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Although salinity satellites can provide high-resolution global sea surface salinity (SSS) data, the satellite data still display large errors close to the coast. In this paper, a nonlinear empirical method based on random forest is proposed to correct two Soil Moisture and Ocean Salinity (SMOS) L3 products in the tropical Indian Ocean, including SMOS BEC and SMOS CATDS data. The agreement between in-situ data and the corrected SMOS data is better than that between in-situ data and the original satellite data. The root-mean-square deviation (RMSD) of the satellite SSS data decreased from 0.366 to 0.275 and from 0.367 to 0.255 for SMOS BEC and SMOS CATDS, respectively. The effect of the correction model was better in the Arabian Sea than in the Bay of Bengal. The RMSD of corrected BEC (CATDS) SSS was reduced from 0.44 (0.48) to 0.276 (0.269), and the correlation coefficient was increased to 0.915 from 0.741(0.801) in the Arabian Sea while the correlation coefficient improved less than 0.02 in the Bay of Bengal. The cross-validation results highlight the robustness and effectiveness of the correction model. Additionally, the effects of different features on the correction model are discussed to demonstrate the vital role of geographical information in the correction of satellite SSS data. The proposed method outperformed other machine-learning methods with respect to the RMSD and correlation coefficient.

Cement production has been increasing rapidly leading to energy consumption, with serious cost implications and environmental challenges. Energy efficiency is a key component required to maintain the cement company’s environmental strategy. In this study, Aspen Plus process model and neural networks are used to assess the energetic efficiency of a precalcining rotary kiln in a cement production process. Aspen Plus process simulator estimated energy efficiency at 61.30 % using the first law of thermodynamic. Further, for the ANN model, kiln feed, kiln gas, calciner gas, clinker cooling air, and primary air were the operation parameters inputs. ANN model is validated and demonstrated it is capable of predicting cement rotary kiln energy efficiency accurately with a correlation coefficient (R2) of 0.991. In conclusion, the Bootstrap aggregated neural network (BANN) was used to search the optimal operational parameters in achieving the lowest mean square error (MSE) of the energy efficiency. The MSE for training, testing, and validation data sets were 3.64 × 10-5, 3.70 × 10-5, and 5.00 × 10-5 for in the estimation of rotary kiln system energy efficiency. To achieve this optimal condition of 61.5 % energy efficiency, the optimal parameters as determined by ANN (BANN) were kiln feed of 205050 kg/hr, kiln fuel gas of 2821 kg/hr, calciner fuel gas of 5648 kg/hr, clinker cooling air of 247463 kg/hr and primary air of 7309 kg/hr. Consequently, it is recommended that ANN should be combined with Bootstrap aggregated neural network (BANN) for effective prediction and monitoring of energy efficiency for precalcining rotary kiln system.

Time and intensity-specific very short-term forecasting or nowcasting is the biggest challenge faced by an aviation meteorologist. Ground-based microwave radiometer (MWR) has been used for nowcasting convective activity and it was established that there is a good comparison between thermodynamic parameters derived from MWR and GPS radiosonde observations, indicating that MWR observations can be used to develop techniques for nowcasting severe convective activity. In this study, efforts have been made to bring out the efficacy of MWR in nowcasting thunderstorms and fog. Firstly, the observations of MWR located at Palam, New Delhi, India have been compared with the nearest radiosonde data to ascertain the variation in respective profiles. Large differences were found in relative humidity (RH), whereas temperatures from MWR were found to be close to radiosonde observed temperature up to 3.5 km. Subsequently, the scatter plots and correlation coefficients of thermodynamic indices/parameters indicated that most of the parameters are either not correlated or have moderate correlation only for 12:00 UTC profiles. The superepoch technique of lagged composite for various thermodynamic indices/parameters to obtain a combined picture of all the thunderstorm and dense fog cases on the time series could not determine any pattern to predict thunderstorm and dense fog with lead time of 2-4 hours. MWR profile for a case of occurrence of thunderstorm was analyzed. No significant variation was observed in most of the indices (as calculated from MWR observed parameters) prior to the occurrence of thunderstorm. RH at freezing level and between 950 and 700 hPa levels were the only parameters, which increased four hours prior to the occurrence.

The COVID-19 lockdown had a large impact on anthropogenic emissions of air pollutants and particularly on nitrogen dioxide (NO2). While the overall NO2 decline over some large cities is well-established, understanding the details remains a challenge since multiple source categories contribute. In this study, a new method of isolation of three components (background NO2, NO2 from urban sources, and NO2 from industrial point sources) is applied to estimate the impact of the COVID-19 lockdown on each of them. The approach is based on fitting satellite data by a statistical model with empirical plume dispersion functions driven by a meteorological reanalysis. Population density and surface elevation data as well as coordinates of industrial sources were used in the analysis. The tropospheric NO2 vertical column density (VCD) values measured by the Tropospheric Monitoring Instrument (TROPOMI) on board the Sentinel-5 Precursor over 261 urban areas for the period from 16 March to 15 June 2020 were compared with the average VCD values for the same period in 2018 and 2019. While the background NO2 component remained almost unchanged, the urban NO2 component declined by −18 % to −28 % over most regions. India, South America, and a part of Europe (particularly, Italy, France, and Spain) demonstrated a −40 % to −50 % urban emission decline. In contrast, the decline over urban areas in China, where the lockdown was over during the analysed period, was, on average, only -4.4±8 %. Emissions from large industrial sources in the analysed urban areas varied greatly from region to region from -4.8±6 % for China to -40±10 % for India. Estimated changes in urban emissions are correlated with changes in Google mobility data (the correlation coefficient is 0.62) confirming that changes in traffic were one of the key elements in the decline in urban NO2 emissions. No correlation was found between changes in background NO2 and Google mobility data. On the global scale, the background and urban components were remarkably stable in 2018, 2019, and 2021, with averages of all analysed areas all being within ±2.5 % and suggesting that there were no substantial drifts or shifts in TROPOMI data. The 2020 data are clearly an outlier: in 2020, the mean background component for all analysed areas (without China) was -6.0%±1.2 % and the mean urban component was -26.7±2.6 % or 20σ below the baseline level from the other years.

This work presents a novel scheme to retrieve soil moisture (SM) from the Cyclone Global Navigation Satellite System (CyGNSS) data, which is accomplished by using a bagged regression trees (BRT) algorithm with the inputs being the CyGNSS-derived products, the corresponding geolocation, and associated climate type. This algorithm is validated with the in situ hourly SM data acquired by China's automatic SM observation stations throughout the year 2018. High consistency between the retrieved SM results and the measured SM is achieved, with a correlation coefficient of 0.86 and a root-mean-square error of 0.05 cm³/cm³. The results obtained in this work indicate that the proposed BRT-based method can effectively estimate SM from CyGNSS data in different scenarios of various station locations and climate types in a near real-time manner.

This letter presents a novel method for instantaneous air temperature under cloudy conditions (Ta,cloudy) estimation using satellite-derived cloud top temperature (CTT), cloud top height (CTH), and Global Forecast System (GFS) forecasts. The radiosonde profiles were used to analyze the relationship between Ta,cloudy and CTT, CTH. The results showed that it is feasible to estimate Ta,cloudy using CTT and CTH, especially for low and middle cloud conditions. Linear and neural network (NN)-based Ta,cloudy estimation models were constructed and validated using the Visible Infrared Imaging Radiometer Suite (VIIRS) CTT, CTH, and GFS Tair for summer 2017 and 2018. The NN model performs better than the linear model, and GFS Tair can obviously improve the accuracy of Ta,cloudy estimation. The correlation coefficient (R), root-mean-square error (RMSE), and bias of the NN model with GFS Tair were 0.953, 1.950 °C, and -0.030 °C, respectively. The estimation model performed better under low and warm clouds than high and cold cloud conditions.

