The convective nature of Stratocumulus topped boundary layers (STBL) involves the motion of updrafts and downdrafts, driven by surface fluxes and radiative cooling, respectively. The balance between shear and buoyant forcings at the surface can determine the organization of updrafts between cellular and roll structures. We investigate the effect of varying shear at the surface and top of the STBL using Large Eddy Simulations, taking DYCOMS II RF01 as a base case. We focus on spatial identification of the following features: coherent updrafts and downdrafts, and observe how they are affected by varying shear. Stronger surface shear organizes the updrafts in rolls, causes less well‐mixed thermodynamic profiles, and decreases cloud fraction and liquid water path (LWP). Stronger top shear also decreases cloud fraction and LWP more than surface shear, by thinning the cloud from the top. Features with stronger top than surface shear are associated with a net downward momentum transport and show early signs of decoupling. Classifying updrafts and downdrafts based on their vertical span and horizontal size confirms the dominance of tall objects spanning the whole STBL. Tall objects occupy 30% of the volume in the STBL, while short ones occupy less than 1%. For updraft and downdraft fluxes, these tall objects explain 65% of the vertical velocity variance and 83% of the buoyancy flux, on average. Stronger top shear also weakens the contribution of downdrafts to the turbulent fluxes and tilts the otherwise vertical development of updrafts.
Electric vehicle (EV) penetration has been increasing in the modern electricity grid and has been complemented by the growth of EV charging infrastructure. This paper addresses the gap in the literature on the EV effects of total electricity costs in commercial buildings by incorporating V0G, V1G, and V2B charging. The electricity costs are minimized in 14 commercial buildings with real load profiles, demand and energy charges. The scientific contributions of this study are the incorporation of demand charges, quantification of EV and smart charging electricity costs and benefits using several representative long-term datasets, and the derivation of approximate equations that simplify the estimation of EV economic impacts. Our analysis is primarily based on an idealized uniform EV commuter fleet case study. The V1G and V2B charging electricity costs as a function of the number of EVs initially diverge with increasing charging demand and then become parallel to one another with the V2B electricity costs being lower than V1G costs. A longer EV layover time leads to higher numbers of V2B charging stations that can be installed such that original (pre-EV) electricity costs are not exceeded, as compared to a shorter layover time. Sensitivity analyses based on changing the final SOC of EVs between 90% to 80% and initial SOC between 50 to 40% (thereby keeping charging energy demand constant) show that the total electricity costs are the same for V0G and V1G charging, while for V2B charging the total electricity costs decrease as final SOC decreases.
The impact of initial states and meteorological variables on stratocumulus cloud dissipation time over coastal land is investigated using a mixed-layer model. A large set of realistic initial conditions and forcing parameters are derived from radiosonde observations and numerical weather prediction model outputs, including total water mixing ratio and liquid water potential temperature profiles (within the boundary layer, across the capping inversion, and at 3 km), inversion-base height and cloud thickness, large-scale divergence, wind speed, Bowen ratio, sea surface fluxes, sky effective radiative temperature, shortwave irradiance above the cloud, and sea level pressure. We study the sensitivity of predicted dissipation time using two analyses. In the first, we simulate 195 cloudy days (all variables covary as observed in nature). We caution that simulated predictions correlate only weakly to observations of dissipation time, but the simulation approach is robust and facilitates covariability testing. In the second, a single variable is varied around an idealized reference case. While both analyses agree in that initial conditions influence dissipation time more than forcing parameters, some results with covariability differ greatly from the more traditional sensitivity analysis and with previous studies: opposing trends are observed for boundary layer total water mixing ratio and Bowen ratio, and covariability diminishes the sensitivity to cloud thickness and inversion height by a factor of 5. With covariability, the most important features extending predicted cloud lifetime are (i) initially thicker clouds, higher inversion height, and stronger temperature inversion jumps, and (ii) boundary forcings of lower sky effective radiative temperature.
Historical observations from radiosondes, buoys, and satellite images are used to generate an analog ensemble (AnEn) solar forecast. In coastal California, Stratocumulus (Sc) clouds appear most frequently during late spring and summer months. Sc clouds form at night and begin to dissipate after sunrise, limiting solar energy generation in the morning hours. The AnEn method categorizes cloudy (as either well-mixed or decoupled) and clear events at the forecast initial time and uses several meteorological variables to find the closest analogs. The AnEn forecast is tested at the NKX weather station in San Diego, CA during May to September 2014-2017. The AnEn forecast has a lower root mean square error than a numerical weather prediction model and 24-hour persistence forecasts. The error is lowest for the clear cases and largest for the cloudy decoupled cases. The AnEn forecast is able to capture Sc dissipation for the well-mixed cases in the early morning, but decoupled cases display higher variability throughout the day and are much harder to predict as a result.
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