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Application of Machine Learning in Atmospheric Observations, Monitoring and Modeling

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A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Atmospheric Techniques, Instruments, and Modeling".

Deadline for manuscript submissions: closed (15 November 2022) | Viewed by 21922

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Special Issue Editors

Dr. Chandrasekar Radhakrishnan

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Guest Editor

Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO 80523, USA
Interests: numerical weather prediction(NWP); satellite and radar data assimilation; machine learning in atmospheric science; nowcasting; air quality modeling and prediction; wind and solar energy forecasting; tropical cyclones; WRF models; WRF-Chem and CAMQ model; PM2.5 and AOD data assimilation
Special Issues, Collections and Topics in MDPI journals

Dr. Haonan Chen

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Guest Editor

National Oceanic and Atmospheric Administration, Washington, DC, USA
Interests: remote sensing of natural disasters; data science in remote sensing; machine learning in geosciences and remote sensing; dual-polarization radar systems and networking; weather radar product system architecture

Prof. Dr. V. Chandrasekar

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Guest Editor

Department of Electrical and Computer Engineering, Colorado State University, 1373 Campus Delivery, Fort Collins, CO 80523, USA
Interests: radar meteorology; radar system and networking; polarimetric analysis and signal processing; wave propagation and remote sensing; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The current availability of a huge supply of weather observations and massive growth in the computational facility gives more opportunities to apply machine learning (ML) techniques in the field of weather observations, monitoring and prediction. The recent readily available sophisticated and optimized ML algorithms can learn complex weather phenomena from the data, which empirical algorithms and mathematical models cannot capture. This Special Issue encourages articles that discuss the application of ML techniques in the field of weather observation, monitoring and prediction systems. Weather observations are more crucial in understanding weather patterns and validating weather and climate models. Therefore, this Special Issue focuses on papers that address the usage of ML techniques to improve the quality of weather observations—in particular, improving satellite and weather radar retrievals and learning the complex relationship between different weather observation instruments. Automatic weather monitoring and warning systems are essential to give timely warnings to people to save their lives from severe weather events. Integrating Artificial Intelligence (AI) with severe weather monitoring and warning systems will improve the system's performance. This Special Issue seeks articles that discuss how ML techniques can be integrated to improve severe weather monitoring and warning systems in real time. Though the numerical weather prediction (NWP) model has satisfactory accuracy, it still needs improvement. Therefore, this issue is expected to include articles that use ML techniques to correct NWP model bias, improve the model physics parametrizations, blending multiple NWP models, etc. to enhance the overall model forecast skills. Articles related to ML-based nowcasting and image processing on weather observations are also encouraged for this Special Issue.

Dr. Chandrasekar Radhakrishnan
Dr. Haonan Chen
Prof. Dr. V. Chandrasekar
Guest Editors

Manuscript Submission Information

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Keywords

Machine learning
Weather observations
Weather monitoring
Real time warning system
Weather forecasting and nowcasting

Published Papers (10 papers)

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Research

17 pages, 11202 KiB

Open AccessArticle

Automatic Center Detection of Tropical Cyclone Using Image Processing Based on the Operational Radar Network

by Sun-Jin Mo and Ji-Young Gu

Atmosphere 2023, 14(1), 168; https://doi.org/10.3390/atmos14010168 - 12 Jan 2023

Viewed by 1688

Abstract

This study presents the algorithm ACTION, defined as automatic center detection of tropical cyclone (TC) using image processing based on the operational radar network. Based on the high visibility of weather radar imagery, a TC’s motion vector is calculated from the continuous image change using optical flow, producing the TC’s rotation center. The algorithm performance was verified by analyzing the typhoons (TCs in the northwestern Pacific) that affected the Korean Peninsula from 2018–2021, demonstrating a high detection rate of 80.8% within an error distance of 40 km against the best track of the Korea Meteorological Administration (KMA). The detection rate was 100% for typhoons with temporally consistent morphological characteristics. ACTION automatically generates TC center information upon the TC’s initial entry inside the observation radius even in the absence of uniform radar data. As ACTION is capable of performing real-time calculations that are directly applied to rapidly generated weather radar images, it is currently being utilized by the KMA. The high temporal resolution TC center information calculated through ACTION is expected to improve the efficiency of TC forecasting. Full article

