Evaluation of NASA POWER Reanalysis Products to Estimate Daily Weather Variables in a Hot Summer Mediterranean Climate

This study aims to evaluate NASA POWER reanalysis products for daily surface maximum (Tmax) and minimum (Tmin) temperatures, solar radiation (Rs), relative humidity (RH) and wind speed (Ws) when compared with observed data from 14 distributed weather stations across Alentejo Region, Southern Portugal, with a hot summer Mediterranean climate. Results showed that there is good agreement between NASA POWER reanalysis and observed data for all parameters, except for wind speed, with coefficient of determination (R2) higher than 0.82, with normalized root mean square error (NRMSE) varying, from 8 to 20%, and a normalized mean bias error (NMBE) ranging from –9 to 26%, for those variables. Based on these results, and in order to improve the accuracy of the NASA POWER dataset, two bias corrections were performed to all weather variables: one for the Alentejo Region as a whole; another, for each location individually. Results improved significantly, especially when a local bias correction is performed, with Tmax and Tmin presenting an improvement of the mean NRMSE of 6.6 °C (from 8.0 °C) and 16.1 °C (from 20.5 °C), respectively, while a mean NMBE decreased from 10.65 to 0.2%. Rs results also show a very high goodness of fit with a mean NRMSE of 11.2% and mean NMBE equal to 0.1%. Additionally, bias corrected RH data performed acceptably with an NRMSE lower than 12.1% and an NMBE below 2.1%. However, even when a bias correction is performed, Ws lacks the performance showed by the remaining weather variables, with an NRMSE never lower than 19.6%. Results show that NASA POWER can be useful for the generation of weather data sets where ground weather stations data is of missing or unavailable.

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Characteristics of LDAPS-Predicted Surface Wind Speed and Temperature at Automated Weather Stations with Different Surrounding Land Cover and Topography in Korea

Atmosphere ◽

10.3390/atmos11111224 ◽

2020 ◽

Vol 11 (11) ◽

pp. 1224

Author(s):

Dong-Ju Kim ◽

Geon Kang ◽

Do-Yong Kim ◽

Jae-Jin Kim

Keyword(s):

Wind Speed ◽

Land Cover ◽

Local Heating ◽

Surface Wind ◽

Surface Wind Speed ◽

Mean Bias Error ◽

Bias Error ◽

Flat Terrain ◽

Weather Stations ◽

Grid Points

We investigated the characteristics of surface wind speeds and temperatures predicted by the local data assimilation and prediction system (LDAPS) operated by the Korean Meteorological Administration. First, we classified automated weather stations (AWSs) into four categories (urban flat (Uf), rural flat (Rf), rural mountainous (Rm), and rural coastal (Rc) terrains) based on the surrounding land cover and topography, and selected 25 AWSs representing each category. Then we calculated the mean bias error of wind speed (WE) and temperature (TE) using AWS observations and LDAPS predictions for the 25 AWSs in each category for a period of 1 year (January–December 2015). We found that LDAPS overestimated wind speed (average WE = 1.26 m s−1) and underestimated temperature (average TE = −0.63 °C) at Uf AWSs located on flat terrain in urban areas because it failed to reflect the drag and local heating caused by buildings. At Rf, located on flat terrain in rural areas, LDAPS showed the best performance in predicting surface wind speed and temperature (average WE = 0.42 m s−1, average TE = 0.12 °C). In mountainous rural terrain (Rm), WE and TE were strongly correlated with differences between LDAPS and actual altitude. LDAPS underestimated (overestimated) wind speed (temperature) for LDAPS altitudes that were lower than actual altitude, and vice versa. In rural coastal terrain (Rc), LDAPS temperature predictions depended on whether the grid was on land or sea, whereas wind speed did not depend on grid location. LDAPS underestimated temperature at grid points on the sea, with smaller TE obtained for grid points on sea than on land.

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Performance Evaluation of Models Established for the Estimation of Diffused Solar Radiation: Case Study Lahore, Pakistan

International Journal of Economic and Environmental Geology ◽

10.46660/ojs.v10i1.226 ◽

2019 ◽

Vol 10 (1) ◽

pp. 113-119

Author(s):

Saif Ur Rehman ◽

Muhammad Shoaib ◽

Imran Siddiqui ◽

S. Zeeshan Abbas

Keyword(s):

