Climatological normals are 30-year averages of meteorological variables and their derived statistics,used as predictors of future climate conditions and climatic backgrounds for certain regions.Decadally updated normals have changed in response to climate warming.Therefore,it is essential to investigate the differences between the new(1991-2020)and old(1981-2010)climatological normals in Guangdong Province.In this study,basic climate warming facts were revealed through temperature analysis based on data from 86 national meteorological observatories.Subsequently,multiple climate indicators derived from daily temperature and precipitation were analyzed.Temperature-derived indicators show that in the new climatic stage,Guangdong Province experienced an increase of 3.9 d in the annual average of high-temperature days and a decrease of 1.1 d in low-temperature days,while cold-wave days decreased by 0.2 d,reflecting climate warming.Precipitation patterns shifted across various temporal scales:annually,seasonally,monthly,during the flood season,and the"Dragon Boat Water"period.Notably,the amount of rainstorms and the number of rainstorm days increased,whereas total rainfall and rainfall days decreased.Furthermore,the rainy season began an average of 5.0 d later in the new normal than in the old.Multi-scale analysis revealed that the temperature differences between the new and old climatological normals were primarily driven by a dominant linear warming trend,whereas precipitation differences were mainly dominated by interannual variability,which explains the insignificant change in annual total precipitation.
Research on atmospheric electric field distortion factors is crucial for enhancing the accuracy of lightning monitoring and warnings.In this study,two mill installation cases were established.Based on an established three-dimensional corona discharge model,the impact of corona discharge of the lightning rod on the ground atmospheric electric field(E)was investigated by comparing two situations,with and without corona,while considering the influence of wind.In the absence of wind,State 1 represented the condition with corona,whereas State 2 represented the condition without corona.In State 1,the measurement results of E were not affected by the rod.Conversely,in State 2,the corona discharge of the rod caused a distortion of E;the lower the height of the rod,the higher the degree of distortion caused by the corona on E.When wind was considered,the position of the mill affected the distortion of E.The distortion coefficient was ki(i=3,4,5)and ki<1.Specifically,k3 was the distortion coefficient when the mill was installed in the windward area,k4 when it was installed in the leeward area,and k5 when it was installed in the sidewind area.The smaller ki,the greater the shielding effect.The highest shielding effect was exhibited by exhibited,followed by k5 and k4.The leeward installation yielded the greatest shielding effect,whereas the windward installation offered the lowest shielding effect.When the same case was considered,the greater the wind speed,the more the corona charge of the tip was released,and the stronger the shielding effect on E.When two different cases were adopted,the relationship between the wind speed(v)and the revision coefficient(p)was fitted.The values of p and v exhibited an exponential functional relationship.
Downdrafts tend to suppress convection under the influence of the western Pacific subtropical high(WPSH);however,local short-term heavy rainfall(STHR)events still occur frequently in coastal cities,and predicting the con-vective initiation(CI)of such processes remains challenging.Based on ERA5 reanalysis data and an automatic weather station(AWS)network,the meteorological conditions of STHR and non-rainfall(NR)events were compared and analyzed using hourly synthesis analysis methods.The findings are as follows.(1)Suppression by the WPSH results in stronger vertical upward motion during STHR events,with-0.04≤ω≤-0.03 Pa s-1,than during NR events,with-0.03≤ω≤-0.015 Pa s-1.(2)STHR events,with over 80%AWSs≥38 ℃,exhibit quicker temperature rises,higher peak tempera-tures,and more extensive areas of heat coverage compared to NR events,with only 16.5%AWSs≥38 ℃.(3)STHR events,with a convergence of the Q vector divergence of-0.4×10-11 hPa-1 s-3,compared to NR events,with a con-vergence of the Q vector divergence of-0.2×10-11 hPa-1 s-3,are also accompanied by more intense surface convergence and upward motion,conditions which reveal the significance of local dynamics in triggering such weather events.(4)The weaker suppressive effect of the WPSH,combined with a significant mesoscale thermodynamic force near the ground,creates a multiscale resonance effect,which is a key condition for triggering local convection.(5)The lower temperature-dew point temperature spread(T-Td)at 2 m and relative humidity below 850 hPa for STHR events indicate that moisture conditions are sufficient,but not necessary,for triggering local convection.This study provides new insights into CI mechanisms under the influence of the WPSH for STHR events,thus offering important clues for future improvements in forecasting capabilities.
