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Recent work has shown that bulk-Richardson (Rib) parameterizations for friction velocity, sensible heat flux, and latent heat flux have similar, and in some instances better, performance than long-standing parameterizations from Monin–Obukhov similarity theory (MOST). In this work, we expanded upon new Rib parameterizations and developed parameterizations of turbulence statistics, i.e., standard deviations in the 30-min u (horizontal), υ (meridional), and w (vertical) wind components (i.e., σu, συ, and σw, respectively), which allowed us to derive Rib-based parameterizations of turbulent kinetic energy (e), and standard deviations in the 30-min temperature and moisture measurements (σθ and σq, respectively). We used datasets from three 10-m micrometeorological towers installed during the Land Atmosphere Feedback Experiment (LAFE) conducted in Oklahoma from 1 to 31 August 2017 and evaluated the new parameterizations by comparing them against parameterizations from MOST. We used the LAFE datasets and fully independent datasets obtained from two micrometeorological towers installed in Alabama between February 2016 and April 2017 to evaluate the performance of the parameterizations. Based on the slope of the relationship between the observed and parameterized turbulence statistics (mb) and the coefficient of correlation (r), we found that the Rib relationships generally performed better than MOST at parameterizing συ, σw, σθ, and σq, and the Rib relationships performed better at low wind speeds than at high wind speeds. These results, coupled with recent developments of Rib parameterizations for surface-layer momentum, heat, and moisture fluxes, provide further evidence to consider using Rib-based parameterizations in weather forecasting models. Significance Statement Deficiencies in Monin–Obukhov similarity theory (MOST) are well known, yet MOST forms the basis in weather forecasting models for describing heat, moisture, and momentum transfer between the land surface and atmosphere. We expanded upon previous work suggesting a MOST alternative called the bulk-Richardson approach. We used data collected from meteorological towers installed in Oklahoma and compared the bulk-Richardson approach with MOST. We evaluated these two approaches using data from meteorological towers installed in Oklahoma and Alabama and found that, overall, the bulk-Richardson approach performed better than MOST in determining the 30-min variability in temperature, moisture, and wind. This result provides additional motivation to use a bulk-Richardson approach in weather forecasting models because doing so will likely yield improved forecasts.

Measurements of three flux towers operated during the land atmosphere feedback experiment (LAFE) are used to investigate relationships between surface fluxes and variables of the land–atmosphere system. We study these relations by means of two machine learning (ML) techniques: multilayer perceptrons (MLP) and extreme gradient boosting (XGB). We compare their flux derivation performance with Monin–Obukhov similarity theory (MOST) and a similarity relationship using the bulk Richardson number (BRN). The ML approaches outperform MOST and BRN. Best agreement with the observations is achieved for the friction velocity. For the sensible heat flux and even more so for the latent heat flux, MOST and BRN deviate from the observations while MLP and XGB yield more accurate predictions. Using MOST and BRN for latent heat flux, the root mean square errors (RMSE) are 107 Wm$$^{-2}$$ - 2 and 121 Wm$$^{-2}$$ - 2 , respectively, as well as the intercepts of the regression lines are $$\approx 110$$ ≈ 110 Wm$$^{-2}$$ - 2 . For the ML methods, the RMSEs reduce to 31 Wm$$^{-2}$$ - 2 for MLP and 33 Wm$$^{-2}$$ - 2 for XGB as well as the intercepts to just 4 Wm$$^{-2}$$ - 2 for MLP and $$-1$$ - 1 Wm$$^{-2}$$ - 2 for XGB with slopes of the regression lines close to 1, respectively. These results indicate significant deficiencies of MOST and BRN, particularly for the derivation of the latent heat flux. In fact, in contrast to the established theories, feature importance weighting demonstrates that the ML methods base their improved derivations on net radiation, the incoming and outgoing shortwave radiations, the air temperature gradient, and the available water contents, but not on the water vapor gradient. The results imply that further studies of surface fluxes and other turbulent variables with ML techniques provide great promise for deriving advanced flux parameterizations and their implementation in land–atmosphere system models.

