Ground Meteorological Data Research Articles

Machine-Learning-Based Imputation Method for Filling Missing Values in Ground Meteorological Observation Data

Ground meteorological observation data (GMOD) are the core of research on earth-related disciplines and an important reference for societal production and life. Unfortunately, due to operational issues or equipment failures, missing values may occur in GMOD. Hence, the imputation of missing data is a prevalent issue during the pre-processing of GMOD. Although a large number of machine-learning methods have been applied to the field of meteorological missing value imputation and have achieved good results, they are usually aimed at specific meteorological elements, and few studies discuss imputation when multiple elements are randomly missing in the dataset. This paper designed a machine-learning-based multidimensional meteorological data imputation framework (MMDIF), which can use the predictions of machine-learning methods to impute the GMOD with random missing values in multiple attributes, and tested the effectiveness of 20 machine-learning methods on imputing missing values within 124 meteorological stations across six different climatic regions based on the MMDIF. The results show that MMDIF-RF was the most effective missing value imputation method; it is better than other methods for imputing 11 types of hourly meteorological elements. Although this paper applied MMDIF to the imputation of missing values in meteorological data, the method can also provide guidance for dataset reconstruction in other industries.

Design and Implementation of Ground Meteorological Data Storage Alarm System for Other Provinces

The provincial information center receives meteorological data from other provinces through satellite broadcasts. In response to the low efficiency and inability to promptly address issues such as data nonexistence and data loss caused by malfunctions in the manual monitoring mode, an analysis of the data transmission process and user access methods was conducted. Based on the Python language, a ground meteorological data storage alarm system was developed for the Northwestern region. This system enables real-time monitoring of the storage status of hourly ground data, facilitates the generation of statistical reports on missing station information within any given time frame, and promptly sends alarm notifications via enterprise WeChat. The results indicate that this system assists operational and maintenance personnel in promptly identifying and addressing faults, ensuring the orderly and timely storage of meteorological data from the Northwestern region into the local database and thereby providing users with higher-quality data services.

Estimating Seasonal and Interannual Variations in Precipitation in Rural Eastern Africa: A Case Study in Longido, Tanzania

Access to water is a limiting factor for development in many semi-arid regions, contributing to food insecurity and environmental stresses on the local population. Additionally, some rural areas still have limited quantitative data on weather and associated rainfall patterns. This study analyzes ground meteorological data from a station installed at Longido, Tanzania and performs time series decomposition modelling of complementary Integrated Multi-satellite Retrievals (IMERG) data to quantify the amount, distribution, and variability of this essential resource. The seasonal rainfall pattern at Longido is bimodal with a large peak between March and May and a smaller peak between October and November. Interannual variability in rainfall is only weakly correlated with El Niño and the Indian Ocean Dipole indices; however, the highest observed rainfall does occur in a year with numerous simultaneous storms in the southern Indian Ocean. This analysis will help improve water management planning in the locality and points to a need to promote and support water storage as a method to meet the needs of the local population

An approach to select optimum inputs for hydrological modeling to improve simulation accuracy in data-scarce regions

Study regionThe source regions of the Yellow River and Yangtze River in the central-eastern part of the Tibetan Plateau. Study focusHydrological model is an important tool in the simulation of watershed hydrology. However, as more and more basic geographic data becomes publicly available and shared globally, researchers are developing a 'symptom' of difficulty in choosing data, so a systematic comparative analysis for model input data selection is necessary. We tested the effects of different types, sources, and resolutions of input data on the model output results based on the SWAT model, and focused on the mechanism of the role of different input data in the model and how to select an appropriate input data for similar studies. New hydrological insights for the regionThe results show that: the meteorological data is crucial in the model's runoff simulation, and ground meteorological observation station data outperforms reanalysis data such as CFSR. Optimizing CFSR significantly improves the model's performance. The DEM resolution minimally impacts runoff simulation, as the difference in results is primarily due to the topographic characteristics of the watershed. DEM selection should consider TWI complexity and its compatibility with the watershed network. The selection of LULC data has little effect on the simulation, and the best input data combination is OBS + 90 m DEM + CNLULC. These findings assist input data selection for similar watersheds using the SWAT model.

