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The physical parameterizations have important influence on model performance in precipitation simulation and prediction; however, previous investigations are seldom conducted at very high resolution over Hebei Province, which is often influenced by extreme events such as droughts and floods. In this paper, the influence of parameterization schemes and analysis nudging on precipitation simulation is investigated using the WRF (weather research and forecasting) model with many sensitivity experiments at the cumulus "gray-zone" resolution (5 km). The model performance of different sensitivity simulations is determined by a comparison with the local high-quality observational data. The results indicate that the WRF model generally reproduces the distribution of precipitation well, and the model tends to underestimate precipitation compared with the station observations. The sensitivity simulation with the Tiedtke cumulus parameterization scheme combined with the Thompson microphysics scheme shows the best model performance, with the highest temporal correlation coefficient (0.45) and lowest root mean square error (0.34 mm/day). At the same time, analysis nudging, which incorporates observational information into simulation, can improve the model performance in precipitation simulation. Further analysis indicates that the negative bias in precipitation may be associated with the negative bias in relative humidity, which in turn is associated with the positive bias in temperature and wind speed. This study highlights the role of parameterization schemes and analysis nudging in precipitation simulation and provides a valuable reference for further investigations on precipitation forecasting applications. [ABSTRACT FROM AUTHOR]

Among the critical global crises curbing world development and sustainability, air quality degradation has been a long-lasting and increasingly urgent one and it has been sufficiently proven to pose severe threats to human health and social welfare. A higher level of model prediction accuracy can play a fundamental role in air quality assessment and enhancing human well-being. In this paper, four types of machine learning models—random forest model, ridge regression model, support vector machine model, extremely randomized trees model—were adopted to predict PM2.5 concentration in ten cities in the Jing-Jin-Ji region of north China based on multi-sources spatiotemporal data including air quality and meteorological data in time series. Data were fed into the model by using the rolling prediction method which is proven to improve prediction accuracy in our experiments. Lastly, the comparative experiments show that at the city level, RF and ExtraTrees models have better predictive results with lower mean absolute error (MAE), root mean square error (RMSE), and higher index of agreement (IA) compared to other selected models. For seasonality, level four models all have the best prediction performances in winter time and the worst in summer time, and RF models have the best prediction performance with the IA ranging from 0.93 to 0.98 with an MAE of 5.91 to 11.68 μg/m3. Consequently, the demonstration of how each model performs differently in each city and each season is expected to shed light on environmental policy implications. [ABSTRACT FROM AUTHOR]

Satellite-based precipitation products (SPPs) provide valuable precipitation information for various applications. Their performance, however, varies significantly from region to region due to various data sources and production processes. This paper aims to evaluate four selected SPPs (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Climate Data Record (PERSIANN-CDR), Multi-Source Weighted-Ensemble Precipitation (MSWEP), Gauge-adjusted Global Satellite Mapping of Precipitation (GSMAP-gauge), and Global Precipitation Measurement (GPM)) over mainland China from 2016 to 2019. Both conventional statistical indicators (e.g., correlation coefficients (CC), root mean square error (RMSE), mean absolute error (MAE), relative bias (RB), and Nash–Sutcliffe efficiency (NSE)) and categorical indicators (probability of detection (POD), probability of true detection (POTD), false-alarm rate (FAR), and critical success index (CSI)) are used for quantitative analysis. The results show that: (1) GSMAP-gauge and GPM perform best in reproducing the spatial distribution pattern of precipitation over mainland China, whereas SPPs generally underestimate summer precipitation with a high frequency of no-rain cases. (2) MSWEP has the best capability for recording precipitation events, although some parts of northern China exhibit abnormal overestimations for winter precipitation. (3) All SPPs, especially the PERSIANN-CDR, significantly underestimate the precipitation in the mountainous areas of southwestern China. (4) The GSMAP-gauge and GPM outperformed the other two of the four SPPs, in terms of the probability density function of daily precipitation for cases (PDFc) and the probability density function of daily precipitation for volume (PDFv). Generally, PERSIANN-CDR shows the poorest performance when compared to the other three products. The product's algorithm for estimating heavy precipitation and mountainous precipitation needs further improvement. [ABSTRACT FROM AUTHOR]

