Mean Bias Error Research Articles

Assessment of reference evapotranspiration forecasting in the Mediterranean climate of Central Chile using the ASCE standardized Penman-Monteith equation, the Hargreaves-Samani equation, and weather predictions from the Global Forecast System model

Accurate estimation of crop evapotranspiration (ETc) is essential for effective agricultural water resource planning. To determine ETc it is common multiplying the reference evapotranspiration (ETref) by an appropriate crop coefficient (Kc). Forecasting ETref could be particularly beneficial for irrigation scheduling. In this work, daily and cumulative (7 days) ETref predictions were estimated with the Penman-Monteith (PM) and the Hargreaves-Samani (HS) models using environmental variables obtained from the Global Forecast System (GFS). Results were compared with ETref calculated with the ASCE (American Society of Civil Engineers) PM equation from measured daily values of solar radiation, air temperature, relative humidity and wind speed. Four weather stations located in Central Chile during 2020 to 2022 were used. The best results were obtained using the HS equation, for all weather stations, the statistical indicators for daily ETref predictions fall within the following ranges: Accuracy (defined as the percentage of daily ETref predictions with an absolute error within 1mmd−1) varies between 72.8% and 91.8%; the root mean square error (RMSE) ranges from 0.59mmd−1 to 0.98mmd−1; the normalized root mean square error (NRMSE) is between 16.1% and 24.1%; the mean bias error (MBE) is between 0.10mmd−1 and 0.36mmd−1; and the normalized mean bias error (NMBE) varies from 2.5% to 8.3%. On the other hand, the indicators for the 7-day cumulative ETref forecasts were: RMSE from 2.86mm to 5.08mm; NRMSE from 11.0% to 16.3%; MBE from 0.66mm to 2.57mm; and NMBE from 2.4% to 8.3%. These results suggest that HS ETref values predicted from GFS temperature forecasts could be effective for foreseeing near-future water demand and enhancing the accuracy of irrigation scheduling and management in Central Chile.

EVALUATION OF DAILY SATELLITE AND REANALYSIS OF RAINFALL DATA OVER SOUTH SUMATRA REGION

The limitations of good rainfall data due to constraints on direct measurements can be overcome by using satellite data or reanalysis data. The use of this data must, of course, go through a validation process first. This research aims to evaluate daily data from the Tropical Rainfall Measurement Mission (TRMM) version 3B42RT (TRMM_3B42RT) and European Center for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5 (ERA5) data against data collected from 17 rain gauges in the Sumatra region South. Evaluation is carried out based on the Correlation Coefficient (CC), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Bias Error (MBE) values between the data. In addition, the estimation capabilities of TRMM_3B42RT and ERA5 were evaluated based on the Probability of Detection (POD), False Alarm Ratio (FAR), and Critical Success Index (CSI) values. The results show a very high correlation between TRMM_3B42RT and ERA5 with rain gauge data, especially in terms of monthly data. These values (monthly data) for TRMM_3B42RT and ERA5 data are 0.3-0.9 and 0.2-0.9, respectively. The RMSE values of TRMM_3B42RT and ERA5 data in monthly analysis are 75-250mm/month and 100-180mm/month, respectively. The forecasting performance of TRMM_3B42RT and ERA5 shows good results, especially for moderate rainfall in daily data and heavy rainfall in monthly data. The results of this analysis show tha the monthly data TRMM_3B42RT is more in line with the station data and can be used in further research.

A probabilistic integration of LSTM and Gaussian process regression for uncertainty-aware reservoir water level predictions

ABSTRACT Reservoir-level forecasting, while being crucial for optimal operation, is challenged by complex physical processes and changing climate conditions. Machine learning approaches offer deterministic predictions but often neglect system physics and uncertainty. This article presents a probabilistic data-driven approach combining long short-term memory (LSTM) and Gaussian process regression (GPR) to provide both point forecasts and uncertainty estimates. The hybrid model leverages LSTM’s fitting capabilities with GPR’s robust Bayesian frameworks for uncertainty estimation in non-linear problems, offering accurate predictions without extensive high-fidelity modelling, and avoiding frequent training and parameter optimization. Evaluation with real reservoir data from India shows the model’s superiority over the vanilla LSTM for both univariate and multivariate scenarios. The proposed model achieved a Nash-Sutcliffe efficiency of 0.97 to 0.98, a mean biased error of −0.5634 to −1.0314 for 10-day forecasts, and a continuous ranked probability score of 5.80 and 1.87 for the Bhakra and Pong reservoirs, respectively.

