The synergistic effects of the extreme heat wave events in Beijing in June of 2023 and Paris in July of 2019 and urbanization on the urban thermal environment were simulated using the O (100 m) RMAPS-Urban model combined with data from automatic meteorological stations and ERA5 data.The mechanism was analyzed through the points of weather and climate background,topography and urban characteristics.The results show that the O (100 m) model combined with high-resolution land classification data can reasonably simulate the fine spatio-temporal distribution of near-surface meteorological elements and urban heat island.During the heat wave events,under the background of high-altitude ridge and clear air breeze,the contribution of urban factors to daytime air temperature rise increased by 0.20 ℃ and 0.55 ℃ in Beijing and Paris,and increased by 1.40 ℃ and 0.83 ℃ at night.During the heat wave events,the sensible heat flux in Paris is stronger than that in Beijing,resulting in greater contribution of urbanization to daytime temperature rise,while the higher ground heat storage in Beijing is the main reason for the greater contribution of urbanization to nighttime temperature rise.Urbanization has significantly slowed down the southerly mountain winds in Beijing at night,resulting in a "southeast higher and northwest lower" impact of urbanization on temperature in Beijing,while Paris has a smoother terrain and is not affected by valley winds.

The composition of urban morphology has a significant impact on local meteorological conditions and urban living environment.Based on the local climate zones (LCZ) theory and the observational data of automatic weather station from 2018 to 2022,this study analyzed the composition of urban morphology and the distributions of climate comfort index and its main influencing factors in Beijing,and quantified the differences in meteorological conditions and human comfort index in different urban functional areas.The results indicate that the urban area of Beijing is composed of 10 types of building underlying surfaces and 6 types of natural underlying surfaces,and the building underlying surfaces account for 52.0% of the total area,among which the open mid-rise buildings (LCZ 5) has the greatest contribution and the low regatation area (LCZ-D) has the widest distribution in the natural surface type.The main factors affecting climate comfort index,such as air temperature,relative humidity,and wind speed,vary significantly across different LCZ underlying surfaces.In general,the climate comfort index is characterized by "comfortable","hot","comfortable",and "freezing cold" in spring,summer,autumn,and winter,respectively.Due to the physical properties of building underlying surfaces and the impact of human heat emissions,urban central areas have better climate comfort index in spring,autumn,and winter,while it is opposite in summer.There is a proportion of 32.5% to 38.2% in the total time for human body feeling "comfortable" on various LCZ underlying surfaces,among which ranking in descending order as LCZ-A,LCZ-B,LCZ-G,LCZ-D,LCZ 7,LCZ 6,LCZ 5,LCZ 8,LCZ 4,LCZ- E,LCZ 3,LCZ 1,and LCZ 2.The climate comfort index is better on natural underlying surfaces than building underlying surfaces,and better on open building underlying surfaces than dense building underlying surfaces,also better for high-rise buildings than medium and low rise buildings.

Based on the observation data of radiation and heat flux during summer in Fengxian,a suburban area in Shanghai,Surface Urban Energy and Water Balance Scheme(SUEWS)and its sensitivity to surface cover and human activities were evaluated.The results indicated that the model can well reproduce the diurnal variation of radiation flux,and can simulate the diurnal variation and magnitude of sensible heat flux and latent heat flux well.By comparing the average Bowen ratio of dry and wet months in summer,it is found that the Bowen ratio of dry months(0.74)is close to twice that of wet months(0.40),indicating that air humidity has a significant impact on heat fluxes.The sensitivity tests of several groups of underlying surface coverage show that compared with trees,sensible heat flux and latent heat flux simulated by SUEWS are more sensitive to grass area.When the natural surface coverage is constant,the Bowen ratio decreases significantly with the increase in grass area.When the population density increases,the simulated anthropogenic heat flux significantly increases,resulting in a significant increase in the simulated sensible heat flux and Bowen ratio.

