Special Issue "Artificial Intelligence and Statistical Techniques to Advance Weather Forecasting and Impact Modeling"

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 (28 July 2022) | Viewed by 3640

Special Issue Editors

Dr. Md Abul Ehsan Bhuiyan
E-Mail Website
Guest Editor
Northeast Climate Adaptation Science Center, University of Massachusetts Amherst, Amherst, MA 01003, USA
Interests: artificial intelligence; climate variability; weather and climate extremes; power outage modelling; wind speed modelling; natural hazard vulnerability assessments; statistical and dynamical downscaling
Special Issues, Collections and Topics in MDPI journals
Dr. Diego Cerrai
E-Mail Website
Guest Editor
Civil and Environmental Engineering, School of Engineering, University of Connecticut, Storrs, CT 06269, USA
Interests: impact modeling; meteorology; power outages; power restoration; wildfires.
Dr. Nishan Kumar Biswas
E-Mail Website
Guest Editor
Goddard Space Flight Center, National Aeronautics and Space Administration, Greenbelt, MD 20771, USA
Interests: satellite remote sensing; hydrological process modelling; extreme weather prediction; cloud computing and AI application in hydrology

Special Issue Information

Dear Colleagues,

Accurate estimation of weather variables such as temperature, wind, precipitation, humidity, and soil moisture is critical for hydrometeorological and weather impact modeling applications. Remote sensing observations, through their capability of covering large areas, are the primary source of weather variables estimation. It is important to evaluate the performance of radar and satellite-based estimates for hydrological, meteorological, and hydrometeorological applications on a global scale. Therefore, assessing and adjusting the sources of error is essential for extending the use of satellite-based (soil moisture, precipitation, etc.) estimates for water resources application. Different statistical and artificial intelligence (AI) techniques are used to improve the estimates by merging multisource datasets. This Special Issue aims at presenting the state-of-the-art in data science and machine/deep learning techniques to correct radar and satellite-based observations for hydrometeorological applications. Moreover, improvements in weather variables estimation have a direct effect on impact modeling accuracy, since weather variable errors non-linearly propagate and often magnify into models which quantitatively describe the relationships. We therefore welcome contributions which quantitatively describe the relationships between weather variables and their impact through explainable artificial intelligence, and which quantify how weather variables estimation improvements relate to impact modeling improvements. We also believe that this Special Issue will provide AI-based high-performance methods for weather variable estimation and weather impact modeling for future advancements in the domain.

Dr. Md Abul Ehsan Bhuiyan
Dr. Diego Cerrai
Dr. Nishan Kumar Biswas
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

  • data science
  • artificial intelligence
  • machine/deep learning
  • weather impact modeling
  • uncertainty quantification
  • weather forecasting
  • severe weather
  • remote sensing
  • advanced machine learning algorithms for satellite-based precipitation assimilation
  • machine learning for remote sensing applications
  • advances in precipitation retrieval algorithms
  • deep learning applications on spatial data
  • new satellite missions
  • quantitative precipitation estimation
  • cloud computing
  • ML application in hydrological predictions
  • AI application data-sparse regions
  • cloud computing in hydrology.

