Deep Learning Algorithms for Weather Forecasting and Climate Prediction

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

Deadline for manuscript submissions: 14 June 2024 | Viewed by 3896

Special Issue Editor


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Guest Editor
School of Software Engineering, Tongji University, Shanghai, China
Interests: climate prediction; weather forecasting; spatio-temporal data analysis; artificial intelligence and its interpretability; machine learning

Special Issue Information

Dear Colleagues,

Human society and natural ecosystems are vulnerable to weather and climate change, which have considerable impacts. Therefore, there is an urgent need for useful and credible information for weather and climate services. In recent years, deep learning is widely used in meteorological research and have made great progress in tasks such as weather forecasting and climate prediction. However, deep learning still has two problems: the first is the poor generalization ability of models, which often underestimate or even miss extreme events, and the second is the lack of physical consistency, weak interpretability, and poor credibility of "black box" models. Recent advances such as graph convolutional networks, attention mechanisms, and full residual encoder–decoders have helped to enhance learning using limited samples, reducing overfitting and bias. In addition, introducing a priori physical knowledge in deep learning models by designing special network structures or loss functions can improve the forecasting skill and interpretability of AI.

This Special Issue aims to promote the publication of original research and reviews that focus on deep learning algorithms for weather forecasting and climate prediction. Submissions are welcome covering a wide range of topics, including, but not limited to:

  1. the application of deep learning in climate prediction;
  2. the application of deep learning in weather forecasting, such as NAO, tropical cyclone, precipitation, etc;
  3. the comparison of different methods to illustrate the effectiveness of deep learning;
  4. the interpretation of the models and results.

Dr. Shijin Yuan
Guest Editor

Manuscript Submission Information

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Keywords

  • climate prediction
  • severe weather forecasting
  • spatio-temporal data analysis
  • artificial intelligence and its interpretability
  • machine learning
  • intelligent algorithm development
  • data assimilation

Published Papers (4 papers)

