Meteorological Observation Stations Research Articles

Evaluation of High-Resolution Crop Model Meteorological Forcing Datasets at Regional Scale: Air Temperature and Precipitation over Major Land Areas of China

Air temperature and precipitation are two important meteorological factors affecting the earth’s energy exchange and hydrological process. High quality temperature and precipitation forcing datasets are of great significance to agro-meteorology and disaster monitoring. In this study, the accuracy of air temperature and precipitation of the fifth generation of atmospheric reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ERA5) and High-Resolution China Meteorological Administration Land Data Assimilation System (HRCLDAS) datasets are compared and evaluated from multiple spatial–temporal perspectives based on the ground meteorological station observations over major land areas of China in 2018. Concurrently, the applicability to the monitoring of high temperatures and rainstorms is also distinguished. The results show that (1) although both forcing datasets can capture the broad features of spatial distribution and seasonal variation in air temperature and precipitation, HRCLDAS shows more detailed features, especially in areas with complex underlying surfaces; (2) compared with the ground observations, it can be found that the air temperature and precipitation of HRCLDAS perform better than ERA5. The root-mean-square error (RMSE) of mean air temperature are 1.3 °C for HRCLDAS and 2.3 °C for ERA5, and the RMSE of precipitation are 2.4 mm for HRCLDAS and 5.4 mm for ERA5; (3) in the monitoring of important weather processes, the two forcing datasets can well reproduce the high temperature, rainstorm and heavy rainstorm events from June to August in 2018. HRCLDAS is more accurate in the area and magnitude of high temperature and rainstorm due to its high spatial and temporal resolution. The evaluation results can help researchers to understand the superiority and drawbacks of these two forcing datasets and select datasets reasonably in the study of climate change, agro-meteorological modeling, extreme weather research, hydrological processes and sustainable development.

Detection of wind corridors based on “Climatopes”: a study in central Ji’nan

Wind corridor blueprints are an essential reference for climate planning that takes into account zones of fresh cold air, air channels as well as heat island alleviation zones. This article addresses the matter of how to plan urban wind corridors more efficiently by proposing a “ventilation potential coefficient” (VPC) as well as providing an index for natural cold sources. The study additionally proposes a surface urban heat island (SUHI) index as a means of evaluating the severity of the heat island effect. Based on these three indexes, a new method of categorizing urban climatic zones, called “Climatopes”, is developed. Using the above methods, data from 2016 meteorological observation stations and Landsat8 satellites, as well as urban planning materials and basic geographical information on a scale of 1:2000 are used to create an Urban Climate Analysis Map (UCAnMap) and formulate a plan for the construction of wind corridors in the urban centre of Ji’nan. The results reveal that the intensity of urban heat islands in Ji’nan is closely related to a lack of natural cold sources, the specific layout of different types of land use as well as poor planning of urban “function zones”. Moreover, there is a significant correlation between the VPC and other factors such as surface roughness length, building height, the density of building frontal area and floor area ratios. The potential wind corridors could also be determined based on the VPC. Based on an UCAnMap composed of 11 Climatopes and background wind environments, this article ascertains that it would be possible to construct a network of 3 Level One corridors (each over 500 m wide) and 11 Level Two corridors (each over 80-m wide). Additionally, regulatory measures and protective legislation are proposed for the corridors, as well as for the city’s overall development.

Методика оценки выработки электроэнергии солнечными электростанциями с использованием данных многолетних наблюдений метеостанций

Методика оценки выработки электроэнергии солнечными электростанциями с использованием данных многолетних наблюдений метеостанций

The Application of a Decision Tree and Stochastic Forest Model in Summer Precipitation Prediction in Chongqing

