Developing High-Quality Field Program Sounding Datasets

Paul E. Ciesielski Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado

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Patrick T. Haertel Department of Geology and Geophysics, Yale University, New Haven, Connecticut

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Richard H. Johnson Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado

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Junhong Wang National Center for Atmospheric Research, Boulder, Colorado

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Scot M. Loehrer National Center for Atmospheric Research, Boulder, Colorado

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Enormous resources of time, effort, and finances are expended in collecting field program rawinsonde (sonde) datasets. Correcting the data and performing quality control (QC) in a timely fashion after the field phase of an experiment are important for facilitating scientific research while interest is still high and funding is available. However, a variety of issues (different sonde types, ground station software, data formats, quality control issues, sonde errors, etc.) often makes working with these datasets difficult and time consuming.

Our experience working with sounding data for several field programs has led to the design of a general procedure for creating user-friendly, bias-reduced, QCed sonde datasets. This paper describes the steps in this procedure, gives examples for the various processing stages, and provides access to software tools to aide in this process.

Enormous resources of time, effort, and finances are expended in collecting field program rawinsonde (sonde) datasets. Correcting the data and performing quality control (QC) in a timely fashion after the field phase of an experiment are important for facilitating scientific research while interest is still high and funding is available. However, a variety of issues (different sonde types, ground station software, data formats, quality control issues, sonde errors, etc.) often makes working with these datasets difficult and time consuming.

Our experience working with sounding data for several field programs has led to the design of a general procedure for creating user-friendly, bias-reduced, QCed sonde datasets. This paper describes the steps in this procedure, gives examples for the various processing stages, and provides access to software tools to aide in this process.

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