Tropical Cyclone Wind Speeds from WindSat, AMSR and SMAP: Algorithm Development and Testing

Meißner, Thomas; Ricciardulli, Lucrezia; Manaster, Andrew

doi:10.3390/rs13091641

Cited by 23 publications

(32 citation statements)

References 72 publications

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“…This is not the case here, as there is no apparent attenuation or artificial rotation of the wind direction vectors (typically perpendicular to the spacecraft orbiting direction) even in regions of intense rain. The consistent wind fields and smooth wind direction retrievals in Figure 15 confirm that the three ASCAT scatterometers generally provide reliable observations in Tropical Cyclones for wind speeds, at least up to the Category 1 threshold (33 m/s) [26,48,49,72]. For this specific day, the ASCAT maximum winds (~35-36 m/s,~68-70 kt) displayed in Figure 15 are within the range of estimates from the SATCON dataset [73] at the same time (~60 kt ± 20 kt), which includes Best-Track data, Dvorak technique, and satellite-based estimates (http://tropic.ssec.wisc.edu/realtime/satcon/202102B.html, accessed on 26 August 2021).…”

Section: Example: Ascat Wind Retrievals In Tropical Cyclones (Tcs)supporting

confidence: 59%

“…Similar results were found for analyses using ASCAT-A and -C (not shown). The SMAP radiometer winds have been extensively validated versus oil platform anemometers, in situ airborne measurements, and Best-Track estimates in Tropical Cyclones, and verified versus hurricane forecast model wind fields [21,[48][49][50]. The ASCAT retrievals are remarkably unbiased versus these radiometers for high wind speeds, up to 35 m/s.…”

Section: High Wind Speed Validation Using Radiometersmentioning

confidence: 98%

“…Because of SMAP's low frequency, its wind retrievals are particularly valuable at high winds and are not affected by rain [48]. Specially-trained algorithms for winds in rain have been developed for WindSat, AMSR-E, and AMSR-2 [49,50].…”

Section: Mw Radiometer Windsmentioning

confidence: 99%

“…The combination of these effects often caps the ASCAT wind retrievals to about 42 m/s. Examples of intense ASCAT wind retrievals in tropical and extratropical cyclones and signal saturation at high winds are shown in [48,49].…”

Section: Example: Ascat Wind Retrievals In Tropical Cyclones (Tcs)mentioning

confidence: 99%

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Intercalibration of ASCAT Scatterometer Winds from MetOp-A, -B, and -C, for a Stable Climate Data Record

Ricciardulli

Manaster

2021

Remote Sensing

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Scatterometers provide very stable ocean vector wind data records. This is because they measure the ratio of backscattered to incident microwave signal over the ocean surface as opposed to an absolute quantity (e.g., emitted microwave signal). They provide an optimal source of observations for building a long ocean vector wind Climate Data Record (CDR). With this objective in mind, observations from different satellite platforms need to be assessed for high absolute accuracy versus a common ground truth and for fine cross-calibration during overlapping periods. Here we describe the methodology for developing a CDR of ocean surface winds from the C-band ASCAT scatterometers onboard MetOp-A, -B, and -C. This methodology is based on the following principles: a common Geophysical Model Function (GMF) and wind algorithm developed at Remote Sensing Systems (RSS) and the use of in situ and satellite winds to cross-calibrate the three scatterometers within the accuracy required for CDRs, about 0.1 m/s at the global monthly scale. Using multiple scatterometers and radiometers for comparison allows for the opportunity to isolate sensors that are drifting or experiencing step-changes as small as 0.05 m/s. We detected and corrected a couple of such changes in the ASCAT-A wind record. The ASCAT winds are now very stable over time and well cross-calibrated with each other. The full C-band wind CDR now covers 2007-present and can be easily extended in the next decade with the launch of the MetOp Second Generation scatterometers.

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Section: Example: Ascat Wind Retrievals In Tropical Cyclones (Tcs)supporting

confidence: 59%

Section: High Wind Speed Validation Using Radiometersmentioning

confidence: 98%

Section: Mw Radiometer Windsmentioning

confidence: 99%

Section: Example: Ascat Wind Retrievals In Tropical Cyclones (Tcs)mentioning

confidence: 99%

See 2 more Smart Citations

Intercalibration of ASCAT Scatterometer Winds from MetOp-A, -B, and -C, for a Stable Climate Data Record

Ricciardulli

Manaster

2021

Remote Sensing

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“…The fast and accurate global 3D wind field measurements are required for improving the global forecasting as well as for utilizing wind energy and achieving sustainable energy supply [1,2]. Today, it is difficult to obtain the wind data in polar regions, over vast oceans, and on high elevations [3,4]. Therefore, satellite wind measurement technology is widely explored to obtain the global wind field.…”

Section: Introductionmentioning

confidence: 99%

The Potential of Satellite Sounding Observations for Deriving Atmospheric Wind in All-Weather Conditions

Zhang

Weng

2021

Remote Sensing

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Atmospheric wind is an essential parameter in the global observing system. In this study, the water vapor field in Typhoon Lekima and its surrounding areas simulated by the Weather Research and Forecasting (WRF) model is utilized to track the atmospheric motion wind through the Farneback Optical Flow (OF) algorithm. A series of experiments are conducted to investigate the influence of temporal and spatial resolutions on the errors of tracked winds. It is shown that the wind accuracy from tracking the specific humidity is higher than that from tracking the relative humidity. For fast-evolving weather systems such as typhoons, the shorter time step allows for more accurate wind retrievals, whereas for slow to moderate evolving weather conditions, the longer time step is needed for smaller retrieval errors. Compared to the traditional atmospheric motion vectors (AMVs) algorithm, the Farneback OF wind algorithm achieves a pixel-wise feature tracking and obtains a higher spatial resolution of wind field. It also works well under some special circumstances such as very low water vapor content or the region where the wind direction is parallel to the moisture gradient direction. This study has some significant implications for the configuration of satellite microwave sounding missions through their derived water vapor fields. The required temporal and spatial resolutions in the OF algorithm critically determine the satellite revisiting time and the field of view size. The brightness temperature (BT) simulated through Community Radiative Transfer Model (CRTM) is also used to track winds. It is shown that the error of tracking BT is generally larger than that of tracking water vapor. This increased error may result from the uncertainty in simulations of brightness temperatures at 183 GHz.

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Planetary cloud tracking

Liu

Salazar

2022

Exp Fluids

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Tropical Cyclone Wind Speeds from WindSat, AMSR and SMAP: Algorithm Development and Testing

Abstract: The measurement of ocean surface wind speeds in precipitation from satellite microwave radiometers is a challenging task. Rain attenuates the signal that is emitted from the ocean surface.

Cited by 23 publications

References 72 publications

Intercalibration of ASCAT Scatterometer Winds from MetOp-A, -B, and -C, for a Stable Climate Data Record

Intercalibration of ASCAT Scatterometer Winds from MetOp-A, -B, and -C, for a Stable Climate Data Record

The Potential of Satellite Sounding Observations for Deriving Atmospheric Wind in All-Weather Conditions

Planetary cloud tracking

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