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The atmosphere exhibits variability across different time scales. Currently, in the field of atmospheric science, statistical filtering is one of the most widely used methods for extracting signals on certain time scales. However, signal extraction based on traditional statistical filters may be sensitive to missing data points, which are particularly common in meteorological data. To address this issue, this study applies a new type of temporal filters based on a one-dimensional convolution neural network (1D-CNN) and examines its performance on reducing such uncertainties. As an example, we investigate the advantages of a 1D-CNN bandpass filter in extracting quasi-biweekly-to-intraseasonal signals (10–60 days) from temperature data provided by the Hong Kong Observatory. The results show that the 1D-CNN achieves accuracies similar to a 121-point Lanczos filter. In addition, the 1D-CNN filter allows a maximum of 10 missing data points within the 60-point window length, while keeping its accuracy higher than 80% (R2 > 0.8). This indicates that the 1D-CNN model works well even when missing data points exist in the time series. This study highlights another potential for applying machine learning algorithms in atmospheric and climate research, which will be useful for future research involving incomplete time series and real-time filtering. [ABSTRACT FROM AUTHOR]

In this study the changes of tropical cyclone (TC) size from 2001 to 2021 are analyzed based on linear and quadratic curve fittings of the National Hurricane Center (NHC) / Joint Typhoon Warning Center (JTWC) best track data, based on the radius of maximum wind (RMW) and the average radius of 34-kt wind (AR34), in three oceanic basins of the North Atlantic (NATL), the Western North Pacific (WPAC) and the Eastern North Pacific (EPAC). The computations are done separately for two categories of tropical cyclones: tropical storms (TS) and hurricanes (HT). Size changes of landfalling and non-landfalling TCs are also discussed. Results show that there is a great inter-basin variability among the changes in TC sizes. Major conclusions include: 1) overall, the inner cores of TSs have become larger in all three basins, with the increasing tendencies being significant in the NATL and WAPC, while those of HTs mostly get smaller or remain similar; 2) meanwhile, comparatively large inter-basin differences are observed for the TC outer core sizes, and the sizes of landfalling TCs; 3) particularly, a significant decrease in landfalling HT outer core size is observed over the EPAC; 4) in contrast, significant increases in landfalling TS inner core size are found over the NATL and WPAC. The presented analysis results could benefit future research about TC forecasts, storm surge studies, and the cyclone climate and its changes. [ABSTRACT FROM AUTHOR]

Initialization and the spin‐up effect are critical to model performance in the four‐dimensional variational data assimilation (4DVar) system. The incremental analysis update (IAU) technique is able to combat these problems. This study introduces the IAU technique into the 4DVar framework (IAU‐4DVar) of the operational China Meteorological Administration Global Forecast System (CMA‐GFS) and evaluates the performance of the IAU‐4DVar. In this IAU‐4DVar framework, the initial increments optimally estimated are split up into equally weighted forcing terms and integrated into the tangent model and its adjoint model. Then, the optimal estimation of the analysis increment is gradually introduced into the model integration during the assimilation window. A set of two‐month assimilation and forecast experiments were established and executed in this study, and the preliminary results show that the IAU technique can reduce the spin‐up effect of the initial analysis increment from the operational 4DVar system with digital filter initialization (DFI‐4DVar) by retaining the balance in dynamics and thermodynamics. In the optimal minimization process, the IAU‐4DVar converges faster than the DFI‐4DVar with a reduction of the computational cost of up to ca 40%. In addition, the introduction of IAU‐4DVar into the CMA‐GFS model could have a positive impact on the analysis and forecast skills relative to the operational DFI‐4DVar. [ABSTRACT FROM AUTHOR]