Despite many observational studies on the atmospheric boundary layer (ABL) depth zi variability across various time scales (e.g., diurnal, seasonal, annual, and decadal), zi variability before, during, and after frontal passages over land, or simply zi variability as a function of weather patterns, has remained relatively unexplored. In this study, we provide an empirical framework using 5 years (2014–18) of daytime rawinsonde observations and surface analyses over 18 central and southeastern U.S. sites to report zi variability across frontal boundaries. By providing systematic observations of front-relative contrasts in zi (i.e., zi differences between warm and cold sectors, ) and boundary layer moisture (i.e., ABL-q) regimes in summer and winter, we propose a new paradigm to study zi changes across cold-frontal boundaries. For most cases, we found deeper zi over the warm sector than the cold sector in both summer and winter, although with significant site-to-site variability in Δzi. Additionally, our results show a positive ΔqABL (i.e., frontal contrasts in ABL-q) in summer and winter, supporting what is typically observed in midlatitude cyclones. We found that a front-relative ΔqABL of 1 g kg−1 often yielded at least a 100-m Δzi across the frontal boundary in both summer and winter. This work provides a synoptic-scale basis for zi variability and establishes a foundation for model verification to examine the impact of airmass exchange associated with advection on zi. This work will advance our understanding of ABL processes in synoptic environments and help unravel sources of front-relative zi variability. Significance Statement The atmospheric boundary layer (ABL) is the lowermost part of the atmosphere adjacent to Earth’s surface. The irregular motion of air inside the ABL plays an essential role in relocating air near the surface to the free troposphere. Meteorologists use ABL depth in weather forecast models to determine the atmosphere’s ability to dilute or enrich tracers within the ABL. However, knowledge about the changes in ABL depth during stormy conditions remains incomplete. Here, we investigate how the ABL depth varies before and after cold-frontal passages. We found that ABL depths were much deeper before the cold-frontal passages than after. This knowledge will help us develop new approaches to consider how storms modify the ABL in weather forecast models.

A series of meteorological measurements with a small Uncrewed Aircraft System (sUAS) was collected at Oliver Springs Airport, Tennessee. The sUAS provides a unique observing system capable of obtaining vertical profiles of meteorological data within the lowest few hundred meters of the boundary layer. The measurements benefit simulated plume predictions by providing more accurate meteorological data to a dispersion model. The sUAS profiles can be used directly to drive HYSPLIT dispersion simulations. When using sUAS data covering a small domain near a release and meteorological model fields covering a larger domain, simulated pollutants may be artificially increased or decreased near the domain boundary due to inconsistencies in the wind fields between the two meteorological inputs. Numerical experiments using the Weather Research and Forecasting (WRF) model with observational nudging reveal that incorporating sUAS data improves simulated wind fields and can significantly affect mixing characteristics of the boundary layer, especially during the morning transition period of the planetary boundary layer. We conducted HYSPLIT dispersion simulations for hypothetical releases for three case study periods using WRF meteorological fields with and without assimilating sUAS measurements. The comparison of dispersion results on 15 and 16 December 2021 shows that using sUAS observational nudging is more significant under weak synoptic conditions than strong influences from regional weather. Very different dispersion results were introduced by the meteorological fields used. The observational nudging produced not just a sUAS-nudged wind flow but also adjusted meteorological fields that further impacted the mixing calculation in HYSPLIT.

The ability of high-resolution mesoscale models to simulate near-surface and sub-surface meteorological processes is critical for representing land-atmosphere feedback processes. The High Resolution Rapid Refresh (HRRR) model is a 3-km numerical weather prediction model that has been used operationally since 2014. In this study, we evaluated the HRRR over the contiguous U.S. from 1 January 2021 through 31 December 2021. We compared the 01-, 03-, 06-, 12-, 18-, 24-, 30-, and 48-hour forecasts against observations of air and surface temperature, shortwave radiation, and soil temperature and moisture from the 114 stations of the U.S. Climate Reference Network (USCRN) and evaluated the HRRR’s performance for different geographic regions and land cover types. We found that the HRRR well-simulated air and surface temperatures, but underestimated soil temperatures when temperatures were subfreezing. The HRRR had the largest overestimates in shortwave radiation under cloudy skies, and there was a positive relationship between the shortwave radiation mean bias error (MBE) and air temperature MBE that was stronger in summer than winter. Additionally, the HRRR underestimated soil moisture when the values exceeded about 0.2 m3 m−3, but overestimated soil moisture when measurements were below this value. Consequently, the HRRR exhibited a positive soil moisture MBE over the drier areas of the Western U.S. and negative MBE over the Eastern U.S. Although caution is needed when applying conclusions regarding HRRR’s biases to locations with subgrid-scale land cover variations, general knowledge of HRRR’s biases will help guide improvements to land surface models used in high-resolution weather forecasting models.

