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Artificial Intelligence for Weather and Climate

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "AI Remote Sensing".

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 35124

Special Issue Editors


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Guest Editor
Royal Meteorological Institute of Belgium, Ringlaan 3, 1180 Brussel, Belgium
Interests: Earth radiation budget; atmospheric remote sensing; climate monitoring; weather forecast

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Guest Editor
ETRO Department, Vrije Universiteit Brussel, Pleinlaan 2, B-1050 Brussels, Belgium
Interests: information processing; image and video transmissions over networks; image processing

Special Issue Information

Dear Colleagues,

 

Artificial intelligence (AI) is an explosively growing field of computer science which is expected to transform many aspects of society in a profound way. Following a series of scientific and technological breakthroughs, AI techniques are witnessing a growing interest and successful deployments in a plethora of domains and applications. AI techniques analyze large amounts of unstructured and heterogeneous data, and discover and exploit complex and intricate relationships between these data, without recourse to an explicit analytical treatment of those relationships.

 

As AI techniques are data driven, in principle they are well suited for application in weather forecasting (WF) and climate monitoring (CM), as they rely on a vast amount of meteorological observations, with a prominent place for satellite remote sensing. 

 

The main objective of the Special Issue is to draw the attention of the remote sensing community to the rapidly evolving domain of modern AI and its applications. For this Special Issue, we invite contributions related to the application of AI techniques to WF and CM. Particular areas that could be addressed include:

  • Observations of quality control/bias correction/data fusion
  • Nowcasting
  • Data assimilation
  • Process parameterization
  • Postprocessing of NWP output
  • Multimodel superensembles
  • Warnings for high-impact weather
  • Subseasonal and seasonal forecast
  • Decadal climate prediction

Dr. Steven Dewitte
Prof. Adrian Munteanu
Dr. Richard Mueller
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence weather forecast climate monitoring and prediction meteorological observations nowcasting weather warnings

Published Papers (10 papers)

