Dual‐polarized C‐ and Ku‐band ocean backscatter response to hurricane‐force winds

Fernandez, D.E.; Carswell, J.; Frasier, Stephen J.; Chang, Paul; Black, Peter G.; Marks, Frank D.

doi:10.1029/2005jc003048

Cited by 117 publications

(59 citation statements)

References 26 publications

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“…It is obvious that in major hurricanes, rain contamination, the horizontal resolution of the data, and saturation of the backscatter signal at wind speeds greater than about 90 kt (46.3 m s 638 conditions (e.g., Fernandez et al 2006), make it impossible for QuikSCAT to measure the maximum wind speed in the inner core. Future scatterometer instruments must be able to measure hurricane-force winds (up to and including category 5 strength) in heavy rain conditions to be useful in the analysis of the intensity of major hurricanes.…”

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confidence: 99%

“…) [equivalent to category 2 on the Saffir-Simpson hurricane scale (e.g., Simpson 1974)] can be produced in rain-free areas (e.g., Fernandez et al 2006), but this is not possible in most TCs. The retrievals are impacted by moderate to heavy rain, but winds of tropical storm force can sometimes be estimated from QuikSCAT in rainy conditions with careful forecaster interpretation.…”

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confidence: 99%

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The Operational Use of QuikSCAT Ocean Surface Vector Winds at the National Hurricane Center

Brennan

Hennon

Knabb

2009

Weather and Forecasting

110

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The utility and shortcomings of near-real-time ocean surface vector wind retrievals from the NASA Quick Scatterometer (QuikSCAT) in operational forecast and analysis activities at the National Hurricane Center (NHC) are described. The use of QuikSCAT data in tropical cyclone (TC) analysis and forecasting for center location/identification, intensity (maximum sustained wind) estimation, and analysis of outer wind radii is presented, along with shortcomings of the data due to the effects of rain contamination and wind direction uncertainties. Automated QuikSCAT solutions in TCs often fail to show a closed circulation, and those that do are often biased to the southwest of the NHC best-track position. QuikSCAT winds show the greatest skill in TC intensity estimation in moderate to strong tropical storms. In tropical depressions, a positive bias in QuikSCAT winds is seen due to enhanced backscatter by rain, while in major hurricanes rain attenuation, resolution, and signal saturation result in a large negative bias in QuikSCAT intensity estimates.QuikSCAT wind data help overcome the large surface data void in the analysis and forecast area of NHC's Tropical Analysis and Forecast Branch (TAFB). These data have resulted in improved analyses of surface features, better definition of high wind areas, and improved forecasts of high-wind events. The development of a climatology of gap wind events in the Gulf of Tehuantepec has been possible due to QuikSCAT wind data in a largely data-void region.The shortcomings of ocean surface vector winds from QuikSCAT in the operational environment at NHC are described, along with requirements for future ocean surface vector wind missions. These include improvements in the timeliness and quality of the data, increasing the wind speed range over which the data are reliable, and decreasing the impact of rain to allow for accurate retrievals in all-weather conditions.

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confidence: 99%

mentioning

confidence: 99%

The Operational Use of QuikSCAT Ocean Surface Vector Winds at the National Hurricane Center

Brennan

Hennon

Knabb

2009

Weather and Forecasting

110

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“…The viewing directions could be up-wind, cross-wind and down-wind. In addition, sea surface cross section measurements for wind-roughened sea surfaces found by Fernandez et al (2006) were interpolated to yield comparable relationships for 25, 30, 35, 45 m/s, with no rain. These results are shown in Fig.…”

Section: Dual Frequency Precipitation Retrieval Over An Inhomogeneousmentioning

confidence: 99%

“…4. Linear fits to those cross sections, including the wind roughened sea surface measurements by Fernandez et al (2006) (----). The NRCS computed from the model R(x, z) distribution found in our study was (---).…”

Section: Dual Frequency Precipitation Retrieval Over An Inhomogeneousmentioning

confidence: 99%

Rainfall observation from X-band, space-borne, synthetic aperture radar

Weinman

Marzano

Plant

et al. 2009

Nat. Hazards Earth Syst. Sci.

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Abstract. Satellites carrying X-band Synthetic Aperture Radars (SAR) have recently been launched by several countries. These provide new opportunities to measure precipitation with higher spatial resolution than has heretofore been possible. Two algorithms to retrieve precipitation from such measurements over land have been developed, and the retrieved rainfall distributions were found to be consistent. A maritime rainfall distribution obtained from dual frequency (X and C-band) data was used to compute the Differential Polarized Phase Shift. The computed Differential Polarized Phase Shift compared well with the value measured from space. Finally, we show a comparison between a recent Xband SAR image of a precipitation distribution and an observation of the same rainfall from ground-based operational weather radar. Although no quantitative comparison of retrieved and conventional rainfall distributions could be made with the available data at this time, the results presented here point the way to such comparisons.

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“…This is because XMOD is tuned and validated through VV-polarization TS-X/TD-X images and the DWD with the wind speeds up to 25 m/s. Moreover, the signal saturation problem also exists for SAR in tropical cyclone [30,31].…”

Section: Introductionmentioning

confidence: 99%

An Empirical Algorithm for Wave Retrieval from Co-Polarization X-Band SAR Imagery

Shao

Wang

et al. 2017

Remote Sensing

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Abstract:In this study, we proposed an empirical algorithm for significant wave height (SWH) retrieval from TerraSAR-X/TanDEM (TS-X/TD-X) X-band synthetic aperture radar (SAR) co-polarization (vertical-vertical (VV) and horizontal-horizontal (HH)) images. As the existing empirical algorithm at X-band, i.e., XWAVE, is applied for wave retrieval from HH-polarization TS-X/TD-X image, polarization ratio (PR) has to be used for inverting wind speed, which is treated as an input in XWAVE. Wind speed encounters saturation in tropical cyclone. In our work, wind speed is replaced by normalized radar cross section (NRCS) to avoiding using SAR-derived wind speed, which does not work in high winds, and the empirical algorithm can be conveniently implemented without converting NRCS in HH-polarization to NRCS in VV-polarization by using X-band PR. A total of 120 TS-X/TD-X images, 60 in VV-polarization and 60 in HH-polarization, with homogenous wave patterns, and the coincide significant wave height data from European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis field at a 0.125 • grid were collected as a dataset for tuning the algorithm. The range of SWH is from 0 to 7 m. We then applied the algorithm to 24 VV and 21 HH additional SAR images to extract SWH at locations of 30 National Oceanic and Atmospheric Administration (NOAA) National Data Buoy Center (NDBC) buoys. It is found that the algorithm performs well with a SWH stander deviation (STD) of about 0.5 m for both VV and HH polarization TS-X/TD-X images. For large wave validation (SWH 6-7 m), we applied the empirical algorithm to a tropical cyclone Sandy TD-X image acquired in 2012, and obtained good result with a SWH STD of 0.3 m. We concluded that the proposed empirical algorithm works for wave retrieval from TS-X/TD-X image in co-polarization without external sea surface wind information.

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Dual‐polarized C‐ and Ku‐band ocean backscatter response to hurricane‐force winds

Cited by 117 publications

References 26 publications

The Operational Use of QuikSCAT Ocean Surface Vector Winds at the National Hurricane Center

The Operational Use of QuikSCAT Ocean Surface Vector Winds at the National Hurricane Center

Rainfall observation from X-band, space-borne, synthetic aperture radar

An Empirical Algorithm for Wave Retrieval from Co-Polarization X-Band SAR Imagery

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