SearcharxivSearch

arXiv · 1011.3564

Climatology of extratropical atmospheric wave packets in the northern hemisphere

Abstract

Planetary and synoptic scale wave-packets represent one important component of the atmospheric large-scale circulation. These dissipative structures are able to rapidly transport eddy kinetic energy, generated locally (e.g. by baroclinic conversion), downstream along the upper tropospheric flow. The transported energy, moving faster than individual weather systems, will affect the development of the next meteorological system on the leading edge of the wave packet, creating a chain of connections between systems that can be far apart in time and space, with important implications on predictability. In this work we present an automated recognition of atmospheric wave packets which allows the extraction of all the relevant properties, such as location, duration and velocity. Behind this tool lies the need to investigate atmospheric variability in its full complexity, bridging the low-frequency steady-state approach with the storm-tracks lagrangian approach. We have applied the algorithm to the daily analysis (every 12h) from 1958-2010, building an extended climatology of waves packets with different spectral properties. We show that wave packets characteristics over Northern Hemisphere exhibit a strong seasonal dependence, both in their spectral component and in their distribution and localization. The maximum activity is reached in the cold months, from autumn to spring, with a slight weakening in mid-winter and a clear minimum of activity in summer. Preferential areas of genesis are shown to be the Western and Central-Pacific and Western-Atlantic while areas of lyses are the eastern borders of Pacific and Atlantic. We envisage possible applications of this algorithm also for predictability studies and operational activities.

Explore related subjects

Keep this discovery

BibTeXRIS

Federico Grazzini, Valerio Lucarini. 2010-11-16. Climatology of extratropical atmospheric wave packets in the northern hemisphere. https://arxiv.org/abs/1011.3564

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Windowed Envelope Statistics for Time-Domain Significant Wave Height Estimation From HF Radar

Significant wave height (SWH) retrieval from high-frequency (HF) radar typically relies on a weak second-order Doppler continuum that is sensitive to noise, interference, and spectral leakage. This letter presents a Windowed Envelope Statistics Estimator (WESE) that operates directly on beam-formed time-domain voltages. A second-order term obtained from a Neumann expansion of the rough-surface field equation motivates quadratic compensation of localized radar features. WESE extracts the mean, standard deviation, or variance from overlapping windows of the in-phase, quadrature, or envelope-magnitude sequence, followed by quadratic compensation, rank ordering, least-squares regression, and causal smoothing. Evaluation used 335 synchronized hourly observations from a 13.385 MHz, 12-element WERA system at Argentia, Newfoundland and Labrador. The optimal configuration used quadrature variance, a 16-sample window, 896 retained chronological samples, and 30-h smoothing, achieving an RMSE of 0.152 m and a Pearson correlation of 0.978. This represents RMSE reductions of 32.1% and 18.7% relative to previously reported linear and second-order compensated ordered-statistics models, respectively. The results demonstrate robust time-domain SWH estimation without explicit Doppler-spectrum construction.

physics.ao-ph

KiloDA: Reconstructing kilometer-scale near-surface wind states from sparse station observations

Accurate kilometer-scale near-surface winds are important for understanding atmospheric processes over complex terrain, yet remain difficult to reconstruct from sparse and unevenly distributed observations. Here we introduce KiloDA, a diffusion framework for hourly kilometer-scale wind reconstruction from surface stations. KiloDA learns the statistical distribution and spatial structure of wind fields from historical 3-km Weather Research and Forecasting (WRF) model forecasts. At each reconstruction time, no contemporaneous WRF field is used. Instead, station observations provide the only constraints on the current atmospheric state and guide posterior sampling from the learned prior. In idealized WRF experiments, KiloDA recovers localized wind structures when only 0.24% of grid cells are observed and shows an overall advantage over conventional interpolation across terrain conditions and wind speed regimes. This capability largely transfers to real observations. In a fully withheld region, KiloDA reduces the median wind speed root mean square error (RMSE) by 19% relative to ERA5 reanalysis, using only observations outside the region, with the largest improvements over high-elevation and high-relief terrain. A random station holdout further confirms that this advantage extends across different complex-terrain locations and holdout configurations. These results show that historical model archives can provide useful structural knowledge for reconstructing kilometer-scale wind fields from sparse observations without requiring an accurate model estimate of the current atmospheric state.

physics.ao-ph