Evaluation of cloud base height in the North American Regional Reanalysis using ceilometer observations

Abstract

Future climate change predictions by global climate models or earth system models diverge significantly, most likely due to their different cloud responses to global warming. There is an uncertainty as to how the cloud frequency (or cloud fraction) and height will change, in turn, affecting the sign, and amount of cloud feedbacks. While satellite observations have been very useful in augmenting information on clouds, it is mostly related to cloud tops, and there is a lack of information on cloud base height (CBH). In this study, a unique record of CBH information was collected at 706 Automated Surface Observing System (ASOS) ceilometer stations to evaluate the ability of the North American Regional Reanalysis (NARR) model to correctly simulate similar information. It was found that NARR can capture the geographical distribution and the seasonal variation of CBH and cloud base frequency (CBF). On average, the CBF values of NARR were 7% fewer and the CBH values of NARR were 631 m lower than those from observations that span the distance from the surface to 7,600 m. NARR simulates CBH better in arid area in the west of the contiguous United States (CONUS) than in humid areas, where NARR frequently predicted cloud bases too low compared with observations. In the west coast area of the CONUS, the discontinuity between high cloud bases over arid inland areas and low marine cloud layers from the Pacific Ocean over coastal areas produced especially large deviations between the NARR-simulated and ASOS-observed CBHs.

Intraregional Comparisons of the Near‐Storm Environments of Storms Dominated by Frequent Positive Versus Negative Cloud‐to‐Ground Flashes

“…The NARR obtains CBH by taking the lowest level above the surface at which the cloud water/ice mixing ratio is greater than 10 −6 kg kg −1 . If no clouds are inferred by this method, the NARR algorithm checks for any convective clouds and returns their CBHs (An et al., 2020).…”

The 2019 Mississippi and Missouri River Flooding and Its Impact on Atmospheric Boundary Layer Dynamics

“…Dirmeyer et al (2013) illustrated SM‐precipitation feedbacks, whereas Halder et al (2015) outlined the need for both advanced LAFP and observations in simulating hydroclimatic variability over an extended region. Understanding of the ABL kinematics and thermodynamic features under extreme wet regime and associated LAFP is important since ABL features modulate numerous atmospheric processes, for example, CI, aerosol‐cloud microphysics, and turbulent mixing of aerosols and pollutants (e.g., An et al, 2019; Dirmeyer & Halder, 2016, 2017). Furthermore, the precipitation‐SM feedback process involves multiple nonlinear interactions among soil‐water storage, runoff, evapotranspiration, orographic features, vegetation cover, and precipitation types (Schubert et al, 2004).…”

The Effect of Climatological Variables on Future UAS-Based Atmospheric Profiling in the Lower Atmosphere

“…Moreover, the FAA requires the UAS to remain at least 152.4 m below the bases of clouds, and the UAS cannot fly in clouds for the flight restrictions being considered. An et al found that the NARR estimates the cloud base to be lower than the true cloud base in humid climates [19]. To account for this, our study filtered out a NARR product of combined low cloud and medium cloud percentage corresponding to clear skies when determining unsuccessful flights.…”