Monin–Obukhov similarity theory (MOST) has long been used to represent surface–atmosphere exchange in numerical weather prediction (NWP) models. However, recent work has shown that bulk Richardson (Rib) parameterizations, rather than traditional MOST formulations, better represent near-surface wind, temperature, and moisture gradients. So far, this work has only been applied to unstable atmospheric regimes. In this study, we extended Rib parameterizations to stable regimes and developed parameterizations for the friction velocity (u*), sensible heat flux (H), and latent heat flux (E) using datasets from the Land-Atmosphere Feedback Experiment (LAFE). We tested our new Rib parameterizations using datasets from the Verification of the Origins of Rotation in Tornadoes Experiment-Southeast (VORTEX-SE) and compared the new Rib parameterizations with traditional MOST parameterizations and MOST parameterizations obtained using the LAFE datasets. We found that fitting coefficients in the MOST parameterizations developed from LAFE datasets differed from the fitting coefficients in classical MOST parameterizations which we attributed to the land surface heterogeneity present in the LAFE domain. Regardless, the new Rib parameterizations performed just as well as, and in some instances better than, the classical MOST parameterizations and the MOST parameterizations developed from the LAFE datasets. The improvement was most evident for H, particularly for H under unstable conditions, which was based on a better 1:1 relationship between the parameterized and observed values. These findings provide motivation to transition away from MOST and to implement bulk Richardson parameterizations into NWP models to represent surface–atmosphere exchange.

DOI: 10.37421/Virol Curr Res.2020.4.114 is not valid yet [https://doi.org/10.37421/Virol%20Curr%20Res.2020.4.114]. Copyright © 2022 The Authors. Outbreaks of COVID-19 in passengers and crew in ships at sea continue to pose a problem for conventional epidemiology. In one instance the crew of an Argentinian fishing trawler, who were quarantined and tested negative before sailing, contracted the disease after 35 days at sea. In another instance a livestock ship had crew that was isolated and confined becoming sick with presumed COVID-19 whilst sailing in mid-ocean.

Rawinsonde observations have long been used to estimate the atmospheric boundary layer depth (BLD), which is an important parameter for monitoring air quality, dispersion studies, weather forecast models, and inversion systems for estimating regional surface–atmosphere fluxes of tracers. Although many approaches exist for deriving the BLDs from rawinsonde observations, the bulk Richardson approach has been found to be most appropriate. However, the impact of errors in the measured thermodynamic and kinematic fields on the estimated BLDs remains unexplored. We argue that quantifying BLD error (δBLD) estimates is equally as important as the BLDs themselves. Here we quantified δBLD by applying the bulk Richardson method to 35 years of rawinsonde data obtained from three stations in the United States: Sterling, Virginia; Amarillo, Texas; and Salt Lake City, Utah. Results revealed similar features in terms of their respective errors. A −2°C bias in temperature yielded a mean δBLD ranging from −15 to 200 m. A +2°C bias in temperature yielded a mean δBLD ranging from −214 to +18 m. For a −5% relative humidity bias, the mean δBLD ranged from −302 to +7 m. For a +5% relative humidity bias, the mean δBLD ranged from +2 to +249 m. Differences of ±2 m s−1 in the winds yielded BLD errors of ~±300 m. The δBLD increased as a function of BLD when introducing errors to the thermodynamic fields and decreased as a function of BLD when introducing errors to the kinematic fields. These findings expand upon previous work evaluating rawinsonde-derived δBLD by quantifying δBLD arising from rawinsonde-derived thermodynamic and kinematic measurements. Knowledge of δBLD is critical in, for example, intercomparison studies where rawinsonde-derived BLDs are used as references.

