AUTHOREA
Log in
Sign Up
Browse Preprints
LOG IN
SIGN UP
Essential Site Maintenance
: Authorea-powered sites will be updated circa 15:00-17:00 Eastern on Tuesday 5 November.
There should be no interruption to normal services, but please contact us at
[email protected]
in case you face any issues.
David R Fitzjarrald
Public Documents
2
Evaluating sources of surface bias in HRRR using New York State Mesonet
Lanxi Min
and 6 more
April 24, 2021
Key Points: The HRRR model surface thermodynamic biases are seasonally dependent, presenting a systematic warm and dry bias during the warm season. The primary locations of the summer warm and dry biases are over farmland, on days with optically thick clouds. A hydrological bias underestimating of spring snow melt is consistent with subsequent summer warm and dry biases over farmland.
Evaluating the Land-Atmosphere-Cloud Interaction in HRRR using New York State Mesonet
Lanxi Min
and 5 more
January 02, 2021
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.