scholarly journals Evaluation of models for the dew point temperature determination

2017 ◽  
Vol 3 (20) ◽  
pp. 241-257
Author(s):  
Krzysztof Górnicki ◽  
Radosław Winiczenko ◽  
Agnieszka Kaleta ◽  
Aneta Choińska
Keyword(s):  
Dew Point ◽  
Mean Bias Error ◽  
Bias Error ◽  
Mean Square ◽  
Ann Model ◽  
Chi Square ◽  
Point Temperature ◽  
Dew Point Temperature ◽  
Temperature Determination ◽  
The Mean

The accuracy of the available from the literature models for the dew point temperature determination was compared. The proposal of the modelling using artificial neural networks was also given. The experimental data were taken from the psychrometric tables. The accuracies of the models were measured using the mean bias error MBE, root mean square error RMSE, correlation coefficient R, and reduced chi-square χ2. Model M3, especially with constants A=237, B=7.5, gave the best results in determining the dew point temperature (MBE: -0.0229 – 0.0038 K, RMSE: 0.1259 – 0.1286 K, R=0.9999, χ2: 0.0159 – 0.0166 K2). Model M1 with constants A=243.5, B=17.67 and A=243.3, B=17.269 can be also considered as appropriate (MBE=-0.0062 and -0.0078 K, RMSE=0.1277 and 0.1261 K, R=0.9999, χ2=0.0163 and 0.0159 K2). Proposed ANN model gave the good results in determining the dew point temperature (MBE=-0.0038 K, RMSE=0.1373 K, R=0.9999, χ2=0.0189 K2).

scholarly journals Performance of potential evapotranspiration models in Peninsular Malaysia

2021 ◽  
Author(s):  
Goh Ee Hang ◽  
Jing Lin Ng ◽  
Yuk Feng Huang ◽  
Stephen Luo Sheng Yong
Keyword(s):  
Potential Evapotranspiration ◽  
Peninsular Malaysia ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Bias Error ◽  
Mean Square ◽  
Estimation Model ◽  
Baseline Model ◽  
Resources Management ◽  
The Mean

Abstract Potential evapotranspiration (PET) is an important parameter for the operation of irrigation projects and water resources management. The globally recognized PET estimation model, the FAO-56 Penman–Monteith (FAO-56 PM) model, had been criticized for its requirement of many detailed meteorological variables, but nevertheless has been accepted as the baseline model in many worldwide studies. The performances of different PET models can be found to be excellent for a specific location but may not be representative in other regions. The aim of this study is to select the most suitable PET model to estimate PET in Malaysia. Three radiation-based models and four temperature-based models were compared with the FAO-56 PM model at seven selected meteorological stations in Peninsular Malaysia. The mean bias error, relative error (Re) and normalized root-mean-square error (NRMSE) and coefficient of determination (R2) were used to evaluate the performances of the PET models. The Re values of Turc models were below 0.2 at all stations, while Priestly–Taylor, Thornthwaite, Thornthwaite-corrected and Blaney–Criddle models were above 0.2. The Makkink and Hargreaves–Samani models were below 0.2 at most of the stations. Thus, the Turc model was recommended as the best model to estimate PET in Peninsular Malaysia.

scholarly journals Evaluation of Antarctic Ozone Profiles derived from OMPS-LP by using Balloon-borne Ozonesondes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Edgardo Sepúlveda ◽  
Raul R. Cordero ◽  
Alessandro Damiani ◽  
Sarah Feron ◽  
Jaime Pizarro ◽  
...  
Keyword(s):  
Radiative Forcing ◽  
Lower Stratosphere ◽  
Stratospheric Ozone ◽  
Mean Bias Error ◽  
Bias Error ◽  
Mean Square ◽  
Stratospheric Warming ◽  
Sudden Stratospheric Warming ◽  
Antarctic Ozone ◽  
The Mean

AbstractPredicting radiative forcing due to Antarctic stratospheric ozone recovery requires detecting changes in the ozone vertical distribution. In this endeavor, the Limb Profiler of the Ozone Mapping and Profiler Suite (OMPS-LP), aboard the Suomi NPP satellite, has played a key role providing ozone profiles over Antarctica since 2011. Here, we compare ozone profiles derived from OMPS-LP data (version 2.5 algorithm) with balloon-borne ozonesondes launched from 8 Antarctic stations over the period 2012–2020. Comparisons focus on the layer from 12.5 to 27.5 km and include ozone profiles retrieved during the Sudden Stratospheric Warming (SSW) event registered in Spring 2019. We found that, over the period December-January–February-March, the root mean square error (RMSE) tends to be larger (about 20%) in the lower stratosphere (12.5–17.5 km) and smaller (about 10%) within higher layers (17.5–27.5 km). During the ozone hole season (September–October–November), RMSE values rise up to 40% within the layer from 12.5 to 22 km. Nevertheless, relative to balloon-borne measurements, the mean bias error of OMPS-derived Antarctic ozone profiles is generally lower than 0.3 ppmv, regardless of the season.

scholarly journals Improvement of Hourly Surface Solar Irradiance Estimation Using MSG Rapid Scanning Service