Nitrogen dioxide (NO2) is an important precursor of atmospheric aerosol. Forecasting urban NO2 concentration is vital for effective control of air pollution. This paper proposes a hybrid deep learning model for predicting daily average NO2 concentrations on the next day, based on atmospheric pollutants, meteorological data, and historical data during 2014 to 2020 in five coastal cities of Shandong peninsula, northern China. A random Forest (RF) algorithm was used to select input variables to reduce data dimensionality trained by the sequence to sequence (Seq2Seq) the model and describe how the Seq2Seq model understands each predictor variable. The hybrid model combining an RF with Seq2Seq network (RF-S2S) was evaluated and achieved a Pearson's correlation coefficient of 0.93, a Nash–Sutcliffe coefficient (NS) of 0.79, a Root Mean Square Error (RMSE) of 5.85 µg/m3, a Mean Absolute Error (MAE) of 4.50 µg/m3, and a Mean Absolute Percentage Error (MAPE) of 20.86%. Feature selection by an RF model improves the performance of the Seq2Seq model, reducing errors by 19.7% (RMSE), 20.3% (MAE), and 29.3% (MAPE), respectively. Carbon monoxide (CO) and PM10 are two common, important features influencing the prediction of NO2 concentrations in coastal areas of northern China. The results of RF-S2S models can capture general trends and disruptions more accurately than can long-short term memory (LSTM) models with and without feature selection. The decreasing tendency of NO2 from 2014 to 2020 illustrated by the empirical mode decomposition (EMD) method is one important obstacle to improving the RF-S2S prediction accuracy. An EMD-based RF-S2S model could help to perform the short-term forecast of NO2 concentrations efficiently. [ABSTRACT FROM AUTHOR]

Methane (CH4) is the second-largest greenhouse gas emitted by human activity and natural sources after carbon dioxide (CO2). Its relatively short lifetime in the atmosphere (about 12 years) means that we can mitigate the human impacts of climate change in a relatively short period of time by reducing CH4 emissions. The creation of CH4 emissions management policies can be based on the distribution maps of surface CH4 concentration that are in large-scale and at high-resolution. The estimate of CH4 emissions with broad coverage are provided by currently extensively used satellite data supplemented with data from model simulations. However, it is at low spatial resolution. In this paper, through the combination of atmospheric CH4 observations from the TROPOMI sensor and wind data from the ECMWF global reanalysis, a straightforward divergence method is proposed to estimate the surface CH4 emissions in China from March 2019 to September 2022 at a resolution of 7 km × 7 km. This method was compared with the average annual CH4 emissions of Emissions Database for Global Atmospheric Research (EDGARv7.0), and the Root Mean Square Error (RMSE) is 2.53 kg/km2/h and within error envelop (EE) is 72.93%, which represents the proportion of reliable values under certain uncertain conditions. We estimated that the average annual CH4 emissions in China from 2019 to 2022 is 81 Tg, with the lowest emissions in 2021 (75 Tg) due to the impact of COVID-19. In 2021, the largest anthropogenic emissions in China are from agriculture, energy activities and livestock, accounting for 28% (20.8 Tg), 25% (18.9 Tg) and 19% (13.9 Tg) of total emissions, respectively, while wetlands, as the largest natural source, produce 14% (10.5 Tg) of CH4 emissions. [ABSTRACT FROM AUTHOR]

