Como incorporar o microclima na previsão de recursos eólicos?

Over the years I've had a couple of hobbies that required the ability to make detailed observations and predictions about localized movements of air. Starting in the mid 1970s and continuing until 2001 I build, flew and for 20 years competed with Radio Controlled Sailplanes. Then from about 2005 until the present I have been involved to varying degrees in small bore rifle accuracy competitions. Flying radio controlled sailplanes is not hard, but unless you can find a thermal (column of heated rising air) to keep the model aloft, your flight will be short lived. The good news is that mother-nature provide all of the indicators.

Strategies include deploying high-resolution weather models, utilizing Lidar and Sodar instruments for wind profile measurements, employing machine learning algorithms trained on microclimate data, integrating information from local weather stations, and applying numerical weather prediction models that account for small-scale atmospheric features. Data assimilation techniques, historical wind data analysis, and an understanding of seasonal and diurnal variations further refine predictions. Collaboration with meteorological experts, community engagement, and leveraging local wind farm data contribute to a comprehensive approach.

While flying RC sailplanes, I was introduced to a book titles "A River of Air". Unfortunately I can't find my copy so what follows is my recollection of the author's writing. In one chapter the author likened the atmosphere to babbling brook with eddy currents and swirling turbulence around, over and behind rocks to help the reader visualize how air moves around buildings, hills, trees, etc. In another chapter he used a pot of water on a stove to illustrate how thermals form and how the atmosphere responds to their formation. Both of these illustrations are helpful in understanding microclimate.

In my opinion, to understand localized wind, you need you understand thermals. Using the a pot of water on a stove as an analog, as water close to the bottom of the pot is warmed by the stove tiny bubbles form and grow until they become buoyant enough to break away from the bottom. As the bubbles break free the bottom and float upwards, water from all sides flows in to fill the void. This is very close to how thermals are created. The sun heats the ground unevenly causing localized hot spots that warm the air above unevenly. Eventually a bubble of air warmer than the surrounding air (i.e., a thermal) detaches from the surface and rises. As the thermal rises, cool air rushes in from all sides to fill the void causing localized wind.

To my knowledge, microclimate modelling for wind resource forecasting largely employs advanced techniques such as Computational Fluid Dynamics (CFD) to simulate wind flow around complex terrains and Lidar technology to measure detailed wind characteristics. More accurate modelling needs incorporation of remote sensing data from satellites and drones to account for land cover and vegetation impact, while leveraging upon machine learning algorithms for analyzing historical weather patterns. Additionally, models could utilize weather radars to monitor real-time atmospheric conditions. Integrating these methodologies creates precise microclimate models, optimizing wind resource forecasts for optimal renewable energy production.

In shooting competitions, we used wind flags to indicate the local direction and strength of the wind. In soaring we rely more on mother-nature's indicators; wind direction and strength hundreds of yards away was determined by looking at foliage. Thermals passing by caused rapid and significant wind direction changes. Noticeable changes in temperature and humidity would indicate that a thermal was forming were you were standing. Small birds normally feed on swarms of bugs close tot he ground, so seeing small birds hundreds of feet up could indicate a thermal broken free from the ground. All of these and more observations were used to develop the accurate understanding of the local microclimate necessary to keep the sailplane in a thermal.

Through careful observation, an "understanding" of the local microclimate was developed that would allow you to accurately predict when the next thermal was expected, which direction it would travel. Launching the sailplanes became a matter of waiting for the next cycle as predicted by a wind shift or temperature change. The best competitors were very adept at making these predictions. Are accurate microclimate predictions possible? Definitely! How do you predict microclimates? Through the use of sensors that mimic human senses; sight, feel, and even smell. Then use this information as inputs to a modelling system that can detect the patterns and predict the next cycle.