Snowy Days Research Articles

An Experimental Study on the Variation of Atmospheric Magnetic-Field Intensity Related to Dust, Haze, Rain, Snow, and Thunderstorms

Simultaneous measurements of the vertical magnetic field (VMF), three-dimensional electric field, ambient temperature, ambient relative humidity, particle mass concentration, and three-dimensional velocity are conducted during dust, haze, rain, snow, and thunderstorms. The hourly VMF variation (the rate of VMF increment and time increment with a 1-h time interval) is used to evaluate the disturbance level of the atmospheric magnetic field during the abovementioned weather conditions. There is no significant difference in the hourly VMF variations between hazy days and fair weather. However, the hourly VMF variations on dusty, rainy, snowy, and thunderstorm days differ from those in fair weather, implying that these weather conditions significantly affect the atmospheric magnetic field. On hazy days, although the haze particles are charged, the VMF does not change compared with in fair weather, which suggests that the horizontal electric field generated by haze particles is an irrotational field. On dusty and snowy days, the heterogeneity of the charge distribution forms a rotational horizontal electric field, which can induce the VMF to disturb the atmospheric magnetic field. During rain and thunderstorms, the hourly variation in the VMF is larger than in dust and snow, which can be attributed to the rotational horizontal electric field generated by raindrops and clouds. In addition, the hourly variation in the VMF in thunderstorms is one order of magnitude larger than that during days without thunderstorms, indicating that the VMF induced by the cumulonimbus cloud electric field is more significant than that in other weather conditions.

Significance of plumage colour for winter survival in the Tawny Owl (Strix aluco): revisiting the camouflage hypothesis

In snowy winters, the survival of Tawny Owls Strix aluco of the grey colour morph is suggested to be better than that of the brown (or red) morph, partly due to their paler plumage, which is presumed to provide better camouflage against predation. If the difference in the susceptibility to predation between the colour morphs is significant, this should probably be reflected in the relation between the number of snow‐covered days (i.e. the length of the winter) and the number and survival of owls of different colour. In a population in southern Finland there were no significant associations between the number of snowy days during winter and the number of breeding Tawny Owls of different colour morphs in the following spring, but the number of owls, in particular red ones, varied inversely with the number of snowy days in the winter of the previous year. The time lag suggests that the number of snow‐covered days (or some related factors) primarily influenced the pre‐breeding survival of young red owls. However, I found no effect of snow days on the proportion of colour morphs, suggesting only small differences in morph‐specific survival under long‐lasting winter conditions.

Deep factor analysis for weather varied sense-through-foliage target detection

In this paper, the influence of seasonal variation on target detection accuracy and the effectiveness of deep factor analysis (DFA) in signal denoising are studied. To extensively verify the universality of the DFA_based approach, a variety of target objects, including no target, human, wood board and iron cabinet targets, are measured in foliage environment under four different weather conditions. Then, after removing background noise from the collected data, deep factor analysis is carried out to reduce the impact of noise. The experimental results show that the influence of weather variation on target detection can be effectively eliminated by DFA_based algorithm, which can improve the average classification accuracy in all seasons. Finally, by means of cross validation, the effectiveness of DFA_based algorithm on signal denoising and the influence on target detection accuracy are further studied. The method is stable and universal in any weather conditions, even in hazy and snowy days, which can be stable at about 93%.

Research on the Cascade Vehicle Detection Method Based on CNN

This paper introduces an adaptive method for detecting front vehicles under complex weather conditions. In the field of vehicle detection from images extracted by cameras installed in vehicles, backgrounds with complicated weather, such as rainy and snowy days, increase the difficulty of target detection. In order to improve the accuracy and robustness of vehicle detection in front of driverless cars, a cascade vehicle detection method combining multifeature fusion and convolutional neural network (CNN) is proposed in this paper. Firstly, local binary patterns, Haar-like and orientation gradient histogram features from the front vehicle are extracted, then principal-component-analysis dimension reduction and serial-fusion processing are performed on the input image. Furthermore, a preliminary screening is conducted as the input of a support vector machine classifier based on the acquired fusion features, and the CNN model is employed to validate cascade detection of the filtered results. Finally, an integrated data set extracted from BDD, Udacity, and other data sets is utilized to test the method proposed. The recall rate is 98.69%, which is better than the traditional feature algorithm, and the recall rate of 97.32% in a complex driving environment indicates that the algorithm possesses good robustness.