Meteorological disasters are the result of the interaction of multiple factors and multiple systems. In order to improve the accuracy of prediction, it is necessary not only to consider the characteristics and cycles of each subsystem, but also to study the interaction of all systems. Based on the summer precipitation data and 130 circulation indexes of 34 national meteorological observation stations in Chongqing from 1961 to 2010, the prediction model of Chongqing summer precipitation was established based on the decision tree and the stochastic forest algorithm based on machine learning, and the prediction test of 2011–2018 was carried out independently by the model. Compared with the results of the single-factor prediction model, the trend consistency rate increased by 37.5% and 12.5% respectively. In addition, when using the random forest model to predict summer precipitation in Chongqing from 2014 to 2018, the 5-year average Ps, Cc and PC scores were 84.6, 0.27 and 67.1, respectively, which were significantly improved compared with 72.4, −0.12 and 52.9 of the current climate forecasting methods, and the forecast quality of the random forest was relatively stable. The multi-system collaborative impact model based on decision tree and random forest algorithm can achieve high accuracy and stability. Thus, this method can not only be an effective means for the diagnosis and prediction of climate causes, but also has a good theoretical and practical value for the prediction of extreme disasters.

Spatiotemporal changes in heavy precipitation events in the Beijing-Tianjin-Hebei region revealed by hourly meteorological station observations

Changes in heavy precipitation events (HPEs) are closely related to hydrological disasters, such as floods and urban water logging. This study investigated the spatiotemporal changes in HPEs by using hourly precipitation data from the warm season (from May to September) from 1980 to 2016 in the Beijing-Tianjin-Hebei (BTH) region. Sen’s slope estimator and the modified Mann-Kendall test were used to detect the temporal trend. A circular statistical approach was used to analyze the diurnal distribution of HPEs. The study found that the temporal changes in the HPEs in the BTH region could be divided into three stages: 1980–1994, 1994–2002, and 2002–2016. The first period and the last period witnessed an increase in the frequency and maximum intensity of HPEs, while the second period experienced a decreasing trend in the frequency and maximum intensity of HPEs. By comparing the HPEs in the first and last periods, the study revealed that the short-duration HPEs (HPEs of 1–6 h in duration) became more frequent and more extreme, while the long-duration HPEs (HPEs lasting longer than 6 h) became less frequent and less extreme. In addition, the study found that the peak time of the short-duration HPEs occurred from 19:00 local standard time (LST) to 24:00 LST; however, the peak time of the long-duration HPEs occurred from 01:00 LST to 06:00 LST. Moreover, the peak time of short-duration HPEs tended to be more concentrated than that of long-duration HPEs. Because an increasing number of short-duration HPEs occurred in the period of 2002–2016, the peak time of the HPEs in the BTH region was delayed and more concentrated. Local temperature showed an obvious correlation with the changes in HPEs at the decadal timescale. Our study provided useful information about spatiotemporal changes in precipitation events at the sub-daily scale in the BTH region.

Mechanisms of an extreme fog and haze event in the megacities of central and eastern China

In this study, the authors conduct a detailed analysis of a prolonged fog and haze (FAH) event in the megacities of central and eastern China in January 2013. The persistence and intensification mechanisms of high impact FAH weather are analyzed using surface and sounding data from national meteorological observation stations and national environmental pollutant observations. The large-scale circulation was characterized by the weak and stable polar vortex in Eurasia, a zonally elongated circulation spanning from the midlatitudes to high latitudes at 500 hPa, and the West Pacific Subtropical High extending further westward. At the surface, the FAH regions were under control by a weak easterly and southeasterly at the back or bottom of the surface high-pressure system, which was favorable for the accumulation of high aerosol pollutant concentrations. The formation and intensification of the FAH have an apparent correlation with the variation in meteorological variables and aerosols. The strengthening of the extreme fog was due to a combination of advection and radiation effects, among which alternative temperature advection played the key role in the maintenance and deterioration of FAH. The boundary layer height indicated the strengthening and weakening of the FAH. The persistence of weak vertical wind shear in the middle-lower layer and weak vertical motion were favorable dynamic factors for the FAH.

