Observation Stations In China Research Articles

Screening Approach of the Langley Calibration Station for Sun Photometers in China

A sun photometer is a type of photometer that points at the sun, and it has been playing an increasingly important role in characterizing aerosols across the world. As long as the solar photometer is accurately calibrated, the optical thickness of the aerosol can be obtained from the measured value of this device. When the calibration of a single instrument is not accurate, the inversion quantity varies greatly. The calibration constant of the sun photometer changes during its use process; thus, calibrations are frequently needed in order to ensure the accuracy of the measured value. The calibration constant of the solar photometer is usually determined using the Langley method. Internationally, AERONET has two Langley calibration stations: the Mauna Loa observatory in the United States and the Izaña observatory in Spain. So far, the International Comparison and Calibration System has been established in Beijing, similar to AERONET at GSFC, but the Langley calibration system has not yet been established. Therefore, it is necessary to select a suitable calibration station in China. This paper studies the requirements of the calibration station using the Langley method. We used long-term records of satellite-derived measurements and survey data belonging to the aerosol optical thickness data of SNPP/VIIRS, CERES, MERRA-2, etc., in order to gain a better understanding of whether these stations are suitable for calibration. From the existing astronomical observation stations, meteorological stations, and the Sun–Sky Radiometer Observation Network (SONET) observation stations in China, the qualified stations were selected. According to the statistical data from the Ali observatory, the monthly average of clear sky is 20.21 days, and it is always greater than 15 days. The monthly average of aerosol is not more than 0.15 and is less than 0.3. We believe that the atmosphere above the Ali observatory is stable, and the results show that the Ali observatory has excellent weather conditions. This study can provide a selection of calibration sites for solar photometer calibrations in China that may need to be further characterized and evaluated, and at the same time provide a method to exclude unsuitable calibration sites.

TECX-TCN: Prediction of ionospheric total electron content at different latitudes in China based on XGBoost algorithm and temporal convolution network

The correction of ionospheric delay error through the estimation of ionospheric total electron content (TEC) parameters is very important for the accuracy and rapid convergence of precise point positioning (PPP), especially for single frequency PPP position and double frequency undifferenced and uncombined PPP models. This paper studies the prediction method of ionospheric TEC by analyzing various influencing factors. Firstly, considering the influence of latitude, the TEC values of different observation stations in China at middle and low latitudes are used for research. Secondly, due to the influence of solar storms, the years with and without solar storms are selected for comparative study. At the same time, the moving average value of TEC and the lag value of each solar and geomagnetic index are also added. Because the influence of each feature on ionospheric TEC is not clear, the importance score of each feature is obtained according to the EXtreme Gradient Boosting (XGBoost) algorithm to establish a reasonable feature combination. Then, the temporal convolution network (TCN) is used to train according to data of the collected solar cycle and predict data for the next year. Through experiments, the prediction model with feature selection can obtain higher prediction accuracy, the error reaches ∼2TECU at middle latitude region and ∼3 TECU at low latitude region. Finally, compared with other algorithms, our ionospheric TEC prediction algorithm based on XGBoost and TCN (TECX-TCN) significantly has better performance. When the solar activity is low, its RMSE can reach ∼2.5TECU, the correlation coefficient can reach ∼0.9, and even in the low latitude area with high solar activity, its RMSE can reach ∼5TECU, the correlation coefficient can reach ∼0.85.

A dataset of ultraviolet radiation reconstruction in China during 2015–2020

Ultraviolet radiation data can provide data support for many fields such as agriculture, ecology, environmental science, materials science, and human health. They have important application value in ecosystem succession, climate change research and environmental impact assessment. But with so little observational data and a short time scale, obtaining accurate, long-time, and wide range UV radiation data will provide scientific data support for relevant research. Therefore, based on the observation data of existing stations, in this paper, we constructed a method suitable for the reconstruction of ultraviolet radiation at the national scale, and combined with the conventional meteorological data observed for a long time, so as to obtain a dataset of ultraviolet radiation reconstruction. During the production of this dataset, first, we got the historical data of total surface radiation in accordance with the "hybrid model". Then, we created the UV radiation estimation formula by using clear sky index and other parameters. Finally, we calculated the reconstructed UV radiation data of 705 observation stations in China followed by the process of accuracy verification. The time range of this dataset is during 2015–2020.