Classification and analysis of sea‐effect snowbands for Danube Sea area in Black Sea

AbstractIn this study, for the Danube Sea Area (Danube Region—western part of the Black Sea), the structure of the sea effect snow (SES) bands, meteorological conditions in which SES bands occurred, and SES band types are analysed for the period 2009–2018. In the 10‐year period, snowy times were determined using aviation reports published by four airports within the borders of the Marmara Region. SES events and SES bands were determined by examining the several satellite images (Channel 9‐IR, Natural Colour‐RGB, Airmass‐RGB, Channel 6‐VW) obtained from the Spinning‐Enhanced Visible and Infrared Imager (SEVIRI) device on MSG‐2 (Meteosat‐9) and MSG‐3 (Meteosat‐10) satellites. The Danube Region is divided into six sub‐regions. Five different types of SES bands were analysed separately for each sub‐region. Some meteorological conditions that are known to play an important role in the formation of SES bands (presence, thickness and level of the inversion layer; wind structure in the upper levels over the sea; sea surface temperature—SST) were analysed for the snowy times with sea‐effect. According to the preliminary results; a total of 186 days were found to be snowy, 75 days of these snowy days occurred with sea‐effect, and SES bands were seen in 8 days but did not cause snowfall in the Marmara Region. Type‐2 (T‐2) band was the most common band type (85%) over the Danube Region. The inversion layer(s) were observed between the level of 850–700 hPa in 52 days of 75 snowy days with sea effect. The majority of the direction of the upper‐level winds (at 850 and 700 hPa) was found to be northern (North‐N, Northwest‐NW, Northeast‐NE). The lowest average SST value (6.02°C) observed in the sub‐region ‘A’ and the highest average SST value (8.66°C) was observed in the sub‐region ‘F’.

A sustainable framework for spatial planning of photovoltaic solar farms using GIS and multi-criteria assessment approach in Central Anatolia, Turkey

Convenient photovoltaic (PV) solar farms location choosing is a substantial issue in terms of renewable energy policies and spatial planning. This study is carried out to contribute to the growth, expansion, advancement, and location selection of PV farms using Geographical Information System (GIS), Analytic Hierarchy Process, Weighted Linear Combination, and Inverse Distance Weighting approaches. All maps were prepared for 17 evaluation criteria and suitability classification in GIS environment in the province of Kayseri (Turkey). Global Horizontal Irradiation was identified to be the most essential sub-criteria, followed by aspect, land use/cover, slope, distance from power network, temperature, precipitation, total snowy days, relative humidity, distance from land transportation network and others. The results show that an extensive percentage (3.70 % of the study area is highly suitable, and 40.01 % is moderately suitable) of the study area has a unique capacity for the establishment of PV farms. The results indicate that the western region of the study area is the most convenient for the installation of PV farms. In this study, the location selection decisions of existing PV farms investments were evaluated. Most of the existing PV farm’s locations frequently match up with the convenient zones presented in this study. This study provides a considerable, utility, and sustainable framework for spatial planning of PV farms in order to develop regional renewable energy and environmental conservation policies and to raise the awareness of the public.

Deep Dense Multi-Scale Network for Snow Removal Using Semantic and Depth Priors.

Images captured in snowy days suffer from noticeable degradation of scene visibility, which degenerates the performance of current vision-based intelligent systems. Removing snow from images thus is an important topic in computer vision. In this paper, we propose a Deep Dense Multi-Scale Network (DDMSNet) for snow removal by exploiting semantic and depth priors. As images captured in outdoor often share similar scenes and their visibility varies with depth from camera, such semantic and depth information provides a strong prior for snowy image restoration. We incorporate the semantic and depth maps as input and learn the semantic-aware and geometry-aware representation to remove snow. In particular, we first create a coarse network to remove snow from the input images. Then, the coarsely desnowed images are fed into another network to obtain the semantic and depth labels. Finally, we design a DDMSNet to learn semantic-aware and geometry-aware representation via a self-attention mechanism to produce the final clean images. Experiments evaluated on public synthetic and real-world snowy images verify the superiority of the proposed method, offering better results both quantitatively and qualitatively. https://github.com/HDCVLab/Deep-Dense-Multi-scale-Network https://github.com/HDCVLab/Deep-Dense-Multi-scale-Network.