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Research

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29 pages, 16759 KiB  
Article
A Novel Hybrid Intelligent SOPDEL Model with Comprehensive Data Preprocessing for Long-Time-Series Climate Prediction
by Zeyu Zhou, Wei Tang, Mingyang Li, Wen Cao and Zhijie Yuan
Remote Sens. 2023, 15(7), 1951; https://doi.org/10.3390/rs15071951 - 06 Apr 2023
Cited by 3 | Viewed by 1765
Abstract
Long-time-series climate prediction is of great significance for mitigating disasters; promoting ecological civilization; identifying climate change patterns and preventing floods, drought and typhoons. However, the general public often struggles with the complexity and extensive temporal range of meteorological data when attempting to accurately [...] Read more.
Long-time-series climate prediction is of great significance for mitigating disasters; promoting ecological civilization; identifying climate change patterns and preventing floods, drought and typhoons. However, the general public often struggles with the complexity and extensive temporal range of meteorological data when attempting to accurately forecast climate extremes. Sequence disorder, weak robustness, low characteristics and weak interpretability are four prevalent shortcomings in predicting long-time-series data. In order to resolve these deficiencies, our study gives a novel hybrid spatiotemporal model which offers comprehensive data preprocessing techniques, focusing on data decomposition, feature extraction and dimensionality upgrading. This model provides a feasible solution to the puzzling problem of long-term climate prediction. Firstly, we put forward a Period Division Region Segmentation Property Extraction (PD-RS-PE) approach, which divides the data into a stationary series (SS) for an Extreme Learning Machine (ELM) prediction and an oscillatory series (OS) for a Long Short-term Memory (LSTM) prediction to accommodate the changing trend of data sequences. Secondly, a new type of input-output mapping mode in a three-dimensional matrix was constructed to enhance the robustness of the prediction. Thirdly, we implemented a multi-layer technique to extract features of high-speed input data based on a Deep Belief Network (DBN) and Particle Swarm Optimization (PSO) for parameter searching of a neural network, thereby enhancing the overall system’s learning ability. Consequently, by integrating all the above innovative technologies, a novel hybrid SS-OS-PSO-DBN-ELM-LSTME (SOPDEL) model with comprehensive data preprocessing was established to improve the quality of long-time-series forecasting. Five models featuring partial enhancements are discussed in this paper and three state-of-the-art classical models were utilized for comparative experiments. The results demonstrated that the majority of evaluation indices exhibit a significant optimization in the proposed model. Additionally, a relevant evaluation system showed that the quality of “Excellent Prediction” and “Good Prediction” exceeds 90%, and no data with “Bad Prediction” appear, so the accuracy of the prediction process is obviously insured. Full article
(This article belongs to the Special Issue Artificial Intelligence for Weather and Climate)
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19 pages, 5330 KiB  
Article
Three-Dimensional Gridded Radar Echo Extrapolation for Convective Storm Nowcasting Based on 3D-ConvLSTM Model
by Nengli Sun, Zeming Zhou, Qian Li and Jinrui Jing
Remote Sens. 2022, 14(17), 4256; https://doi.org/10.3390/rs14174256 - 29 Aug 2022
Cited by 9 | Viewed by 2016
Abstract
Radar echo extrapolation has been widely developed in previous studies for precipitation and storm nowcasting. However, most studies have focused on two-dimensional radar images, and extrapolation of multi-altitude radar images, which can provide more informative and visual forecasts about weather systems in realistic [...] Read more.
Radar echo extrapolation has been widely developed in previous studies for precipitation and storm nowcasting. However, most studies have focused on two-dimensional radar images, and extrapolation of multi-altitude radar images, which can provide more informative and visual forecasts about weather systems in realistic space, has been less explored. Thus, this paper proposes a 3D-convolutional long short-term memory (ConvLSTM)-based model to perform three-dimensional gridded radar echo extrapolation for severe storm nowcasting. First, a 3D-convolutional neural network (CNN) is used to extract the 3D spatial features of each input grid radar volume. Then, 3D-ConvLSTM layers are leveraged to model the spatial–temporal relationship between the extracted 3D features and recursively generate the 3D hidden states correlated to the future. Nowcasting results are obtained after applying another 3D-CNN to up-sample the generated 3D hidden states. Comparative experiments were conducted on a public National Center for Atmospheric Research Data Archive dataset with a 3D optical flow method and other deep-learning-based models. Quantitative evaluations demonstrate that the proposed 3D-ConvLSTM-based model achieves better overall and longer-term performance for storms with reflectivity values above 35 and 45 dBZ. In addition, case studies qualitatively demonstrate that the proposed model predicts more realistic storm evolution and can facilitate early warning regarding impending severe storms. Full article
(This article belongs to the Special Issue Artificial Intelligence for Weather and Climate)
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14 pages, 3113 KiB  
Communication
The Prediction of the Tibetan Plateau Thermal Condition with Machine Learning and Shapley Additive Explanation
by Yuheng Tang, Anmin Duan, Chunyan Xiao and Yue Xin
Remote Sens. 2022, 14(17), 4169; https://doi.org/10.3390/rs14174169 - 25 Aug 2022
Cited by 5 | Viewed by 1644
Abstract
