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

Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 22918

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Special Issue Editors

Prof. Mark A. Bourassa

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Guest Editor

Florida State University, 600 W College Ave, Tallahassee, FL 32306, USA
Interests: remote sensing (e.g.; ocean winds and currents); boundary-layers (ocean and atmosphere); air-sea interaction; the observing system
Special Issues, Collections and Topics in MDPI journals

Dr. Stephen R. Guimond

E-Mail Website
Guest Editor

University of Maryland Baltimore County and NASA Goddard Space Flight Center, Baltimore/Greenbelt, MD, USA
Interests: remote sensing with a focus on airborne Doppler radar including designing algorithms for computing geophysical variables such as winds, latent heat and precipitation; geophysical fluid dynamics with a focus on hurricanes, convection, turbulence and computational methods

Dr. Heather M. Holbach

E-Mail Website
Guest Editor

Northern Gulf Institute, NOAA/AOML/HRD, Florida State University, Tallahassee, FL 32306, USA
Interests: remote sensing; tropical cyclones; air-sea interaction

Special Issue Information

Dear Colleagues,

Ocean-based extreme weather events are among the most challenging environments for remote sensing of surface properties, yet these systems have a disproportionately large impact on society and the Earth’s energy and water cycles.

This special issue focuses on remote sensing of the ocean surface (e.g., wind speed and direction, wave characteristics, temperature, humidity, sea spray, white cap fraction and precipitation), observations related to the oceanic and atmospheric boundary layers, and the coupling of the boundary-layer with the free atmosphere. Connections between the remote sensing measurements and the dynamics of extreme weather events are especially encouraged. Furthermore, techniques that better link remote sensing to in situ data are also welcome, as well as improvements in the calibration (accuracy and parameter space).

Topics include satellite and airborne observations, as well as sub-surface observations.

Prof. Mark A. Bourassa
Dr. Stephen R. Guimond
Dr. Heather M. Holbach
Guest Editors

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Keywords

Ocean surface winds
Surface waves
Extreme conditions
Boundary layer
Precipitation
Mesoscale structures
Turbulence
Radar

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Published Papers (7 papers)

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23 pages, 5643 KiB

Open AccessArticle

Assessment of Saildrone Extreme Wind Measurements in Hurricane Sam Using MW Satellite Sensors

by Lucrezia Ricciardulli, Gregory R. Foltz, Andrew Manaster and Thomas Meissner

Remote Sens. 2022, 14(12), 2726; https://doi.org/10.3390/rs14122726 - 7 Jun 2022

Cited by 17 | Viewed by 4345

Abstract

In 2021, a novel NOAA-Saildrone project deployed five uncrewed surface vehicle Saildrones (SDs) to monitor regions of the Atlantic Ocean and Caribbean Sea frequented by tropical cyclones. One of the SDs, SD-1045, crossed Hurricane Sam (Category 4) on September 30, providing the first-ever surface-ocean videos of conditions in the core of a major hurricane and reporting near-surface winds as high as 40 m/s. Here, we present a comprehensive analysis and interpretation of the Saildrone ocean surface wind measurements in Hurricane Sam, using the following datasets for direct and indirect comparisons: an NDBC buoy in the path of the storm, radiometer tropical cyclone (TC) winds from SMAP and AMSR2, wind retrievals from the ASCAT scatterometers and SAR (RadarSat2), and HWRF model winds. The SD winds show excellent consistency with the satellite observations and a remarkable ability to detect the strength of the winds at the SD location. We use the HWRF model and satellite data to perform cross-comparisons of the SD with the buoy, which sampled different relative locations within the storm. Finally, we review the collective consistency among these measurements by describing the uncertainty of each wind dataset and discussing potential sources of systematic errors, such as the impact of extreme conditions on the SD measurements and uncertainties in the methodology. Full article

(This article belongs to the Special Issue High Winds and High Seas)

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26 pages, 3192 KiB

Open AccessArticle

High Wind Geophysical Model Function Modeling for the HY-2A Scatterometer Using Neural Network

by Xuetong Xie, Jing Wang and Mingsen Lin

Remote Sens. 2022, 14(10), 2335; https://doi.org/10.3390/rs14102335 - 12 May 2022

