Challenges to Satellite Sensors of Ocean Winds: Addressing Precipitation Effects

Weissman, David E.; Stiles, B.; Hristova-Veleva, S. M.; Long, D.G.; Smith, Derrick W.; Hilburn, Kyle; Jones, W. Linwood

doi:10.1175/jtech-d-11-00054.1

Cited by 67 publications

(59 citation statements)

References 36 publications

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“…Weissman [8] proposed that the azimuth variation in the wind-induced backscatter can still be detected and is useful for estimating the wind direction (provided that the rain attenuation is only moderately large). According to the residual analyses of different collocated data in the present study, the STDs and RMSEs of the wind direction in H2SCAT are slightly higher under rain conditions than under rain-free conditions, especially at low and moderate wind speeds.…”

Section: Discussionmentioning

confidence: 99%

“…Although several studies noted the rain effects on scatterometer wind data, and attempts to reduce these effects are numerous [8], the nonlinear relationship between the rain effects (attenuation and roughening) and wind retrieval model functions are difficult to elucidate. The effects of different rain rates on the wind retrieval ability of H2SCAT must be investigated in further study.…”

Section: Discussionmentioning

confidence: 99%

“…2016, 8,390 3 of 18 wind products. To investigate the effect of rain on the quality of the H2SCAT sea surface winds, we employ the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center morphing method (CMORPH) rain data [45].…”

Section: Wind Observations By Ndbc and Tao Buoysmentioning

confidence: 99%

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Evaluation of HY-2A Scatterometer Wind Vectors Using Data from Buoys, ERA-Interim and ASCAT during 2012–2014

et al. 2016

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Abstract:The first Chinese operational Ku-band scatterometer on board Haiyang-2A (HY-2A), launched in August 2011, is designed for monitoring the global ocean surface wind. This study estimates the quality of the near-real-time (NRT) retrieval wind speed and wind direction from the HY-2A scatterometer for 36 months from 2012 to 2014. We employed three types of sea-surface wind data from oceanic moored buoys operated by the National Data Buoy Center (NDBC) and the Tropical Atmospheric Ocean project (TAO), the European Centre for Medium Range Weather Forecasting (ECMWF) reanalysis data (ERA-Interim), and the advanced scatterometer (ASCAT) to calculate the error statistics including mean bias, root mean square error (RMSE), and standard deviation. In addition, the rain effects on the retrieval winds were investigated using collocated Climate Prediction Center morphing method (CMORPH) precipitation data. All data were collocated with the HY-2A scatterometer wind data for comparison. The quality performances of the HY-2A NRT wind vectors data (especially the wind speeds) were satisfactory throughout the service period. The RMSEs of the HY-2A wind speeds relative to the NDBC, TAO, ERA-Interim, and ASCAT data were 1.94, 1.73, 2.25, and 1.62 m¨s´1, respectively. The corresponding RMSEs of the wind direction were 46.63˝, 43.11˝, 39.93˝, and 47.47˝, respectively. The HY-2A scatterometer overestimated low wind speeds, especially under rainy conditions. Rain exerted a diminishing effect on the wind speed retrievals with increasing wind speed, but its effect on wind direction was robust at low and moderate wind speeds. Relative to the TAO buoy data, the RMSEs without rain effect were reduced to 1.2 m¨s´1 and 39.68˝for the wind speed direction, respectively, regardless of wind speed. By investigating the objective laws between rain and the retrieval winds from HY-2A, we could improve the quality of wind retrievals through future studies.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Evaluation of HY-2A Scatterometer Wind Vectors Using Data from Buoys, ERA-Interim and ASCAT during 2012–2014

et al. 2016

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“…Rain affects all wind retrievals from all microwave sensors but with varying degrees [Tournadre and Quilfen, 2005;Portabella et al, 2012;Weissman et al, 2012]. Rain-contaminated retrievals were discarded by using rain flags embedded in the products.…”