Profiles of Temperature (1000 hPa to 0.1 hPa at 40 pressure levels) retrieved by physical approach algorithm in cloud-free areas from Indian National Satellite-3D repeat (INSAT-3DR) satellite has been validated with ground-based in situ measurements from GPS based radiosonde measurements and then the same is compared with Atmospheric Infrared Sounder (onboard on NASA Aqua Satellite) derived profiles of temperature. The accuracy of these retrievals is a challenge and backbone of improvements in forecast accuracy. The statistical analysis is done by using INSAT-3DR and Atmospheric Infrared Sounder (onboard on NASA Aqua Satellite) data (September –December-2020). The retrieval performance and accuracy are examined in terms of biases, correlation coefficients (CC), and root means square error (RMSE) which is carried out over the land, hilly and coastal areas and found slight variations in the lower middle and upper levels of the troposphere. The key findings of this study INSAT −3DR vs AIRS are as follows: The correlation coefficient (CC) is greater than 0.7 above 15° N at all pressure levels except 150 hPa (CC ∼ 0.2 between 22° N to 35° N). The CC values are random (0.2 to 0.7) below 15° N to 7° N at all pressure levels. The warm bias of the order of (∼0.0 K to 2.5 K) from 1000 to 600 hPa over Indian land and ocean region except for some areas/, 25° N to 35° N have cold biases of the order of −2.0 K to −4.0 K. Himalayan region have a warm bias - ranges 0.0 to 4.0 K at all pressure levels. Similarly, the findings of INSAT-3R & GPS radiosonde are as follows: All the coastal stations show the random bias of the order of ∼ 3.0 K to + 3.0 K hPa above 100 hPa. RMSE value observed of ∼ 4.0 K for the stations (land, hilly, coastal) at each pressure level. The correlation coefficient (CC) of land and hilly regions is higher (0.7 to 1.0) as compared to coastal stations (0.5 to 0.6). INSAT-3DR derived temperature profiles from surface to 10.0 hPa have large bias overland (plain and desert) ranges from −3.0 K to + 3.0 K as compared to coastal and hilly regions −2.5 K to + 2.5 K, which may be attributed to the large surface emissivity values over land. In general, a positive bias of the order of (+1 to + 3 °K) from 1000 to 800 hPa, ± (1 to 2 °K) at 800 to 200 hPa & ± (1 to 3 °K) above 200 hPa have been observed with INSAT-3DR and radiosonde data. This study is found very useful in daily operational weather forecasting and analysis of weather systems at different temporal and spatial scales with more confidence.

The hydrodynamic model for Khour Al-Zubair port was introduced using the Mike 21 Flow Model FM. 2D numerical model using continuity equations and the Navier-Stokes equation to simulate hydrodynamic characteristics. Field data were collected over two different periods and on a complete tidal cycle (13 h). The model was run from October 15 to December 15, 2020, with a time step (10 sec) based on water levels and wind data. Calibration and validation of the model are evaluated based on statistical criteria (Root Mean Square Error “RMSE”, Mean Absolute Error “MAE” and correlation coefficient “R”). Depending on the values of the statistical criteria, it can be said that the results of the model were encouraging as the difference between the measured, and calculated data is not perceptible. Moreover, the results showed the behavior of tidal currents for one day of a complete cycle and the velocity of the flow in the ebb state, causing the accumulation of pollutants in the area more than in the flood state.

Calculating the motion vectors based on the infrared observations can complement the gaps of the passive microwave and radar observations. So, it is an essential step in numerical weather models and other meteorological applications. FengYun-4 (FY4A) geosynchronous satellite was launched in 2016 by the Chinese government. Carrying on board the Advanced Geosynchronous Radiation Imager (AGRI), FY4A can provide instantaneous observations from visible to infrared wavelength. However, the ability of FY4A to capture precipitation motions still needs to be explored, and the optimal scale for generating the motion vectors remains unclear. In this study, a reasonable sliding window strategy is applied to calculate the correlation coefficients between two image sequences to determine the motion vectors. Based on this, the China Merged Precipitation Analysis (CMPA) data is recognized as the true precipitation data and is compared with the 10.7 µm observations of AGRI aboard FY4A. The results in August 2018 show that based on the quantified directions of the motion vectors between the CMPA and FY4A-TBB, the correlation coefficient (CC) and Root Mean Square Error (RMSE) are about 0.64 and 2.1 respectively, which are acceptable at the resolution of 0.5°. The result indicates that FY4A can capture precipitation motions. Meanwhile, as the resolution becomes lower, the probability distributions of the motion vectors in FY4A-TBB are more similar to those in CMPA, and they become stable at 0.5°. Therefore, 0.5° is considered the optimal resolution to generate the motion vectors for FY4A-TBB. The study results have great potentials for the precipitation estimate retrieval and fusion forFY4A and also FY4B.

Accurately determining the height of the planetary boundary layer (PBL) is important since it can affect the climate, weather, and air quality. Ground-based infrared hyperspectral remote sensing is an effective way to obtain this parameter. Compared with radiosonde measurements, its temporal resolution is much higher. In this study, a method to retrieve the PBL height (PBLH) from the ground-based infrared hyperspectral radiance data is proposed based on machine learning. In this method, the channels that are sensitive to temperature and humidity profiles are selected as the feature vectors, and the PBLHs derived from radiosonde are taken as the true values. The support vector machine (SVM) is applied to train and test the data set, and the parameters are optimized in the process. The data set collected at the Atmospheric Radiation Measurement (ARM) program Southern Great Plains (SGP) from 2012 to 2015 is analyzed. The instruments used in this letter include Atmospheric Emitted Radiance Interferometer (AERI), Vaisala CL31 ceilometer, and radiosonde. It shows that the root mean square error (RMSE) between the PBLHs calculated by the proposed method using AERI data and those from radiosonde data can be within 370 m, and the square correlation coefficient (SCC) is greater than 0.7. Compared with the PBLHs derived from the ceilometer, it can be found that the new method is more stable and less affected by clouds.

The discrete element method has been used to simulate the particle flow in a ball mill under different operating conditions. The model was validated by comparing the simulated results of the flow pattern and input power with those measured from a same-scale laboratory mill. The impact energy of the particles under different operating conditions was analysed in detail. The results showed that the impact energy was affected by the operating conditions of the mill and can be linked to the grinding rate for a given material. The correlation between impact energy and grinding rate follows first-order grinding kinetics. Mill performance decreases with increasing mill size. Furthermore, a data-driven machine learning framework has been proposed to predict the impact energy for different operating conditions. It was found that the prediction for mills with diameters of 2000 and 3000 mm based on the training model developed by mills with diameter less than 254 mm could be achieved with an accuracy of 80% and a correlation coefficient of 0.9. Through the combination of DEM simulation and data-driven approach, the computing time required in the determination of impact energy for large scale mills can be dramatically reduced.

The Directional Polarimetric Camera (DPC) is a polarization sensor mounted on Chinese Gaofen-5 satellite. It has the capability of observing multispectral, multiangular, and polarized light, and can be used to monitor global aerosols and clouds. This work developed an adaptive algorithm to retrieve aerosol properties based on DPC measurements. It performs initial surface property estimations, aerosol parameter retrievals, surface parameter adjustments, and result assessments. In the algorithm, it allows global aerosol optical depths (AODs) and Angstrom exponents (AEs) to be retrieved. The AOD values show good consistency with that from AErosol RObotic NETwork (AERONET) and Moderate Resolution Imaging Spectroradiometer (MODIS). The regression line between AODs from DPC and AERONET is y = 0.895x + 0.056, with a correlation coefficient of 0.894, and 49% of the DPC-retrieved AODs are within the expected error range of ±(15%AOD + 0.05). For the AEs, the regression line is y = 0.728x + 0.395, the correlation coefficient is 0.763, and 41.4% of the AEs are within the error range of (AE ± 0.4). We also investigated the possible influence of AOD errors retrieved from the DPC measurements over three typical areas. It was found that though the DPC-retrieved dust aerosols did not show so good consistency with the MODIS measurements as the smoke aerosols, the retrievals still matched well on the whole. It demonstrates the potential of DPC and the adaptive algorithm for aerosol remote sensing.

Downward longwave radiation (DLR) at the surface is a key variable of interest in fields, such as hydrology and climate research. However, existing DLR estimation methods and DLR products are still problematic in terms of both accuracy and spatiotemporal resolution. In this article, we propose a deep convolutional neural network (DCNN)-based method to estimate hourly DLR at 5-km spatial resolution from top of atmosphere (TOA) brightness temperature (BT) of the Himawari-8/Advanced Himawari Imager (AHI) thermal channels, combined with near-surface air temperature and dew point temperature of ERA5 and elevation data. Validation results show that the DCNN-based method outperforms popular random forest and multilayer perceptron-based methods and that our proposed scheme integrating multisource data outperforms that only using remote sensing TOA observations or surface meteorological data. Compared with state-of-the-art CERES-SYN and ERA5-land DLR products, the estimated DLR by our proposed DCNN-based method with physical multisource inputs has higher spatiotemporal resolution and accuracy, with correlation coefficient (CC) of 0.95, root-mean-square error (RMSE) of 17.2 W/m², and mean bias error (MBE) of -0.8 W/m² in the testing period on the Tibetan Plateau.

