Spatiotemporal Modes Characteristics and SARIMA Prediction of Total Column Water Vapor over China during 2002–2022 Based on AIRS Dataset

The total column water vapor (TCWV) is a relatively active component in the atmosphere and an important detection object of climate change. Exploring the spatiotemporal modes characteristics of TCWV and predicting its changing trends can provide a reference for human beings to deal with climate change and formulate corresponding countermeasures. The TCWV data over China region by using the Atmospheric Infrared Sounder (AIRS) dataset from 2002 to 2022 were obtained. The empirical orthogonal function (EOF) analysis, linear regression, Mann-Kendall (M-K) mutation test, Seasonal Autoregressive Integrated Moving Average (SARIMA) model and other methods were used to discuss the spatiotemporal modes characteristics of TCWV in the China region on the monthly, seasonal, and annual scales and verify the rationality of the forecast of the monthly average trend of TCWV in the next year. The obtained results show that: (1) The annual and seasonal scales spatial distributions of TCWV in China are roughly consistent, with obvious latitudinal distribution characteristics. That is, the TCWV in the low latitude region, especially in the tropical region, is larger, and it gradually decreases with the increase of the latitude. Furthermore, the TCWV in the eastern region is higher than that in the western region at the same latitude; (2) The EOF analysis results show that its first mode can better reflect the typical distribution characteristics of the southeast-northwest positive distribution in China; (3) From 2002 to 2022, the TCWV in China shows an upward trend and the TCWV increases at a rate of 0.0413 kg/m2 per year, which may be related to the long-term increase of air temperature in recent years; (4) The inter-monthly variation of TCWV shows a slightly positive skewed ‘bell-shaped’ curve, with the maximum in summer, the minimum in winter and the similar distribution in spring and autumn. As can be seen from the M-K curves of the four seasons, each season has different mutation points; (5) Forecasting the TCWV was done using time series monthly average values from September 2002 to February 2022. SARIMA (3, 1, 3) × (0, 1, 1, 12) was identified as the best model. This model passed the residual normality test and the forecasting evaluation statistics show that MAPE = 2.65%, MSE = 0.3229 and the R2-score = 0.9949. As demonstrated by the results, the SARIMA model is a good model for forecasting TCWV in the China region

Spatiotemporal Modes Characteristics and SARIMA Prediction of Total Column Water Vapor over China during 2002–2022 Based on AIRS Dataset

The total column water vapor (TCWV) is a relatively active component in the atmosphere and an important detection object of climate change. Exploring the spatiotemporal modes characteristics of TCWV and predicting its changing trends can provide a reference for human beings to deal with climate change and formulate corresponding countermeasures. The TCWV data over China region by using the Atmospheric Infrared Sounder (AIRS) dataset from 2002 to 2022 were obtained. The empirical orthogonal function (EOF) analysis, linear regression, Mann-Kendall (M-K) mutation test, Seasonal Autoregressive Integrated Moving Average (SARIMA) model and other methods were used to discuss the spatiotemporal modes characteristics of TCWV in the China region on the monthly, seasonal, and annual scales and verify the rationality of the forecast of the monthly average trend of TCWV in the next year. The obtained results show that: (1) The annual and seasonal scales spatial distributions of TCWV in China are roughly consistent, with obvious latitudinal distribution characteristics. That is, the TCWV in the low latitude region, especially in the tropical region, is larger, and it gradually decreases with the increase of the latitude. Furthermore, the TCWV in the eastern region is higher than that in the western region at the same latitude; (2) The EOF analysis results show that its first mode can better reflect the typical distribution characteristics of the southeast-northwest positive distribution in China; (3) From 2002 to 2022, the TCWV in China shows an upward trend and the TCWV increases at a rate of 0.0413 kg/m2 per year, which may be related to the long-term increase of air temperature in recent years; (4) The inter-monthly variation of TCWV shows a slightly positive skewed ‘bell-shaped’ curve, with the maximum in summer, the minimum in winter and the similar distribution in spring and autumn. As can be seen from the M-K curves of the four seasons, each season has different mutation points; (5) Forecasting the TCWV was done using time series monthly average values from September 2002 to February 2022. SARIMA (3, 1, 3) × (0, 1, 1, 12) was identified as the best model. This model passed the residual normality test and the forecasting evaluation statistics show that MAPE = 2.65%, MSE = 0.3229 and the R2-score = 0.9949. As demonstrated by the results, the SARIMA model is a good model for forecasting TCWV in the China region