Tropical cyclone (TC) genesis forecasting is essential for daily operational practices during the typhoon season. The updated version of the Tropical Regional Atmosphere Model for the South China Sea (CMA-TRAMS) offers forecasters reliable numerical weather prediction (NWP) products with improved configurations and fine resolution. While traditional evaluation of typhoon forecasts has focused on track and intensity, the increasing accuracy of TC genesis forecasts calls for more comprehensive evaluation methods to assess the reliability of these predictions. This study aims to evaluate the effectiveness of the CMA-TRAMS for cyclogenesis forecasts over the western North Pacific and South China Sea. Based on previous research and typhoon observation data over five years, a set of localized, objective criteria has been proposed. The analysis results indicate that the CMA-TRAMS demonstrated superiority in cyclogenesis forecasts, predicting 6 out of 22 TCs with a forecast lead time of up to 144 h. Additionally, over 80% of the total could be predicted 72 h in advance. The model also showed an average TC genesis position error of 218.3 km, comparable to the track errors of operational models according to the annual evaluation. The study also briefly investigated the forecast of Noul (2011). The forecast field of the CMA-TRAMS depicted thermal and dynamical conditions that could trigger typhoon genesis, consistent with the analysis field. The 96-hour forecast field of the CMA-TRAMS displayed a relatively organized threedimensional structure of the typhoon. These results can enhance understanding of the mechanism behind typhoon genesis, fine-tune model configurations and dynamical frameworks, and provide reliable forecasts for forecasters. [ABSTRACT FROM AUTHOR]

Each physical process in a numerical weather prediction (NWP) system may have many different parameterization schemes. Early studies have shown that the performance of different physical parameterization schemes varies with the weather situation to be simulated. Thus, it is necessary to select a suitable combination of physical parameterization schemes according to the variation of weather systems. However, it is rather difficult to identify an optimal combination among millions of possible parameterization scheme combinations. This study applied a simple genetic algorithm (SGA) to optimizing the combination of parameterization schemes in NWP models for typhoon forecasting. The feasibility of SGA was verified with the simulation of Typhoon Mujigae (2015) by using the Weather Research and Forecasting (WRF) model and Typhoon Higos (2020) by using the Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) modeling system. The results show that SGA can efficiently obtain the optimal combination of schemes. For Typhoon Mujigae (2015), the optimal combination can be found from the 1,304,576 possible combinations by running only 488 trials. Similar results can be obtained for Typhoon Higos (2020). Compared to the default combination proposed by the COAWST model system, the optimal combination scheme significantly improves the simulation of typhoon track and intensity. This study provides a feasible way to search for the optimal combinations of physical parameterization schemes in WRF and COAWST for more accurate typhoon simulation. This can help provide references for future development of NWP models, and for analyzing the coordination and adaptability of different physical process parameterization schemes under specific weather backgrounds. [ABSTRACT FROM AUTHOR]

Although the North Atlantic (NA) hurricane season has been reported to start increasingly early, historical long-term changes in NA spring tropical cyclone (TC) activity have not been examined in previous studies. We find that spring TCs have become more frequent and intense, and they have more closely approached the US coastline over the past four decades, thus increasing the probability of landfall. A long-term increase in the NA sea surface temperature (SST) and northward incursion of warm water may be responsible for the increasing number, intensity, and northward shift of spring TCs. In addition, the interdecadal winter NA SST tripole mode may induce a North Atlantic Oscillation (NAO)-like atmospheric response in the following spring, which controls spring TC tracks via modulating the strength and position of the NA subtropical high (or the Bermuda high). The superposition of the interdecadal positive winter SST tripole on the NA warming trend has contributed to the anomalous enhancement and northward shift of spring TC activity, increasing the spring TC risks in the US in the past six years. [ABSTRACT FROM AUTHOR]

Copyright of Advances in Atmospheric Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Landfalling typhoons can cause disasters over large regions. The government and emergency responders need to take measures to mitigate disasters according to the forecast of landfall position, while slight timing error can be ignored. The reliability of operational model forecasts of typhoon landfall position needs to be evaluated beforehand, according to the forecasts and observation of historical cases. In the evaluation of landfalling typhoon track, the traditional method based on point-to-point matching methods could be influenced by the predicted typhoon translation speed. Consequently, the traditional track evaluation method may result in a large track error even if the predicted landfall position is close to observation. The purpose of this paper is to address the above issue using a simple evaluation method of landfalling typhoon track forecast based on the time neighborhood approach. In this new method, the timing error was lessened to highlight the importance of the position error during the landfall of typhoon. The properties of the time neighborhood method are compared with the traditional method based on numerical forecast results of 12 landfalling typhoon cases. Results demonstrated that the new method is not sensitive to the sampling frequency, and that the difference between the time neighborhood and traditional method will be more obvious when the moving speed of typhoon is moderate (between 15–30 km h−1). The time neighborhood concept can be easily extended to a broader context when one attempts to examine the position error more than the timing error. [ABSTRACT FROM AUTHOR]