Ultra-short-term Radiation Prediction based on FY-4 Satellite Cloud Images and Artificial Neural Network

Surface solar radiation is affected by many random mutation factors, which makes the ultra-short-term prediction face great challenges. In this paper, the surface radiation observation station in the northwest (Dunhuang) desert area with broad PV prospects is selected as the research object. The input parameters of the test sample are: cloud forecast value, reflectivity and brightness temperature value of a satellite cloud image closest to the forecast time. The MATLAB software is used to model the prediction program and to predict the surface solar radiation in the next 10 minutes. A combined algorithm of satellite cloud images and neural network is applied to predict surface solar radiation for the next 10 minutes and is compared with the measured surface solar radiation. The model is a lightweight calculation model, it satisfies the calculation precision of engineering requirements. The results show that the diurnal variation trend of measured and predicted radiation values is basically the same. Among them, the prediction accuracy of the model for cloudy days is higher, while for snowy days with more abrupt changes, the prediction error of abrupt points is larger. The model can provide reference for ultra-short-term prediction of surface radiation.

Effects of Rain and Snow on the Air Quality Index, PM2.5 Levels, and Dry Deposition Flux of PCDD/Fs

The effects of eighteen precipitation (rain and snow) events in both Jinan and Qingdao cities in Shandong Province, China, on the air quality and dry deposition flux in PCDD/Fs were investigated. A total of fifteen precipitation events were positive for AQI reductions. In these events, the AQI ranged between 29 and 195 and averaged 79, and the PM2.5 concentration ranged between 10 and 146 µg m–3 and averaged 39 µg m–3. A comparison of the average of the fifteen events during and after the precipitation, respectively, with that before the precipitation, showed AQI reduction fractions of 23% and 32%, respectively, while those for the PM2.5 concentration were 27% and 42%, respectively; the PCDD/F dry deposition ranged between 145.7 and 1152 pg WHO2005-TEQ m–2 day–1 and averaged 476.7 pg WHO2005-TEQ m–2 day–1. A comparison of the above events after the precipitation showed that the reduction in the fraction of the PCDD/F dry deposition flux averaged 35%. However, the other three precipitation events demonstrated an elevation in the AQI, where the AQI ranged between 24 and 147 and averaged 91, and the PM2.5 concentration ranged between 4 and 112 µg m–3 and averaged 42 µg m–3. A comparison of the values during and after the precipitation, respectively with those before the precipitation, revealed increased AQI fractions of –18% and 28%, respectively, while those for the PM2.5 concentration were 16% and 51%, respectively. In the three AQI elevation events, the PCDD/F dry deposition flux ranged between 191.3 and 946.2 pg WHO2005-TEQ m–2 day–1 and averaged 473.0 pg WHO2005-TEQ m–2 day–1. A comparison of the period after the precipitation with that before the precipitation showed that the increase in the fraction of the PCDD dry deposition flux averaged 20%. The above results revealed that, in general, rain and snow did improve the air quality in the areas of interest. This was due to the fact that the particulates or dissolved gaseous pollutants were scavenged out of the air and carried the aerosols down to the ground. However, in some events, after rainy or snowy days, increases in the source emissions and reductions in the atmospheric vertical convection did result in an elevation in the AQI, PM2.5 concentrations, and PCDD/F dry deposition in the ambient air. The results of this study provided useful information contributing to air quality management.

Rain and snow event cooling effect: a comparison on outdoor and indoor thermal comfort in Ardabil, northwest of Iran

Human thermal comfort is influenced by different climatic parameters. The effect of rain and snow events on human thermal comfort has been less explored in the available literature. The work presented here investigates outdoor and indoor thermal comfort using the physiological equivalent temperature (pet) and wind chill temperature (WCT) indices and Givoni bioclimatic chart for rainy and snowy days as well as a day prior to and a day post snow and rain events in Ardabil. Results indicated that snow and rain events have cooling effects on both indoor and outdoor thermal sensation. The cooling effect of rainy and snowy days is higher than a day prior to and a day post events. Also, the cooling effect of the snow and rain increases by the intensity of the events (> 10 mm/day). Based on the PET and WCT outputs, the cooling effect of the snow events with intensity higher than 10 mm a day is about 1.77 and 0.80 °C less than events under 10-mm a day intensity. The same pattern was observed for rain events. Analysis of the cooling effect of event duration on outdoor thermal comfort indicated that the events with more than 2-day duration have more cooling effect on thermal sensation. Finally, due to different cooling effects of snow and rain events on indoor thermal sensation, different types of bioclimatic strategies should be used inside the buildings during snowy and rainy days in Ardabil.

