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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Atmospheric Remote Sensing".

Deadline for manuscript submissions: closed (20 October 2022) | Viewed by 22681

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Dr. Marina Astitha

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Civil and Environmental Engineering, University of Connecticut, Storrs, CT 06269, USA
Interests: atmospheric physics, dynamics, and chemistry; prediction of extreme weather events; integration of multimedia modeling systems using machine learning; real-time weather and air quality forecasting; uncertainties in atmospheric and air quality modeling systems
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Dear Colleagues,

Extreme weather in the form of tornadoes, thunderstorms, hurricanes, winter storms, and heat waves is the cause of major societal disruptions with severe impacts on the economy, environment, and human lives worldwide. Accurate prediction of such events is crucial for managing emergency response and mitigation of impacts. In addition, better understanding of uncertainty in severe weather prediction improves situational awareness and influences confidence in impact modeling that relies on the weather prediction outcome.

The Special Issue on“Prediction of Extreme Weather Events” aims to bring together current state-of-the-art science research on the tools and methodologies to accurately predict extreme weather such as tornadoes, hurricanes, nor’easters, winter storms, and heat waves, among others. Topics that discuss the influence of severe weather prediction toward preparing for and managing impacts of severe weather (e.g., floods, droughts, landslides, wildfires, social impacts) will also be favored. Manuscripts that include remote sensing products to evaluate and/or improve extreme weather prediction will be preferred.

Prof. Marina Astitha
Guest Editor

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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.

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Keywords

Extreme weather events
Forecasting
Impact studies
Storms
Severe convection
Remote sensing
Uncertainty

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Related Special Issue

Prediction of Extreme Weather Events II in Remote Sensing

Published Papers (8 papers)

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25 pages, 10567 KiB

Open AccessArticle

Exploring the Real-Time WRF Forecast Skill for Four Tropical Storms, Isaias, Henri, Elsa and Irene, as They Impacted the Northeast United States

by Ummul Khaira and Marina Astitha

Remote Sens. 2023, 15(13), 3219; https://doi.org/10.3390/rs15133219 - 21 Jun 2023

Cited by 4 | Viewed by 2276

Abstract

Tropical storm Isaias (2020) moved quickly northeast after its landfall in North Carolina and caused extensive damage to the east coast of the United States, with electric power distribution disruptions, infrastructure losses and significant economic and societal impacts. Improving the real-time prediction of tropical storms like Isaias can enable accurate disaster preparedness and strategy. We have explored the configuration, initialization and physics options of the Weather Research and Forecasting (WRF) model to improve the deterministic forecast for Isaias. The model performance has been evaluated based on the forecast of the storm track, intensity, wind and precipitation, with the support from in situ measurements and stage IV remote sensing products. Our results indicate that the Global Forecasting System (GFS) provides overall better initial and boundary conditions compared to the North American Model (NAM) for wind, mean sea level pressure and precipitation. The combination of tropical suite physics options and GFS initialization provided the best forecast improvement, with error reduction of 36% and an increase of the correlation by 11%. The choices for model spin-up time and forecast cycle did not affect the forecast of the storm significantly. In order to check the consistency of the result found from the investigation related to TS Isaias, Irene (2011), Henri (2021) and Elsa (2021), three other tropical storms, were also investigated. Similar to Isaias, these storms are simulated with NAM and GFS initialization and different physics options. The overall results for Henri and Elsa indicate that the models with GFS initialization and tropical suite physics reduced error by 44% and 57%, respectively, which resonates with the findings from the TS Isaias investigation. For Irene, the initialization used an older GFS version and showed increases in error, but applying the tropical physics option decreased the error by 20%. Our recommendation is to consider GFS for the initialization of the WRF model and the tropical physics suite in a future tropical storm forecast for the NE US. Full article

(This article belongs to the Special Issue Prediction of Extreme Weather Events)

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27 pages, 30416 KiB

Open AccessArticle

Identification and Analysis of Heatwave Events Considering Temporal Continuity and Spatial Dynamics

by Yanqun Ren, Jinping Liu, Tongchang Zhang, Masoud Jafari Shalamzari, Arfan Arshad, Tie Liu, Patrick Willems, Huiran Gao, Hui Tao and Tingli Wang