Extraction of Energy Information from Analog Meters Using Image Processing

There has been an ongoing effort to increase the number of advanced metering infrastructure (AMI) devices to improve system observability. When deployed across distribution secondary networks, AMI provides building-level load and consumption information, which can be used to improve grid management strategies. A barrier to implementation is the significant upgrade costs associated with retrofitting existing meters with network-capable sensing. One economic way is to use image processing methods to extract usage information from images of the existing meters. This paper presents a solution that uses online data exchange of power consumption information to a cloud server without modifying the existing electromechanical analog meters. In this framework, a systematic approach to extract energy data from images is applied to replace the manual reading process. A case study is presented where the digital imaging approach is compared to the averages determined by visual readings over a one-month period

The Global Distribution of Cirrus Clouds Reflectance Based on MODIS Level-3 Data

Cirrus clouds are crucially important to weather, climate and earth energy balance studies. The distribution of cirrus reflectance with latitude and season is an interesting topic in atmospheric sciences. The monthly mean Level-3 MODIS cirrus reflectance is used to analyze the global distribution of cirrus clouds, which covers a period from 1 March 2000 to 28 February 2018. The latitude, from 90° S to 90° N, is divided into 36 latitude zones with 5° interval. Data in each latitude zone are analyzed. The research results show that the slopes of cirrus reflectance variation in the Northern and Southern Hemisphere are −1.253 × 10−4/year and –1.297 × 10−4/year, respectively. The yearly-average cirrus reflectance reveals strong negative correlation with time in the Northern Hemisphere, i.e., the correlation coefficient is −0.761. Then the statistical analysis of cirrus reflectance is performed in different seasons, the results show that cirrus reflectance varies obviously with seasonal change. Additionally, for the [30°, 90°] latitude regions, cirrus reflectance reaches the minimum in summer and the maximum in winter in the Southern and Northern Hemisphere

Analysis of Influencing Factors of SST in Tropical West Indian Ocean Based on COBE Satellite Data

The time-frequency domain analysis of the sea surface temperature (SST) in the tropical western Indian Ocean was conducted using wavelet analysis, cross wavelet transform (XWT), the Mann–Kendall (MK) test, and other methods based on COBE-SST data for the last 50 years (1974–2020). From the perspective of time-frequency combination, examining the data of precipitation, sea surface heat flux, total cloud cover, and long-wave radiation, helped contribute to exploring the periodic changes of SST. Moreover, the Western Hemisphere Warm Pool (WHWP) was selected to analyze the role of SST from 1974 to 2020. Present results have demonstrated that the SST in the western Indian Ocean was in a stage of rising, particularly in 1998. According to the fast Fourier transform of the filtered SST time series, the tropical western Indian Ocean SST has a short period of 3–6 years, a medium period of about 10 years, and a long period of 40 years. The SST in the tropical western Indian Ocean has a resonance period of 2–6 years with precipitation, a resonance period of 2–6 years with sea surface heat flux, a resonance period of 4–5 years with total cloud cover, and a resonance period of 2–5 years with long-wave radiation. Importantly, SST was negatively associated with precipitation, total cloud cover, and long-wave radiation, and positively for sea surface heat flux before 1997. Seasonal migration activities are significantly correlated with the WHWP and the tropical western Indian Ocean SST. The spatial lattice point correlation coefficient is generally from 0.6 to 0.9, and the inter-annual serial correlation value is more than 0.89. Furthermore, the two exist with a resonance period of 2–5 years

Analysis and Prediction of Wind Speed Effects in East Asia and the Western Pacific Based on Multi-Source Data

With the increasing problem of global warming caused by the massive use of fossil fuels, biomass energy as a renewable energy source has attracted widespread attention throughout the globe. In this paper, we analyzed the spatial and temporal variation in wind energy in the East Asia and Western Pacific areas using IGRA site data, ERA5, and NCEP/NCAR reanalysis data from 2000 to 2021, and multi-variate empirical orthogonal function (MV-EOF) decomposition with the Pettitt mutation test, and the seasonal autoregression integrated moving average (SARIMA) model was used to predict the trend of wind speed. The spatial and temporal variations in wind energy in East Asia and Western Pacific areas were analyzed, and it was found that the richer wind-energy resources were mainly concentrated in the “Three Norths” (North China, Northwest China, and Northeast China) and Mongolia, followed by the Western Pacific areas. In addition, the T’ai-hang Mountains and the Qinghai-Tibet Plateau in China block the wind resources in the eastern and southern regions of East Asia, resulting in a shortage of wind resources in this region. In addition, the summer wind speed is significantly lower than in the other three seasons. The first-mode contributions of the MV-EOF wind field and geopotential heights, respectively, are 29.47% and 37.75%. The results show that: (1) There are significant seasonal differences in wind-energy resources in the study area, with the lowest wind speed in summer and the highest wind speed in winter. (2) The wind energy in the study area has significant regional characteristics. For example, China’s Qinghai-Tibet Plateau, Inner Mongolia, Xinjiang region, and Mongolia are rich in wind-energy resources. (3) Wind-energy resources in the study area have gradually increased since 2010, mainly due to changes in large-scale oceanic and atmospheric circulation patterns caused by global warming

