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Original Paper
Characteristics of Surface Solar Radiation under Different Air Pollution Conditions over Nanjing, China: Observation and Simulation
Hao LUO, Yong HAN, Chunsong LU, Jun YANG, Yonghua WU
2019, 36(10): 1047-1059. doi: 10.1007/s00376-019-9010-4
Surface solar radiation (SSR) can affect climate, the hydrological cycle, plant photosynthesis, and solar power. The values of solar radiation at the surface reflect the influence of human activity on radiative climate and environmental effects, so it is a key parameter in the evaluation of climate change and air pollution due to anthropogenic disturbances. This study presents the characteristics of the SSR variation in Nanjing, China, from March 2016 to June 2017, using a combined set of pyranometer and pyrheliometer observations. The SSR seasonal variation and statistical properties are investigated and characterized under different air pollution levels and visibilities. We discuss seasonal variations in visibility, air quality index (AQI), particulate matter (PM10 and PM2.5), and their correlations with SSR. The scattering of solar radiation by particulate matter varies significantly with particle size. Compared with the particulate matter with aerodynamic diameter between 2.5 μm and 10 μm (PM2.5−10), we found that the PM2.5 dominates the variation of scattered radiation due to the differences of single-scattering albedo and phase function. Because of the correlation between PM2.5 and SSR, it is an effective and direct method to estimate PM2.5 by the value of SSR, or vice versa to obtain the SSR by the value of PM2.5. Under clear-sky conditions (clearness index ≥0.5), the visibility is negatively correlated with the diffuse fraction, AQI, PM10, and PM2.5, and their correlation coefficients are −0.50, −0.60, −0.76, and −0.92, respectively. The results indicate the linkage between scattered radiation and air quality through the value of visibility.
Applying Anomaly-based Weather Analysis to the Prediction of Low Visibility Associated with the Coastal Fog at Ningbo-Zhoushan Port in East China
Weihong QIAN, Jeremy Cheuk-Hin LEUNG, Youli CHEN, Siyuan HUANG
2019, 36(10): 1060-1077. doi: 10.1007/s00376-019-8252-5
Low visibility episodes (visibility < 1000 m) were studied by applying the anomaly-based weather analysis method. A regional episode of low visibility associated with a coastal fog that occurred from 27 to 28 January 2016 over Ningbo-Zhoushan Port, Zhejiang Province, East China, was first examined. Some basic features from the anomalous weather analysis for this case were identified: (1) the process of low visibility mainly caused by coastal fog was a direct response to anomalous temperature inversion in the lower troposphere, with a warm center around the 925 hPa level, which was formed by a positive geopotential height (GPH) anomaly in the upper troposphere and a negative GPH anomaly near the surface; (2) the positive humidity anomaly was conducive to the formation of coastal fog and rain; (3) regional coastal fog formed at the moment when the southwesterly wind anomalies transferred to northeasterly wind anomalies. Other cases confirmed that the low visibility associated with coastal fog depends upon low-level inversion, a positive humidity anomaly, and a change of wind anomalies from southwesterly to northeasterly, rain and stratus cloud amount. The correlation coefficients of six-hourly inversion, 850−925-hPa-averaged temperature, GPH and humidity anomalies against visibility are −0.31, 0.40 and −0.48, respectively, reaching the 99% confidence level in the first half-years of 2015 and 2016. By applying the anomaly-based weather analysis method to medium-range model output products, such as ensemble prediction systems, the anomalous temperature−pressure pattern and humidity−wind pattern can be used to predict the process of low visibility associated with coastal fog at several days in advance.
Main Detrainment Height of Deep Convection Systems over the Tibetan Plateau and Its Southern Slope
Quanliang CHEN, Guolu GAO, Yang LI, Hongke CAI, Xin ZHOU, Zhenglin WANG
2019, 36(10): 1078-1088. doi: 10.1007/s00376-019-9003-3
Deep convection systems (DCSs) can rapidly lift water vapor and other pollutants from the lower troposphere to the upper troposphere and lower stratosphere. The main detrainment height determines the level to which the air parcel is lifted. We analyzed the main detrainment height over the Tibetan Plateau and its southern slope based on the CloudSat Cloud Profiling Radar 2B_GEOPROF dataset and the Aura Microwave Limb Sounder Level 2 cloud ice product onboard the A-train constellation of Earth-observing satellites. It was found that the DCSs over the Tibetan Plateau and its southern slope have a higher main detrainment height (about 10−16 km) than other regions in the same latitude. The mean main detrainment heights are 12.9 and 13.3 km over the Tibetan Plateau and its southern slope, respectively. The cloud ice water path decreases by 16.8% after excluding the influences of DCSs, and the height with the maximum increase in cloud ice water content is located at 178 hPa (about 13 km). The main detrainment height and outflow horizontal range are higher and larger over the central and eastern Tibetan Plateau, the west of the southern slope, and the southeastern edge of the Tibetan Plateau than that over the northwestern Tibetan Plateau. The main detrainment height and outflow horizontal range are lower and broader at nighttime than during daytime.