The western Pacific subtropical high (WPSH) substantially affects the climate in the Pacific and East Asia. Previous studies have revealed that the springtime Indo‐Pacific warm pool (IPWP) sea surface temperature zonal gradient (SSTG) could be used as a predictor of the subsequent summertime WPSH's intensity. Here, we find that the interannual variability of the springtime IPWP SSTG has greatly decreased after the late 1990s, accompanied by the weakened relationship between the springtime IPWP SSTG and the following summertime WPSH, which may reduce the efficiency of the springtime IPWP SSTG as a key predictor for the summertime WPSH in recent decades. This observed recent weakening IPWP SSTG–WPSH relationship could be largely contributed by the decadal shift of the El Niño–Southern Oscillation (ENSO) and the WPSH around the late 1990s. The ENSO regime shift from the eastern Pacific (EP) type to the central Pacific (CP) type could alter the spatial pattern of the springtime IPWP sea surface temperature (SST) dipole and further weaken the local air–sea interaction between the underlying IPWP SST and the WPSH. From another perspective of the WPSH decadal shift, the WPSH‐related first leading mode before (after) the late 1990s, characterized by a large‐scale uniform (dipole) pattern with an oscillating period of ~4–5 year (~2–3 year), tended to promote a stronger (weaker) linkage with the springtime IPWP SSTG. In addition, the recent enhancement of the tropical Atlantic SST influences is considered to possibly promote the decadal shifts of the ENSO and the WPSH‐related leading mode. After the springtime tropical Atlantic SST was added as a predictor, the predicting skills of the empirical equation for the summertime WPSH could be substantially improved. The results herein have important implications for the further improvement of the seasonal WPSH prediction, which is of great practical significance in the prevention and mitigation of climate disasters. [ABSTRACT FROM AUTHOR]

The Indo-Pacific warm pool (IPWP), which affects the global climate system through supporting tropical convection, has been reported to expand significantly under greenhouse warming. Although early research revealed that the sea surface temperature (SST) threshold for deep convection (σconv) increases with global warming, many latest relevant works were still conducted based on the traditional IPWP definition (e.g., static SST = 28 °C threshold, and is referred to as the oceanic warm pool, OWP28). Here, we claim that the OWP28 expansion differs from the deep convection favoring pool (DCFP) area change and may not reflect the direct impacts of Indo-Pacific warming on the climate system. Results show that, because of the long-term increase in σconv, the DCFP expands at a rate 2.6 times slower than the OWP28 from 1979 to 2020. The difference reaches 12–27 times from 2015–2100 under different emission scenarios, based on CMIP6 model simulations. While the OWP28 expands to the eastern Pacific, the DCFP will remain within the Indian Ocean and western Pacific Ocean regardless of emission levels. This study emphasizes the necessity of considering the response of the relationship between deep convection and SST to climate change when studying the long-term variability of the IPWP. [ABSTRACT FROM AUTHOR]

A record-breaking severe ozone (O3) pollution episode occurred in the Pearl River Delta (PRD) in early autumn 2019 when the PRD was under the influence of a Pacific subtropical high followed by Typhoon Mitag. In this study, we analyzed the effects of meteorological and photochemical processes on the O3 concentration in the PRD during this episode by carrying out the Weather Research Forecast–Community Multiscale Air Quality (WRF-CMAQ) model simulations. Results showed that low relative humidity, high boundary layer height, weak northerly surface wind, and strong downdrafts were the main meteorological factors contributing to O3 pollution. Moreover, delayed sea breezes that lasted into the night would transport O3 from the sea back to the land and resulted in secondary O3 maxima at night. In addition, O3 and its precursors stored in the residual layer above the surface layer at night can be mixed down to the surface in the next morning, further enhancing the daytime ground-level O3 concentration on the following day. Photochemical production of O3 , with a daytime average production rate of about 7.2 ppbh-1 (parts per billion), is found to be the predominate positive contributor to the O3 budget of the boundary layer (0–1260 m) during the entire O3 episode, while the horizontal and vertical transport fluxes are the dominant negative contributors. This O3 episode accounted for 10 out of the yearly total of 51 d when the maximum daily 8 h average (MDA8) O3 concentration exceeded the national standard of 75 ppb in the PRD in 2019. Based on these results, we propose that the enhanced photochemical production of O3 during the episode is a major cause of the most severe O3 pollution year since the official O3 observation started in the PRD in 2006. Moreover, since this O3 episode is a synoptic-scale phenomenon covering the entire eastern China, we also suggest that the enhanced photochemical production of O3 in this O3 episode is a major cause of the extraordinarily high O3 concentrations observed in eastern China in 2019. [ABSTRACT FROM AUTHOR]

