scholarly journals High Spatial Resolution Simulation of Sunshine Duration over the Complex Terrain of Ghana

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1743 ◽  
Author(s):  
Mustapha Adamu ◽  
Xinfa Qiu ◽  
Guoping Shi ◽  
Isaac Kwesi Nooni ◽  
Dandan Wang ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Sunshine Duration ◽  
Model Performance ◽  
Temporal Distribution ◽  
Absolute Error ◽  
In Situ Observation ◽  
Observation Data ◽  
Atmospheric Conditions ◽  
Proposed Model ◽  
The Mean

In this paper, we propose a remote sensing model based on a 1 × 1 km spatial resolution to estimate the spatio-temporal distribution of sunshine percentage (SSP) and sunshine duration (SD), taking into account terrain features and atmospheric factors. To account for the influence of topography and atmospheric conditions in the model, a digital elevation model (DEM) and cloud products from the moderate-resolution imaging spectroradiometer (MODIS) for 2010 were incorporated into the model and subsequently validated against in situ observation data. The annual and monthly average daily total SSP and SD have been estimated based on the proposed model. The error analysis results indicate that the proposed modelled SD is in good agreement with ground-based observations. The model performance is evaluated against two classical interpolation techniques (kriging and inverse distance weighting (IDW)) based on the mean absolute error (MAE), the mean relative error (MRE) and the root-mean-square error (RMSE). The results reveal that the SD obtained from the proposed model performs better than those obtained from the two classical interpolators. This results indicate that the proposed model can reliably reflect the contribution of terrain and cloud cover in SD estimation in Ghana, and the model performance is expected to perform well in similar environmental conditions.

scholarly journals A Comparison of Models for Estimating Solar Radiation from Sunshine Duration in Croatia

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Tihomir Betti ◽  
Ivana Zulim ◽  
Slavica Brkić ◽  
Blanka Tuka
Keyword(s):  
Mean Absolute Error ◽  
Sunshine Duration ◽  
Model Performance ◽  
Absolute Error ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
Mean Square ◽  
Using Data ◽  
Statistical Indicators

The performance of seventeen sunshine-duration-based models has been assessed using data from seven meteorological stations in Croatia. Conventional statistical indicators are used as numerical indicators of the model performance: mean absolute percentage error (MAPE), mean bias error (MBE), mean absolute error (MAE), and root-mean-square error (RMSE). The ranking of the models was done using the combination of all these parameters, all having equal weights. The Rietveld model was found to perform the best overall, followed by Soler and Dogniaux-Lemoine monthly dependent models. For three best-performing models, new adjusted coefficients are calculated, and they are validated using separate dataset. Only the Dogniaux-Lemoine model performed better with adjusted coefficients, but across all analysed locations, the adjusted models showed improvement in reduced maximum percentage error.

scholarly journals A Continuous Blood Pressure Estimation Method Using Photoplethysmography by GRNN-Based Model

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7207
Author(s):  
Zheming Li ◽  
Wei He
Keyword(s):  
Blood Pressure ◽  
Estimation Method ◽  
Absolute Error ◽  
Disease Diagnosis ◽  
Generalized Regression Neural Network ◽  
British Hypertension Society ◽  
Proposed Model ◽  
The Mean ◽  
Blood Pressure Estimation ◽  
The Time Domain

Compared with diastolic blood pressure (DBP) and systolic blood pressure (SBP), the blood pressure (BP) waveform contains richer physiological information that can be used for disease diagnosis. However, most models based on photoplethysmogram (PPG) signals can only estimate SBP and DBP and are susceptible to noise signals. We focus on estimating the BP waveform rather than discrete BP values. We propose a model based on a generalized regression neural network to estimate the BP waveform, SBP and DBP. This model takes the raw PPG signal as input and BP waveform as output. The SBP and DBP are extracted from the estimated BP waveform. In addition, the model contains encoders and decoders, and their role is to be responsible for the conversion between the time domain and frequency domain of the waveform. The prediction results of our model show that the mean absolute error is 3.96 ± 5.36 mmHg for SBP and 2.39 ± 3.28 mmHg for DBP, the root mean square error is 5.54 for SBP and 3.45 for DBP. These results fulfill the Association for the Advancement of Medical Instrumentation (AAMI) standard and obtain grade A according to the British Hypertension Society (BHS) standard. The results show that the proposed model can effectively estimate the BP waveform only using the raw PPG signal.