Long‐Term Relationship Between foF2 From China Ionospheric Stations and Solar Activity During the 24th Solar Activity Cycle

AbstractThis paper employs the complete data of six ionospheric observation stations in China in the 24th solar activity cycle and researches the variation of frequency of the F2 layer (foF2) and the relationship with the solar activity indices. We discover that the dependence of foF2 on the sunspot number (R) and the solar radio flux of 10.7 cm wavelength (F10.7) is strongest in mid‐solar years, and weakest in high solar years. At the same time, the long‐term variation trends of foF2, R and F10.7 in the complete 24th solar activity cycle are analyzed using the mutual information method in information theory. We utilize the linear and nonlinear methods to compare the effects of long term change relationship between foF2 and R during the rising and falling phases of solar activity. The findings reveal that the sunspot number is more suitable to depict the long‐term trend of foF2 in China, and the relationship between them is better described by nonlinear quasi‐polynomial. This result has important implications for improving foF2 forecasts and the prediction accuracy of some ionospheric models.

Analysis of Extreme Rain and Snow Weather Dynamic and Water Vapor Conditions in Northeast China from 17 to 19 November 2020

Based on hourly precipitation data from 2413 national ground observation stations in China and ERA5 (0.25° × 0.25°), this study analyzes the characteristics and causes of extreme rainfall and snow in northeast China from 17–19 November 2020. The results show that extreme precipitation is mainly attributed to the abnormally strong large-scale low vortex and ground cyclone. The significant high-level and low-level coupling in areas with strong rain and snow is conducive to the continuous upward motion, which provides favorable dynamic conditions for the generation and development of extreme precipitation. The frontogenesis effect below the 850 hPa level is obvious, and the extreme precipitation period corresponds to the meeting of the north and south front areas. The symmetrical unstable atmosphere of 925 hPa~700 hPa is forced by the frontogenesis, which strengthens the oblique rising of the low layer and increases the instability, leading to the strengthened development of precipitation. For heavy rainfall and snow in early winter in China, water vapor transport is crucial. The extremely strong low-level jet also provides extremely strong water vapor conditions for the occurrence of heavy rain and snow. The analysis of the extreme rain and snow characteristics and formation mechanism of this weather process can deepen the understanding of extreme weather processes, and provide a useful reference for the research and prediction of extreme precipitation processes.

Long-Term Trends and Spatiotemporal Variations in Atmospheric XCH4 over China Utilizing Satellite Observations

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.

Comparisons among four different upscaling strategies for cultivar genetic parameters in rainfed spring wheat phenology simulations with the DSSAT-CERES-Wheat model

Cropping system models are widely used to assess the impacts of and adaptation practices to climate change on agricultural production. However, crop growth simulations at large scales have often lacked consideration of variation in crop cultivars, which were represented by different sets of genetic coefficients in crops models. In this study, taking the phenology of spring wheat (Triticum aestivum L.) as an example, we compared four different strategies for upscaling genetic parameters in phenology simulations at large scales with two experimental datasets. The first dataset was from field experiments comprising 40 different spring wheat cultivars at Altay (2014) and Yangling (2015–2017) station; the second dataset was historical (2010–2014) observed phenology records from 57 national agro-meteorological observation stations in China. The four strategies were the representative cultivar estimated at a single site (SSPs), the representative cultivar estimated at the 57 sites (NRPs), the various representative cultivars estimated at different agro-ecological zones (RRPs), and the virtual cultivars generated from the posterior distributions (VCPs). The posterior distributions aforesaid were established based on the calibrated parameter values of the 40 different spring wheat cultivars planted in Yangling. Then, 1000 sets of VCPs were randomly sampled from the posterior distributions. The results indicated that both the SSPs and NRPs strategy obtained large errors and uncertainties in spring wheat phenology simulations in China since only one representative cultivar was used. The RRPs strategy achieved the second high and the highest accuracy in anthesis and maturity data simulations. The VCPs strategy obtained the highest accuracy in anthesis simulation but relative larger errors in maturity simulation. The VCPs strategy can be directly used in large-scale crop growth simulations without tedious process of calibration. Hence, this strategy is recommended in areas where observations are scarce and for model users who not good at model parameter estimation.