Aircraft observations on a continuous haze pollution event in Shijiazhuang area

A continuous haze event was recorded on November 14th∼17th, 2020 over Shijiazhuang. Two flights of King-air 350 meteorological research aircraft were performed on November 14th and 16th for the retrieval and observations of meteorological elements, aerosols, and black carbon. In this study, we combined airborne data with air pollution data (PM2.5), ground meteorological data, and ERA-5 reanalysis data to describe the vertical distribution of aerosols (namely 0.1–3.0 μm) and black carbon. We further explicated the formation of this haze event. PM2.5 pollution dominated this haze event, and the highest concentration of PM2.5 was 209 μg/m3. The intensity and height of thermal layers highly linked with the vertical transport of pollution. The highest number concentration of aerosols and black carbon was found below the thermal layers on both airborne sounding days. On the 14th, both BC and aerosol concentrations showed unimodal distribution, and the highest concentrations of BC and aerosols were 12683 ng/m3 and 6965.125#/L at 250 m within layer Ⅰ. The intensity of the thermal layer near-ground was weaker on the 16th that the number concentrations of BC and aerosols also remained at high levels in layer Ⅱ. Backward trajectories of air mass indicated the long-range transport of pollution contributed to the high level of pollution on the 16th. Vapor conditions were more favorable for aerosols growth through moisture absorption. The maximum concentration of 943.58#/L was recorded at particles with a diameter of 0.4 μm on the 16th, while 749.26#/L was reached at 0.14 μm on the 14th. The corresponding height was consistent with the height of maximum concentration in the vertical distribution.

Performance of ERA5 data in retrieving precipitable water vapor over Hong Kong

When accurately retrieving atmospheric precipitable water vapor (PWV) using Global Positioning System (GPS) data, errors mainly originate from the effects of atmospheric weighted mean temperature (Tm) and ground meteorological data (e.g. surface pressure and temperature). This article assessed the accuracy of interpolated pressure and temperature from the European Centre for Medium-Range Weather Forecasts (ECMWF) fifth generation reanalysis dataset (ERA5), using actual meteorological data in the Hong Kong region from 2016 to 2020 as a reference.Radiosonde data were used to establish a linear equation (HKTm) between Tm and surface temperature (Ts), and a fitted equation between Tm, Ts, and water vapor pressure (HKTm-e). Then, these equations were evaluated using the Tm estimated by radiosonde with higher accuracy as a reference. On this basis, the PWV obtained from the measured meteorological data and BTm (GPS MET PWV) was compared with the PWV obtained from interpolated ERA5 meteorological data and HKTm-e (GPS ERA5 PWV), and the overall mean Bias and RMSE were −0.08 mm and 0.28 mm, respectively. In addition, PWV obtained from radiosonde data (RS PWV) was used to evaluate the accuracy of PWV obtained from ERA5 data (ERA5 PWV) and GPS ERA5 PWV. Then, the GPS ERA5 PWV was used to analyze the seasonal PWV variation and the effect of elevation on PWV in Hong Kong. The accuracy of ERA5 meteorological data is affected by the level of tropical cyclones in Hong Kong. Most of the differences are negative and the reason for the increased accuracy in temperature is explained based on the measured temperature and interpolated ERA5 temperature variations during all high-level tropical cyclones from 2016 to 2020. This article assessed the feasibility of using ERA5 data to replace the missing measured meteorological data during tropical cyclones. Based on the spatial variation of PWV as the tropical cyclone approaches Hong Kong, it is found that the spatial variation of PWV is related to the direction and location of the tropical cyclone.

