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Extreme Weather Detection, Attribution and Adaptation Design
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Extreme Weather Detection, Attribution and Adaptation Design

A special issue of Climate (ISSN 2225-1154).

Deadline for manuscript submissions: 31 March 2025 | Viewed by 8564

Special Issue Editors

Dr. Chandrasekar Radhakrishnan

E-Mail Website
Guest Editor
Department of Electrical & Computer Engineering, Colorado State University, Fort Collins, CO 80521, USA
Interests: numerical weather and climate modeling/prediction; impact of climate changes on weather extremes; air quality modeling and prediction; satellite and radar data assimilation; machine learning in atmospheric science; radar-based nowcasting; wind and solar energy forecasting; tropical cyclones prediction; WRF models; WRF-Chem and CAMQ model; AOD data assimilation
Special Issues, Collections and Topics in MDPI journals
Dr. Attada Raju

E-Mail Website
Guest Editor
Department of Earth and Environmental Sciences, Indian Institute of Science Education and Research Mohali, Sector 81, S.A.S. Nagar, PO Manauli Mohali, Punjab 140306, India
Interests: regional weather and climate modelling; data assimilation; weather and climate extremes; indian monsoons
Dr. Biswas Sounak

E-Mail Website
Guest Editor
Physical Sciences Laboratory (PSL), The National Oceanic and Atmospheric Administration, Boulder, CO 80305-3328, USA
Interests: atmospheric remote sensing; satellite remote sensing (GPM,TRMM); polarimetric weather radar; radar meteorology; radar rainfall estimation
Dr. Kannan Srinivasa Ramanujam

E-Mail Website
Guest Editor
School of Mechanical Sciences, IIT Bhubaneswar, Odisha 752050, India
Interests: atmospheric radiation; induced rainfall modelling; weather radar; machine learning; and inverse modelling

Special Issue Information

Dear Colleagues,

Extreme precipitation events can lead to substantial loss of property and life. The timely and accurate predictions of these events can potentially mitigate some of these losses by providing decision support to stakeholders and communities. The skillful prediction of such extreme events through numerical weather prediction (NWP), statistical techniques, or their combination in hybrid dynamical-statistical methods is crucial for managing preparedness, emergency response, and mitigation of impacts. However, the prediction of rainfall extremes remains challenging in NWP due to various causes, including model deficiencies and initial-value problems. Several approaches for assimilating precipitation observations in NWP models have been developed in the last few years to improve the model’s initial states and subsequent short-range forecasts. This Special Issue invites papers on observational and numerical modeling studies of extreme events such as flash floods and cloud bursts to understand their spatiotemporal characteristics. In particular, we also encourage authors to explore extreme events related to past and near-future hazards, which would assist policymakers in building societies which are potentially more resilient. Additionally, this Special Issue is expected to include articles that use observations and modeling techniques to understand the physics of rainfall extremes and further enhance overall model forecast skills.

Dr. Chandrasekar Radhakrishnan
Dr. Attada Raju
Dr. Biswas Sounak
Dr. Kannan Srinivasa Ramanujam
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. Climate 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 1800 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

  • numerical weather forecasting and nowcasting
  • applications of machine learning in severe weather prediction
  • data assimilation in numerical weather forecasting models
  • severe weather warning systems
  • spaceborne satellites/radar weather detection
  • impact of climate change on weather extremes

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Published Papers (4 papers)

