Identification of Dominant Warm-Season Latent Heat Flux Patterns in the Lower Mississippi River Alluvial Valley

被引:1
|
作者
Mercer, Andrew [1 ]
Dyer, Jamie [1 ]
机构
[1] Mississippi State Univ, 108 Hilbun Hall, Mississippi State, MS 39762 USA
来源
BIG DATA, IOT, AND AI FOR A SMARTER FUTURE | 2021年 / 185卷
关键词
Kernel Principal Component Analysis; Land-Atmosphere Interactions; Data Mining; VARIABILITY; ENSEMBLE;
D O I
10.1016/j.procs.2021.05.001
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Warm-season precipitation in the Lower Mississippi River Alluvial Valley (LMRAV) is heavily dominated by the rates of evapotranspiration and surface heat fluxes and is a primary water resource for agriculture. However, the stochastic nature of LMRAV warm-season thunderstorms makes precipitation forecasts challenging. The Weather Research and Forecasting Hydrologic (WRF-Hydro) model, coupled with the multi-parameter Noah land surface (Noah-MP) model, has improved estimates of important warm-season precipitation process. Given the widespread agriculture and dominance of crop and forested landscapes over the region, proper assessment of land use / land cover (LULC) is critical in predicting warm-season precipitation patterns. The objective of this study is to quantify simulated latent heat flux sensitivity (important for warm-season precipitation) to temporally updated LULC datasets. Both the model default and annually updated LULC conditions were used to initialize a 16-year WRF-Hydro simulation from which warm-season latent heat flux estimates were obtained. Annual root mean square difference was computed at each gridpoint. Cluster analysis preprocessed with kernel principal component analysis was used to identify spatial RMSD structures that quantified sensitivity to updated LULC conditions. Results showed the largest impacts occurred directly in the LMRAV and for points slightly east and revealed a meteorological link between these regions. (c) 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the Complex Adaptive Systems Conference, June 2021.
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页码:1 / 8
页数:8
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