Remotely Sensed Winds and Wind Stresses for Marine Forecasting and Ocean Modeling

Bourassa, Mark A.; Meißner, Thomas; Cerovečki, Ivana; Chang, Paul; Dong, Xiaolong; Chiara, Giovanna de; Donlon, Craig; Dukhovskoy, Dmitry S.; Elya, Jocelyn; Fore, Alexander; Fewings, Melanie R.; Foster, R. C.; Gille, Sarah T.; Haus, Brian K.; Hristova-Veleva, S. M.; Holbach, Heather M.; Jelenak, Zorana; Knaff, John A.; Kranz, Sven A.; Manaster, Andrew; Mazloff, Matthew R.; Mears, C. A.; Mouche, Alexis; Portabella, Marcos; Reul, Nicolás; Ricciardulli, Lucrezia; Rodríguez, Ernesto; Sampson, Charles R.; Solís, Daniel; Stoffelen, Ad; Stukel, Michael R.; Stiles, B.; Weissman, David E.; Wentz, F. J.

doi:10.3389/fmars.2019.00443

Cited by 92 publications

(83 citation statements)

References 224 publications

(285 reference statements)

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“…Such a transition occurred between different scatterometers onboard QuickSCAT and ASCAT satellites with an overlap period of two years during 2007-2009. Comparative studies have been attempted for the Indian Ocean and elsewhere, to validate sensors against the observations as well as to evaluate outputs of the physical models that use wind-field derived from these two satellites (Schlax et al 2001;Bentamy et al 2008Bourassa et al 2010;Sivareddy et al 2013Sivareddy et al , 2015. These studies found that ASCAT performs better in low-wind speed conditions, whereas QuickSCAT can resolve higher wind-speed conditions better.…”

Section: Introductionmentioning

confidence: 99%

Getting the right wind-forcing for an ecosystem model: A case study from the eastern Arabian Sea

Chakraborty

Nimit

Gupta

2017

Journal of Operational Oceanography

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The state of the ocean simulated through a numerical model is extremely sensitive to the forcing and hence, the choice of appropriate forcing is essential, in particular wind-fields. We compared Regional Ocean Modeling System (ROMS) simulations when forced with two different wind-fields derived from satellite sensors (scatterometers), namely QuickSCAT and ASCAT, while keeping the remaining configurations the same. This study had been carried out during the operational overlapping period of these scatterometers (2007-2009). We observed that the simulations of upper ocean chlorophyll distribution and depth of sub-surface chlorophyll maximum were vastly diverging when the model was forced with different scatterometer forcings. In order to determine which of the forcings yielded coastal productivity closer to observations, we compared model-simulated upper ocean chlorophyll concentration with in situ observations along the eastern Arabian Sea. For all the sub-regions, the QuickSCAT wind-forced coastal productivity was better correlated with in situ chlorophyll than its counterpart. However, this signature was most prominent in the wind-driven upwelling regime of the southeastern Arabian Sea. Additionally, when compared with surface chlorophyll derived from SeaWiFS and MODIS-Aqua satellites, we found that the QuickSCAT simulated interannual variability was in better agreement in comparison to its counterpart.

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Section: Introductionmentioning

confidence: 99%

Getting the right wind-forcing for an ecosystem model: A case study from the eastern Arabian Sea

Chakraborty

Nimit

Gupta

2017

Journal of Operational Oceanography

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“…This scope presently excludes the consideration of records derived from satellite data only as these have a maximum record length of about 40 years and are the subject of other papers in this issue (e.g., Ardhuin et al, 2019;Bourassa et al, 2019;O'Carroll et al, 2019). We note that many of the themes contained in this paper are relevant to both in situ and satellite data.…”

Section: Scope and Terminologymentioning

confidence: 99%

Observing Requirements for Long-Term Climate Records at the Ocean Surface

et al. 2019

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Frontiers in Marine Science | www.frontiersin.org July 2019 | Volume 6 | Article 441 Kent et al. Long-Term Surface Marine Recordsbiases and to improve methods of construction of CDRs. The requirements developed in this paper encompass specific actions involving a variety of stakeholders, including funding agencies, scientists, data managers, observing network operators, satellite agencies, and international co-ordination bodies.

