Meteorological Station Data Research Articles

Construction of Interactive Guidance System and Design of Sharing Platform Under the Background of Agrometeorological Remote Sensing Sensor Network

This subject uses SQL Server database management system as the background database, and designs the meteorological station data table, meteorological element data table, crop data table, and agricultural gas product data table. This paper briefly reviews the development process of agricultural remote sensing and the theoretical basis of its application; Then, the relevant research and applications at home and abroad are expounded from the four fields of crop yield estimation, agricultural resource investigation, agricultural disaster monitoring and precision agricultural management. Finally, it is proposed that agricultural remote sensing should be combined with the ground agricultural observation network technology. The functional modules of the Friendship Platform are analyzed, and then the system implementation is elaborated. The shared platform operates stably, and realizes comprehensive functions such as weather forecast, soil moisture monitoring, meteorological disaster warning and emergency response.

Relationship of Meteorological Data with Heat Stress Effect on Dairy Cows of Smallholder Farmers

Global warming has been increasing heat stress threat in animals, which can be monitored via the Temperature Humidity Index (THI). The present study describes the THI pattern and the relationship between THI and the production performances of dairy cows in a selected area of Bangladesh. The THI value was calculated using weather station data gathered over 35 years. Based on the THI pattern, January and June were identified as the coolest and hottest months, respectively. Consequently, the production performances of 10 crossbred cows with homogenous characteristics were monitored every January and June for a 5-year period. The average THIMEAN was found to be 17% higher in June when compared to January; with this increment of THIMEAN, average milk production was decreased by 24.4% (p < 0.05). The milk fat and protein content were also reduced (p < 0.05) by 14.5 and 15.2%, respectively, suggesting a negative correlation. However, ash content increased by 15.3%, which indicates a positive correlation. In addition, multiple regression analysis revealed that, with each point increase in THIMEAN and rectal temperature, there was a decrease in milk yield of 0.04 and 1.17 kg ECM, respectively. In contrary, each point increase in THIMEAN resulted in 0.059 °C increase of rectal temperature. Taken together, THIMEAN calculated using meteorological station data has a distinct relationship with the production performances of lactating crossbred dairy cows.

Utility of Deep Learning Algorithms in Initial Flowering Period Prediction Models

The application of a deep learning algorithm (DL) can more accurately predict the initial flowering period of Platycladus orientalis (L.) Franco. In this research, we applied DL to establish a nationwide long-term prediction model of the initial flowering period of P. orientalis and analyzed the contribution rate of meteorological factors via Shapely Additive Explanation (SHAP). Based on the daily meteorological data of major meteorological stations in China from 1963–2015 and the observation of initial flowering data from 23 phenological stations, we established prediction models by using recurrent neural network (RNN), long short-term memory (LSTM) and gated recurrent unit (GRU). The mean absolute error (MAE), mean absolute percentage error (MAPE), and coefficient of determination (R2) were used as training effect indicators to evaluate the prediction accuracy. The simulation results show that the three models are applicable to the prediction of the initial flowering of P. orientalis nationwide in China, with the average accuracy of the GRU being the highest, followed by LSTM and the RNN, which is significantly higher than the prediction accuracy of the regression model based on accumulated air temperature. In the interpretability analysis, the factor contribution rates of the three models are similar, the 46 temperature type factors have the highest contribution rate with 58.6% of temperature factors’ contribution rate being higher than 0 and average contribution rate being 5.48 × 10−4, and the stability of the contribution rate of the factors related to the daily minimum temperature factor has obvious fluctuations with an average standard deviation of 8.57 × 10−3, which might be related to the plants being sensitive to low temperature stress. The GRU model can accurately predict the change rule of the initial flowering, with an average accuracy greater than 98%, and the simulation effect is the best, indicating that the potential application of the GRU model is the prediction of initial flowering.

