Developing countries is the area of the globe where land-based, in situ monitoring of weather and climate is at its scarcest, but at the same time has arguably the most potential to benefit society.
Rainfall and temperature can have high spatial variability due to the strong feedback that can exist between the land and atmosphere. Temperature can change rapidly in space due to land-cover heterogeneity and changing altitude over complex mountainous terrain. This means that a weather station tens of kilometers away may measure conditions that have little relevance to your location, making it hard to make informed local decisions.
The goal of this challenge is to create a low-cost, low-power, reliable, accurate, easy-to-install, and maintain weather station, with no mechanical moving parts for measuring all weather conditions with a focus on rain and wind, based on based on ultra-low power machine learning at the edge, that can be deployed locally.