Spectral Estimation of Chlorophyll for Non-Invasive Assessment in Apple Orchards

The main aim of our research was to develop a methodology of chlorophyll content in the leaves of apple trees non-invasive assessment in apple orchards and its adaptation to Early Gold and Golden Reinders based on spectral characteristics of chlorophyll content in the canopy. In each measurement period, 30 samples were collected from each of the two apple cultivars studied. For spectral data collection of leaf samples, an AvaSpec 2048 spectrometer was used in the wavelength range 400–1000 nm in three replicates. Principal component analysis (PCA) with varimax rotation was used to identify the wavelength with the highest factor weight to identify the chlorophyll-sensitive wavelength. The models were calibrated with 2/3 of the values in the database and validated with the remaining 1/3. The simple linear regression method generated the model for estimating chlorophyll. The coefficient of determination (R2) was used to compare the strength of the regression models, and the Root Mean Square Error (RMSE), Normalized Root Mean Square Error (NRMSE), Nash–Sutcliffe efficiency (NSE), Mean Absolute Error (MAE) and Mean Bias Error (MBE) functions were used to measure the accuracy of the estimator models. These metrics help to quickly assess how reliable and accurate a model’s predictions are. Nine indices were obtained based on the precision values, and CHLapple1 performed best (R2 = 0.633, RMSE = 298.28 µg/g, NRMSE = 9.61%, NSE = 0.60, MBE = 84.59, and MAE = 243.39).

Embedded physical constraints in machine learning to enhance vegetation phenology prediction

ABSTRACT Vegetation phenology plays a pivotal role in ecological processes on terrestrial surfaces and the interactions between the biosphere and atmospheric feedback. Current attempts to retrieve vegetation phenology have primarily depended on vegetation indices extracted from satellite remote sensing imagery. These approaches often neglect the underlying physical mechanisms associated with climatic factors, and there is a notable absence of evaluations and comparisons with field-observed phenology inventory data. To address these limitations, this paper proposes an innovative physical constraint neural networks (PCNNs) model that combines machine learning techniques with physical mechanisms to enhance the accuracy of vegetation phenology predictions. By incorporating meteorological variables into a machine learning model and by using the Moderate-Resolution Imaging Spectroradiometer (MODIS) dataset to identify the vegetation phenology of four vegetation types in North America, this study delved into the relationship between vegetation phenology and climate factors as well as its impacts on ecosystems. Our model demonstrated high accuracy compared to machine learning methods without physical mechanisms when validated by field observations from PhenoCam and the USA National Phenology Network (USA-NPN) spanning from 2001 to 2021. The results show that the overall root mean square error (RMSE) with physical constraints is reduced to 12.37 days, higher by 2.6 days than the machine learning method without physical constraints. We compared four vegetation types using different machine learning and traditional rule-based methods, deciduous vegetation (DV) exhibited the most favorable prediction results, with the RMSE and mean bias error (MBE) as low as 5.71 days and 4.06 days in PCNNs model, respectively. This was followed by evergreen needle-leaved forests and mixed forests with RMSE of 12.32 and 13.28 days, respectively. The stressed deciduous vegetation type had the worst prediction result of 19.86 days (RMSE), and the weighted index of agreement (WIA) attained a value of 0.68. These findings suggest that the model embedded with physical constraints significantly boosted prediction accuracy for four common vegetation types, particularly for DV, compared to the unconstrained ML model. It offers valuable insights into the incorporation of physical mechanisms within machine learning models. This research paves the way for substantial advancements in the field of land surface phenology, enabling more accurate and reliable predictions of vegetation phenology in various ecological and climatic contexts.

