Advances in Atmospheric Sciences最新文献

Owing to the persisting hype in pushing toward global carbon neutrality, the study scope of atmospheric science is rapidly expanding. Among numerous trending topics, energy meteorology has been attracting the most attention hitherto. One essential skill of solar energy meteorologists is solar power curve modeling, which seeks to map irradiance and auxiliary weather variables to solar power, by statistical and/or physical means. In this regard, this tutorial review aims to deliver a complete overview of those fundamental scientific and engineering principles pertaining to the solar power curve. Solar power curves can be modeled in two primary ways, one of regression and the other of model chain. Both classes of modeling approaches, alongside their hybridization and probabilistic extensions, which allow accuracy improvement and uncertainty quantification, are scrutinized and contrasted thoroughly in this review.

Despite the maturity of ensemble numerical weather prediction (NWP), the resulting forecasts are still, more often than not, under-dispersed. As such, forecast calibration tools have become popular. Among those tools, quantile regression (QR) is highly competitive in terms of both flexibility and predictive performance. Nevertheless, a long-standing problem of QR is quantile crossing, which greatly limits the interpretability of QR-calibrated forecasts. On this point, this study proposes a non-crossing quantile regression neural network (NCQRNN), for calibrating ensemble NWP forecasts into a set of reliable quantile forecasts without crossing. The overarching design principle of NCQRNN is to add on top of the conventional QRNN structure another hidden layer, which imposes a non-decreasing mapping between the combined output from nodes of the last hidden layer to the nodes of the output layer, through a triangular weight matrix with positive entries. The empirical part of the work considers a solar irradiance case study, in which four years of ensemble irradiance forecasts at seven locations, issued by the European Centre for Medium-Range Weather Forecasts, are calibrated via NCQRNN, as well as via an eclectic mix of benchmarking models, ranging from the naïve climatology to the state-of-the-art deep-learning and other non-crossing models. Formal and stringent forecast verification suggests that the forecasts post-processed via NCQRNN attain the maximum sharpness subject to calibration, amongst all competitors. Furthermore, the proposed conception to resolve quantile crossing is remarkably simple yet general, and thus has broad applicability as it can be integrated with many shallow- and deep-learning-based neural networks.

Abstract

El Niño–Southern Oscillation (ENSO) exhibits a distinctive phase-locking characteristic, first expressed during its onset in boreal spring, developing during summer and autumn, reaching its peak towards winter, and decaying over the next spring. Several studies have demonstrated that this feature arises as a result of seasonal variation in the growth rate of ENSO as expressed by the sea surface temperature (SST). The bias towards simulating the phase locking of ENSO by many state-of-the-art climate models is also attributed to the unrealistic depiction of the growth rate. In this study, the seasonal variation of SST growth rate in the Niño-3.4 region (5°S–5°N, 120°–170°W) is estimated in detail based on the mixed layer heat budget equation and recharge oscillator model during 1981–2020. It is suggested that the consideration of a variable mixed layer depth is essential to its diagnostic process. The estimated growth rate has a remarkable seasonal cycle with minimum rates occurring in spring and maximum rates evident in autumn. More specifically, the growth rate derived from the meridional advection (surface heat flux) is positive (negative) throughout the year. Vertical diffusion generally makes a negative contribution to the evolution of growth rate and the magnitude of vertical entrainment represents the smallest contributor. Analysis indicates that the zonal advective feedback is regulated by the meridional immigration of the intertropical convergence zone, which approaches its southernmost extent in February and progresses to its northernmost location in September, and dominates the seasonal variation of the SST growth rate.

