Special Issue "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: 30 June 2021.

Special Issue Editors

Dr. Steven Dewitte
E-Mail Website
Guest Editor
Royal Meteorological Institute of Belgium, Ringlaan 3, 1180 Brussel, Belgium
Interests: Earth radiation budget; atmospheric remote sensing; climate monitoring; weather forecast
Prof. Adrian Munteanu
E-Mail Website
Guest Editor
ETRO Department, Vrije Universiteit Brussel, Pleinlaan 2, B-1050 Brussels, Belgium
Interests: information processing; image and video transmissions over networks; image processing
Dr. Richard Müller
E-Mail Website
Guest Editor
German Weather Service, Frankfurter Str. 135, 63067 Offenbach, Germany
Interests: remote sensing of surface radiation; clouds and aerosols; sensor calibration; methods for "merging" in situ data with remote sensing data
Special Issues and Collections in MDPI journals

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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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 2400 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 (3 papers)

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Research

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Article
A New Method Based on a Multilayer Perceptron Network to Determine In-Orbit Satellite Attitude for Spacecrafts without Active ADCS Like UVSQ-SAT
Remote Sens. 2021, 13(6), 1185; https://doi.org/10.3390/rs13061185 - 21 Mar 2021
Cited by 1 | Viewed by 1069
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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Article
Artificial Neural Networks to Retrieve Land and Sea Skin Temperature from IASI
Remote Sens. 2020, 12(17), 2777; https://doi.org/10.3390/rs12172777 - 26 Aug 2020
Cited by 1 | Viewed by 1251
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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Letter
Global Cyclone and Anticyclone Detection Model Based on Remotely Sensed Wind Field and Deep Learning
Remote Sens. 2020, 12(19), 3111; https://doi.org/10.3390/rs12193111 - 23 Sep 2020
Viewed by 994
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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