In this study, the stability dependence of turbulent Prandtl number (Pr_t) is quantified via a novel and simple analytical approach. Based on the variance and flux budget equations, a hybrid length scale formulation is first proposed and its functional relationships to well-known length scales are established. Next, the ratios of these length scales are utilized to derive an explicit relationship between Pr_t and gradient Richardson number. In addition, theoretical predictions are made for several key turbulence variables (e.g., dissipation rates, normalized fluxes). The results from our proposed approach are compared against other competing formulations as well as published datasets. Overall, the agreement between the different approaches is rather good despite their different theoretical foundations and assumptions.

The high-fidelity modelling of optical turbulence is critical to the design and operation of a new class of emerging highly sophisticated astronomical telescopes and adaptive optics instrumentation. In this study, we perform retrospective simulations of optical turbulence over the Hawaiian islands using a mesoscale model. The simulated results are validated against thermosonde data. We focus on turbulence in the free atmosphere, above the atmospheric boundary layer. The free atmosphere is particularly important for adaptive optics performance and for sky coverage calculations and hence has significant impact on performance optimization and scheduling of observations. We demonstrate that a vertical grid spacing of 100 m or finer is needed to faithfully capture the intrinsic variabilities of observed clear air turbulence. This is a particularly timely study because the next generation of extremely large telescopes are currently under construction and their associated suite of instruments are in the design phase. Knowledge of the expected accuracy of optical turbulence simulations and real-time forecasts will enable the design teams to (i) test and develop instrument designs and (ii) formulate operational procedure.

A hybrid neural network model, comprising of a convolutional neural network and a multilayer perceptron network, has been developed for day-ahead forecasting of regional scale wind power production. This model requires operational weather forecasts as input and also has the capability to ingest data from ensemble forecasts. Even though the training of the model requires significant computational cost, the actual forecasting can be done within a few minutes on any recent personal computer. The proposed model has demonstrated noteworthy performance at a recent international forecasting competition.

We develop and study two approaches for the prediction of optical refraction effects in the lower atmosphere. Refraction can cause apparent displacement or distortion of targets when viewed by imaging systems or produce steering when propagating laser beams. Low-cost, time-lapse camera systems were deployed at two locations in New Mexico to measure image displacements of mountain ridge targets due to atmospheric refraction as a function of time. Measurements for selected days were compared with image displacement predictions provided by (1) a ray-tracing evaluation of numerical weather prediction data and (2) a machine learning algorithm with measured meteorological values as inputs. The model approaches are described and the target displacement prediction results for both were found to be consistent with the field imagery in overall amplitude and phase. However, short time variations in the experimental results were not captured by the predictions where sampling limitations and uncaptured localized events were factors.