Estimation of the Optimum Tilt Angle of Solar PV Panels to Maximize Incident Solar Radiation in Libya

The most significant factor affecting the performance of a solar photovoltaic (PV) system is its tilt angle. It determines the amount of incident solar energy at the panel surface. In this paper, the optimum tilt angle of solar PV panels is estimated based on measured data recorded in twelve major cities in Libya by changing the panel’s tilt angle from 0∘ up to 90∘ in steps of 1∘ and searching for the corresponding maximum daily total solar radiation. A non-linear regression technique was applied to establish six empirical models to determine the optimum tilt angle in Libya. The accuracy of the models was evaluated using statistical criteria such as Taylor diagrams, root mean square error, mean bias error, and correlation coefficient. The results demonstrated that the monthly optimum tilt angle increased during the winter and decreased during the summer varying from 0∘ to 59∘. In addition, both third-order polynomial and Fourier models presented the best efficiency in estimating the optimum tilt angle with a correlation coefficient of 0.9943. The percent gain in average yearly solar energy received at the monthly optimum tilt angle varies from 12.43% to 17.24% for all studied sites compared to the horizontal surface.

Deep learning approaches for bias correction in WRF model outputs for enhanced solar and wind energy estimation: A case study in East and West Malaysia

Accurate estimation of wind and solar energy potentials is crucial for successfully integrating renewable energy into power grids. Traditional numerical weather prediction models, such as the Weather Research and Forecasting (WRF) model, often suffer from biases that lead to inaccurate energy forecasts. This study employs advanced deep learning (DL) techniques to correct these biases in WRF model outputs, specifically to enhance wind and solar energy estimations in East and West Malaysia. Unlike previous studies, this research integrates a diverse array of DL models: Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and Feedforward Neural Networks (FNN), to address both temporal and spatial prediction challenges. The models were trained and tested using historical weather data and ground-based measurements to improve the accuracy of wind speed and solar radiation predictions. Evaluation metrics, root mean square error (RMSE), mean bias error (MBE), and mean absolute error (MAE), demonstrate the better performance of CNN and FNN models over the sole WRF approach. The findings reveal that CNN achieved the lowest RMSE in wind speed estimation (0.91 in CEMACS and 0.97 in Kuching compared to WRF RMSEs of 1.92 and 1.39). At the same time, FNN significantly improved solar radiation prediction (RMSE of 86.86 in Kuching and 99.23 in CEMACS compared to WRF RMSEs of 154.44 and 370.66). Given the low wind speeds, the corrected data from CNN was used to estimate wind energy at 536 kWh at Kuching and 0 kWh at CEMACS. FNN-corrected data was also used to estimate solar energy at 19 kWh and 18 kWh at Kuching and CEMACS, respectively. This research not only shows the effectiveness of DL in mitigating biases in numerical weather prediction models but also contributes a novel methodology for reliable renewable energy assessments.

Atmospheric CO2 column concentration over Iran: Emissions, GOSAT satellite observations, and WRF-GHG model simulations

Regarding global warming and climate change, carbon dioxide (CO2) is one of the most important greenhouse gases. Simulating CO2 gas at hourly/weekly time intervals and desired vertical resolution is challenging due to the coarse horizontal resolution of global models. In this study, both column-averaged CO2 mixing ratio (XCO2) and vertical cross sections of CO2 mixing ratio were simulated by the Weather Research and Forecast Green House gas (WRF-GHG) model at spatial resolutions of 30 and 10 km for the Middle East region as the first domain, and Iran as the second domain. Simulations consider the primary CO2 sources (anthropogenic, biogenic, fire, and oceanic) and the Copernicus Atmosphere Monitoring Service (CAMS) dataset. XCO2 retrieved from GOSAT satellite observations was employed to evaluate the simulation results of the column-averaged CO2 concentrations in February and August 2010. The evaluations showed that the spatiotemporal variability of meteorological variables was well simulated by WRF-GHG with correlation coefficients r of 0.86–0.92, 0.67–0.75, and 0.76–0.82 for temperature, wind, and relative humidity, respectively, during February and August 2010. The evaluations also indicated that the WRF-GHG simulations outperformed the global model TM3, with mean bias error values of − 0.79 and 0.45 PPMV for WRF-GHG in February and August, respectively. The percentage contribution of net CO2 emissions from human activities in Iran was calculated as (38.33 % and 23.70 %) of the total emissions, respectively, with values of 4.4 and 0.85 kg/km2 in each month. The net emissions contributions of biogenic, fire, and oceanic sources were evaluated in February and August, with biogenic emissions contributing (31.901 % and 27.66 %), biogenic absorption contributing (24.07 % and 46.63 %), fire emissions contributing (5.7 % and 2.064 %), and oceanic emissions contributing (3.23 × 10−6 % and 2.23 × 10−6 %). Large-scale circulations and biogenic activity are responsible for the major features of the spatial and seasonal distribution of CO2 in the area. In February, column mixing ratios are higher in more northern latitudes; in August, they are higher to the south. Furthermore, the simulated vertical cross sections show high CO2 mixing ratios in the mid-lower troposphere and northerly/northeasterly advection in February; the vertical profile is inverted in August with high concentrations in the lower stratosphere associated with southwesterly advection. However, the interaction between the synoptic and sub-synoptic features with the topography determines the precise dispersion and distribution of CO2. Despite the negligible emissions in central and eastern Iran, these factors play an important role in the observed concentrations in February and August. In August, the areas between the 120-day low-level monsoon flow of Sistan in eastern Iran, and the westerlies over western/southwestern Iran and the Zagros Mountain range, interact with the heat-low in the Iranian plains and with the Arabian subtropical high. In February, the dominant winds in western Asia were west winds, with the main source of pollution coming from the northeastern regions flowing towards central and eastern Iran. The equatorward displacement of the polar jet stream and the passage of low-pressure systems from the west in winter cause a temporary reduction in CO2 concentrations.