(This article belongs to the Special Issue Application of Machine Learning in Atmospheric Observations, Monitoring and Modeling)

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16 pages, 2186 KiB

Open AccessArticle

Simulation of the Ozone Concentration in Three Regions of Xinjiang, China, Using a Genetic Algorithm-Optimized BP Neural Network Model

by Qilong Zhao, Kui Jiang, Dilinuer Talifu, Bo Gao, Xinming Wang, Abulikemu Abulizi, Xiaohui Zhang and Bowen Liu

Atmosphere 2023, 14(1), 160; https://doi.org/10.3390/atmos14010160 - 11 Jan 2023

Cited by 2 | Viewed by 1523

Abstract

Accurate ozone concentration simulation can provide a health reference for people’s daily lives. Simulating ozone concentrations is a complex task because near-surface ozone production is determined by a combination of volatile organic compounds (VOCs) and NOx emissions, atmospheric photochemical reactions, and meteorological factors. In this study, we applied a genetic algorithm-optimized back propagation (GA-BP) neural network, multiple linear regression (MLR), BP neural network, random forest (RF) algorithm, and long short-term memory network (LSTM) to model ozone concentrations in three regions of Xinjiang, China (Urumqi, Hotan, and Dushanzi districts) for the first time by inputting wind speed, humidity, visibility, temperature, and wind direction. The results showed that the average relative errors of the model simulations in the Urumqi, Hotan, and Dushanzi districts were BP (61%, 14%, and 16%), MLR (97%, 14%, and 23%), RF (39%, 11%, and 14%), LSTM (50%, 12%, and 16%), and GA-BP (16%, 4%, and 6%) and that the significance coefficients R² were BP (0.73, 0.65, and 0.83), MLR (0.68, 0.62, and 0.74), RF (0.85, 0.80, and 0.88), LSTM (0.78, 0.74, and 0.85), and GA-BP (0.92, 0.93, and 0.94), respectively, with the simulated values of GA-BP being the closest to the true values. The GA-BP model results showed that among the 100 samples with the same wind speed, humidity, visibility, temperature, and wind direction data, the highest simulated ozone concentrations in the Urumqi, Hotan, and Dushanzi districts were 173.5 μg/m³, 114.3 μg/m³, and 228.4 μg/m³, respectively. The results provide a theoretical basis for the effective control of regional ozone pollution in urban areas (Urumqi), dusty areas (Hotan), and industrial areas (Dushanzi) in Xinjiang. Full article

(This article belongs to the Special Issue Application of Machine Learning in Atmospheric Observations, Monitoring and Modeling)

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17 pages, 5914 KiB

Open AccessArticle

A Dual-Attention-Mechanism Multi-Channel Convolutional LSTM for Short-Term Wind Speed Prediction

by Jinhui He, Hao Yang, Shijie Zhou, Jing Chen and Min Chen

Atmosphere 2023, 14(1), 71; https://doi.org/10.3390/atmos14010071 - 30 Dec 2022

Cited by 1 | Viewed by 2058

Abstract

Accurate wind speed prediction plays a crucial role in wind power generation and disaster avoidance. However, stochasticity and instability increase the difficulty of wind speed prediction. In this study, we proposed a dual-attention mechanism multi-channel convolutional LSTM (DACLSTM), collected European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) near-ground element-grid data from some parts of North China, and selected elements with high correlations with wind speed to form multiple channels. We used a convolutional network for the feature extraction of spatial information, a Long Short-Term Memory (LSTM) network for the feature extraction of time-series information, and used channel attention with spatial attention for feature extraction. The experimental results show that the DACLSTM model can improve the accuracy of six-hour lead time wind speed prediction relative to the traditional ConvLSTM model and fully connected network long short-term memory (FC_LSTM). Full article