Effects of changes in land use on the diurnal temperature range: a long-term data analysis

Air temperature is sensitive to changes in local land use. In this study, we investigated the effect of dam lake water on the air temperature based on open climate data. Lake Sayama (and its surroundings), located in Tokorozawa City, 30 km from Tokyo, Japan, was undertaken as the case study. This artificial lake was empty for three years for seismic reinforcement. To remove the effects of time-series changes in the air temperature, nearby observation stations where the air temperature difference did not change from that of Tokorozawa during the analysis period were selected. Analysis of data revealed that the minimum air temperature increased, and the maximum air temperature slightly decreased during the period when the dam reservoir was full throughout the year. Moreover, the reduction in the diurnal range of air temperatures was 1.78 °C in December and 0.46 °C in August. The effect of the lake on the air temperature reflected the size of the lake and the climatic zone. These results highlight the need for maintaining a network of meteorological observation stations, whose data can prove useful for identifying local climate changes due to land use.

Temporal variation of dust aerosol pollution in northern China

By employing the sandstorm dataset from meteorological station observations, the temporal and spatial variations in dust outbreak duration in the source regions and visibility variations over the downwind regions of northern China were analyzed over the past four decades. The statistics and wind field analyses reveal that after the early 2000s, the duration of dust outbreaks over the potential source areas of northern China had decreased by 63% compared with that in the 1970s, which suggests that the dust aerosol emission intensities in the region were significantly reduced. However, although the potential dust aerosol emission intensities were significantly reduced, the visibility during dust aerosol emission periods increased by only 25%. The emerging potential source areas in the eastern regions, variations in the wind field, increases in up-air winds, and decreases in surface winds all contribute to the observed continued high dust aerosol pollution in northern China since the early 2000s.

Evaluation of Gene Expression Method for Rainfall-Runoff Relationship: Case study of the E21A057 Station

A reliable model for the prediction of rainfall-runoff process is important for water resource planning and management. The accurate estimation of monthly rainfall and runoff also can be considered a key element for predicting drought in the near future. Rainfall is the most important factor for flow. Therefore, knowing the amount of rainfall in a basin can help to improve the accuracy of forecasting models for river flow Precipitation and flow have a complex and nonlinear relationship. Additionally, the effect of precipitation on the flow depends on basin characteristics such as topography, shape, slope, type of soil, soil moisture, temperature and evaporation. These parameters are generally considered in conceptual models. However, since the basin characteristics are difficult to determine, in recent years the relationship between precipitation and flow can be made with limited data by artificial intelligence methods. In this study, monthly mean flow values of E21A057 Stream Gauging Station (SGS) are estimated by Genexpression Programming (GEP) and Multiple Linear Regression (MLR) methods by using monthly total precipitation values of 17204 meteorological observation station. Models which are prepared using different input parameters, the GEP method is found to be more successful in almost all cases compared to the MLR method. The most successful model among all models is determined as DM04 using four input parameters in GEP method. These parameters are Precipitation (P), 1 month lagged flow rate (Q-1), 1 month lagged precipitation (P-1) and 2 month lagged precipitation (P-2).

Evaluation of Elazığ Province Precipitation Data by Innovative Trend Analysis

The effect of climate change that have been occurred in worldwide is felt especially in Turkey in recent years. Rainfall is the parameter most affected by climate change. Rainfalls affects the amount of water to be used for drinking, irrigation and electrical energy production by feeding the streams flow. In this study, the total monthly precipitation data of 5 meteorological observation stations in Elazığ province were investigated and the changes of these precipitations under the influence of climate change were analyzed monthly, seasonal and yearly using Innovative Trend Analysis (ITA). When the results obtained by ITA method were evaluated, it was found that rainfall measurements of selected stations were negatively affected by climate change and there was a significant decrease in precipitation when analyzed monthly, seasonal and yearly.

CLUSTERING OF RAINFALL DISTRIBUTION PATTERNS IN PENINSULAR MALAYSIA USING TIME SERIES CLUSTERING METHOD

Time series clustering technique was used in this study to categorize the locations in Peninsular Malaysia according to the similarity of rainfall distribution patterns. Daily rainfall time series data from 12 meteorological observation stations across Peninsular Malaysia have been considered for this study. Four dissimilarity measure methods were examined and compared in terms of accuracy and suitability, namely Euclidean distance (ED), complexity-invariant distance (CID), correlation-based distance (COR) and integrated periodogram-based distance (IP). The average silhouette width (ASW) was used to determine the optimal group number for the rainfall time series data. Using Ward’s hierarchical clustering method, this study found that the rainfall time series in Peninsular Malaysia can be divided into four regions of homogeneous climate zones. Based on the results, the IP was the most suitable dissimilarity measures for clustering rainfall time series data in Peninsular Malaysia, except during the Southwest Monsoon where the COR performed better.