Potential Urban Barrier Effect to Alter Patterns of Cloud‐To‐Ground Lightning in Beijing Metropolis

AbstractThe urban barrier effect is one possible physical mechanism by which the urbanization alters the spatial distribution of cloud‐to‐ground (CG) lightning activity in a city. There is a gap in the preceding research on an urban barrier effect to alter patterns of CG lightning in Beijing, and the urban morphology does not receive enough attention as an important influencing factor. By combining lightning location data, ground meteorological data, and urban morphology datasets, we identify a possible urban barrier effect from the perspective of CG lightning in Beijing during 2010–2017. Analysis of one typical thunderstorm on 13 July 2017 revealed that the barrier effect might result in a bifurcation of low‐level airflow and splitting of the convergence line. A more important finding was the modulation of the barrier effect by the scale and density of the built‐up area, and the numerical simulation experiments further confirmed this potential association.

Coupling meteorological stations data and satellite data for prediction of global solar radiation with machine learning models

The simulation of solar radiation is of great significance to the sustainable development of energy, engineering, and many other fields. The Himawari series of satellites has the characteristics of high temporal, spatial resolution, which helps to solve the problem of insufficient ground radiation observation in China. However, the accuracy of this data needs to be further improved. Thus, four machine learning models with 13 ground and satellite-based input combinations were used to simulate daily solar radiation. The results showed that the simulation accuracy of the model based on a combination of meteorological data from different sources was significantly improved compared with the model based on single-source data. The RMSE was 32.4% and 44.6% lower than those of the model based on the ground meteorological stations data and the model based on the satellite data, respectively. SVM13 model showed the optimal simulation performance compared with other models, and its RMSE and R2 were 1.732 MJ m−2 day−1 and 0.939 in each climate region, respectively. Overall, we conclude that the SVM13 model is the most suitable model, and the model with a complex combination of more meteorological factors as input has higher simulation accuracy than the model with a relatively simple input combination.

Evapotranspiration and Vegetation Cover Classifications Maps Based on Cloud Computing at the Arab Countries Scale

According to the most recent Köppen–Geiger classification, Arab countries are divided into seven climate classes. Ground data availability is limited in developing countries, and ground meteorological data are scarce and concentrated in a few locations, rather than station maintenance capability being adequate for the responsibilities. The current study uses remote sensing and meteorological data to create regional classification maps of reference evapotranspiration (ETo), potential crop evapotranspiration, and vegetation cover in Arab countries from 2005 to 2020. The Stand-alone Remote Sensing Approach to Estimate Reference Evapotranspiration (SARE) was used to estimate ETo using satellite data from 2005 to 2020. The Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) were extracted from MODIS satellite data and used in the SARE model, in addition to elevation (E), Julian day (J), and Latitude (Lat). To validate the SARE model results, the FAO-Penman–Monteith model was applied to 35 ground meteorological stations distributed across Arab countries to cover all climate classes based on the most recent Köppen–Geiger climate classification. Google Earth Engine was used to create the classification. The statistical indices produced acceptable results, with average RMSE values ranging from 6.9 to 17.3 (mm/month), while correlation coefficient (r) and index of agreement (d) values are more significant than 0.9. To be included in the ETc calculation, the crop coefficient (Kc) was calculated using NDVI 250 m spatial resolution. The density of the vegetation cover is used to classify it (low to high). The average vegetation cover was calculated to be greater than 31.5 Mha. The minimum vegetation cover was 14.9 Mha, and the maximum vegetation cover was 49.2 Mha. 15.8 Mha can be cultivated without supplementary irrigation for at least one agricultural season, according to the rainfall classification map.