(This article belongs to the Special Issue Application of Machine Learning in Atmospheric Observations, Monitoring and Modeling)

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15 pages, 2447 KiB

Open AccessArticle

Deep Quantified Visibility Estimation for Traffic Image

by Fang Zhang, Tingzhao Yu, Zhimin Li, Kuoyin Wang, Yu Chen, Yan Huang and Qiuming Kuang

Atmosphere 2023, 14(1), 61; https://doi.org/10.3390/atmos14010061 - 28 Dec 2022

Cited by 3 | Viewed by 2563

Abstract

Image-based quantified visibility estimation is an important task for both atmospheric science and computer vision. Traditional methods rely largely on meteorological observation or manual camera calibration, which restricts its performance and generality. In this paper, we propose a new end-to-end pipeline for single image-based quantified visibility estimation by an elaborate integration between meteorological physical constraint and deep learning architecture design. Specifically, the proposed Deep Quantified Visibility Estimation Network (abbreviated as DQVENet) consists of three modules, i.e., the Transmission Estimation Module (TEM), the Depth Estimation Module (DEM), and the Extinction coEfficient Estimation Module (E3M). Casting on these modules, the meteorological prior constraint can be combined with deep learning. To validate the performance of DQVENet, this paper also constructs a traffic image dataset (named QVEData) with accurate visibility calibration. Experimental results compared with many state-of-the-art methods on QVEData demonstrate the effectiveness and superiority of DQVENet. Full article

(This article belongs to the Special Issue Application of Machine Learning in Atmospheric Observations, Monitoring and Modeling)

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18 pages, 3411 KiB

Open AccessArticle

Lightning Identification Method Based on Deep Learning

by Zheng Qian, Dongdong Wang, Xiangbo Shi, Jinliang Yao, Lijun Hu, Hao Yang and Yongsen Ni

Atmosphere 2022, 13(12), 2112; https://doi.org/10.3390/atmos13122112 - 16 Dec 2022

Cited by 1 | Viewed by 1497

Abstract

In this study, a deep learning method called Lightning-SN was developed and used for cloud-to-ground (CG) lightning identification. Based on artificial scenarios, this network model selects radar products that exhibit characteristic factors closely related to lightning. Advanced time of arrival and direction lightning positioning data were used as the labeling factors. The Lightning-SN model was constructed based on an encoder–decoder structure with 25 convolutional layers, five pooling layers, five upsampling layers, and a sigmoid activation function layer. Additionally, the maximum pooling index method was adopted in Lightning-SN to avoid characteristic boundary information loss in the pooling process. The gradient harmonizing mechanism was used as the loss function to improve the model performance. The evaluation results showed that the Lightning-SN improved the segmentation accuracy of the CG lightning location compared with the traditional threshold method, according to the 6-minute operating period of the current S-band Doppler radar, exhibiting a better performance in terms of lightning location identification based on high-resolution radar data. The model was applied to the Ningbo area of Zhejiang Province, China. It was applied to the lightning hazard prevention in the hazardous chemical park in Ningbo. The composite reflectivity and radial velocity were the two dominant factors, with a greater influence on the model performance than other factors. Full article

(This article belongs to the Special Issue Application of Machine Learning in Atmospheric Observations, Monitoring and Modeling)

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21 pages, 12022 KiB

Open AccessArticle

Cloud Nowcasting with Structure-Preserving Convolutional Gated Recurrent Units

by Samuel A. Kellerhals, Fons De Leeuw and Cristian Rodriguez Rivero

Atmosphere 2022, 13(10), 1632; https://doi.org/10.3390/atmos13101632 - 7 Oct 2022