In this study,the SAL(structure amplitude and location) spatial verification method is used to evaluate the 12~36 hours total precipitation forecast performance of five China Meteorological Administration (CMA) models for the strongest precipitation day during the remnants of Typhoon "Meihua" over Northeast China in September of 2022 (from 08:00 on September 16 to 08:00 on September 17,with the typhoon category as a tropical depression).The results can be summarized as follows: The CMA-GFS model exhibits the best forecast of precipitation structure,amplitude,and location in this case,while for the other models tend to underestimate extreme precipitation,except CMA-BJ.Due to its well-performance in predicting the location,intensity,and moving speed of the 850 hPa low-level jet,the CMA-GFS model has the best precipitation forecast.Meanwhile,the faster speed of the low-level jet predicted by the CMA-TYM model leads to a smaller area of significant rainfall exceeding 100 mm.The CMA-GFS model demonstrates better forecasting performance as the lead time shortens,with notable advantages in now-casting forecasts but limited skill for longer lead times.In contrast,although CMA-TYM model exhibits poorer structure and amplitude performance in approaching lead time,it is the earliest model to provide indicative signals for the general location and magnitude of heavy precipitation.The summary of CMA models forecast performance in typhoon remnant system precipitation aims to enhance the applicability of numerical weather models in China in similar scenarios of the future.

Using the daily precipitation data from 94 national stations in Shaanxi province from 1971 to 2021,the method of Detrended Fluctuation Analysis (DFA) was used to determine the extreme precipitation threshod,and the method such as linear trend estimation,moving average,power spectrum analysis,and generalized extreme value distribution were utilized to analyze the spatial and temporal distribution,and probability characteristics of extreme precipitation.The disaster-causing index of heavy rain floods were further established based on the disaster data.The results show that the extreme precipitation thresholds in Shaanxi province generally show a decreasing trend from south to north.The high-value areas are mainly distributed at the southern border of Hanzhong and Ankang.The extreme precipitation frequencies in various parts of Shaanxi province have shown an increasing trend in recent years,being most significant in the northern region.The DFA threshold is slightly higher than that of the 2 a return period.The DFA threshold has a large dispersion due to unique terrain leffect in some areas.There are obvious high-value areas in southern Loess Plateau and northwestern Guanzhong,and the threshold span crossing Qinling Mountains is large.Combined with the extreme precipitation thresholds,the disaster causing rainfall in different regions,and the terrain and regional vulnerability,the recommended thresholds for southern,central,and northern Shaanxi province are 80 mm,55 mm,and 50 mm,respectively,which can provide a reference for disaster prevention and heavy precipitation warning in various regions.

This study utilized atmospheric environmental monitoring data and routine meteorological data in Mudanjiang,Heilongjiang province from 2014 to 2023,as well as global data assimilation system (GDAS) data provided by the National Centers for Environmental Prediction in the United States,to analyze the characteristics of six major atmospheric pollutants and investigate the correlation between meteorological elements and PM_2.5 concentration.By combining methods such as the Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT),Potential Source Contribution Function (PSCF),and Concentration Weighted Trajectory (CWT),the transport paths and contribution sources of PM_2.5 during typical heavy air pollution periods were studied.The results showed that among the pollution events in Mudanjiang over the past 10 years,days with excessive PM_2.5 concentration ocurred most frequently,making it the primary pollutant affecting the atmospheric environment quality.Particularly,from 2014 to 2017,PM_2.5 pollution occurred frequently,lasted for a long time,and was usually severe.Backward trajectory analysis indicated that the transport paths of PM_2.5 pollution in the west and southwest directions had a significant impact on atmospheric pollution in Mudanjiang.In the transport trajectories of typical heavy pollution events,long-distance trajectories accounted for 34.33%,with representing regions including the Mongolian Plateau and southern Siberia,while medium and short-distance trajectory accounted for 65.67%,with representing regions such as Harbin,Changchun,and Yanbian Korean Autonomous Prefecture.Contribution source analysis revealed that pollution sources from old industrial cities in northern Liaoning province and Yanbian Prefecture in Jilin province contributed the most to PM_2.5 concentration in Mudanjiang (WPSCF>0.9),and they were also the key contribution sources for PM_2.5 concentration (WCWT >100).

Based on the European Centre for meaium-Range Weather forecasts(ECMWF) model with a horizontal resolution of 0.1°×0.1° and the observation data from 20 national automatic weather sites in Beijing from July 1,2019 to March 15,2024,the characteristics of the model for 2 m temperature bias forecasted by ECMWF model during the historical heating season were analyzed.Extreme random forest,decision tree,gradient boosting tree,linear regression,and Lasso regression methods were used to correct the 2 m temperature forecasts from ECMWF model.The results show that the overall 2 m temperature forecast during the historical heating season in 2019-2023 in the urban area of Beijing by ECMWF is low,with the largest bias occurring in the afternoon and with the average bias of -2.3 ℃.While the 2 m temperature forecast in the suburban area is low in the morning and high in the afternoon,with the largest positive and negative bias occurring at 07:00 and 16:00,being 1.7 ℃ and -2.2 ℃,respectively.After the correction by machine learning method,the mean bias and root mean square error of urban and suburban sites in Beijing in the 2023 heating season (from November 7,2023 to March 15,2024) are significantly decreased,in which the correction effect of the extreme random forest is the best,and the improvement rates of root mean square error in the urban and suburban areas are 24.2% and 35.4%.After the correction by machine learning method,the accuracy of daily mean temperature forecast bias within ±0.5 ℃,±1.0 ℃ and ±2.0 ℃ at 9 sites in urban area of Beijing in the 2023 heating season is significantly improved,with the maximum improvement rates of 31%,44% and 40%,respectively,and the extreme random forest and decision tree have the best performance.