Estimation and Long-term Trend Analysis of Surface Solar Radiation in Antarctica: A Case Study of Zhongshan Station

Long-term, ground-based daily global solar radiation (DGSR) at Zhongshan Station in Antarctica can quantitatively reveal the basic characteristics of Earth’s surface radiation balance and validate satellite data for the Antarctic region. The fixed station was established in 1989 and conventional radiation observations started much later in 2008. In this study, a random forest (RF) model for estimating DGSR was developed using ground meteorological observation data, and a high-precision, long-term DGSR dataset was constructed. Then, the trend of DGSR from 1990 to 2019 at Zhongshan Station, Antarctica, was analyzed. The RF model, which performs better than other models, shows a desirable performance of DGSR hindcast estimation with an R2 of 0.984, root-mean-square error of 1.377 MJ/m2, and mean absolute error of 0.828 MJ/m2. The trend of DGSR annual anomalies increased during 1990-2004 and then began to decrease after 2004. Note that the maximum value of annual anomalies occurred during approximately 2004/05 and is mainly related to the days with precipitation (especially those related to good weather during the polar day period) at this station. In addition to clouds and water vapor, bad weather conditions (such as snowfall, which can result in low visibility and then decreased sunshine duration and solar radiation) were the other major factors affecting solar radiation at this station. The high-precision, long-term estimated DGSR dataset enables us to further study and understand the role of Antarctica in global climate change and the interactions between snow, ice and atmosphere.

Study on Ecological Water Requirement of Vegetation in Wuhai Based on GIS

Wuhai City is located in Inner Mongolia, belongs to the arid and semi-arid area of Northwest China, where precipitation is scarce, vegetation coverage is low and evaporation is large. With the exploitation of coal resources, the cultivated land area is gradually reduced and the surface is seriously damaged. After mining, a large number of mines and coal gangue piles are left behind, causing serious ecological problems. In order to ensure the normal growth of vegetation in Wuhai City, it is of great significance to study the ecological water demand of vegetation in Wuhai City for the realization of reasonable allocation of water resources, ecological restoration of mines and sustainable development of the city. Based on remote sensing satellite image data, combined with land use type map and ground meteorological data, this paper analyzes the spatial and temporal variation characteristics of normalized difference vegetation index (NDVI) in Wuhai City, calculates the evapotranspiration of vegetation in reference year, and obtains the ecological water demand of vegetation in Wuhai City by using area quota method according to different water demand spatial areas. The results showed that: in the total ecological water demand of vegetation in Wuhai City, the water demand of medium coverage grassland was the largest, reaching 407.59 mm, while that of forest land was the smallest, reaching 309.01 mm. The other water requirements from large to small are: low coverage grassland, high coverage grassland, sparse forest land, shrub land. Grassland accounts for 98% of ecological water demand, while woodland only accounts for 2%. The total ecological water demand is 326 million m3.

Regression Models for Soil Water Storage Estimation Using the ESA CCI Satellite Soil Moisture Product: A Case Study in Northeast Portugal

The European Space Agency Climate Change Initiative Soil Moisture (ESA CCI SM) product provides soil moisture estimates from radar satellite data with a daily temporal resolution. Despite validation exercises with ground data that have been performed since the product’s launch, SM has not yet been consistently related to soil water storage, which is a key step for its application for prediction purposes. This study aimed to analyse the relationship between soil water storage (S), which was obtained from soil water balance computations with ground meteorological data, and soil moisture, which was obtained from radar data, as affected by soil water storage capacity (Smax). As a case study, a 14-year monthly series of soil water storage, produced via soil water balance computations using ground meteorological data from northeast Portugal and Smax from 25 mm to 150 mm, were matched with the corresponding monthly averaged SM product. Linear (I) and logistic (II) regression models relating S with SM were compared. Model performance (r2 in the 0.8–0.9 range) varied non-monotonically with Smax, with it being the highest at an Smax of 50 mm. The logistic model (II) performed better than the linear model (I) in the lower range of Smax. Improvements in model performance obtained with segregation of the data series in two subsets, representing soil water recharge and depletion phases throughout the year, outlined the hysteresis in the relationship between S and SM.

