A Three-dimensional Convolutional-Recurrent Network for Convective Storm Nowcasting

Very short-term convective storm forecasting, termed nowcasting, has long been an important issue and has attracted substantial interest. Existing nowcasting methods rely principally on radar images and are limited in terms of nowcasting storm initiation and growth. Real-time re-analysis of meteorological data supplied by numerical models provides valuable information about three-dimensional (3D), atmospheric, boundary layer thermal dynamics, such as temperature and wind. To mine such data, we here develop a convolution-recurrent, hybrid deep-learning method with the following characteristics: (1) the use of cell-based oversampling to increase the number of training samples; this mitigates the class imbalance issue; (2) the use of both raw 3D radar data and 3D meteorological data re-analyzed via multi-source 3D convolution without any need for handcraft feature engineering; and (3) the stacking of convolutional neural networks on a long short-term memory encoder/decoder that learns the spatiotemporal patterns of convective processes. Experimental results demonstrated that our method performs better than other extrapolation methods. Qualitative analysis yielded encouraging nowcasting results.Comment: 13 pages, 11 figures, accepted by 2019 IEEE International Conference on Big Knowledge The copyright of this paper has been transferred to the IEEE, please comply with the copyright of the IEE

A Deep Learning Approach to Radar-based QPE

In this study, we propose a volume-to-point framework for quantitative precipitation estimation (QPE) based on the Quantitative Precipitation Estimation and Segregation Using Multiple Sensor (QPESUMS) Mosaic Radar data set. With a data volume consisting of the time series of gridded radar reflectivities over the Taiwan area, we used machine learning algorithms to establish a statistical model for QPE in weather stations. The model extracts spatial and temporal features from the input data volume and then associates these features with the location-specific precipitations. In contrast to QPE methods based on the Z-R relation, we leverage the machine learning algorithms to automatically detect the evolution and movement of weather systems and associate these patterns to a location with specific topographic attributes. Specifically, we evaluated this framework with the hourly precipitation data of 45 weather stations in Taipei during 2013-2016. In comparison to the operational QPE scheme used by the Central Weather Bureau, the volume-to-point framework performed comparably well in general cases and excelled in detecting heavy-rainfall events. By using the current results as the reference benchmark, the proposed method can integrate the heterogeneous data sources and potentially improve the forecast in extreme precipitation scenarios.Comment: 22 pages, 11 figures. Published in Earth and Space Scienc

Radar-based Hail-producing Storm Detection Using Positive Unlabeled Classification

Machine learning methods have been widely used in many fields of weather forecasting. However, some severe weather, such as hailstorm, is difficult to be completely and accurately recorded. These inaccurate data sets will affect the performance of machine-learning-based forecasting models. In this paper, a weather-radar-based hail-producing storm detection method is proposed. This method utilizes the bagging class-weighted support vector machine to learn from partly labeled hail case data and the other unlabeled data, with features extracted from radar and sounding data. The real case data from three radars of North China are used for evaluation. Results suggest that the proposed method could improve both the forecast accuracy and the forecast lead time comparing with the commonly used radar parameter methods. Besides, the proposed method works better than the method with the supervised learning model in any situation, especially when the number of positive samples contaminated in the unlabeled set is large

Relative Importance of Radar Variables for Nowcasting Heavy Rainfall: A Machine Learning Approach

Highly short-term forecasting, or nowcasting, of heavy rainfall due to rapidly evolving mesoscale convective systems (MCSs) is particularly challenging for traditional numerical weather prediction models. To overcome such a challenge, a growing number of studies have shown significant advantages of using machine learning (ML) modeling techniques with remote sensing data, especially weather radar data, for high-resolution rainfall nowcasting. To improve ML model performance, it is essential first and foremost to quantify the importance of radar variables and identify pertinent predictors of rainfall that can also be associated with domain knowledge. In this study, a set of MCS types consisting of convective cell, mesoscale convective cell, diagonal squall line, and parallel squall line, was adopted to categorize MCS storm cells, following the fuzzy logic algorithm for storm tracking, over the Korean Peninsula. The relationships between rain rates and over 15 variables derived from data products of dual-polarimetric weather radar were investigated and quantified via 5 ML regression methods and a permutation importance algorithm. As an applicational example, ML classification models were also developed to predict locations of storm cells. Recalibrated ML regression models with identified pertinent predictors were coupled with the ML classification models to provide early warnings of heavy rainfall. Results imply that future work needs to consider MCS type information to improve ML modeling for nowcasting and early warning of heavy rainfall

Previsão de níveis de precipitação usando redes neurais artificiais

The planning of various human activities, such as agriculture, construction, transportation, tourism, leisure, among others, are delimited to a greater or lesser extent by the climatic conditions, especially rainfall amounts and temperature values. Climate forecast, i.e. forecast for months ahead, are negatively impacted by the dynamics of the atmosphere-earth-ocean system, causing various levels of uncertainties. Currently, many traditional and modern methods support the forecast of climate conditions; however, the necessary accuracy is not reached. Thus, in this thesis were analyzed how the artificial neural networks could contribute in the rainfall forecast. Artificial neural networks are a method of the artificial intelligence area that has had an accelerated development in recent decades, where a considerable number of applications with satisfactory results have positioned these networks as the state of the art in several areas of knowledge. Precipitation levels from three cities of Ecuador were forecasted using as predictors atmospheric and oceanic variables. The results obtained show that the artificial neural networks were able to predict the rain a month ahead with accuracy for Guayaquil of 89%, Portoviejo of 100% and Esmeraldas of 74%, results considered satisfactory and encouraging for the use of artificial intelligence techniques in the operational climatic forecast.O planejamento de diversas atividades humanas, tais como agricultura, construção, transporte, turismo, lazer, entre outras, são delimitadas em maior ou menor grau pelas condições climáticas, especialmente quantidades de chuvas e valores de temperaturas. As previsões climáticas, definidas como previsões de meses à frente, são impactadas negativamente pela dinâmica do sistema atmosfera-terra-oceano, causando diversos níveis de incertezas. Existem na atualidade vários métodos tradicionais e modernos que auxiliam na previsão das condições climáticas, mas que não a conseguem predizer com uma exatidão necessária. Assim, nesta tese foram analisadas como as redes neurais artificiais podem ajudar nas tarefas da previsão da chuva. As redes neurais artificiais são um método da área da inteligência artificial que tem tido um desenvolvimento acelerado nas décadas recentes, em que um número considerável de aplicações com resultados satisfatórios tem posicionado estas redes nas principais linhas de pesquisa em diversas áreas do conhecimento. Níveis de precipitação de três cidades do Equador foram prognosticados, utilizando como prognosticadores variáveis atmosféricas e oceânicas. Os resultados obtidos mostram que as redes neurais artificiais conseguiram predizer a chuva um mês à frente com exatidões para Guaiaquil de 89%, Portoviejo de 100% e Esmeraldas de 74%, resultados considerados satisfatórios e encorajadores para o uso de técnicas de inteligência artificial na previsão climática operacional