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Kyla Drushka

Principal Oceanographer

Affiliate Assistant Professor, Oceanography

Email

kdrushka@apl.washington.edu

Phone

206-543-6858

Education

B.S. Physics, McGill University, 2004

Ph.D. Physical Oceanography, Scripps Institution of Oceanography, 2011

Publications

2000-present and while at APL-UW

Salinity Rain Impact Model (RIM) for SMAP

Jacob, M.M., W.L. Jones, A. Santos-Garcia, K. Drushka, W.E. Asher, and C.M. Scavuzzo, "Salinity Rain Impact Model (RIM) for SMAP," IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 12, 16-79-1687, doi:10.1109/JSTARS.2019.2907275, 2019.

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15 Jul 2019

When oceanic rainfall occurs, it creates a vertical salinity profile that is fresher at the surface. This freshwater lens is mixed downward by turbulent diffusion, dissipating over a few hours until the upper layer (1–5 m depth) becomes well mixed. Thus, there will be a transient bias between the in situ bulk salinity and the satellite-measured sea surface salinity (SSS) (representative of the first centimeter of the ocean depth). Based on measurements of Aquarius (AQ) SSS under rainy conditions, a model called rain impact model (RIM) was developed to assess the SSS variations due to the accumulation of rainfall prior to the time of the AQ observation. RIM uses ocean surface salinities from hybrid coordinate ocean model and the NOAA global precipitation product, climate prediction center morphing, to estimate changes in the near-surface salinity profile. Also, the RIM analysis has been applied to soil moisture and ocean salinity with similar results observed. The Soil Moisture Active Passive (SMAP) satellite carries an L-band radiometer, which measures SSS over a swath of 1000 km at 40-km resolution. SMAP can extend AQ salinity data record with improved temporal/spatial sampling. This paper describes RIM that simulates the effects of rain accumulation on SMAP SSS, showing good correlation between the model and the observed SSS values. Given the better resolution of SMAP, the goal of this paper is to continue the previous analysis of AQ to better understand the effects of the instantaneous and accumulated rain on the salinity measurements.

Global patterns of submesoscale surface salinity variability

Drushka, K., W.E. Asher, J. Sprintall, S.T. Fille, and C. Hoang, "Global patterns of submesoscale surface salinity variability," J. Phys. Oceanogr., 49, 1669-1685, doi: 10.1175/JPO-D-19-0018.1, 2019.

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1 Jul 2019

Surface salinity variability on O(1–10) km lateral scales (the submesoscale) generates density variability and thus has implications for submesoscale dynamics. Satellite salinity measurements represent a spatial average over horizontal scales of approximately 40–100 km but are compared to point measurements for validation, so submesoscale salinity variability also complicates validation of satellite salinities. Here, we combine several databases of historical thermosalinograph (TSG) measurements made from ships to globally characterize surface submesoscale salinity, temperature, and density variability. In river plumes; regions affected by ice melt or upwelling; and the Gulf Stream, South Atlantic, and Agulhas Currents, submesoscale surface salinity variability is large. In these regions, horizontal salinity variability appears to explain some of the differences between surface salinities from the Aquarius and SMOS satellites and salinities measured with Argo floats. In other words, apparent satellite errors in highly variable regions in fact arise because Argo point measurements do not represent spatially averaged satellite data. Salinity dominates over temperature in generating submesoscale surface density variability throughout the tropical rainbands, in river plumes, and in polar regions. Horizontal density fronts on 10-km scales tend to be compensated (salinity and temperature have opposing effects on density) throughout most of the global oceans, with the exception of the south Indian and southwest Pacific oceans between 20° and 30°S, where fronts tend to be anticompensated.

Estimating rain-generated turbulence at the ocean surface using the active controlled flux technique

Asher, W.E., K. Drushka, A.T. Jessup, E.J. Thompson, and D. Clark, "Estimating rain-generated turbulence at the ocean surface using the active controlled flux technique," Oceanography, 32, 108-115, doi:10.5670/oceanog.2019.218, 2019.

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14 Jun 2019

Rain-generated lenses of fresher water at the ocean surface affect satellite remote sensing of salinity, mixed-layer dynamics, and air-sea exchange of heat, momentum, and gases. Understanding how rain and wind generate turbulence at the ocean surface is important in modeling the generation and evolution of these fresh lenses. This paper discusses the use of the active controlled flux technique (ACFT) to determine relative levels of turbulence in the top centimeter of the ocean surface in the presence of rain. ACFT measurements were made during the 2016 second Salinity Processes in the Upper-ocean Regional Study (SPURS-2) in the eastern equatorial Pacific Ocean. The data show that at wind speeds below 4 m s-1, the turbulence dissipation rate at the ocean surface (as parameterized by the water-side surface renewal time constant) is correlated with the instantaneous rain rate. However, at higher wind speeds, the wind stress dominates turbulence production and rain is not a significant source of turbulence. There is also evidence that internal waves can be a significant source of turbulence at the ocean surface under non-raining conditions when a diurnal warm layer is present.

More Publications

Inventions

Continuous Underway Multi-sensor Profiler

Record of Invention Number: 48207

Peter Gaube, Kyla Drushka

Disclosure

15 Nov 2017

Acoustics Air-Sea Interaction & Remote Sensing Center for Environmental & Information Systems Center for Industrial & Medical Ultrasound Electronic & Photonic Systems Ocean Engineering Ocean Physics Polar Science Center
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