A hybrid mutational Northern Goshawk and elite opposition learning artificial rabbits optimizer for PEMFC parameter estimation

For the purpose of simulating, controlling, evaluating, managing and optimizing PEMFCs it is necessary to develop accurate mathematical models. The present study develops a mathematical model which uses empirical or semi-empirical equations to estimate unknown model parameters through optimization techniques. This thesis calculates, analyzes and discusses the sum of squares error (SSE) between measured and estimated current and voltage values using parameters derived from multiple optimization techniques for six commercially available PEMFCs: BCS 500 W-PEMFC, 500 W SR-12 PEMFC, Nedstack PS6 PEMFC, H-12 PEMFC, HORIZON 500 W PEMFC and a 250 W-stack PEMFC. To minimize the SSE between measured and estimated current values under these new models we employ an advanced version of Artificial Rabbits Optimization called Mutational Northern goshawk and Elite opposition learning-based Artificial Rabbits Optimizer (MNEARO). Additionally SSE, Absolute Error (AE), and Mean Bias Error (MBE) are computed for different recent methods according to literature on voltage measurement. Other optimization algorithms including ARO, TLBO, DE and SSA are used for comparative analysis purposes. On top of that MNEARO outperforms others in terms of both computational cost as well as solution quality while experiments carried out using benchmark problems indicate its superiority over other meta-heuristics approaches.

Development and evaluation of site-specific evapotranspiration models in Malawi through a comparative analysis of existing models

Locally based information on reference evapotranspiration pertinent to efficient irrigation water management is scarce in developing countries. This compromises the accuracy in determining irrigation crop water requirements since most existing reference evapotranspiration models are empirically based and less accurate unless calibrated to local conditions. The FAO Penman-Monteith equation has been proven to estimate reference evapotranspiration for different environments worldwide. However, its intensive data input requirements impede utilization of the model in developing countries due to inadequate climate data collection and management capacities. Therefore, a need existed to develop site-specific models that are less data intensive. A study was conducted aimed at developing site-specific evapotranspiration models using comparative analysis of the Kharrufa, Linacre, Hargreaves Original and Hargreaves Modified empirical models during the wet and dry season. Performance of each model was compared with the FAO Penman-Monteith model, regarded as the standard model. The models whose Root Mean Square Error (RMSE), Mean Bias Error (MBE) and coefficient of determination (R2) were satisfactory were selected. The selected models were developed statistically through regression analysis. Performance of the Kharrufa model was satisfactory comparatively as observed from its RMSEs of 1.02 mm/day and MBE -0.34 to 0.8 for the dry season. However, the model less accurately estimated reference evapotranspiration during the wet season (RMSE 2.18 mm/day and MBE 1.03 to 2.13). The study recommends the use of the Kharrufa model in the study locations in dry seasons while utilization in wet season may require further studies to ascertain the model's useability and reliability.

