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Use of Remote Sensing for High Impact Weather

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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 (31 July 2022) | Viewed by 20938

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Dr. Vinay Kumar

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Guest Editor

Department of Physical and Environmental Sciences, Texas A&M University, Corpus Christi, TX 78412, USA
Interests: research on critical issues; precipitation and extreme events
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Ismail Gultepe

E-Mail Website
Guest Editor

Faculty of Engineering and Applied Science, Ontario Technical University, Oshawa, ON L1G 0C5, Canada
Interests: clouds; cold weather systems; cloud microphysics; precipitation; arctic weather; aviation meteorology; aircraft and ground based in-situ and remote sensing observations of the atmosphere, including satellites, radars, lidars, as well as microwave radiometers
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Use of remote sensing for atmospheric research and extreme weather research is important for weather forecast and climate change, and develop new technologies. Multi-decadal datasets of high spatial and temporal resolution and with reasonable accuracy based on remote sensing platforms e.g. satellites, radars, as well as lidars are needed for improving the research on weather forecast, climate change, and adaptation for extreme weather conditions.

Remote sensing platforms provide extensive datasets over data-sparse regions of the Earth that are related to clouds and its microphysics, lightning, precipitation types (e.g. rain, drizzle, snow), precipitation intensity, ozone, aerosol optical properties, soil moisture, and groundwater. Long-term datasets are useful to investigate the atmospheric processes for extreme weather events and climate change conditions. Advanced cloud and precipitation radars on satellites provide measurements of cloud microphysics and precipitation characteristics that use radar reflectivity factor and other meteorological parameters such as temperature discriminate particle phase. Clouds Imageries based on advanced baseline instrument (ABI) platforms are used for nowcasting and track of weather extremes, as well as cloud water and ice crystal properties e.g. liquid water path (LWP) and ice water path (IWP), effective size (Reff), as well as optical thickness. Lidars placed on satellites or on the ground provide aerosol, ice cloud, as well as wind components to study dynamics of the environment, and can be used for data assimilation for NWP model simulations.These suggest that remote sensing platforms are essential for studying extreme weather events and important weather events that can include tracks of weather systems, polar vortex, climate change, ocean warming, the role of aerosols in surface heat budget, fog, rain, snowstorms as well as cloud physical structure and distribution.

This special-Issue invites research articles related to weather and extreme events that uses satellites, radars, lidars, as well related ground and spaced based platforms; These can be related to GPM, TRMM, CloudSat, TMI, MODIS, GOES, GRACE, SMOS, CALIPSO, VIIRM, HIMAWARI, ICESat-2 and AVHRR, as well as newly developed satellite based systems for wind and aerosol, and clouds. Manuscripts involving above topics and other remote sensing platforms that can explain a particular phenomenon, event or process, can be suitable for this special issue. If you need any additional information, please contact editors or to journal.

Dr. Vinay Kumar
Prof. Dr. Ismail Gultepe
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. Remote Sensing is an international peer-reviewed open access semimonthly 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 2700 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

Remote sensing of weather and surface
Cloud microphysics and aerosols
Precipitation intensity, amount, and duration
Cold weather systems e.g., snow, icing, frost
Aviation meteorology, icing, turbulence, and visibility
Extreme weather and climate regimes
Arctic weather

Published Papers (8 papers)

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Research

21 pages, 10314 KiB

Open AccessArticle

Seasonal and Microphysical Characteristics of Fog at a Northern Airport in Alberta, Canada

by Faisal S. Boudala, Di Wu, George A. Isaac and Ismail Gultepe

Remote Sens. 2022, 14(19), 4865; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14194865 - 29 Sep 2022