Modeling of Thermodynamic Consolidation of Sea Ice Ridges Drifting in the Water with Changing Temperature

Oceanographic and ice conditions in the region of Spitsbergen Bank in the Barents Sea were investigated in research cruises of the “Polarsyssel” in 2017–2019. Trajectories of ice drift were constructed using GPS data of the buoys deployed on the floes in the research cruises. The duration of the ice season in the region was analyzed using ice charts. The air temperature and wind velocities were analyzed using the data of meteorological stations on Bear Island and Hopen Island. Fieldwork on drifting ice showed the existence of thick consolidated floes with drafts up to 8 m, which were identified as completely consolidated sea ice ridges. The presence of such floes is dangerous for winter navigation along Spitsbergen Bank. A model of thermodynamic consolidation of ice ridges was formulated to investigate the thermodynamic evolution of ice ridges. The observed air and sea water temperatures were used in the boundary conditions on top and bottom surfaces of sea ice rubble. It was shown that the regular interaction of sea ice rubble with Atlantic and Arctic waters in the region of Spitsbergen Bank leads to almost complete consolidation of the ice rubble with an initial macro-porosity 0.2 for 150 days.

Complementarity Characteristics of Actual and Potential Evapotranspiration and Spatiotemporal Changes in Evapotranspiration Drought Index over Ningxia in the Upper Reaches of the Yellow River in China

Based on energy balance theory, using Theil–Sen median trend analysis and the Mann–Kendall test, this research studied the applicability of the complementary theory of evapotranspiration (ET) over Ningxia in the Upper Reaches of the Yellow River with MOD16 ET product and the measured data of meteorological stations, based on which ET drought index (EDI) was proposed for the first time. Moreover, the usability of EDI was also verified and its influencing factors were analyzed. The results revealed that there was a complementary relationship between AET and PET in 91.1% of the area in Ningxia, including strictly complementary and asymmetrically complementary relationships in 69.2% and 21.9% of the total area, respectively. EDI ranged from 0 to 1 and was useful to accurately reflect the degree of drought of the study area on the annual and monthly scales. From 2001 to 2020, the average annual EDI was 0.66, and the smallest monthly EDI was in January and the largest was in May. EDI of different time scales had different influencing factors. Precipitation was the most influencing factor of annual EDI, but the influencing factors of monthly EDI was different over time. However, surface non-precipitation water replenishment, such as irrigation, had great impact both on annual EDI and monthly EDI. The application scope of the theory of ET complementarity was extended to the study area for the first time, and EDI was proposed and applied, which will provide a theoretical basis and empirical reference for drought research based on ET data in arid and semi-arid areas.

Comparison of CLDAS and Machine Learning Models for Reference Evapotranspiration Estimation under Limited Meteorological Data

The accurate calculation of reference evapotranspiration (ET0) is the fundamental basis for the sustainable use of water resources and drought assessment. In this study, we evaluate the performance of the second-generation China Meteorological Administration Land Data Assimilation System (CLDAS) and two simplified machine learning models to estimate ET0 when meteorological data are insufficient in China. The results show that, when a weather station lacks global solar radiation (Rs) data, the machine learning methods obtain better results in their estimation of ET0. However, when the meteorological station lacks relative humidity (RH) and 2 m wind speed (U2) data, using RHCLD and U2CLD from the CLDAS to estimate ET0 and to replace the meteorological station data obtains better results. When all the data from the meteorological station are missing, estimating ET0 using the CLDAS data still produces relevant results. In addition, the PM–CLDAS method (a calculation method based on the Penman–Monteith formula and using the CLDAS data) exhibits a relatively stable performance under different combinations of meteorological inputs, except in the southern humid tropical zone and the Qinghai–Tibet Plateau zone.