Accurate prediction of PM2.5 concentration is one of the key tasks of air pollution assessment, early warning, and treatment. In this paper, four monitoring sites were arranged in Jiangbei New District of Nanjing City, China. The environmental parameters such as PM2.5/PM10 concentration, temperature, and humidity were monitored from January to February 2020. A gated recurrent unit (GRU) network based on the PM2.5 concentration prediction model was established to predict PM2.5 concentration. The mean relative error (MRE), root mean square error (RMSE), and Pearson correlation coefficient were selected as the evaluation criteria for the accuracy of the GRU model. The data set was divided into a training set, a test set and a validation set at a ratio of 7:2:1, and the GRU model was used to predict the hourly value of PM2.5 concentration in the next week. The prediction results show that the Pearson correlation coefficients between the predicted values and the monitored values of the four monitoring sites have reached more than 0.9, reflecting a strong correlation. The relative average errors are around 10%. The GRU model prediction of NJAU (Nanjing Agricultural University)-Pukou Campus Site is the most accurate, and the correlation coefficient, MRE, and RMSE are 0.970, 7.85%, and 9.6049, respectively, reflecting the good prediction performance of the model. Therefore, this research supports the prediction of air quality in different cities and regions, so people can take protective measures in advance and reduce the damage caused by air pollution to human bodies. [ABSTRACT FROM AUTHOR]

The 2-m temperature is one of the important meteorological elements, and improving the accuracy of medium- and long-term forecasts of the 2-m temperature is important. The similarity forecasting method is widely used as a calibration technique in the statistical postprocessing of numerical weather prediction (NWP). In this study, the analogue ensemble averaging method is used to correct the deterministic forecast of the 2-m temperature with a forecast lead time from 180 h to 348 h using the CMA-GEPS model. The bias, mean absolute error (MAE), and root mean square error (RMSE) are used as the evaluation metrics. In comparison with NWP, the systematic error of the model for 2-m temperature is effectively reduced during each forecast period when using the analogue ensemble averaging method. In addition, the differences in forecast errors between regions are reduced, and the accuracy of 2-m temperature forecasts over complex terrain, especially in Southwest China, Northwest China, and North China, is improved using this method. In the future, there is certainly potential to apply the analogue ensemble averaging method to the bias correction of medium- and long-term forecasts of more meteorological elements. [ABSTRACT FROM AUTHOR]

Currently, satellite-based soil moisture (SM) products and land surface model assimilation techniques are widely utilized. However, the presence of systematic errors in the observation process, algorithmic discrepancies between products, and variations in spatial and temporal scales result in diverse accuracy characteristics and applicability. This study evaluates three prominent SM products in China, namely, the Essential Climate Variable Soil Moisture (ECV), the European Centre for Medium-Range Weather Forecasts' Fifth-Generation Land Surface Reanalysis Data (ERA5-Land), and the Global Land Surface Data Assimilation System (GLDAS). The evaluation was conducted using extended triple collocation (ETC) analysis and in situ validation methods at a monthly scale from 2000 to 2020. The ETC analysis results show that among the three products, GLDAS exhibits the highest correlation coefficient (CC) and the lowest standard deviation of error (ESD), indicating its superior performance in China. ECV and ERA5-Land follow, with slightly lower performance. In the in situ validation results, ERA5-Land displays the highest correlation, capturing the temporal trend of the ground SM well. Comparatively, in terms of overall accuracy, ECV performs the best, with a slightly smaller mean error (ME) and root mean square error (RMSE) than GLDAS, and ERA5-Land has the lowest accuracy. The discrepancy between the in situ validation results and ETC analysis can be attributed to the limited number of sites and their representativeness errors. Notably, ERA5-Land exhibits a highly consistent trend of interannual fluctuations between ESD and precipitation. Furthermore, a strong association is observed between the ME and RMSE of ECV and GLDAS and precipitation. These findings serve as valuable references for future SM studies in China. [ABSTRACT FROM AUTHOR]