Remote Sens. 2023, 15(5), 1369; https://doi.org/10.3390/rs15051369 - 28 Feb 2023

Cited by 7 | Viewed by 3606

Abstract

In the context of global warming, the general increase in temperature has led to an increase in heatwave events, as well as a dramatic intensification of economic losses and social risks. This study employs the latest intensity–area–duration (IAD) framework that takes into account the temporal continuity and spatial dynamics of extreme events to identify regional heatwave events, and extracts key parameters of heatwave events to study the associated changes in frequency, intensity, influence area, and duration in seven geographic subregions of China in the 1979–2018 period. Heatwaves of all durations increased in frequency and intensity during the research period, with shorter heatwaves increasing in frequency and intensity at a faster rate than longer heatwaves. Among the seven geographic subregions, Xinjiang (XJ) and Southern China (SC) are the regions with the most frequent heatwave occurrence, while the Southwest (SW) and SC have the highest increase in heatwave frequency. In terms of regional distributions, XJ has the strongest heatwave event intensity and the largest affected area, while SC has the longest duration. However, in terms of spatial trends, SC, XJ, and the SW have the highest rates of intensity growth, influence area, and duration, respectively. In addition, heatwaves with extended durations and vast influence areas are more likely to occur in SC, and their frequency is on the rise. During the study period, the intensity, influence area, and length of heatwave occurrences in China exhibited an upward tendency, and it was shown that the longer the duration, the greater the intensity and the broader the influence area. In addition, the evolutionary characteristics of heatwave events with the longest duration indicate a certain consistency in their intensity and influence. These findings can contribute to the development of strategies to prepare for and mitigate the adverse effects of heatwave occurrences. Full article

(This article belongs to the Special Issue Prediction of Extreme Weather Events)

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20 pages, 9733 KiB

Open AccessArticle

Abrupt High PM Concentration in an Urban Calm Cavity Generated by Internal Gravity Waves and a Shallow Coastal Atmospheric Boundary Layer with the Influence of the Yellow Dust from China

by Doo-Sun Choi, Soo-Min Choi and Hyo Choi

Remote Sens. 2023, 15(2), 372; https://doi.org/10.3390/rs15020372 - 7 Jan 2023

Cited by 1 | Viewed by 1662

Abstract

Using GRIMM-1107 aerosol sampler, GOES-9 DCD satellite images, HYSPLIT model of backward trajectory and 3D-meteorological WRF-3.6 model, high particulate matter concentrations were investigated at Gangneung city in the Korean east coast which consists of Mt. Taeglyung in its west and the East Sea in its east on 00:00LST March 26~00:00 LST 4 April 2004. During a Yellow Dust period, the maximum PM₁₀ (PM_2.5 and PM₁) concentration at the city was about 3.3 (1.1 and 1.01) times higher than one in the non-dust period. After the transported dust from the Gobi Desert and Nei-Mongo by strong northwesterly wind passed over Mt. Taegulyang and moved down toward the city. Then the dust was trapped inside a calm cavity generated by the confront of internal gravity waves (IGW) over the city and the eastward movement of the trapped dust is prohibited by the easterly onshore wind from the East Sea, and the trapped dust further combined with particulate and gaseous emitted from the road vehicles and heating boilers of the city at 09:00LST, March 30 (beginning time of office hours), causing high PM concentrations. On mid-day, as the combined dust due to daytime sufficient thermal convection rises up to the top of the thermal internal boundary layer (TIBL) of a 300 m depth from the coast to the top of the mountain, the ground-based PM concentrations in the city are much lower at 15:00LST due to the higher thickness of the TIBL than at 09:00LST. At night, particulates emitted from many road vehicles after the end of office hours and residential heating boilers could combine with both dust transported from the Nei-Mongo and falling dust uplifted from the ground surface of the city during the day, and they were trapped inside a calm cavity by the IGV under much shallower stable nocturnal surface inversion layer than the TIBL, causing more dust to be accumulated near the surface and showing the maximum PM concentrations at 20:00 LST. Full article

(This article belongs to the Special Issue Prediction of Extreme Weather Events)