Cited by 4 | Viewed by 1880

Abstract

Nowcasting of clouds is a challenging spatiotemporal task due to the dynamic nature of the atmosphere. In this study, the use of convolutional gated recurrent unit networks (ConvGRUs) to produce short-term cloudiness forecasts for the next 3 h over Europe is proposed, along with an optimisation criterion able to preserve image structure across the predicted sequences. This approach is compared against state-of-the-art optical flow algorithms using over two and a half years of observations from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument onboard the Meteosat Second Generation satellite. We show that the ConvGRU trained using our structure-preserving loss function significantly outperforms the optical flow algorithms with an average change in

R^{2}

, mean absolute error and structural similarity of 12.43%, −8.75% and 9.68%, respectively, across all time steps. We also confirm that merging multiple optical flow algorithms into an ensemble yields significant short-term performance increases (<1 h), and that nowcast skill can vary significantly across different European regions. Furthermore, our results show that blurry images resulting from using globally oriented loss functions can be avoided by optimising for structural similarity when producing nowcasts. We thus showcase that deep-learning-based models using locally oriented loss functions present a powerful new way to produce accurate cloud nowcasts, with important applications to be found in solar power forecasting. Full article

(This article belongs to the Special Issue Application of Machine Learning in Atmospheric Observations, Monitoring and Modeling)

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12 pages, 1211 KiB

Open AccessArticle

Visibility Prediction Based on Machine Learning Algorithms

by Yu Zhang, Yangjun Wang, Yingqian Zhu, Lizhi Yang, Lin Ge and Chun Luo

Atmosphere 2022, 13(7), 1125; https://doi.org/10.3390/atmos13071125 - 16 Jul 2022

Cited by 11 | Viewed by 3078

Abstract

In this study, ground observation data were selected from January 2016 to January 2020. First, six machine learning methods were used to predict visibility. We verified the accuracy of the method with and without principal components analysis (PCA) by combining actual examples with the European Centre for Medium-Range Weather Forecast (ECMWF) data and National Centers for Environmental Prediction (NECP) data. The results show that PCA can improve visibility prediction. Neural networks have high accuracy in machine learning algorithms. The initial visibility data plays an important role in the visibility forecast and can effectively improve forecast accuracy. Full article

(This article belongs to the Special Issue Application of Machine Learning in Atmospheric Observations, Monitoring and Modeling)

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16 pages, 1015 KiB

Open AccessArticle

Multitask Learning Based on Improved Uncertainty Weighted Loss for Multi-Parameter Meteorological Data Prediction

by Junkai Wang, Lianlei Lin, Zaiming Teng and Yu Zhang

Atmosphere 2022, 13(6), 989; https://doi.org/10.3390/atmos13060989 - 20 Jun 2022

Viewed by 1698

Abstract

With the exponential growth in the amount of available data, traditional meteorological data processing algorithms have become overwhelmed. The application of artificial intelligence in simultaneous prediction of multi-parameter meteorological data has attracted much attention. However, existing single-task network models are generally limited by the data correlation dependence problem. In this paper, we use a priori knowledge for network design and propose a multitask model based on an asymmetric sharing mechanism, which effectively solves the correlation dependence problem in multi-parameter meteorological data prediction and achieves simultaneous prediction of multiple meteorological parameters with complex correlations for the first time. The performance of the multitask model depends largely on the relative weights among the task losses, and manually adjusting these weights is a difficult and expensive process, which makes it difficult for multitask learning to achieve the expected results in practice. In this paper, we propose an improved multitask loss processing method based on the assumptions of homoscedasticity uncertainty and the Laplace loss distribution and validate it using the German Jena dataset. The results show that the method can automatically balance the losses of each subtask and has better performance and robustness. Full article

(This article belongs to the Special Issue Application of Machine Learning in Atmospheric Observations, Monitoring and Modeling)

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21 pages, 2598 KiB

Open AccessArticle

Predicting Meteorological Variables on Local Level with SARIMA, LSTM and Hybrid Techniques

by Antonios Parasyris, George Alexandrakis, Georgios V. Kozyrakis, Katerina Spanoudaki and Nikolaos A. Kampanis