Polarimetric radar data are an important tool for quantitative precipitation estimation (QPE), which is essential for monitoring and forecasting precipitation. Previous studies have shown that the drop size distribution (DSD) and polarimetric radar parameters of typhoon-induced precipitation differ significantly from those of other types of rainfall. South China is a region that frequently experiences typhoons and heavy rainfall, which can cause serious disasters. Therefore, it is critical to develop a QPE algorithm that is suitable for typhoon precipitation over South China. In this study, we constructed four simple QPE estimators, R(ZH), R(ZH, ZDR), R(KDP) and R(KDP, ZDR) based on two-dimensional video disdrometer (2DVD) DSD observations of typhoon-induced precipitation over South China in 2017–2018. We analyzed the DSD characteristics and the estimation accuracy of these four QPE estimators in the reflectivity–differential reflectivity (ZH–ZDR) space, as well as the S-band polarimetric radar (S-POL) data of seven typhoon-induced precipitation events that affected South China in 2017–2019. We used these data to quantitatively determine the optimal ranges of the estimators and establish a typhoon precipitation QPE algorithm for typhoon-induced precipitation over South China (2DVD-Typhoon). The evaluation results showed that: (1) compared to R(ZH) and R(KDP), R(ZH, ZDR) and R(KDP, ZDR) had lower performance in estimating typhoon-induced rainfall after incorporating the polarimetric parameter ZDR, as strong crosswind of the typhoon caused some bias in the raindrop-induced ZDR; (2) the 2DVD-Typhoon algorithm utilizes the respective advantages of the individual estimators to generate the best QPE results; (3) the QPE performance of 2DVD-Typhoon and the Colorado State University–Hydrometeor Identification Rainfall Optimization (CSU-HIDRO) is used as a comparison for hourly rainfall, cumulative rainfall and different rainfall intensity. The comparison shows that 2DVD-Typhoon gives a better normalized error (NE), root mean square error (RMSE) and correlation coefficient (CC), indicating its strength in rainfall estimation for typhoons over South China. The above results provide theoretical support for improving typhoon-induced rainfall monitoring and numerical weather forecasting models in South China. [ABSTRACT FROM AUTHOR]

The ensemble optimal interpolation method was used in this study to conduct an examination of the assimilations of significant wave height (SWH) data from HY-2A satellite altimeter based on the WAVEWATCH III global ocean wave model. The results suggested that the ensemble optimal interpolation method using HY-2A SWH data played a positive role in enhancing the accuracy of the global ocean wave simulations and could effectively improve the deviations of SWH in the simulation processes. The root mean square errors of the NDBC buoy inspections were improved by 7 to 44% after the assimilation, and those of China's offshore buoy inspections were improved by 3 to 11% after the assimilation. It was observed that the farther the buoys were from the shore, the better the effects of the assimilation improvements. The root mean square errors of the Jason-2 satellite data validations were improved by 17% after the assimilation, with monthly improvements of 8–25%. The improvements occurred in most of the global oceans, particularly in the Southern Ocean, the Eastern Pacific Ocean and the Indian Ocean. The results obtained in this research can be used as a reference for the operational applications of China's ocean satellite data in ocean wave data assimilation and prediction. [ABSTRACT FROM AUTHOR]

Odor measurement is a crucial element of odor management and regulation. This paper introduced a collaborative implementation of interlaboratory evaluation of olfactometry between Japan and China. An international comparison of olfactometry using the triangular odor bag method was carried out for the first time between Japan and China in 2018. A total of 134 olfactometry laboratories (130 Japanese and 4 Chinese) participated in the test, and the odor index of the test odorant (dimethyl disulfide with a concentration of 10.7 ppm) was measured three times at each laboratory. In the interlaboratory evaluation, a reference value and repeatability and reproducibility standard deviations were determined on the basis of measurement results of 13 'excellent qualified laboratories' designated by the Japan Association on Odor Environment. Evaluation results of trueness and precision of the 133 laboratories that conducted duplicate or triplicate measurements showed that 110 (108 Japanese and 2 Chinese) and 104 (102 Japanese and 2 Chinese) laboratories (82.7% and 78.2%) conformed to the criterion of trueness and precision, respectively, and 87 (86 Japanese and 1 Chinese) laboratories (65.4%) conformed to both. Based on the meaningful experiences in 2018, a continuous international collaboration between Japan and China in the field of olfactometry should be implemented for the improvement of the quality of olfactometry laboratories and the reliability of odor measurement in both countries. [ABSTRACT FROM AUTHOR]