Vertical Structures of Convective and Stratiform Clouds in Boreal Summer over the Tibetan Plateau and Its Neighboring Regions
Yafei YAN, Yimin LIU
2019, 36(10): 1089-1102. doi: 10.1007/s00376-019-8229-4
Cloud is essential in the atmosphere, condensing water vapor and generating strong convective or large-scale persistent precipitation. In this work, the relationships between cloud vertical macro- or microphysical properties, radiative heating rate, and precipitation for convective and stratiform clouds in boreal summer over the Tibetan Plateau (TP) are analyzed and compared with its neighboring land and tropical oceans based on CloudSat/CALIPSO satellite measurements and TRMM precipitation data. The precipitation intensity caused by convective clouds is twofold stronger than that by stratiform clouds. The vertical macrophysics of both cloud types show similar features over the TP, with the region weakening the precipitation intensity and compressing the cloud vertical expansion and variation in cloud top height, but having an uplift effect on the average cloud top height. The vertical microphysics of both cloud types under conditions of no rain over the TP are characterized by lower-level ice water, ice particles with a relatively larger range of sizes, and a relatively lower occurrence of denser ice particles. The features are similar to other regions when precipitation enhances, but convective clouds gather denser and larger ice particles than stratiform clouds over the TP. The atmospheric shortwave (longwave) heating (cooling) rate strengthens with increased precipitation for both cloud types. The longwave cooling layer is thicker when the rainfall rate is less than 100 mm d−1, but the net heating layer is typically compressed for the profiles of both cloud types over the TP. This study provides insights into the associations between clouds and precipitation, and an observational basis for improving the simulation of convective and stratiform clouds over the TP in climate models.
Microphysical Characteristics of Precipitation during Pre-monsoon, Monsoon, and Post-monsoon Periods over the South China Sea
Qingwei ZENG, Yun ZHANG, Hengchi LEI, Yanqiong XIE, Taichang GAO, Lifeng ZHANG, Chunming WANG, Yanbin HUANG
2019, 36(10): 1103-1120. doi: 10.1007/s00376-019-8225-8
Raindrop size distribution (RSD) characteristics over the South China Sea (SCS) are examined with onboard Parsivel disdrometer measurements collected during marine surveys from 2012 to 2016. The observed rainfall is divided into pre-monsoon, monsoon, and post-monsoon periods based on the different large-scale circumstances. In addition to disdrometer data, sounding observation, FY-2E satellite, SPRINTARS (Spectral Radiation-Transport Model for Aerosol Species), and NCEP reanalysis datasets are used to illustrate the dynamical and microphysical characteristics associated with the rainfall in different periods. Significant variations have been observed in respect of raindrops among the three periods. Intercomparison reveals that small drops (D < 1 mm) are prevalent during pre-monsoon precipitation, whereas medium drops (1−3 mm) are predominant in monsoon precipitation. Overall, the post-monsoon precipitation is characterized by the least concentration of raindrops among the three periods. But, several large raindrops could also occur due to severe convective precipitation events in this period. Classification of the precipitation into stratiform and convective regimes shows that the lg(Nw) value of convective rainfall is the largest (smallest) in the pre-monsoon (post-monsoon) period, whereas the Dm value is the smallest (largest) in the pre-monsoon (post-monsoon) period. An inversion relationship between the coefficient A and the exponential b of the ZR relationships for precipitation during the three periods is found. Empirical relations between Dm and the radar reflectivity factors at Ku and Ka bands are also derived to improve the rainfall retrieval algorithms over the SCS. Furthermore, the possible causative mechanisms for the significant RSD variability in different periods are also discussed with respect to warm and cold rain processes, raindrop evaporation, convective activities, and other meteorological factors.
Long-term Correlations and Extreme Wind Speed Estimations
Lei LIU, Fei HU
2019, 36(10): 1121-1128. doi: 10.1007/s00376-019-9031-z
In this paper, we use fluctuation analysis to study statistical correlations in wind speed time series. Each time series used here was recorded hourly over 40 years. The fluctuation functions of wind speed time series were found to scale with a universal exponent approximating to 0.7, which means that the wind speed time series are long-term correlated. In the classical method of extreme estimations, data are commonly assumed to be independent (without correlations). This assumption will lead to an overestimation if data are long-term correlated. We thus propose a simple method to improve extreme wind speed estimations based on correlation analysis. In our method, extreme wind speeds are obtained by simply scaling the mean return period in the classical method. The scaling ratio is an analytic function of the scaling exponent in the fluctuation analysis.
