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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]

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]

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]

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]

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]

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]

In this study, the seasonal variations of Madden-Julian Oscillation (MJO) are simulated with a 2.5-layer model governed by a set of perturbation equations, considering the effects of boundary layer friction convergence, diabatic heating, moisture feedback and background flow, under a non-resting basic state. The governing perturbation equations are written under a basic state defined by the 30-year climatology of observation (reanalysis), in which daily/monthly climatic wind, geopotential and moisture act as a background forcing influencing the evolution and propagation of MJO. Under this climatic background state, the precipitation of basic state is no longer zero but equal to the 30-year precipitation climatology, which is generally larger in the tropical region and has a dominant seasonal cycle in the subtropical region. Diabatic heating of convection is the key factor that produces the coupled Rossby-Kelvin structure of MJO and is a function of precipitation perturbation in the model. In common practice, precipitation perturbation can be derived from precipitation parametrization schemes, which usually consider only positive values during calculation because negative precipitation does not physically exist in the real world. However, the model in the study is run under a non-zero rainfall background, thus precipitation perturbation is not constraint to be non-negative. In this case, "negative precipitation" area is referred to as abnormally dry area, and leads to "diabatic cooling" (i.e. weaker diabatic heating compared to the basic state) to the troposphere.Model simulations show that the climatic state and "negative precipitation" are two indispensable factors to the MJO seasonal variations. The climatic state provides a crucial background circulation and moisture distribution that favors MJOs / Boreal Summer Intraseasonal Oscillations (BSISOs), as reported in previous studies. On top of the climatic state, our results indicate that the "negative precipitation" effect is also essential to the simulation of MJO in different seasons. The MJO seasonal variations are less obvious if the model only incorporates positive precipitation. Results also reveal that the "negative precipitation" effectively damps shortwaves. [ABSTRACT FROM AUTHOR]