Detection of Changes in Terrestrial Ecosystems of Ukraine Using Remote Sensing Data

In recent years, Ukraine has been affected by climate change. This has led to frequent extreme weather events (heavy / high rains, floods, droughts, squalls). As a result of droughts, desertification is one of the most dangerous and transient consequences of modern climate change. The research is devoted to the diagnostic assessment of the modern climate of Ukraine. Remote sensing data and instrumental observations of 30 weather stations of Ukraine were used. Temperature increase was registered in the study area by all stations, which significantly affected the level of precipitation. At the moment there is not enough moisture for the Earth’s surface. Precipitation in Ukraine is currently characterized by an uneven distribution. It leads to accelerated processes of soil degradation and it’s fertility loss. The aim of the study was to identify areas prone to desertification using satellite imagery and meteorological observations. Over the past 17 years (2000-2017), the average air temperature in Ukraine has increased by 1.5 ºC. Particularly anomalous warming has been recorded in recent years, starting in 2015. During the XXI century, a slight decrease in precipitation was observed in Ukraine. Both a decrease in precipitation and an increase in temperature may lead to a decrease in soil moisture levels. According to ground meteorological data, the tendency of dryness in Ukraine was confirmed. Lack of water leads to prompt manifestation of this process. Water indexes were used to estimate the moisture content of surface soils. It is possible to assess the susceptibility of the desert area to climate change. Relevant quantitative information on water availability in Ukraine is provided. Two water indices (Normalized Difference Infrared Index NDII and Ratio Drought Index RDI) have been taken estimate the moisture content. It can be estimated from the MODIS MOD13C2 product data obtained from the MODIS satellite sensor and used for regional research. The main conclusion of this study is to determine the changes in natural terrestrial ecosystems in Ukraine. This was shown on the basis of temperature and humidity. Such trends may lead to changes in the biodiversity of the territory and loss of natural soil properties.

A Comparison of the Accuracy of Multi-satellite Precipitation Estimation and Ground Meteorological Records Over Southwestern Nigeria

Multi-satellite rainfall observations are compared with ground-based meteorological stations data in this study. Both ground and satellite meteorological datasets were analysed and synchronized in this study. Satellite rainfall datasets used are Tropical Rainfall Measuring Mission (TRMM), Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) and African Rainfall Estimation (RFE 2.0), which were obtained from the archives of the NOAA Climate Prediction Centre. The ground meteorological data were obtained from the Nigerian Meteorological Agency (NIMET) Lagos office in Oshodi, for selected stations in Southwest of Nigeria, for the period 1998–2016. Descriptive and inferential analyses were used to analyse the dataset. Spatial and temporal trends analyses were carried out to compare rainfall estimates from satellite and ground stations. Generally, correlation results show that the relationship between satellite datasets and ground observation (NIMET) appears remarkably high in all stations with R2> 0.70. This relationship is greatly strong between TRMM and NIMET in Abeokuta, Akure, and Osogbo with R2 = 0.99, 0.98 and 0.98, respectively. CHIRPS also show high correlation with NIMET in these three stations with R2> 0.90, but RFE-NIMET relationship appears much more in Osogbo with R2 > 0.90. It is also obvious that TRMM and CHIRPS have a very high relationship in almost all locations with R2> 0.87. The major finding from this study is that though in situ meteorological data may be used for local studies of small areas, satellite rainfall estimates may also be used to augment such data, over a larger area. Besides, satellite rainfall estimates are more useful for areas with larger spatial and temporal coverage.