Based on daily maximum and minimum temperature observation data from 110 surface meteorological stations in the Dongting Lake basin from 1961 to 2022,the percentile threshold was used to determine the warm day threshold and the warm night threshold for each station.The spatial-temporal variation characteristics of warm days,warm nights,compound high temperature,high temperature heat wave frequency,high temperature heat wave days,and high temperature heat wave intensity in the Dongting Lake basin were analyzed.The results show that the warm day threshold and the warm night threshold in the Dongting Lake basin are gradually increasing from southwest to northeast,which is obviously affected by the topography of high in southwest and low in northeast.The average annual warm days and warm night days in the basin show a significant increasing trend.The former has a climate tendency rate of 1.6 d per decade,while the latter reaches 2.6 d per decade,indicating that the number of warm night days increases faster than the number of warm day days.The trend of changes in the number of warm days and warm nights in the watershed shows a low center and a high surrounding area in space.In addition,the number and intensity of composite high temperature days in the watershed also show a significant increasing (enhancement) trend,with climate tendency rates of 1.3 d per decade and 3.2 ℃ per decade,respectively.Since 2000,the number and intensity of composite high temperature days have shown an accelerated growth (enhancement) trend.The frequency and number of days of high-temperature heat waves in the watershed show a spatial distribution pattern of increasing from southwest to northeast,while the intensity of high-temperature heat waves increases from south to north.During the study period,the frequency,number of days and intensity of high temperature heat waves in the Dongting Lake basin showed a significant increasing trend,and the climate tendency rates were 0.2 times per decade,1.3 d per decade and 2.0 ℃ per decade,respectively.The variation trend of heat wave frequency and heat wave intensity show differences in the three zones of north,middle,and south.The heat wave frequency and intensity in the two zones of north and south are significantly increased (enhanced) compared to those in the central region.The significantly increased stations are mostly distributed in the north and south,while there are fewer in the central region.The change trend of annual average heat wave days in the basin increases from the center of the basin to the surrounding areas,with the most pronounced increase in the northeastern Dongting Lake,and the climate tendency rate as high as 3.0 d per decade.

Based on meteorological data,maize development period records and unit yield information from the primary maize producing areas in Heilongjiang province from 1981 to 2021,this study selected temperature and soil moisture as the key variables to construct the daily mean temperature and the relative soil moisture of ten days in 0~10 cm soil layer series for maize sowing.Based on the skewness-kurtosis test method of sample series distribution fitting,the suitable temperature and the relative soil moisture indicators were constructed by using the confidence interval estimation method for maize sowing in different regions of the main maize producing areas in Heilongjiang Province.An independent maize sowing samples was reserved for validation and yield differences associated with varying sowing dates were further analyzed.The results show that the meteorological conditions are favorable in 2012,2014,and 2016,but unfavorable in 2010 and 2013 during the maize sowing period.The developed indicators effectively capture the suitability of temperature and soil moisture conditions for maize sowing in Heilongjiang province.The indicators verify that the coincidence rates of the temperature for maize sowing are 76.6% and 12.3%,and the relative soil moisture are 37.0% and 45.6% within the appropriate and more appropriate ranges.There are significant variations in maize yield among different sowing dates,because there is a mutual compensatory mechanism for meteorological conditions throughout the growth period of maize in different years.Compared to adjacent years of shorter emergence time,the average yield decreases by 14.5% in years of longer emergence time after sowing.The main reason for the delayed sowing of maize is that the temperature and water conditions are both not suitable,or either is not suitable,during the sowing periods in 2010 and 2013,compared with normal sowing years,the yield reduction of 4.4% to 27.5% at representative stations.The temperature and soil moisture conditions such as low temperature and drought,low temperature and waterlogging during the sowing period are not suitable for maize sowing,which affect the rapid emergence of maize after sowing and could easily lead to reduced yields.

