Aakash Sane

Aakash Sane

Brown University

H-index: 4

North America-United States

About Aakash Sane

Aakash Sane, With an exceptional h-index of 4 and a recent h-index of 4 (since 2020), a distinguished researcher at Brown University, specializes in the field of Ocean Surface Boundary Layers, Machine Learning, Ocean Modelling, Soap Films.

His recent articles reflect a diverse array of research interests and contributions to the field:

Analyzing Zooplankton grazing spatial variability in the Southern Ocean using deep learning

Parameterizing Vertical Turbulent Mixing Coefficients In The Ocean Surface Boundary Layer Using Machine Learning

Learning Machine Learning with Lorenz-96

Parameterizing vertical mixing coefficients in the ocean surface boundary layer using neural networks

Evaluating coupled climate model parameterizations via skill at reproducing the monsoon intraseasonal oscillation

Consistent Predictability of the Ocean State Ocean Model (OSOM) using Information Theory and Flushing Timescales.

Internal vs Forced Variability metrics for Geophysical Flows using Information theory

Aakash Sane Information

University

Brown University

Position

Graduate Student

Citations(all)

47

Citations(since 2020)

45

Cited By

19

hIndex(all)

4

hIndex(since 2020)

4

i10Index(all)

1

i10Index(since 2020)

1

Email

University Profile Page

Brown University

Aakash Sane Skills & Research Interests

Ocean Surface Boundary Layers

Machine Learning

Ocean Modelling

Soap Films

Top articles of Aakash Sane

Analyzing Zooplankton grazing spatial variability in the Southern Ocean using deep learning

Authors

Gian Giacomo Navarra,Aakash Sane,Curtis Deutsch

Published Date

2024/3/7

To elucidate the complex dynamics of zooplankton grazing and its impact on the organic carbon pump, we leveraged machine learning algorithms to analyze extensive datasets encompassing zooplankton behavior, environmental variables, and carbon flux measurements. Specifically, we employed regression models to establish predictive relationships between zooplankton grazing rates and key environmental factors, such as Potential Temperature, Sea Ice extension and iron availability.The results demonstrate the potential of machine learning in discerning patterns and nonlinear relationships within the data, offering insights into the factors influencing zooplankton grazing dynamics. Additionally, the models provide a predictive framework to estimate the contribution of zooplankton to the organic carbon pump under varying environmental conditions. We have further analyzed the results by using two explainable …

Parameterizing Vertical Turbulent Mixing Coefficients In The Ocean Surface Boundary Layer Using Machine Learning

Authors

Aakash Sane,Brandon Reichl,Alistair Adcroft,Laure Zanna NYU

Published Date

2024/2/22

Parameterizing Vertical Turbulent Mixing Coefficients In The Ocean Surface Boundary Layer Using Machine Learning Page 1 Parameterizing Vertical Turbulent Mixing Coefficients In The Ocean Surface Boundary Layer Using Machine Learning Neural Networks And Equation Discovery Aakash Sane (Princeton University) Brandon Reichl (NOAA – GFDL) Alistair Adcroft (Princeton University) Laure Zanna (NYU) CESM OMWG Feb. 8th, 2024 m2lines.github.io Page 2 1. Neural networks - within the existing energetics based physics framework to improve vertical diffusivity in the OSBL. 2. Networks implemented in MOM6. 3. JRA forced simulations performed: Bias reduction in shallow mixed layer depth and upper ocean stratification. 4. Equation Discovery: Successes, challenges, results. Key points: Page 3 Vertical mixing (OSBL) Page 4 Vertical mixing Changing ad-hoc parameters changes rate of ocean warming! …

Learning Machine Learning with Lorenz-96

Authors

Dhruv Balwada,Ryan Abernathey,Shantanu Acharya,Alistair Adcroft,Judith Brener,V Balaji,Mohamed Aziz Bhouri,Joan Bruna,Mitch Bushuk,Will Chapman,Alex Connolly,Julie Deshayes,Carlos Fernandez-Granda,Pierre Gentine,Anastasiia Gorbunova,Will Gregory,Arthur Guillaumin,Shubham Gupta,Marika Holland,Emmanuel Johnsson,Julien Le Sommer,Ziwei Li,Nora Loose,Feiyu Lu,Paul O'gorman,Pavel Perezhogin,Brandon Reichl,Andrew Ross,Aakash Sane,Sara Shamekh,Tarun Verma,Janni Yuval,Lorenzo Zampieri,Cheng Zhang,Laure Zanna