Small uncrewed aircraft systems (sUxS) are now being routinely used not only for sampling atmospheric boundary layer (ABL) processes and land-atmosphere interactions but also have significant potential to improve weather forecasting at National Weather Service (NWS) Weather Forecast Offices (WFOs). In the present study, we used observations obtained from a Meteomatics Meteodrone SSE sUxS flown on 31 days between 20 August and 10 December 2020 near Oliver Springs, Tennessee, located 35 km northwest of Knoxville, Tennessee. We flew the sUxS up to 700 m above ground level, starting around sunrise and continuing every half hour until 3.5–4.0 hours past sunrise under synoptically quiescent, fair weather conditions. These datasets were provided in real time to the local NWS WFO in Morristown, Tennessee and used by forecasters there to assist with short-term operational forecasting needs. The sUxS profiles also provided finescale details on the early-morning transition over complex terrain and how this evolution varied during the late summer to winter period, which can be used to support the initialization of numerical weather prediction models.

Copyright © The Athors 2022. As the SARS-CoV-2 pandemic is nearing its eventual end we focus on what we believe are two key omissions from the mainstream scientific literature and which have significant implications for how mankind manages the next global pandemic. We therefore review data, observations, analyses and conclusions from our series of papers published through 2020 and 2021 on its likely cometary origin and global spread. We also revisit our long held understanding of the superior effectiveness of intra-nasal vaccines against respiratory tract pathogens that involve induction of dimeric secretory IgA antibodies. While these two oversights seem disparate, together they provide us with new insights into our collective awareness of how we might view and address the next global pandemic. We begin with our hypothesis of the likely cometary origin of the SARS-CoV-2 virus via a bolide strike in the stratosphere on the night of October 11 2019 on the 40o N line over Jilin in NE China. Further global spread most likely occurred via prevailing wind systems transporting both the pristine cometary virus followed by continuing strikes from the same primary source as well as prior human-passaged virus transmitted by person to person spread and through contaminated dust in global wind systems. We also include a discussion of our prior work on data relating to vaccine protective efficacy. Finally we review the totality of evidence concerning the likely origin and global spread of the predominant variants of the virus ‘Omicron’ (+Delta mix?) from early to mid-December 2021 and extending into the first week January 2022. We describe the striking data showing the large numbers of infectious cases per day and outline the scale of what appears to be a global pandemic phenomenon, the causes of which are unclear and not completely understood. Firstly, these essentially simultaneous and sudden global-wide epidemic COVID-19 out breaks, appear to be largely correlated with events external to the Earth, probably causing globally correlated precipitation events. They appear related broadly to “Space Weather” events that render the Earth vulnerable to cosmic pandemic pathogen attack particularly during times of the minima of the Sunspot Solar Cycle which we are now currently passing through. Secondly, we argue that these sudden global-wide epidemic outbreaks of COVID-19 are specifically largely influenced by global wind transport and deposition mechanisms, the physics of which we need to further explore and comprehend. We conclude on an optimistic note for mankind. Given our prior knowledge of the effectiveness against respiratory tract pathogens of mucosal immunity involving induction of dimeric secretory IgA antibodies, we consider that the recently published intra-nasal vaccine data from laboratories based at the University of California, San Francisco and, independently at Yale University. These latter studies hold out great promise for the future development of both pan-specific and specific immunity against future pandemics caused by suddenly emergent respiratory pathogens, whether viral, bacterial or fungal.

Since drought and excessive rainfall can have significant socioeconomic impacts, it is important to have accurate high-resolution gridded datasets that can help improve analysis and forecasting of these conditions. One such widely used dataset is the Parameter-Elevation Regressions on Independent Slopes Model (PRISM). PRISM uses a digital elevation model (DEM) to obtain gridded elevation analyses and then uses a regression analysis along with approximately 15 000 surface precipitation measurements to produce a 4-km resolution daily precipitation product over the conterminous United States. The U.S. Climate Reference Network (USCRN) consists of 114 stations that take highly accurate meteorological measurements across all regions of the United States. A comparison between the USCRN and PRISM was performed using data from 2006 to 2018. There were good comparisons between the two datasets across nearly all seasons and regions; most mean daily differences were