The thermal condition over the Tibetan Plateau (TP) plays a vital role in the South Asian high (SAH) and the Asian summer monsoon (ASM); however, its prediction skill is still low. Here, two machine learning models are employed to address this problem. Expert [...] Read more.
The thermal condition over the Tibetan Plateau (TP) plays a vital role in the South Asian high (SAH) and the Asian summer monsoon (ASM); however, its prediction skill is still low. Here, two machine learning models are employed to address this problem. Expert knowledge and distance correlation are used to select the predictors from observational datasets. Both linear and nonlinear relationships are considered between the predictors and predictands. The predictors are utilized for training the machine learning models. The prediction skills of the machine learning models are higher than those of two state-of-the-art dynamic operational models and can explain 67% of the variance in the observations. Moreover, the SHapley Additive exPlanation method results indicate that the important predictors are mainly from the Southern Hemisphere, Eurasia, and western Pacific, and most show nonlinear relationships with the predictands. Our results can be applied to find potential climate teleconnections and improve the prediction of other climate signals. Full article
(This article belongs to the Special Issue Artificial Intelligence for Weather and Climate)
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21 pages, 1033 KiB  
Article
End-to-End Prediction of Lightning Events from Geostationary Satellite Images
by Sebastian Brodehl, Richard Müller, Elmar Schömer, Peter Spichtinger and Michael Wand
Remote Sens. 2022, 14(15), 3760; https://doi.org/10.3390/rs14153760 - 05 Aug 2022
Cited by 6 | Viewed by 2064
Abstract
While thunderstorms can pose severe risks to property and life, forecasting remains challenging, even at short lead times, as these often arise in meta-stable atmospheric conditions. In this paper, we examine the question of how well we could perform short-term (up to 180 [...] Read more.
While thunderstorms can pose severe risks to property and life, forecasting remains challenging, even at short lead times, as these often arise in meta-stable atmospheric conditions. In this paper, we examine the question of how well we could perform short-term (up to 180 min) forecasts using exclusively multi-spectral satellite images and past lighting events as data. We employ representation learning based on deep convolutional neural networks in an “end-to-end” fashion. Here, a crucial problem is handling the imbalance of the positive and negative classes appropriately in order to be able to obtain predictive results (which is not addressed by many previous machine-learning-based approaches). The resulting network outperforms previous methods based on physically based features and optical flow methods (similar to operational prediction models) and generalizes across different years. A closer examination of the classifier performance over time and under masking of input data indicates that the learned model actually draws most information from structures in the visible spectrum, with infrared imaging sustaining some classification performance during the night. Full article
(This article belongs to the Special Issue Artificial Intelligence for Weather and Climate)
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22 pages, 7099 KiB  
Article
Two-Stage Spatiotemporal Context Refinement Network for Precipitation Nowcasting
by Dan Niu, Junhao Huang, Zengliang Zang, Liujia Xu, Hongshu Che and Yuanqing Tang
Remote Sens. 2021, 13(21), 4285; https://doi.org/10.3390/rs13214285 - 25 Oct 2021
Cited by 11 | Viewed by 2280
Abstract
Precipitation nowcasting by radar echo extrapolation using machine learning algorithms is a field worthy of further study, since rainfall prediction is essential in work and life. Current methods of predicting the radar echo images need further improvement in prediction accuracy as well as [...] Read more.
Precipitation nowcasting by radar echo extrapolation using machine learning algorithms is a field worthy of further study, since rainfall prediction is essential in work and life. Current methods of predicting the radar echo images need further improvement in prediction accuracy as well as in presenting the predicted details of the radar echo images. In this paper, we propose a two-stage spatiotemporal context refinement network (2S-STRef) to predict future pixel-level radar echo maps (deterministic output) more accurately and with more distinct details. The first stage is an efficient and concise spatiotemporal prediction network, which uses the spatiotemporal RNN module embedded in an encoder and decoder structure to give a first-stage prediction. The second stage is a proposed detail refinement net, which can preserve the high-frequency detailed feature of the radar echo images by using the multi-scale feature extraction and fusion residual block. We used a real-world radar echo map dataset of South China to evaluate the proposed 2S-STRef model. The experiments showed that compared with the PredRNN++ and ConvLSTM method, our 2S-STRef model performs better on the precipitation nowcasting, as well as at the image quality evaluating index and the forecasting indices. At a given 45 dBZ echo threshold (heavy precipitation) and with a 2 h lead time, the widely used CSI, HSS, and SSIM indices of the proposed 2S-STRef model are found equal to 0.195, 0.312, and 0.665, respectively. In this case, the proposed model outperforms the OpticalFlow method and PredRNN++ model. Full article
(This article belongs to the Special Issue Artificial Intelligence for Weather and Climate)
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21 pages, 3311 KiB  
Article
A Deep Learning Multimodal Method for Precipitation Estimation
by Arthur Moraux, Steven Dewitte, Bruno Cornelis and Adrian Munteanu
Remote Sens. 2021, 13(16), 3278; https://doi.org/10.3390/rs13163278 - 19 Aug 2021
Cited by 19 | Viewed by 3769
