Gab Abramowitz

Biographic Data

ID6818890
NAMEGab Abramowitz
GIVEN NAMESGab
FAMILY NAMEAbramowitz
SIGNATUREABRAMOWITZ G
AFFILIATIONSUNSW Sydney
ORCID0000-0002-4205-001X
VERIFIEDYes
TOTAL WORKS5
TOTAL CITATIONS0
WORKS AS AUTHOR5
WORKS AS EDITOR0
FIRST PUBLICATION YEAR2013
LATEST PUBLICATION YEAR2025
H-INDEX0

  • Electric vehicle battery storage displacement of grid storage in Australia’s renewable future

    Open AccessElona Rey-Costa, Donna Green et al.ARTICLEEnvironmental Research Letters2025

    Electric vehicles (EVs) with vehicle-to-grid (V2G) technology offer a dual solution to decarbonise the energy and transport sectors. This paper evaluates the potential for Australia’s passenger and light commercial EV fleet to replace large-scale battery storage under a 100% renewable energy system in the National Electricity Market. By simulating various levels of V2G battery availability, we assess the impact on energy production costs, renewab…

  • Predicting Australian energy demand variability using weather data and machine learning

    Open AccessDoug Richardson, Sanaa Hobeichi et al.ARTICLEEnvironmental Research Letters2024

    Managing energy systems requires understanding the variability of energy demand, which on daily timescales is driven primarily by the weather. Historical records of demand typically cover 1–2 decades which may be too short to capture the range of possible demand, particularly for a climate with high interannual variability such as that of Australia. Predicting demand using long records of weather data opens the possibility of more robustly estima…

  • Comparison of a novel machine learning approach with dynamical downscaling for Australian precipitation

    Open AccessNidhi Nishant, Sanaa Hobeichi et al.ARTICLEEnvironmental Research Letters2023

    Dynamical downscaling (DD), and machine learning (ML) based techniques have been widely applied to downscale global climate models and reanalyses to a finer spatiotemporal scale, but the relative performance of these two methods remains unclear. We implement an ML regression approach using a multi-layer perceptron (MLP) with a novel loss function to downscale coarse-resolution precipitation from the Bureau of Meteorology Atmospheric high-resoluti…

  • Land surface models systematically overestimate the intensity, duration and magnitude of seasonal-scale evaporative droughts

    Open AccessAnna Ukkola, Martin G De Kauwe et al.ARTICLEEnvironmental Research Letters2016

    Land surface models (LSMs) must accurately simulate observed energy and water fluxes during droughts in order to provide reliable estimates of future water resources.We evaluated 8 different LSMs (14 model versions) for simulating evapotranspiration (ET) during periods of evaporative drought (Edrought) across six flux tower sites.Using an empirically defined Edrought threshold (a decline in ET below the observed 15th percentile), we show that LSM…

  • Optimally choosing small ensemble members to produce robust climate simulations

    Open AccessJason P Evans, Fei Ji et al.ARTICLEEnvironmental Research Letters2013

    This study examines the subset climate model ensemble size required to reproduce certain statistical characteristics from a full ensemble. The ensemble characteristics examined are the root mean square error, the ensemble mean and standard deviation. Subset ensembles are created using measures that consider the simulation performance alone or include a measure of simulation independence relative to other ensemble members. It is found that the ind…