Analytical model for solar radiation transmitting the curved transparent surface of solar greenhouse

This study introduces and examines an improved mathematical model to calculate the total transmitted solar radiation captured by the curved surface of a solar greenhouse. The MATLAB platform is used to calculate the total solar radiation and takes reflected radiation from the outdoor ground into account, and the results were verified in a field test. First, mathematical expression to calculate the equivalent reflected transmissivity of the greenhouse curved surface is developed. The variation for different solar greenhouse aspect ratios on the equivalent transmissivities of beam radiation, scattered radiation, and ground-reflected radiation is simulated for four typical sunny days. Besides, the optimal solar greenhouse aspect ratio is determined for the different latitudes in six cities. Then, the equivalent beam transmissivity is compared between traditional simplified inclined planes and the actual curved surfaces of a specific solar greenhouse, which implies that the calculation results of curved surfaces are more accurate. Additionally, the transmitted solar radiation is calculated for both sunny and snowy days. The results indicate that the intensity of the radiation reflected from outdoor ground is in the same order of magnitude as the scattered radiation. This means reflected sunlight should not be neglected, especially for cities located at high altitudes.

New insights into the world's longest series of monthly snowfall (Parma, Northern Italy, 1777–2018)

AbstractThe emergence of decreasing trends in snowfall frequency and snow depth highlights the challenges arising from shifts in snow regimes. In particular, snow‐dependent southern Europe regions may be negatively impacted by such changes. Snow regimes strongly influence the water availability in reservoirs and groundwater. This study presents the world's longest series of monthly snowfall (fresh snow depth, snow days per year, days with snow on the ground) for the Parma Observatory, northern Italy (44°48′N and 10°19′E) between 1777 and 2018. These datasets were collected over the centuries using early rain gauges and recently, SIAP tipping bucket models. Sources of inhomogeneity were identified and analysed through the study of metadata and of nonparametric tests on datasets. Homogenized time series were obtained after correcting the observed snowfall data for the instruments response and localization characteristics, for the observing practices with the help of the standard normal homogeneity tests. The Buishand and the Mann–Kendall tests were further applied to check the correction and detect possible monotonic trends. Over the study period, days per year of snow decreased after the change point detected in 1897, in association with a rise in surface air temperature (including a distinct urban warming trend). In this study high numbers of snowy days per year (hereafter referred as snowfall frequency) and snow depth values during the latest phase of the LIA at Parma are consistent with a cycle of minimum solar activity, suggesting that enhanced, solar‐induced blocking activity dominated, with the arrival into the Mediterranean of large cold air masses developing over Siberia and northern Europe. Over the last century, snow appears to have resonance‐like characteristics as similar trends were observed over the Northern Hemisphere, where the extent of the snow cover has been reported to markedly decline in the transition spring and autumn seasons.

Correction of Inhomogeneities in Observed Land Surface Temperatures over China

AbstractLand surface temperature Ts and near-surface air temperature Ta are two main metrics that reflect climate change. Recently, based on in situ observations, several studies found that Ts warmed much faster than Ta in China, especially after 2000. However, we found abnormal jumps in the Ts time series during 2003–05, mainly caused by the transformation from manual to automatic measurements due to snow cover. We explore the physical mechanism of the differences between automatic and manual observations and develop a model to correct the automatic observations on snowy days in the observed records of Ts. Furthermore, the nonclimatic shifts in the observed Ts were detected and corrected using the RHtest method. After corrections, the warming rates for Ts-max, Ts-min, and Ts-mean were 0.21°, 0.34°, and 0.25°C decade−1, respectively, during the 1960–2014 period. The abnormal jump in the difference between Ts and Ta over China after 2003, which was mentioned in existing studies, was mainly caused by inhomogeneities rather than climate change. Through a combined analysis using reanalyses and CMIP5 models, we found that Ts was consistent with Ta both in terms of interannual variability and long-term trends over China during 1960–2014. The Ts minus Ta (Ts − Ta) trend is from −0.004° to 0.009°C decade−1, accounting for from −3.19% to 5.93% (from −3.09% to 6.39%) of the absolute warming trend of Ts (Ta).