Abstract
To improve precipitation estimation accuracy, new methods, which are able to merge different precipitation measurement modalities, are necessary. In this study, we propose a deep learning method to merge rain gauge measurements with a ground-based radar composite and thermal infrared satellite imagery. The [...] Read more.
To improve precipitation estimation accuracy, new methods, which are able to merge different precipitation measurement modalities, are necessary. In this study, we propose a deep learning method to merge rain gauge measurements with a ground-based radar composite and thermal infrared satellite imagery. The proposed convolutional neural network, composed of an encoder–decoder architecture, performs a multiscale analysis of the three input modalities to estimate simultaneously the rainfall probability and the precipitation rate value with a spatial resolution of 2 km. The training of our model and its performance evaluation are carried out on a dataset spanning 5 years from 2015 to 2019 and covering Belgium, the Netherlands, Germany and the North Sea. Our results for instantaneous precipitation detection, instantaneous precipitation rate estimation, and for daily rainfall accumulation estimation show that the best accuracy is obtained for the model combining all three modalities. The ablation study, done to compare every possible combination of the three modalities, shows that the combination of rain gauges measurements with radar data allows for a considerable increase in the accuracy of the precipitation estimation, and the addition of satellite imagery provides precipitation estimates where rain gauge and radar coverage are lacking. We also show that our multi-modal model significantly improves performance compared to the European radar composite product provided by OPERA and the quasi gauge-adjusted radar product RADOLAN provided by the DWD for precipitation rate estimation. Full article
(This article belongs to the Special Issue Artificial Intelligence for Weather and Climate)
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17 pages, 1057 KiB  
Article
A New Method Based on a Multilayer Perceptron Network to Determine In-Orbit Satellite Attitude for Spacecrafts without Active ADCS Like UVSQ-SAT
by Adrien Finance, Mustapha Meftah, Christophe Dufour, Thomas Boutéraon, Slimane Bekki, Alain Hauchecorne, Philippe Keckhut, Alain Sarkissian, Luc Damé and Antoine Mangin
Remote Sens. 2021, 13(6), 1185; https://doi.org/10.3390/rs13061185 - 21 Mar 2021
Cited by 7 | Viewed by 3212
Abstract
Climate change is largely determined by the radiation budget imbalance at the Top Of the Atmosphere (TOA), which is generated by the increasing concentrations of greenhouse gases (GHGs). As a result, the Earth Energy Imbalance (EEI) is considered as an Essential Climate Variable [...] Read more.
Climate change is largely determined by the radiation budget imbalance at the Top Of the Atmosphere (TOA), which is generated by the increasing concentrations of greenhouse gases (GHGs). As a result, the Earth Energy Imbalance (EEI) is considered as an Essential Climate Variable (ECV) that has to be monitored continuously from space. However, accurate TOA radiation measurements remain very challenging. Ideally, EEI monitoring should be performed with a constellation of satellites in order to resolve as much as possible spatio-temporal fluctuations in EEI which contain important information on the underlying mechanisms driving climate change. The monitoring of EEI and its components (incoming solar, reflected solar, and terrestrial infrared fluxes) is the main objective of the UVSQ-SAT pathfinder nanosatellite, the first of its kind in the construction of a future constellation. UVSQ-SAT does not have an active determination system of its orientation with respect to the Sun and the Earth (i.e., the so-called attitude), a prerequisite in the calculation of EEI from the satellite radiation measurements. We present a new effective method to determine the UVSQ-SAT’s in-orbit attitude using its housekeeping and scientific sensors measurements and a well-established deep learning algorithm. One of the goals is to estimate the satellite attitude with a sufficient accuracy for retrieving the radiative fluxes (incoming solar, reflected solar, terrestrial infrared) on each face of the satellite with an uncertainty of less than ±5 Wm2 (1σ). This new method can be extended to any other satellites with no active attitude determination or control system. To test the accuracy of the method, a ground-based calibration experiment with different attitudes is performed using the Sun as the radiative flux reference. Based on the deep learning estimation of the satellite ground-based attitude, the uncertainty on the solar flux retrieval is about ±16 Wm2 (1σ). The quality of the retrieval is mainly limited by test conditions and the number of data samples used in training the deep learning system during the ground-based calibration. The expected increase in the number of training data samples will drastically decrease the uncertainty in the retrieved radiative fluxes. A very similar algorithm will be implemented and used in-orbit for UVSQ-SAT. Full article
(This article belongs to the Special Issue Artificial Intelligence for Weather and Climate)
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22 pages, 4205 KiB  
Article
Artificial Neural Networks to Retrieve Land and Sea Skin Temperature from IASI
by Sarah Safieddine, Ana Claudia Parracho, Maya George, Filipe Aires, Victor Pellet, Lieven Clarisse, Simon Whitburn, Olivier Lezeaux, Jean-Noël Thépaut, Hans Hersbach, Gabor Radnoti, Frank Goettsche, Maria Martin, Marie Doutriaux-Boucher, Dorothée Coppens, Thomas August, Daniel K. Zhou and Cathy Clerbaux
Remote Sens. 2020, 12(17), 2777; https://doi.org/10.3390/rs12172777 - 26 Aug 2020
Cited by 11 | Viewed by 3620
Abstract
