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

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  • Journal Title:
    Procedia Computer Science
  • Personal Author:
  • NOAA Program & Office:
    OAR (Oceanic and Atmospheric Research)
  • Description:
    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.
  • Keywords:
  • Source:
    Procedia Computer Science, 185, 1-8
  • DOI:
  • ISSN:
    1877-0509
  • Format:
    PDF
  • Publisher:
    Elsevier BV
  • Document Type:
    Journal Article
  • Funding:
    Grant no. NA19OAR4590411
  • License:
  • Rights Information:
    CC BY-NC-ND
  • Compliance:
    Library
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