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Evaluating the Land-Atmosphere-Cloud Interaction in HRRR using New York State Mesonet
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  • Lanxi Min,
  • David R Fitzjarrald,
  • Yuyi Du,
  • Brian E J Rose,
  • Jia Hong,
  • Qilong Min
Lanxi Min
University at Albany

Corresponding Author:lmin@albany.edu

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David R Fitzjarrald
University at Albany
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Yuyi Du
University at Albany
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Brian E J Rose
University at Albany
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Jia Hong
University at Albany
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Qilong Min
University at Albany
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Abstract

Key Points:  The HRRR model near surface thermodynaic biases are seasonally dependent. A systematic warm and dry bias present during the warm season.  The summer warm and dry biases over farmland are consistently larger than forest.  Hydrological bias of spring snow melt might eventually lead to the summer warm and dry biases over farmland. Abstract The High-Resolution Rapid Refresh (HRRR) model version 3 meteorological fields were examined using data from New York State Mesonet (NYSM) sites from an entire year. In this work, the land surface, atmosphere and cloud coupling systems are evaluated as an integrated system. The physical processes influencing soil hydrological, surface thermodynamic processes from surface fluxes to boundary layer convection are investigated from both temporal (seasonal and diurnal) and spatial perspectives. Results show that the model 2m surface biases are seasonally dependent, with warm and dry bias present during the warm season, and an extreme cold bias during the night in winter. The summer warm bias includes both a land-surface-induced bias and a cloud-induced bias. Inacurate representation of energy partition and soil hydrological process across different land use types and hydrological bias of spring snow melt in the land surface model is the main source of the land-surface induced bias. A feedback loop linking cloud presence, radiative flux changes and temperature contributes to the cloud-induced bias. The positive solar radiation bias increases from clear sky to overcast sky conditions, beyond simply the lack of aerosols in the current version. The most significant bias occurs during overcast and thick cloud conditions associated with frontal passage and thunderstorms.