The complex terrain in mountainous areas distorts solar illumination, which brings a strong topographic effect on optical remote-sensing observations. Although many efforts have been done to correct this effect via normalizing solar illumination induced differences, there are still high uncertainty, especially for poor illuminated surfaces. In this study, a downwelling shortwave radiation (DSR)-based correction (DSRC) method was proposed. The topographic effects were accounted by normalizing DSR differences at different topographic conditions, and a stratified correction strategy was applied by separating the image into different groups according to normalized difference vegetation index (NDVI) to consider the spectral differences of different land-cover types. The DSRC method was applied to nine Landsat 8 scenes with high-resolution DSR data acquired by downscaling the Meteosat Second Generation (MSG) DSR product. The performance analysis indicates that the correlation coefficient between the corrected surface reflectance and illumination conditions notably decreased. Compared with SCS+ C, empirical rotation, Statistical-Empirical, and Modified Minnaert methods, the DSRC method well retains inherent spectral pattern and provides good advantages in normalizing the aspect differences of surface reflectance. Furthermore, the comparison of NDVI values before and after correction indicated that DSRC preserved the original values and successfully corrected the overestimated NDVI values of poor illuminated surfaces. The corrected NDVI time series provide more reasonable cycle of the phenology of vegetated surfaces than the original series. In summary, the DSRC method has a strong potential for reducing topographic effects that currently limit the applications of remotely sensed data in mountainous areas.

The rapidly changing land use from agriculture to aquaculture and its emerging adverse impact on various biophysical environments along the Rasulpur River bank, West Bengal, India have been quantified by using Landsat data from 2003 to 2017. The biophysical indices like Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Normalized Difference Bareness Index (NDBaI) and Soil Adjusted Vegetation Index (SAVI) as well as Land Surface Temperature (LST) were estimated to extract the changing relationship between each other’s using Pearson’s correlation coefficient, generalized multiple linear regression (GMLR) model and Multiple Linear Regression (MLR) model. Results revealed the decreasing trend of NDVI and SAVI along with increasing trends of NDWI, MNDWI and NDBaI controlled land surface temperature (LST). Hence, both the regression models were well fitted at the significance level of p

Estimation of spatial variability of soil organic carbon (SOC) content is important for agricultural management and environmental studies. In this study, geo-spatial prediction of SOC content was conducted to evaluate and compare geostatistical techniques of ordinary kriging (OK) and cokriging (CK) with hyperspectral satellite data (Hyperion) as an auxiliary variable. The study area located in western Uttar Pradesh, India. Hyperspectral satellite-derived spectral colour indices and spectral band reflectance used as auxiliary variables in CK. Spectral colour indices, viz. brightness index, coloration index, hue index and saturation index, were computed using atmospherically corrected Hyperion spectral bands reflectance data. Results of correlation analysis revealed that the spectral reflectance of Hyperion band 57 (1033.88 nm wavelength) has the highest correlation coefficient (r) with the SOC content (− 0.86) followed by brightness index (− 0.79), coloration index (− 0.78), hue index (− 0.77) and saturation index (− 0.66). Both spherical and exponential models best fitted the semivariograms with SOC and spectral reflectance of Hyperion band 57 and also with SOC and soil coloration index as auxiliary variables. Cross-validation results indicated that among the kriging methods, CK performed best using exponential semivariogram model and soil coloration index as auxiliary variable for spatial prediction of SOC content with highest R2 (0.653) and lowest RMSE (0.136%) values. In general, CK methods were superior to OK in spatial prediction of SOC content. A combination of cokriging geostatistical technique and remotely sensed hyperspectral data as auxiliary variables is very useful for improved and reliable spatial prediction of SOC content.

The retrieval of aerosol properties over land from satellite sensors has always been a challenge. At present, several different algorithms for retrieving aerosol optical depth (AOD) have been developed from different satellite sensors. While each algorithm has its own advantages, the accuracy of AOD retrieval still needs to be further improved. To improve the retrieval accuracy of aerosol algorithms, it is necessary to provide a better method to describe the surface properties. In the current study, a new aerosol retrieval algorithm for Moderate Resolution Imaging Spectroradiometer (MODIS) images at a high spatial resolution of 500 m is proposed based on a priori bidirectional reflectance distribution function (BRDF) shape parameters database, which is reconstructed via the 3-D discrete cosine transform (DCT-PLS) method. Then, the surface reflectances are calculated from BRDF model (i.e., RossThick-LiSparse), and a non-Lambertian forward model used to describe the surface anisotropy. The new algorithm is used for processing the MODIS over the Beijing-Tianjin-Hebei of China, and Southeastern United States of America regions, and results are validated against AERONET AOD measurements as well as compared with the MODIS AOD products. The comparison showed that the estimation scheme of surface reflectance in this new algorithm significantly improved the AOD retrievals accuracy, with average correlation coefficient ~0.965 and root-mean-square error ~0.125; the number of AOD retrievals falling within expected error has increased to ~80.1%, and the overestimation uncertainty has been reduced compared with MODIS products. Due to the high spatial resolution and continuous spatial distributions of the AOD retrievals by the new algorithm, therefore, it can well-captured aerosol details over mixed surfaces and better useful for air pollution studies than the MODIS products at local and urban scales.

Easy access to accurate and reliable climate data is a crucial concern in hydrological modeling. In this regard, gridded climate data have recently been provided as an alternative to observational data. However, those data should be first evaluated and corrected to guarantee their validity and accuracy. This study offered a new approach to assess the ECMWF gridded precipitation data based on some indicators including, correlation coefficient (CC), and normalized root-mean-square error (NRMSE), and absolute error (AE) in daily and monthly intervals (2007–2017) across different climatic and geographical areas of Iran. Besides, an artificial neural network (ANN) model was utilized to correct the ECMWF precipitation product. According to the results, NRMSE was less than 2 (in 93% of stations) and 5 (in 63% of stations) on monthly and daily scales, respectively. Moreover, CC was above 0.6 in 58% and 94% of stations on daily and monthly scales, respectively. The AEs values were from −0.5 to 0.5, in 80% (daily scale) and 50% (monthly scale) of stations. Having corrected the ECMWF precipitation product by ANN, the number of stations with NRMSE 0.6 increased from 58% to 87% on the daily timescale.

The gas hydrate formation in pipelines of industries and chemical plants can cause various operational damages and can increase economic risks. Hence, the knowledge of hydrate formation conditions has become a critical research study to overcome the problems arising from the formation of hydrates. In this study, we applied an algorithm to develop an LSSVM model to predict the formation temperature of natural gas hydrate for a comprehensive range of data points. Total 188 experimental data points were applied from the literature for the development of the LSSVM model. The input parameter was finalized based on the structure of hydrates by each gas species. The results obtained by the LSSVM model have good accuracy as compared with empirical correlations available in the literature. This model gave the squared correlation coefficient (R2), and root mean square error of 0.9901 and 0.59974, respectively. The composition of gases may affect the phase equilibrium condition of gas hydrates. The applied algorithm revealed that the developed LSSVM model could become a good alternative for calculating the formation temperature of hydrate for the range of all data sets. The results showed that the proposed LSSVM model could be applicable for the prediction of hydrate formation temperature for all data points.

Quantifying forest systems is of importance for ecological services and economic benefits in ecosystem models. This study aims to map the percent tree cover (PTC) of various forest stands in the Buyuk Menderes Basin, located in the western part of Turkey with different characteristics in the Mediterranean and Terrestrial transition regions Sentinel-2 data with 10-m spatial resolution. In recent years, some researches have been carried out in different fields to show the capabilities and potential of Sentinel-2 satellite sensors. However, the limited number of PTC researches conducted with Sentinel-2 images reveals the importance of this study. This study aimed to demonstrate reliable PTC data in landscape planning or ecosystem modeling by introducing an advanced approach with high spatial, spectral, and temporal resolution and more cost-effective. In this study, a regression tree algorithm, one of the popular machine learning techniques for ecological modeling, was used to estimate the tree cover's dependent variable based on high-resolution monthly metrics' spectral signatures. Six frames of TripleSat images were used as training data in the regression tree. Monthly Sentinel-2 bands and produced metrics including NDVI, LAI, fCOVER, MSAVI2, and MCARI were almost the first time used as predictor variables. Stepwise linear regression (SLR) was applied to select these predictor bands in the regression tree and a correlation coefficient of 0.83 was obtained. Result PTC maps were produced and the results were evaluated based on coniferous and broadleaf. The results were tested using high spatial resolution TripleSat images and higher model accuracy was determined in both forest types. The high correlation is due to the Sentinel 2 satellite's band characteristics and the metrics are directly related to the tree cover. As a result, the high-accuracy availability of the Sentinel2 satellite is seen to map the PTC on a regional scale, including complex forest types between the Mediterranean and terrestrial transition climates.

