A grand challenge from the wind energy industry is to provide reliable forecasts on mountain winds several hours in advance at microscale (∼100 m) resolution. This requires better microscale wind-energy physics included in forecasting tools, for which field observations are imperative. While mesoscale (∼1 km) measurements abound, microscale processes are not monitored in practice nor do plentiful measurements exist at this scale. After a decade of preparation, a group of European and U.S. collaborators conducted a field campaign during 1 May–15 June 2017 in Vale Cobrão in central Portugal to delve into microscale processes in complex terrain. This valley is nestled within a parallel double ridge near the town of Perdigão with dominant wind climatology normal to the ridges, offering a nominally simple yet natural setting for fundamental studies. The dense instrument ensemble deployed covered a ∼4 km × 4 km swath horizontally and ∼10 km vertically, with measurement resolutions of tens of meters and seconds. Meteorological data were collected continuously, capturing multiscale flow interactions from synoptic to microscales, diurnal variability, thermal circulation, turbine wake and acoustics, waves, and turbulence. Particularly noteworthy are the extensiveness of the instrument array, space–time scales covered, use of leading-edge multiple-lidar technology alongside conventional tower and remote sensors, fruitful cross-Atlantic partnership, and adaptive management of the campaign. Preliminary data analysis uncovered interesting new phenomena. All data are being archived for public use.

Emissions from Kīlauea volcano, known locally as “vog” for volcanic smog, pose significant environmental and health risks to the Hawaiian community. The Vog Measurement and Prediction (VMAP) project was conceived to help mitigate the negative impacts of Kīlauea’s emissions. To date, the VMAP project has achieved the following milestones: i) created a custom application of the Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT, hereafter Vog model) to produce statewide forecasts of the concentration and dispersion of sulfur dioxide (SO2) and sulfate aerosol from Kīlauea volcano; ii) developed an ultraviolet (UV) spectrometer array to provide near-real-time volcanic gas emission rate measurements for use as input into the Vog model; iii) developed and deployed a stationary array of ambient SO2 and meteorological sensors to record the spatial characteristics of Kīlauea’s gas plume in high temporal and spatial resolution for model verification; and iv) developed web-based tools to facilitate the dissemination of observations and model forecasts to provide guidance for safety officials and the public, and to raise awareness of the potential hazards of volcanic emissions to respiratory health, agriculture, and general aviation. Wind fields and thermodynamic data from the Weather Research and Forecasting (WRF) Model provide input to the Vog model, with a statewide grid spacing of 3 km and a 1-km grid covering the island of Hawaii. Validation of the Vog model forecasts is accomplished with reference to data from Hawaii State Department of Health ground-based air quality monitors. VMAP results show that this approach can provide useful guidance for the people of Hawaii.

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<div class="page" title="Page 1"><div class="section"><div class="layoutArea"><div class="column"><p><span>Deterministic model forecasts do not convey to the end users the forecast uncertainty the models possess as a result of physics parameterizations, simplifications in model representation of physical processes, and errors in initial conditions. This lack of understanding leads to a level of uncertainty in the forecasted value when only a single deterministic model forecast is available. Increasing computational power and parallel software architecture allows multiple simulations to be carried out simultaneously that yield useful measures of model uncertainty that can be derived from ensemble model results. The Hybrid Single Particle Lagrangian Integration Trajectory and Dispersion model has the ability to generate ensemble forecasts. A meteorological ensemble was formed to create probabilistic forecast products and an ensemble mean forecast for volcanic emissions from the Kilauea volcano that impacts the state of Hawai’i. The probabilistic forecast products show uncertainty in pollutant concentrations that are especially useful for decision-making regarding public health. Initial comparison of the ensemble mean forecasts with observations and a single model forecast show improvements in event timing for both sulfur dioxide and sulfate aerosol forecasts. </span></p></div></div></div></div><p> </p>