This paper celebrates Professor Yongqi GAO’s significant achievement in the field of interdisciplinary studies within the context of his final research project Arctic Climate Predictions: Pathways to Resilient Sustainable Societies - ARCPATH (https://www.svs.is/en/projects/finished-projects/arcpath). The disciplines represented in the project are related to climatology, anthropology, marine biology, economics, and the broad spectrum of social-ecological studies. Team members were drawn from the Nordic countries, Russia, China, the United States, and Canada. The project was transdisciplinary as well as interdisciplinary as it included collaboration with local knowledge holders. ARCPATH made significant contributions to Arctic research through an improved understanding of the mechanisms that drive climate variability in the Arctic. In tandem with this research, a combination of historical investigations and social, economic, and marine biological fieldwork was carried out for the project study areas of Iceland, Greenland, Norway, and the surrounding seas, with a focus on the joint use of ocean and sea-ice data as well as social-ecological drivers. ARCPATH was able to provide an improved framework for predicting the near-term variation of Arctic climate on spatial scales relevant to society, as well as evaluating possible related changes in socioeconomic realms. In summary, through the integration of information from several different disciplines and research approaches, ARCPATH served to create new and valuable knowledge on crucial issues, thus providing new pathways to action for Arctic communities.

Abstract

Since the Beijing 2022 Winter Olympics was the first Winter Olympics in history held in continental winter monsoon climate conditions across complex terrain areas, there is a deficiency of relevant research, operational techniques, and experience. This made providing meteorological services for this event particularly challenging. The China Meteorological Administration (CMA) Earth System Modeling and Prediction Centre, achieved breakthroughs in research on short- and medium-term deterministic and ensemble numerical predictions. Several key technologies crucial for precise winter weather services during the Winter Olympics were developed. A comprehensive framework, known as the Operational System for High-Precision Weather Forecasting for the Winter Olympics, was established. Some of these advancements represent the highest level of capabilities currently available in China. The meteorological service provided to the Beijing 2022 Games also exceeded previous Winter Olympic Games in both variety and quality. This included achievements such as the “100-meter level, minute level” downscaled spatiotemporal resolution and forecasts spanning 1 to 15 days. Around 30 new technologies and over 60 kinds of products that align with the requirements of the Winter Olympics Organizing Committee were developed, and many of these techniques have since been integrated into the CMA’s operational national forecasting systems.

These accomplishments were facilitated by a dedicated weather forecasting and research initiative, in conjunction with the preexisting real-time operational forecasting systems of the CMA. This program represents one of the five subprograms of the WMO’s high-impact weather forecasting demonstration project (SMART2022), and is also a part of their Regional Association (RA) II Research Development Project (Hangzhou RDP). Therefore, the research accomplishments and meteorological service experiences from this program will be carried forward into forthcoming high-impact weather forecasting activities. This article provides an overview and assessment of this program and the operational national forecasting systems.

The East Asian trough (EAT) profoundly influences the East Asian spring climate. In this study, the relationship of the EATs among the three spring months is investigated. Correlation analysis shows that the variation in March EAT is closely related to that of April EAT. Extended empirical orthogonal function (EEOF) analysis also confirms the co-variation of the March and April EATs. The positive/negative EEOF1 features the persistent strengthened/weakened EAT from March to April. Further investigation indicates that the variations in EEOF1 are related to a dipole sea surface temperature (SST) pattern over the North Atlantic and the SST anomaly over the tropical Indian Ocean. The dipole SST pattern over the North Atlantic, with one center east of Newfoundland Island and another east of Bermuda, could trigger a Rossby wave train to influence the EAT in March–April. The SST anomaly over the tropical Indian Ocean can change the Walker circulation and influence the atmospheric circulation over the tropical western Pacific, subsequently impacting the southern part of the EAT in March–April. Besides the SST factors, the Northeast Asian snow cover could change the regional thermal conditions and lead to persistent EAT anomalies from March to April. These three impact factors are generally independent of each other, jointly explaining large variations in the EAT EEOF1. Moreover, the signals of the three factors could be traced back to February, consequently providing a potential prediction source for the EAT variation in March and April.

Shallow convection plays an important role in transporting heat and moisture from the near-surface to higher altitudes, yet its parameterization in numerical models remains a great challenge, partly due to the lack of high-resolution observations. This study describes a large eddy simulation (LES) dataset for four shallow convection cases that differ primarily in inversion strength, which can be used as a surrogate for real data. To reduce the uncertainty in LES modeling, three different large eddy models were used, including SAM (System for Atmospheric Modeling), WRF (Weather Research and Forecasting model), and UCLA-LES.

