Well-established wildfire smoke models such as HRRR-Smoke and BlueSky predict smoke concentrations one to three days in advance. They present these forecasts as gridded maps of modeled PM₂.₅ concentrations, hour by hour. This focus on concentration suits state air-quality agencies concerned with maximum human-health impacts. It is less useful to someone who needs to know where the plume will be over the next few hours as it meanders — whether it will cross a particular highway this afternoon, or where to place an air quality monitor tonight to capture the point of greatest impact.
Large man-made bodies of water at mining sites such as tailings ponds are designed for water to evaporate. Exposure to the greatest tool for evaporation – the sun – means water level changes require water balance tracking due to precipitation and evaporation.
When working with geospatial data on Google Earth (for example, from our AEREarth tool), you might need the elevations for some coordinates. If it’s only a handful, they’re easily found with Google Earth’s interface. But let’s say you want elevations for dozens, hundreds, or even thousands of locations.
We recently wrote a post about a handy Excel workbook you can use to query elevations for a set of coordinates in the Google Maps Application Programming Interface (API). Here, we’ll go over how to do the same thing using Python.