It's impolite to zoom in on global hydrological models

2020 ◽  
Author(s):  
Jerom Aerts ◽  
Albrecht Weerts ◽  
Willem van Verseveld ◽  
Niels Drost ◽  
Rolf Hut ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Large Scale ◽  
Transfer Functions ◽  
Model Performance ◽  
Observation Data ◽  
Hydrological Models ◽  
Climate Data ◽  
Local Observation ◽  
Fair Principles ◽  
Computational Resources

<p>Large scale or global hydrological models (GHMs) show promise in enabling us to accurately predict floods, droughts, navigation hazards, reservoir operations, and many more water related issues. As opposed to regional hydrological models that have many parameters that need to be calibrated or estimated using local observation data (Sood and Smakhtin 2015). GHMs are able to simulate regions that lack observation data, whilst applying a uniform approach for parameter estimation (Döll, Kaspar, and Lehner 2003; Widén‐Nilsson et al. 2009). Up until recently the GHMs used coarse modelling grids of around 0.5 to 1 degree spatial resolution. However, due to advances in satellite data, climate data, and computational resources, GHMs are modelling on higher resolutions (up to 200 meters) that raise questions about how these models can be adjusted in order to take advantage of the finer modelling grid.</p><p>In this study, we carry out an extensive assessment of how changes in spatial resolution affect the simulations of the Wflow SBM model for 8 basins in the Continental United States. This is done by comparing the model states and fluxes at three spatial resolutions, namely 3 km, 1km, and 200m. A hypothesis driven approach is used to investigate why changes in states and fluxes are taking place at different spatial resolutions and how they relate to model performance. The latter is determined by validating river discharge, snow extent, soil moisture, and actual evaporation. In addition, we make use of two sets of parameters that rely on different pedo-transfer functions. Further investigating the role parameterization in conjunction with changes in spatial resolution.</p><p>By carrying out this study within the eWaterCycle II framework we showcase our ability to handle large datasets (forcing and validation) whilst always complying to the FAIR principles. Furthermore, this study is setup in such that it is scalable in terms of case study areas and hydrological models.</p>

scholarly journals Simple and Low-Cost Procedure for Monthly and Yearly Streamflow Forecasts during the Current Hydrological Year

Water ◽  
2018 ◽  
Vol 10 (8) ◽  
pp. 1038 ◽  
Author(s):  
Fernando Delgado-Ramos ◽  
Carmen Hervás-Gámez
Keyword(s):  
Low Cost ◽  
Model Performance ◽  
Absolute Error ◽  
Integrated Water Resources Management ◽  
Coefficient Of Determination ◽  
Water Volume ◽  
Cumulative Probability ◽  
Water Users ◽  
The Mean ◽  
The Right

Accurately forecasting streamflow values is essential to achieve an efficient, integrated water resources management strategy and to provide consistent support to water decision-makers. We present a simple, low-cost, and robust approach for forecasting monthly and yearly streamflows during the current hydrological year, which is applicable to headwater catchments. The procedure innovatively combines the use of well-known regression analysis techniques, the two-parameter Gamma continuous cumulative probability distribution function and the Monte Carlo method. Several model performance statistics metrics (including the Coefficient of Determination R2; the Root-Mean-Square Error RMSE; the Mean Absolute Error MAE; the Index of Agreement IOA; the Mean Absolute Percent Error MAPE; the Coefficient of Nash-Sutcliffe Efficiency NSE; and the Inclusion Coefficient IC) were used and the results showed good levels of accuracy (improving as the number of observed months increases). The model forecast outputs are the mean monthly and yearly streamflows along with the 10th and 90th percentiles. The methodology has been successfully applied to two headwater reservoirs within the Guadalquivir River Basin in southern Spain, achieving an accuracy of 92% and 80% in March 2017. These risk-based predictions are of great value, especially before the intensive irrigation campaign starts in the middle of the hydrological year, when Water Authorities have to ensure that the right decision is made on how to best allocate the available water volume between the different water users and environmental needs.

scholarly journals The Ångström–Prescott Regression Coefficients for Six Climatic Zones in South Africa