Short‐term prediction of photovoltaic power generation based on neural network prediction model

AbstractReal‐time monitoring and accurate prediction of photovoltaic (PV) power generation operation parameters are essential to ensure stable operation. In this paper, a set of online PV power generation parameter measurement and monitoring devices characterized by simple structure, high sampling accuracy, small data fluctuations, and ease of measurement, are designed. Sensors based on the zero‐flux principle are employed in the real‐time collection of the output electrical signals in the process of PV power generation, realizing the accurate collection of electric parameter signals. Next, the basic structure and working principle of PV cells are analyzed, a mathematical model of PV cells for engineering purposes is established, a wavelet neural network is selected to predict the short‐term PV power generation, and particle swarm optimization and adding momentum are used to optimize the weight of wavelet neural network (WNN) as well as the parameters of the wavelet basis function. Finally, the historical power generation data and meteorological data of the power station are taken as the training samples to train and simulate the prediction sub‐models of different weather types to verify the effectiveness and accuracy of the PV power generation short‐term prediction model for optimizing the WNN based on the particle swarm optimization algorithm. The research results of this paper can realize real‐time monitoring of the output parameters and accurate prediction and evaluation of power generation during the operation of the PV‐power‐generation system.

Temporal differences of rainfall in Erbil province based on remote sensing data and meteorological station data between 1981-2020

Abstract Climate and changing its elements are very important because they affect water resources, agriculture, the environment and many other aspects of human life. This study aims to show the amount of monthly and annual rainfall and to verify spatiotemporal differences in the Province of Erbil. It also aims to compare satellite data with annual data from meteorological stations in the study area. The methods used in this research are comparison, analysis and statistical methods to show the direction of rain changes such as Kernel and Kriging. The study used meteorological station data between 2001 and 2020 and remote sensing data (CHIRPS) from 1981 to 2020. The study found that the average annual rainfall between 1981 and 2020 was 609 ± 122 mm, as well as a 40-year increase in rainfall by only 1.88 mm, according to CHIRPS data. Data from Erbil governorate stations confirm that rainfall increased by 106 mm from 2001 to 2020, but the change is not significant statistically (p-value = 0.72), with the exception of Shorsh and Koya stations in the province, where rainfall decreased in the mentioned period. With regard to the comparison between remote sensing data and annual data from meteorological stations in the study area between 2001 and 2020, there appears to be an important strong positive relationship between them, because the R2 is (0.7), (p-value) is (0.001) and the error scale (RMSE) is (83.04) mm.

Decadal change of spring dust activity in western Iran and its mechanism

Western Iran is an important dust source region in Middle East, with strong dust activities occurring in springtime. Based on a three-hourly meteorological station data, remarkable decadal change of dust frequency in the spring season has been found in the west and southwest of Iran, with less dust activities during 1992–2005 (hereafter as “P1”) and more frequent dust activities occurring during 2006–2015 (hereafter as “P2”). The decadal change signal in dust activities is closely associated with the corresponding decadal difference in precipitation and atmospheric moisture transportation in the region. Compared with P1 period during 1992–2005, anomalous moisture divergence over the center of Middle East can be found in P2 period during 2006–2015, suggesting less moisture transport to the western Iran from the Arabian Sea, Red Sea, and the Persian Gulf, hence there is relatively less precipitation and dry soil moisture over the main dust source regions in the study region, which is favorable for more frequent dust emissions in P2 period. Meanwhile, westerly anomaly in P2 period can also be found in western Iran and upstream regions, such as Iraq and Syria, which is favorable for more dust transport to western Iran from upstream dust source region in Middle East. Furthermore, negative SST anomalies in central and western North Pacific and positive SST anomalies in the eastern North Pacific can be found in P1 period, which is corresponding to positive phase of Pacific Decadal Oscillation (PDO). Conversely, a negative phase of PDO can be found during P2 period. This suggests that PDO is the key influential factor for the decadal change of spring dust activities in western Iran.