Study on factors affecting corn yield based on the Cobb-Douglas production function

This paper presents an analysis of the quantitative correlations between corn yield and its influencing factors in Daqing City, China by establishing a Cobb–Douglas production function model. The effective precipitation, corn planting area and chemical fertilizer and pesticide application rates were selected as the influencing factors of corn yield. Using the Cobb–Douglas production function model, the output elasticity and degree of influence for each factor on increasing grain yield were calculated. The current fertilizer and pesticide application rates, effective precipitation and planting area had positive effects on increasing corn yield, and the Daqing City area has the potential to produce more corn. Among the four influencing factors, the amounts of pesticide applications had the greatest impact on corn yield, followed by planting area, amounts of chemical fertilizer applications and, finally, effective precipitation. In this study, we used remote-sensing images combined with meteorological station data to calculate the effective precipitation in corn fields in Daqing City. The accuracy of this method was 0.01%–11.0% greater than that of the traditional effective precipitation calculation method. The innovation was the use of Thiessen polygons to calculate regional precipitation by combining satellite images with ground meteorological station data. The insufficient sensitivity of satellite inversion for precipitation (short and heavy rainfalls cannot be detected) and insufficient temporal resolution were avoided by using precipitation data from ground meteorological stations. Using satellite image interpretations, the weight coefficient of precipitation could be confirmed according to the location and size of the study area, improving the accuracy of Thiessen polygons in calculating regional precipitation. However, this method still has limitations. When calculating precipitation over a short time, it can be limited by the cloudiness of satellite images. When calculating the long-term precipitation trend, it can be limited by incomplete precipitation data from surface meteorological stations.

An Empirical Model for Estimating Precipitable Water Vapor on the Tibetan Plateau

Using the ground meteorological and radiosonde data of 122 atmospheric stations from 1958 to 2008 all over China, excellent numerical correlations between precipitable water vapor (PWV) and ground humidity parameters such as ground water vapor pressure (e) have been derived; by introducing two parameters, latitude φ and altitude H, we establish an empirical model for estimating the total amount of PWV from e in different climatic areas in China, especially on the vast Tibetan Plateau with complex topography. In the validation, we make use of the ground meteorological data of the atmospheric weather station from 2016 July 12 to December 22 at the Ali observatory (32.°30N, 80.°05E, 5050 m above sea level), which is located in the most western part of the Tibetan Plateau, to obtain the total amount of PWV by the empirical model; compared with the PWV derived from radiosonde observations at Ali, the model shows a very good performance estimating PWV, and presents very well the seasonal and diurnal variations of PWV. The total amount of PWV derived from the empirical model at the observatory agree with that from radiosonde observations very well, with the correction coefficient R = 0.958 and the root mean square error RMSE = 1.04 mm; the errors between the model and observations are a bit larger in summer when PWV is rich, and small in winter when PWV values are usually less than 2.0 mm. Based on the empirical model, the median PWV value in 2017 at the Ali observatory was about 2.9 mm, and the values were less than 2.0 mm for half the 2017. Overall, the total amount of PWV derived from the empirical model is proved to be reliable, and the empirical model is excellent and accurate, allowing it to be implemented as an operational tool for estimating PWV at an astronomical site.

Mesoscale characteristics of a rare severe hailstorm event

This study analyzed the environmental field, physical quantify fields, convection parameters and radar echo characteristics of a severe hailstorm that occurred on June 18, 2013, due to a mesoscale convective system. The analysis was performed using ground meteorological data, sounding data and GFS-ANL (gfs 0.5° × 0.5°) reanalysis data in combination with satellite cloud imagery and Doppler radar observations. The results showed that this rare hailstorm event was generated under the influence of a weakening and eastwardly moving low vortex over Lake Balkhash; and a distinct shear line in the middle and lower troposphere over the western South Xinjiang. Strong vertical wind shears, deep conditionally unstable stratification, ample water vapor in the lower troposphere and suitable wet-bulb zero heights are important contributors to the formation of severe hailstorms. An increase in convective available potential energy (CAPE) and a decrease in the lifting index are strong indicators of a severe hailstorm. For the studied hailstorm event, the time corresponding to the maximum value of the normalized CAPE was close to the timing of the actual hailstorm. The severe hailstorm, consisting of multiple supercells, exhibited notable characteristics of hail clouds and supercells. The formation, disappearance and replacement of strong convection cells occurred while the supercells were sustained. The tracking and forecasting results obtained using the Thunderstorm Identification, Tracking, Analysis and Nowcasting system are found to be, to a certain extent, able to indicate of the direction of the supercell storms during this severe hailstorm event.

