Evaluating Medium-Range Forecast Performance of Regional-Scale Circulation Patterns
نویسندگان
چکیده
Abstract Accurate subseasonal-to-seasonal (S2S) weather forecasts are crucial to making important decisions in many sectors. However, significant gaps exist between the needs of society and what forecasters can produce, especially at weekly longer lead times. We hypothesize that by clustering atmospheric states into a number predefined categories, noise be reduced and, consequently, medium-range improved. Self-organizing map (SOM)-based was used on daily mean sea level pressure (MSLP) data from North American Regional Reanalysis categorize synoptic-scale circulation for eastern America 1979 2016 28 discrete patterns. Then, using two goodness-of-fit metrics, relative skill four different forecasting methods over 90-day time studied: 1) pattern (CP) forecast, 2) raw forecast output Climate Forecast System (CFS) operated National Centers Environmental Prediction (NCEP), 3) simple climatology 4) persistence forecast. As expected, both CP CFS generally decreased rapidly first day, coming parity with after 10–12 days when correlation, 7–10 root-mean-square error (RMSE). Most importantly, this study found most skillful method 8–11-day RMSE. On spatial basis, decreases latitudinally north south. This thus demonstrates potential utility categorical or pattern–based 1–2-week
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ژورنال
عنوان ژورنال: Weather and Forecasting
سال: 2023
ISSN: ['0882-8156', '1520-0434']
DOI: https://doi.org/10.1175/waf-d-22-0149.1