8.2 Toward Agile Weather Forecast Product Development

Thursday, 10 January 2019: 8:45 AM
North 221AB (Phoenix Convention Center - West and North Buildings)
Nadia Smith, Science and Technology Corporation, Columbia, MD; and K. D. White

We came together as a team to explore why forecasters were not fully exploiting the NOAA-Unique Combined Atmospheric Processing System (NUCAPS) suite of products. Our team consisted of a product developer schooled in meeting static requirements, and a meteorologist routinely running into operational data gaps. Together we worked to identify the blockages that can often occur in the research to operations process. What emerged was a renewed appreciation for the importance of effective communication. The lack of a common, shared vocabulary that communicates functionality with clarity, and at times, a misunderstanding of the others’ needs and capabilities, slowed understanding and created misconceptions. A one-way development flow saw products discharged into the operational stream which failed to fill the data gaps and did not adequately address operational needs or proposed applications. We now use and promote an agile approach to development where forecasters work in a closer, collaborative environment, sometimes face-to-face, with product developers in evaluating, redefining and improving operational weather products. The interaction between operational scientists and research scientists might best be summed up by the dialogue, “Here is what I need” and “This is what is possible”, respectively. It is these needs and possibilities that we now attempt to properly address instead. We will share our personal experiences in overcoming communication blockages and the lessons we have learned in improving product design through agile development that fosters understanding. This not only fills the operational data gaps more efficiently but improves the interface between forecasters and the general public. As a result, decisions about severe weather can be communicated with improved clarity because forecasters have more in-depth knowledge about the products they use.
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