Energies ◽  
2020 ◽  
Vol 13 (20) ◽  
pp. 5418
Author(s):  
Brighton Mabasa ◽  
Meena D. Lysko ◽  
Henerica Tazvinga ◽  
Sophie T. Mulaudzi ◽  
Nosipho Zwane ◽  
...  
Keyword(s):  
South Africa ◽  
Sunshine Duration ◽  
Absolute Error ◽  
Regression Coefficients ◽  
Solar Irradiation ◽  
Mean Bias Error ◽  
Bias Error ◽  
Observation Data ◽  
Statistical Validation ◽  
Worst Case

The South African Weather Service (SAWS) manages an in situ solar irradiance radiometric network of 13 stations and a very dense sunshine recording network, located in all six macroclimate zones of South Africa. A sparsely distributed radiometric network over a landscape with dynamic climate and weather shifts is inadequate for solar energy studies and applications. Therefore, there is a need to develop mathematical models to estimate solar irradiation for a multitude of diverse climates. In this study, the annual regression coefficients, a and b, of the Ångström–Prescott (AP) model, which can be used to estimate global horizontal irradiance (GHI) from observed sunshine hours, were calibrated and validated with observed station data. The AP regression coefficients were calibrated and validated for each of the six macroclimate zones of South Africa using the observation data that span 2013 to 2019. The predictive effectiveness of the calibrated AP model coefficients was evaluated by comparing estimated and observed daily GHI. The maximum annual relative Mean Bias Error (rMBE) was 0.371%, relative Mean Absolute Error (rMAE) was 0.745%, relative Root Mean Square Error (rRMSE) was 0.910%, and the worst-case correlation coefficient (R2) was 0.910. The statistical validation metrics results show that there is a strong correlation and linear relation between observed and estimated GHI values. The AP model coefficients calculated in this study can be used with quantitative confidence in estimating daily GHI data at locations in South Africa where daily observation sunshine duration data are available.

scholarly journals Air quality over Europe: modeling gaseous and particulate pollutants and the effect of precursor emissions

2013 ◽  
Vol 13 (3) ◽  
pp. 6681-6705
Author(s):  
E. Tagaris ◽  
R. E. P. Sotiropoulou ◽  
N. Gounaris ◽  
S. Andronopoulos ◽  
D. Vlachogiannis
Keyword(s):  
Air Quality ◽  
Western Europe ◽  
Model Performance ◽  
Absolute Error ◽  
Low Concentrations ◽  
Particulate Pollutants ◽  
The Mean ◽  
The Difference ◽  
Eastern And Western Europe ◽  
Pm2.5 Emissions

Abstract. Air quality over Europe using Models-3 (i.e. CMAQ, MM5, SMOKE) modeling system is performed for winter (i.e. January, 2006) and summer (i.e. July, 2006) months with the 2006 TNO gridded anthropogenic emissions database. Higher ozone concentrations are illustrated in southern Europe while higher NO2 concentrations are simulated over western Europe. Elevated SO2 concentrations are simulated over eastern Europe while elevated PM2.5 levels are simulated over eastern and western Europe. Results suggest that NO2 and PM2.5 are underpredicted, SO2 is overpredicted while Max8hrO3 is overpredicted for low concentrations and is underpredicted for the higher ones. Speciated PM2.5 components suggest that NO3 is dominant during winter in western Europe and in a few eastern countries due to the high NO2 concentrations. During summer NO3 is dominant only in regions with elevated NH3 emissions. For the rest of the domain SO4 is dominant. Low OC concentrations are simulated mainly due to the uncertain representation of SOA formation. The difference between observed and predicted concentrations for each country is assessed for the gaseous and particulate pollutants. The simultaneous precursor emissions change applying scaling factors on NOx, SO2 and PM2.5 emissions based on the observed/predicted ratio for each country seems to statistically enhance model performance (in gaseous pollutants the improvement in root mean square is up to 5.6 ppbV, in the index of agreement is up to 0.3 and in the mean absolute error is up to 4.2 ppbV while the related values in PM2.5 are 4.5 μg m−3, 0.2 and 3.5 μg m−3, respectively).