The innovative development of atmospheric background stations in China: Thoughts and recommendations

Atmospheric background monitoring refers to the long-term observation of atmospheric composition that is well mixed and not affected by local pollution at a fixed location which is far from the influence of human activities. Atmospheric background monitoring can reflect the long-term changes of atmospheric composition caused by human activities on a global or regional scale. Therefore, it plays a very important role in both socioeconomic development and scientific research. Since the 1950s, some observation stations abroad have carried out background observation of the main atmospheric components such as carbon dioxide, ozone and atmospheric heavy metals. Additionally, the Global Ozone Observing System (GO₃OS) and the Background Air Pollution Monitoring Network (BAPMoN) were established by the World Meteorological Organization (WMO) in 1957 and 1968 to fulfill long-term and continuous observation of the chemical constituents of reactive gases, greenhouse gases, aerosols and acid rain. In 1989, WMO integrated GO₃OS and BAPMoN into the Global Atmosphere Watch (GAW). WMO/GAW has achieved great accomplishments owing to its long-term, systematic and accurate observation of the physical and chemical characteristics of atmospheric composition. During the period of 1981 to 2007, China Meteorological Administration (CMA) has built six representative regional GAW (Global Atmosphere Watch) stations, i.e., Shangdianzi (117.12°E, 40.65°N, <sc>293.3 m</sc> asl), Lin’an (119.75°E, 30.28°N, <sc>138.6 m</sc> asl), Longfengshan (127.60°E, 44. 73°N, <sc>331 m</sc> asl), Akedala (87.93°E, 47.10°N, <sc>562 m</sc> asl), Shangri-La (99.73°E, 28.01°N, <sc>3580 m</sc> asl), and Jinsha (114.2°E, 29.63°N, <sc>750 m</sc> asl), following the construction criteria of the GAW regional background stations. In addition, in accordance with the requirements of GAW global background station site selection, the Chinese government and the UN Global Environmental Facility (GEF) jointly invested and established a global GAW station in China, namely, the China GAW Baseline Observatory (CGAWBO), located at Mt. Waliguan (100.89°E, 36.28°N, <sc>3816 m</sc> asl) in Qinghai Province in 1994. These seven global/regional GAW stations build the current operational network of atmospheric background observation stations in China. Thanks to their long-term continous and precise atmospheric composition monitoring data and special representations, the stations at Mt. Waliguan, Shangdianzi, Lin’an, and Longfengshan were selected as the National Field Scientific Observation and Research Stations by the Ministry of Science and Technology of the People’s Republic of China in 2005. It is well known that the long-time series and excellent data on meteorological and atmospheric composition obtained from these seven global/regional GAW stations have played an unique role in China’s efforts on addressing climate change, climate change diplomacy and negotiation, air pollution control, and scientific research. With China’s socioeconomic development stepping into the new era, however, the deficiencies of the current GAW stations have become increasingly significant. Major problems lie in the unbalanced and insufficient development of these stations, as well as in the lack of innovation and demonstration. In this article, the status and the achievements of these seven stations are reviewed and summarized for the first time, followed by the identification and analysis of major problems of development. At last, it is recommended that future endeavors should be concentrated on enhancing the comprehensive observation capability and international cooperation, as well as further improving the construction and capacities of the national field scientific research stations. Besides, it is also suggested that the development of atmospheric background station shall serve China’s national and local development strategy, as well as strengthen its role in China’s progress of ecological civilization.

Study of uncertainty of satellite and reanalysis precipitation products and their impact on hydrological simulation.

Satellite and reanalysis precipitation products are potential alternatives in hydrological studies, and it is very important to evaluate their accuracy and potential use for reliable simulations. In this study, three precipitation products (Tropical Rainfall Measuring Mission 3B43 Version 7 (TRMM 3B43), spatial interpolation grid data based on 2472 national meteorological observation stations in China (GRID_0.5), and National Centers for Environmental Prediction-Climate Forecast System Reanalysis (NCEP-CFSR)) were evaluated against gauge observations in the Xiangxi River watershed of Hubei Province. The performance results indicated that the results of the three precipitation products were correlated with those of the rain gauges; however, there were differences among the three products. TRMM 3B43 tended to overestimate precipitation with the highest correlation coefficient, while NCEP-CFSR tended to underestimate precipitation with the least satisfactory performance, and the performance of GRID_0.5 ranked between them. However, the annual and monthly mean errors differed, as the errors of most of the results driven by NCEP-CFSR were lowest. The errors varied at different time scales. During years with high precipitation, the results were often underestimated, while the results are often overestimated during years with low precipitation. According to the average monthly results, the GRID_0.5 results were closest to the gauge observations for most months. During the wet season, TRMM 3B43 performed better, while NCEP-CFSR precipitation performed better during the dry season. The errors from precipitation to streamflow, NPS pollution, and water environmental capacity (WEC) driven by the three precipitation products increased gradually, ranging from 10% for precipitation to over 20% for NPS pollution and almost 100% for WEC. The error increase for NCEP-CFSR was lower than that of the other two products. Although the simulation error from precipitation to the WEC results driven by the three precipitation products gradually increased, the degree of overestimation and underestimation became smaller.