Precise calculations for determining the water requirements of plants and the extent of evapotranspiration are crucial in determining the volume of water consumed for plant production. In order to estimate evapotranspiration over an extended area, different remote sensing algorithms require numerous climatological variables; however, climatological variable measurements cover only limited areas thus resulting into erroneous calculations over extended areas. The exploiting of both data mining and remote sensing technologies allows for the modeling of the evapotranspiration process. In this research, the physical-based SEBAL evapotranspiration algorithm was remodeled using M5 decision tree equations in GIS. The input variables of the M5 decision tree consisted of Albedo, emissivity, and Normalized Difference Water Index (NDWI) which were defined as absorbed light, transformed light, and plant moisture, respectively. After extracting the best equations in the M5 decision tree model for 8 April 2019, these equations were modeled in GIS using python scripts for 8 April 2019 and 3 April 2020, respectively. The calculated correlation coefficient (R2), mean absolute error (MAE), and root mean squared error (RMSE) for 8 April 2019 were 0.92, 0.54, and 0.42, respectively, and for 3 April 2020 were 0.95, 0.31, and 0.23 in order. Moreover, for the further evaluation of the model, a sensitivity analysis and an uncertainty analysis were carried out. The analysis revealed that evapotranspiration is more sensitive to Albedo than the two other model inputs, and when applying data mining techniques instead of SEBAL, the estimation of evapotranspiration has a lower accuracy.

Air pollution is a severe problem growing over time. A dense air-quality monitoring network is needed to update the people regarding the air pollution status in cities. A low-cost sensor device (LCSD) based dense air-quality monitoring network is more viable than continuous ambient air quality monitoring stations (CAAQMS). An in-field calibration approach is needed to improve agreements of the LCSDs to CAAQMS. The present work aims to propose a calibration method for PM2.5 using domain adaptation technique to reduce the collocation duration of LCSDs and CAAQMS. A novel calibration approach is proposed in this work for the measured PM2.5 levels of LCSDs. The dataset used for the experimentation consists of PM2.5 values and other parameters (PM10, temperature, and humidity) at hourly duration over a period of three months data. We propose new features, by combining PM2.5, PM10, temperature, and humidity, that significantly improved the performance of calibration. Further, the calibration model is adapted to the target location for a new LCSD with a collocation time of two days. The proposed model shows high correlation coefficient values (R2) and significantly low mean absolute percentage error (MAPE) than that of other baseline models. Thus, the proposed model helps in reducing the collocation time while maintaining high calibration performance.

The frequency of extreme rainfall events in India has increased substantially during the recent decades, causing increased risk of inland flooding. This study investigated the impact and role of four different cumulus parameterization schemes (CPSs) in simulating heavy rainfall events (HRE) over Tamil Nadu State that occurred on 01 December 2015 with five different time step sizes (TSSs) using the Weather Research and Forecasting (WRF) model. Numerical simulations were carried out using a 3-km horizontal grid resolution with relevant forcing parameters from NCEP Global Forecasting System (GFS) datasets. The rainfall data were obtained from the Chennai Doppler Weather Radar station and two automated weather stations (AWS) located at the National Centre for Sustainable Coastal Management (NCSCM 13.01° N, 80.23° E) and Madhavaram AWS (13.2° N, 80.2° E), respectively. The results indicate that simulated HRE using the WRF model is more sensitive to the choice of CPS at different TSS. It is seen that with an increase in TSS, the Kain–Fritsch (KF) scheme over-predicts HRE, while the Grell–Freitas (GF) convection scheme provides reasonably better prediction at 20 s TSS. The results clearly demonstrate that TSS is more sensitive to HRE forecasts in the WRF model, and the corresponding probability of detection (POD), critical success index (CSI) and bias varied from 0 to 81% as TSS increased from 6.67 to 20 s. Also, this study clearly reveals that TSS is very sensitive to the model CPSs in HRE forecasting. Variations attributed to TSS and CPS are reflected not only in rainfall magnitude but also in terms of rainfall location pattern, and depend on changes in simulated distribution of hydrometers, rate of latent heat, vertical velocity and wind flow patterns. Statistical analysis was also carried out to verify model-computed HRE forecasts against the data from two automatic weather stations (AWS). This study indicates that the correlation coefficient (CC) was very good, while the root mean square error (RMSE) was smaller for simulations that used high TSS with simplified Arakawa–Schubert (SAS) and GF CPSs. Numerical simulations that employed KF with high TSS showed a higher intensity in the rainfall forecast.

Deriving large-scale and high-quality precipitation products from satellite remote-sensing spectral data is always challenging in quantitative precipitation estimation (QPE), and limited studies have been conducted even using China's latest Fengyun-4A (FY-4A) geostationary satellite. Taking three rainstorm events over South China as examples, a machine-learning-based regression model was established using the random forest (RF) method to derive QPE from FY-4A observations, in conjunction with cloud parameters and physical quantities. The cross-validation results indicate that both daytime (DQPE) and nighttime (NQPE) RF algorithms performed well in estimating QPE, with the bias score, correlation coefficient and root-mean-square error of DQPE (NQPE) of 2.17 (2.42), 0.79 (0.83) and 1.77 mm h−1 (2.31 mm h−1), respectively. Overall, the algorithm has a high accuracy in estimating precipitation under the heavy-rain level or below. Nevertheless, the positive bias still implies an overestimation of precipitation by the QPE algorithm, in addition to certain misjudgements from non-precipitation pixels to precipitation events. Also, the QPE algorithm tends to underestimate the precipitation at the rainstorm or even above levels. Compared to single-sensor algorithms, the developed QPE algorithm can better capture the spatial distribution of land-surface precipitation, especially the centre of strong precipitation. Marginal difference between the data accuracy over sites in urban and rural areas indicate that the model performs well over space and has no evident dependence on landscape. In general, our proposed FY-4A QPE algorithm has advantages for quantitative estimation of summer precipitation over East Asia.

The solar seawater greenhouse desalination is a plant that simulates the natural water cycle through seawater evaporation and condensation into freshwater. In the present work, the radial basis function neural network integrated with different optimization algorithms is presented to predict the water production capacity considering the effect of various parameters on the performance seawater greenhouse system. Different statistical metrics are employed to examine the performance of the proposed models. Also, in order to obtain a more satisfactory performance of water production forecasting in a seawater greenhouse, the effect of neurons’ numbers in the hidden layer of the proposed neural network is studied. According to the obtained results, the forecasting accuracy of the proposed radial basis function neural network optimized with the hybrid particle swarm optimization–gray wolf optimizer algorithm with nine neurons in the hidden layer with the correlation coefficient and coefficient of determination of 0.998 and 0.996 in training phase is much better than those of the other models. Also, the best values for the front greenhouse dimension are obtained as Width = 125 m, Length = 200 m, and Height = 4 m. Also, the roof transparency is obtained by 0.6.

Floods are natural disasters that widely affect people and goods. Its frequency and magnitude are projected to substantially increase due to the ongoing environmental change. At regional and national levels, some efforts have been made in predicting floods at a short-term range. However, the usefulness of flood prediction increases as the time lead increases. The objective of this work is therefore to investigate flood sensitivity to climate indexes in West Africa as a basis for seasonal flood forecasting. The methodology consists of optimizing the relationship between Annual Maximal Discharge (AMD), a proxy for flood discharge and various climate indexes using correlation coefficient, linear regression and statistical modeling based on 56 river gauging stations across West Africa. The climate indexes considered are the Sea Surface Temperature (SST) of the Tropical Northern Atlantic (TNA), SST of the Tropical Southern Atlantic (TSA), the Sea Level Pressure (SLP) of the Southern Oscillation Indexes (SOI) and the detrended El-Nino Southern Oscillation indexes. It was found that SOI/SLP indexes are the most strongly related to the AMD for the investigated stations with generally high, positive, and statistically significant correlation. The TSA/SST indexes indicated both positive and negative statistically significant correlations with river discharge in the region. The percentage change in AMD per unit change in SOI/SLP for most of the statistically significant stations is within 10 % and 50 % indicating a strong relationship between these two variables. This relationship could serve as a basis for seasonal flood forecasting in the study area.