Results show that the different models generally exhibit similar behavior for each shallow convection case, despite some differences in the details of the convective structure. In addition to grid-averaged fields, conditionally sampled variables, such as in-cloud moisture and vertical velocity, are also provided, which are indispensable for calculation of the entrainment/detrainment rate. Considering the essentiality of the entraining/detraining process in the parameterization of cumulus convection, the dataset presented in this study is potentially useful for validation and improvement of the parameterization of shallow convection.

This paper investigates the homogeneity of United States aircraft reconnaissance data and the impact of these data on the homogeneity of the tropical cyclone (TC) best track data for the seasons 1949–1987 generated by the China Meteorological Administration (CMA). The evaluation of the reconnaissance data shows that the minimum central sea level pressure (MCP) data are relatively homogeneous, whereas the maximum sustained wind (MSW) data show both overestimations and spurious abrupt changes. Statistical comparisons suggest that both the reconnaissance MCP and MSW were well incorporated into the CMA TC best track dataset. Although no spurious abrupt changes were evident in the reconnaissance-related best track MCP data, two spurious changepoints were identified in the remainder of the best-track MCP data. Furthermore, the influence of the reconnaissance MSWs seems to extend to the best track MSWs unrelated to reconnaissance, which might reflect the optimistic confidence in making higher estimates due to the overestimated extreme wind “observations”. In addition, the overestimation of either the reconnaissance MSWs or the best track MSWs was greater during the early decades compared to later decades, which reflects the important influence of reconnaissance data on the CMA TC best track dataset. The wind–pressure relationship (WPR) used in the CMA TC best track dataset is also evaluated and is found to overestimate the MSW, which may lead to inhomogeneity within the dataset between the aircraft reconnaissance era and the satellite era.

Abstract

Recent studies on tropical cyclone (TC) intensity change indicate that the development of a vertically aligned TC circulation is a key feature of its rapid intensification (RI), however, understanding how vortex alignment occurs remains a challenging topic in TC intensity change research. Based on the simulation outputs of North Atlantic Hurricane Wilma (2005) and western North Pacific Typhoon Rammasun (2014), vortex track oscillations at different vertical levels and their associated role in vortex alignment are examined to improve our understanding of the vortex alignment during RI of TCs with initial hurricane intensity. It is found that vortex tracks at different vertical levels oscillate consistently in speed and direction during the RI of the two simulated TCs. While the consistent track oscillation reduces the oscillation tilt during RI, the reduction of vortex tilt results mainly from the mean track before RI. It is also found that the vortex tilt is primarily due to the mean vortex track before and after RI. The track oscillations are closely associated with wavenumber-1 vortex Rossby waves that are dominant wavenumber-1 circulations in the TC inner-core region. This study suggests that the dynamics of the wavenumber-1 vortex Rossby waves play an important role in the regulation of the physical processes associated with the track oscillation and vertical alignment of TCs.

This study investigates the evolution of an extreme anomalous anticyclone (AA) event over Northeast Asia, which was one of the dominant circulation systems responsible for the catastrophic extreme precipitation event in July 2021 in Henan, and further explores the significant impact of this AA on surface temperatures beneath it. The results indicate that this AA event over Northeast Asia was unprecedented in terms of intensity and duration. The AA was very persistent and extremely strong for 10 consecutive days from 13 to 22 July 2021. This long-lived and unprecedented AA led to the persistence of warmer surface temperatures beyond the temporal span of the pronounced 500-hPa anticyclonic signature as the surface air temperatures over land in Northeast Asia remained extremely warm through 29 July 2021. Moreover, the sea surface temperatures in the Sea of Japan/East Sea were extremely high for 30 consecutive days from 13 July to 11 August 2021, persisting well after the weakening or departure of this AA. These results emphasize the extreme nature of this AA over Northeast Asia in July 2021 and its role in multiple extreme climate events, even over remote regions. Furthermore, possible reasons for this long-lasting AA are explored, and it is suggested to be a byproduct of a teleconnection pattern over extratropical Eurasia during the first half of its life cycle, and of the Pacific–Japan teleconnection pattern during the latter half.