Section: Quikscatmentioning

confidence: 99%

“…Passive microwave radiometers have no observations under rain conditions, while the C-band ASCAT and the Ku-band QuikSCAT have different responses to rain, causing persistent interscatterometer differences in high winds over the overlapping areas [Weissman et al, 2012]. One case analysis is presented in Figures 6a-6f, in which satellite wind observations of Hurricane Bill on 22 August 2009 from four sensors are examined.…”

Section: Sensitivity Of the Daily Mean Field To High Winds And Rainmentioning

confidence: 99%

Insights on the OAFlux ocean surface vector wind analysis merged from scatterometers and passive microwave radiometers (1987 onward)

Jin

2014

JGR Oceans

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A high-resolution global daily analysis of ocean surface vector winds (1987 onward) was developed by the Objectively Analyzed air-sea Fluxes (OAFlux) project. This study addressed the issues related to the development of the time series through objective synthesis of 12 satellite sensors (two scatterometers and 10 passive microwave radiometers) using a least-variance linear statistical estimation. The issues include the rationale that supports the multisensor synthesis, the methodology and strategy that were developed, the challenges that were encountered, and the comparison of the synthesized daily mean fields with reference to scatterometers and atmospheric reanalyses. The synthesis was established on the bases that the low and moderate winds (<15 m s 21 ) constitute 98% of global daily wind fields, and they are the range of winds that are retrieved with best quality and consistency by both scatterometers and radiometers. Yet, challenges are presented in situations of synoptic weather systems due mainly to three factors: (i) the lack of radiometer retrievals in rain conditions, (ii) the inability to fill in the data voids caused by eliminating rainflagged QuikSCAT wind vector cells, and (iii) the persistent differences between QuikSCAT and ASCAT high winds. The study showed that the daily mean surface winds can be confidently constructed from merging scatterometers with radiometers over the global oceans, except for the regions influenced by synoptic weather storms. The uncertainties in present scatterometer and radiometer observations under high winds and rain conditions lead to uncertainties in the synthesized synoptic structures.

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The emission and scattering of L‐band microwave radiation from rough ocean surfaces and wind speed measurements from the Aquarius sensor

2014

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In order to achieve the required accuracy in sea surface salinity (SSS) measurements from Lband radiometers such as the Aquarius/SAC-D or SMOS (Soil Moisture and Ocean Salinity) mission, it is crucial to accurately correct the radiation that is emitted from the ocean surface for roughness effects. We derive a geophysical model function (GMF) for the emission and backscatter of L-band microwave radiation from rough ocean surfaces. The analysis is based on radiometer brightness temperature and scatterometer backscatter observations both taken on board Aquarius. The data are temporally and spatially collocated with wind speeds from WindSat and F17 SSMIS (Special Sensor Microwave Imager Sounder) and wind directions from NCEP (National Center for Environmental Prediction) GDAS (Global Data Assimilation System). This GMF is the basis for retrieval of ocean surface wind speed combining L-band H-pol radiometer and HHpol scatterometer observations. The accuracy of theses combined passive/active L-band wind speeds matches those of many other satellite microwave sensors. The L-band GMF together with the combined passive/active L-band wind speeds is utilized in the Aquarius SSS retrieval algorithm for the surface roughness correction. We demonstrate that using these L-band wind speeds instead of NCEP wind speeds leads to a significant improvement in the SSS accuracy. Further improvements in the roughness correction algorithm can be obtained by adding VV-pol scatterometer measurements and wave height (WH) data into the GMF.

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Challenges to Satellite Sensors of Ocean Winds: Addressing Precipitation Effects

Cited by 67 publications

References 36 publications

Evaluation of HY-2A Scatterometer Wind Vectors Using Data from Buoys, ERA-Interim and ASCAT during 2012–2014

Evaluation of HY-2A Scatterometer Wind Vectors Using Data from Buoys, ERA-Interim and ASCAT during 2012–2014

Insights on the OAFlux ocean surface vector wind analysis merged from scatterometers and passive microwave radiometers (1987 onward)

The emission and scattering of L‐band microwave radiation from rough ocean surfaces and wind speed measurements from the Aquarius sensor

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