Sunshine duration is an important indicator of the amount of solar radiation received in a region and an important input parameter for the study of atmospheric energy balance, climate change, ecosystem evolution, and social sustainability. Currently, extrapolation and interpolation of data from meteorological stations are the most common methods used to calculate sunshine duration on a regional scale. However, it is difficult to obtain high precision sunshine duration in areas lacking ground observation or where sunshine duration is highly heterogeneous on the ground. In this paper, a new method is proposed to estimate sunshine duration with hourly total cloud amount (CTA) data from sunrise to sunset derived from the Fengyun-2G geostationary meteorological satellite (FY-2G). This method constructs a new index known as daytime mean total cloud coverage amount and provides quadratic equations relating daytime mean total cloud coverage amount to relative sunshine duration in different seasons. The method was validated with ground observation data for 2016 from 18 meteorological stations in the Three-River Headwaters Region of Qinghai Province, China. For individual stations, the coefficient of determination (R2) between estimated and measured sunshine was at least 0.894, the RMSE (root mean square error) was 0.977 h/day or less, the MAE (mean absolute error) was 0.824 h/day or less, the RE (relative error) was 0.150 or lower, and the value of d was 0.963 or greater, which validated that the proposed method can effectively predict daily sunshine duration. These equations can also provide higher precision estimates of regional-scale sunshine duration. This was demonstrated by comparing, for the entire study region, the spatial distribution of sunshine duration estimated from season-based equations with results from three different interpolation methods based on ground observations. Overall, the study confirms that total cloud amount measures from a geostationary satellite can be used to successfully estimate sunshine duration. [ABSTRACT FROM AUTHOR]

For better modeling the vertical distribution and variations of water vapor, a 10-year time-series of a newly derived water vapor parameter (termed IRPWV), defined as the ratio of water vapor density (WVD) to the total precipitable water vapor (TPWV), was statistically analyzed. This research showed that the vertical distribution of IRPWV presents a periodic pattern and is highly correlated with the relative magnitude of its corresponding TPWV when compared with the other TPWVs in the same time range. Six TPWV ranges were first chosen to determine the relative magnitude and then used to classify the IRPWV vertical distributions of TPWV. For the periodic variations in each of the six classified IRPWV vertical distribution time-series, a temporal IRPWV model was developed accordingly with six sets of coefficients. The new models were validated by comparing their predictions against the reference values from sounding data at 12 radiosonde stations in China, and their performance was also evaluated against that of the commonly used exponential model. Results showed that, first, the proportions of the height range that had reduced annual root mean square error (RMSE) of WVD in all height ranges within all TPWV ranges were over 75% at the 12 stations. Then, the annual RMSEs of the WVD for all the stations were reduced by at least 11%, 20%, 43%, 48%, 40%, 38%, 32%, 35%, 32%, and 28% in each of the 10 selected height ranges, respectively. [ABSTRACT FROM AUTHOR]

Cloud droplet size distribution (CDSD) is a critical characteristic for a number of processes related to clouds, considering that cloud droplets are formed in different sizes above the cloud-base. This paper analyzes the in-situ aircraft measurements of CDSDs and aerosol concentration ( N a ) performed in stratiform clouds in Hebei, China, in 2015 to reveal the characteristics of cloud spectral width, commonly known as relative dispersion (ε , ratio of standard deviation (σ) to mean radius (r) of the CDSD). A new algorithm is developed to calculate the contributions of droplets of different sizes to ε. It is found that small droplets with the size range of 1 to 5.5 μm and medium droplets with the size range of 5.5 to 10 μm are the major contributors to ε, and the medium droplets generally dominate the change of ε. The variation of ε with N a can be well explained by comparing the normalized changes of σ and r ( k σ / σ and k r / r ), rather than k σ and k r only ( k σ is Δσ/Δ N a and k r is Δr/Δ N a ). From the perspective of external factors affecting ε change, the effects of N a and condensation are examined. It is found that ε increases initially and decreases afterward as N a increases, and "condensational broadening" occurs up to 1 km above cloud-base, potentially providing observational evidence for recent numerical simulations in the literature. [ABSTRACT FROM AUTHOR]