Model simulations suggest that Madden–Julian oscillation (MJO) activity changes under the anthropogenic climate change background. However, satellite observations, which provide information on MJO convection activity, are not available before the 1970s, hindering research on the long-term historical variability of MJO. This study aims at extending the data length of MJO indices that include both MJO circulation and convection features, such as the widely used Real-Multivariate MJO (RMM) Index, to the pre-satellite era. This paper introduces a new MJO Index construction method, in which the outgoing longwave radiation (OLR) input is derived from upper-level geopotential, and names it as the Geopotential-Based Multivariate MJO (GMM) Index. The GMM Index is derived from 1902 to 2008 based on the 20th century reanlaysis product, by assuming that the relationship between OLR and geopotential does not change over time, and is compared with the filtered version of the RMM (FMM) Index during 1981–2008 and historical observed precipitation records in the 20th century. The GMM Index is shown to (1) have the same climatological properties as the FMM Index, (2) be statistically highly correlated to the FMM Index, and (3) be able to indicate MJO activities and MJO's convection features in the pre-satellite era. The overall bivariate correlation between the FMM and GMM indices based on ERA-20C is 0.964. Evaluation results confirm the validity of the proposed MJO Index construction method, which could capture MJO convection activity in the pre-satellite era and can be applied to all MJO indices that require OLR inputs. This study provides an alternative way that overcomes the difficulty of historical MJO studies and will be beneficial to our understanding of the long-term change of MJO. [ABSTRACT FROM AUTHOR]

Copyright of Advances in Atmospheric Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Six dust storms that originated from Mongolia and moved to northern China in Spring 2021 are investigated by the anomaly variable‐based analysis method, which is compared to the traditional total‐variable based analysis method. Strong surface wind is a force to form dust storms as commonly known in current practice. However, dust area depicted by total‐wind model product analysis and operational forecast has a large bias to observation even for leading 24 hr. The analysis method of anomaly based variables showed that the domain of an airborne dust storm observed from satellite images is usually located between two neighboring convergence lines of 850 hPa anomalous winds. This connection of dust storm associated with the anomalous wind convergence extends southwestward from an anomalous cyclone and moves from Mongolia to northern China or Northeast China. It is thermodynamically associated with an anomalous thermal contrast, which has a spatial structure of wind, temperature, and geopotential height anomalies in the troposphere. We further found that numerical weather prediction products of the ECMWF model are able to indicate the anomalous wind pattern with lead times of 4–8 days by applying the anomaly based analysis approach. Therefore, this approach can not only depict dust area better but also reveal predictors much easier compared to the traditional total‐variable or full‐field‐based analysis method. Key Points: Dust storm can be well detected by the anomaly based analysis of atmospheric variablesA dust domain is located between two neighboring convergence lines of 850 hPa anomalous windsThe ECMWF model can predict the anomalous wind pattern for leading 4–8 days [ABSTRACT FROM AUTHOR]