Mapping evapotranspiration variability over a complex oasis-desert ecosystem based on automated calibration of Landsat 7 ETM+ data in SEBAL

Fragmented ecosystems of the desiccated Aral Sea seek answers to the profound local hydrologically- and water-related problems. Particularly, in the Small Aral Sea Basin (SASB), these problems are associated with low precipitation, increased temperature, land use and evapotranspiration (ET) changes. Here, the utility of high-resolution satellite dataset is employed to model the growing season dynamic of near-surface fluxes controlled by the advective effects of desert and oasis ecosystems in the SASB. This study adapted and applied the sensible heat flux calibration mechanism of Surface Energy Balance Algorithm for Land (SEBAL) to 16 clear-sky Landsat 7 ETM+ dataset, following a guided automatic pixels search from surface temperature Ts and Normalized Difference Vegetation Index NDVI ( ). Results were comprehensively validated with flux components and actual ET (ETa) outputs of Eddy Covariance (EC) and Meteorological Station (KZL) observations located in the desert and oasis, respectively. Compared with the original SEBAL, a noteworthy enhancement of flux estimations was achieved as follows: – desert ecosystem ETa R2 = 0.94; oasis ecosystem ETa R2 = 0.98 (P < 0.05). The improvement uncovered the exact land use contributions to ETa variability, with average estimates ranging from 1.24 mm to 6.98 mm . Additionally, instantaneous ET to NDVI (ETins-NDVI) ratio indicated that desert and oasis consumptive water use vary significantly with time of the season. This study indicates the possibility of continuous daily ET monitoring with considerable implications for improving water resources decision support over complex data-scarce drylands.

Recent trends of snow avalanche regime in the Central Caucasus (Elbrus region as an example)

The climate change during cold seasons of 1995–2017 in the Central Caucasus is estimated, and its influence on the avalanche regime is shown. Data on the avalanche releases in the Central Caucasus for the period 1968– 2017 together with observations of high-altitude meteorological stations were used for the analysis. The paper presents estimates of snowiness of the winters and their frequency of occurrence in the area under investigation. The winter snowiness was noted to decrease since the beginning of the 2000s. The last decade of the period was not snowy, especially its series of six winters having very small amounts of snow. It is shown that in the second half of the XX century the heaviest snowfalls took place mostly in Januaries, and they were followed by releases of avalanches with the volumes exceeding 1 million cubic metres. In the early 2000‑ies, intensive January snowfalls were observed later, i.e. during the winter-spring period. In the warmer months March and April, the destructive potential of avalanches was noticeably smaller. In the present time, the warming and decrease of winter snowiness resulted in significant diminution of the avalanche hazard in the region. At the same time, on the background of general warming the certain increase in inter-seasonal variability of air temperature was noted. These changes may be compared to the warming of 1910–1945 when during its warmest phase the Europe suffered with one of the harshest winters in 1941/42. The swing of the «temperature pendulum» indicates that a harsh winter with heavy snowfalls and avalanches with catastrophic consequences may occur on the background of winters with mild and moderate avalanche danger. This is one of probable scenarios in the development of avalanche activity in the Greater Caucasus in the context of the current climate change.