In the Tropical Regional Atmospherical Model System of South China of the China Meteorological Administration (CMA-TRAMS), the skin sea surface temperature ( T s ) remains fixed during the forecast time. This limits the model's performance in describing interactions between air and sea. The offline diagnostics and online analysis coupled with the CMA-TRAMS of T s prognostic scheme were discussed. The results of the offline diagnostics showed that the profile shape parameter, ν, and initial temperature, T b , were sensitive to the forecasted T s . Based on our observations, when ν was set to 0.2 and T b was the averaged T s without obvious diurnal variation, the forecasted T s was relatively reasonable. The forecasted T s of CMA-TRAMS after coupling with the T s scheme had diurnal variations during the overall forecast time, which was different from the fixed T s from the uncoupled model. There existed a certain difference of forecasted T s between uncoupled and coupled models in those days influenced by typhoons. The biases and Root Mean Square Errors (RMSEs) for the temperature and moisture in the lower layer and those for the wind speed in most layers were reduced and, therefore, the accuracy of environmental field forecasting was improved from the coupled model. The typhoon track errors after 36-h decreased due to the improvement of steering flow on the west side of subtropical high from the coupled model. However, the difference of typhoon intensity errors was insignificant, which might mean that the differences of forecasted T s and heat flux between uncoupled and coupled model are small. The reasons for the small difference need to be further investigated. [ABSTRACT FROM AUTHOR]

With the intensification of global warming and economic development in China, the near-surface ozone (O3) concentration has been increasing recently, especially in the Beijing-Tianjin-Hebei (BTH) region, which is the political and economic center of China. However, O3 has been measured in real time only over the past few years, and the observational records are discontinuous. Therefore, we propose a new method (WRFC-XGB) to establish a near-surface O3 concentration dataset in the BTH region by integrating the Weather Research and Forecasting with Chemistry (WRF-Chem) model with the extreme gradient boosting (XGBoost) algorithm. Based on this method, the 8-h maximum daily average (MDA8) O3 concentrations are obtained with full spatiotemporal coverage at a spatial resolution of 0.1° × 0.1° across the BTH region in 2018. Two evaluation methods, sample- and station-based 10-fold cross-validation (10-CV), are used to assess our method. The sample-based (station-based) 10-CV evaluation results indicate that WRFC-XGB can achieve excellent accuracy with a high coefficient of determination (R2) of 0.95 (0.91), low root mean square error (RMSE) of 13.50 (17.70) µg m−3, and mean absolute error (MAE) of 9.60 (12.89) µg m−3. In addition, superb spatiotemporal consistencies are confirmed for this model, including the estimation of high O3 concentrations, and our WRFC-XGB model outperforms traditional models and previous studies in data mining. In addition, the proposed model can be applied to estimate the O3 concentration when it has not been measured. Furthermore, the spatial distribution analysis of the MDA8 O3 in 2018 reveals that O3 pollution in the BTH region exhibits significant seasonality. Heavy O3 pollution episodes mainly occur in summer, and the high O3 loading is distributed mainly in the southern BTH areas, which will pose challenges to atmospheric environmental governance for local governments. [ABSTRACT FROM AUTHOR]

