Zobrazeno 1 - 10 of 21 pro vyhledávání: '"Fan-wei Zeng"'
1
Akademický článek
A novel delins (c.773_819+47delinsAA) mutation of the PCCA gene associated with neonatal-onset propionic acidemia: a case report
Autor: Hai-rong Wang, Yan-qiu Liu, Xue-lian He, Jun Sun, Fan-wei Zeng, Cheng-bin Yan, Hao Li, Shu-yang Gao, Yun Yang
Publikováno v: BMC Medical Genetics, Vol 21, Iss 1, Pp 1-7 (2020)
Abstract Background Propionic acidemia (PA)(OMIM#606054) is an inborn error of branched-chain amino acid metabolism, caused by defects in the propionyl-CoA carboxylase (PCC) enzyme which encoded by the PCCA and PCCB genes. Case presentation Here we r
Externí odkaz: https://doaj.org/article/97f740796a8941f5994909435bc18dd4
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2
Akademický článek
An Observation‐Driven Approach to Improve Vegetation Phenology in a Global Land Surface Model
Autor: Jana Kolassa, Rolf H. Reichle, Randal D. Koster, Qing Liu, Sarith Mahanama, Fan‐Wei Zeng
Publikováno v: Journal of Advances in Modeling Earth Systems, Vol 12, Iss 9, Pp n/a-n/a (2020)
Abstract An empirical model calibration approach is presented that aims to approximate missing biosphere processes in a global land surface model without the need for substantial model structural changes. The strategy is implemented here using the NA
Externí odkaz: https://doaj.org/article/a552bf16646a4128b891aef8ccaa56ce
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3
Akademický článek
Evaluating and Quantifying the Climate-Driven Interannual Variability in Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) at Global Scales
Autor: Alvaro Ivanoff, G. James Collatz, Fan-Wei Zeng, Jorge E. Pinzon
Publikováno v: Remote Sensing, Vol 5, Iss 8, Pp 3918-3950 (2013)
Satellite observations of surface reflected solar radiation contain information about variability in the absorption of solar radiation by vegetation. Understanding the causes of variability is important for models that use these data to drive land su
Externí odkaz: https://doaj.org/article/d17cae145ff24749a63cbd4a2d2214e0
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5
An Observation‐Driven Approach to Improve Vegetation Phenology in a Global Land Surface Model
Autor: Randal D. Koster, Fan-Wei Zeng, Rolf H. Reichle, Jana Kolassa, Sarith Mahanama, Qing Liu
Publikováno v: Journal of Advances in Modeling Earth Systems, Vol 12, Iss 9, Pp n/a-n/a (2020)
An empirical model calibration approach is presented that aims to approximate missing biosphere processes in a global land surface model without the need for substantial model structural changes. The strategy is implemented here using the NASA Catchm
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4403670d7bb73c17627044c9d0aa8b47
https://doi.org/10.1029/2020ms002083
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8
The impact of spatiotemporal variability in atmospheric CO2 concentration on global terrestrial carbon fluxes
Autor: Benjamin Poulter, Eunjee Lee, Fan Wei Zeng, Brad Weir, Randal D. Koster, Lesley Ott
Publikováno v: Biogeosciences. 15:5635-5652
Land carbon fluxes, e.g., gross primary production (GPP) and net biome production (NBP), are controlled in part by the responses of terrestrial ecosystems to atmospheric conditions near the Earth's surface. The Coupled Model Intercomparison Project P
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::3f8a3042506c39eda52a5b07b7c5f853
https://doi.org/10.5194/bg-15-5635-2018
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9
Global relationships among traditional reflectance vegetation indices (NDVI and NDII), evapotranspiration (ET), and soil moisture variability on weekly timescales
Autor: Thomas R. H. Holmes, Joanna Joiner, Christopher Hain, Martha C. Anderson, Fan-Wei Zeng, Randal D. Koster, Elizabeth M. Middleton, Rolf H. Reichle, Yasuko Yoshida
Publikováno v: Remote sensing of environment. 219
Monitoring the effects of water availability on vegetation globally using satellites is important for applications such as drought early warning, precision agriculture, and food security as well as for more broadly understanding relationships between
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::da530de3d06b4c11664b92095a00f52e
https://pubmed.ncbi.nlm.nih.gov/31217640
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