A re-evaluated data set of nitrogen dioxide ( NO2 ) column densities over Rome for the years 1996 to 2017 is here presented. This long-term record is obtained from ground-based direct sun measurements with a MkIV Brewer spectrophotometer (serial number #067) and further reprocessed using a novel algorithm. Compared to the original Brewer algorithm, the new method includes updated NO2 absorption cross sections and Rayleigh scattering coefficients, and it accounts for additional atmospheric compounds and instrumental artefacts, such as the spectral transmittance of the filters, the alignment of the wavelength scale, and internal temperature. Moreover, long-term changes in the Brewer radiometric sensitivity are tracked using statistical methods for in-field calibration. The resulting series presents only a few (about 30) periods with missing data longer than 1 week and features NO2 retrievals for more than 6100 d, covering nearly 80 % of the considered 20-year period. The high quality of the data is demonstrated by two independent comparisons. In the first intensive campaign, Brewer #067 is compared against another Brewer (#066), recently calibrated at the Izana Atmospheric Observatory through the Langley method and there compared to reference instrumentation from the Network for the Detection of Atmospheric Composition Change (NDACC). Data from this campaign show a highly significant Pearson's correlation coefficient of 0.90 between the two series of slant column densities (SCDs), slope 0.98 and offset 0.05 DU (Dobson units; 1.3×1015 molec . cm - 2 ). The average bias between the vertical column densities is 0.03 DU ( 8.1×1014 molec . cm - 2 ), well within the combined uncertainty of both instruments. Brewer #067 is also independently compared with new-generation instrumentation, a co-located Pandora spectrometer (#117), over a 1-year-long period (2016–2017) at Sapienza University of Rome, showing linear correlation indices above 0.96 between slant column densities, slope of 0.97, and offset of 0.02 DU ( 5.4×1014 molec . cm - 2 ). The average bias between vertical column densities is negligible ( −0.002 DU or - 5.4 × 10 13 molec . cm - 2 ). This, incidentally, represents the first intercomparison of NO2 retrievals between a MkIV Brewer and a Pandora instrument. Owing to its accuracy and length, the Brewer data set collected in Rome can be useful for satellite calibration/validation exercises, comparison with photochemical models, and better aerosol optical depth estimates ( NO2 optical depth climatology). In addition, it can be employed to identify long-term trends in NO2 column densities in a metropolitan environment, over two decades witnessing important changes in environmental policies, emission loads and composition, and the effect of a worldwide economic recession, to offer just a few examples. The method can be replicated on the more than 80 MkIV spectrophotometers operating worldwide in the frame of the international Brewer network. The NO2 data set described in this paper can be freely accessed at https://doi.org/10.5281/zenodo.4715219 ( Diemoz and Siani , 2021 ) .

This study assesses the spatiotemporal development of land use systems and climate variability in Southwestern Ghana over the past five decades using integrated remote sensing techniques and existing literature. We demonstrated the relationship between Normalized Difference Vegetative Index, Normalized Difference Water Index, Normalized Difference Built-up Index, surface temperature and precipitation using geoinformatics and Pearson’s correlation coefficient (r). We found change in land use systems in Southwestern Ghana to be immensely driven by economic and socio-political factors. Interestingly, some biophysical factors have somewhat contributed to this change. Findings revealed a drastic decline in forested areas (−334.8 km2 yr−1) and waterbodies (−4.79 km2 yr−1), along with a dramatic increase in built-up (+137.93 km2 yr−1) and farmlands/shrubs (+131.97 km2 yr−1). Change in prevailing microclimatic conditions can be associated with land cover change, considering the impact of major drivers observed over the given period. Results showed a very weak positive correlation between vegetation and temperature (r = 0.214). Similarly, built-up correlated positively with vegetation (r = 0.165), water-index (r = 0.818; strong correlation or evidence of association) and temperature (r = 0.266). In contrast, other used variables correlated negatively with precipitation. The study serves a seminal guide to land use developers and institutors for effective and sustainable use of natural resources.

Climate change has caused a rise in temperature extremes, particularly heatwaves, in recent decades. Physical-empirical models are developed in this study using two classical machine learning algorithms, namely decision tree (DT) and random forests, and a novel hybrid technique consists of Ada-Boost Regression and decision tree (ABR-DT) for forecasting annual heatwave days (HWDs) of Iran from synoptic predictors. The daily temperature data of Princeton Meteorological Forcing were extracted for 27 points to estimate the annual number of HWDs, and the National Centers for Environmental Prediction reanalysis data were used as predictors. The major synoptic variables were extracted for four pressure levels (e.g., 300, 500, 850, and 1000 hPa) and three monthly time lags. The Principal Component Analysis was employed to reduce the diverse predictors and their features to the most optimal structure. The grid point-based performance evaluation revealed the superiority of ABR-DT, which showed a correlation coefficient (CC) of 0.860 and meant absolute error (MAE) of 6.929, using only specific humidity and wind component as predictors. The spatial performance indices over eight different climate regions of Iran also showed the better performance of ABR-DT, which improved the CC and MAE of its two alternatives up to 185 and 19%. The study identified the optimal parameter combination as the predictors of heatwaves by examining the effects of numerous weather components. The results proved the proposed hybrid forecasting approach’s effectiveness in predicting heatwave days, a devastating hazard, for many regions.

In this study, we propose a sequence-to-sequence neural network architecture to jointly estimate the plant area index (PAI) and wet biomass of canola and soybean. The PAI and wet biomass have considerable importance for crop growth stage mapping and monitoring. RADARSAT-2 quad-pol data along with in situ measurements of canola and soybean obtained from the SMAPVEX16 campaign over Manitoba, Canada, are utilized for evaluating the efficiency and accuracy of the proposed estimation methodology. The analysis indicates promising results for the two crops with a correlation coefficient $(r)$ in the range of 0.69–0.87. The results also confirm intercorrelation between the PAI and wet biomass for canola and soybean.

Estimating monthly runoff variation, especially in ungauged basins, is inevitable for water resource planning and management. The present study aimed to evaluate the regionalization methods for determining regional parameters of the rainfall-runoff model (i.e., GR2M model). Two regionalization methods (i.e., regression-based methods and distance-based methods) were investigated in this study. Three regression-based methods were selected including Multiple Linear Regression (MLR), Random Forest (RF), and M5 Model Tree (M5), and two distance-based methods included Spatial Proximity Approach and Physical Similarity Approach (PSA). Hydrological data and the basin's physical attributes were analyzed from 37 runoff stations in Thailand's southern basin. The results showed that using hydrological data for estimating the GR2M model parameters is better than using the basin's physical attributes. RF had the most accuracy in estimating regional GR2M model’s parameters by giving the lowest error, followed by M5, MLR, SPA, and PSA. Such regional parameters were then applied in estimating monthly runoff using the GR2M model. Then, their performance was evaluated using three performance criteria, i.e., Nash–Sutcliffe Efficiency (NSE), Correlation Coefficient (r), and Overall Index (OI). The regionalized monthly runoff with RF performed the best, followed by SPA, M5, MLR, and PSA. The Taylor diagram was also used to graphically evaluate the obtained results, which indicated that RF provided the products closest to GR2M's results, followed by SPA, M5, PSA, and MLR. Our finding revealed the applicability of machine learning for estimating monthly runoff in the ungauged basins. However, the SPA would be recommended in areas where lacking the basin's physical attributes and hydrological information.

Research focus (problem description, short background): The energy efficiency of a district heating (DH) system can be improved by fine-tuning supply and return temperatures and flow rates without need for invasive renovation of the end user heating systems. Research methods: We employ a correlation coefficient to examine the relationship between the local supply temperature and the use of heat; research relies on data hourly heat generation records from each heat generation facility in the DH system of Omsk, Russia, 2016–2017. Key research results/findings: The correlation declines the further we go from winter. Firstly, the relationship between the transition states and the outside temperature results from the relationship between external conditionings and heat parameters in DH. Secondly, both the energy consumption and the supply temperature are affected by the infrastructure, other weather conditions, and energy price; they are also highly dependent on consumer behavior, which is arbitrary, heterogeneous, and interdependent. Thirdly, the regional heating load is influenced by the characteristics of a building cluster. Although nearly all DH plants have a control system in place, we find that human factor jeopardizes the performance of such systems. Main conclusions and recommendations: To generalize these results to other systems, we emphasize that the effective diffusion coefficients and effective heat-loss coefficients are influenced by the thermal dynamics in the network and change on typical time scale (one year), hence their uncertainty. This study has produced a technique for visualizing the relationship that exists in a DH system between supply temperature and heat consumption; this technique is expected to contribute to the dynamic modeling, control, and planning of future integrated energy systems. The load profiles are dynamic; a thermal storage could be used to shift the timing of heating loads to minimize the flow through DH networks.

