What Is the Predictability Limit of Midlatitude Weather?
By Fuqing Zhang, Y. Q. Sun, L. Magnusson, Roberto Buizza, Shian‐Jiann Lin, Jan‐Huey Chen, Kerry A. Emanuel. Journal of the Atmospheric Sciences, 2019
211 citations recorded by the source corpus.
Understanding the predictability limit of day-to-day weather phenomena such as midlatitude winter storms and summer monsoonal rainstorms is crucial to numerical weather prediction (NWP). This predictability limit is studied using unprecedented high-resolution global models with ensemble experiments of the European Centre for Medium-Range Weather Forecasts (ECMWF; 9-km operational model) and identical-twin experiments of the U.S. Next-Generation Global Prediction System (NGGPS; 3 km). Results suggest that the predictability limit for midlatitude weather may indeed exist and is intrinsic to the underlying dynamical system and instabilities even if the forecast model and the initial conditions are nearly perfect. Currently, a skillful forecast lead time of midlatitude instantaneous weather is around 10 days, which serves as the practical predictability limit. Reducing the current-day initial-condition uncertainty by an order of magnitude extends the deterministic forecast lead times of day-to-day weather by up to 5 days, with much less scope for improving prediction of small-scale phenomena like thunderstorms. Achieving this additional predictability limit can have enormous socioeconomic benefits but requires coordinated efforts by the entire community to design better numerical weather models, to improve observations, and to make better use of observations with advanced data assimilation and computing techniques.
Subfields: Atmospheric Science, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/dfc1a6aa4424fcc7af189ef1f0e0a5af87014593
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Citations in the corpus
- WeatherBench 2: A Benchmark for the Next Generation of Data‐Driven Global Weather Models (2023, background): “Around half of the remaining 5 days of potential skill stem from model improvements, the other half from better initial conditions (Zhang et al., 2019; Selz et al., 2022).”
- Can Machines Learn to Predict Weather? Using Deep Learning to Predict Gridded 500‐hPa Geopotential Height From Historical Weather Data (2019, background): “For synoptic-388 scale mid-latitude weather systems, such error saturation and loss of predictability oc-389 curs after roughly two weeks (Selz, 2019; Zhang et al., 2019).”
- ClimaX: A foundation model for weather and climate (2023, unknown): “These factors limit their use in many scenarios, especially in simulating atmospheric variables quickly at very short time scales (e.g., a few hours) or accurately at long time scales (e.g., beyond 5-7 days) (Zhang et al., 2019).”