Results 161 to 170 of about 7,780 (201)

GOES-R land surface products at Western Hemisphere eddy covariance tower locations. [PDF]

Sci Data
Losos D, Hoffman S, Stoy PC.
europepmc   +1 more source

To What Extend Does the Eddy Covariance Footprint Cutoff Influence the Estimation of Surface Energy Fluxes Using Two Source Energy Balance Model and High-Resolution Imagery in Commercial Vineyards? [PDF]

Proc SPIE Int Soc Opt Eng, 2020
Nassar A, Torres-Rua A, Kustas W, Nieto H, McKee M, Hipps L, Alfieri J, Prueger J, Alsina MM, McKee L, Coopmans C, Sanchez L, Dokoozlian N.   +12 more
europepmc   +1 more source

Achieve Medium‐Range SST Forecast by Rolling 1‐Day Deep Learning

Geophysical Research Letters, Volume 53, Issue 18, 28 September 2026.
Abstract Deep learning has revolutionized short‐term Sea Surface Temperature (SST) forecasting, but often struggles with cumulative error propagation and operational complexity in medium‐range predictions. To achieve lightweight and temporally continuous SST forecast, this study introduces two novel training strategies, Rolling Training and Multistep ...
Nanxiang Huang, Jiangnan He, Wenfang Lu, Young‐Heon Jo   +3 more
wiley   +1 more source

Intercomparison of sensible and latent heat flux measurements from combined eddy covariance, energy balance, and Bowen ratio methods above a grassland prairie. [PDF]

Sci Rep
Billesbach DP, Arkebauer TJ, Sullivan RC.   +2 more
europepmc   +1 more source

OCO‐3 Meets ECOSTRESS: Insights Into Ecosystem Diurnal Water‐Use Efficiency From Co‐Located Solar‐Induced Fluorescence and Thermal Observations

Geophysical Research Letters, Volume 53, Issue 18, 28 September 2026.
Abstract Terrestrial ecosystem regulation of carbon and water fluxes is critical for constraining climate–biosphere feedbacks but remains poorly quantified across space and time. Here, we evaluate whether co‐located observations from NASA's Orbiting Carbon Observatory‐3 (OCO‐3) and the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station
Zoe Amie Pierrat, Thomas P. Kurosu, Abhishek Chatterjee, Joshua B. Fisher, Maggie Johnson, Le Kuai, Kanishka Mallick, Nicholas Parazoo, Benjamin Wiebe, Kerry Cawse‐Nicholson   +9 more
wiley   +1 more source

Assessing Spatial Representativeness of Global Flux Tower Eddy-Covariance Measurements Using Data from FLUXNET2015. [PDF]

Sci Data
Fang J, Fang J, Chen B, Zhang H, Dilawar A, Guo M, Liu S.   +6 more
europepmc   +1 more source

Structure of Boundary Layer Wind and Turbulence in Super Typhoon Ragasa and Typhoon Wipha During Landfall

Journal of Geophysical Research: Atmospheres, Volume 131, Issue 18, 28 September 2026.
Abstract Boundary layer turbulence and vertical mixing exert great control over the structure and evolution of tropical cyclones (TCs). However, scaling and parameterizing them remains challenging due to their inherent complexity and limited observations.
Junyi He, Yucheng Xue, Feng Hu, Yikang Liu, Qiusheng Li, Honglong Yang, Pak Wai Chan   +6 more
wiley   +1 more source

Fire‐Atmosphere Coupling Starts Upwind: Selective Redistribution of Turbulence at the Fire‐Induced Rear Inflow

Journal of Geophysical Research: Atmospheres, Volume 131, Issue 17, 16 September 2026.
Abstract Wildfires alter local atmospheric conditions, yet the spatial extent and frequency‐dependent characteristics of fire‐induced inflow perturbations remain largely unquantified. This study applies Multivariate Empirical Mode Decomposition (MEMD) to analyze high‐frequency horizontal wind speed data from a tower positioned at varying distances (∼80–
Gitesh Wasson, Marwan Katurji, Peyman Zawar‐Reza, Andres Valencia, Jiawei Zhang, Tara Strand, H. Grant Pearce, Jessica Kerr   +7 more
wiley   +1 more source

Assessment of spongy moth infestation impacts on forest productivity and carbon loss using the Sentinel-2 satellite remote sensing and eddy covariance flux data. [PDF]

Ecol Process
Hussain N, Gonsamo A, Wang S, Arain MA.
europepmc   +1 more source

Stochastic Condensation With Information Flow for Causality Analysis

Geophysical Research Letters, Volume 53, Issue 17, 16 September 2026.
Abstract Water vapor concentration in turbulent clouds fluctuates over a wide range of scales, challenging physical understanding of droplet growth mechanisms, prediction of droplet size distributions, and attribution of causal connections. We generalize a minimalist Brownian‐motion model (McGraw & Liu, 2006, https://doi.org/10.1029/2005gl023545 ...
Yaohui Shu, Yangang Liu, Tao Zhang, Robert McGraw, Xiaolin Li   +4 more
wiley   +1 more source