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.

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.

A three-day precipitation event was simulated using Weather Research and Forecasting (WRF) model coupled with a Spectral Bin Microphysical (SBM) scheme. On January 6th, a synoptic frontal system passed through NYS, and was followed by a lake effect event that lasted to January 8th, 2014. With a focus of aerosol indirect effect over the complex terrain of NYS, three sensitivity studies on this event were conducted: 1) a Control Case with background aerosol of low concentration, 2) an IN Case with doubled ice nuclei (IN) concentration, and 3) a CCN Case with tripled cloud condensate nuclei (CCN) number than the Control Case. In the frontal system, the ice-to-liquid partition within the cloud and in the surface precipitation was strongly affected by extra INs and CCNs. For the lake effect event with a much colder atmosphere, extra IN had large impact on the cloud by forming more ice crystals directly over the lake and invigorating the vertical motion within the lake effect cloud band, resulting in taller and narrower cloud band as well as increased precipitation over the downwind Tug Hill. Whereas the more numerous small cloud droplets and lower rimming efficiency associated with extra CCNs buffered the increased ice collection rate, resulting in a similar convection intensity and surface precipitation to the Control Case.