This data is then subjected to different statistical formulas to produce a range of verification findings isolating different areas of interest. The types of statistical verification data produced include: accuracy, frequency bias, false alarm ratio, success ratio, hit rate, and so on.
3) Multiple Category Methods – this method also uses contingency tables where different forecast categories are compared with observed categories. This method is advantageous in that forecast errors can be more easily recognised. The problem is that fewer statistical analyses can be conducted on the findings since results are difficult to present as a single number. Histograms may be used to represent category frequencies.
4) Continuous Variables Methods – a table is used to record forecast and observed values at regular time intervals e.g. one day. Findings may be plotted on graphs and a range of statistical computations performed on them to determine bias, error, probability, skill, and so forth.
5) Probabilistic Forecast Methods – these aim to give the probability of a weather event occurring. Probabilities are produced with a range of 0 and 100% (represented as 0 and 1). A better forecast would have a probability towards either of the extremes rather than somewhere nearer 0.5, and this is referred to as sharpness. Graphs of reliability are produced and a range of complex statistical formula may be applied to findings.
Other Verification Methods
In addition to standard verification techniques, a number of scientific (diagnostic) methods are used to verify data which are beyond the scope of this course. They are much more complex.