Impact of global climate change on the surface runoff of the Chatkal River

Charvak reservoir is the largest reservoir in Uzbekistan. It is surrounded by the Tien Shan Mountains. The Chatkal River begins from the western part of The Tien Shan Mountains and flows into the Charvak reservoir. The average annual water discharge is 108 m3/s and is the largest right tributary of The Chirchik River. Based on long-term observational data, intraannual flow distribution was calculated. And trends were determined, tests for homogeneity were carried out, differential-integral curves in the observation series were evaluated. Average flow rates the error does not exceed 10%, for the coefficient of variation; the error does not exceed 15%. 4. Analysis of the difference integral curve showed since the 90s of the last century to the present time, a high water period has been observed on The Chatkal River and its tributaries. This is mainly due to the increase in annual precipitation. There is no significant change in long-term annual precipitation. However, shift happening with the type of precipitation. Namely, in the last two decades more rainy days than cold snowy days were observed.

Long‐term variability and trends of extended winter snowfall in Turkey and the role of teleconnection patterns

AbstractIn this study, a climatological analysis of daily new snow cover data from 93 meteorological stations (10–2,400 m above sea level) over Turkey during the winter (December–March) period of 1967–2006 is presented. Series of snow cover depth (SCD) (≥1 cm) and number of snowy days (SDs) were analysed to determine the variability and statistically significant trends in the snow climatology. The results showed an inverse relation between SDs and SCD over the central (CAR) and eastern (EAR) Anatolian regions. While positive trends in SDs were observed in the CAR (77% of all stations), the new SCD decreased in time. On the other hand, the intensity of the daily snowfall over the EAR increased due to decreasing (increasing) SD (SCD) trends in the region, especially between the 500 and 1,000 m layer. It was found that higher SCDs occur in the EAR during winter with positive patterns in the east Atlantic/western Russia. The daily mean sea level pressure composite maps of high SCD (>5 cm) show that strong northeasterly flows enable the penetration of cold polar air to the EAR due to the interaction between the Siberian high‐pressure centre over Europe and the low‐pressure centre over the Caspian Sea. These results help further understanding of the regional variation of the snowfall pattern in Turkey and its response to global climatic change.

Changing Probabilities of Days with Snow and Rain in the Atlantic Sector of the Arctic under the Current Warming Trend

AbstractTrends in the probabilities of days with liquid, solid, and mixed precipitation are discussed on annual and intra-annual scales along with their relationship to air temperature in the Atlantic sector of the Arctic. Data on weather phenomena were used to identify precipitation phases. The data cover various periods but all series extend to 2017. Trends in the annual air temperature and probability of precipitation phases for various long-term periods are discussed and differences in the mean air temperature and probability of precipitation phases between 1979–97 (insignificant warming) and 1999–2017 (significant warming) on an intra-annual scale. In the studied region, the precipitation phases were sensitive to warming and atmospheric circulation to various degrees, depending on the phase, mean climate, month, and local conditions. The probability of days with rainfall increased (by +1% to +3% per decade), whereas the probability of days with snowfall decreased (by −1.5% to −2.4% per decade). The increasing trends in the probability of rainy days at all stations and decreasing trends in the probability of snowy days in the southern part of the region were warming induced. The most significant and widespread trends in snowy and rainy days were found in September. The probability of days with mixed precipitation exhibited no trends due to an inverse reaction to warming in the warmer and colder parts of the year. Temporal variability in the probability of precipitation phases was significantly linked to three teleconnection patterns playing a role in various parts of the year.

Climate control on snowfall days in peninsular Italy

The advent of instrumental and digital weather observatories has not eliminated the substantial gaps within surface meteorological time series. In particular, the lack of long-term homogeneous snowfall records may be a major impediment for hydrologic studies. We tested the hypothesis that reliable estimates of snowy days per year (SDY) can be produced for peninsular Italy, based on weather and site measurements from a limited set of stations centrally located in the Central Mediterranean Area. The core weather data are those from three observatories: Naples (40° 51′ N, 14° 15′ E, 150 m a.s.l.), Rome (41° 53′ N, 12° 28′ E, 100 m a.s.l.) and Montevergine (40° 56′ N, 14° 43′ E, 1280 m a.s.l.). A linear multivariate regression model (LMRM) was developed with candidate predictors (elevation, snowfall days and winter temperatures) for SDY reconstructions in the Benevento Valley (calibration site), whose homogenised SDY time series covers the period 1869–2018 (the longest in Southern Italy, extending back to 1832 thanks to documentary proxies but with missing values from 1911 to 1949). Three validation sites across peninsular Italy were considered: Vesuvius (40° 49′ N, 14° 24′ E, 605 m a.s.l.), Perugia (43° 05′ N, 12° 30′ E, 205 m a.s.l.) and Padua (45° 23′ N, 11° 51′ E, 15 m a.s.l.). The percent relative mean absolute error (%RMAE) for Benevento Valley was 20.2%, and though higher model errors were encountered at validation sites, they were in an acceptable range (32.6, 39.3 and 39.5% at Vesuvius, Perugia and Padua, respectively). Our SDY reconstruction in the Benevento Valley parallels, the pattern of water discharge occurred in the region during the same period, whose fluctuations result in changes of the pattern of snowfall days (i.e. decreasing snowfall days in recent times was accompanied by decreasing groundwater levels). This corroborates that the approach used to reconstruct SDY data takes dominant climate controlling factors of hydrological changes. We conclude that the LMRM, a statistically developed model, is physically meaningful and may be reasonably used for estimating SDY in peninsular Italy roughly down from the sub-Alpine range.