Cited by 3 | Viewed by 1470

Abstract

Reduction in visibility (Vis) due to fog is one of the deadliest severe weather hazards affecting aviation and public transportation. Nowcasting/forecasting of Vis reduction due to fog using current models is still problematic, with most using some type of empirical parameterization. To improve the models, further observational studies to better understand fog microphysics and seasonal variability are required. To help achieve these goals, the seasonal and microphysical characteristics of different fog types at Cold Lake airport (CYOD), Alberta, Canada were analyzed using hourly and sub-hourly METAR data. Microphysical and meteorological measurements obtained using the DMT Fog Monitor FM-120 and the Vaisala PWD22 were examined. The results showed that radiation fog (RF) dominates at CYOD in summer while precipitation, advection and cloud-base-lowering fogs mostly occur in fall and winter. All fog types usually form at night or early morning and dissipate after sunrise. The observed dense fog events (Vis < 400 m) were mainly caused by RF. The observed mean fog particle spectra (

n (D)

) for different fog types and temperatures showed bimodal n(D) (with two modes near 4 μm and 17–25 μm; the maximum total number concentration (

N_{d}

) was 100 cm⁻³ and 20 cm⁻³, respectively, corresponding to each mode). Parameterizations of Vis as a function of liquid water content (

LWC

) and

N_{d}

were developed using both the observed Vis and calculated Vis based on

n (D)

. It was found that the observed Vis was higher than the calculated Vis for warm fog with

LWC

> 0.1 gm⁻³ and most of the mass was contributed by the large drops. Based on the observed Vis, the relative error of the visibility parameterization as a function of both

LWC

and

N_{d}

(32%) was slightly lower than that (34%) using

LWC

alone for warm fogs. Full article

(This article belongs to the Special Issue Use of Remote Sensing for High Impact Weather)

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15 pages, 3879 KiB

Open AccessArticle

Evaluations of the Climatologies of Three Latest Cloud Satellite Products Based on Passive Sensors (ISCCP-H, Two CERES) against the CALIPSO-GOCCP

by Faisal S. Boudala and Jason A. Milbrandt

Remote Sens. 2021, 13(24), 5150; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13245150 - 18 Dec 2021

Cited by 5 | Viewed by 2212

Abstract

In this study, the climatologies of three different satellite cloud products, all based on passive sensors (CERES Edition 4.1 [EBAF4.1 and SYN4.1] and ISCCP–H), were evaluated against the CALIPSO-GOCCP (GOCCP) data, which are based on active sensors and, hence, were treated as the reference. Based on monthly averaged data (ocean + land), the passive sensors underestimated the total cloud cover (TCC) at lower (TCC < 50%), but, overall, they correlated well with the GOCCP data (r = 0.97). Over land, the passive sensors underestimated the TCC, with a mean difference (MD) of −2.6%, followed by the EBAF4.1 and ISCCP-H data with a MD of −2.0%. Over the ocean, the CERES-based products overestimated the TCC, but the SYN4.1 agreed better with the GOCCP data. The ISCCP-H data on average underestimated the TCC both over oceanic and continental regions. The annual mean TCC distribution over the globe revealed that the passive sensors generally underestimated the TCC over continental dry regions in northern Africa and southeastern South America as compared to the GOCCP, particularly over the summer hemisphere. The CERES datasets overestimated the TCC over the Pacific Islands between the Indian and eastern Pacific Oceans, particularly during the winter hemisphere. The ISCCP-H data also underestimated the TCC, particularly over the southern hemisphere near 60° S where the other datasets showed a significantly enhanced TCC. The ISCCP data also showed less TCC when compared against the GOCCP data over the tropical regions, particularly over the southern Pacific and Atlantic Oceans near the equator and also over the polar regions where the satellite retrieval using the passive sensors was generally much more challenging. The calculated global mean root meant square deviation value for the ISCCP-H data was 6%, a factor of 2 higher than the CERES datasets. Based on these results, overall, the EBAF4.1 agreed better with the GOCCP data. Full article

(This article belongs to the Special Issue Use of Remote Sensing for High Impact Weather)

