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8 Concluding Thoughts
Pages 168-175

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From page 168...
... In this chapter we address some human dimensions associated with the selection and provision of mesoscale information and we enumerate the highest observing system priorities associated with the critical gaps identified elsewhere in the report. PRESERVING AND ENHANCING THE DIVERSITY OF INVESTMENT A major implementation challenge is to retain the energy, enthusiasm, and diverse investments that have led to our current condition, while also introducing an appropriate degree of centralization for the purposes of coordination and integration to maximize the national benefit.
From page 169...
... This is especially important in the case of costly three-dimensional observations, which enable short-range numerical weather prediction, the nowcasting of high impact weather, and chemical weather predictions. THE EVOLVING HUMAN DIMENSION The societal uses of mesoscale information are evolving rapidly, and these are increasingly interactive with the technical enterprise of weather prediction and climate monitoring.
From page 170...
... The Committee has surveyed needs for mesoscale observations in six application areas: weather and climate, energy, public health and safety, transportation, water resources and food production, and research. Commensurate with the Committee's charge, our surveys have emphasized regional and urban short-term applications, paying special attention to the atmospheric boundary layer within the continental U.S.
From page 171...
... CONCLUDING THOUGHTS 171 TABLE 8.1  Application sector gaps for various parameters Weather Public Food and Health and Transpor- and Sector/ Variable Climate Energy Safety tation Water Surface wind speed and X X X X X direction Surface temperature X X X X X Surface relative humidity X X X X X Surface pressure X X X Visibility X X X Precipitation rate X X X X Snow cover and depth X X X Precipitation amount X X X X X Precipitation type X X X X sea-surface temperature X Lightning X X X planetary boundary layer� X X X X height Soil-moisture and soil- X X X X X temperature profiles Direct and diffuse X X X X radiation Vertical wind profiles X X X X Vertical temperature X X X X profiles Vertical humidity profiles X X X X Hydrometeor mixing ratios X Reservoir temperature/ X X water temperature Stream flow X X X Ag climate variables X X Icing near surface X X Air quality -- surface X X X Air quality -- aloft X X Cloud cover/ sky view X X X Surface turbulence X X X parameters continued
From page 172...
... • Icing near the surface • Vertical profiles of temperature • Surface turbulence parameters If one wants to know where multiple, cross-cutting needs can be met through an investment in new or improved observing systems, Table 8.1 provides fairly specific guidelines. Observations drive all environmental monitoring and prediction systems.
From page 173...
... Assets required to profile the lower troposphere above the near-surface layer (first 10) are too limited in what they measure, too sparsely or unevenly distributed, sometimes too coarse in vertical resolution, sometimes limited to regional areal coverage, and clearly do not qualify as a mesoscale network of national dimensions.
From page 174...
... 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.
From page 175...
... Local strengths are heralded by the proliferation of surface meteorological stations, which are often tailored to satisfy the monitoring needs of a particular application. The national gaps result from weaknesses in the federal government's observational infrastructure as they pertain to mesoscale numerical weather prediction and chemical weather prediction.


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