Learning a Precipitation Indicator from Traffic Speed Variation Patterns

It is common sense that traffic participants tend to drive slower under rain or snow conditions, which has been confirmed by many studies in the field of transportation research. When analyzing the relation between precipitation events and traffic speed observations, it was shown that by using extra weather information, road speed prediction models can be improved. Conversely, traffic speed variation patterns of multiple roads may also provide an indirect indication of weather conditions. In this paper, we attempt to learn such a model, which can detect the appearance of precipitation events, using only road speed observations, for the case of New York City. With a seasonal trend decomposition model Prophet, residuals between the observations and the model were used as features to represent the level of anomaly as compared to the normal traffic situation. Based on the timestamps of weather records on sunny days versus rainy or snowy days, features were extracted from traffic data and assigned to the corresponding labels. A binary classifier was then trained on six-month training data and achieved an accuracy of 91.74% when tested on the remaining two-month test data. We show that there is a significant correlation between the precipitation events and speed variation patterns of multiple roads, which can be used to train a binary indicator. This indicator can detect those precipitation events, which have a significant influence on the city traffic. The method has also a great potential to improve the emergency response of cities where massive real-time traffic speed observations are available.

Principal features of Chornohora climate (Ukrainian Carpathians)

AbstractChornohora is the highest mountain ridge in the Ukrainian Carpathians with 6 peaks of an altitude over 2,000 m above sea level (Hoverla is the highest peak, 2,061 m a.s.l). Its climate is explored less than other mountain ridges in Europe. The massif is a climatic barrier for air masses on NW-SE line. To describe the climate of this area data from the weather station at Pozhyzhevska alpine meadow for the years 1961–2010 were used. The seasonal and long-term variability of air temperature, atmospheric precipitation and snow cover were investigated on the background of air circulation types. The results show that general features of Chornohora climate depend both, on elevation above sea level and on air circulation. Lowest temperature is observed at N-NE circulation and highest precipitation – at western air inflow. Long-term changes of examined climate elements in Chornohora show significant increase in mean (0.13°/10 years) and minimum (0.22°C/10 years) air temperature as well as in snow cover depth and number of snowy days.

Research on the influence of weather conditions on urban night light environment

The increasingly serious urban light pollution has deepened the relevant research, and weather conditions indeed have great impact on the urban night light environment. Based on the SQM instrument, fish-eye camera and weather-related systems, this paper analyzes the changing law of night sky with time and weather. The brightness of the typical clear night sky changes regularly with time, and mainly includes five phases: rapid decline phase, slow decline phase, unstable decline phase, smooth phase, and rapid increase phase of sky brightness. In two phases of the smooth sky brightness, the average sky brightness in the high and low brightness phase respectively is 18.123 mag/arcsecond2 and 18.82 mag/arcsecond2, and about 15 times and 8 times higher than those of the natural night sky. This paper establishes the regression model of typical clear night sky brightness in rapid decline phase and rapid increase phase of sky brightness. The sky magnitude brightness in rainy weather is much lower than that in clear weather, the difference is about 3 mag/arcsecond2, the brightness can be reach 15.63 mag / arcsecond2; the average magnitude brightness in snowy days is about 0.17 mag/arcsecond2 higher than that in cloudy weather. There is a significant correlation among the air quality index, the ground illumination ratio of moon, the atmospheric visibility and the sky brightness. The deepened air pollution can also intensify light pollution, which can increase to 3 and 10 times higher than the night sky brightness under the moderate and severe air pollution. The lunar cycle has the least impact on light pollution in clear days, the sky brightness with the full moon is about 2 and 3 times higher than that without the moon.