Average Mean Bias Error Research Articles

CO Fluxes in Western Europe during 2017–2020 Winter Seasons Inverted by WRF-Chem/Data Assimilation Research Testbed with MOPITT Observations

The study of anthropogenic carbon monoxide (CO) emissions is crucial to investigate anthropogenic activities. Assuming the anthropogenic CO emissions accounted for the super majority of the winter CO fluxes in western Europe, they could be roughly estimated by the inversion approach. The CO fluxes and concentrations of four consecutive winter seasons (i.e., December–February) in western Europe since 2017 were estimated by a regional CO flux inversion system based on the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) and the Data Assimilation Research Testbed (DART). The CO retrievals from the Measurements Of Pollution In The Troposphere instrument (MOPITT) version 8 level 2 multi-spectral Thermal InfraRed (TIR)/Near-InfraRed (NIR) CO retrieval data products were assimilated by the inversion system. The analyses of the MOPITT data used by the inversion system indicated that the mean averaging kernel row sums of the surface level was about 0.25, and the difference percentage of the surface-level retrievals relative to a priori CO-mixing ratios was 14.79%, which was similar to that of the other levels. These results suggested the MOPITT’s surface-level observations contained roughly the same amount of information as the other levels. The inverted CO fluxes of the four winter seasons were 6198.15 kilotons, 4939.72 kilotons, 4697.80 kilotons, and 5456.19 kilotons, respectively. Based on the assumption, the United Nations Framework Convention on Climate Change (UNFCCC) inventories were used to evaluate the accuracy of the inverted CO fluxes. The evaluation results indicated that the differences between the inverted CO fluxes and UNFCCC inventories of the three winter seasons of 2017–2019 were 13.36%, −4.59%, and −4.76%, respectively. Detailed surface-CO concentrations and XCO comparative analyses between the experimental results and the external Community Atmosphere Model with Chemistry (CAM-Chem) results and the MOPITT data were conducted. The comparative analysis results indicated that the experimental results of the winter season of 2017 were obviously affected by high boundary conditions. The CO concentrations results of the experiments were also evaluated by the CO observation data from Integrated Carbon Observation System (ICOS), the average Mean Bias Error (MBE), and the Root Mean Square Error (RMSE) between the CO concentrations results of the inversion system, and the ICOS observations were −22.43 ppb and 57.59 ppb, respectively. The MBE and RMSE of the inversion system were 17.53-ppb and 4.17-ppb better than those of the simulation-only parallel experiments, respectively.

Advances in aerosol optical depth evaluation from broadband direct normal irradiance measurements

Aerosols are part of the attenuation processes that impact solar radiation within the atmosphere. They influence the availability and spectrum of the solar resource for each location at the earth’s surface. The present study presents advances in the development of a methodology intended to estimate the aerosol optical depth (AOD) at a given location from broadband direct normal irradiance (DNI) measurements and an appropriate radiative transfer model (RTM) operated backwards. For this purpose, databases provided by AERONET and BSRN at 16 stations throughout the world are jointly employed as inputs to the proposed methodology. The validation of two RTMs (SMARTS and SOLIS) is first undertaken to estimate DNI under clear-sky conditions at each station, assuming both AOD and additional atmospheric inputs are known from sunphotometric measurements. Results indicate that both models achieve good performance, characterized by a relative rRMSE of 3.2% for SMARTS and 3.8% for SOLIS. In the second, and most important stage, the AOD at 550 nm (AOD550) is derived using these models again, but in an iterative mode, now using the 1-minute DNI measurements as inputs. Periods of clear line of sight to the sun first need to be selected from the irradiance measurement record. This, along with other difficulties, make this operation prone to errors when only DNI measurements are available. In spite of this, the results show that AOD can be estimated with a 16-site average mean bias error of only between −0.024 and 0.015 AOD unit and an absolute RMSE between 0.025 and 0.050 AOD unit (compared to the AERONET ground truth), depending on model. Notable improvements are obtained if secondary atmospheric variables are extracted from the MERRA-2 reanalysis and are included as inputs for local computations. The present results suggest that the method is able to compare favorably with AOD estimates from MERRA-2 predictions or MODIS observations, for instance.