Predicting Green Water Footprint of Sugarcane Crop Using Multi-Source Data-Based and Hybrid Machine Learning Algorithms in White Nile State, Sudan

Water scarcity and climate change present substantial obstacles for Sudan, resulting in extensive migration. This study seeks to evaluate the effectiveness of machine learning models in forecasting the green water footprint (GWFP) of sugarcane in the context of climate change. By analyzing various input variables such as climatic conditions, agricultural data, and remote sensing metrics, the research investigates their effects on the sugarcane cultivation period from 2001 to 2020. A total of seven models, including random forest (RF), extreme gradient boosting (XGBoost), and support vector regressor (SVR), in addition to hybrid combinations like RF-XGB, RF-SVR, XGB-SVR, and RF-XGB-SVR, were applied across five scenarios (Sc) which includes different combinations of variables used in the study. The most significant mean bias error (MBE) was recorded in RF with Sc3 (remote sensing parameters), at 5.14 m3 ton−1, followed closely by RF-SVR at 5.05 m3 ton−1, while the minimum MBE was 0.03 m3 ton−1 in RF-SVR with Sc1 (all parameters). SVR exhibited the highest R2 values throughout all scenarios. Notably, the R2 values for dual hybrid models surpassed those of triple hybrid models. The highest Nash–Sutcliffe efficiency (NSE) value of 0.98 was noted in Sc2 (climatic parameters) and XGB-SVR, whereas the lowest NSE of 0.09 was linked to SVR in Sc3. The root mean square error (RMSE) varied across different ML models and scenarios, with Sc3 displaying the weakest performance regarding remote sensing parameters (EVI, NDVI, SAVI, and NDWI). Effective precipitation exerted the most considerable influence on GWFP, contributing 81.67%, followed by relative humidity (RH) at 7.5% and Tmax at 5.24%. The study concludes that individual models were as proficient as, or occasionally surpassed, double and triple hybrid models in predicting GWFP for sugarcane. Moreover, remote sensing indices demonstrated minimal positive influence on GWFP prediction, with Sc3 producing the lowest statistical outcomes across all models. Consequently, the study advocates for the use of hybrid models to mitigate the error term in the prediction of sugarcane GWFP.

Validation of 3D thermal simulations of the Double C Block, a novel composite masonry unit, using in-situ U-value measurements

Energy efficient building envelopes, particularly innovative prefabricated building components, can significantly contribute to lower the building industry energy-related emissions. In this research the composite masonry unit Double C Block (DCB) is quantitatively tested by means of three-dimensional thermal simulation validated by in-situ U-value measurements carried out in both summer and winter conditions.The simulations were enhanced by the experimental measurement of the dry thermo-physical material properties (specific heat capacity, thermal conductivity, and density) in laboratory conditions for both concrete and polyurethane. The samples were extracted from real-scale DCB prototypes. Furthermore, site-specific boundary conditions of Malta — representative a case study of a Mediterranean climate − were utilised in the simulations. Both laboratory and in-situ assessments of thermal transmittance, or U-value, had their uncertainty specified as part of the calculations.The validation exercise was performed by means of hourly recorded surface temperature and the selected indices were the root mean square error and mean absolute error. Results showed that these indices were within the recommended accuracy thresholds during both winter and summer assessments. For the hourly-based heat flux measurements, the mean bias error and coefficient of variation of the root mean square error were found lower than values reported in literature, thus supporting the robustness of the overall simulation outputs. Hence the 3D CVM simulation was experimentally validated by in-situ assessments.The convergence value of the experimental U-value provided respectively 1.51 ± 0.20 W/(m2K) (winter) and 1.44 ± 0.19 W/(m2K) (summer). Then, the validated 3D CVM model was adapted to calculate the theoretical U-value of DCB, using monthly values of recorded surface temperatures obtained by the test cells (in both seasons). The predicted thermal transmittance was of 1.47 W/(m2K), in both conditions, and this almost matches the above-mentioned experimental assessments and thresholds established in previous works.

Improving PM10 sensor accuracy in urban areas through calibration in Timișoara

Low-cost particulate matter sensors (LCS) are vital for improving the spatial and temporal resolution of air quality data, supplementing sparsely placed official monitoring stations. Despite their benefits, LCS readings can be biased due to the physical properties of aerosol particles and device limitations. An optimization model is essential to enhance LCS data accuracy. This paper presents a calibration study of the LCS network of Timișoara, Romania. The calibration began by selecting LCS devices near National Air Quality Monitoring Network (NAQMN) stations and developing parametric models, choosing the best for broader application. Plantower, Sensirion, and Honeywell sensors showed comparable accuracy. Calibration involved clusters within a 750 m radius around NAQMN stations. Models incorporating RH corrections and multiple linear regression (MLR) were fitted. The best model was validated against data from unseen sensors, leading to mean bias errors (MBE) within 9-17% and RMSEs of 33-35%, within sensor uncertainty margins. Applied to the city-wide LCS network, the model identified several stations regularly exceeding the EU daily PM10 threshold, unnoticed by NAQMN stations due to their limited coverage. The study highlights the necessity of granular monitoring to accurately capture urban air quality variations.