Surface skin temperature (Tskin) derived from infrared remote sensors mounted on board satellites provides a continuous observation of Earth’s surface and allows the monitoring of global temperature change relevant to climate trends. In this study, we present a fast retrieval method [...] Read more.
Surface skin temperature (Tskin) derived from infrared remote sensors mounted on board satellites provides a continuous observation of Earth’s surface and allows the monitoring of global temperature change relevant to climate trends. In this study, we present a fast retrieval method for retrieving Tskin based on an artificial neural network (ANN) from a set of spectral channels selected from the Infrared Atmospheric Sounding Interferometer (IASI) using the information theory/entropy reduction technique. Our IASI Tskin product (i.e., TANN) is evaluated against Tskin from EUMETSAT Level 2 product, ECMWF Reanalysis (ERA5), SEVIRI observations, and ground in situ measurements. Good correlations between IASI TANN and the Tskin from other datasets are shown by their statistic data, such as a mean bias and standard deviation (i.e., [bias, STDE]) of [0.55, 1.86 °C], [0.19, 2.10 °C], [−1.5, 3.56 °C], from EUMETSAT IASI L-2 product, ERA5, and SEVIRI. When compared to ground station data, we found that all datasets did not achieve the needed accuracy at several months of the year, and better results were achieved at nighttime. Therefore, comparison with ground-based measurements should be done with care to achieve the ±2 °C accuracy needed, by choosing, for example, a validation site near the station location. On average, this accuracy is achieved, in particular at night, leading to the ability to construct a robust Tskin dataset suitable for Tskin long-term spatio-temporal variability and trend analysis. Full article
(This article belongs to the Special Issue Artificial Intelligence for Weather and Climate)
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12 pages, 409 KiB  
Perspective
Artificial Intelligence Revolutionises Weather Forecast, Climate Monitoring and Decadal Prediction
by Steven Dewitte, Jan P. Cornelis, Richard Müller and Adrian Munteanu
Remote Sens. 2021, 13(16), 3209; https://doi.org/10.3390/rs13163209 - 13 Aug 2021
Cited by 33 | Viewed by 8640
Abstract
Artificial Intelligence (AI) is an explosively growing field of computer technology, which is expected to transform many aspects of our society in a profound way. AI techniques are used to analyse large amounts of unstructured and heterogeneous data and discover and exploit complex [...] Read more.
Artificial Intelligence (AI) is an explosively growing field of computer technology, which is expected to transform many aspects of our society in a profound way. AI techniques are used to analyse large amounts of unstructured and heterogeneous data and discover and exploit complex and intricate relations among these data, without recourse to an explicit analytical treatment of those relations. These AI techniques are unavoidable to make sense of the rapidly increasing data deluge and to respond to the challenging new demands in Weather Forecast (WF), Climate Monitoring (CM) and Decadal Prediction (DP). The use of AI techniques can lead simultaneously to: (1) a reduction of human development effort, (2) a more efficient use of computing resources and (3) an increased forecast quality. To realise this potential, a new generation of scientists combining atmospheric science domain knowledge and state-of-the-art AI skills needs to be trained. AI should become a cornerstone of future weather and climate observation and modelling systems. Full article
(This article belongs to the Special Issue Artificial Intelligence for Weather and Climate)
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13 pages, 4944 KiB  
Letter
Global Cyclone and Anticyclone Detection Model Based on Remotely Sensed Wind Field and Deep Learning
by Ming Xie, Ying Li and Kai Cao
Remote Sens. 2020, 12(19), 3111; https://doi.org/10.3390/rs12193111 - 23 Sep 2020
Cited by 5 | Viewed by 3185
Abstract
Cyclone detection is a classical topic and researchers have developed various methods of cyclone detection based on sea-level pressure, cloud image, wind field, etc. In this article, a deep-learning algorithm is incorporated with modern remote-sensing technology and forms a global-scale cyclone/anticyclone detection model. [...] Read more.
Cyclone detection is a classical topic and researchers have developed various methods of cyclone detection based on sea-level pressure, cloud image, wind field, etc. In this article, a deep-learning algorithm is incorporated with modern remote-sensing technology and forms a global-scale cyclone/anticyclone detection model. Instead of using optical images, wind field data obtained from Mean Wind Field-Advanced Scatterometer (MWF-ASCAT) is utilized as the dataset for model training and testing. The wind field vectors are reconstructed and fed to the deep-learning model, which is built based on a faster-region with convolutional neural network (faster-RCNN). The model consists of three modules: a series of convolutional and pooling layers as the feature extractor, a region proposal network that searches for the potential areas of cyclone/anticyclone within the dataset, and the classifier that classifies the proposed region as cyclone or anticyclone through a fully-connected neural network. Compared with existing methods of cyclone detection, the test results indicate that this model based on deep learning is able to reduce the number of false alarms, and at the same time, maintain high accuracy in cyclone detection. An application of this method is presented in the article. By processing temporally continuous data of wind field, the model is able to track the path of Hurricane Irma in September, 2017. The advantages and limitations of the model are also discussed in the article. Full article
(This article belongs to the Special Issue Artificial Intelligence for Weather and Climate)
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