Different from traditional methods, this paper uses machine learning methods to solve the nowcasting problem of severe convective weather. Therefore, we propose a spatiotemporal convolutional block for nowcasting. The network adopts the encoder-decoder structure and proposes a new spatiotemporal causal convolution for timing feature extraction. Our network inputs 5 frames of images to predict the weather conditions for the next 30 minutes. The experimental results show that the results of our network structure are better and the training time is shorter. Compared with other networks, it can better capture the temporal and spatial correlation.
Accurate recognition of convective initiation (CI) is important to locate severe hazardous weather events. Early identifying CI can provide warning signals so that people can prepare for the coming natural disasters. Modern geostationary satellite and Doppler weather radar can provide high spatial-temporal resolution imageries to monitor CI. In this study, CI refers to Doppler radar image having reflectivity greater than 35dBZ at the first time. This paper presents a deep learning method for early recognition of CI using multi-source observation data, including geostationary satellite and Doppler weather radar imagery. We use the 3D U-Net method which is composed of three-dimensional convolution, pooling, down sampling and up sampling. The North China area is selected as the study domain. The experimental results show that the proposed method can recognize CI effectively while the false alarms still need to be reduced in future work.
Doppler radar is the main remote sensing equipment to monitor severe convective weather which has significant threats to social and economic activities. It is important to accurately predict the time and location of severe weather events. In this study, we use a deep learning technique to predict severe weather events based on radar images. Firstly, we transform the prediction problem into a binary classification problem and use Generative Adversarial Networks (GANs) to construct a classifier. Then Doppler radar images are used to train the model. The critical success index, probability of detection, and false alarm ratio are used to evaluate the prediction results. The experimental results show that the GANs model provides satisfactory results.
The purpose of convective storms nowcasting is to predict whether a region will experience strong convective weather in the next 30 minutes. Unlike traditional prediction, we study the problem of convective storm nowcasting from the perspective of machine learning. First, this paper divides the research area into a number of position-fixed small cells, then transforms the nowcasting problem into a question: Is there a radar echo > 35 dBZ in a cell within 30 minutes? Secondly, the problem of nowcasting is formulated as a kind of spatiotemporal classification learning. From this point of view, this paper introduces a sliding oversampling method to mitigate the class imbalance issue of convective storm nowcasting. A spatiotemporal convolutional network nowcasting method is proposed which is in less computational cost and easier to train than recurrent neural network. In experiments, this spatiotemporal convolution network can better captures temporal and spatial correlations, compared with previous studies, therefore results in better predictive performance. Although no sophisticated tracking algorithms are used, storm movement trends and storm growth can be predicted with reasonable skill.
Convective storm nowcasting refers to the prediction of the convective weather initiation, development, and decay in a very short term (typically 0 ~ 2 h) .Despite marked progress over the past years, severe convective storm nowcasting still remains a challenge. With the boom of machine learning, it has been well applied in various fields, especially convolutional neural network (CNN). In this paper, we build a servere convective weather nowcasting system based on CNN and hidden Markov model (HMM) using reanalysis meteorological data. The goal of convective storm nowcasting is to predict if there is a convective storm in 30min. In this paper, we compress the VDRAS reanalysis data to low-dimensional data by CNN as the observation vector of HMM, then obtain the development trend of strong convective weather in the form of time series. It shows that, our method can extract robust features without any artificial selection of features, and can capture the development trend of strong convective storm.
Nowcasting or very short-term forecasting convective storms is still a challenging problem due to the high nonlinearity and insufficient observation of convective weather. As the understanding of the physical mechanism of convective weather is also insufficient, the numerical weather model cannot predict convective storms well. Machine learning approaches provide a potential way to nowcast convective storms using various meteorological data. In this study, a deep belief network (DBN) is proposed to nowcast convective storms using the real-time re-analysis meteorological data. The nowcasting problem is formulated as a classification problem. The 3D meteorological variables are fed directly to the DBN with dimension of input layer 6*6*80. Three hidden layers are used in the DBN and the dimension of output layer is two. A box-moving method is presented to provide the input features containing the temporal and spatial information. The results show that the DNB can generate reasonable prediction results of the movement and growth of convective storms.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.