A case study of NeuralProphet and nonlinear evaluation for high accuracy prediction in short-term forecasting in PV solar plant

Prediction of the energy, active production from the PV solar plant is a challenge in cloudy weather or with clouds over the solar plant; therefore, it has impact in the planning of the power system, especially in the season analysis and prediction accuracy adjustments, for example in holidays. In 2022, some authors published some analysis associated to horizontal pyranometers and the limits in the evaluation of the data, the Mean Bias Error of Daily Solar Irradiation average (MIEave) ranges from 0.17% to 2.86% associated a sudden change in the weather, it increases the “risk of misestimating the potential electricity generation” with short-term error of more than 50% and the Global Horizontal Irradiance (GHI) has a mean bias error (MBE) of at least ±8% [1]. In this research article, a novel proposal for short-term forecasting combines the satellite with meteorological station data and statistical model associated to the new seasonality analysis by using two approaches: i) NeuralProphet, Ridge regression, ii) Long Short-Term Memory with convolutional neural networks. Besides, it requires three KPI as feedback, it is the mean absolute error (MAE), relative Root mean square error (RMSE), and mean absolute percentage error (MAPE). The results demonstrate a MAPE of 5.93% and a computational time 852.10 s and the comparison with new predictions methods from 2019 to 2021. This research article illustrates the new approach with the forecasting method in a case of the PV solar plant in Peru and proves the robustness and seasonality results, and new short-terms improvements associated to external influence as cloudy conditions and resource availability. Our findings are an improvement of the model MAPE 12.14%–5.93%; even compared with the literature and currently models as ARIMA-LSTM with 10.57%, LSTM with NN and G, SARIMA and SVM considering Gaussian White Noise with 8.14% and Prophet with SVM with 8.81%.

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.

Photovoltaic Output Prediction Method Based on Weather Forecast and Machine Learning

This paper conducts prediction research based on the historical power generation data and meteorological data of a photovoltaic power station. The obtained data is firstly classified by season, and the correlation analysis of meteorological factors is carried out. The data closely related to PV generation is selected for prediction, which improves the prediction efficiency. Then the Kmeans algorithm is used to cluster the historical data, and data are divided into four types: sunny, partly cloudy, cloudy, and rainy, further each type of data is separately trained to obtain four training models. The classified data is decomposed by EEMD, and a set of frequency-distributed IMF components and a monotonic distribution of residual components are obtained. Use support vector machine to predict the dimension-reduced components, and reconstruct the result to obtain the final predicted value. After obtaining the future weather forecast data, select the same type of historical data set for model training. The results show that the prediction accuracy will be significantly improved after signal processing in cloudy and rainy weather.

Mapping the Thermal State of Permafrost in Northeast China Based on the Surface Frost Number Model

Under the influence of climate change and human activities, the southern boundary of the permafrost region in Northeast China, which is located at the southern edge of the permafrost area of Eurasia, has moved north, the surface temperature has increased, and the thickness of the frozen layer has decreased. At present, there is a lack of classification standards or a map of the thermal state of permafrost that can reflect the dynamic change characteristics of permafrost in Northeast China. A vegetation impact factor consisting of normalized difference vegetation index and forest canopy closure was introduced into MODIS LST products, in order to improve the applicability of products in Northeast China. Based on the improved MODIS LST data, this study analyzed the distribution and change of the mean annual surface temperature and the surface frost number (SFnc) from 2003 to 2019. SFnc was used as the standard to classify the thermal state of permafrost, and a map of the thermal state distribution and changes of permafrost in Northeast China, with a spatial resolution of 1 km, was produced. Compared with the observation data of meteorological stations and field monitoring data, the reliability of classification results was nearly 95%. The map showed that there was no area of extremely stable permafrost (SFnc ≥ 0.667), the area of stable permafrost (0.55 ≤ SFnc< 0.667) changed from 14.9 × 104 km2 to 6.5 × 104 km2, the SFnc reduced from 0.564 to 0.557, the area of semi-stable permafrost (0.51 < SFnc < 0.55) changed from 17.68 × 104 km2 to 17.77 × 104 km2, the SFNc reduced from 0.529 to 0.528, and the area of transitional or unstable permafrost (0.49 ≤ SFnc ≤ 0.51) changed from 8.67 × 104 km2 to 9.56 × 104 km2. The thermal state of permafrost decreased and the distribution of stable permafrost shrank, due to continuous rising air temperature. The overall change characteristics of the thermal state distribution of permafrost were that the southern boundary of the permafrost region moved northward, the regional permafrost thermal state decreased, and there was an increased region of semi-stable, transitional or unstable permafrost from the frozen soil thawed at the edge of the permafrost region with higher stability. The permafrost region in Northeast China has lush vegetation, and the continuous degradation of permafrost will change the vegetation growth environment and affect the global carbon cycle process. This work will provide important data support for climate change feedback, natural disaster process research, and an early warning and prevention of terrestrial ecosystem response in the permafrost region of Eurasia.