The combined effects of ENSO and Arctic Oscillation on wintertime fog days in eastern China

The data of fog days from weather observation stations in China and the NCEP/NCAR re-analysis data from 1954 to 2007 are used to investigate the combined effects of El Niño and Southern Oscillation (ENSO) and Arctic Oscillation (AO) on the number of winter fog days in eastern China. The results show that during El Niño, the enhanced low-level southwesterly warm-moist airflow could lead to the temperature rise and humidity increase in eastern China. Note that the rate of humidity increase is faster than the rate of temperature rise, which makes the air in eastern China easy to be saturated. Besides, in winter, North China is dominated by the sinking airflow, so a large-value area of fog days appears in eastern China with the center in North China. While in La Niña years, the atmospheric circulation and its influence on the fog days are the opposite. During the positive AO period, the East Asian trough weakens and the low-level westerly jet moves northward, preventing northwesterly cold air from moving southward. The warming and humidification of North China and the slight temperature drop in South China would cause more fog days in North China and fewer fog days in South China. The effect of negative AO is opposite to that of positive AO. The combined effects of ENSO and AO are far greater than the sum of their individual effects. Under El Niño and positive AO, the number of fog days would increase significantly in North China during the whole winter. Besides, ENSO has greater impacts than AO during early winter and vice versa during later winter.

What Large Sample Size Is Sufficient for Hydrologic Frequency Analysis?—A Rational Argument for a 30-Year Hydrologic Sample Size in Water Resources Management

The calculation of hydrologic frequency is an important basic step in the planning and design stage of any water conservancy project. The purpose of the frequency analysis is to deduce the hydrologic variables under different guarantee rates, and to provide hydrologic information for water conservancy project planning and design. The calculation of hydrologic frequency requires that the sample size is large enough, as only then can the statistical characteristics of samples take the place of the total statistical eigenvalues. This means that the samples can reveal the statistical characteristics of hydrologic variables and identify the randomness rule of hydrologic phenomena. Many countries in the East Asian monsoon climate zone (China, Japan and South Korea) have stipulated a sample size of 30 years for hydrologic frequency analysis. In this paper the rationality of the 30-year sample size is proved by analyzing the periodic and random rules of hydrologic phenomenon and the influencing mechanism of solar activity, and by adopting the general conclusion of the sampling theorem. Then, using the wavelet analysis method to examine annual precipitation data in a long series generated from representative precipitation observation stations in China, the strong-weak cycle of solar activity is proved to be 10 years, which is consistent with the wet-dry cycle of the representative precipitation stations (10–12 years). Finally, adopting numerical modeling to analyze the normal distribution of randomly generated samples and long-range annual precipitation data collected from representative stations, hypothesis testing (u, F and t) is used to prove that a 30-year sample size is reasonable. This research provides a reference as to how to prove the necessary sample size for relevant statistical analyses (for example, how large the sample should be for analyzing hydrologic factors trend evolution, hydrologic data consistency and ergodicity of statistical samples), thus ensuring the reliability of the analytical results.

Heat Waves in China: Definitions, Leading Patterns, and Connections to Large‐Scale Atmospheric Circulation and SSTs

AbstractBased on the daily maximum temperatures (Tmax) from 587 surface observation stations in China during 1959–2013, heat waves are detected using both absolute and relative definitions. The spatiotemporal variations of heat wave occurrence/duration/amplitude are compared between the two definitions. Considering the significant differences in regional climatology, relative threshold is more meaningful to detect the local extremes. By utilizing the empirical orthogonal function, the integral index heat wave total intensity is decomposed into three dominant modes: interdecadal (ID), interannual‐tripole (IA‐TR), and interannual‐dipole (IA‐DP) modes. The ID mode shows uniform anomalies over the whole China, with the maximum in north, and its corresponding time series depict notable interdecadal variations with a turning point around mid‐1990s. The IA‐DP mode exhibits opposite‐signed anomalies over north and south China. The IA‐TR mode shows an anomalous tripole pattern with negative anomalies over central China and positive anomalies over north and south China in its positive phase. Both the IA‐DP and IA‐TR patterns are more obvious since mid‐1990s with mainly year‐to‐year variations before that. All the three modes are controlled by anomalous high‐pressure systems, which are accompanied by local‐scale dry land conditions. The diabatic heating associated with anomalous convective activities over tropical western Pacific triggers Rossby wave trains propagating northward along the East Asia, which causes abnormal heat waves through descending motion over the high‐pressure nodes. In turn, the severe convections are generated by enhanced Walker circulation in the tropical Pacific due to warming and/or cooling sea surface temperature (SST) anomalies in the tropical western and eastern Pacific, respectively.