Most of the socio-economic activities of Sri Lanka are centred in the coastal regions, which can be described according to its unique geographical and geophysical backgrounds. It is estimated that Sri Lanka has a shore line about 1,700 km in length. Therefore, better knowledge and understanding of the characteristics of sea level is very essential. The sea level has traditionally been measured mainly at tide gauges set up along the coast line. However, the number of tide gauge stations is few and sparse in most countries. But, with the advancements of satellite altimetry technology, it is now accepted as an alternative technique to study regional and global sea level changes. The basic concept in altimetry is the range measurements to the water surface measured from the altimeter antenna. The surface of the sea, or sea level, is continuously changing for many reasons. Here, some changes are fast whereas others occur gradually. Among them, some factors are natural causes, while others occur due to man-made activities such as the greenhouse effect. These changes can be regional or extend at a global scale. The main aim of this research was to examine the trends in sea level rise around the Sri Lankan coastline using two decades of satellite altimetry data from 1993 to 2012. The computed sea level rise trends around Sri Lanka is around 2.5 to 3 mm per year, with a variance of +/- 0.6 mm accuracy. The obtained trend of sea level rise from the altimetry data was validated with tide gauge data in Colombo, and found to be highly correlated, with a correlation coefficient (R2) of 0.93 for annual mean sea level. This study confirms that satellite altimetry can be successfully used to investigate the long term sea level trend studies, particularly in the absence of long term tide data.

Quantification of the distribution of the CO2 dry-air mixing ratio (XCO2) is crucial for understanding the carbon cycle. However, clouds and aerosols in the line of light create spectral interference with CO2 signals. This interference can result in a low yield of XCO2 retrievals, thus limiting the application of these valuable satellite data. In this study, we developed an innovative methodology to obtain XCO2 maps of high spatial and temporal resolution using satellite data. The method first interpolates the spatial properties using an empirical Bayesian kriging (EBK) algorithm. Then, the temporal properties are modulated based on a CO2 curve database that was constructed using temporal contours and transfer learning techniques. We applied this method to obtain spatiotemporal XCO2 maps over mainland China using the Orbiting Carbon Observatory 2 (OCO-2) data product OCO-2_L2_Lite_FP 9r for the period from January 1 to December 31, 2019. The correlation coefficient ( $R^{2}$ ) was 0.8056, and the average absolute prediction error [root-mean-square error (RMSE)] was 0.9951. In the research area of mainland China, the vacancy validation strategy was adopted and yielded $R^{2}$ and RMSE of 0.8230 and 0.9746, respectively. We used the 2018–2019 ground-based data from four Total Carbon Column Observing Network (TCCON) sites in Europe and 2016 Hefei sites in mainland China to evaluate the performance of this new mapping method, respectively. Also, we obtained $R^{2}$ of 0.8690 and the RMSE of 0.9056 in Europe and $R^{2}$ of 0.8473 and the RMSE of 0.7026 in mainland China, proving the robustness and high precision of our method. This mapping technique is capable of filling the spatiotemporal gaps of satellite measurements with the high accuracy and resolution needed for its scientific application; thus, it has the potential to augment the scientific returns of satellite missions (e.g., USA OCO-2 Japan GOSAT and Chinese TanSat).

The indirect rainfall estimates by satellites and numerical models are the alternative options for the regions lacking enough and accurate ground observations. However, these indirect estimates often lack homogeneity and need to be evaluated before application. This study used gauge observations to test the accuracy of recently produced high-resolution satellite-based and numerical model output rainfall products over Ethiopia. Tropical Applications of Meteorology Using Satellite data and Ground-Based Observations (TAMSAT v3.1), Climate Hazard group Infrared Precipitation with Stations (CHIRPS v2.0), and the ERA5 reanalysis products were evaluated at monthly, seasonal, and annual temporal scales for the years 1992–2009. The satellite products showed nearly similar characteristics with much better accuracy than the model reanalysis output, which underestimated the rainfall amounts. Both satellite and reanalysis products captured the shapes of the rainfall at a monthly scale but less accurately at a seasonal scale. In general, the satellite-based products outperformed the reanalysis data set with a high correlation coefficient and index of agreement values, as well as low Root Mean Square Error and BIAS values. On the other hand, the reanalysis (ERA5) product showed a considerable underestimation in all sites. Therefore, satellite-based products are more reliable for researches in the region. However, the algorithms in both satellites need further calibration for a better estimation of seasonal rainfall amounts.

The present study proposes a Geographic Information System-based runoff and sediment yield model. The model derives the study watershed characteristics using the Digital Elevation Model. The model calculates the runoff (using the NRCS curve number method) and sediment yield (using MUSLE) on each cell (pixel) of the DEM and routes these hydrological parameters over the overland flow cells into the drainage channels to the watershed outlet using Time-Area histogram method. The backend and frontend of the model is developed in Python and HTML + JavaScript code, respectively. The model develops the runoff hydrograph and sediment graph at the watershed outlet. The performance of the developed model has been assessed by comparing the model output with the observed runoff and sediment yield measured at the outlet of forest micro-watershed located in Shivalik foot hills of North-West India. The statistical analysis reveals the reasonably well performance of the developed model in simulating runoff and sediment yield as is corroborated by low values of PBIAS, MAPE, MBE and RMSE and high values of correlation coefficient and model efficiency.

Groundwater is the major source of municipal and private potable water supply for meeting the drinking, domestic, agricultural and industrial requirements on man around the world. The cost of analyzing water quality in the laboratory to ascertain its potability is usually high and sometimes not available. Groundwater samples were collected from fifty (50) spatially referenced bore well locations in Warri and its environs in the dry and wet seasons (November 2019 to January 2020) in the study area. The water samples were analyzed for twenty-six (26) physical, chemical and bacteriological parameters both in the field and laboratory in line with APHA standard procedures for testing water and waste water inorder to evaluate the status of potability of groundwater across Warri, Delta State Nigeria. The data analysis tool in Microsoft Excel was used to explore and study the interrelationship between some conservative parameters measured in the field (pH, EC, TDS, and DO) as independent variables and some cations, anions and heavy metals (Na, Mg, Ca, HCO3, SO4 Cl, Fe, Cd, Cr, Cu and Pb) analysed in the laboratory as dependent variables. The results obtained from the parameters analysed insitu in the field which are cheap to perform and easily affordable were used to check and evaluate and the inter-relationships with some cations, anions and heavy metals. Highly correlated water quality parameters were determined by correlation coefficient (R) values obtained from correlation matrix and related by Regression equations (models). The regression models can be adopted to predict the concentration of these cations, anions and heavy metals before the rigorous laboratory analysis, to serve as a quick check for concentration of most disease-causing pollutants and to save time, money and resources, especially the near absence of AAS for analysing heavy metals in a good number of laboratories. The regression models developed in the study can be used for monitoring the water quality parameters by knowing the concentration of independent parameters obtained in the field alone. There is a relationship between variables which show that one variable actually causes changes in another variable. It was observed that multiple regression models can predict most parameters at 5% level of significance. Significantly positive correlation at 1 and 5% was found between many parameters. This technique studied and calculated the correlation coefficients between various physico-chemical parameters of drinking water and provided an excellent device for the calculation of parameter values within realistic degree of accuracy. The results proved to be easiest, useful, and rapid means for monitoring of water quality with the help of systematic calculations of correlation coefficient. It is recommended to treat groundwater prior to domestic use.

The impact of soil moisture (SM) in the regional climate model RegCM4.6 is investigated for the simulation of Indian summer monsoon seasonal rainfall and its spatiotemporal variability. For this purpose, the model is initialized with soil moisture from five different sources such as the (1) European Space Agency Climate Change Initiative (ESACCI), (2) Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), (3) Climate Prediction Center (CPC), (4) ECMWF's reanalysis (ERA) of the twentieth century (ERA-20C), and (5) corrected ERA (CERA). There is a notable discrepancy in the magnitude and distribution of SM among the different sources. In the first stage of the experiment, the model is integrated for two deficit (1982 and 1987), two excess (1983 and 1988), and two normal (1984 and 1989) monsoon seasons to identify the best suitable SM source. Soil moisture tends to have a positive relationship with precipitation and evapotranspiration and a negative relation with sensible heat flux. The CPC SM having a correlation coefficient of 0.6 performs better than the other sources, and it is hence used for long-term simulations of the recent 18 monsoon seasons. We suggest that the initialization of RegCM using CPC data is suitable since the model skill is higher than the other sources of SM. Comprehensive statistical analyses also confirm that the CPC is a better choice over the other sources, and the skill of the model, while using CPC improves over the control experiment. The performance index is higher with the CPC data than the control experiment for a longer-period simulations. The number of phase-synchronizing events based on the standardized anomaly index suggest that the CPC anomaly is aligned in the same direction to the observed rainfall anomaly in 13 of the 18 seasons, whereas the control experiment is aligned for nine of the 18 monsoon seasons. Also, the skill of the model increases from 0.48 with the control experiment to 0.56 with the CPC-initialized experiment.