A minute-resolution downscaling algorithm for high-resolution global irradiance time series

Numerous studies have demonstrated the significant impact of the resolution of solar irradiation data on the outcomes of hourly production models. Accurate integration of photovoltaic (PV) systems sometimes demands a high-resolution global horizontal irradiance (GHI) time series to capture the rapid fluctuations in PV power output induced by swift irradiance changes. Most of the available databases provide data at hourly resolution, leading to a lack of accuracy in PV simulations. Those existing hourly averages of global horizontal irradiance in open sources fail to represent this volatility adequately, especially when PV systems are coupled with fast ramp rate technologies. In the present work, an easy-to-use algorithm is implemented to synthesize high-resolution GHI time series from hourly averaged and clear sky irradiance datasets. By employing Linear interpolation, a technique that helps to achieve the desired time resolution and afterward computing critical factors, the algorithm identifies periods characterized by short-term weather phenomena, thus creating a high-resolution time series that accurately represents these dynamics. Avoiding the probabilistic components and machine learning techniques conserves computational power and reduces calculation time, but this comes at the cost of reduced fidelity in reproducing the results. Improving accuracy in PV simulations is not always directly related to reproducing real phenomena, but enhancing the amount of information contained in the data is sufficient. This study’s approach enhances user-friendliness and facilitates seamless integration into existing energy modeling frameworks, aiming for representation with sub-hourly time steps. The algorithm’s effectiveness is demonstrated by applying the model to hourly averaged data to revert them to a one-minute time step, and finally comparing the synthetically produced one-minute GHI data to the original measured data. The comparative analysis between synthesized and measured data demonstrated a strong agreement, with normalized mean bias error (MBE) values ranging between 1.8% and 9.6% and normalized root mean square error (NRMSE) values between 2.7% and 16.1%, depending on weather conditions. Additionally, the coefficient of determination (R2) consistently remained above 0.64. Successful algorithm validation makes our algorithm suitable for use in meteorological datasets and locations, with similar climatic characteristics.

New multivariate models for the estimation of earth’s albedo and its variation with meteorological parameters over Kano, Nigeria

The study of what happens to sunlight as it goes through the atmosphere is vital to many fields of science because over time, reflection, scattering, and absorption in the atmosphere have changed the amount of solar radiation that reaches the Earth's surface. This study examined the variation in albedo over Kano, situated in the Sahelian climatic zone of Nigeria. The study's data, which covered the years 1984–2022, totaled thirty-nine, was obtained from the National Aeronautics and Space Administration (NASA). Utilizing monthly average daily measured meteorological data on global solar radiation, wind speed, mean temperature, surface pressure, and relative humidity, eleven models of three categories based on two, three, and four variable regression models were developed. Five validation indicators were used to verify the regression models: the t-test, coefficient of determination (R2), mean percentage error (MPE), root mean square error (RMSE), and mean bias error (MBE). The developed models that were considered most appropriate for estimating surface albedo were ranked; the three-variable regression model ALB7, which included the wind speed, relative humidity, and mean temperature, outperformed the previously employed generalized method from literature in estimating surface albedo over Kano. The variation of measured albedo with meteorological parameters depicts a direct relationship for clearness index and wind speed; an inverse relationship was observed for surface pressure. Mean temperature and surface pressure exhibit an inverse relationship during the wet season and vice versa for the dry season.