Establishing Unusual-Weather Detection System Prototype Using Onboard Sensor Information

Weather has been closely associated with flight safety since the beginning of aviation history, with many aviation accidents and incidents occurring due to unusual weather conditions near airports. The current aviation unusual-weather detection systems use the weather information provided by ground meteorological observation stations in the airport vicinity. They can only detect unusual weather conditions near the ground surface because all the ground meteorological observation stations are at almost the same elevation. Consequently, this Paper aims to augment the current system by collecting vertical weather information in the air from a new data source, namely, Automatic Dependent Surveillance-Broadcast systems. This Paper evaluates the correlation between actual aircraft Automatic Dependent Surveillance-Broadcast data and unusual weather conditions based on several unusual-weather detection algorithms and then uses machine learning to establish the system prototype to detect aviation unusual weather. These unusual-weather detection algorithms have been verified in this Paper to distinguish between normal and unusual weather conditions. The developed machine-learning model can provide accuracy rates of close to 98% for detecting aviation unusual weather conditions. Finally, we develop a system prototype graphical user interface with which users can interact through graphical icons and indicators. Users can directly get information regarding aircraft and weather from this system prototype. The testing of the developed models is validated by the actual Automatic Dependent Surveillance-Broadcast signals broadcast by several aircraft recorded at the airport.

A Statistical–Distributed Model of Average Annual Runoff for Water Resources Assessment in DPR Korea

Water resource management is critical for the economic development of the Democratic People’s Republic of Korea (DPRK), where runoff plays a central role. However, long and continuous runoff data at required spatial and temporal scales are generally not available in many regions in DPRK, the same as in many countries around the world. A common practice to fill the gaps is to use some kind of interpolation or data-infilling methods. In this study, the gaps in annual runoff data were filled using a distributed runoff map. A novel statistical–distributed model of average annual runoff was derived from 50 years’ observation on 200 meteorological observation stations in DPRK, considering the influence of climatic factors. Using principal component analysis, correlation analysis and residual error analysis, average annual precipitation, average annual precipitation intensity, average annual air temperature, and hot seasonal air temperature were selected as major factors affecting average annual runoff formation. Based on the water balance equation and assumptions, the empirical relationship for runoff depth and impact factors was established and calibrated. The proposed empirical model was successfully verified by 93 gauged stations. The cartography of the average annual runoff map was automatically implemented in ArcGIS. A case study on the Tumen River Basin illustrated the applicability of the proposed model. This model has been widely used for the development and management of water resources by water-related institutes and design agencies in DPRK. The limitation of the proposed model and future works are also discussed, especially the impacts of climate changes and topology changes and the combination with the physical process of runoff formation.

Changes in Snow Phenology from 1979 to 2016 over the Tianshan Mountains, Central Asia

Snowmelt from the Tianshan Mountains (TS) is a major contributor to the water resources of the Central Asian region. Thus, changes in snow phenology over the TS have significant implications for regional water supplies and ecosystem services. However, the characteristics of changes in snow phenology and their influences on the climate are poorly understood throughout the entire TS due to the lack of in situ observations, limitations of optical remote sensing due to clouds, and decentralized political landscapes. Using passive microwave remote sensing snow data from 1979 to 2016 across the TS, this study investigates the spatiotemporal variations of snow phenology and their attributes and implications. The results show that the mean snow onset day (Do), snow end day (De), snow cover duration days (Dd), and maximum snow depth (SDmax) from 1979 to 2016 were the 78.2nd day of hydrological year (DOY), 222.4th DOY, 146.2 days, and 16.1 cm over the TS, respectively. Dd exhibited a spatial distribution of days with a temperature of &lt;0 °C derived from meteorological station observations. Anomalies of snow phenology displayed the regional diversities over the TS, with shortened Dd in high-altitude regions and the Fergana Valley but increased Dd in the Ili Valley and upper reaches of the Chu and Aksu Rivers. Increased SDmax was exhibited in the central part of the TS, and decreased SDmax was observed in the western and eastern parts of the TS. Changes in Dd were dominated by earlier De, which was caused by increased melt-season temperatures (Tm). Earlier De with increased accumulation of seasonal precipitation (Pa) influenced the hydrological processes in the snowmelt recharge basin, increasing runoff and earlier peak runoff in the spring, which intensified the regional water crisis.