Journal

Authorea Preprints

Published Date

2023/12/27

Learning Machine Learning with Lorenz-96 Page 1 P osted on 27 Dec 2023 — The cop yrigh t holder is the author/funder. All righ ts reserv ed. No reuse with out p erm ission. — h ttps://doi.org/10.22541/essoar.170365239.95851488/v1 — This is a preprin t and has not b een p eer-review ed. Data ma y b e preliminary . Learning Machine Learning with Lorenz-96 Dhruv Balwada1, Ryan Abernathey1, Shantanu Acharya2, Alistair Adcroft3, Judith Brener4, V Balaji5, Mohamed Aziz Bhouri6, Joan Bruna2, Mitch Bushuk7, Will Chapman4, Alex Connolly6, Julie Deshayes8, Carlos Fernandez-Granda2, Pierre Gentine6,9, Anastasiia Gorbunova10, Will Gregory3, Arthur Guillaumin11, Shubham Gupta12, Marika Holland4, Emmanuel Johnsson10, Julien Le Sommer10, Ziwei Li2, Nora Loose3, Feiyu Lu7, Paul O’gorman13, Pavel Perezhogin2, Brandon Reichl7, Andrew Ross2, Aakash Sane3, Sara Shamekh6, Tarun Verma3, …

Parameterizing vertical mixing coefficients in the ocean surface boundary layer using neural networks

Authors

Aakash Sane,Brandon G Reichl,Alistair Adcroft,Laure Zanna

Journal

Journal of Advances in Modeling Earth Systems

Published Date

2023/10

Vertical mixing parameterizations in ocean models are formulated on the basis of the physical principles that govern turbulent mixing. However, many parameterizations include ad hoc components that are not well constrained by theory or data. One such component is the eddy diffusivity model, where vertical turbulent fluxes of a quantity are parameterized from a variable eddy diffusion coefficient and the mean vertical gradient of the quantity. In this work, we improve a parameterization of vertical mixing in the ocean surface boundary layer by enhancing its eddy diffusivity model using data‐driven methods, specifically neural networks. The neural networks are designed to take extrinsic and intrinsic forcing parameters as input to predict the eddy diffusivity profile and are trained using output data from a second moment closure turbulent mixing scheme. The modified vertical mixing scheme predicts the eddy diffusivity …

Evaluating coupled climate model parameterizations via skill at reproducing the monsoon intraseasonal oscillation

Authors

Patrick Orenstein,Baylor Fox-Kemper,Leah Johnson,Qing Li,Aakash Sane

Journal

Journal of Climate

Published Date

2022/3/15

Empirically generated indices are used to evaluate the skill of a global climate model in representing the monsoon intraseasonal oscillation (MISO). This work adapts the method of Suhas et al., an extended empirical orthogonal function (EEOF) analysis of daily rainfall data with the first orthogonal function indicating MISO strength and phase. This method is applied to observed rainfall and Community Earth System Model (CESM1.2) simulation results. Variants of the CESM1.2 including upper ocean parameterizations for Langmuir turbulence and submesoscale mixed layer eddy restratification are used together with the EEOF analysis to explore sensitivity of the MISO to global upper ocean process representations. The skill with which the model variants recreate the MISO strength and persistence is evaluated versus the observed MISO. While all model versions reproduce the northward rainfall propagation …

Consistent Predictability of the Ocean State Ocean Model (OSOM) using Information Theory and Flushing Timescales.