Air surface temperature (AST) is a crucial importance element for many applications such as hydrology, agriculture, and climate change studies. The aim of this study is to develop regression equation for calculating AST and to analyze and investigate the effects of atmospheric parameters (O3, CH4, CO, H2Ovapor, and outgoing longwave radiation (OLR)) on the AST value in Iraq. Dataset retrieved from the Atmospheric Infrared Sounder (AIRS) at EOS Aqua Satellite, spanning the years of 2003 to 2016, and multiple linear regression were used to achieve the objectives of the study. For the study period, the five atmospheric parameters were highly correlated (R, 0.855-0.958) with predicted AST. Statistical analyses in terms of β showed that OLR (0.310 to 1.053) contributes significantly in enhancing AST values. Comparisons among selected five stations (Mosul, Kanaqin, Rutba, Baghdad, and Basra) for the year 2010 showed a close agreement between the predicted and observed AST from AIRS, with values ranging from 0.9 to 1.5 K and for ground stations data, within 0.9 to 2.6 K. To make more complete analysis, also, comparison between predicted and observed AST from AIRS for four selected month in 2016 (January, April, July, and October) has been carried out. The result showed a high correlation coefficient (R, 0.87 and 0.95) with less variability (RMSE ≤ 1.9) for all months studied, indicating model's capability and accuracy. In general, the results indicate the advantage of using the AIRS data and the regression analysis to investigate the impact of the atmospheric parameters on AST over the study area.

The Gravity Recovery and Climate Experiment (GRACE) satellite has proven adept at monitoring, characterizing, and predicting hydrological variables. This paper attempted to investigate the applicability of using nonlinear autoregressive with exogenous input (NARX) neural network to forecast GRACE terrestrial water storage anomalies over 11 basins. By using six hydrological indicators as external inputs and forming all possible combinations of these variables, we have found the appropriate external inputs of the optimal NARX model for each basin. In addition, the number of acceptable time delays and the number of hidden neurons are adjusted to train out the optimal NARX model. The results reveal that the NARX models with one time delay perform better than the models with higher than one delay, and the structures of the optimal NARX models vary with basins. The performance of the optimal NARX models of the 11 basins falls into “very good” category whether during training, validating, or testing period. In comparison with GRACE Follow On (GRACE-FO) results, the predictions from the optimal NARX models are satisfactory, with the highest correlation coefficient of 0.97 in the Amazon basin and the lowest correlation coefficient of 0.62 over the Yangtze basin. The results shown here not only bridge the data gap between GRACE and GRACE-FO but also facilitate the applications of using the NARX neural network to predict climate extremes like droughts and flooding.

Land surface models (LSMs) are typically forced with observed precipitation and surface meteorology and hence the soil moisture estimates obtained from LSM do not reflect the contribution of irrigation to the soil moisture estimates. However, the satellite retrievals of soil moisture estimates do register the signature of the irrigation effects. It is suggested that the soil moisture estimates obtained from LSM may reflect the role of irrigation if they are assimilated with soil moisture estimated from satellites. The present study evaluates the improvement of soil moisture estimates obtained from Noah LSM by ingesting them with the satellite-derived Advanced Scatterometer (ASCAT) soil moisture retrievals over the Indian domain for the year 2012. The above ingesting of soil moisture estimates is performed using the land information system (LIS). The improved soil moisture estimates are validated with the in situ India Meteorological Department (IMD) soil moisture observations and also with the high-resolution Indian Monsoon Data Assimilation and Analysis (IMDAA) regional reanalysis data. The percentages of grid points over the Indian domain where the improvement parameter shows positive values are 59.14% (winter), 69.17% (pre-monsoon), 43.59% (monsoon), and 77.53% (post-monsoon). Furthermore, the forecast impact parameter also indicates the positive impact of data assimilation. Also, 12 of the 22 stations show reduced RMSE soil moisture error after data assimilation is performed while only 6 of the 22 stations show higher correlation coefficient in soil moisture without data assimilation, when validated with the in situ IMD soil moisture observations. The study has also evaluated the irrigation impact of ASCAT in the assimilated soil moisture using triple collocation (TC) method. For the TC analysis, the model-based Global Land Data Assimilation System (GLDAS), Catchment Land Surface Model (CLSM), and MERRA (Modern-Era Retrospective analysis for Research and Applications) Land data set together with soil moisture model outputs with and without ASCAT assimilation are used to calculate the error and correlation coefficient of each of the two set of triplets. The results of the TC analysis further conclusively show the positive impact of irrigation effects in the ASCAT-assimilated soil moisture model output.

Background The atmosphere is usually contaminated by heavy metals from different natural and anthropogenic sources. The levels of heavy metals in the atmosphere often become harmful and cause environmental pollution with serious health consequences to humans when their concentrations exceed certain threshold limits. The determination of the atmospherically suspended particulate matter in general and toxic trace metallic concentrations in particular, is, therefore, necessary for the evaluation of the sources and fate of atmospherically deposited suspended particulate matter. The assessment of the concentration levels of suspended particulate trace metals like Zn and Pb in the cities of Enugu and Abakaliki southeastern Nigeria was performed to assess the concentrations of atmospherically suspended metals in the area, their sources, and their pollution status. Results Results of the study showed that Zn concentration levels were not significant at all the sampling points, while the concentrations of Pb particulate matter which ranged from 0.11 to 0.91 µg/m3 exceeded the international revised EPA Pb standard of 0.15 µg/m3 but within the approved Federal Ministry of Environment (FMEnv) Nigeria, 24 h long duration of 1 µg/m3, and National Ambient Air Quality Standard (NAAQS) 1 h duration (1 µg/m3). The enrichment factor (EF) showed that Pb had a dominant crustal source, while Zn was determined to be of minor to moderate crust enrichment. The multivariate analysis carried out using principal component analysis (PCA) based on varimax rotation and Hierarchical Cluster Analysis (HCA) plotted in biplot and dendrogram diagrams revealed the levels of dependency and grouping, whereas Pearson’s Correlation Coefficient (PC) revealed a strong link between the two metallic pollutants Conclusion This study therefore which applied statistical and pollution assessment of atmospherically suspended particulate matter has revealed the importance of monitoring atmospherically suspended materials resulting from anthropogenic sources like mining, quarrying, construction, etc. Similarly, the use of statistical techniques like PCA, HCA, and Pearson’s Correlation Coefficient to the study of ambient air quality has proved to be a useful tool in the assessment of atmospheric pollution. The findings of the present study therefore will be invaluable in the monitoring of particulate matter pollution of the atmosphere and their control measures, especially in mining districts.

High frequency (HF) radar often indirectly inverts wind field from wave height field, so the accuracy, range, and spatial resolution of the estimated wind field are usually limited by the wave field estimation performance. However, the sea surface current including the wind-driven current can often be accurately measured by radar. Thus, based on the wind-driven current estimated by HF radar, a new wind speed inversion algorithm is proposed in this letter. First, the relationship between wind speed and wind-driven current speed is established according to the ocean dynamics theory, but it contains several uncertain parameters. To avoid solving these parameters, an artificial neural network is used to train an accurate model between wind-driven current speed and wind speed. Final, the wind-driven current estimated by radar is substituted into the model to extract wind speed. A field experiment shows that the average correlation coefficient between the radar-estimated wind speed and the reference wind speed is 0.86, and the average root mean square error is 2.38 m/s. In addition, the proposed algorithm has larger measurement range and better spatial resolution than traditional algorithms.

Live fuel moisture content (LFMC) is an essential variable to model fire danger and behaviour. This paper presents the first application of deep learning to LFMC estimation based on the historical LFMC ground samples of the Globe-LFMC database, as a step towards operational daily LFMC mapping in the Contiguous United States (CONUS). One-year MODerate resolution Imaging Spectroradiometer (MODIS) time series preceding each LFMC sample were extracted as the primary data source for training. The proposed temporal convolutional neural network for LFMC (TempCNN-LFMC) comprises three 1-D convolutional layers that learn the multi-scale temporal dynamics (features) of one-year MODIS time series specific to LFMC estimation. The learned features, together with a few auxiliary variables (e.g., digital elevation model), are then passed to three fully connected layers to extract the non-linear relationships with LFMC. In the primary training and validation scenario, the neural network was trained using samples from 2002 to 2013 and then adopted to estimating the LFMC from 2014 to 2018, achieving an overall root mean square error (RMSE) of 25.57% and a correlation coefficient (R) of 0.74. Good consistency on spatial patterns and temporal trends of accuracy was observed. The trained model achieved a similar RMSE of 25.98%, 25.20% and 25.93% for forest, shrubland, and grassland, respectively, without requiring prior information on the vegetation type.

