Neural networks & Weather prediction
Peter Polák
Faculty of Electrical Engineering and Informatics
Department of Cybernetics and Artificial Intelligence
peter.polak.6@student.tuke.sk
Abstract - The need of weather prediction with sufficient accuracy can be seen
when considering the benefits that it would bring to whether the control flow of
rivers in agricultural interests or only as an little help in selecting the holiday
period. While the necessary data for the needs of such a prediction have been
available for a long time, the complex relations between data and their effect on
the prediction is proving to be difficult to use classical analysis, and here by the
integration of neural network we can achieve learning these relationships instead
of their complex analysis, thus using less effort we can obtain sufficiently
accurate prediction.
Key words - artificial intelligence, neural networks, weather prediction, weather
forecast
I. INTRODUCTION
Weather forecast is the most wanted product of meteorological services,
where user requirements are, that prediction accuracy should be the highest and
the predicted interval longest possible.
The weather forecast is actually about prediction of atmospheric events in a
given area taking into consideration that the atmosphere is a nonlinear dynamic
system whose state at some future point in time is not adequately predictable in
the deterministic sense.
Nowadays, there are these 3 methods mainly used for weather prediction:
 Numerical
 Statistical
 Synoptic
Current predictions are the result of using a combination of these methods,
where each predictive method captures atmospheric phenomena only in a
particular approximation, thus predictions have rather probabilistic than
deterministic nature.
Numerical methods are based on numerical integration of differential
equations, which in certain model approaches describe dynamics and
thermodynamics of the atmosphere.
Statistical forecasting of meteorological elements or meteorological fields is
based on the statistical characteristics of files meteorological elements.
In the synoptic forecasts meteorologist based on his subjective experience and
by empirical rules extrapolates future development of atmospheric processes,
which can be much more efficient realizable by an appropriate neural network.
II. HOW TO PREDICTION WITH NN
Neural network is a massively parallel processor, which tends to preservation
of experimental knowledge and their further use.
Neural networks are very convenient in prediction because they assume the
role of so-called universal function approximator, which in this case actually
means that thanks to main property of the neural networks, which I mentioned
above, we can learn neural network to simulate the course of the weather, and
thus to predict its future short-term development with insufficient accuracy.
Regarding the model of neural network topology for weather prediction, the
most common form is the use of multilayer feed forward neural networks, also
referred as MLP. This topology is composed of several layers of neurons, where
each layer is fully connected with the next.
To be able to learn neural network something, it is first necessary to obtain
enough samples of training data, in this case it means the meteorological data
from the certain area of the atmosphere. It is also necessary to decide which
meteorological variables select for appropriate prediction and these selected
variables are then ours neural network inputs. Problem occurs when variables
are supplied to the input of the network, where could values like "overcast" be a
problem, therefore normalization of inputs is required, where the values of the
variables are modified appropriately to the actual inputs values of the neural
network, where eg. value "overcasts" will be assigned the value 0.3 in the
interval (-1,1), where -1 represents the state of "storm" and 1 is "sun". Of course
for values of the inputs, we need the values of outputs and here we come to
another problem, which is to determine the length of the forecast period, as I
mentioned above, it is one of the parameters that we want to be the longest, but
the larger he gets, so more we decrease even more important parameter
accuracy. After selecting the forecast period and obtaining input and output
data, we can move on to learning phase of the neural network.
In the learning phase we bring to the inputs of the neural network normalized
variables of meteorological data, and calculate the net output according to
weights values. If the output corresponds to the desired value, then we continue
with the next element of the training set, however, if the output does not
respond, there is a readjusting of weight values, which consists in calculating
the weights change by back propagation of errors method. The whole learning
process is repeated a number of cycles or until the error function reaches the
desired value.
After completion of learning, the testing phase takes place. Test data have the
same format as the training and the difference is that weight of neural network
do not change anymore, it is only a determination of error, and thus the accuracy
of the network.
III. REALIZATION
A. Prediction of temperature and state of clouds
The most common objective of weather prediction either using neural network
or not remains forecast temperature and clouds condition. Nowadays almost
every Meteorological Institute takes into account of these parameters also the
output of a dedicated neural network for that purpose.
B. Rainfall prediction
Hydro meteorologist Tony Hall from the National Weather Service in Texas
using the software BrainMaker, which is used to implement the application
using the neural network, created model consisting of four interconnected
neural network for predicting rainfall in the area. Two neural networks are used
to determine the amount of precipitation, one for summer and one for winter
season, where nineteen input variables are used for both. The other two neural
networks determine the probability of these deductions. The overall accuracy of
the system reached 85%.
C. Solar activity prediction
Dr. Henrik Lundstedt from Lund Observatory in Sweden created a system to
predict solar harmful activities. This solar activity resulting in a loss of signal,
power outages, pipeline corrosion, failure of geological measurements
equipment and other adverse effects, which could be prevented by their
prediction. Designed neural network has 37 inputs, describing the behavior of
the solar wind, the result of which are mentioned malicious activities.
IV. CONCLUSION
By integration of a neural network to the weather prediction has been
simplified approach to achieving the predictions with maintaining a sufficiently
high accuracy, which in my opinion will lead to even more frequent deployment
of neural network in such systems.
REFERENCES
[1] ADAMCAK, Jan – Weather prediction using neural networks
http://neuron.tuke.sk/adamcak/theses/Adamcak-Jaksa-BC-thesis-2010.pdf
[2] SINCAK, Peter – Neural networks: Master approach 1
http://www.student.ui.sk/ZNS?action=AttachFile&do=view&target=NS1.pdf
[3] Rainfall prediction
http://www.calsci.com/Weather.html
[4] Solar activity prediction
http://www.calsci.com/SolarFlares.html