Published Papers (3 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Article
Evaluation of Technology for the Analysis and Forecasting of Precipitation Using Cyclostationary EOF and Regression Method
Atmosphere 2022, 13(3), 500; https://doi.org/10.3390/atmos13030500 - 21 Mar 2022
Cited by 1 | Viewed by 549
Abstract
Precipitation time series exhibit complex fluctuations and statistical changes. Existing research stops short of a simple and feasible model for precipitation forecasting. In this article, the authors investigate and forecast precipitation variations in South Korea from 1973 to 2021 using cyclostationary empirical orthogonal [...] Read more.
Precipitation time series exhibit complex fluctuations and statistical changes. Existing research stops short of a simple and feasible model for precipitation forecasting. In this article, the authors investigate and forecast precipitation variations in South Korea from 1973 to 2021 using cyclostationary empirical orthogonal function (CSEOF) and regression methods. First, empirical orthogonal function (EOF) and CSEOF analyses are used to examine the periodic changes in the precipitation data. Then, the autoregressive integrated moving average (ARIMA) method is applied to the principal component (PC) time series derived from the EOF and CSEOF precipitation analyses. The fifteen leading EOF and CSEOF modes and their corresponding PC time series clearly reflect the spatial distribution and temporal evolution characteristics of the precipitation data. Based on the PC forecasts of the EOF and CSEOF models, the EOF–ARIMA composite model and CSEOF–ARIMA composite model are used to obtain quantitative precipitation forecasts. The comparison results show that both composite models have good performance and similar accuracy. However, the performance of the CSEOF–ARIMA model is better than that of the EOF–ARIMA model under various measurements. Therefore, the CSEOF–ARIMA composite forecast model can be considered an efficient and feasible technology representing an analytical approach for precipitation forecasting in South Korea. Full article
Show Figures

Figure 1

Article
Temperature Forecasting Correction Based on Operational GRAPES-3km Model Using Machine Learning Methods
Atmosphere 2022, 13(2), 362; https://doi.org/10.3390/atmos13020362 - 21 Feb 2022
Cited by 1 | Viewed by 527
Abstract
Postprocess correction is essential to improving the model forecasting result, in which machine learning methods play more and more important roles. In this study, three machine learning (ML) methods of Linear Regression, LSTM-FCN and LightGBM were used to carry out the correction of [...] Read more.
Postprocess correction is essential to improving the model forecasting result, in which machine learning methods play more and more important roles. In this study, three machine learning (ML) methods of Linear Regression, LSTM-FCN and LightGBM were used to carry out the correction of temperature forecasting of an operational high-resolution model GRAPES-3km. The input parameters include 2 m temperature, relative humidity, local pressure and wind speed forecasting and observation data in Shaanxi province of China from 1 January 2019 to 31 December 2020. The dataset from September 2018 was used for model evaluation using the metrics of root mean square error (RMSE), average absolute error (MAE) and coefficient of determination (R2). All three machine learning methods perform very well in correcting the temperature forecast of GRAPES-3km model. The RMSE decreased by 33%, 32% and 40%, respectively, the MAE decreased by 33%, 34% and 41%, respectively, the R2 increased by 21.4%, 21.5% and 25.2%, respectively. Among the three methods, LightGBM performed the best with the forecast accuracy rate reaching above 84%. Full article
Show Figures

Figure 1

Article
Artificial Intelligence-Based Techniques for Rainfall Estimation Integrating Multisource Precipitation Datasets
Atmosphere 2021, 12(10), 1239; https://doi.org/10.3390/atmos12101239 - 23 Sep 2021
Cited by 8 | Viewed by 1608
Abstract
This study presents a comprehensive investigation of multiple Artificial Intelligence (AI) techniques—decision tree, random forest, gradient boosting, and neural network—to generate improved precipitation estimates over the Upper Blue Nile Basin. All the AI methods merged multiple satellite and atmospheric reanalysis precipitation datasets to [...] Read more.
This study presents a comprehensive investigation of multiple Artificial Intelligence (AI) techniques—decision tree, random forest, gradient boosting, and neural network—to generate improved precipitation estimates over the Upper Blue Nile Basin. All the AI methods merged multiple satellite and atmospheric reanalysis precipitation datasets to generate error-corrected precipitation estimates. The accuracy of the model predictions was evaluated using 13 years (2000–2012) of ground-based precipitation data derived from local rain gauge networks in the Upper Blue Nile Basin region. The results indicate that merging multiple sources of precipitation substantially reduced the systematic and random error statistics in the Upper Blue Nile Basin. The proposed methods have great potential in predicting precipitation over the complex terrain region. Full article
Show Figures

Figure 1

Back to TopTop