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Research

13 pages, 408 KiB  
Article
It Is Normal: The Probability Distribution of Temperature Extremes
by Nir Y. Krakauer
Climate 2024, 12(12), 204; https://doi.org/10.3390/cli12120204 - 2 Dec 2024
Viewed by 633
Abstract
The probability of heat extremes is often estimated using the non-stationary generalized extreme value distribution (GEVD) applied to time series of annual maximum temperature. Here, this practice was assessed using a global sample of temperature time series, from reanalysis (both at the grid [...] Read more.
The probability of heat extremes is often estimated using the non-stationary generalized extreme value distribution (GEVD) applied to time series of annual maximum temperature. Here, this practice was assessed using a global sample of temperature time series, from reanalysis (both at the grid point and the region scale) as well as station observations. This assessment used forecast negative log-likelihood as the main performance measure, which is particularly sensitive to the most extreme heat waves. It was found that the computationally simpler normal distribution outperforms the GEVD in providing probabilistic year-ahead forecasts of temperature extremes. Given these findings, it is suggested to consider alternatives to the GEVD for assessing the risk of extreme heat. Full article
(This article belongs to the Special Issue Extreme Weather Detection, Attribution and Adaptation Design)
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16 pages, 1308 KiB  
Article
Classification of Rainfall Intensity and Cloud Type from Dash Cam Images Using Feature Removal by Masking
by Kodai Suemitsu, Satoshi Endo and Shunsuke Sato
Climate 2024, 12(5), 70; https://doi.org/10.3390/cli12050070 - 12 May 2024
Cited by 1 | Viewed by 1858
Abstract
Weather Report is an initiative from Weathernews Inc. to obtain sky images and current weather conditions from the users of its weather app. This approach can provide supplementary weather information to radar observations and can potentially improve the accuracy of forecasts However, since [...] Read more.
Weather Report is an initiative from Weathernews Inc. to obtain sky images and current weather conditions from the users of its weather app. This approach can provide supplementary weather information to radar observations and can potentially improve the accuracy of forecasts However, since the time and location of the contributed images are limited, gathering data from different sources is also necessary. This study proposes a system that automatically submits weather reports using a dash cam with communication capabilities and image recognition technology. This system aims to provide detailed weather information by classifying rainfall intensities and cloud formations from images captured via dash cams. In models for fine-grained image classification tasks, there are very subtle differences between some classes and only a few samples per class. Therefore, they tend to include irrelevant details, such as the background, during training, leading to bias. One solution is to remove useless features from images by masking them using semantic segmentation, and then train each masked dataset using EfficientNet, evaluating the resulting accuracy. In the classification of rainfall intensity, the model utilizing the features of the entire image achieved up to 92.61% accuracy, which is 2.84% higher compared to the model trained specifically on road features. This outcome suggests the significance of considering information from the whole image to determine rainfall intensity. Furthermore, analysis using the Grad-CAM visualization technique revealed that classifiers trained on masked dash cam images particularly focused on car headlights when classifying the rainfall intensity. For cloud type classification, the model focusing solely on the sky region attained an accuracy of 68.61%, which is 3.16% higher than that of the model trained on the entire image. This indicates that concentrating on the features of clouds and the sky enables more accurate classification and that eliminating irrelevant areas reduces misclassifications. Full article
(This article belongs to the Special Issue Extreme Weather Detection, Attribution and Adaptation Design)
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21 pages, 8488 KiB  
Article
Evaluation of the 3DVAR Operational Implementation of the Colombian Air Force for Aircraft Operations: A Case Study
by Jhon Edinson Hinestroza-Ramirez, Juan Ernesto Soto Barbosa, Andrés Yarce Botero, Danilo Andrés Suárez Higuita, Santiago Lopez-Restrepo, Lisseth Milena Cruz Ruiz, Valeria Sólorzano Araque, Andres Céspedes, Sara Lorduy Hernandez, Richard Caceres, Giovanni Jiménez-Sánchez and Olga Lucia Quintero
Climate 2023, 11(7), 153; https://doi.org/10.3390/cli11070153 - 20 Jul 2023
Viewed by 1834
Abstract
This manuscript introduces an exploratory case study of the SIMFAC’s (Sistema de Información Meteorológica de la Fuerza Aérea Colombiana) operational implementation of the Weather Research and Forecasting (WRF) model with a 3DVAR (three-dimensional variational) data assimilation scheme that provides meteorological information for military, [...] Read more.
This manuscript introduces an exploratory case study of the SIMFAC’s (Sistema de Información Meteorológica de la Fuerza Aérea Colombiana) operational implementation of the Weather Research and Forecasting (WRF) model with a 3DVAR (three-dimensional variational) data assimilation scheme that provides meteorological information for military, public, and private aviation. In particular, it investigates whether the assimilation scheme in SIMFAC’s implementation improves the prediction of the variables of interest compared to the implementation without data assimilation (CTRL). Consequently, this study compares SIMFAC’S 3DVAR-WRF operational implementation in Colombia with a CTRL with the same parameterization (without 3DVAR assimilation) against the ground and satellite observations in two operational forecast windows. The simulations are as long as an operational run, and the evaluation is performed using the root mean square error, the mean fractional bias, the percent bias, the correlation factor, and metrics based on contingency tables. It also evaluates the model’s results according to the regions of Colombia, accounting for the country’s topographical differences. The findings reveal that, in general, the operational forecast (3DVAR) is similar to the CTRL without data assimilation, indicating the need for further improvement of the 3DVAR-WRF implementation. Full article
(This article belongs to the Special Issue Extreme Weather Detection, Attribution and Adaptation Design)
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22 pages, 4962 KiB  
Article
Numerical Simulation of Winter Precipitation over the Western Himalayas Using a Weather Research and Forecasting Model during 2001–2016
by Pravin Punde, Nischal, Raju Attada, Deepanshu Aggarwal and Chandrasekar Radhakrishnan
Climate 2022, 10(11), 160; https://doi.org/10.3390/cli10110160 - 25 Oct 2022
Cited by 2 | Viewed by 2734
Abstract
In the present study, dynamically downscaled Weather Research and Forecasting (WRF) model simulations of winter (DJF) seasonal precipitation were evaluated over the Western Himalayas (WH) at grey zone configurations (at horizontal resolutions of 15 km (D01) and 5 km (D [...] Read more.
In the present study, dynamically downscaled Weather Research and Forecasting (WRF) model simulations of winter (DJF) seasonal precipitation were evaluated over the Western Himalayas (WH) at grey zone configurations (at horizontal resolutions of 15 km (D01) and 5 km (D02)) and further validated using satellite-based (IMERG; 0.1°), observational (IMD; 0.25°), and reanalysis (ERA5; 0.25° and IMDAA; 0.108°) gridded datasets during 2001–2016. The findings demonstrate that both model resolutions (D01 and D02) are effective at representing precipitation characteristics over the Himalayan foothills. Precipitation features over the region, on the other hand, are much clearer and more detailed, with a significant improvement in D02, emphasizing the advantages of higher model grid resolution. Strong correlations and the lowest biases and root mean square errors indicate a closer agreement between model simulations and reanalyses IMDAA and ERA5. Vertical structures of various dynamical and thermodynamical features further confirm the improved and more realistic in WRF simulations with D02. Moreover, the seasonal patterns of upper tropospheric circulation, vertically integrated moisture transport, surface temperature and cloud cover show more realistic simulation in D02 compared to coarser domain D01. The categorical statistics reveal the efficiency of both D01 and D02 in simulating moderate and heavy precipitation events. Overall, our study emphasizes the significance of high-resolution data for simulating precipitation features specifically over complex terrains like WH. Full article
(This article belongs to the Special Issue Extreme Weather Detection, Attribution and Adaptation Design)
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