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21 pages, 9224 KiB

Open AccessArticle

Orographic Construction of a Numerical Weather Prediction Spectral Model Based on ASTER Data and Its Application to Simulation of the Henan 20·7 Extreme Rainfall Event

by Yingjie Wang, Jianping Wu, Xiangrong Yang, Jun Peng and Xiaotian Pan

Remote Sens. 2022, 14(15), 3840; https://doi.org/10.3390/rs14153840 - 8 Aug 2022

Cited by 2 | Viewed by 1974

Abstract

Numerical weather prediction (NWP) has become an important method of predicting extreme weather events, but orography is one of the key factors affecting the performance of NWPs. In this paper, based on Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) elevation data, a method for constructing a global orographic dataset suitable for NWP spectral models is investigated. The Yin-He global spectrum model (YHGSM) is used to simulate the early and peak periods of the extreme rainfall event on 20 July 2021 in Henan Province, China, and the heavy rain in Beijing in order to verify the effectiveness and superiority of the proposed orographic construction method. It is demonstrated that in a few cases the direct two-dimensional filter can sometimes simulate more intense rainfall, but in general, the bidirectional one-dimensional filter is better than the direct two-dimensional filter in orographic processing, and the bidirectional one-dimensional filter can filter out more of the small-scale orographic information. The effect of the higher orographic resolution before conversion to spectral space is not very obvious, but it is demonstrated that the simulation results are better for the heavy-rainfall level. In conclusion, in most cases, the simulations conducted using the new global orographic dataset based on ASTER data are better than those obtained using the model’s original orography, especially for torrential and extreme rainfall. These conclusions provide a reference for future predictions of and research on extreme rainfall events. Full article

(This article belongs to the Special Issue Prediction of Extreme Weather Events)

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17 pages, 6328 KiB

Open AccessArticle

Can Satellite-Based Thermal Anomalies Be Indicative of Heatwaves? An Investigation for MODIS Land Surface Temperatures in the Mediterranean Region

by Ilias Agathangelidis, Constantinos Cartalis, Anastasios Polydoros, Thaleia Mavrakou and Kostas Philippopoulos

Remote Sens. 2022, 14(13), 3139; https://doi.org/10.3390/rs14133139 - 29 Jun 2022

Cited by 8 | Viewed by 2694

Abstract

In recent years, an exceptional number of record-shattering temperature extremes have been observed, resulting in significant societal and environmental impacts. The Mediterranean region is particularly thermally vulnerable, frequently suffering from intense and severe heatwaves. Using daily temperature observations from 58 weather stations (NOAA Global Historical Climatology Network daily database) in the Mediterranean area, past heatwave episodes were initially detected. A daily LST time series was developed using Land Surface Temperature (LST) products from Moderate Resolution Imaging Spectroradiometer (MODIS) (Terra & Aqua satellites) for a 19-year period (2002–2020) at the station locations. LST anomalies were identified using percentile-based indices. It was found that remotely sensed-based LST presents the potential for understanding and monitoring heatwave events, as surface thermal anomalies were generally indicative of heatwaves. Approximately 42% (39%) of heatwave days during daytime (nighttime) coincided with LST anomalies; conversely, 51% of daytime LST anomalies overlapped with the exact days of a heatwave (38% at night). Importantly, the degree of association was significantly higher for extremely hot days (up to an 80% match) and long-lasting heatwaves (up to an 85% match). Rising trends in frequency and duration were observed for both heatwaves and LST anomalies. The results advance the understanding of surface-atmosphere coupling during extreme temperature days and reflect the suitability of thermal remote sensing in heatwave preparedness strategies. Full article

(This article belongs to the Special Issue Prediction of Extreme Weather Events)

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21 pages, 5618 KiB

Open AccessArticle

Comparing Precipitation during Typhoons in the Western North Pacific Using Satellite and In Situ Observations