Essai d'application de modèles météorologiques au calcul du moment d'apparition de deux phases phénologiques | Application of Meteorological Models for the Calculation of the Beginning of Two Phenological Phases

2000 ◽  
Vol 151 (10) ◽  
pp. 385-397
Author(s):  
Bernard Primault
Keyword(s):  
Sunshine Duration ◽  
Fruit Trees ◽  
Swiss Alps ◽  
Winter Solstice ◽  
Phenological Data ◽  
Thermal Thresholds ◽  
The Mean ◽  
Two Parameters ◽  
Meteorological Models ◽  
Phenological Phases

Many years ago, a model was elaborated to calculate the«beginning of the vegetation's period», based on temperatures only (7 days with +5 °C temperature or more). The results were correlated with phenological data: the beginning of shoots with regard to spruce and larch. The results were not satisfying, therefore, the value of the two parameters of the first model were modified without changing the second one. The result, however, was again not satisfying. Research then focused on the influence of cumulated temperatures over thermal thresholds. Nevertheless, the results were still not satisfying. The blossoming of fruit trees is influenced by the mean temperature of a given period before the winter solstice. Based on this knowledge, the study evaluated whether forest trees could also be influenced by temperature or sunshine duration of a given period in the rear autumn. The investigation was carried through from the first of January on as well as from the date of snow melt of the following year. In agricultural meteorology, the temperature sums are often interrelated with the sunshine duration, precipitation or both. However,the results were disappointing. All these calculations were made for three stations situated between 570 and 1560 m above sea-level. This allowed to draw curves of variation of the two first parameters (number of days and temperature) separately for each species observed. It was finally possible to specify the thus determined curves with data of three other stations situated between the first ones. This allows to calculate the flushing of the two tree species, if direct phenological observation is lacking. This method, however, is only applicable for the northern part of the Swiss Alps.

Simple explicit model of liquid mean flow in region above axial impeller

1985 ◽  
Vol 50 (11) ◽  
pp. 2396-2410
Author(s):  
Miloslav Hošťálek ◽  
Ivan Fořt
Keyword(s):  
Vortex Flow ◽  
Flow Model ◽  
Quantitative Agreement ◽  
Mean Flow ◽  
Flat Bottom ◽  
Proposed Model ◽  
Minimum Number ◽  
The Mean ◽  
Model Equations ◽  
Radial Circulation

The study describes a method of modelling axial-radial circulation in a tank with an axial impeller and radial baffles. The proposed model is based on the analytical solution of the equation for vortex transport in the mean flow of turbulent liquid. The obtained vortex flow model is tested by the results of experiments carried out in a tank of diameter 1 m and with the bottom in the shape of truncated cone as well as by the data published for the vessel of diameter 0.29 m with flat bottom. Though the model equations are expressed in a simple form, good qualitative and even quantitative agreement of the model with reality is stated. Apart from its simplicity, the model has other advantages: minimum number of experimental data necessary for the completion of boundary conditions and integral nature of these data.

scholarly journals Technical note: Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores

2019 ◽  
Vol 23 (10) ◽  
pp. 4323-4331 ◽  
Author(s):  
Wouter J. M. Knoben ◽  
Jim E. Freer ◽  
Ross A. Woods
Keyword(s):  
Time Series ◽  
Coefficient Of Variation ◽  
Ad Hoc ◽  
Model Performance ◽  
Technical Note ◽  
Mean Flow ◽  
Model Adequacy ◽  
Robust Model ◽  
The Mean ◽  
Adequacy Assessment

Abstract. A traditional metric used in hydrology to summarize model performance is the Nash–Sutcliffe efficiency (NSE). Increasingly an alternative metric, the Kling–Gupta efficiency (KGE), is used instead. When NSE is used, NSE = 0 corresponds to using the mean flow as a benchmark predictor. The same reasoning is applied in various studies that use KGE as a metric: negative KGE values are viewed as bad model performance, and only positive values are seen as good model performance. Here we show that using the mean flow as a predictor does not result in KGE = 0, but instead KGE =1-√2≈-0.41. Thus, KGE values greater than −0.41 indicate that a model improves upon the mean flow benchmark – even if the model's KGE value is negative. NSE and KGE values cannot be directly compared, because their relationship is non-unique and depends in part on the coefficient of variation of the observed time series. Therefore, modellers who use the KGE metric should not let their understanding of NSE values guide them in interpreting KGE values and instead develop new understanding based on the constitutive parts of the KGE metric and the explicit use of benchmark values to compare KGE scores against. More generally, a strong case can be made for moving away from ad hoc use of aggregated efficiency metrics and towards a framework based on purpose-dependent evaluation metrics and benchmarks that allows for more robust model adequacy assessment.

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