On the Study of Influences of Different Factors on the Rapid Tropospheric Tomography

A rapid tropospheric tomography system was developed by using algebraicreconstruction technique. Influences of different factors on the tomographic results, including theground meteorological data, the multi-Global Navigation Satellite System (GNSS) observations, theground station distribution and the tomographic horizontal resolution, were systematicallyinvestigated. In order to exclude the impacts from discrepancies of water vapor informationbetween input observations and references on the tomographic results, the latest reanalysisproducts, ERA5, which were taken as references for result evaluations, were used to simulate slantwet delay (SWD) observations at GNSS stations. Besides, the slant delays derived from GNSSprocessing were also used to evaluate the reliability of simulated observations. Tomography resultsshow that the input both SWD and ground meteorological data could improve the tomographicresults where SWD mainly improve the results at middle layers (500 to 5000m, namely 2 to 16 layer)and ground meteorological data could improve the humidity fields at bottom layers further (0 to500m, namely 0 to 2 layer). Compared to the usage of Global Positioning System (GPS) only SWD,the inclusion of multi-GNSS SWD does not significantly improve the tomographic results at alllayers due to the almost unchanged dispersion of puncture points of GNSS signals. However,increases in the ground GNSS stations can benefit the tomography, with improvements of morethan 10% at bottom and middle layers. Higher tomographic horizontal resolution can furtherslightly improve the tomographic results (about 3-6% from 0.5 to 0.25 degrees), which, however,will also increase the computational burden at the same time.

Exploring management approaches for water and energy in the data-scarce Tekeze-Atbara Basin under hydrologic uncertainty

ABSTRACT This study examines management approaches for hydropower generation and irrigation and domestic water supply for the Tekeze-Atbara, a transboundary river between Ethiopia, Eritrea and Sudan, in above- and below-normal hydrologic conditions, considering current and future water demand scenarios. Satellite data are used to substitute for unavailable or inaccessible ground meteorological and dam data. Based on three examined coordination scenarios, the analysis finds that coordinating the management of the Sudanese dams would bring significant benefits to water supply and energy generation. An optimization analysis is necessary to reveal the full value of coordination of dams in the Tekeze-Atbara Basin.

Boundary layer perspective assessment of air pollution status in Wuhan city from 2013 to 2017.

This study used daily air pollution data (PM2.5, PM10, SO2, NO2, CO, and O3) from nine monitoring stations in Wuhan city to calculate the air quality index (AQI) from 2013 to 2017. Together with this data, L-band sounding data, ground meteorological data, and air mass back trajectories were also used to describe the dynamics of atmospheric boundary layer (ABL) during pollution process. Analysis of the results shows that, even though the city is still polluted, the number of polluted days was decreasing. Ranking the years in terms of pollution status shows that the year 2013 was the most polluted year while the year 2017 was the cleanest year. Average annual limit of PM10, PM2.5, and NO2 during these 5years were 1.3~1.8, 1.5~2.7, and 1.2~1.5 times higher than the annual average acceptable limit, respectively. The average ratio of PM2.5/PM10 for 5years was 0.67 which signifies that a significant portion of PM2.5 accounted for the total mass of PM10. Moreover, the condition of ABL during the pollution process shows the dominance of strong ground inversion and weak to calm winds. These conditions are not favorable for horizontal and vertical mixing of air pollutants and prevent dilution of pollutants with clean air. Mean cluster analysis of air mass back trajectory shows that pollutants of local origin were more important than the trans-boundary movement of air pollutants. This indicates that the observed pollution in Wuhan was more of local origin.