Quantification of soil erosion in small watersheds on the Loess Plateau based on a modified soil loss model

The technology of slope vegetation system stability enhancement is an important part of the comprehensive ecological security improvement in small watersheds area of the Loess Plateau. The results of the comprehensive soil erosion improvement in the sub-basin are used to give an evaluation of the effectiveness of the gully slope vegetation restoration project. Soil erosion quantification distribution in the Sheep Sap Gully sub-basin of the Loess Plateau hilly gully area was simulated and explored by combining the modified universal soil loss equation RUSLE model with GIS and RS spatial information technology. The quantitative values of LS factor were extracted using DEM data, the R factor of rainfall erosion force was calculated using meteorological monitoring station data around this region, the K factor of erosion resistance was obtained based on the soil survey database, and the C factor and P factor of soil and water conservation measures were obtained by combining MODIS image data and previous research experience. The study concluded that (1) The erosion area ratio within the study area is 36.33%; (2) The quantitative grading standard of the pattern, the size of the erosion distribution area at all levels is ranked as light>moderate>very strong>strong>intense; (3) Under the conditions of land use and vegetation cover, strong erosion is mostly found in farming areas with sparse vegetation, while weak erosion is found in areas with lush vegetation such as forests and grasslands; (4) In terms of spatial distribution, erosion is greater in the south-western part of the basin than in the north-eastern part, and there is also strong erosion in the south-western part. The results of the study provide a reference for research into integration and synthesis of ecological security technologies for gully and slope management projects. The research content provides the basis and support for watershed governance and soil and water resource management and conservation.

How Well Does the ERA5 Reanalysis Capture the Extreme Climate Events Over China? Part II: Extreme Temperature

The fifth-generation atmospheric reanalysis of the European Center for Medium-Range Weather Forecasts (ERA5) is the latest reanalysis product. However, the reliability of ERA5 to capture extreme temperatures is still unclear over China. Hence, based on conventional meteorological station data, a new criterion (DISO) was used to validate the ERA5 capturing extreme temperature indices derived from the Expert Team on Climate Change Detection and Indices (ETCCDI) across the six subregions of China on different timescales. The conclusions are as follows: the original daily temperatures (mean temperature, maximum temperature, and minimum temperature) can be well reproduced by ERA5 reanalysis over China. ERA5 tends to exhibit more misdetection for the duration of extreme temperature events than extreme temperature intensity and frequency. In addition, ERA5 performed best in the summer and worst in the winter, respectively. The trend of absolute indices (e.g., TXx and TNx), percentile-based indices (e.g., TX90p, TX10p, TN90p, and TN10p), and duration indices (e.g., WSDI, CSDI, and GSL) can be captured by ERA5, but ERA5 failed to capture the tendency of the diurnal temperature range (DTR) over China. Spatially, ERA5 performs well in southeastern China. However, it remains challenging to accurately recreate the extreme temperature events in the Tibetan Plateau. The elevation difference between the station and ERA5 grid point contributes to the main bias of reanalysis temperatures. The accuracy of ERA5 decreases with the increase in elevation discrepancy.

