Thomas Sherman
Thomas Sherman
CRCL Solutions
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Cited by
Cited by
Wind estimation in the lower atmosphere using multirotor aircraft
RT Palomaki, NT Rose, M van den Bossche, TJ Sherman, SFJ De Wekker
Journal of Atmospheric and Oceanic Technology 34 (5), 1183-1191, 2017
Analysis of random forest modeling strategies for multi-step wind speed forecasting
D Vassallo, R Krishnamurthy, T Sherman, HJS Fernando
Energies 13 (20), 5488, 2020
Characterizing the impact of particle behavior at fracture intersections in three-dimensional discrete fracture networks
T Sherman, JD Hyman, D Bolster, N Makedonska, G Srinivasan
Physical Review E 99 (1), 013110, 2019
A review of spatial Markov models for predicting pre-asymptotic and anomalous transport in porous and fractured media
T Sherman, NB Engdahl, G Porta, D Bolster
Journal of Contaminant Hydrology 236, 103734, 2021
A spatial Markov model for upscaling transport of adsorbing-desorbing solutes
T Sherman, A Paster, G Porta, D Bolster
Journal of contaminant hydrology 222, 31-40, 2019
Characterizing the influence of fracture density on network scale transport
T Sherman, J Hyman, M Dentz, D Bolster
Journal of Geophysical Research: Solid Earth 125 (1), e2019JB018547, 2020
Parameterizing the spatial Markov model from breakthrough curve data alone
T Sherman, A Fakhari, S Miller, K Singha, D Bolster
Water Resources Research 53 (12), 10888-10898, 2017
A dual domain stochastic lagrangian model for predicting transport in open channels with hyporheic exchange
T Sherman, KR Roche, DH Richter, AI Packman, D Bolster
Advances in water resources 125, 57-67, 2019
Subgrid theory for storm surge modeling
AB Kennedy, D Wirasaet, A Begmohammadi, T Sherman, D Bolster, ...
Ocean Modelling 144, 101491, 2019
Predicting downstream concentration histories from upstream data in column experiments
T Sherman, A Foster, D Bolster, K Singha
Water Resources Research 54 (11), 9684-9694, 2018
Characterizing reactive transport behavior in a three-dimensional discrete fracture network
T Sherman, G Sole-Mari, J Hyman, MR Sweeney, D Vassallo, D Bolster
Transport in Porous Media, 1-21, 2021
Upscaling transport of a sorbing solute in disordered non periodic porous domains
T Sherman, EB Janetti, GR Guédon, G Porta, D Bolster
Advances in water resources 139, 103574, 2020
Predicting vertical concentration profiles in the marine atmospheric boundary layer with a Markov chain random walk model
HJ Park, T Sherman, LS Freire, G Wang, D Bolster, P Xian, A Sorooshian, ...
Journal of Geophysical Research: Atmospheres 125 (19), e2020JD032731, 2020
Time-Series Forecasting Energy Loads: A Case Study in Texas
R Rice, K North, G Hansen, D Pearson, O Schaer, T Sherman, D Vassallo
2022 Systems and Information Engineering Design Symposium (SIEDS), 196-201, 2022
Markovian Models for Microplastic Transport in Open‐Channel Flows
L Xing, D Bolster, H Liu, T Sherman, DH Richter, K Rocha‐Brownell, Z Ru
Water Resources Research 58 (8), e2021WR031746, 2022
Upscaling of Solute Plumes in Periodic Porous Media Through a Trajectory‐Based Spatial Markov Model
E Bianchi Janetti, T Sherman, GR Guédon, D Bolster, GM Porta
Water Resources Research 56 (12), e2020WR028408, 2020
Eighth Annual NOAA Open Data Dissemination
K Duffy, M Robertson, T Lavoi, P Keown, T Sherman, R Chandra, N Merati, ...
103rd AMS Annual Meeting, 2023
Machine learning methods for improving wind speed and wind energy production forecasting
T Sherman
103rd AMS Annual Meeting, 2023
Upscaling linear chemical reactions in porous media; a case study of Dry Creek, ID
Z Sherman, T Sherman, D Bolster
AGU Fall Meeting Abstracts 2022, H25P-1302, 2022
Upscaling linear chemical reactions in porous and fractured media with a continuous time random walk framework
Z Sherman, T Sherman, D Bolster
AGU Fall Meeting Abstracts 2021, H45J-1285, 2021
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