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Research

24 pages, 5033 KiB  
Article
Hierarchical Predictions of Fine-to-Coarse Time Span and Atmospheric Field Reconstruction for Typhoon Track Prediction
by Shengye Yan, Zhendong Zhang and Wei Zheng
Atmosphere 2024, 15(5), 605; https://doi.org/10.3390/atmos15050605 - 16 May 2024
Viewed by 181
Abstract
The prediction of typhoon tracks in the Northwest Pacific is key to reducing human casualties and property damage. Traditional numerical forecasting models often require substantial computational resources, are high-cost, and have significant limitations in prediction speed. This research is dedicated to using deep [...] Read more.
The prediction of typhoon tracks in the Northwest Pacific is key to reducing human casualties and property damage. Traditional numerical forecasting models often require substantial computational resources, are high-cost, and have significant limitations in prediction speed. This research is dedicated to using deep learning methods to address the shortcomings of traditional methods. Our method (AFR-SimVP) is based on a large-kernel convolutional spatio-temporal prediction network combined with multi-feature fusion for forecasting typhoon tracks in the Northwest Pacific. In order to more effectively suppress the effect of noise in the dataset to enhance the generalization ability of the model, we use a multi-branch structure, incorporate an atmospheric reconstruction subtask, and propose a second-order smoothing loss to further improve the prediction ability of the model. More importantly, we innovatively propose a multi-time-step typhoon prediction network (HTAFR-SimVP) that does not use the traditional recurrent neural network family of models at all. Instead, through fine-to-coarse hierarchical temporal feature extraction and dynamic self-distillation, multi-time-step prediction is achieved using only a single regression network. In addition, combined with atmospheric field reconstruction, the network achieves integrated prediction for multiple tasks, which greatly enhances the model’s range of applications. Experiments show that our proposed network achieves optimal performance in the 24 h typhoon track prediction task. Our regression network outperforms previous recurrent network-based typhoon prediction models in the multi-time-step prediction task and also performs well in multiple integration tasks. Full article
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21 pages, 11653 KiB  
Article
Data-Driven Global Subseasonal Forecast for Intraseasonal Oscillation Components
by Yichen Shen, Chuhan Lu, Yihan Wang, Dingan Huang and Fei Xin
Atmosphere 2023, 14(11), 1682; https://doi.org/10.3390/atmos14111682 - 13 Nov 2023
Viewed by 804
Abstract
As a challenge in the construction of a “seamless forecast” system, improving the prediction skills of subseasonal forecasts is a key issue for meteorologists. In view of the evolution characteristics of numerical models and deep-learning models for subseasonal forecasts, as forecast times increase, [...] Read more.
As a challenge in the construction of a “seamless forecast” system, improving the prediction skills of subseasonal forecasts is a key issue for meteorologists. In view of the evolution characteristics of numerical models and deep-learning models for subseasonal forecasts, as forecast times increase, the prediction skill for high-frequency components will decrease, as the lead time is already far beyond the predictability. Meanwhile, intraseasonal low-frequency components are essential to the change in general circulation on subseasonal timescales. In this paper, the Global Subseasonal Forecast Model (GSFM v1.0) first extracted the intraseasonal oscillation (ISO) components of atmospheric signals and used an improved deep-learning model (SE-ResNet) to train and predict the ISO components of geopotential height at 500 hPa (Z500) and temperature at 850 hPa (T850). The results show that the 10–30 day prediction performance of the SE-ResNet model is better than that of the model trained directly with original data. Compared with other models/methods, this model has a good ability to depict the subseasonal evolution of the ISO components of Z500 and T850. In particular, although the prediction results from the Climate Forecast System Version 2 have better performance through 10 days, the SE-ResNet model is substantially superior to CFSv2 through 10–30 days, especially in the middle and high latitudes. The SE-ResNet model also has a better effect in predicting planetary waves with wavenumbers of 3–8. Thus, the application of data-driven subseasonal forecasts of atmospheric ISO components may shed light on improving the skill of seasonal forecasts. Full article
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21 pages, 3442 KiB  
Article
Research and Application of Intelligent Weather Push Model Based on Travel Forecast and 5G Message
by Yuan Yuan, Fengchen Fu, Yaling Li, Yao Xing, Lei Wang, Hao Zheng and Wei Ye
Atmosphere 2023, 14(11), 1658; https://doi.org/10.3390/atmos14111658 - 5 Nov 2023
Cited by 1 | Viewed by 1147
Abstract
In the realm of daily activity planning, precise weather forecasting services hold paramount significance. However, the prevalent dissemination of weather forecasts through conventional channels like radio, television, and the internet often yields only generalized regional predictions. This limitation contributes to diminished forecast reach, [...] Read more.
In the realm of daily activity planning, precise weather forecasting services hold paramount significance. However, the prevalent dissemination of weather forecasts through conventional channels like radio, television, and the internet often yields only generalized regional predictions. This limitation contributes to diminished forecast reach, inadequate accuracy, and a lack of individualization, thwarting the effective distribution of meteorological insights and inhibiting the fulfillment of personalized forecast demands. Addressing these concerns, our study proposes a personalized weather forecasting approach that harnesses machine learning techniques and leverages the 5G messaging platform. By amalgamating projected user travel data, we augment personalized weather reports and extend user coverage to achieve tailored, timely, and high-quality weather services. Concretely, our research commences with an extensive analysis of large-scale user travel behavior data to extract pertinent travel attributes. Subsequently, we construct a user’s future location prediction model—dubbed the Loc-PredModel—by employing the Extreme Gradient Boosting (XGBoost) algorithm to forecast users’ trip destinations and arrival times. Anchored in the anticipated outcomes of user travel behavior, personalized weather data reports are formulated. Experimental results underscore the Loc-PredModel’s remarkable predictive prowess, demonstrating a root mean squared error (RMSE) value of 0.208 and a coefficient of determination (R2) value of 0.935, affirming its efficacy in prognosticating users’ trip destinations and arrival times. Furthermore, our 5G message-driven platform, rooted in intelligent personalized meteorological services, underwent testing within Chengdu city and garnered positive user feedback. Our research effectively surmounts the limitations of conventional weather forecasting platforms by furnishing users with more precise and customized weather information predicated on behavioral analysis and the 5G information ecosystem. This study not only advances the theoretical groundwork of intelligent meteorology but also offers invaluable insights and guidance for future advancement. By providing users with a more personalized and timely intelligent meteorological service experience, our approach exhibits transferability, with the research methodology and model potentially extendable nationwide or even on a larger scale beyond the study’s Chengdu-based scope. Full article
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13 pages, 6903 KiB  
Article
Estimating Daily Temperatures over Andhra Pradesh, India, Using Artificial Neural Networks
by Gubbala Ch. Satyanarayana, Velivelli Sambasivarao, Peddi Yasaswini and Meer M. Ali
Atmosphere 2023, 14(10), 1501; https://doi.org/10.3390/atmos14101501 - 28 Sep 2023
Viewed by 1216
Abstract
In the recent past, Andhra Pradesh (AP) has experienced increasing trends in surface air mean temperature (SAT at a height of 2 m) because of climate change. In this paper, we attempt to estimate the SAT using the GFDL-ESM2G (Geophysical Fluid Dynamics Laboratory [...] Read more.
In the recent past, Andhra Pradesh (AP) has experienced increasing trends in surface air mean temperature (SAT at a height of 2 m) because of climate change. In this paper, we attempt to estimate the SAT using the GFDL-ESM2G (Geophysical Fluid Dynamics Laboratory Earth System Model version 2G), available from the Coupled Model Intercomparison Project Phase-5 (CMIP5). This model has a mismatch with the India Meteorological Department (IMD)’s observations during April and May, which are the most heat-prone months in the state. Hence, in addition to the SAT from the model, the present paper considers other parameters, such as mean sea level pressure, surface winds, surface relative humidity, and surface solar radiation downwards, that have influenced the SAT. Since all five meteorological parameters from the GFDL-ESM2G model influence the IMD’s SAT, an artificial neural network (ANN) technique has been used to predict the SAT using the above five meteorological parameters as predictors (input) and the IMD’s SAT as the predictand (output). The model was developed using 1981–2020 data with different time lags, and results were tested for 2021 and 2022 in addition to the random testing conducted for 1981–2020. The statistical parameters between the IMD observations and the ANN estimations using GFDL-ESM2G predictions as input confirm that the SAT can be estimated accurately as described in the analysis section. The analysis conducted for different regions of AP reveals that the diurnal variations of SAT in the IMD observations and the ANN predictions over three regions (North, Central, and South AP) and overall AP compare well, with root mean square error varying between 0.97 °C and 1.33 °C. Thus, the SAT predictions provided in the GFDL-ESM2G model simulations could be improved statistically by using the ANN technique over the AP region. Full article
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