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“…The diurnal cycle and the day-to-day variability of ocean winds can be substantial [Bourassa et al, 2010]. The blended product is composed of 4 samples per day, from 1992 to 2012.…”

Section: Diurnal Cycle and Day-to-day Variabilitymentioning

confidence: 99%

“…The temporal variability of the blended product has also been analyzed, from diurnal to decadal scales. As noted by Bourassa et al [2010], the diurnal cycle of ocean winds can be linked to cloud formation and it is of particular interest for ocean forcing (mixing and air/sea fluxes). For example, Lee et al [2008] showed that the mixed layer they simulate with an ocean model is increased when using winds with a fully resolved diurnal cycle in contrast to wind data smoothed over 24 hours.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Two decades [1992–2012] of surface wind analyses based on satellite scatterometer observations

Desbiolles

Bentamy

Blanke

et al. 2017

Journal of Marine Systems

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Surface winds (equivalent neutral wind velocities at 10 m) from scatterometer missions since 1992 have been used to build up a 20-year climate series. Optimal interpolation and kriging methods have been applied to continuously provide surface wind speed and direction estimates over the global ocean on a regular grid in space and time. The use of other data sources such as radiometer data (SSM/I) and atmospheric wind reanalyses (ERA-Interim) has allowed building a blended product available at 1/4° spatial resolution and every 6 hours from 1992 to 2012. Sampling issues throughout the different missions (ERS-1, ERS-2, QuikSCAT, and ASCAT) and their possible impact on the homogeneity of the gridded product are discussed. In addition, we assess carefully the quality of the blended product in the absence of scatterometer data (1992 to 1999). Data selection experiments show that the description of the surface wind is significantly improved by including the scatterometer winds. The blended winds compare well with buoy winds (1992-2012) and they resolve finer spatial scales than atmospheric reanalyses, which make them suitable for studying air-sea interactions at mesoscale. The seasonal cycle and interannual variability of the product compare well with other long-term wind analyses. The product is used to calculate 20-year trends in wind speed, as well as in zonal and meridional wind components. These trends show an important asymmetry between the southern and northern hemispheres, which may be an important issue for climate studies. Highlights► 20-year blended of high-resolution (0.25deg, 6h) wind product from scaterrometry ► Blended product suitable for studying air-sea interactions at mesoscale. ► The product is used to calculate 20-year trends in wind speed, as well as in zonal and meridional wind components.A long record of ocean surface wind observations is essential for climate research and for addressing a variety of operational and scientific issues. Surface wind vectors are indeed the key drivers of oceanic and atmospheric processes that regulate the global and regional climate [e.g., Ricciardulli and Wentz, 2013]. Ocean winds are routinely used as the primary forcing function of numerical hydrodynamic models of the ocean circulation [e.g., Grima et al., 1999; Carton and Giese, 2009;Wunsch et al. 2009;Desbiolles et al., 2016] and of surface gravity waves [e.g., et al., 1988;Tolman 2002] at global and regional scales. Ocean winds are considered as the most important variable for investigating storm surges and wave forecasts at various space and time scales [Debernard et al., 2002]. They drive the variability of ocean processes such as coastal upwelling, primary productivity, cross-shelf transport, deep-water formation, ice transport, and they are of fundamental importance for the reliable estimation of air-sea momentum fluxes (wind stress vector), turbulent heat fluxes (latent and sensible), and gas exchanges (e.g. CO 2 and H 2 O). Longterm change in global winds is an important forcing and an indicator of cli...

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Remotely Sensed Winds and Wind Stresses for Marine Forecasting and Ocean Modeling

Cited by 92 publications

References 224 publications

Getting the right wind-forcing for an ecosystem model: A case study from the eastern Arabian Sea

Getting the right wind-forcing for an ecosystem model: A case study from the eastern Arabian Sea

Observing Requirements for Long-Term Climate Records at the Ocean Surface

Two decades [1992–2012] of surface wind analyses based on satellite scatterometer observations

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