Verification of Remotely Sensed Sea Surface Winds in Hurricanes

Uhlhorn, Eric W.; Black, Peter G.

doi:10.1175/1520-0426(2003)020<0099:vorsss>2.0.co;2

Cited by 175 publications

(111 citation statements)

References 34 publications

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“…The ratio of 6H* and 10H* has similar characteristics (Figure 4f), and the value is approximately 0.92. These results are in good agreement with the previous results [15,28]. expression [46], and clw-MPM93 expression [45], respectively.…”

Section: Wind Speed Retrieval Modelsupporting

confidence: 92%

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Hurricane Wind Speed Estimation Using WindSat 6 and 10 GHz Brightness Temperatures

et al. 2016

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Abstract:The realistic and accurate estimation of hurricane intensity is highly desired in many scientific and operational applications. With the advance of passive microwave polarimetry, an alternative opportunity for retrieving wind speed in hurricanes has become available. A wind speed retrieval algorithm for wind speeds above 20 m/s in hurricanes has been developed by using the 6.8 and 10.7 GHz vertically and horizontally polarized brightness temperatures of WindSat. The WindSat measurements for 15 category 4 and category 5 hurricanes from 2003 to 2010 and the corresponding H*wind analysis data are used to develop and validate the retrieval model. In addition, the retrieved wind speeds are also compared to the Remote Sensing Systems (RSS) global all-weather product and stepped-frequency microwave radiometer (SFMR) measurements. The statistical results show that the mean bias and the overall root-mean-square (RMS) difference of the retrieved wind speeds with respect to the H*wind analysis data are 0.04 and 2.75 m/s, respectively, which provides an encouraging result for retrieving hurricane wind speeds over the ocean surface. The retrieved wind speeds show good agreement with the SFMR measurements. Two case studies demonstrate that the mean bias and RMS difference are 0.79 m/s and 1.79 m/s for hurricane Rita-1 and 0.63 m/s and 2.38 m/s for hurricane Rita-2, respectively. In general, the wind speed retrieval accuracy of the new model in hurricanes ranges from 2.0 m/s in light rain to 3.9 m/s in heavy rain.

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Section: Wind Speed Retrieval Modelsupporting

confidence: 92%

“…An SFMR is an airborne remote sensing instrument designed for retrieving surface wind speed and rainfall in TCs [28]. The first operational SFMR was flown on the WP-3D aircraft in 2004 [29].…”

Section: Airborne Stepped-frequency Microwave Radiometer (Sfmr) Measumentioning

confidence: 99%

Hurricane Wind Speed Estimation Using WindSat 6 and 10 GHz Brightness Temperatures

et al. 2016

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“…Ophelia was in a location that resulted in numerous QuikSCAT overpasses at or near a time when aircraft reconnaissance was sampling the cyclone. A comparison of the 12.5-and 25-km QuikSCAT maximum winds with the maximum flight-level reconnaissance winds (adjusted to the surface using a 90% reduction factor) and surface wind estimates from the NOAA Stepped-Frequency Microwave Radiometer (SFMR; Uhlhorn and Black 2003) is shown in Fig. 16.…”

Section: ) Tropical Depressionsmentioning

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 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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“…The 53rd Weather Reconnaissance Squadron of the United States Air Force Reserve Command (AFRC) flew five reconnaissance missions into eastern North Pacific tropical cyclones during 2009: one in Andres, two in Jimena, and two in Rick. These missions provided flight-level data as well as surface wind estimates from the Stepped-Frequency Microwave Radiometer (SFMR; Ulhorn and Black 2003). Land-based radars from the Meteorological Service of Mexico were also useful for monitoring TCs during 2009.…”

Section: Datamentioning

confidence: 99%

Eastern North Pacific Hurricane Season of 2009

Kimberlain

Brennan

2011

Monthly Weather Review

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The 2009 eastern North Pacific hurricane season had near normal activity, with a total of 17 named storms, of which seven became hurricanes and four became major hurricanes. One hurricane and one tropical storm made landfall in Mexico, directly causing four deaths in that country along with moderate to severe property damage. Another cyclone that remained offshore caused an additional direct death in Mexico. On average, the National Hurricane Center track forecasts in the eastern North Pacific for 2009 were quite skillful.

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Verification of Remotely Sensed Sea Surface Winds in Hurricanes

Cited by 175 publications

References 34 publications

Hurricane Wind Speed Estimation Using WindSat 6 and 10 GHz Brightness Temperatures

Hurricane Wind Speed Estimation Using WindSat 6 and 10 GHz Brightness Temperatures

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

Eastern North Pacific Hurricane Season of 2009

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