Given the intent of the model for use in fine precipitation forecasting with adequate lead time,it is of interest to examine the way in which forecasts of warm season precipitation over Hainan Island behave as a result of the rapidly updated hourly model throughout 24 initializations.Standard metrics were performed on the quantitative precipitation forecasts(QPFs)from CMA-GD(R3),a global/regional assimilation and prediction system,and the cycle of the hourly assimilation and forecast system,in April-September of 2022-2024 over Hainan Island,to understand the way in which the seasonal,diurnal,and spatial variations change in relation to decreasing forecast lead times.The findings indicated that"rain or no rain"QPFs generally exhibited a superior performance at short lead times,with the 1-h forecast being optimal.As the rainfall threshold increased,the forecast performance deteriorated in the first five forecast hours,whereas the nighttime QPF skill often exceeded the daytime performance.Significant instabilities were identified in the mountainous region and the surrounding areas when 24 forecasts that were valid at the same forecast hour were compared.The 1-h model update enhanced the reliability of the 2-m temperature,2-m dew point,and 10-m wind field across most of the island.However,persistent biases remained,including underestimation of temperature and dew point at night and in central areas,and overestimation of wind speed with strengthened afternoon wind convergence.These biases contributed to uncertainty in the model's prediction of rainfall events over Hainan Island.
Convective initiations(CIs)in western Jiangnan,China,were examined using radar data spanning April-September 2018-2021.Our approach combined objective identification and subjective validation to identify,track,and validate the CIs,thereby producing a highly accurate CI dataset.Using this dataset,we investigated the spatiotemporal variations and environmental conditions associated with CIs and revealed distinct seasonal and diurnal patterns of CI events.Spatially,CIs occur more frequently south of the Nanling Mountains and less frequently in the north.Seasonally,they were most frequent from June to August,and least frequent in April and September,following a unimodal dis-tribution.The CIs exhibited pronounced afternoon convection,particularly from June to August,when most occurred between 11:00 and 19:00 local time(UTC+8 h).Terrain significantly influenced the spatial variation in the CIs.North of the Nanling Mountains,CIs occurred near higher mountains,whereas south of the range,they were concentrated near smaller mountains and along the Guangdong coast.Using K-means clustering,CIs that could develop into Mesoscale Convective Systems were classified into four circulation types:Western Pacific subtropical high(WPSH)control(Type I),WPSH edge(Type II),southwest airflow(Type III),and low trough shear(Type IV).The CIs of Types I and II were primarily attributed to afternoon thermal convection,which occurs under conditions of high moisture and thermal in-stability.Triggers for these CIs mostly occurred near high-elevation terrain.In contrast,Types III and IV were driven primarily by the synergy of abundant moisture conditions and synoptic dynamic factors,such as low-level jets,upper-level troughs,and shear lines.These types exhibited higher frequency in the south,with high-frequency CI trigger zones particularly observed in regions with strong moisture-flux convergence and near complex terrain.
This study is the first to comprehensively investigate the climatology and environmental conditions of rapid shrinkage(RS)in four tropical cyclone(TC)sizes,including radii of the outermost closed isobar(ROCI),17.5 m s-1 winds(R17),25.7 m s-1 winds(R26),and 32.9 m s-1 winds(R33)over the western North Pacific(WNP)during 2004-2022.The 5th percentile of the 24-h change rate of TC size was used to define the RS in TC size,and the range between the 5th and 95th percentiles of the 24-h change rate of TC size was used to define the non-rapid change(non-RC)in TC size.The RS thresholds for the four TC sizes over the WNP were then obtained.The initial mean sizes and initial mean latitudes of the TC centers of the RS cases were larger and higher than those of the non-RC cases.RS in TC sizes mainly occurred during the decay stage,except for RS in ROCI.Composite analysis showed that environmental conditions such as sea surface temperature,temperature profile,relative humidity profile,and inward angular momentum transport of the RS cases were favorable for RS in TC size.The mean brightness temperature and mean hourly rainfall rate of RS and non-RC in R17,R26,and R33 showed that they were in a significantly more unfavorable convective environment for RS than for non-RC.The environmental conditions differed among the RS of the four TC sizes.Sea surface temperature and atmospheric conditions may cause RS in TC sizes mainly by influencing atmospheric instability,atmospheric moisture,TC pre-cipitation,vertical motion of the TC,radial inflow,and tangential wind of the TC.