Solar Radiation ◽

Goodness Of Fit ◽

Statistical Error ◽

Global Solar Radiation ◽

Coefficient Of Determination ◽

Mean Bias Error ◽

Percentage Error ◽

Bias Error ◽

Clearness Index ◽

Diffuse Solar Radiation

A suitable design of solar power project requires accurate measurements of solar radiation for the site ofinvestigation. Such measurements play a pivotal role in the installation of PV systems. While conducting such studies,in general, global solar radiation (GSR) is recorded, whereas diffuse component of solar radiation on a horizontalsurface is seldom recorded. The objective of the present study is to assess diffuse solar radiation (DSR) on horizontalsurfaces by using polynomial models for Lahore, Pakistan (27.89 N, 78.08 E) and by correlating clearness index withdiffuse fraction. The established models are compared with some of the existing models from the literature.Performance of models is evaluated by employing five goodness-of-fit (GoF) tests that are, mean bias error (MBE),root mean square (RMSE), Coefficient of Determination (R2), Mean Absolute Percentage Error (MAPE) and Akaike’sInformation Criterion (AIC). The comparison of the results of goodness-of-fit tests with those of existing modelsindicate that the models established in the present study are performed better as compared to the existing models. Thevalues of statistical error analysis further suggested that a cubic model with a good accuracy of 97.5% and AIC of -22.8is relatively more suitable for this climatic region for estimating diffuse solar radiation. The study shows that the modeldeveloped is in good agreement with Elhadidy and Nabi model with an accuracy of 96.1% and AIC of 4.4 andsatisfactory results are obtained for Lahore. The findings can help to give a generous understanding of solar radiation inorder to optimize the solar energy conversion systems. The results of this study provide a better understanding of theassociations between global solar radiation, clearness index and diffused fraction for the region under study.

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Estimation of Daily Reference Evapotranspiration from NASA POWER Reanalysis Products in a Hot Summer Mediterranean Climate

Agronomy ◽

10.3390/agronomy11102077 ◽

2021 ◽

Vol 11 (10) ◽

pp. 2077

Author(s):

Gonçalo C. Rodrigues ◽

Ricardo P. Braga

Keyword(s):

Bias Correction ◽

Reference Evapotranspiration ◽

Estimation Method ◽

Accurate Method ◽

Weather Data ◽

Maximum Temperature ◽

Weather Variables ◽

Local Bias ◽

Southern Portugal ◽

Weather Stations

This study aims at assessing the accuracy of estimating daily reference evapotranspiration (ETo) computed with NASA POWER reanalysis products. Daily ETo estimated from local observations of weather variables in 14 weather stations distributed across Alentejo Region, Southern Portugal were compared with ETo derived from NASA POWER weather data, using raw and bias-corrected datasets. Three different methods were used to compute ETo: (a) FAO Penman-Monteith (PM); (b) Hargreaves-Samani (HS); and (c) MaxTET. Results show that, when using raw NASA POWER datasets, a good accuracy between the observed ETo and reanalysis ETo was observed in most locations (R2 > 0.70). PM shows a tendency to over-estimating ETo with an RMSE as high as 1.41 mm d−1, while using a temperature-based ET estimation method, an RMSE lower than 0.92 mm d−1 is obtained. If a local bias correction is adopted, the temperature-based methods show a small over or underestimation of ETo (–0.40 mm d−1≤ MBE < 0.40 mm d−1). As for PM, ETo is still underestimated for 13 locations (MBE < 0 mm d−1) but with an RMSE never higher than 0.77 mm d−1. When NASA POWER raw data is used to estimate ETo, HS_Rs proved the most accurate method, providing the lowest RMSE for half the locations. However, if a data regional bias correction is used, PM leads to the most accurate ETo estimation for half the locations; also, when a local bias correction is performed, PM proved the be the most accurate ETo estimation method for most locations. Nonetheless, MaxTET proved to be an accurate method; its simplicity may prove to be successful not only when only maximum temperature data is available but also due to the low data required for ETo estimation.

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Performance Evaluation of Models Established for the Estimation of Diffused Solar Radiation: Case Study Lahore, Pakistan

International Journal of Economic and Environmental Geology ◽

10.46660/ijeeg.vol10.iss1.2019.226 ◽

2019 ◽

Vol 10 (1) ◽

pp. 113-119

Author(s):

Saif Ur Rehman ◽

Muhammad Shoaib ◽

Imran Siddiqui ◽

S. Zeeshan Abbas

Keyword(s):

Solar Radiation ◽

Goodness Of Fit ◽

Statistical Error ◽

Global Solar Radiation ◽

Coefficient Of Determination ◽

Mean Bias Error ◽

Percentage Error ◽

Bias Error ◽

Clearness Index ◽

Diffuse Solar Radiation

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Characteristics of LDAPS-predicted Surface Wind Speed and Temperature at Automated Weather Stations with Different Surrounding Land Cover and Topography in Korea

