Each model forecast tells a story about the weather events to unfold. As a forecaster, you employ diagnostics to understand and interpret that story, in order to modify it, blend it with other stories, and generate your own forecast. This lesson will help you sift through the abundance of model data so you can understand and interpret the model’s story. Other lessons cover evaluating the model’s story against observations and against your conceptual models of the evolving situation, blending the stories, and adjusting the forecast to add value over an objective forecast. The diagnostic approaches in this lesson can be used in any of the first three steps in the forecast process. Since the model’s story may provide insight into the forecast problem of the day, diagnostics may identify the key processes resulting in the model’s forecast, and your understanding of the model forecast can help you assess its plausibility. This lesson is broken into three parts. Each is self-contained. Feel free to take them separately as you have time. Part 1 addresses the different insights you can get from different ways of visualizing the model data Part 2 addresses extracting and distilling the large-scale signature from complex and noisy-looking forecast fields using quasigeostrophic diagnostics Part 3 addresses extracting model signals using non-quasigeostrophic approaches, which are more suitable than quasigeostrophic approaches for mesoscale features. The lesson has one quiz, thus it is best to attempt the quiz after you have reviewed all parts of the lesson.
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