Mathematical statistics, correlation analysis, Piper and Gibbs diagrams, and geographic information system- based multi-criteria decision analysis were used to study the hydrochemical characteristics and identification of hydrochemical ions affected by human activities of the springs in the south of Yanbian City, China. Four criteria were selected: land use/land cover, village density, distance to towns, and distance to main roads. The improved entropy method was used to assign weight to each criterion, followed by evaluating the human activities impact index map, which was used to extract the human activities impact index of springs. The correlation coefficient was calculated to identify the hydrochemical parameters affected by human activities. The results show that the main hydrochemical parameters are Ca2+ among cations and HCO3− among anions. Ca2+, Mg2+, HCO3−, Cl−, and total dissolved solids (TDS) have a strong correlation and similar spatial distribution, showing a decreasing trend from northwest to southeast. Most hydrochemical parameters show a similar spatial distribution trend. The hydrochemical types of springs are HCO3-Ca, HCO3-Ca•Mg, HCO3-Na•Ca, and HCO3-Ca. In the study area, Na+, K+, TFe, Mn2+, F−, PO43−, and oxygen consumption are negligibly affected by human activities, Mg2+, HCO3−, and Cl− were slightly affected, and TDS and total hardness (TH) were strongly affected. With a correlation coefficient of 0.913, nitrate exhibited the highest correlation with the human activities impact index; it was significantly affected by human activities. We conclude that nitrate was the most affected by human activities, followed by TH, TDS, and other hydrochemical parameters.

Determination of gas adsorption capacity under geological conditions is essential in evaluating shale gas resource potential. A quantitative determination of gas adsorption capacity was proposed through 1) investigating controlling geological factors (including both internal ones and external ones) of gas adsorption capacity in organic-rich marine shale with geochemical analysis, XRD diffraction, field-emission scanning electron microscopy, and methane sorption isotherms; 2) defining the relationship between gas adsorption capacity and single controlling factor; 3) establishing a comprehensive determination model with the consideration of all these controlling factors. The primary controlling factors of the sorption capacity for the studied O3w-Lower S1l shale are TOC, illite and quartz, temperature, pressure, Ro, and moisture (water saturation). Specifically, TOC, thermal maturity, illite, and pressure are positively correlated with sorption capacity, whereas, quartz and temperature contribute negatively to the sorption capacity. We present the quantitative model along with application examples from the Wufeng-Lower Longmaxi Shale in the southeast Sichuan Basin, west China, to demonstrate the approach in shale gas evaluation. The result shows that the comprehensive determination model provides a good and unbiased estimate of gas adsorption capacities with a high correlation coefficient (0.96) and bell-shaped residues centered at zero.

Simultaneous measurements of ion-mobility spectra of both polarities with a Neutral Air Ion Spectrometer (NAIS) operating in the mobility range 3.16–0.00133 cm2 V−1 s−1 (mass diameter range 0.36–47.1 nm) and concentration of Radon (222Rn) were carried out at Pune (18° 31′ N, 73° 55′ E, 560 m above mean sea level). 222Rn progenies measured by a Radon detector, RTM 2200, and surface meteorological parameters during the period January 2012 to December 2012 were analysed. During this period, NPF events were observed on 28 days and 222 days were without any event (non-event). NPF events mostly occurred by photochemistry in the morning hours of the pre-monsoon season (~ 62%) during the hottest months (April and May) of the year. Authors studied different features of new particle formation (NPF) events, and their dependence on meteorological parameters. The annual mean diurnal variations of different categories of ions show a primary maximum in the morning hour along with the secondary maxima in the evening hour and a minimum in the afternoon. The results are explained in terms of the atmospheric boundary layer changes and katabatic wind blowing along the hill slope surrounded by the measurement site. The computed ion production rate correlates (correlation coefficient R = 0.67) well with the observed small cluster ions. Also, the role of temperature and humidity on the ion concentration on both for the event and non-event days are discussed. Using the principal component analysis (PCA), the first five principal components were found to represent more than 98% of the total variance on event and non-event days. Even the first principal component explained about ~ 86% (65%) of the total variance on non-event (event) days. The statistical analysis also confirms that the small and large—ions on non-event days originated from a similar physical/chemical background.

The Pingluo area, as an experimental study area in Yinchuan, has been subjected to major environmental degradation due to soil salinization problems. Soil salinization is one of the main problems of land degradation in arid and semiarid regions. In the present study, remote sensing was integrated with mathematical modeling to evaluate soil salinization adequately. To detect soil salinization, soil water content and electrical conductivity of soil samples were analyzed. The reflectance of soil samples was measured using a spectrometer (SR-3500) with 1024 bands. Indices of soil salinity, vegetation and drought were analyzed using Landsat images over the study area. Based on Landsat images, physicochemical analysis, reflectance of sensitive bands for soil salinization and environmental indices, canopy response salinity index (CRSI), perpendicular drought index (PDI) and enhanced normalized difference vegetation index (ENDVI), a new model was established for simulation and prediction of soil salinization in the study area. Correlation analyses and multiple regression methods were used to construct an accurate model. The results showed that green, blue and near-infrared light was significantly correlated with soil salinity and that the spectral parameters improved this correlation significantly. Therefore, the model was more effective when combining spectral parameters with sensitive bands with modeling. After mathematical transformation of soil reflectance, the correlations of bands sensitive to soil salinization were 0.739 and 0.7 for electrical conductivity and water content, respectively. After transformation of vegetation reflectance, the correlation coefficient of soil salinity became 0.577. After inversion of the model based on soil hyperspectral and water content, the significance became 0.871 and 0.726, respectively, which can be used to predict soil salinity and water content. The spectral soil salinity model had a coefficient of 0.739 for soil salinity prediction. Among the salinity indices, the CRSI was selected as the most significant, with R2 of 0.571, whereas the R2 for PDI reached only 0.484. Among the vegetation indices, the ENDVI had the highest response to soil salinity, with R2 of 0.577. After scale conversion, the correlation percentages between CRSI and measured soil salinity and between ENDVI and measured soil salinity increased to 16.2% and 8.5%, respectively. Following the correlation between PDI and soil water content, the percentage of correlation increased to 11.6%. The integration of hyperspectral remote sensing, ground methods and an inversion method for salinity is a very important and effective technique for rapid and nondestructive monitoring of soil salinization.

The study aims to assess the trace metals and physicochemical properties of water in the adjacent to the Sylhet gas blowout area. Trace metals were analyzed using atomic absorption spectrophotometer, whereas physicochemical parameters were evaluated in-situ state using portable instruments and also in the laboratory. Trace metals Pb, Cd, and Ni were found in the water samples higher than the acceptable limit by WHO standards, whereas the concentration of Cu and Zn were within acceptable limit, respectively. The correlation coefficient matrix and factor loading analysis spectacle that the interrelationship among the physicochemical parameters, trace elements, as well as other ions are moderate to strongly corellated which reflecting the homogeneous source of origin. According to contamination factor, Nemerow multi-factor index, pollution load index, and also, potential ecological risk index, the water of the region is quite polluted in case of Pb, Cd, and Ni but unpolluted for Cu and Zn. The water quality index indicates that treatment of water is required before using it for domestic purposes. The health quotient and hazard index results are less than standard value 1 suggesting that there is no noncarcinogenic risk in the area. The carcinogenic analysis shows that the lifetime incremental cancer risk mean value of Cd and Ni are fairly insignificant and Pb is more significant for children to cause health problem. The ILCR value of Cd and Ni are insignificant whereas Pb is significant to pose health risk for adults. Physicochemical parameters revealed that the water was slightly acidic and soft in nature implying to avoid the water from this area for drinking purposes. At the end, it can be concluded that this study will be useful for the residence as well as the policymaker to take the protective surveillance measures around the areas.

Constructed wetland is commonly used as a practice to reduce non-point source pollutants and as a stormwater treatment system. For many years, the evaluation of water quality assessment for the constructed wetland is using normal sampling and laboratory work. However, in line with the technology expansion, the prediction for water quality using modelling has been developed. This study focuses on the prediction of water quality parameter for constructed wetland under tropical climate using Artificial Neural Networks (ANN) and Multiple Linear Regressions Analysis (MLRA). There are five input parameters such as water quality at the inlet point, detention time, depth of water, ratio length to width, and rainfall. The output parameters consist of the water quality at the outlet point namely Biochemical Oxygen Demand (BOD5), Chemical Oxygen Demand (COD), Total Phosphorus (TP), Total Nitrogen (TN), and Total Suspended Solid (TSS). Squared correlation coefficient (R2) and root mean square error (RMSE) were applied to assess the model presentation and the result indicated that the ANN model shows excellent performance compared to MLRA. The R2 value for each output parameter is higher than 0.90 and the RMSE values were closer to zero. However, TN has shown a very good pollutant removal in constructed wetland compared to other water quality tested. Findings from this study will contribute towards the enhancement of design performance and guideline for constructed wetlands under tropical climate.