The suitability of meteorological conditions is related to the efficiency,cost and other issues of agricultural activities.In Hinggan League,the agricultural activities in rice production mainly include seedling cultivation,transplanting,and harvesting.Based on the influence of relevant meteorological factors on rice seedling cultivation,transplantation and harvest,a suitability calculation function for relevant meteorological elements was established using a fuzzy membership method.The weight of each influencing factor was determined through expert consultation.On this basis,utilizing rice growth observation data from 2010 to 2022 in Horqin Right Wing Front Banner,a comprehensive meteorological suitability model and classification index for rice seedling cultivation,transplantation,and harvesting in Hinggan League were developed.The results show that the comprehensive meteorological suitability for rice seedling cultivation,transplantation,and harvesting has been moderate or relatively moderate over the past three years.The meteorological suitability for rice seedling cultivation and transplantation varies greatly in different years,whereas the meteorological suitability for harvesting has changed relatively little.The models and classification indices align well with the actual rice production conditions in Hinggan League.When integrated with refined forecasts of temperature,precipitation,and other meteorological variables,these models can facilitate the prediction of optimal periods for agricultural activities.

Based on daily temperature data from 61 stations in Liaoning province from 1981 to 2020,referring to national standards (GB/T 42074—2022) and combining with agricultural production characteristics,the temperature indicators for seasonal division in Liaoning province were adjusted using a 5-d moving average method.A new temperature index was proposed,with a 5 d running average temperature >20 ℃ for spring and autumn,≥20 ℃ for summer,and<6 ℃ for winter.Based on national standard and local standard (new temperature index),the four seasons of Liaoning province were divided,and the phenological characteristics of Shenyang,the ecological landscape,and ecological tourism activities were analyzed.The above analysis indicates that using the national standard to divide the four seasons in Liaoning province is not suitable to the local climate,phenology and social activities,and the local temperature index is more suitable for seasonal division than the national standard.Analysis of ERA5 300 hPa meridional wind revealed that the transition time of meridional wind of westerly jet is basically consistent with the seasonal transition time obtained from localtemperature index in Liaoning province.

Based on meteorological disaster data in Shanghai from 2012 to 2021,this study investigated the mechanisms by analyzing the disaster regularity of meteorological conditions trigger disasters and employs an optimal division method to derive district-specific threshold indicators for rainstorm and gale disasters.The results show that the average precipitation with an hourly rainfall intensity over 30.7 mm,6-hour cumulative rainfall over 64.5 mm and 24-hour cumulative rainfall over 91.7 mm are prone to induce waterlogging,and the average wind speed over 12.4 m·s^-1 are associated with gale disasters.There are significant differences in the disaster threshold among different administrative regions.For the rainstorm,the hourly rainfall intensity threshold in downtown area is generally higher than that in suburban areas,but the 24-hour cumulative rainfall threshold is obviously lower than that in suburban areas.For strong wind,the threshold of disaster caused by strong wind in downtown area is significantly lower than that in suburban areas.This study provides a quantitative reference for meteorological disaster prevention and urban governance in each district of Shanghai by developing the disaster threshold.

Based on SCMOC refined grid forecast products and observation data of traffic meteorological stations,using the Fully Connected Neural Network model (FCNN),the hourly road surface temperature was forecasted for the next 24 hours in winter along the complex section of the G6 Beijing-Xizang Highway in Inner Mongolia.The results show that according to the experiment,the optimal training period of FCNN model is 20 d.Increasing the time characteristic variable could reduce the forecasting error of the road surface temperature effectively,MAE decreases by 0.3~0.8 ℃,AR increases by 4%~18%.The final FCNN model of road surface temperature forecast is developed with high accuracy,in which ME,MAE and RMSE are -0.2~0.0 ℃,1.1~1.3 ℃,and 1.6~1.9 ℃,respectively,r exceeds 0.94,AR is above 90%,and better performance for temperature below 0 ℃.From the perspective of daily variation of each test index,except for the poor forecast performance from 12:00 to 15:00,the other time periods have good forecast performance,with the mean values of ME,MAE,RMSE,r,and AR are -0.07 ℃,1.20 ℃,1.58 ℃,0.94,and 93%,respectively.Case studies conducted for stable weather,cold wave weather,and snowfall weather during the winter of 2022 confirm that the FCNN model maintains a forecasting MAE within 2.0 ℃ across different weather scenarios,demonstrating its applicability for road surface temperature prediction under diverse meteorological conditions.