2019 ◽  
Vol 11 (1) ◽  
pp. 66 ◽  
Author(s):  
Donatello Gallucci ◽  
Filomena Romano ◽  
Domenico Cimini ◽  
Francesco Di Paola ◽  
Sabrina Gentile ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Solar Irradiance ◽  
Mean Bias Error ◽  
Bias Error ◽  
Mean Square ◽  
Preliminary Assessment ◽  
Temporal Sampling ◽  
The Mean ◽  
Advanced Model ◽  
Rapid Scanning

The purpose of this work is to explore the effect of temporal sampling on the accuracy of the hourly mean Surface Solar Irradiance (SSI) estimation. An upgraded version of the Advanced Model for the Estimation of Surface Solar Irradiance from Satellite (AMESIS), exploiting data from the Meteosat Second Generation Rapid Scanning Service (MSG-RSS), has been used to evaluate the SSI. The assessment of the new version of AMESIS has been carried out against data from two pyranometers located in Southern (Tito) and Northern (Ispra) Italy at an altitude of 760 m and 220 m, respectively. The statistical analysis of the comparison between hourly mean SSI estimates based on temporal sampling every five minutes shows a quantitative improvement compared to those based on 15-minute sampling. In particular, for the whole dataset in Tito, the correlation increases from 0.979 to 0.998, the Root Mean Square Error (RMSE) decreases from 45.16 W/m2 to 13.19 W/m2 and the Mean Bias Error (MBE) is reduced from −0.67 W/m2 to −0.02 W/m2. For the whole dataset in Ispra, the correlation increases from 0.995 to 0.998, the RMSE decreases from 24.85 W/m2 to 15.59 W/m2, whereas the MBE increases from 3.84 W/m2 to 4.58 W/m2. This preliminary assessment shows that higher temporal sampling can improve SSI monitoring over areas featuring frequent and rapid solar irradiance variation.

scholarly journals Improving efficiency and lowering operating costs of evaporative cooling

2021 ◽  
Vol 338 ◽  
pp. 01027
Author(s):  
Jan Taler ◽  
Bartosz Jagieła ◽  
Magdalena Jaremkiewicz
Keyword(s):  
Electricity Consumption ◽  
Open Circuit ◽  
Dew Point ◽  
Operating Costs ◽  
Water Evaporation ◽  
Total Water ◽  
Point Temperature ◽  
Dew Point Temperature ◽  
Adiabatic Cooling ◽  
Positive Effect

Cooling towers, or so-called evaporation towers, use the natural effect of water evaporation to dissipate heat in industrial and comfort installations. Water, until it changes its state of aggregation, from liquid to gas, consumes energy (2.257 kJ/kg). By consuming this energy, it lowers the air temperature to the wet-bulb temperature, thanks to which the medium can be cooled below the ambient temperature. Evaporative solutions are characterized by continuous water evaporation (approx. 1.5% of the total water flow) and low electricity consumption (high EER). Evaporative (adiabatic) cooling also has a positive effect on the reduction of electricity consumption of cooled machines. Lowering the relative humidity (RH) by approx. 2% lowers the wet-bulb temperature by approx. 0.5°C, which increases the efficiency of the tower, operating in an open circuit, expressed in kW, by approx. 5%, while reducing water consumption and treatment costs. The use of the M-Cycle (Maisotsenko cycle) to lower the temperature of the wet thermometer to the dew point temperature will reduce operating costs and increase the efficiency of cooled machines.

scholarly journals The Analysis of Dynamic O/D Adjustment for Bicycle Traffic Demand Estimation with AIMSUN Simulation Model: A Case Study of Nakhon Sawan Municipality in Thailand

2018 ◽  
Vol 12 (1) ◽  
pp. 352-365 ◽  
Author(s):  
Karn Chalermwongphan ◽  
Prapatpong Upala
Keyword(s):  
Simulation Model ◽  
Root Mean Square ◽  
Goodness Of Fit ◽  
Simulation Software ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
Mean Square ◽  
Goodness Of Fit Test ◽  
Traffic Demand

Aim: This research aimed to present the process of estimating bicycle traffic demand in order to design bike routes that meet the daily transportation needs of the people in Nakhon Sawan Municipality. Methods: The primary and secondary traffic data were collected to develop a virtual traffic simulation model with the use of the AIMSUN simulation software. The model validation method was carried out to adjust the origin and destination survey data (O/D matrix) by running dynamic O/D adjustment. The 99 replication scenarios were statistically examined and assessed using the goodness-of-fit test. The 9 measures, which were examined, included: 1) Root Mean Square Error (RMSE), 2) Root Mean Square Percentage Error (RMSPE%), 3) Mean Absolute Deviation (MAD), 4) Mean Bias Error (MBE), 5) Mean Percentage Error (MPE%), 6) Mean Absolute Percentage Error (MAPE%), 7) Coefficient of Determination (R2), 8) GEH Statistic (GEH), and 9) Thiel’s U Statistic (Theil’s U). Results: The resulting statistical values were used to determine the acceptable ranges according to the acceptable indicators of each factor. Conclusion: It was found that there were only 8 scenarios that met the evaluation criteria. The selection and ranking process was consequently carried out using the multi-factor scoring method, which could eliminate errors that might arise from applying only one goodness-of-fit test measure.

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.

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