Exploring Sources of Surface Bias in HRRR Using New York State Mesonet. Issue 20 (19th October 2021)

• Record Type: Journal Article
• Authors: Min, Lanxi; Fitzjarrald, David R.; Du, Yuyi; Rose, Brian E. J.; Hong, Jia; Min, Qilong
• Is Part Of: Journal of geophysical research. Volume 126:Issue 20(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-10-19
• Subjects: HRRR model evaluation -- New York State Mesonet -- surface energy balance -- soil moisture -- cloud; Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1029/2021JD034989
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract: In recent years, there has been increasing demand for applications of short‐term forecasting of renewable energy potential and assessments of the likelihood of extreme weather events using the High‐Resolution Rapid Refresh (HRRR) model. Examining the biases in the newest version of HRRR is necessary to promote further model development. Using data from one of the most comprehensive and dense monitoring networks, New York State Mesonet (NYSM), we evaluate the HRRR version 3 meteorological fields for an entire year. In this work, the land‐atmosphere‐cloud coupling system is evaluated as an integrated whole. We investigate the physical processes influencing the soil hydrological balance and the thermodynamic interactions, from surface fluxes up to the level of boundary layer convection from both temporal (seasonal and diurnal) and spatial perspectives. Results show that the model 2 m temperature and humidity biases are seasonally dependent, with warm and dry bias present during the warm season, and an extreme nocturnal cold bias in winter. The summer warm bias includes both a land‐surface‐induced bias and a cloud‐induced bias. Inaccurate representation of energy partition and soil hydrological process across different land use types as well as a hydrological bias in describing spring snowmelt are identified as the main source of the land‐surface‐induced bias. A feedback loop linking cloud presence, flux changes, and temperature contributes to the cloud‐induced bias. The positive solar radiation bias increases from clear sky to overcast sky conditions. The most significant bias occurs during overcast and thick cloud conditions associated with frontal passage and thunderstorms. Key Points: The High‐Resolution Rapid Refresh 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 snowmelt is consistent with subsequent summer warm and dry biases over farmland