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16 pages, 35374 KiB

Open AccessArticle

The Performance of Commonly Used Surface-Based Instruments for Measuring Visibility, Cloud Ceiling, and Humidity at Cold Lake, Alberta

by Faisal S. Boudala, Ismail Gultepe and Jason A. Milbrandt

Remote Sens. 2021, 13(24), 5058; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13245058 - 13 Dec 2021

Cited by 5 | Viewed by 1845

Abstract

Data from automated meteorological instruments are used for model validation and aviation applications, but their measurement accuracy has not being adequately tested. In this study, a number of ground-based in-situ, remote-sensing instruments that measure visibility (VIS), cloud base height (CBH), and relative humidity (RH) were tested against data obtained using standard reference instruments and human observations at Cold Lake Airport, Alberta, Canada. The instruments included the Vaisala FS11P and PWD22 (FSPW), a profiling microwave radiometer (MWR), the Jenoptik ceilometer, Rotronic, Vaisala WXT520, AES-Dewcell RH, and temperature sensors. The results showed that the VIS measured using the FSPWs were well correlated with a correlation coefficient (R) of 0.84 under precipitation conditions and 0.96 during non-precipitating conditions (NPC), indicating very good agreement. However, the FS11P on average measured higher VIS, particularly under NPC. When the FSPWs were compared against human observation, a significant quantization in the data was observed, but less was noted during daytime compared to nighttime. Both probes measured higher VIS compared to human observation, and the calculated R was close to 0.6 for both probes. When the FSPWs were compared against human observation for VIS < 4 km, the calculated mean difference (MD) for the PWD22 (MD ≈ 0.98 km) was better than the FS11P (MD ≈ 1.37 km); thus, the PWD22 was slightly closer to human observation than the FS11P. No significant difference was found between daytime and nighttime measured VIS as compared to human observation; the instruments measured slightly higher VIS. Two extinction parameterizations as functions of snowfall rate were developed based on the VFPs measurements, and the results were similar. The Jenoptik ceilometer generally measured lower CBH than human observation, but the MWR measured larger CBHs for values <2 km, while CBHs were underestimated for higher CBHs. Full article

(This article belongs to the Special Issue Use of Remote Sensing for High Impact Weather)

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26 pages, 5284 KiB

Open AccessArticle

Development and Assessment of High-Resolution Radar-Based Precipitation Intensity-Duration-Curve (IDF) Curves for the State of Texas

by Dawit T. Ghebreyesus and Hatim O. Sharif

Remote Sens. 2021, 13(15), 2890; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13152890 - 23 Jul 2021

Cited by 11 | Viewed by 2068

Abstract

Conventionally, in situ rainfall data are used to develop Intensity Duration Frequency (IDF) curves, which are one of the most effective tools for modeling the probability of the occurrence of extreme storm events at different timescales. The rapid recent technological advancements in precipitation sensing, and the finer spatio-temporal resolution of data have made the application of remotely sensed precipitation products more dominant in the field of hydrology. Some recent studies have discussed the potential of remote sensing products for developing IDF curves. This study employs a 19-year NEXRAD Stage-IV high-resolution radar data (2002–2020) to develop IDF curves over the entire state of Texas at a fine spatial resolution. The Annual Maximum Series (AMS) were fitted to four widely used theoretical Extreme Value statistical distributions. Gumble distribution, a unique scenario of the Generalized Extreme Values (GEV) family, was found to be the best model for more than 70% of the state’s area for all storm durations. Validation of the developed IDFs against the operational Atlas 14 IDF values shows a ±27% difference in over 95% of the state for all storm durations. The median of the difference stays between −10% and +10% for all storm durations and for all return periods in the range of (2–100) years. The mean difference ranges from −5% for the 100-year return period to 8% for the 10-year return period for the 24-h storm. Generally, the western and northern regions of the state show an overestimation, while the southern and southcentral regions show an underestimation of the published values. Full article

(This article belongs to the Special Issue Use of Remote Sensing for High Impact Weather)