A Global Evaluation of Radar‐Derived Digital Elevation Models: SRTM, NASADEM, and GLO‐30

AbstractThis study evaluates global radar‐derived digital elevation models (DEMs), namely the Shuttle Radar Topography Mission (SRTM), NASADEM and GLO‐30 DEMs. We evaluate their accuracy over bare‐earth terrain and characterize elevation biases induced by forests using global Lidar measurements from the Ice, Cloud, and Land Elevation Satellite (ICESat)'s Geoscience Laser Altimeter System (GLAS), the Global Ecosystem Dynamics Investigation (GEDI) and the ICESat‐2 Advanced Topographic Laser Altimeter System (ATLAS) instruments collected on locally flat terrain. Our analysis is based on error statistics calculated for each DEM tile, which are then summarized as global error percentiles, providing a regional characterization of DEM quality. We find NASADEM to be a significant improvement upon the SRTM V3. Over bare ground areas, the mean elevation bias and root mean square error (RMSE) improved from 0.68 to 2.50 m respectively to 0.00 and 1.5 m as compared to ICESat/GLAS. GLO‐30 is more accurate with bare ground elevation bias and RMSE were below 0.05 and 0.55 m. Similar improvements were observed when compared to GEDI and ICESat‐2 measurements. The DEM biases associated with the presence of vegetation vary linearly with canopy height, and more closely follow the percentile of Lidar Relative Height (RH50). Other factors such as canopy density, radar frequency and Lidar technology also contribute to observed elevation biases. This global analysis highlights the potential of various technologies for mapping of Earth's topography, and the need for more advanced remote sensing observations that can resolve vegetation structure and sub‐canopy ground elevation.

Evaluation of WRF model performance with different microphysics schemes for extreme rainfall prediction in Lagos, Nigeria: Implications for urban flood risk management

In Nigeria, particularly in urban areas like Lagos, flooding is a frequent natural hazard. In 2011, Lagos experienced one of its worst floods resulting in significant economic losses and displacement of people. In recent years, Lagos has continued to grapple with flooding challenges, with an equally significant flood episode occurring in 2021. This study focuses on predicting floods and forecasting extremely heavy rainfall in West Africa's equatorial zone using the Weather Research and Forecasting (WRF) model, particularly in humid tropical environments like Lagos. The study discusses the need to review existing flood models and adopt alternative flood models to address the limitations of flood prediction. As potential causes of these rainfall episodes, the interconnections between synoptic systems such as tropical easterly waves, southwesterly winds related to the West African Monsoon, and local topography and oceanic conditions are investigated. Three key metrics: root mean square error (RMSE), mean bias (MB), and mean absolute error (MAE) are used to assess the effectiveness of the computational model. Results indicate that the WRF model, specifically when using the Thompson parameterisation, can estimate the amount of rainfall accumulated over a 24-h period. This suggests that the model can predict the size of daily precipitation during intense rain events. The Thompson scheme shows better performance compared to the WSM6 scheme while evaluating the stations and episodes. During the rainfall episode on July 10, 2011, Thompson's spatial rainfall predictions were better than WSM6, resulting in a decrease in root mean square error (RMSE) of 15–31% depending on the area. Simulations of the July 2021 episode also show better performance, with a decrease in RMSE of 11–25% when comparing Thompson to WSM6 scheme. The Thompson scheme’s improved ability is directly linked to a more accurate depiction of the microphysical mechanisms that control the rainfall formation. By explicitly simulating the dynamics of ice crystals and graupel, it is possible to accurately replicate the processes of orographic lifting and moist convection that are responsible for driving intense monsoon precipitation. In addition, Thompson scheme shows a reduced degree of systemic bias in comparison to WSM6, with a 75% reduction in the average bias in rainfall accumulation over the research area. The combination of the advanced Thompson microphysics method and WRF's atmospheric dynamics shows a high level of accuracy in predicting intense rainfall and the risk of floods in this area with diverse tropical topography.

An attention fused sequence -to-sequence convolutional neural network for accurate solar irradiance forecasting and prediction using sky images

