The method based on the accumulation of local knowledge or oral communication is often effective – although the forecast tomorrow will be the same as today it works in 70%. The synoptic method is based on the collection of initial data from various sources. Data is systematized and applied to synoptic maps. From several previous weather maps, based on an understanding of weather phenomena, and based on a subjective assessment, the synoptic evaluates changes that will occur. The numerical models are a modern method of weather forecasting. Most models are based on statistical methods or basic physics principles.
Weather forecasting models
Weather can be predicted using various methods. What the synoptician chooses depends on his experience, on the amount of data available to him, on the complexity of the weather situation and on the degree of accuracy required for a given forecast.
The simplest method of forecasting the weather is the „no change” method (this is not a joke). It is assumed that the weather during the period for which the forecast is prepared will remain the same as at the time of the forecast. For example, if we have a sunny day with a temperature of 25 ° C, the forecast for the next day predicts the sun and the temperature of 25 ° C. If today 50 mm of rain fell, according to this method tomorrow the rainfall will be 50 mm.
This method of forecasting is used when the weather elements change very slowly, so when the atmospheric conditions show high dynamics it becomes unreliable.
Such a forecast method is used not only for short-term forecasting, but also for forecasting broadly understood weather or climate conditions. For example, it is predicted that after a hot and dry month it will also be dry and hot. Some of the forecasting methods, eg numeric, give a forecast for no more than 10 days, which makes the „no change” method worthy of application in terms of long-term forecasts.
Knowing the speed and direction of movement of weather elements such as fronts, barric systems, areas of cloudiness or precipitation, it is possible to predict where these elements will be at a given time. Let’s assume that, than it is some 1,600 miles west of where we make the forecast, and moves to the east at 400 km a day, using the trend method (actually pure mathematics) you can predict that it will be in a given place four days: 1,600 km / 400 km per day = 4 days.
This method is often used to forecast short-term order of a few hours, e.g. rainfall. For example, if we know that the storm is 100 km north and travels south at 50 km / h it is easy to predict that it will reach a given place in two hours.
Let’s take another example: the cold front line in the last 24 hours has shifted by 1500 km, using the trend method one could predict that in the next 24 hours it will move another 1500 km in the previous direction. And what if the front slows down (it will accelerate), it may even stop, which if it changes direction and intensity – here the trend method becomes unreliable because it refers to the situation when a given weather element moves at the same speed and in the same direction for a long time.
The Climatology Method
This method is very simple. The basis for making the forecast are climate data collected for many years. For example, if we want to predict the weather in Warsaw on July 4, we calculate the average of weather conditions for Warsaw from previous years, taking only those from July 4. If we forecast rainfall, we calculate the average of rainfall that occurred on July 4 in previous years; similarly, the temperature is predicted. Let us assume that the average temperature is 18 ° C, and the average rainfall for that day is 2.7 mm – the forecast prepared by the climatic method will predict on July 4 in Warsaw 18 ° C and a fall of 2.7 mm. The climatic method fulfills its role when the current weather situation is typical for a given season; however, when the synoptic situation is atypical, this method becomes unreliable.
This method is more complicated than the previous one. The current synoptic situation is compared with the similar one that occurred in the past and knowing the course of that situation can be assumed that the weather on the day on which it is forecast will behave similarly. The name of the method is obvious because a forecast is being prepared by analogy. Suppose we have a hot day and a cool atmospheric front is approaching. We remember a similar situation that occurred a week ago (a hot day with an approaching cold front) and moreover there were afternoon storms. Using the analogy method, it can be predicted that storms will occur in the current situation in the afternoon.
The analogy method is not easy to use, because it is very difficult to find the most similar situation in the past, and finding the same is impossible. Very rare weather elements (bar systems, fronts) form in the same places and follow the same path; even the smallest difference between the current situation and the analogous one to it can make the analogy method unreliable; however, its accuracy increases over time as there is more and more data describing the current situation and the probability of finding the most similar from the past is growing, and thus – the accuracy of the forecast increases.
Here, computers are used. The entire set of programs (forecasting models) is run on super-fast computers that predict weather components such as temperature, pressure, wind and precipitation. After receiving the results of these programs, the synoptics analyze their interaction in order to present the final weather forecast. The disadvantage of this method is that the equations used in these models are inaccurate, which reduces the accuracy of the forecast. In addition, there are many gaps in the input data, which describe the initial situation for a given model, and this is due to the fact that very little observation is carried out in the mountains and over the oceans. In short, if the input data describing the initial state is incomplete, the numerical weather forecast will not be accurate.
Despite the flaws, numerical weather forecasting is probably the best method when it comes to short-term forecasts compared to the above-mentioned ones. Unfortunately, not many people have access to the results obtained from numerical models, and analysis of these is not straightforward, therefore the best method of forecasting for a beginner synoptic is the trend method or the analogy method.