New Technological Advancements for Improved Weather Analytic Services

United States is experiencing an increasing number of weather events that have caused billions of dollar worth of damage. A total of 15 weather and climate disasters with losses amounting to a total of $46.0 billion in damages were reported in 2016 in US alone. These statistics are from NOAA’s National Centers for Environmental Information (NCEI) that tracks US weather and climate events.

The main reasons for the rising number of events are attributed to the increase in population and dwelling of the population in areas with vulnerable infrastructure that become hazardous during extreme events. In US the geography is such that the regions are more prone to extreme high/low temperature, winter storms, inland floods, western drought and wildfire seasons. These events will become more frequent as population concentration and regional changes continue to evolve.

In the event of a natural disaster, the process of learning from these experiences and beginning the recovery process has always been a challenging task for all governments. It is not surprising that the local and state governments are at the frontline of recovery implementation. They are facing this hurdle even with the present-day technologies not fully coping with the damage assessment and recovery process undertaken. So, what is missing in the present-day damage assessment and recovery process? What is required is a combined effort of government experiences coupled with analysis from the technical side that can drive improved disaster-recovery outcomes. Opportunities for improvement lies not only with the federal agencies in the US, but also a host of NGOs and corporate agencies providing technical support for predicting such events and aiding the emergency restoration activities.

Early warning functions play an important role in alerting the federal government and the distressed community in the event of a potential breakdown. This serves as a first step of preventive action for averting further complex emergencies. Disaster preparedness is the key involving sound analysis of disaster risks and establishing good connectivity with early warning systems. Improved weather predictions are vital in preparing for extreme weather events, thus saving lives and minimizing damages to infrastructure.

Weather forecasting prior to an extreme weather event is critical to the preparedness and capacity planning for various sectors, companies and governmental entities. Advance warnings are only effective with better understanding of the weather parameters prior to extreme events such as hurricane winds, tornados, hail storms, and the like. Analyzing weather variables (like temperature, wind speed, cloud cover, etc.) are a decisive factor in forecast verification that shed light on a particular verification issue.

Macrosoft’s Mine-Weather is designed to provide analysts with a flexible analytical platform for verifying and comparing the accuracy of weather forecasts under a variety of different weather conditions. Mine-Weather combines several weather forecast sources with observation data to zero-in on forecast accuracy specifically during the storm periods. In addition to National Weather Service observational sites, actual station data from private providers are also included for analysis. Mine-Weather system is now in use at a large electric utility in the North-Eastern US. Extreme weather events are expected to increase in the coming days and systems like Mine-Weather can help make a difference in reducing the losses.

Please feel free to contact us for further in-depth discussions on Mine-Weather and how utilities can take advantage of its functions.

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About the Author

Dr. Ronald Mueller

Ron is CEO and Founder of Macrosoft, Inc.. Ron heads up all company strategic activities, and directs day-to-day work of the Leadership Team at Macrosoft. Ron is also Macrosoft’s Chief Scientist, defining and structuring Macrosoft’s path forward on new technologies and products, such as Cloud; Big Data; and AI. Ron has a Ph.D. in Theoretical Physics from New York University, and worked in physics for over a decade at Yale University, The Fusion Energy Institute in Princeton, NJ, and at Argonne National Laboratory. Ron also worked at Bell Laboratories in Murray Hill, NJ., where he managed a group on Big Data, including very early work on neural networks. Ron has a career-long passion in ultra-large-scale data processing and analysis including: predictive analytics; data mining, machine learning and neural networks.

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