Atmosphere 2022, 13(6), 878; https://doi.org/10.3390/atmos13060878 - 28 May 2022

Cited by 8 | Viewed by 2698

Abstract

The choice of holiday destinations is highly depended on climate considerations. Nowadays, since the effects of the climate crisis are being increasingly felt, the need for accurate weather and climate services for hotels is crucial. Such a service could be beneficial for both the future planning of tourists’ activities and destinations and for hotel managers as it could help in decision making about the planning and expansion of the touristic season, due to a prediction of higher temperatures for a longer time span, thus causing increased revenue for companies in the local touristic sector. The aim of this work is to calculate predictions on meteorological variables using statistical techniques as well as artificial intelligence (AI) for a specific area of interest utilising data from an in situ meteorological station, and to produce valuable and reliable localised predictions with the most cost-effective method possible. This investigation will answer the question of the most suitable prediction method for time series data from a single meteorological station that is deployed in a specific location; in our case, in a hotel in the northern area of Crete, Greece. The temporal resolution of the measurements used was 3 h and the forecast horizon considered here was up to 2 days. As prediction techniques, seasonal autoregressive integrated moving average (SARIMA), AI techniques like the long short-term memory (LSTM) neural network and hybrid combinations of the two are used. Multiple meteorological variables are considered as input for the LSTM and hybrid methodologies, like temperature, relative humidity, atmospheric pressure and wind speed, unlike the SARIMA that has a single variable. Variables of interest are divided into those that present seasonality and patterns, such as temperature and humidity, and those that are more stochastic with no known seasonality and patterns, such as wind speed and direction. Two benchmark techniques are used for comparison and quantification of the added predictive ability, namely the climatological forecast and the persistence model, which shows a considerable amount of improvement over the naive prediction methods, especially in the 1-day forecasts. The results indicate that the examined hybrid methodology performs best at temperature and wind speed forecasts, closely followed by the SARIMA, whereas LSTM performs better overall at the humidity forecast, even after the correction of the hybrid to the SARIMA model. Lastly, different hybrid methodologies are discussed and introduced for further improvement of meteorological predictions. Full article

(This article belongs to the Special Issue Application of Machine Learning in Atmospheric Observations, Monitoring and Modeling)

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16 pages, 8484 KiB

Open AccessArticle

Usefulness of Automatic Hyperparameter Optimization in Developing Radiation Emulator in a Numerical Weather Prediction Model

by Park Sa Kim and Hwan-Jin Song

Atmosphere 2022, 13(5), 721; https://doi.org/10.3390/atmos13050721 - 30 Apr 2022

Cited by 5 | Viewed by 1604

Abstract

To improve the forecasting accuracy of a radiation emulator in a weather prediction model over the Korean peninsula, the learning rate used in neural network training was automatically optimized using the Sherpa. The Sherpa experiment results were compared with two control simulation results using learning rates of 0.0001 and 1 for different batch sizes (full to 500). In the offline evaluation, the Sherpa results showed significant improvements in predicting longwave/shortwave heating rates and fluxes compared to the lowest learning rate results, whereas the improvements compared to the highest learning rate were relatively small because the optimized values by the Sherpa were 0.4756–0.6656. The online evaluation results over one month, which were linked with the weather prediction model, demonstrated the usefulness of Sherpa on a universal performance for the radiation emulator. In particular, at the full batch size, Sherpa contributed to reducing the one-week forecast errors for longwave/shortwave fluxes, skin temperature, and precipitation by 39–125%, 137–159%, and 24–26%, respectively, compared with the two control simulations. Considering the widespread use of parallel learning based on full batch, Sherpa can contribute to producing robust results regardless of batch sizes used in neural network training for developing radiation emulators. Full article

(This article belongs to the Special Issue Application of Machine Learning in Atmospheric Observations, Monitoring and Modeling)

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