Changes in temporal concentration property of summer precipitation in China during 1961–2010 based on a new index

Based on the property of entropy, a new index Q was defined to measure the temporal concentration property of summertime daily rainfall in China, based on daily precipitation data collected at 553 observation stations in China during 1961–2010. Furthermore, changes in the temporal concentration property of summer precipitation in China were investigated. The results indicate that the regions with larger Q values were located in most parts of Northwest China and the north of the Yellow River, where daily precipitation tended to become temporally concentrated during the study period. On the contrary, smaller Q values were found in eastern Tibetan Plateau, southeastern Northwest China, and most parts of Southwest and South China. The most obvious increasing trend of Q index was found in South China and most parts of Southwest China, where precipitation showed a temporal concentration trend. However, a decreasing trend of Q index was found in Northwest China, the Tibetan Plateau, and the north of the Huaihe River. Variations of the Q index and the summer rainfall total during 1961–2010 in China both exhibited an increasing trend, implying larger temporal variability in rainfall attributes. It is illustrated that the summer precipitation in general became more temporally concentrated with more intense rainfall events and wetter days.

Support vector machine-based open crop model (SBOCM): Case of rice production in China.

Existing crop models produce unsatisfactory simulation results and are operationally complicated. The present study, however, demonstrated the unique advantages of statistical crop models for large-scale simulation. Using rice as the research crop, a support vector machine-based open crop model (SBOCM) was developed by integrating developmental stage and yield prediction models. Basic geographical information obtained by surface weather observation stations in China and the 1:1000000 soil database published by the Chinese Academy of Sciences were used. Based on the principle of scale compatibility of modeling data, an open reading frame was designed for the dynamic daily input of meteorological data and output of rice development and yield records. This was used to generate rice developmental stage and yield prediction models, which were integrated into the SBOCM system. The parameters, methods, error resources, and other factors were analyzed. Although not a crop physiology simulation model, the proposed SBOCM can be used for perennial simulation and one-year rice predictions within certain scale ranges. It is convenient for data acquisition, regionally applicable, parametrically simple, and effective for multi-scale factor integration. It has the potential for future integration with extensive social and economic factors to improve the prediction accuracy and practicability.

Exploring the relationship between vegetation and dust-storm intensity (DSI) in China

It is difficult to estimate the effects of vegetation on dust-storm intensity (DSI) since land surface data are often recorded aerially while DSI is recorded as point data by weather stations. Based on combining both types of data, this paper analyzed the relationship between vegetation and DSI, using a panel data-analysis method that examined six years of data from 186 observation stations in China. The multiple regression results showed that the relationship between changes in vegetation and variance in DSI became weaker from the sub-humid temperate zone (SHTZ) to dry temperate zone (DTZ), as the average normalized difference vegetation index decreased in the four zones in the study area. In the SHTZ and DTZ zones, the regression model could account for approximately 24.9% and 8.6% of the DSI variance, respectively. Lastly, this study provides some policy implications for combating dust storms.

Frequency analysis and its spatiotemporal characteristics of precipitation extreme events in China during 1951–2010

This study investigates frequency analysis and its spatiotemporal characteristics of precipitation extremes based on annual maximum of daily precipitation (AMP) data of 753 observation stations in China during the period 1951–2010. Several statistical methods including L-moments, Mann-Kendall test (MK test), Student’s t test (t test) and analysis of variance (F-test) are used to study different statistical properties related to frequency and spatiotemporal characteristics of precipitation extremes. The results indicate that the AMP series of most sites have no linear trends at 90 % confidence level, but there is a distinctive decrease trend in Beijing-Tianjin-Tangshan region. The analysis of abrupt changes shows that there are no significant changes in most sites, and no distinctive regional patterns within the mutation sites either. An important innovation different from the previous studies is the shift in the mean and the variance which are also studied in this paper in order to further analyze the changes of strong and weak precipitation extreme events. The shift analysis shows that we should pay more attention to the drought in North China and to the flood control and drought in South China, especially to those regions that have no clear trend and have a significant shift in the variance. More important, this study conducts the comprehensive analysis of a complete set of quantile estimates and its spatiotemporal characteristic in China. Spatial distribution of quantile estimation based on the AMP series demonstrated that the values gradually increased from the Northwest to the Southeast with the increment of duration and return period, while the increasing rate of estimation is smooth in the arid and semiarid region and is rapid in humid region. Frequency estimates of 50-year return period are in agreement with the maximum observations of AMP series in the most stations, which can provide more quantitative and scientific basis for decision making.