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25 pages, 11886 KiB

Open AccessArticle

Advection Fog over the Eastern Yellow Sea: WRF Simulation and Its Verification by Satellite and In Situ Observations

by Eunjeong Lee, Jung-Hoon Kim, Ki-Young Heo and Yang-Ki Cho

Remote Sens. 2021, 13(8), 1480; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13081480 - 12 Apr 2021

Cited by 8 | Viewed by 2521

Abstract

An observed sea fog event over the Eastern Yellow Sea on 15–16 April 2012 was reproduced in the Weather Research and Forecasting (WRF) simulation with high-resolution to investigate the roles of physical processes and synoptic-scale flows on advection fog with phase transition. First, it was verified by a satellite-based fog detection algorithm and in situ observation data. In the simulation, longwave (infrared) radiative cooling (LRC) with a downward turbulent sensible heat flux (SHF), due to the turbulence after sunset, triggered cloud formation over the surface when warm-moist air advection occurred. At night, warm air advection with continuous cooling due to longwave radiation and SHF near the surface modulated the change of the SHF from downward to upward, resulting in a drastic increase in the turbulent latent heat flux (LHF) that provided sufficient moisture at the lower atmosphere (self-moistening). This condition represents a transition from cold-sea fog to warm-sea fog. Enhanced turbulent mixing driven by a buoyancy force increased the depth of the sea fog and the marine atmospheric boundary layer (MABL) height, even at nighttime. In addition, cold air advection with a prevailing northerly wind at the top of the MABL led to a drastic increase in turbulent mixing and the MABL height and rapid growth of the height of sea fog. After sunrise, shortwave radiative warming in the fog layers offsetting the LRC near the surface weakened thermal instability, which contributed to the reduction in the MABL height, even during the daytime. In addition, dry advection of the northerly wind induced dissipation of the fog via evaporation. An additional sensitivity test of sea surface salinity showed weaker and shallower sea fog than the control due to the decrease in both the LHF and local self-moistening. Detailed findings from the simulated fog event can help to provide better guidance for fog detection using remote sensing. Full article

(This article belongs to the Special Issue Use of Remote Sensing for High Impact Weather)

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22 pages, 2626 KiB

Open AccessArticle

Spatio-Temporal Distribution of Deep Convection Observed along the Trans-Mexican Volcanic Belt

by José Francisco León-Cruz, Cintia Carbajal Henken, Noel Carbajal and Jürgen Fischer

Remote Sens. 2021, 13(6), 1215; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13061215 - 23 Mar 2021

Cited by 6 | Viewed by 2975

Abstract

Complex terrain features—in particular, environmental conditions, high population density and potential socio-economic damage—make the Trans-Mexican Volcanic Belt (TMVB) of particular interest regarding the study of deep convection and related severe weather. In this research, 10 years of Moderate-Resolution Imaging Spectroradiometer (MODIS) cloud observations are combined with Climate Hazards Group Infrared Precipitation with Station (CHIRPS) rainfall data to characterize the spatio-temporal distribution of deep convective clouds (DCCs) and their relationship to extreme precipitation. From monthly distributions, wet and dry phases are identified for cloud fraction, deep convective cloud frequency and convective precipitation. For both DCC and extreme precipitation events, the highest frequencies align just over the higher elevations of the TMVB. A clear relationship between DCCs and terrain features, indicating the important role of orography in the development of convective systems, is noticed. For three sub-regions, the observed distributions of deep convective cloud and extreme precipitation events are assessed in more detail. Each sub-region exhibits different local conditions, including terrain features, and are known to be influenced differently by emerging moisture fluxes from the Gulf of Mexico and the Pacific Ocean. The observed distinct spatio-temporal variabilities provide the first insights into the physical processes that control the convective development in the study area. A signal of the midsummer drought in Mexico (i.e., “canícula”) is recognized using MODIS monthly mean cloud observations. Full article

(This article belongs to the Special Issue Use of Remote Sensing for High Impact Weather)

