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Observing Weather and Climate from the Ground Up: A Nationwide Network of Networks
mittee believes that low power wireless communication is an important and underused pathway for the mitigation of competing exposure requirements. A single surface station may economically achieve optimal exposures in a local area for winds, precipitation, radiation, and properties of soil, road and water surfaces, etc., as long as data rates and distances are compatible with low power wireless communications.
Geography and Demography
The Committee repeatedly returned to concerns about urban, coastal, and mountainous regions as these affect the mix of surface-based mesoscale observing systems. Mountains, coastlines, and cities have greater importance than their surface areas would imply. Ironically, these are consistently undersampled relative to their needs. All three create their own weather, which is often poorly resolved in synoptic Numerical Weather Prediction models. Considering the danger of traveling in the winter or fighting forest fires in the summer, the need for observations in the mountains goes beyond that for weather forecasting alone. Coastlines and cities, both of which have high concentrations of people, also take on special importance, particularly when one considers the need for observations to response to a release of toxic substances, treating the roads in respond to an ice storm or blizzard, or evacuate people in advance of hurricane landfall.
The effect of these considerations on priorities is somewhat uncertain. Cities have special needs at the mesoscale owing to population density and high exposure to a very wide range of human activity over very short distances. However, coastlines and mountains harbor considerable meteorological and environmental complexity often not experienced in other regions. While sections of coastline are often densely populated, mountains are not, suggesting fewer observations in mountainous regions, which is consistent with past practice. However mountains are where surface observations are by far the least representative of the surrounding area, harboring large gradients of atmospheric properties; they are often suspected of being the major source of error in numerical prediction for regions downstream, such as cities and coastlines. There is no easy way out of this conundrum except to rely on testbeds, observing system experiments, and observing system simulation experiment for guidance in mesoscale observation design; and to gain additional skill as computational capacity increases along with our ability to better resolve and understand atmospheric structure.