Reconstruction of daily ultraviolet radiation for nine observation stations in China

The aim of this study was to reconstruct daily ultraviolet (UV) radiation (daily cumulation, similarly hereinafter) for nine observation stations in China, using a radiative transfer model combined with empirical relationships. Measurements of global total solar (G) radiation, UV radiation, and their ratio (UV/G) at the nine stations were analyzed for the period 2006–2011. The daily cloud and aerosol attenuation ratios were defined to quantitatively evaluate the effect of cloud and aerosols on radiation, based on measurements and simulated data under cloudless-sky conditions. A semi-empirical reconstruction method for UV radiation was established at each station using cloud modification factors, and by applying an equation that relates G and UV radiation measurements, as well as their corresponding simulations under cloudless conditions. Daily UV radiation values were reconstructed and compared with ground-based measurements for the nine stations, and the results revealed most of the slope values to be close to one, and the determination coefficients ranged from 89.5 to 95.7 %. The values of mean absolute bias error (MABE) and root-mean-square error (RMSE) were below 15 and 18 % for all stations, respectively, and at most stations were even lower than 10 %. A comparison of monthly measured and reconstructed UV radiation for the nine stations was also performed, revealing the values of MABE and RMSE to be even smaller with values of less than 5 %. According to these results, we conclude that the method established in this study has good applicability and transferability.

Intensity of dust storms in China from 1980 to 2007: A new definition

In the process of studying dust-storm events, we have to face an important scientific problem: How to define dust-storm intensity (DSI)? This study provides a comprehensive definition of DSI in terms of the frequency, duration, and visibility of dust storms, and uses it to measure the trend of annual changes in dust-storm activities. With dust-storm data from 186 meteorological observation stations in China, the trend in DSI was studied for the period 1980–2007. This trend differs from those based on the frequency of dust storms, which are often used in the literature. In this study, average DSIs after 2000 were underestimated using frequency alone to measure the dust activities, compared with those before 2000. According to the spatiotemporal distribution, there are four modes of change in DSI over the period, namely a significantly decreasing trend, an increasing trend, a mode in which dust storm activity remained constant, and a two-peak mode.

Fourier analysis‐based air temperature movement analysis and forecast

Climate change and climate variability have become issues of global concern. Conventional Fourier analysis is the most common analysis method in the frequency domain. In this study, an evaluation model using limited terms of Fourier series is introduced to describe yearly air temperature movement by month and daily air temperature by hour. Then a forecast method based on the Fourier analysis in the least-square sense is proposed that incorporating with the least-square method, the Fourier-series model is extended to forecast that predict future temperature movements based on limited previous observation values. The forecast model is built by finding its optimum Fourier coefficients in the least-square sense based on the previous observed temperature movements. Experiments including yearly and daily air temperature evaluation and forecast at several observation stations in China, yield satisfied results agreeing well with actual observation values. Experimental results demonstrate that the Fourier evaluation model evaluates the annual and the daily air temperature movements most closely by about 5-term and 11-term Fourier series, respectively. The forecast model predicts both the annual and daily air temperature movements most fit with about 4-term Fourier series. Result analysis indicates workability and effectiveness of the proposed.

Application of Grey Theory to Ionospheric Short-term Forecasting

By analysis of historical data of the ionosphere, it is suggested to apply grey theory to ionospheric short-term forecasting, grey range information entropy is defined to determine the optimum grey length of the sample sequence, the prediction model based on residual error is constructed, and the observation data of multiple ionospheric observation stations in China are adopted for test. The prediction result indicates that the average grey range information entropy calculation results reflect the cyclical effects of solar rotation, precision of the forecasting method in high latitudes is higher than low latitudes, and its error is large relatively in more intense solar activity season, the effect of forecasting 1 day in advance of average relative residuals are less than 1 MHz, the average precision is more than 90%. It provides a new way of thinking for the ionospheric foF2 short-term forecast in the future.