Results 71 to 80 of about 5,527 (257)
Daily time series of zonal‐mean zonal wind (m·s−1) at 10 hPa and 60° N from 1950 to 2021 from the ERA5 reanalysis. This shows huge variability in some seasons and very little in others. We provide evidence that high‐level observations, radiosonde and satellite, are more important during the extended winter season with its very large variability ...
Bruce Ingleby, Inna Polichtchouk
wiley +1 more source
An opportunity index to anticipate when subseasonal predictions are useful
Simultaneously active subseasonal windows of forecast opportunity can be combined into a single opportunity index, which can be used operationally to anticipate enhanced or reduced subseasonal prediction skill. For predictions of temperature anomalies in Switzerland during summer—a region and season with particularly low predictability—skill can nearly
Dominik Büeler +4 more
wiley +1 more source
Diagnosing the 11‐year solar cycle's influence on the East Atlantic pattern
A previously unidentified solar‐cycle response in the East Atlantic pattern is found in late winter at lag +3 years with larger amplitude than the NAO response. A statistically significant NAO response to the solar cycle is seen in late winter at lag 0 years.
Stergios Misios +4 more
wiley +1 more source
We document for the first time how the assimilation of CS2SMOS observations improves the model representation of Arctic sea‐ice thickness (SIT) and its variability: biases are reduced (top row), while excessive variability in the Beaufort Sea and lack of variability in the ice pack are both corrected (bottom row).
Jiping Xie +3 more
wiley +1 more source
The North Atlantic Oscillation in the Atlantic-European SLP*
An analysis of the signature of the North Atlantic Oscillation (NAO) in the Atlantic-European sea level pressure (SLP) is presented for observed (German Weather Service) and ECMWF T21 model data. The former time series consists of 1881–1984 January to December fields and the latter of 42 monthly fields from 3 permanent January simulations.
openaire +3 more sources
Forecast‐Error Diagnostics in Neural Weather Models
Deep learning weather prediction models enable efficient forecast‐error diagnostics through auto‐differentiation and low computational cost. We apply grid‐point relaxation and gradient‐based error sensitivity to identify key forecast‐error sources. Results show that medium‐range forecasts in the midlatitudes benefit most from relaxing the stratosphere ...
Uroš Perkan +2 more
wiley +1 more source
Epistemic and aleatoric uncertainty quantification in weather and climate models
Aleatoric and epistemic uncertainties over time on weather and climate time‐scales, estimated through ensembles that sample aleatoric and epistemic uncertainty using Bayesian neural networks for parameterisations in the Lorenz 1996 model. The spread shows the 16th and 84th percentiles.
Laura A. Mansfield +1 more
wiley +1 more source
This study reveals that the combined effects of the North Atlantic Oscillation (NAO) and anomalous Indo‐Pacific Walker circulation on the excitation of the wave train along the wintertime subtropical jet strongly depend on their phase combination. Their impacts interfere constructively or destructively over South Asia, leading to notable differences in
Yuki Asazuma +2 more
wiley +1 more source
The wintertime North Atlantic Oscillation (NAO) and East Atlantic Pattern (EA) are the two leading modes of North Atlantic pressure variability and have a substantial impact on winter weather in Europe.
L. H. Baker +3 more
doaj +1 more source
Ensemble reliability and the signal‐to‐noise paradox in ECMWF subseasonal forecasts
We derive a general expression for the ratio of predictable components (RPC) in terms of correlation, spread–error ratio, and total variance ratio. Physical constraints on the admissible solutions (i.e., real‐valued and non‐negative variances) provide a mechanism to identify statistically paradoxical sample combinations of reliability and correlation ...
Christopher D. Roberts, Frederic Vitart
wiley +1 more source