Authors

A Sane,B Fox-Kemper,D Ullman,C Kincaid,L Rothstein

Journal

Journal of Geophysical Research: Oceans

Published Date

2021

The Ocean State Ocean Model OSOM is an application of the Regional Ocean Modeling System spanning the Rhode Island waterways, including Narragansett Bay, Mt. Hope Bay, larger rivers, and the Block Island Shelf circulation from Long Island to Nantucket. This paper discusses the physical aspects of the estuary (Narragansett and Mount Hope Bays and larger rivers) to evaluate physical circulation predictability. This estimate is intended to help decide if a forecast and prediction system is warranted, to prepare for coupling with biogeochemistry and fisheries models with widely disparate timescales, and to find the spin-up time needed to establish the climatological circulation of the region. Perturbed initial condition ensemble simulations are combined with metrics from information theory to quantify the predictability of the OSOM forecast system–i.e., how long anomalies from different initial conditions persist. The predictability timescale in this model agrees with readily estimable timescales such as the freshwater flushing timescale evaluated using the total exchange flow (TEF) framework, indicating that the estuarine dynamics rather than chaotic transport is the dominant model behavior limiting predictions. The predictability of the OSOM is ~ 7 to 40 days, varying with parameters, region, and season.

Internal vs Forced Variability metrics for Geophysical Flows using Information theory

Authors

Aakash Sane,Baylor Fox-Kemper

Journal

Earth and Space Science Open Archive ESSOAr

Published Date

2020/11/22

We demonstrate the use of information theory metrics, Shannon entropy and mutual information, for measuring internal and forced variability in general circulation coastal and global ocean models. These metrics have been applied on spatially and temporally averaged data. A combined metric reliably delineates intrinsic and extrinsic variability in a wider range of circumstances than previous approaches based on variance ratios that therefore assume Gaussian distributions. Shannon entropy and mutual information manage correlated fields, apply to any distribution, and are insensitive to outliers and a change of units or scale. Different metrics are used to quantify internal vs forced variability in (1) idealized Gaussian and uniformly distributed data, (2) an initial condition ensemble of a realistic coastal ocean model (OSOM), (3) the GFDL-ESM2M climate model large ensemble. A metric based on information theory partly agrees with the traditional variance-based metric and identifies regions where non-linear correlations might exist. Mutual information and Shannon entropy are used to quantify the impact of different boundary forcings in a coastal ocean model ensemble. Information theory enables ranking the potential impacts of improving boundary and forcing conditions across multiple predicted variables with different dimensions. The climate model ensemble application shows how information theory metrics are robust even in a highly skewed probability distribution (Arctic sea surface temperature) resulting from sharply non-linear behavior (freezing point).

See List of Professors in Aakash Sane University(Brown University)

Aakash Sane FAQs

What is Aakash Sane's h-index at Brown University?

The h-index of Aakash Sane has been 4 since 2020 and 4 in total.

What are Aakash Sane's top articles?

The articles with the titles of

Analyzing Zooplankton grazing spatial variability in the Southern Ocean using deep learning

Parameterizing Vertical Turbulent Mixing Coefficients In The Ocean Surface Boundary Layer Using Machine Learning

Learning Machine Learning with Lorenz-96

Parameterizing vertical mixing coefficients in the ocean surface boundary layer using neural networks

Evaluating coupled climate model parameterizations via skill at reproducing the monsoon intraseasonal oscillation

Consistent Predictability of the Ocean State Ocean Model (OSOM) using Information Theory and Flushing Timescales.

Internal vs Forced Variability metrics for Geophysical Flows using Information theory

are the top articles of Aakash Sane at Brown University.

What are Aakash Sane's research interests?

The research interests of Aakash Sane are: Ocean Surface Boundary Layers, Machine Learning, Ocean Modelling, Soap Films

What is Aakash Sane's total number of citations?

Aakash Sane has 47 citations in total.

What are the co-authors of Aakash Sane?

The co-authors of Aakash Sane are Baylor Fox-Kemper, David S. Ullman, Tal Ben-Horin.

    Co-Authors

    H-index: 54
    Baylor Fox-Kemper

    Baylor Fox-Kemper

    Brown University

    H-index: 22
    David S. Ullman

    David S. Ullman

    University of Rhode Island

    H-index: 19
    Tal Ben-Horin

    Tal Ben-Horin

    North Carolina State University

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