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22 pages, 5915 KiB

Open AccessArticle

A Detection of Convectively Induced Turbulence Using in Situ Aircraft and Radar Spectral Width Data

by Jung-Hoon Kim, Ja-Rin Park, Soo-Hyun Kim, Jeonghoe Kim, Eunjeong Lee, SeungWoo Baek and Gyuwon Lee

Remote Sens. 2021, 13(4), 726; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13040726 - 17 Feb 2021

Cited by 9 | Viewed by 2811

Abstract

A commercial aircraft, departing from Seoul to Jeju Island in South Korea, encountered a convectively induced turbulence (CIT) at about z = 2.2 km near Seoul on 28 October 2018. At this time, the observed radar reflectivity showed that the convective band with cloud tops of z = 6–7 km passed the CIT region with high values of spectral width (SW; larger than 4 m s^–1). Using the 1 Hz wind data recorded by the aircraft, we estimated an objective intensity of the CIT as a cube root of eddy dissipation rate (EDR) based on the inertial range technique, which was about 0.33–0.37 m^2/3 s⁻¹. Radar-based EDR was also derived by lognormal mapping technique (LMT), showing that the EDR was about 0.3–0.35 m^2/3 s⁻¹ near the CIT location, which is consistent with in situ EDR. In addition, a feasibility of the CIT forecast was tested using the weather and research forecast (WRF) model with a 3 km horizontal grid spacing. The model accurately reproduced the convective band passing the CIT event with an hour delay, which allows the use of two methods to calculate EDR: The first is using both the sub-grid and resolved turbulent kinetic energy to infer the EDR; the second is using the LMT for converting absolute vertical velocity (and its combination with the Richardson number) to EDR-scale. As a result, we found that the model-based EDRs were about 0.3–0.4 m^2/3 s⁻¹ near the CIT event, which is consistent with the estimated EDRs from both aircraft and radar observations. Full article

(This article belongs to the Special Issue Use of Remote Sensing for High Impact Weather)

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21 pages, 3953 KiB

Open AccessArticle

Proportional Trends of Continuous Rainfall in Indian Summer Monsoon

by Vinay Kumar, K. Sunilkumar and Tushar Sinha

Remote Sens. 2021, 13(3), 398; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13030398 - 24 Jan 2021

Cited by 8 | Viewed by 2983

Abstract

A comprehensive study on the Indian summer monsoonal rainfall (ISMR) is performed in the light of decadal changes in the continuous rainfall events and the number of rainy days using 68 years (1951–2018) of gridded rain gauge data. Non-parametric Mann–Kendall’s test is applied on total rainfall amount, the number of rainy days, number of continuous rainfall events, and rainfall magnitude to find trends over different climatic zones of India for the two periods, 1951–1984 and 1985–2018. Our results found a decreasing trend for more than 4-days of continuous rainfall events during the recent 34 years (1985–2018) compared to 1951–1984. The rate of increase/decrease in extreme/continuous rainfall events does not follow a similar trend in number of continuous rainfall events and magnitude. Moreover, the rainfall is shifted towards a lesser number of continuous rainfall days with higher magnitudes during 1985–2018. During the crop’s sow season (i.e., the first 45 days from the onset date of Indian monsoon), the total number of rainy days decreased by a half day during the last 34 years. Over the Central and North East regions of India, the number of rainfall days decreased by ~0.1 days/yr and ~0.3 days/yr, respectively, during 1985–2018. Overall, the decreasing trends in continuous rainfall days may escalate water scarcity and lead to lower soil moisture over rain-fed irrigated land. Additionally, an upsurge in heavy rainfall episodes will lead to an unexpected floods. On a daily scale, rainfall correlates with soil moisture and evaporation up to 0.87 over various land cover and land use regions of India. Continuous light-moderate rainfall seems to be a controlling factor for replenishing soil moisture in upper levels. A change in rainfall characteristics may force the monsoon-fed rice cultivation period to adopt changing rainfall patterns. Full article

(This article belongs to the Special Issue Use of Remote Sensing for High Impact Weather)

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