As the second most abundant greenhouse gas after carbon dioxide (CO2), methane not only plays an important role in global and regional photochemical reactions, but also has an important impact on energy balance and climate change. To explore the long-term trends and spatiotemporal variation of methane concentration over China, we verified the accuracy of the column-averaged, dry air-mixing ratio of CH4 (abbreviated as XCH4 hereafter) merged by SCIAMACHY and GOSAT products, utilizing the data of six surface observation stations in China and the surrounding areas. The root mean square error (RMSE) was mostly less than 2.5%, and the correlation coefficients (r) were 0.77, 0.84, 0.66, 0.42, 0.62 and 0.75. Furthermore, we analyzed the temporal and spatial variation patterns of the XCH4 concentration over China from 2003 to 2020. The results showed that the XCH4 concentration had an increasing trend over China from 2003 to 2020; the average growth rate was 6.64 ppb·a−1, and the value range of the increase rate was from 4.66 ppb·a−1 to 8.46 ppb·a−1. The lowest XCH4 concentration was located over Tibet (1764.03 ppb), and the high values were located in the Sichuan Basin, Central China (Hunan, Hubei, and Henan) and East China (Anhui and Jiangxi) (1825–1845 ppb). The XCH4 concentration was higher in autumn and summer, low in winter and spring, and had obvious seasonal variations. Human factors such as population density, GDP and energy consumption have a significant impact on the XCH4 concentration over China. [ABSTRACT FROM AUTHOR]

Fine particulate matter (PM2.5) has a serious impact on human health. Forecasting PM2.5 levels and analyzing the pollution sources of PM2.5 are of great significance. In this study, the Lagrangian particle dispersion (LPD) model was developed by combining the FLEXPART model and the Bayesian inventory optimization method. The LPD model has the capacity for real-time forecasting and determination of pollution sources of PM2.5, which refers to the contribution ratio and spatial distribution of each type of pollution (industry, power, residential, and transportation). In this study, we applied the LPD model to the Beijing-Tianjin-Hebei (BTH) region to optimize the a priori PM2.5 emission inventory estimates during 15–20 March 2018. The results show that (1) the a priori estimates have a certain degree of overestimation compared with the a posteriori flux of PM2.5 for most areas of BTH; (2) after optimization, the correlation coefficient (R) between the forecasted and observed PM2.5 concentration increased by an average of approximately 10%, the root mean square error (RMSE) decreased by 30%, and the IOA (index of agreement) index increased by 16% at four observation sites (Aotizhongxin_Beijing, Beichenkejiyuanqu_Tianjin, Dahuoquan_Xintai, and Renmingongyuan_Zhangjiakou); and (3) the main sources of pollution at the four sites mainly originated from industrial and residential emissions, while power factory and transportation pollution accounted for only a small proportion. The concentration of PM2.5 forecasts and pollution sources in each type of analysis can be used as corresponding reference information for environmental governance and protection of public health. [ABSTRACT FROM AUTHOR]

The capability to estimate monsoon rainfall is investigated by using S-band polarimetric radar (S-POL) and two-dimensional Video Disdrometer (2DVD) during 2017–2018 in South China. Based on 2 years of 2DVD raindrop size distribution (DSD) observations of monsoon precipitation systems, four different quantitative precipitation estimation (QPE) algorithms were obtained, including R(ZH), R(ZH, ZDR), R(KDP), and R(KDP, ZDR). In order to clearly demarcate the optimal ranges of the four QPE algorithms by considering the impact of the monsoon precipitation system of South China, the optimal ranges of the four QPE algorithms were integrated together according to the characteristics of different QPE algorithms in the reflectivity-differential reflectivity (ZH-ZDR) space distribution by reference to 8 monsoon rainfall events from 2016 to 2020 observed in Guangzhou and Yangjiang S-POL. Then, an optimal algorithm was proposed for the quantitative estimation of monsoon precipitation in South China (2DVD-SCM) using S-POL. The 2DVD-SCM was tested by comparing it with a traditional radar QPE algorithm PPS (WSR-88D Precipitation Processing System); a classical QPE algorithm CSU-HIDRO (Colorado State University-Hydrometeor Identification Rainfall Optimization) for the polarimetric radar; a piecewise fitting algorithm LPA-PFM (Piecewise Fitting Method) based on laser raindrop spectrum. The rainfall event one-by-one test results show that the 2DVD-SCM algorithm performs obviously better than the other three algorithms in most of the rainfall events. The hourly accumulated rainfalls estimated by the 2DVD-SCM algorithm are agreed well with rain gauge observations. The normalized errors (NE) and the root mean square errors (RMSE) values of 2DVD-SCM are remarkably less than the other three algorithms, and the correlation coefficient (CC) values are higher. The results of the classified rain rate test show that the NE and RMSE values of the 2DVD-SCM algorithm are the lowest in all classified rain rates. The overall evaluation results show that the 2DVD-SCM algorithm performs obviously better than the existing three algorithms and have the potential to apply in S-band polarimetric radar monsoon rainfall estimation operational system in South China. [ABSTRACT FROM AUTHOR]

