Michael Ferrari

Michael Ferrari is the Director of Climate Modeling and Senior Scientist at CSC, where he is working with the NASA Goddard Space Flight Center on developing commercial tools and applications from their data and models. He also serves on the American Meteorological Society Board of Societal Impacts of Weather and Climate. For the past five years, Michael served as the vice president and director of applied research at Weather Trends International. His primary research interests lie at the interface of climate science, 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 emphasis on weather and agriculture/energy considerations, extreme events, risk quantification and natural hazards. In addition, he builds data-driven tools 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 and Evolutionary Biology from Rutgers.

New tools and techniques for applying climate data

A workshop shows early signs of climate scientists and data scientists coming together.

Climate cycles, machine learning and improved models were all part of the discussions at the first New York Academy of Sciences Workshop on Climate Informatics.

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Interest in renewable energy could benefit data services

The need for temperature, wind, and solar analytics will likely increase.

The increase of large-scale infrastructure investments in the alternative energy sector will likely be accompanied by demand for data-driven services that can optimize efficiency of the related operational costs.

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The ecology of risk

Financial stability can benefit from approaches grounded in the natural sciences.

Large-scale events that have disrupted supply chains underscore the importance of viewing the world through a spatial lens.

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Industrial ecology and big data

How can massive environmental datastreams create new markets?

Because companies are tracking their inputs and byproducts carefully, there has been an exponential increase in the amout of efficiency/environmental data available for primary stakeholders and investors.

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La Nina and global commodities

The connection between the La Nina phenomenon and food prices.

In the weather and climate community, 2010 will be remembered as a year where the strong La Nina pattern exerted a significant influence on global agricultural production.

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An ensemble approach to weather forecasting

A potential India-U.S. partnership could lead to better forecasting through collaboration.

A potential new partnership between U.S. agencies and the Indian Meteorological Department could could open up an "ensemble approach" to forecasting that encourages collaboration and breaks down proprietary barriers.

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Growing new data streams

There's considerable promise in data sources targeting the global agricultural community.

High-quality and high-margin products will come to market that have their roots in agricultural data acquisition and repackaging.

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Seeing green from space

Seeing green from space

How satellites and sensors can assess the health of crops.

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.

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Weather data and the supply chain

Weather data and the supply chain

The predictive power of weather info, as illustrated by cows and La Niña.

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).

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Sensor networks and the future of forecasting

Sensor networks and the future of forecasting

Data and low-cost sensor networks can spot extreme weather before it hits.

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.

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