Effects of Climate Changes on the Pasture Productivity From 1961 to 2016 in Sichuan Yellow River Source, Qinghai-Tibet Plateau, China

Meteorological station data from 1961 to 2016 in the Sichuan Yellow River Source (SYRS) was used to analyze the trends in precipitation and temperature. The Thornthwaite Memorial model and GIS technology were used to calculate the response of pasture productivity to climate change. A climate prediction model of pasture productivity was established to predict its response to precipitation and temperature. The results showed that: (1) the annual precipitation presents a slight downward trend, at a rate of −10.16 mm⋅(10a)–1. The average annual temperature exhibited an upward trend, at (10a)–1, and the productivity of herbage exhibited a linearly increasing trend, with a rate of increase of 80.07 g⋅m–2⋅(10a)–1. (2) In terms of spatial distribution, the pasture productivity decreased from southwest to northeast. The influence of temperature on pasture productivity was greater than that of precipitation in the SYRS. (3) The “warm-wet” climate was conducive to increasing pasture productivity. The annual average temperature was predicted to increase by 1 or 2°C, and the annual average precipitation was predicted to increase by 10 or 20% with an average increase between 7.15 and 14.30%. (4) Grassland degradation continues to occur and ecological restoration measures should be implemented to control grassland degradation.

Assessment of Daily of Reference Evapotranspiration Using CLDAS Product in Different Climate Regions of China

Reference Crop evapotranspiration (ET0) datasets based on reanalysis products can make up for the time discontinuity and the spatial insufficiency of surface meteorological platform data, which is of great significance for water resources planning and irrigation system formulation. However, a rigorous evaluation must be conducted to verify if reanalysis products have application values. This study first evaluated the ability of the second-generation China Meteorological Administration Land Data Assimilation System (CLDAS) dataset for officially estimating ET0 (the local meteorological station data is used as the reference dataset). The results suggest that the temperature data of CLDAS have high accuracy in all regions except the Qinghai Tibet Plateau (QTP) region. In contrast, the global solar radiation data accuracy is fair, and the relative humidity and wind speed data quality are poor. The overall accuracy of ET0 is acceptable other than QTP, but there are also less than 15% (103) of stations with significant errors. In terms of seasons, the error is largest in summer and smallest in winter. Additionally, there are inter-annual differences in the ET0 of this data set. Overall, the CLDAS dataset is expected to have good applicability in the Inner Mongolia Grassland area for estimating ET0, Northeast Taiwan, the Semi Northern Temperate zone, the Humid and Semi Humid warm Temperate zone, and the subtropical region. However, there are certain risks in other regions. In addition, of all seasons, summer and spring have the slightest bias, followed by autumn and winter. From 2017 to 2020, bias in 2019 and 2020 are the smallest, and the areas with large deviation are south of climate zone 3, the coastal area of climate zone 6, and the boundary area of climate zone 7.

Spatio-temporal variation of seasonal heat islands mapping of Pakistan during 2000–2019, using day-time and night-time land surface temperatures MODIS and meteorological stations data

Urban heat islands (UHIs) create severe human health risks. Pakistan has about 220 million inhabitants, with 287 people per Km2 on average. Over the last 100 years, the maximum extreme temperature during the previous summer and winter seasons has been recorded. The forecast for the coming 50 years indicates a temperature rise in Pakistan, directly affecting physical and mental health. To address this challenge, this paper analyzes the spatial variability in Heat Islands (HI) in Pakistan from 2000 to 2019, combining MODIS satellite imagery with data from fifty meteorological stations. Different statistical and geostatistical models were applied, including geographically weighted regression (GWR), simple regression, and Geostatistical interpolation models. The results reveal the variations in nocturnal thermal on a fixed temperature of 11 °C, variations in frequency, and the change in day and night temperatures during summer and winter. Data fusion between MODIS and data from fifty metrological stations in Pakistan shows that spatial-temporal variation in the patterns of HI must be considered in planning processes when responding to climate change. The heat island's maximum nocturnal temperatures indicated a high rate of extension and magnitude compared to the daily recorded temperature, which implies that the overall area in the metropolitan category is covered by a temperature value of greater than 20 °C, raising severity to 10 °C in June. During June, intense night-time temperature values spread their concentration, heading towards the urban center over the Islamabad, Gilgit, Peshawar, Lahore, Quetta, and Karachi station sectors. During the day, the extension difference of the heat island was in the range of 8–10 °C, declining steadily from summer 2006 to summer 2015 before finally covering almost the entire metropolitan area. Higher values emerged after 2015–2019, rising in magnitude and expansion until 2019.

