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Methods that combine in-situ measurements, statistical methods, and model simulations with remotely sensed data provide a pathway for improving the robustness of surface flux products. For this research, we acquired eddy-covariance fluxes along a five-tower transect in a snowy montane forest over three consecutive winters to characterize near-field variability of the subcanopy environment. The novel experiment design enabled discriminating near-field evaposublimation sources. Boosted regression trees reveal that the predictive capacity of state variables change with season and storm cycle frequency. High rates of post-storm evaposublimation of canopy-intercepted snow at this site were constrained by short residence time of snow in the canopy due to throughfall and melt. The snow melt-out date for open vs. closed canopy conditions depended on total snowfall accumulation. Compared with low accumulation years, the snow melt-out date under the dense canopy during the high accumulation winter was later than for the open area, as shading became more important later in the season. The field experiments informed an environmental response function that was used to integrate ERA5-Land latent heat flux data at 20-km nominal resolution with USFS Tree Canopy Fraction data at 30-m resolution and showed near-field flux variability that was not resolved in model simulations. Previous evaposublimation results from experiments in alpine and subalpine environments do not directly translate to a montane forest due to differences in process rates. 

Abstract Daytime atmospheric boundary layer (ABL) dynamics—including potential temperature budgets, water vapour budgets, and entrainment rates—are presented from in situ flight data taken on six afternoons near Fresno in the San Joaquin Valley (SJV) of California during July/August 2016. The flights took place as a part of the California Baseline Ozone Transport Study aimed at investigating transport pathways of air entering the Central Valley from offshore and mixing down to the surface. Midday entrainment velocity estimates ranged from 0.8 to 5.4 cm s −1 and were derived from a combination of continuously determined ABL heights during each flight and model-derived subsidence rates, which averaged -2.0 cm s −1 in the flight region. A strong correlation was found between entrainment velocity (normalized by the convective velocity scale) and an inverse bulk ABL Richardson number, suggesting that wind shear at the ABL top plays a significant role in driving entrainment. Similarly, we found a strong correlation between the entrainment efficiency (the ratio of entrainment to surface heat fluxes with an average of 0.23 ± 0.15) and the wind speed at the ABL top. We explore the synoptic conditions that generate higher winds near the ABL top and propose that warm anomalies in the southern Sierra Nevada mountains promote increased entrainment. Additionally, a method is outlined to estimate turbulence kinetic energy, convective velocity scale ( w * ), and the surface sensible heat flux in the ABL from a slow, airborne wind measurement system using mixed-layer similarity theory. 

Abstract Integrated quadrant analysis is a novel technique to identify and to characterize the trajectory and strength of turbulent coherent structures in the atmospheric surface layer. By integrating the three-dimensional velocity field characterized by traditional quadrant analysis with respect to time, the trajectory history of individual coherent structures can be preserved with Eulerian turbulence measurements. We develop a method to identify the ejection phase of coherent structures based on turbulence kinetic energy (TKE). Identifying coherent structures within a time series using TKE performs better than identifying them with the streamwise and vertical velocity components because some coherent structures are dominated by the cross-stream velocity component as they pass the sensor. By combining this identification method with the integrated quadrant analysis, one can animate or plot the trajectory of individual coherent structures from high-frequency velocity measurements. This procedure links a coherent ejection with the subsequent sweep and quiescent period in time to visualize and quantify the strength and the duration of a coherent structure. We develop and verify the method of integrated quadrant analysis with data from two field studies: the Eclipse Boundary Layer Experiment (EBLE) in Corvallis, Oregon in August 2017 (grass field) and the Vertical Cherry Array Experiment (VACE) in Linden, California in November 2019 (cherry orchard). The combined TKE identification method and integrated quadrant analysis are promising additions to conditional sampling techniques and coherent structure characterization because the identify coherent structures and couple the sweep and ejection components in space. In an orchard (VACE), integrated quadrant analysis verifies each coherent structure is dominated by a sweep. Conversely, above the roughness sublayer (EBLE), each coherent structure is dominated by an ejection. 

Highlights Machine learning can incorporate a variety of data from low-cost sensors and estimate actual ET by comparison with short-term, higher-cost measurements. On-farm weather monitoring can be leveraged to estimate site-specific crop-water requirements. Expanding spatial coverage of weather and actual ET through on-farm monitoring will facilitate localization and leverage publicly available weather data to guide irrigation decisions and improve irrigation water management. Abstract . One of the basic challenges to adopting science-based irrigation scheduling is providing reliable, site-specific estimates of actual crop water demand. While agro-meteorology networks cover most agricultural production areas in the U.S., widely spaced stations represent regionally specific, rather than site-specific, conditions. A variety of low to moderate cost commercial weather stations are available but do not provide directly useful information, such as actual evapotranspiration (ETa), or the ability to incorporate additional sensors. We demonstrate that machine learning methods can provide real-time, site-specific information about ETa and crop water demand using on-farm sensors and public weather information. Two years of field experiments were conducted at four irrigated field sites with crops including snap beans, alfalfa, and pasture. On-farm data were compared to publicly available data originating at nearby agro-meteorology network stations. The machine learning procedure can robustly estimate ETa using data from a few basic sensors, but the resulting estimate is sensitive to the range of conditions that are used as training data. The results demonstrate that machine learning can be used with affordable sensors and publicly available data to improve local estimates of crop water demand when high-quality measurements can be co-located for short periods of time. Supplementary sensors can also be integrated into a tailored monitoring plan to estimate crop stress and other operational considerations. Keywords: Agro-meteorology, Irrigation requirement, Machine learning, Site-specific Irrigation.