The remnant of Typhoon Haikui(2311)caused extreme precipitation in the Guangdong-Hong Kong-Macao Greater Bay Area,breaking multiple precipitation records in Shenzhen and Hong Kong.In this study,three microphysical schemes,LIUMA,WSM6,and THOM,were selected for a simulation based on CMA-MESO V6.0.Forecast performance,errors in the prediction of various meteorological elements,microphysical variables,and their changes were compared across the different schemes.Additionally,latent heat sensitivity tests were conducted for further analysis.The LIUMA run exhibited a slightly higher threat score(TS)and a higher bias than the other two runs.In the region experiencing heavy precipitation,all three runs showed peaks of significant snow and graupel totals near the 0 ℃layer,and peaks of rainwater near 3 km.The contribution of ice-phase particles varied among the schemes,with WSM6 contributing mostly graupel and THOM and LIUMA contributing mostly snow.The LIUMA run demonstrated a significantly larger net latent heat release near the 0 ℃layer compared to the WSM6 and THOM runs and forecasted stronger warm core structures and remnants of a landfalling tropical cyclone(LTC).All three runs predicted a warm-core structure located to the east of the low-level center of the LTC remnant,which slowed the westward movement of the remnant and facilitated the maintenance of heavy precipitation.Compared to the WSM6 and THOM experiments,the LIUMA experiment released significantly more latent heat near the 0 ℃layer and forecast a stronger warm-core structure and LTC remnants,leading to more false alarms.Latent heat sensitivity tests revealed the importance of latent heat feedback from microphysical processes,indicating that a decrease in latent heat could lead to a rapid westward weakening of the LTC remnant and an increase in forecast error.The model also exhibited significant uncertainty in its extreme precipitation forecasts,which could affect the overall forecast field through latent heat release.
With the continuous development of remote-sensing technology,the capabilities and applications of geosta-tionary meteorological satellites have gradually expanded.They play an important role in monitoring climate change,promoting the formulation of environmental protection policies,and reducing the damage caused by natural disasters.This study investigated the current state and emerging trends in geostationary meteorological satellite research.We system-atically reviewed the developmental history of these satellites across various countries and performed a comparative analysis of their status over different periods.Using bibliometric methods,this study revealed the knowledge structure of the field and forecasted future trends.Key findings indicate that geostationary satellites have evolved through three generations,improving from spin to three-axis stabilization,and transitioning from single payloads to multiple instruments.Since 1990,a significant growth in publications and citations has been observed,particularly in the United States and China,reflecting increased global cooperation.The co-citation analysis identified 19 clusters,with machine learning emerging as a prominent focus.Over the last 65 years,research has shifted from theoretical analysis to the integration of artificial intelligence(AI)technologies,with leading regions transitioning from the United States and European Union to China.This study suggests four critical directions for future exploration:leveraging AI,enhancing weather analysis capabilities,utilizing multi-satellite data,and exploring ecological monitoring applications.This study offers valuable insights to researchers and policymakers by providing a comprehensive overview of the development and prospects within the field.
To address the complexities associated with forecasting low-probability,low-visibility fog events and the underlying nonlinear interdependencies among various influencing variables,we present an attention mechanism-em-bedded long short-term memory(ATT-LSTM)deep learning model for sea fog visibility hazard prediction.This archi-tecture seamlessly incorporates ATT into the conventional LSTM neural network framework.This integration enables the model to adaptively assign weights to the input features,thereby distinguishing between salient and non-salient variables.This targeted allocation enhances the contribution of considerable factors within the LSTM forecasting algorithm,opti-mizes input data,and assigns varying levels of attention to each variable.Consequently,the model substantially mitigates prediction errors in multivariate scenarios.An empirical analysis employing an independent dataset encompassing 303 foggy days over a biennial period confirmed the superior performance of the proposed ATT-LSTM model.Comparative evaluations with LSTM,logistic classification regression,and support vector machine classification regression models revealed that the ATT-LSTM model achieved a recall rate of 37%,a precision rate of 48%,an accuracy rate of 91%,and a threat score(TS)of 0.26.Among the assessed methodologies,the ATT-LSTM model outperformed the others in terms of recall,accuracy,and TS metrics.These findings confirm that the ATT-LSTM model offers a potent and innovative deep learning approach for enhancing the accuracy of low-visibility sea fog hazard predictions.
Autumn rain in western China(ARWC)is a unique and significant precipitation phenomenon that occurs during the summer-to-winter transition of the atmospheric circulation.Using the fifth generation of global climate and weather reanalysis data from the European Centre for Medium-Range Weather Forecasts and CN05.1 grid precipitation data,this study examined the anomalous characteristics and mechanisms of ARWC by combining the synergistic effect of the westerly jet and meridional wind.Over the past 60 years,ARWC has exhibited significant interdecadal and interannual variations,as well as a north-south seesaw pattern.The westerly jet index(meridional wind index)exhibited a negative(positive)correlation with precipitation in the northern autumn rain zone(NARZ),and a positive(negative)correlation with precipitation in the southern autumn rain zone(SARZ).The coupling of a weak meridional southerly wind with a southward westerly jet and a strong meridional southerly wind with a northward westerly jet are the two primary modes that synergistically influence the ARWC.These synergistic effects cause significant atmospheric changes throughout the troposphere.The contrasting circulation structure,temperature advection,vertical motion,and water vapor flux contributed to the opposite precipitation anomalies observed in the NARZ and SARZ.A new comprehensive index that reflects the coupled synergistic effect is proposed to characterize the anomalous changes in ARWC.This study improves the un-derstanding of the anomalous characteristics and mechanisms of ARWC.
