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Special Issue "Simulation and Modeling of Climate: Recent Trends, Current Progress and Future Directions"

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

Deadline for manuscript submissions: 8 May 2023 | Viewed by 608

Special Issue Editors

Dr. Jinbo Xie
E-Mail Website
Guest Editor
Lawrence Livermore National Lab, Atmosphere, Earth & Energy Division, 7000 East Avenue, Livermore, CA 94550, USA
Interests: earth system model development and application; decadal prediction; sub-grid orographic parameterization; land–atmosphere coupling
Dr. Shuaiqi Tang
E-Mail Website
Guest Editor
Pacific Northwest National Lab, Atmospheric Sciences and Global Change Division, 902 Battelle Blvd, Richland, WA 99354, USA
Interests: climate model development and evaluation; aerosol, cloud and precipitation processes; objective analysis of field observations
Special Issues, Collections and Topics in MDPI journals
Dr. Yujin Zeng
E-Mail Website
Guest Editor
Program in Atmospheric and Oceanic Sciences, Princeton University, Princeton, NJ 08544, USA
Interests: land surface modeling; hydrology; climate modeling
Dr. Yi Qin
E-Mail Website
Guest Editor
Lawrence Livermore National Lab, Atmosphere, Earth & Energy Division, 7000 East Avenue, Livermore, CA 94550, USA
Interests: cloud feedback and climate sensitivity; cloud parameterization; climate change

Special Issue Information

Dear Colleagues,

General circulation models (GCMs) or Earth system models (ESMs) are important tools to understand how the climate has changed in the past and may change in the future. These models use mathematical equations to describe the behavior of factors and processes that impact climate and simulate the interactions between important drivers of the climate—including atmosphere, oceans, land surface and ice—with different levels of detail. Despite their progress over the past few decades, the most up-to-date Coupled Model Intercomparison Project (CMIP6) shows that the models still exhibit large uncertainties in climate simulations and projections. Tremendous efforts are being conducted to improve climate model simulations through improvements of dynamical core, more sophisticated physical parameterizations, better coupling between different climate components, constraints from observations and increasing model resolution. As the rapid development of computer sciences quickly expands to many other fields in recent years, some studies are leveraging machine learning (ML) for to aid in understanding climate patterns and improving simulations. With these progresses in the climate modeling and simulation community, more efforts are needed to analyze the trends and progress in the current climate simulation and modeling, and to identify potential future directions.

In this Special Issue, we focus on the recent trends, current progress, and future directions of climate modeling and simulation. Topics in this Special Issue include but are not limited to those outlined below:

  • development of climate models, including dynamic core, physical parameterizations and more
  • improving predictability of the earth system by machine learning
  • model simulations, evaluation, analysis and benchmarking
  • uncertainty quantification
  • evaluation of regional or global simulations using in situ or remote-sensing observations
  • regional climate change
  • coupling of different climate components (e.g., land–atmosphere coupling, air–sea interaction) and their climate impacts
  • evaluation of the cmip6 simulations and their improvement from the previous cmips

Dr. Jinbo Xie
Dr. Shuaiqi Tang
Dr. Yujin Zeng
Dr. Yi Qin
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 submissions that pass pre-check are 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. Atmosphere is an international peer-reviewed open access monthly 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 2000 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

  • model development
  • machine learning
  • model simulations, evaluation, analysis and benchmarking
  • uncertainty quantification
  • regional/global simulation evaluation
  • climate change
  • CMIP/CMIP6

Published Papers (1 paper)

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Research

Article
Short-Term Rainfall Prediction Based on Radar Echo Using an Improved Self-Attention PredRNN Deep Learning Model
Atmosphere 2022, 13(12), 1963; https://doi.org/10.3390/atmos13121963 - 24 Nov 2022
Viewed by 245
Abstract
Accurate short-term precipitation forecast is extremely important for urban flood warning and natural disaster prevention. In this paper, we present an innovative deep learning model named ISA-PredRNN (improved self-attention PredRNN) for precipitation nowcasting based on radar echoes on the basis of the advanced [...] Read more.
Accurate short-term precipitation forecast is extremely important for urban flood warning and natural disaster prevention. In this paper, we present an innovative deep learning model named ISA-PredRNN (improved self-attention PredRNN) for precipitation nowcasting based on radar echoes on the basis of the advanced PredRNN-V2. We introduce the self-attention mechanism and the long-term memory state into the model and design a new set of gating mechanisms. To better capture different intensities of precipitation, the loss function with weights was designed. We further train the model using a combination of reverse scheduled sampling and scheduled sampling to learn the long-term dynamics from the radar echo sequences. Experimental results show that the new model (ISA-PredRNN) can effectively extract the spatiotemporal features of radar echo maps and obtain radar echo prediction results with a small gap from the ground truths. From the comparison with the other six models, the new ISA-PredRNN model has the most accurate prediction results with a critical success index (CSI) of 0.7001, 0.5812 and 0.3052 under the radar echo thresholds of 10 dBZ, 20 dBZ and 30 dBZ, respectively. Full article
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