Multifunctional mesoscale observing networks

被引:45
作者
Dabberdt, WF
Schlatter, TW
Carr, FH
Friday, EWJ
Jorgensen, D
Koch, S
Pirone, M
Ralph, FM
Sun, JZ
Welsh, P
Wilson, JW
Zou, XL
机构
[1] Vaisala Inc, Boulder, CO 80305 USA
[2] Natl Ocean & Atmospher Adm, Ocean & Atmospher Res Forecast Syst Lab, Boulder, CO USA
[3] Univ Oklahoma, Norman, OK 73019 USA
[4] Natl Ocean & Atmospher Adm, Ocean & Atmospher Res, Natl Severe Storms Lab, Boulder, CO USA
[5] Atmospher & Environm Res Inc, Lexington, MA USA
[6] Natl Ocean & Atmospher Adm, Ocean & Atmospher Res, Environm Technol Lab, Boulder, CO USA
[7] Ctr Atmospher Res, Boulder, CO USA
[8] NOAA, Natl Weather Serv, Jacksonville, FL USA
[9] Florida State Univ, Tallahassee, FL 32306 USA
关键词
D O I
10.1175/BAMS-86-7-961
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
More than 120 scientists, engineers, administrators, and users met on 8-10 December 2003 in a workshop format to discuss the needs for enhanced three-dimensional mesoscale observing networks. Improved networks are seen as being critical to advancing numerical and empirical modeling for a variety of mesoscale applications, including severe weather warnings and forecasts, hydrology, air-quality forecasting, chemical emergency response, transportation safety, energy management, and others. The participants shared a clear and common vision for the observing requirements: existing two-dimensional mesoscale measurement networks do not provide observations of the type, frequency, and density that are required to optimize mesoscale prediction and nowcasts. To be viable, mesoscale observing networks must serve multiple applications, and the public, private, and academic sectors must all actively participate in their design and implementation, as well as in the creation and delivery of value-added products. The mesoscale measurement challenge can best be met by an integrated approach that considers all elements of an end-to-end solution-identifying end users and their needs, designing an optimal mix of observations, defining the balance between static and dynamic (targeted or adaptive) sampling strategies, establishing long-term test beds, and developing effective implementation strategies. Detailed recommendations are provided pertaining to nowcasting, numerical prediction and data assimilation, test beds, and implementation strategies.
引用
收藏
页码:961 / 982
页数:22
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