10.20944/preprints202009.0024.v1 ◽

2020 ◽

Author(s):

Dong-Ju Kim ◽

Geon Kang ◽

Do-Yong Kim ◽

Jae‒Jin Kim

Keyword(s):

Wind Speed ◽

Land Cover ◽

Local Heating ◽

Surface Wind ◽

Surface Wind Speed ◽

Mean Bias Error ◽

Bias Error ◽

Flat Terrain ◽

Weather Stations ◽

Grid Points

We investigated the characteristics of surface wind speeds and temperatures predicted by the local data assimilation and prediction system (LDAPS) operated by the Korean Meteorological Administration. First, we classified automated weather stations (AWSs) into four categories [urban flat (Uf), rural flat (Rf), rural mountainous (Rm), and rural coastal (Rc) terrains] based on the surrounding land cover and topography, and selected 25 AWSs representing each category. Then we calculated the mean bias error of wind speed (WE) and temperature (TE) using AWS observations and LDAPS predictions for the 25 AWSs in each category for a period of 1 year (January–December 2015). We found that LDAPS overestimated wind speed (average WE = 1.26 m s–1) and underestimated temperature (average TE = –0.63°C) at Uf AWSs located on flat terrain in urban areas because it failed to reflect the drag and local heating caused by buildings. At Rf, located on flat terrain in rural areas, LDAPS showed the best performance in predicting surface wind speed and temperature (average WE = 0.42 m s–1, average TE = 0.12°C). In mountainous rural terrain (Rm), WE and TE were strongly correlated with differences between LDAPS and actual altitude. LDAPS underestimated (overestimated) wind speed (temperature) for LDAPS altitudes that were lower than actual altitude, and vice versa. In rural coastal terrain (Rc), LDAPS temperature predictions depended on whether the grid was on land or sea, whereas wind speed did not depend on grid location. LDAPS underestimated temperature at grid points on the sea, with smaller TE obtained for grid points on sea than on land.

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Hourly Ground-Level PM2.5 Estimation Using Geostationary Satellite and Reanalysis Data via Deep Learning

Remote Sensing ◽

10.3390/rs13112121 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2121

Author(s):

Changsuk Lee ◽

Kyunghwa Lee ◽

Sangmin Kim ◽

Jinhyeok Yu ◽

Seungtaek Jeong ◽

...

Keyword(s):

Cross Validation ◽

Ground Level ◽

Reanalysis Data ◽

Training Dataset ◽

Coefficient Of Determination ◽

Mean Bias Error ◽

Bias Error ◽

Air Quality Model ◽

Quality Model ◽

Near Surface

This study proposes an improved approach for monitoring the spatial concentrations of hourly particulate matter less than 2.5 μm in diameter (PM2.5) via a deep neural network (DNN) using geostationary ocean color imager (GOCI) images and unified model (UM) reanalysis data over the Korean Peninsula. The DNN performance was optimized to determine the appropriate training model structures, incorporating hyperparameter tuning, regularization, early stopping, and input and output variable normalization to prevent training dataset overfitting. Near-surface atmospheric information from the UM was also used as an input variable to spatially generalize the DNN model. The retrieved PM2.5 from the DNN was compared with estimates from random forest, multiple linear regression, and the Community Multiscale Air Quality model. The DNN demonstrated the highest accuracy compared to that of the conventional methods for the hold-out validation (root mean square error (RMSE) = 7.042 μg/m3, mean bias error (MBE) = −0.340 μg/m3, and coefficient of determination (R2) = 0.698) and the cross-validation (RMSE = 9.166 μg/m3, MBE = 0.293 μg/m3, and R2 = 0.49). Although the R2 was low due to underestimated high PM2.5 concentration patterns, the RMSE and MBE demonstrated reliable accuracy values (<10 μg/m3 and 1 μg/m3, respectively) for the hold-out validation and cross-validation.

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Space-Time Sea Surface pCO2 Estimation in the North Atlantic Based on CatBoost

Remote Sensing ◽

10.3390/rs13142805 ◽

2021 ◽

Vol 13 (14) ◽

pp. 2805

Author(s):

Hongwei Sun ◽

Junyu He ◽

Yihui Chen ◽

Boyu Zhao

Keyword(s):