An image compression method for coal mine undergroud

For difficult transmission and storage of a large of video monitoring image data of coal mine underground, an image compression method was proposed which was based on improved compressive sending algorithm. Firstly, collected images data are spared by Coiflet2 wavelet transform principle. Secondly, the spared data is measured by measurement matrix to get measurement data. Finally, the images are refactored by orthogonal matching tracking algorithm and inverse transformation. The experimental results show that the method can effectively compress image and reconstruct the image

Study of Time-Frequency Domain Characteristics of the Total Column Ozone in China Based on Wavelet Analysis

Ozone is a very important trace gas in the atmosphere, it is like a “double-edged sword”. Because the ozone in the stratosphere can effectively help the earth’s organisms to avoid the sun’s ultraviolet radiation damage, the ozone near the ground causes pollution. Therefore, it is essential to explore the time-frequency domain variation characteristics of total column ozone and have a better understanding of its cyclic variation. In this paper, based on the monthly scale dataset of total column ozone (TCO) (September 2002 to February 2023) from Atmospheric Infrared Sounder (AIRS) carried by NASA’s Aqua satellite, linear regression, coefficient of variation, Mann-Kendall (M-K) mutation tests, wavelet analysis, and empirical orthogonal function decomposition (EOF) analysis were used to analyze the variation characteristics of the TCO in China from the perspectives of time domain, frequency domain, and spatial characteristics. Finally, this study predicted the future of TCO data based on the seasonal autoregressive integrated moving average (SARIMA) model in the time series algorithm. The results showed the following: (1) From 2003 to 2022, the TCO in China showed a slight downward trend, with an average annual change rate of −0.29 DU/a; the coefficient of variation analysis found that TCO had the smallest intra-year fluctuations in 2008 and the largest intra-year fluctuations in 2005. (2) Using the M-K mutation test, it was found that there was a mutation point in the total amount of column ozone in 2016. (3) Using wavelet analysis to analyze the frequency domain characteristics of the TCO, it was observed that TCO variation in China had a combination of 14-year, 6-year, and 4-year main cycles, where 14 years is the first main cycle with a 10-year cycle and 6 years is the second main cycle with a 4-year cycle. (4) The spatial distribution characteristics of the TCO in China were significantly different in each region, showing a distribution characteristic of being high in the northeast and low in the southwest. (5) Based on the EOF analysis of the TCO in China, it was found that the variance contribution rate of the first mode was as high as 52.85%, and its spatial distribution of eigenvectors showed a “-” distribution. Combined with the trend analysis of the time coefficient, this showed that the TCO in China has declined in the past 20 years. (6) The SARIMA model with the best parameters of (1, 1, 2) × (0, 1, 2, 12) based on the training on the TCO data was used for prediction, and the final model error rate was calculated as 1.34% using the mean absolute percentage error (MAPE) index, indicating a good model fit

Spatiotemporal Characteristics and Influencing Factors of Sunshine Duration in China from 1970 to 2019

In order to alleviate global warming and the energy crisis, it is of great significance to develop and utilize solar energy resources. Sunshine duration (SD) is considered to be the best substitute for solar radiation and a key factor in evaluating solar energy resources. Therefore, the spatial and temporal characteristics of SD and the reasons for its changes have received extensive attention and discussion. Based on the data of 415 meteorological stations from 1970 to 2019, this paper uses linear trend analysis, Mann–Kendall mutation analysis, the Hurst index, empirical orthogonal decomposition, correlation analysis and partial correlation analysis to analyze the spatiotemporal characteristics of SD and its relationship with influencing factors. The results show that the annual SD in China shows a downward trend, with a climate trend rate of −37.93 h/10a, and a significant decline from 1982 to 2019. The seasonal SD shows a downward trend, and the downward trend is most obvious in summer. The annual and seasonal SD will still show a downward trend in the future. The spatial distribution of SD not only has an overall consistent distribution but also takes the Yellow River from Ningxia to Shandong as the boundary, showing a north–south opposite distribution. Annual SD has a significant positive correlation, a significant negative correlation, a positive correlation and a negative correlation with wind speed, precipitation, temperature and relative humidity, respectively, and it is most closely related to wind speed and precipitation. In addition, the change in SD may also be related to human activities