by Jessica R. P. Sutton, Alexandra Jakobsen, Kathryn Lanyon and Venkat Lakshmi

Remote Sens. 2022, 14(4), 877; https://doi.org/10.3390/rs14040877 - 12 Feb 2022

Cited by 5 | Viewed by 2906

Abstract

Typhoons are known for causing heavy precipitation, very strong winds, and storm surges. With climate change, the occurrence, strength, and duration of typhoons are changing. Daily, weekly, and monthly precipitation from in situ stations from the NOAA Global Historical Climatological Network (GHCN) were compared in the Western North Pacific from 2000 to 2018 against two widely used datasets: NASA’s TRMM TMPA and PERSIANN-CDR. Additionally, precipitation levels during twenty-five typhoons were compared using precipitation estimates. There have been reductions in the average number of typhoons per year from 1959 to present and by month during the months of August, September, and October. Satellite-derived precipitation estimates from PERSIANN and TRMM TMPA explained approximately 50% of the variation in weekly cumulative precipitation and approximately 72% of the variation in monthly cumulative precipitation during the study period (March 2000–December 2018) when using all available stations. When analysis was completed using only stations close to the best track for the entire duration of a typhoon, 62% of the variation was explained, which is comparable to the weekly and monthly cumulative comparisons. However, most of the stations available and with sufficient data were not located in the tracks of the typhoons. It is of utmost importance to better understand typhoon events by utilizing precipitation data from satellite remote sensing in the Western North Pacific. Full article

(This article belongs to the Special Issue Prediction of Extreme Weather Events)

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19 pages, 58620 KiB

Open AccessArticle

Spatial and Temporal Variation Characteristics of Heatwaves in Recent Decades over China

by Jinping Liu, Yanqun Ren, Hui Tao and Masoud Jafari Shalamzari

Remote Sens. 2021, 13(19), 3824; https://doi.org/10.3390/rs13193824 - 24 Sep 2021

Cited by 21 | Viewed by 3523

Abstract

Global warming and rapid socioeconomic development increased the risk of regional and global disasters. Particularly in China, annual heatwaves (HWs) caused many fatalities and substantial property damage, with an increasing trend. Therefore, it is of great scientific value and practical importance to analyze the spatiotemporal changes of HW in China for the sustainable development of regional socioeconomic and disaster risk management. In this study, based on gridded maximum temperature product and specific humidity dataset, an HW evaluation algorithm, considering the impact of humidity on the human body and the characteristics of HW in China, was employed to generate daily HW state at light, moderate, and severe levels for the period 1979–2018. Consequently, the regional differences at three HW levels were revealed, and the changing trend of HW onset, termination, and duration in each subregion was analyzed. The results show that in the three levels, the frequency and duration of HW in China had a significant increasing trend, generally characterized by the advancement of HW onset and the postponement of HW termination. The HW influence at light, moderate and severe levels decreased gradually, with the light level occurring the earliest and terminating the latest. Among the seven subregions, the largest HW frequency happened to be mainly in XJ (Xinjiang), SC (Southern China), and NC (Northern China), while the variations of HW onset and termination had noticeable regional differences at the three levels. The findings presented in this study can provide the essential scientific and technological support for national and regional disaster prevention mitigation and adaptation to extreme climate events. Full article

(This article belongs to the Special Issue Prediction of Extreme Weather Events)

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15 pages, 1057 KiB

Open AccessTechnical Note

Rapid Growth of Tropical Cyclone Outer Size over the Western North Pacific

by Yi Li, Youmin Tang, Shuai Wang and Xiaojing Li

Remote Sens. 2023, 15(2), 486; https://doi.org/10.3390/rs15020486 - 13 Jan 2023

Cited by 2 | Viewed by 2009

Abstract

The concept of rapid growth (RG) of tropical cyclones (TCs) in the north Atlantic basin was recently proposed. RG can represent a dangerous change in TC structure because it can rapidly ramp up the TC destructive potential. However, the nature of RG behaviour remains obscure over the western north Pacific (WNP), where nearly one third of global TCs occur. In this study, TC RG in the WNP is investigated using TC best-tracks and reanalysis of data. We first define TC RG in the WNP as an increase of at least 84 km in the radius of a gale-force wind within 24 h, corresponding to the 90th percentile of all over-water changes. Monte Carlo experiments demonstrate the robustness of the threshold. Similar to that occurring in the north Atlantic, RG in the WNP is associated with the highest level of destructive potential. In addition, RG over the WNP occurs closer to the coast than for TCs in the Atlantic and more RG events in the WNP are accompanied by rapid intensification, which may significantly increase their destructive potential in a worst case scenario. Composite analysis shows that certain dynamic processes, such as radial inflow, may play an important role in the occurrence of RG. This study suggests that, apart from rapid intensification, TC RG is another important factor to consider for TC-related risk assessment in the WNP. Full article

(This article belongs to the Special Issue Prediction of Extreme Weather Events)

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