Abstract
Many natural disasters in South America are linked to meteorological phenomena. Therefore, forecasting and monitoring climatic events are fundamental issues for society and various sectors of the economy. In the last decades, machine learning models have been developed to tackle different issues in society, but there is still a gap in applications to applied physics. Here, different machine learning models are evaluated for precipitation prediction over South America. Currently, numerical weather prediction models are unable to precisely reproduce the precipitation patterns in South America due to many factors such as the lack of region-specific parametrizations and data availability. The results are compared to the general circulation atmospheric model currently used operationally in the National Institute for Space Research (INPE: Instituto Nacional de Pesquisas Espaciais), Brazil. Machine learning models are able to produce predictions with errors under 2 mm in most of the continent in comparison to satellite-observed precipitation patterns for different climate seasons, and also outperform INPE’s model for some regions (e.g., reduction of errors from 8 to 2 mm in central South America in winter). Another advantage is the computational performance from machine learning models, running faster with much lower computer resources than models based on differential equations currently used in operational centers. Therefore, it is important to consider machine learning models for precipitation forecasts in operational centers as a way to improve forecast quality and to reduce computation costs.
Highlights
South America (SA) is located between 12oN and 55oS, covering lands in both low and medium latitudes, and has a diversified geography with the presence of the Andes, a narrow strip of mountainous region stretching from north to south on the SA west coast; vast plains containing huge aquatic surfaces made up of rivers such as the Amazon and the Orinoco; the largest tropical rain-forest in the world, the Amazonian equatorial forest; one of the most arid areas on the planet, the Atacama desert, located in northern Chile; and the Patagonia region at the southern end of South America, limited by the Pacific Ocean in the west up to the Andes (Chilean part) and from the Andes up to the Atlantic Ocean to the east (Argentinean part)
The performance of neural networks developed in TensorFlow (NNTensorFlow) and Multi-Particle Collision Algorithm (MPCA) (NN-MPCA) applied to the test dataset (2019) are analyzed in comparison with observation data (GPCP) and an operational model at CPTEC
This paper shows the capability of neural networks for seasonal precipitation forecasting over South America
Summary
South America (SA) is located between 12oN and 55oS, covering lands in both low and medium latitudes, and has a diversified geography with the presence of the Andes, a narrow strip of mountainous region stretching from north to south on the SA west coast; vast plains containing huge aquatic surfaces made up of rivers such as the Amazon and the Orinoco; the largest tropical rain-forest in the world, the Amazonian equatorial forest; one of the most arid areas on the planet, the Atacama desert, located in northern Chile; and the Patagonia region at the southern end of South America, limited by the Pacific Ocean in the west up to the Andes (Chilean part) and from the Andes up to the Atlantic Ocean to the east (Argentinean part). Krasnopolsky and co-authors [2] proposed an approach based on neural networks for the development of a parameterization of stochastic convection for climate models and numerical weather forecast. The authors tested their methods on the National. Researchers who are familiar with the data do not necessarily have the experience of designing neural network architectures and vice versa To this end, there is a great demand for the development of algorithms allowing researchers without any knowledge about machine learning to obtain models for describing their data in an automatic way.
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