Estimation of Photovoltaic Energy in China Based on Global Land High-Resolution Cloud Climatology

As clean, renewable energy, photovoltaic (PV) energy can reduce the ozone-layer loss and climate deterioration caused by the use of traditional types of energy to generate electricity. At present, most PV energy products involve the influence of cloud cover on solar radiation. However, the resolution and precision of most cloud cover data are not fine enough to reflect the actual cloud distribution in local areas. This leads to incorrect distribution results of PV energy in areas with high-spatial-variability clouds. Using high-resolution and high-precision cloud cover data obtained by satellite remote sensing to estimate the distribution of PV energy can solve this problem. In this study, the Global Land High-Resolution Cloud Climatology (GLHCC), a 10-day cloud frequency product with a resolution of 1 km and located in China, was used to construct a cloud-based solar radiation estimation model. Using the inverse relationship between cloud cover and solar radiation, the GLHCC was converted into sunshine percentage data. Using meteorological station data in China, a Least Squares Fit (LSF) and error check were carried out on the A-P, Lqbal, Bahel and Sen Models to determine the optimal solar radiation estimation model (Sen Model). Based on the sunshine percentage data, the Sen Model and terrain shielding factors, the distribution of PV energy in China was estimated. Finally, comparing to the Global Horizontal Irradiance (GHI) of the World Bank and the yearly average global irradiance of the Photovoltaic Geographic Information System (PVGIS), PV energy data in this paper more accurately reflected the distribution of PV energy in China, especially in areas with high-spatial-variability clouds.

Estimation of Daily and Instantaneous Near-Surface Air Temperature from MODIS Data Using Machine Learning Methods in the Jingjinji Area of China

Meteorologically observed air temperature (Ta) is limited due to low density and uneven distribution that leads to uncertain accuracy. Therefore, remote sensing data have been widely used to estimate near-surface Ta on various temporal scales due to their spatially continuous characteristics. However, few studies have focused on instantaneous Ta when satellites overpass. This study aims to produce both daily and instantaneous Ta datasets at 1 km resolution for the Jingjinji area, China during 2018–2019, using machine learning methods based on remote sensing data, dense meteorological observation station data, and auxiliary data (such as elevation and normalized difference vegetation index). Newly released Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 surface Downward Shortwave Radiation (DSR) was introduced to improve the accuracy of Ta estimation. Five machine learning algorithms were implemented and compared so that the optimal one could be selected. The random forest (RF) algorithm outperformed the others (such as decision tree, feedforward neural network, generalized linear model) and RF obtained the highest accuracy in model validation with a daily root mean square error (RMSE) of 1.29 °C, mean absolute error (MAE) of 0.94 °C, daytime instantaneous RMSE of 1.88 °C, MAE of 1.35 °C, nighttime instantaneous RMSE of 2.47 °C, and MAE of 1.83 °C. The corresponding R2 was 0.99 for daily average, 0.98 for daytime instantaneous, and 0.95 for nighttime instantaneous. Analysis showed that land surface temperature (LST) was the most important factor contributing to model accuracy, followed by solar declination and DSR, which implied that DSR should be prioritized when estimating Ta. Particularly, these results outperformed most models presented in previous studies. These findings suggested that RF could be used to estimate daily instantaneous Ta at unprecedented accuracy and temporal scale with proper training and very dense station data. The estimated dataset could be very useful for local climate and ecology studies, as well as for nature resources exploration.