Viewed by 1818

Abstract

Under low to medium wind speeds and no rainfall, the retrieved vector wind from a scatterometer is accurate and reliable. However, under high wind conditions, the currently used geophysical model function (GMF), such as NSCAT-2, for wind vector retrieval has the disadvantage of overestimating the backscattering coefficient, which leads to a decrease in the quality of the retrieved ocean surface winds. To enhance the wind retrieval precision of the HY-2A scatterometer under high wind conditions, a new GMF for high wind (HW-GMF) is established by using the neural network method based on the backscattering coefficient data of the HY-2A scatterometer combined with the wind speed data of the Special Sensor Microwave Imager (SSM/I) and the Final (FNL) operational global analysis wind direction data from the National Centers for Environmental Prediction (NCEP). The absolute value of the mean deviation between the predicted σ⁰ by the HW-GMF and the measured σ⁰ by the HY-2A scatterometer is less than 0.1 dB, indicating that the HW-GMF has high accuracy. To verify the HW-GMF performance, the wind field inversion accuracy of the HW-GMF is compared with that of the NSCAT-2 GMF, a GMF currently used in the data processing of the HY-2A scatterometer. The experimental results show that the deviation between the HW-GMF retrieved wind speed and the SSM/I wind speed is within 2 m/s in the high wind speed range of 15–35 m/s, indicating that the HW-GMF improves the precision of the wind speed inversion of the HY-2A scatterometer under high wind speed conditions. Full article

(This article belongs to the Special Issue High Winds and High Seas)

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27 pages, 7230 KiB

Open AccessArticle

Characterizing Buoy Wind Speed Error in High Winds and Varying Sea State with ASCAT and ERA5

by Ethan E. Wright, Mark A. Bourassa, Ad Stoffelen and Jean-Raymond Bidlot

Remote Sens. 2021, 13(22), 4558; https://doi.org/10.3390/rs13224558 - 12 Nov 2021

Cited by 8 | Viewed by 3573

Abstract

Buoys provide key observations of wind speed over the ocean and are routinely used as a source of validation data for satellite wind products. However, the movement of buoys in high seas and the airflow over waves might cause inaccurate readings, raising concern when buoys are used as a source of wind speed comparison data. The relative accuracy of buoy winds is quantified through a triple collocation (TC) exercise comparing buoy winds to winds from ASCAT and ERA5. Differences between calibrated buoy winds and ASCAT are analyzed through separating the residuals by anemometer height and testing under high wind-wave and swell conditions. First, we converted buoy winds measured near 3, 4, and 5 m to stress-equivalent winds at 10 m (

U_{10 S}

). Buoy

U_{10 S}

from anemometers near 3 m compared notably lower than buoy

U_{10 S}

from anemometers near 4 and 5 m, illustrating the importance of buoy choice in comparisons with remote sensing data. Using TC calibration of buoy

U_{10 S}

to ASCAT in pure wind-wave conditions, we found that there was a small, but statistically significant difference between height adjusted buoy winds from buoys with 4 and 5 m anemometers compared to the same ASCAT wind speed ranges in high seas. However, this result does not follow conventional arguments for wave sheltering of buoy winds, whereby the lower anemometer height winds are distorted more than the higher anemometer height winds in high winds and high seas. We concluded that wave sheltering is not significantly affecting the winds from buoys between 4 and 5 m with high confidence for winds under 18 ms⁻¹. Further differences between buoy

U_{10 S}

and ASCAT winds are observed in high swell conditions, motivating the need to consider the possible effects of sea state on ASCAT winds. Full article

(This article belongs to the Special Issue High Winds and High Seas)

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25 pages, 75877 KiB

Open AccessArticle

Evaluating the Detection of Mesoscale Outflow Boundaries Using Scatterometer Winds at Different Spatial Resolutions

by Georgios Priftis, Timothy J. Lang, Piyush Garg, Stephen W. Nesbitt, Richard D. Lindsley and Themistoklis Chronis

Remote Sens. 2021, 13(7), 1334; https://doi.org/10.3390/rs13071334 - 31 Mar 2021

Cited by 3 | Viewed by 3384

Abstract

Outflow boundaries induced by cold-pools are a key characteristic of convective systems related to microphysical and kinematic processes during the mature stage of their lifecycle. Over the ocean, such kinematic processes are associated with low-level wind modulations that are captured by scatterometers. This study investigates the ability of the Advanced Scatterometer (ASCAT) wind retrievals to detect the outflow boundary associated with an oceanic mesoscale convective system (MCS). Leveraging a new technique to identify cold pools that is based on features that enclose elevated magnitude of the gradient of the wind, termed as ‘Gradient Feature’ (GF), wind retrievals at 50-, 25- and 7-km spatial resolution were utilized to explore how the characteristics of the outflow boundary vary with resolution. Ground-based radar retrievals were also implemented to assess and correct, when possible, the performance of the ASCAT retrievals. The magnitude of the gradient of the wind for the coarser resolution was an order of magnitude smaller (

10^{- 4}

^{- 1}

) than the finer ones (

10^{- 3}

^{- 1}

). An increase in the magnitude of the gradient wind field associated with the outflow boundary was captured by all resolutions and a respective feature was identified by the GF method. The location of the features relative to the distance from the front edge of the MCS decreased with resolution, indicating the importance of the high resolution ASCAT product to capture their extent, as well as additional smaller scale features. The effect of the background wind field on the selection of the final wind field during the ambiguity removal process for the high-resolution product is also discussed. Full article