Previous studies showed that 4D-Var technique used for data assimilation could be modified for weather control. This study demonstrates the ability of 4D-Var to influence the future path of a tropical cyclone by calculating perturbations in WRF simulation. Given the background error covariance matrix, the initial field is improved by the vortex dynamic initialization technique. Our results show that 4D-Var can be applied to control the trajectory of simulated tropical cyclones by producing "optimal" perturbations. In the numerical simulation experiment of Typhoon Mitag in 2019, after this kind of weather control similar to data assimilation, the tropical cyclone moved obviously, and the damaging wind over the coastline weakened. The prediction results after the initial field modified by 4D-Var have a great change, and the position of the tropical cyclone moved about 0.5° southeastward after assimilation, which misses the southeast coast of China. Moreover, the damaging wind is also weakened. Since the 4D-Var is premised on the assumption that the model is perfect and does not consider the model error, then the research plan to consider model error and introduce new methods is discussed in the paper. [ABSTRACT FROM AUTHOR]

It is well‐known that the increasing zonal‐mean trend of sea‐ice concentration (SIC) surrounding Antarctica is up to 2015 so this paper first summarized this trend with opposing regional trends in each season along the latitude band 60°–70°S during 1979–2015. The surface wind trends can partly explain the observed regional SIC trends. By analysing the surface wind, as well as the tropospheric geopotential height (GPH) and temperature trends, the study revealed that the increasing zonal‐mean SIC trend and opposing regional SIC trends surrounding Antarctica are vertically associated with GPH and temperature trends in the troposphere. This relationship is also found in extreme years for seven SIC maximum and four SIC minimum autumns from 1979 to 2015, particularly reconfirmed in the two SIC minimum autumns of 2017 and 2018, indicating that a positive regional SIC anomaly is vertically associated with an anomalously cold air mass centred around 850 hPa in the troposphere which is located beneath a negative GPH anomaly at the upper troposphere, and vice versa. [ABSTRACT FROM AUTHOR]

Copyright of Advances in Atmospheric Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this article, the synoptic-scale spatial structures for raising tornadic and hail storms are compared by analyzing the total and anomalous variable fields from the troposphere to the stratosphere. 15 cases of tornado outbreaks and 20 cases of hail storms that occurred in the central United States during 1980–2011 were studied. The anomalous temperature–height field shows that a tornadic or hail storm usually occurs at the boundary of anomalous warm and cold air masses horizontally in the troposphere. In one side, an anomalous warm air mass in the mid-low troposphere and an anomalous cold air mass in the stratosphere are vertically separated by a positive center of height anomalies at the upper troposphere. In another side, an opposite vertical pattern shows that an anomalous cold air mass in the mid-low troposphere and an anomalous warm air mass in the stratosphere are separated by a negative center of height anomalies at the upper troposphere. Therefore, two pairs of adjacent anomalous warm/cold centers and one pair of anomalous high/low centers combining together form a major tornadic or hail storm paradigm, which can be physically considered as the storage of anomalous potential energy (APE) to generate severe weather. To quantitatively measure the APE, we define an index of anomalous convective instability (ACI) which is a difference of integrating temperature anomalies based on two vertically opposite anomalous air masses. The APE transformation to anomalous kinetic energy, which reduces horizontal and vertical gradients of temperature anomalies, produces anomalous rising and sinking flows in the lower-layer anomalous warm and cold air mass sides, respectively. The intensity of ACI index for tornadic storm cases is 1.5 times larger than that of hail storm cases in average. Thus, this expression of anomalous variables is better than total variables used in the traditional synoptic chart and the ACI index is better than other indices to detect potential tornadic and hail storms in order to understand the environmental conditions affecting severe weather in analytical and model output datasets. [ABSTRACT FROM AUTHOR]

ABSTRACT In the summer of 2010, western Russia experienced extreme heat, which was noted for its exceptional spatial spread, long duration, high intensity and impacts. We use an anomaly-based approach to decompose atmospheric variables into daily climatic components and anomalies from two reanalysis datasets. We show that a surface heat wave event results from a downward extension of an anomalously warm air column below a centre of positive geopotential height anomalies in the upper troposphere. Therefore, we use this approach to analyse all summer regional heat wave events with spatial scales larger than 2000 km and durations greater than 5 days over western Eurasia from 1980 to 2014. Our results demonstrate that the rapid increase in regional heat wave events over western Eurasia since 2010 is a direct response to the increasing frequency of large-scale, quasi-stationary positive centres of maximum height anomalies in the upper troposphere. [ABSTRACT FROM AUTHOR]