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Dynamical numerical weather prediction has remarkably improved over the last decades. Yet postprocessing techniques are needed to calibrate forecasts which are based on statistical and machine learning techniques. With recent advances in the derivation of year-round, large-scale atmospheric circulations, or weather regimes, the question arises of whether this information can be valuable within forecast postprocessing methods. This paper investigates this by proposing a bias correction scheme to integrate the atmospheric circulation state derived from empirical orthogonal functions, referred to as weather patterns, for deterministic short-term, near-surface temperature forecasts based on least absolute shrinkage and selection operator (LASSO) regression. We propose a computational study which first evaluates different weather pattern definitions (spatial domain) to improve temperature forecasts in Europe. As a bias could be associated with the weather pattern at the model initialization time or at the realization time of the forecast, both variants are tested in this study. We show that forecasted weather patterns with the identical spatial domain as the forecast show best skill reaching mean-square-error skill improvements of up to 3% (day ahead) or 1% (week ahead). Only considering land surface improvements in Europe, improvements of 4%–6% for day-ahead forecasts and 1%–5% for week-ahead forecasts are observable. We believe that this study not only introduces a simple yet effective tool to reduce bias in temperature forecasts but also contributes to the active discussion of how valuable weather patterns are and how to use them within forecast calibration techniques. [ABSTRACT FROM AUTHOR]

This paper analyzes the relationships between the circulation regimes of the 500-hPa height (z500) and 250-hPa zonal winds (u250) in the Pacific–North America region during boreal winter, and the 45-day Northern Hemisphere oscillation in z500. The regimes were calculated using a k-means clustering applied to the leading 12 principal components of the combined z500–u250 anomaly fields. We divided the oscillation into eight arbitrary phases. The oscillation phase z500 composite maps are spatially well correlated with regime z500 composites: phases 1–2 are best correlated with the Arctic Low, phases 3–5 are best correlated with the Pacific Trough, phase 6 is best correlated with the Arctic High, and phases 7–8 are best correlated with the Alaskan Ridge. We found that these correlations are generally consistent with the regimes that tend to occur during the individual oscillation phases: the Arctic Low occurs above significance in phases 1–2, the Pacific Trough occurs above significance in phase 3, and Alaskan Ridge occurs above significance in phases 7–8. Therefore, the oscillation has a preferred order with respect to the regimes. The regime transitions indicate a pattern that moves through the Pacific Wavetrain, a regime that appears for k = 5 as a mean state. Transitions out of this regime into different regimes are preferred in different phases of the oscillation. These results imply a possible enhancement to regime prediction using the low-frequency oscillations in combination with regimes. Significance Statement: Subseasonal prediction, weather forecasting in the 2–4-week range, is important for many parts of society, e.g., water managers, emergency response units, and farmers. However, current prediction skill in this time range is low. This paper performs an initial analysis of a possible method to increase weather statistic prediction skill beyond 10 days in the winter for the Pacific–North America region. This is done by combining two ways of looking at large, long-lasting patterns of pressure systems in the atmosphere, which are associated with various weather statistics like precipitation extremes and storminess. The results indicate this method holds potential skill for enhancing subseasonal prediction. Further investigation might yield forecasting improvements in this important time range. [ABSTRACT FROM AUTHOR]

This study examined the dominant structure and characteristics of synoptic-scale (2–8-day periods) waves over northern Eurasia during 40 summer seasons (June–August, 1979–2018). The synoptic-scale wave patterns are isolated using an extended empirical orthogonal function (EEOF) analysis on the 300-hPa geopotential height anomalies, and a composite based on atmospheric circulation fields and gridded precipitation product. The wave patterns are classified into two types from two pairs of EEOF modes. These two different wave types are defined as the polar frontal (PF) mode and Arctic frontal (AF) mode, respectively. The PF-mode waves are initiated in the North Atlantic sector to the west of the British Isles. They propagate eastward across Siberia into the North Pacific, and produce precipitation mainly over the Eurasian polar frontal zone. The AF-mode wave train arcs along the climatological Arctic frontal zone (AFZ). The AF-mode waves originate near the Scandinavian Peninsula. Their eastward passage brings precipitation along the AFZ. The development of the synoptic-scale waves is reflected by unique background conditions over northern Eurasia. The lower-tropospheric baroclinicity in southern Siberia and central Asia favored the baroclinic growth of the PF-mode waves. The AF-mode waves are trapped in the well-organized baroclinic zone along the north coast of the Eurasian continent. The baroclinic zone is coupled with a band of large meridional gradient of potential vorticity in the upper troposphere, suggesting that this band acts as a waveguide for the AF-mode waves. Significance Statement: This study examines the synoptic-scale waves in the 2–8-day range of time scales over northern Eurasia during summer. The synoptic-scale waves are categorized into two distinct types at different latitude bands by the EEOF analysis on the 300-hPa z anomalies. They are defined as polar frontal (PF) mode and Arctic frontal (AF) mode. Then the EEOF-based composite analysis is conducted to detect the large-scale circulation anomalies associated with the propagation of different types of synoptic-scale waves. The structure and characteristics are examined. The roles of the mean background conditions in the development and propagation of the respective types are discussed. The behavior of these wave disturbances as rain-producing weather systems is also examined. [ABSTRACT FROM AUTHOR]