Extreme Weather Detection, Attribution and Adaptation Design

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

Deadline for manuscript submissions: 31 July 2024 | Viewed by 4501

Special Issue Editors


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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
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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

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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

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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

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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

Published Papers (2 papers)

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Research

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
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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 2130
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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