A 3D Nonhydrostatic Compressible Atmospheric Dynamic Core by Multi-moment Constrained Finite Volume Method
Qingchang QIN, Xueshun SHEN, Chungang CHEN, Feng XIAO, Yongjiu DAI, Xingliang LI
2019, 36(10): 1129-1142. doi: 10.1007/s00376-019-9002-4
A 3D compressible nonhydrostatic dynamic core based on a three-point multi-moment constrained finite-volume (MCV) method is developed by extending the previous 2D nonhydrostatic atmospheric dynamics to 3D on a terrain-following grid. The MCV algorithm defines two types of moments: the point-wise value (PV) and the volume-integrated average (VIA). The unknowns (PV values) are defined at the solution points within each cell and are updated through the time evolution formulations derived from the governing equations. Rigorous numerical conservation is ensured by a constraint on the VIA moment through the flux form formulation. The 3D atmospheric dynamic core reported in this paper is based on a three-point MCV method and has some advantages in comparison with other existing methods, such as uniform third-order accuracy, a compact stencil, and algorithmic simplicity. To check the performance of the 3D nonhydrostatic dynamic core, various benchmark test cases are performed. All the numerical results show that the present dynamic core is very competitive when compared to other existing advanced models, and thus lays the foundation for further developing global atmospheric models in the near future.
Analysis and Simulation of the Stratospheric Quasi-zero Wind Layer over Korla, Xinjiang Province, China
Rui YANG, Lingkun RAN, Yuli ZHANG, Yi LIU
2019, 36(10): 1143-1155. doi: 10.1007/s00376-019-9045-6
The stratospheric quasi-zero wind layer (QZWL) is a transition region with low zonal wind speeds in the lower stratosphere at an altitude of ~20 km. The zonal wind direction above the QZWL layer is opposite to that below the QZWL layer and the north–south wind component is small. The atmospheric wind field near the stratospheric QZWL is an important factor affecting the flight altitude and dynamic control of stratospheric airships. It is therefore necessary to study the stratospheric QZWL to provide better environmental information for these aircraft. High-resolution radiosonde data were used to analyze the characteristics of the stratospheric QZWL over Korla, Xinjiang Province, China. A weak wind layer in which the wind direction suddenly reversed from westerly to easterly was observed at ~20 km in the lower stratosphere, characteristic of the stratospheric QZWL. The Weather Research and Forecasting model was used to simulate the profiles of the horizontal wind speed and direction over Korla. The forcing effect of each diagnostic term in the equation on the zonal wind speed was analyzed. The results showed that the advection term was the dominant factor forcing the zonal wind speed. The wave term had a secondary forcing role, although the forcing effect of the wave term on the zonal wind speed was significant in some regions.
A Model Output Machine Learning Method for Grid Temperature Forecasts in the Beijing Area
Haochen LI, Chen YU, Jiangjiang XIA, Yingchun WANG, Jiang ZHU, Pingwen ZHANG
2019, 36(10): 1156-1170. doi: 10.1007/s00376-019-9023-z
In this paper, the model output machine learning (MOML) method is proposed for simulating weather consultation, which can improve the forecast results of numerical weather prediction (NWP). During weather consultation, the forecasters obtain the final results by combining the observations with the NWP results and giving opinions based on their experience. It is obvious that using a suitable post-processing algorithm for simulating weather consultation is an interesting and important topic. MOML is a post-processing method based on machine learning, which matches NWP forecasts against observations through a regression function. By adopting different feature engineering of datasets and training periods, the observational and model data can be processed into the corresponding training set and test set. The MOML regression function uses an existing machine learning algorithm with the processed dataset to revise the output of NWP models combined with the observations, so as to improve the results of weather forecasts. To test the new approach for grid temperature forecasts, the 2-m surface air temperature in the Beijing area from the ECMWF model is used. MOML with different feature engineering is compared against the ECMWF model and modified model output statistics (MOS) method. MOML shows a better numerical performance than the ECMWF model and MOS, especially for winter. The results of MOML with a linear algorithm, running training period, and dataset using spatial interpolation ideas, are better than others when the forecast time is within a few days. The results of MOML with the Random Forest algorithm, year-round training period, and dataset containing surrounding gridpoint information, are better when the forecast time is longer.