(This article belongs to the Special Issue High Winds and High Seas)

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19 pages, 10505 KiB

Open AccessArticle

Analyzing Gaps in Hurricane Rain Coverage to Inform Future Satellite Proposals

by Justin P. Stow, Mark A. Bourassa and Heather M. Holbach

Remote Sens. 2020, 12(17), 2673; https://doi.org/10.3390/rs12172673 - 19 Aug 2020

Cited by 1 | Viewed by 2893

Abstract

This study assesses where tropical cyclone (TC) surface winds can be measured as a function of footprint sizes and wavelengths (Ka- Ku- and C-band). During TCs, most high-resolution surface observations are impeded by considerable ‘rain contamination.’ Under these conditions, high-resolution surface observations typically come from operational aircraft. Other techniques that provide high-resolution surface observations through rain are also hindered somewhat by rain contamination and are very sparse in space and time. The impacts of rain are functions of the remotely sensed wavelength and rain–drop size. Therefore, relative long wavelengths have been used to observe the surface, but at the cost of a larger footprint. We examine how smaller footprint sizes could be used to observe through gaps between moderate to heavy rainbands that circulate around the main low-pressure center of a TC. Aircraft data from the National Oceanic and Atmospheric Administration’s (NOAA’s) WP-3D turboprop aircraft will be used to create realistic maps of rain. Our results provide information on the satellite instrument characteristics needed to see the surface through these gaps. This information is expected to aid in developing hurricane-related applications of new higher-resolution satellites. Full article

(This article belongs to the Special Issue High Winds and High Seas)

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14 pages, 9704 KiB

Open AccessTechnical Note

High-Resolution Polar Low Winds Obtained from Unsupervised SAR Wind Retrieval

by Mathias Tollinger, Rune Graversen and Harald Johnsen

Remote Sens. 2021, 13(22), 4655; https://doi.org/10.3390/rs13224655 - 18 Nov 2021

Cited by 8 | Viewed by 2152

Abstract

High-resolution sea surface observations by spaceborne synthetic aperture radar (SAR) instruments are sorely neglected resources for meteorological applications in polar regions. Such radar observations provide information about wind speed and direction based on wind-induced roughness of the sea surface. The increasing coverage of SAR observations in polar regions calls for the development of SAR-specific applications that make use of the full information content of this valuable resource. Here we provide examples of the potential of SAR observations to provide details of the complex, mesoscale wind structure during polar low events, and examine the performance of two current wind retrieval methods. Furthermore, we suggest a new approach towards accurate wind vector retrieval of complex wind fields from SAR observations that does not require a priori wind direction input that the most common retrieval methods are dependent on. This approach has the potential to be particularly beneficial for numerical forecasting of weather systems with strong wind gradients, such as polar lows. Full article

(This article belongs to the Special Issue High Winds and High Seas)

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14 pages, 5331 KiB

Open AccessTechnical Note

Extreme Wind Speeds Retrieval Using Sentinel-1 IW Mode SAR Data

by Yuan Gao, Jian Sun, Jie Zhang and Changlong Guan

Remote Sens. 2021, 13(10), 1867; https://doi.org/10.3390/rs13101867 - 11 May 2021

Cited by 30 | Viewed by 3460

Abstract

With the improvement in microwave radar technology, spaceborne synthetic aperture radar (SAR) is widely used to observe the tropical cyclone (TC) wind field. Based on European Space Agency Sentinel-1 Interferometric Wide swath (IW) mode imagery, this paper evaluates the correlation between vertical transmitting–horizontal receiving (VH) polarization signals and extreme ocean surface wind speeds (>40 m/s) under strong TC conditions. A geophysical model function (GMF) Sentinel-1 IW mode wind retrieval model after noise removal (S1IW.NR) was proposed, according to the SAR images of nine TCs and collocated stepped frequency microwave radiometer (SFMR) and soil moisture active passive (SMAP) radiometer wind speed measurements. Through curve fitting and regression correction, the new GMF exploits the relationships between VH-polarization normalized radar cross section, incident angle, and wind speed in each sub-swath and covers wind speeds up to 74 m/s. Based on collocated SAR and SFMR measurements of four TCs, the new GMF was validated in the wind speed range from 2 to 53 m/s. Results show that the correlation coefficient, bias, and root mean squared error were 0.89, −0.89 m/s, and 4.13 m/s, respectively, indicating that extreme winds can be retrieved accurately by the new model. In addition, we investigated the relationship between the S1IW.NR wind retrieval bias and the SFMR-measured rain rate. The S1IW.NR model tended to overestimate wind speeds under high rain rates. Full article

(This article belongs to the Special Issue High Winds and High Seas)

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