Michael Ferrari is the vice president and director of applied research at Weather Trends International. His primary research interests lie at the interface of climatology, environmental modeling/analysis, and the subsequent development of commercial applications that can benefit from this research. Michael is a frequent speaker at both scientific and commodity conferences around the world, where his talks focus on the confluence of weather, climate and their relationship to society, with a particular focus on weather and agricultural production and natural hazards. In addition, he builds data-driven applications for the physical commodity and risk management sectors utilizing global weather, satellite-derived, economic and sensor network data. Michael holds a PhD in Geophysical Fluid Dynamics from Rutgers, and a BS in Economics from West Chester. Follow him on Twitter: @aeroculus.
Many satellites capture everything from ocean temperatures, to land reflectance at the surface of the Earth, to global chlorophyll production. Here's a look at how that data can reveal the condition of a country's crops.
A forecast -- weather or otherwise -- is always a blend of art and science. Nothing is foolproof. But in this post, Michael Ferrari shows how simple analysis can reveal a connection between a weather event (La Niña) and commodity production (milk).
Identifying extreme weather patterns can minimize impact when that weather arrives. But to improve long-range forecasts, we'll need to create environmental sensor networks out of phones, satellites and other technology.
Michael Ferrari is the vice president and director of applied research at Weather Trends International. His primary research interests lie at the interface of climatology, environmental modeling/analysis, and the subsequent development of commercial applications that can benefit from this research. Michael is a frequent speaker at both scientific and commodity conferences around the world, where his talks focus on the confluence of weather, climate and their relationship to society, with a particular focus on weather and agricultural production and natural hazards. In addition, he builds data-driven applications for the physical commodity and risk management sectors utilizing global weather, satellite-derived, economic and sensor network data. Michael holds a PhD in Geophysical Fluid Dynamics from Rutgers, and a BS in Economics from West Chester. Follow him on Twitter: @aeroculus.
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