Aneesh Subramanian

Aneesh Subramanian

Associate Professor
Associate Professor in the Department of Atmospheric and Oceanic Sciences at CU Boulder. Research spans weather and climate prediction, subseasonal-to-seasonal forecasting, atmospheric river dynamics, machine learning for Earth system modeling, and data assimilation in coupled ocean-atmosphere systems.

Stochastic representations of model uncertainties at ECMWF: state of the art and future vision

Members in ensemble forecasts differ due to the representations of initial uncertainties and model uncertainties. The inclusion of stochastic schemes to represent model …

martin-leutbecher
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Seasonal and decadal forecasts of Atlantic Sea surface temperatures using a linear inverse model

Predictability of Atlantic Ocean sea surface temperatures (SST) on seasonal and decadal timescales is investigated using a suite of statistical linear inverse models (LIM). …

benjamin-huddart
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Maintaining Momentum in Climate Model Development

As the current funding for climate process teams comes to an end, scientists emphasize the continuing need for teams that translate basic research into improved climate models.

caroline-ummenhofer
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Impact of stochastic physics on tropical precipitation in the coupled ECMWF model

Uncertainties in parametrized processes in general circulation models can be represented as stochastic perturbations to the model formulation. The European Centre for …

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Aneesh Subramanian
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Climate SPHINX: evaluating the impact of resolution and stochastic physics parameterisations in the EC-Earth global climate model

The Climate SPHINX (Stochastic Physics HIgh resolutioN eXperiments) project is a comprehensive set of ensemble simulations aimed at evaluating the sensitivity of present and …

paolo-davini
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A study of reduced numerical precision to make superparameterization more competitive using a hardware emulator in the OpenIFS model

The use of reduced numerical precision to reduce computing costs for the cloud resolving model of superparameterized simulations of the atmosphere is investigated. An approach …

peter-d.-duben
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Simulation of High-Resolution Precipitable Water Data by a Stochastic Model with a Random Trigger

We use a stochastic differential equation (SDE) model with a random precipitation trigger for mass balance to simulate the 20 s temporal resolution column precipitable water …

kimberly-leung
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The skill of atmospheric linear inverse models in hindcasting the Madden-Julian Oscillation

A suite of statistical atmosphere-only linear inverse models of varying complexity are used to hindcast recent MJO events from the Year of Tropical Convection and the …

nicholas-r.-cavanaugh
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The MJO and global warming: a study in CCSM4

The change in Madden-Julian oscillation (MJO) amplitude and variance in response to anthropogenic climate change is assessed in the 1A degrees nominal resolution community …

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Aneesh Subramanian
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