Investigating Spatial and Temporal Variation of Hydrological Processes in Western China Driven by CMADS

The performance of hydrological models in western China has been restricted due to the scarcity of meteorological observation stations in the region. In addition to improving the quality of atmospheric input data, the use hydrological models to analyze Hydrological Processes on a large scale in western China could prove to be of key importance. The Jing and Bortala River Basin (JBR) was selected as the study area in this research. The China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS) is used to drive SWAT model, in order to greatly improve the accuracy of SWAT model input data. The SUFI-2 algorithm is also used to optimize 26 sensitive parameters within the SWAT-CUP. After the verification of two runoff observation and control stations (located at Jing and Hot Spring) in the study area, the temporal and spatial distribution of soil moisture, snowmelt, evaporation and precipitation were analyzed in detail. The results show that the CMADS can greatly improve the performance of SWAT model in western China, and minimize the uncertainty of the model. The NSE efficiency coefficients of calibration and validation are controlled between 0.659–0.942 on a monthly scale and between 0.526–0.815 on a daily scale. Soil moisture will reach its first peak level in March and April of each year in the JBR due to the snow melting process in spring in the basin. With the end of the snowmelt process, precipitation and air temperature increased sharply in the later period, which causes the soil moisture content to fluctuate up and down. In October, there was a large amount of precipitation in the basin due to the transit of cold air (mainly snowfall), causing soil moisture to remain constant and increase again until snowmelt in early spring the following year. This study effectively verifies the applicability of CMADS in western China and provides important scientific and technological support for the spatio-temporal variation of soil moisture and its driving factor analysis in western China.

An Analysis of the Reliability of a New Dataset of Transmission Line Icing Thickness in Southern China

AbstractBased on ERA-Interim data, gauge observations, transmission line icing observational data, and hindcasted predictors from a numerical forecast system of transmission line icing, a new transmission line icing thickness (TLIT) dataset was constructed to solve the problem of limited historical data. The reliability of the dataset was analyzed using case studies and climate data. The results showed that the descriptions of three icing events in southern China by the TLIT were consistent with the actual observational data, and the icing thickness differences were less than 2 mm. The spatial distribution of annual icing days and icing thickness calculated using meteorological observation station icing data (OIT) and the TLIT data had a similar pattern, with small differences in the numerical values. A rotated empirical orthogonal function (REOF) decomposition was conducted for 67 transmission line icing events. It was found that the spatial distributions of the first three characteristic vectors of the TLIT and OIT data were similar, and the correlation coefficients for the time coefficients of the first three characteristic vectors were 0.801, −0.443, and 0.576, respectively. Three key areas were identified based on the first three patterns of REOF, and the average icing thickness of 67 events in southern China and the three key areas was calculated. The correlation coefficients of icing thickness calculated by the TLIT and OIT data for these areas were 0.648, 0.384, 0.565, and 0.599, respectively. The results illustrate that the TLIT data can reflect the temporal and spatial variations of ice thickness in southern China.

Weather Visibility Prediction Based on Multimodal Fusion

Visibility affects all forms of traffic: roads, sailing, and aviation. Visibility prediction is meaningful in guiding production and life. Different from weather prediction, which relies solely on atmosphere factors, the factors that affect meteorological visibility are more complicated, such as the air pollution caused by factory exhaust emission. However, the current prediction of visibility is mostly based on the numerical prediction method similar to the weather prediction. We proposed a method using multimodal fusion to build a weather visibility prediction system in this paper. An advanced numerical prediction model and a method for emission detection were used to build a multimodal fusion visibility prediction system. We used the most advanced regression algorithm, XGBoost, and LightGBM, to train the fusion model for numerical prediction. Through the estimation of factory emission by the traditional detector in the satellite image, we propose to add the result of estimation based on Landsat-8 satellite images to assist the prediction. By testing our numerical model in atmosphere data of various meteorological observation stations in Beijing-Tianjin-Hebei region from 2002 to 2018, our numerical prediction model turns out to be more accurate than other existing methods, and after fusing with emission detection method, the accuracy of our visibility prediction system has been further improved.

Climatic Conditions of Steppe and Tundra Formations in the Altai Highland Depressions

«Вестник Санкт-Петербургского университета. Науки о Земле» — научно-теоретический рецензируемый журнал, обобщающий результаты исследований в области геологии, гидрогеологии и геохимии, петрологии и экологии, ландшафтного планирования и геоморфологии, экономической и социальной географии.