Modelling Agro-Met Station Observations Using Genetic Algorithm

The present work discusses the development of a nonlinear data-fitting technique based on genetic algorithm (GA) for the prediction of routine weather parameters using observations from Agro-Met Stations (AMS). The algorithm produces the equations that best describe the temporal evolutions of daily minimum and maximum near-surface (at 2.5-meter height) air temperature and relative humidity and daily averaged wind speed (at 10-meter height) at selected AMS locations. These enable the forecasts of these weather parameters, which could have possible use in crop forecast models. The forecast equations developed in the present study use only the past observations of the above-mentioned parameters. This approach, unlike other prediction methods, provides explicit analytical forecast equation for each parameter. The predictions up to 3 days in advance have been validated using independent datasets, unknown to the training algorithm, with impressive results. The power of the algorithm has also been demonstrated by its superiority over persistence forecast used as a benchmark. 1. Introduction In the recent past, nonlinear data-adaptive approach of genetic algorithm [1, 2] for the forecast of a particular meteorological parameter has gained considerable ground. This is particularly advantageous when a location-specific forecast of a specific parameter is required and when a long time series of observations (either in situ or remotely sensed) of the relevant parameter exists. The algorithm is computationally extremely cheap and produces good quality forecasts. An additional advantage of genetic algorithm (GA) is that an explicit analytical forecast equation is obtained as an outcome of the algorithm [3, 4]. Also, the algorithm can work with only a small number of data points, typically of the order of a few hundred. Predictive skill of GA has been amply demonstrated in the cases of sea surface temperature (SST) in the Alboran Sea [5] and Arabian Sea [6], summer monsoon rainfall over India [7], SST and sea level anomaly in the Ligurian Sea [8], wave heights in the north Indian Ocean [3] and more recently, for basin-scale predictions of ocean surface wind in the north Indian Ocean [9] and wave heights in the Bay of Bengal [10]. Even tidal currents from a few tidal levels have been predicted using GA [11]. At the outset it should be clarified that we are not attempting to provide an alternate method of weather prediction, which can be done only by a numerical weather prediction (NWP) model, since only such a model can provide multilevel forecasts of a large number of