Wind speed data derived from reanalysis datasets has been used in the plan and design of wind farms in China, but the quality of these kinds of data over China remains unknown. In this study, the performances of five sets of reanalysis data, including National Centers for Environmental Predictions (NCEP)-U.S. Department of Energy (DOE) Reanalysis 2 (NCEP-2), Modern-ERA Retrospective Analysis for Research and Applications (MERRA), Japanese 55-year Reanalysis Project (JRA-55), Interim ECMWF Re-Analysis product (ERA-Interim), and 20th Century Reanalysis (20CR) in reproducing the climatology, interannual variation, and long-term trend of near-surface (10 m above ground) wind speed, for the period of 1979–2011 over continental China are comprehensively evaluated. Compared to the gridded data compiled from meteorological stations, all five reanalysis datasets reasonably reproduce the spatial distribution of the climatology of near-surface wind speed, but underestimate the intensity of the near-surface wind speed in most regions except for Tibetan Plateau where the wind speed is overestimated. All five reanalysis datasets show large weaknesses in reproducing the annual cycle of near-surface wind speed averaged over the continental China. The near-surface wind speed derived from the observations exhibit significant decreasing trends over most parts of continental China during 1979 to 2011. Although the spatial patterns of the linear trends reproduced by reanalysis datasets are close to the observation, the magnitudes are weaker in annual, spring, summer and autumn season. The qualities of all reanalysis datasets are limited in winter. For the interannual variability, except for winter, all five reanalysis datasets reasonably reproduce the interannual standard deviation but with larger amplitude. Quantitative comparison indicates that among the five reanalysis datasets, the MERRA (JRA-55) shows the relatively highest (lowest) skill in terms of the climatology and linear trend. These results call for emergent needs for developing high quality reanalysis data that can be used in wind resource assessment and planning. [ABSTRACT FROM AUTHOR]

High-resolution meteorological data products are crucial for agrometeorological studies. Here, we study the accuracy of an important gridded dataset, the near-surface temperature dataset from the 5 km × 5 km resolution China dataset of meteorological forcing for land surface modeling (published by the Beijing Normal University). Using both the gridded dataset and the observed temperature data from 590 meteorological stations, we calculate nine universal meteorological indices (mean, maximum, and minimum temperatures of daily, monthly, and annual data) and five agricultural thermal indices (first frost day, last frost day, frost-free period, and ≥0 °C and ≥10 °C active accumulated temperature, i.e., AAT0 and AAT10) of the 11 temperature zones over mainland China. Then, for each meteorological index, we calculate the root mean square errors (RMSEs), correlation coefficient and climate trend rates of the two datasets. The results show that the RMSEs of these indices are usually lower in the north subtropical, mid-subtropical, south subtropical, marginal tropical and mid-tropical zones than in the plateau subfrigid, plateau temperate, and plateau subtropical mountains zones. Over mainland China, the AAT0, AAT10, and mean and maximum temperatures calculated from the gridded data show the same climate trends with those derived from the observed data, while the minimum temperature and its derivations (first frost day, last frost day, and frost-free period) show the opposite trends in many areas. Thus, the mean and maximum temperature data derived from the gridded dataset are applicable for studies in most parts of China, but caution should be taken when using the minimum temperature data. [ABSTRACT FROM AUTHOR]