The challenge of accurately forecasting ultra-short-term solar irradiance for photovoltaic systems is complicated by rapidly changing weather, and while ground-based sky images offer potential improvements, effectively extracting spatiotemporal data from these images remains a significant hurdle for current computer vision models. A new hybrid model, "An Attention Fused Sequence-to-Sequence Convolutional Neural Network," is being developed to address this challenge. The model predicts intra-hour GHI, DNI, and DHI with a 10-min lead time by combining a Convolutional Neural Network (for spatial feature extraction from sky images), an attention mechanism (to focus on relevant regions), and a sequence-to-sequence model (for temporal feature extraction from time-series data). The proposed Model is trained using the NREL Solar Radiation Research Laboratory Dataset while evaluating the model with the Mean Bias Error, Mean Absolute Error, Root Mean Squared Error, R Squared, and forecasting skill score. The 10-min and sequence length 2 interval is considered to be the best performing across most of the evaluation metrics with an MBE value is 2.321 W/m2, MAE value of 39.490 W/m2, RMSE value of 62.086 W/m2, R2 value is 0.909 W/m2, FSS value of 23.589 W/m2 and an MBE of 4.876 W/m2, MAE of 56.887 W/m2, RMSE of 85.346 W/m2, R2 of 0.834 and FSS of 24.881 W/m2 respectively. Furthermore, the sensitivity analysis reveals that the proposed model's performance is influenced by both the sequence length and the lead time. The proposed framework outperforms other techniques for ultra-short-term PV generation forecasting, demonstrating its potential for practical deployment in PV systems to improve grid reliability and energy management.

Concurrent Validity and Interunit Reliability of 25-Hz GNSS Units for Profiling Sprinting Performance.

Dennison, L, Duthie, GM, Ehrmann, F, and Psarakis, MA. Concurrent validity and interunit reliability of 25-Hz GNSS units for profiling sprinting performance. J Strength Cond Res XX(X): 000-000, 2024-Profiling sprint performance by assessing within-race velocity or time is crucial for understanding an athlete's capabilities and identifying areas for improvement. While traditional gold standard systems provide valid and reliable measurements, they are often costly, laboratory-based, or impractical for field-based settings. New Global Navigation Satellite System (GNSS) units (25 Hz) with higher sampling rates may address some of these limitations. The purpose of the project was to evaluate the concurrent validity and interunit reliability of 25-Hz GNSS units by examining their agreement with laser devices and timing gates for velocity measurements and sprint/interval times. Concurrent validity was assessed during a real track and field training session. Thirty subjects each completed 3 to 6 maximal 40-meter sprints, for a total of 106 sprints, while being assessed simultaneously through all systems. Interunit reliability was assessed by placing 3 GNSS units on a motor vehicle and completing 60 accelerations from 0 to 60 m. Low mean bias (<1%) and typical error less than <2% for all measurements demonstrate excellent agreement between GNSS and criterion devices. The units demonstrated good reliability for the 0-10 m interval time (intraclass correlation coefficient [ICC] = 0.86), excellent reliability for all remaining 10 m to 60 m intervals (ICC = 0.91-0.99), sprint times 0-60 m (ICC = 0.97), and maximal sprint velocity (ICC = 1.0). These GNSS units provide an efficient and feasible alternative to traditional measurement devices. For coaches, this technology offers a scalable method to concurrently assess the sprint performances of multiple athletes during training and competitions, enabling evidence-based decisions to guide athletic development.

Evaluating a multi-step collocation approach for an ensemble climatological dataset of actual evapotranspiration over Italy

Accurate estimations of actual evapotranspiration (ET) are key in a variety of water balance applications, but divergent results can be obtained due to the large range of available methodologies. The use of an ensemble approach is a suitable alternative, as it summarizes multiple sources in an optimized strategy. In this study, an expert-based multi-step collocation (MC) approach is tested to merge six ET datasets, with the aim of reconstructing a spatiotemporally-consistent monthly dataset for Italy in the climatological period 1991–2020. The merged products are: three water balance datasets (BIG BANG, LSA SAF, and LISFLOOD), two residual surface energy balance model datasets (SSEBop, and ALEXI), and the MODIS standard ET product. The merged product is analyzed for spatio-temporal consistency and evaluated using flux observations from 11 sites. On average, the merged product has higher accuracy (mean absolute difference = 0.47 ± 0.17 mm/d, relative difference = 27.9 ± 7.5 %) than any single base dataset, and it is characterized by limited bias (mean bias error = -0.17 ± 0.26 mm/d), high correlation (r = 0.83 ± 0.10), and more uniform performance across sites. The merged ET dataset is accompanied by an estimation of the ensemble spread, which highlights large differences in ET estimates in some areas and periods characterized by severe water stress, such as in southern Italy during the summer. This large spread seems to be mostly driven by systematic differences among datasets, which affect the estimation of the reference climatology, suggesting how inter-model spread can have a defining role in further improving the merging strategies.