North Atlantic ◽

Function Analysis ◽

Biological Activities ◽

Mixed Layer Depth ◽

Sea Surface ◽

Coefficient Of Determination ◽

Mean Bias Error ◽

Bias Error ◽

The North ◽

The North Atlantic

Sea surface partial pressure of CO2 (pCO2) is a critical parameter in the quantification of air–sea CO2 flux, which plays an important role in calculating the global carbon budget and ocean acidification. In this study, we used chlorophyll-a concentration (Chla), sea surface temperature (SST), dissolved and particulate detrital matter absorption coefficient (Adg), the diffuse attenuation coefficient of downwelling irradiance at 490 nm (Kd) and mixed layer depth (MLD) as input data for retrieving the sea surface pCO2 in the North Atlantic based on a remote sensing empirical approach with the Categorical Boosting (CatBoost) algorithm. The results showed that the root mean square error (RMSE) is 8.25 μatm, the mean bias error (MAE) is 4.92 μatm and the coefficient of determination (R2) can reach 0.946 in the validation set. Subsequently, the proposed algorithm was applied to the sea surface pCO2 in the North Atlantic Ocean during 2003–2020. It can be found that the North Atlantic sea surface pCO2 has a clear trend with latitude variations and have strong seasonal changes. Furthermore, through variance analysis and EOF (empirical orthogonal function) analysis, the sea surface pCO2 in this area is mainly affected by sea temperature and salinity, while it can also be influenced by biological activities in some sub-regions.

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Sorption isotherms and their fitted equations for dried chips of cassava roots (Manihot Esculenta Crantz; Tme-7 variety) and the resulting isosteric heats of sorption

Food Research ◽

10.26656/fr.2017.4(3).340 ◽

2020 ◽

Vol 4 (3) ◽

pp. 703-711

Author(s):

A.S. Ajala ◽

P.O. Ngoddy ◽

J.O. Olajide

Keyword(s):

Relative Humidity ◽

Shelf Life ◽

Gravimetric Method ◽

Sorption Isotherms ◽

Isosteric Heat ◽

Coefficient Of Determination ◽

Mean Bias Error ◽

Isotherm Models ◽

Bias Error ◽

Manihot Esculenta Crantz

Cassava roots are susceptible to deterioration with 24 hrs of harvest; it needs processing into a more stable material such as dried cassava chips to extend its shelf life for long storage. However, improper knowledge of the effect of atmospheric relative humidity on these dried chips during storage makes it mouldy and unacceptable. This work aimed at studying the effect of sorption isotherms on the dried cassava chips. In this study, adsorption and desorption isotherm were carried out using static gravimetric method and data for equilibrium moisture content (EMC) were generated at five (5) temperatures (53, 60, 70, 80, 86oC). These were fitted into four (4) isotherm-models [Oswin, Peleg, the Modified Oswin and GAB]. The statistical criteria to test the models were coefficient of determination (R2 ), reduced chi-square (χ 2 ), root mean square error (RMSE) and mean bias error (MBE). The values of EMC ranged from 7.21-12.44% wb. The values of R2 ranged from 0.95-0.99; χ 2 ranged from 0.008-0.14; RMSE values ranged from 0.06-0.254 while MBE values ranged from -0.0004-1.1E-5. The values of isosteric heat of sorption calculated from the isosteres recorded a range from 6.579 to 67.829 kJ/mole. The Pelegmodel gave the best fit in the relative humidity range of 10 to 80%. The values of EMC show that the chips can have a stable shelf life without spoilage.

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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

The Open Transportation Journal ◽

10.2174/1874447801812010352 ◽

2018 ◽

Vol 12 (1) ◽

pp. 352-365 ◽

Cited By ~ 1

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.

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Prediction model for day-ahead solar insolation using meteorological data for smart grid

E3S Web of Conferences ◽

10.1051/e3sconf/201911106040 ◽

2019 ◽

Vol 111 ◽

pp. 06040

Author(s):

Min Hee Chung

Keyword(s):

Power Generation ◽

Prediction Model ◽

Electric Power ◽

Meteorological Data ◽

Weather Data ◽

Mean Bias Error ◽

The Internet ◽

Bias Error ◽

Photovoltaic Systems ◽

Internet Platform

In the overseas market, power generation and energy service companies have been engaged in the business of providing personalized trading services for the production of electric power through the Internet platform. This is, so that the electric power sharing system between individuals is being developed through the Internet platform. The prediction of insolation is essential for the prediction of power generation for photovoltaic systems. In this study, we present a prediction model for insolation from data observed at the Meteorological Administration. We also present basic data for the development of the insolation prediction model through meteorological parameters provided in future weather forecasts. The prediction model presented is for five years of observation of weather data in the Seoul area. The proposed model was trained by using the feed-forward neural networks, taking into account the daily climatic elements. To validate the reliability of the model, the root mean square error (RMSE), mean bias error (MBE), and mean absolute error (MAE) were used for estimation. The results of this study can be used to predict the solar power generation system and to provide basic information for trading generated output by photovoltaic systems.

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