scholarly journals Detection of sand grains blown by Titan surface winds with the DraGMet/EFIELD sensor on Dragonfly

2021 ◽  
Author(s):  
Audrey Chatain ◽  
Alice Le Gall ◽  
Michel Hamelin ◽  
Jean-Jacques Berthelier ◽  
Ralph D. Lorenz ◽  
...  
Keyword(s):  
Surface Winds

Evaluation of reanalysis and satellite-based sea surface winds using in situ measurements from Chinese Antarctic Expeditions

2013 ◽  
Vol 24 (3) ◽  
pp. 147
Author(s):  
Ming LI ◽  
Qinghua YANG ◽  
Jiechen ZHAO ◽  
Lin ZHANG ◽  
Chunhua LI ◽  
...  
Keyword(s):  
In Situ Measurements ◽  
Sea Surface ◽  
Surface Winds ◽  
Sea Surface Winds

scholarly journals Multimodel Ensemble Projections of Wave Climate in the Western North Pacific Using CMIP6 Marine Surface Winds

2021 ◽  
Vol 9 (8) ◽  
pp. 835
Author(s):  
Mochamad Riam Badriana ◽  
Han Soo Lee
Keyword(s):  
North Pacific ◽  
General Circulation ◽  
Western North Pacific ◽  
Wave Climate ◽  
Coastal Development ◽  
Climate Projections ◽  
Surface Winds ◽  
Time Step ◽  
Wave Characteristics ◽  
Marine Surface

For decades, the western North Pacific (WNP) has been commonly indicated as a region with high vulnerability to oceanic and atmospheric hazards. This phenomenon can be observed through general circulation model (GCM) output from the Coupled Model Intercomparison Project (CMIP). The CMIP consists of a collection of ensemble data as well as marine surface winds for the projection of the wave climate. Wave climate projections based on the CMIP dataset are necessary for ocean studies, marine forecasts, and coastal development over the WNP region. Numerous studies with earlier phases of CMIP are abundant, but studies using CMIP6 as the recent dataset for wave projection is still limited. Thus, in this study, wave climate projections with WAVEWATCH III are conducted to investigate how wave characteristics in the WNP will have changed in 2050 and 2100 compared to those in 2000 with atmospheric forcings from CMIP6 marine surface winds. The wave model runs with a 0.5° × 0.5° spatial resolution in spherical coordinates and a 10-min time step. A total of eight GCMs from the CMIP6 dataset are used for the marine surface winds modelled over 3 hours for 2050 and 2100. The simulated average wave characteristics for 2000 are validated with the ERA5 Reanalysis wave data showing good consistency. The wave characteristics in 2050 and 2100 show that significant decreases in wave height, a clockwise shift in wave direction, and the mean wave period becomes shorter relative to those in 2000.

Extreme Surface Winds During Landfalling Atmospheric Rivers: The Modulating Role of Near-Surface Stability

2021 ◽  
Author(s):  
Terence J. Pagano ◽  
Duane E. Waliser ◽  
Bin Guan ◽  
Hengchun Ye ◽  
F. Martin Ralph ◽  
...  
Keyword(s):  
Water Vapor ◽  
Static Stability ◽  
Vapor Transport ◽  
Water Vapor Transport ◽  
Surface Winds ◽  
Near Surface ◽  
Extreme Surface ◽  
Atmospheric Rivers ◽  
Explained Variance

AbstractAtmospheric rivers (ARs) are long and narrow regions of strong horizontal water vapor transport. Upon landfall, ARs are typically associated with heavy precipitation and strong surface winds. A quantitative understanding of the atmospheric conditions that favor extreme surface winds during ARs has implications for anticipating and managing various impacts associated with these potentially hazardous events. Here, a global AR database (1999–2014) with relevant information from MERRA-2 reanalysis, QuikSCAT and AIRS satellite observations are used to better understand and quantify the role of near-surface static stability in modulating surface winds during landfalling ARs. The temperature difference between the surface and 1 km MSL (ΔT; used here as a proxy for near-surface static stability), and integrated water vapor transport (IVT) are analyzed to quantify their relationships to surface winds using bivariate linear regression. In four regions where AR landfalls are common, the MERRA-2-based results indicate that IVT accounts for 22-38% of the variance in surface wind speed. Combining ΔT with IVT increases the explained variance to 36-52%. Substitution of QuikSCAT surface winds and AIRS ΔT in place of the MERRA-2 data largely preserves this relationship (e.g., 44% compared to 52% explained variance for USA West Coast). Use of an alternate static stability measure–the bulk Richardson number–yields a similar explained variance (47%). Lastly, AR cases within the top and bottom 25% of near-surface static stability indicate that extreme surface winds (gale or higher) are more likely to occur in unstable conditions (5.3%/14.7% during weak/strong IVT) than in stable conditions (0.58%/6.15%).

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