Correction of Eddy Covariance Based Crop ET Considering the Heat Flux Source Area

Eddy covariance (EC) systems are being used to measure sensible heat (H) and latent heat (LE) fluxes in order to determine crop water use or evapotranspiration (ET). The reliability of EC measurements depends on meeting certain meteorological assumptions; the most important of such are horizontal homogeneity, stationarity, and non-advective conditions. Over heterogeneous surfaces, the spatial context of the measurement must be known in order to properly interpret the magnitude of the heat flux measurement results. Over the past decades, there has been a proliferation of ‘heat flux source area’ (i.e., footprint) modeling studies, but only a few have explored the accuracy of the models over heterogeneous agricultural land. A composite ET estimate was created by using the estimated footprint weights for an EC system in the upwind corner of four fields and separate ET estimates from each of these fields. Three analytical footprint models were evaluated by comparing the composite ET to the measured ET. All three models performed consistently well, with an average mean bias error (MBE) of about −0.03 mm h−1 (−4.4%) and root mean square error (RMSE) of 0.09 mm h−1 (10.9%). The same three footprint models were then used to adjust the EC-measured ET to account for the fraction of the footprint that extended beyond the field of interest. The effectiveness of the footprint adjustment was determined by comparing the adjusted ET estimates with the lysimetric ET measurements from within the same field. This correction decreased the absolute hourly ET MBE by 8%, and the RMSE by 1%.

Electromagnetism-Like Algorithm-Based Parameters Estimation of Double-Diode PV-Module Model

An accurate parameters extraction of photovoltaic (PV) module is necessary as it plays an essential role in determining the performance of PV system. Therefore, an electromagnetism-like algorithm (EMLA) is proposed to optimally estimate associated parameters of double-diode PV module model. The parameters are optimized based on a proposed fitness function, which depends on the root mean square error between the experimental and computed PV output current. Seven different experimental data sets of I-V curve are utilized to verify the presented approach under various operation conditions. The results show there is a high fitness between the estimated and realistic I-V curves under different weather conditions. Furthermore, the results are validated with another model that proposed in literature under different statistical indices. Finally, the proposed PV modeling method exhibits under all operation conditions an average root mean square error, average mean bias error and average determination coefficient under all operation conditions were 0.0799, 0.0088, and 0.9863, respectively.

Automated Relative Fundamental Frequency Algorithms for Use With Neck-Surface Accelerometer Signals

Relative fundamental frequency (RFF) has been suggested as a potential acoustic measure of vocal effort. However, current clinical standards for RFF measures require time-consuming manual markings. Previous semi-automated algorithms have been developed to calculate RFF from microphone signals. The current study aimed to develop fully automated algorithms to calculate RFF from neck-surface accelerometer signals for ecological momentary assessment and ambulatory monitoring of voice. Training a set of 2646 /vowel-fricative-vowel/ utterances from 317 unique speakers, with and without voice disorders, was used to develop automated algorithms to calculate RFF values from neck-surface accelerometer signals. The algorithms first rejected utterances with poor vowel-to-noise ratios, then identified fricative locations, then used signal features to determine voicing boundary cycles, and finally calculated corresponding RFF values. These automated RFF values were compared to the clinical gold-standard of manual RFF calculated from simultaneously collected microphone signals in a novel test set of 639 utterances from 77 unique speakers. Automated accelerometer-based RFF values resulted in an average mean bias error (MBE) across all cycles of 0.027 ST, with an MBE of 0.152 ST and -0.252 ST in the offset and onset cycles closest to the fricative, respectively. All MBE values were smaller than the expected changes in RFF values following successful voice therapy, suggesting that the current algorithms could be used for ecological momentary assessment and ambulatory monitoring via neck-surface accelerometer signals.

Operational performance of megawatt-scale grid integrated rooftop solar PV system in tropical wet and dry climates of India

This case study presents the performance of a megawatt-scale grid-connected rooftop solar photovoltaic (PV) plant installed on the building rooftops of an educational institute (GITAM Deemed to be University) located in coastal regions (longitude 83° 23' 6.54'' E and latitude 17° 48' 8.208'' N) of Andhra Pradesh. A framework is proposed to validate the existing simulation models that are used for PV project modeling. The validation is done by comparing the simulated performance with the real-time monitored performance. The studied PV plant consists of 3078 solar panels and 23 inverters. For the analysis, we recorded the PV plant operational data for 12 months from 1st October 2018 to 30th September 2019. Based on the monitored data and by following the proposed framework, performance analysis is carried out. The results include the estimated parameters like energy outputs, yield factor, capacity factor, performance ratio, and the error matrices. The solar PV plant supplied energy of 1325.42 MWh to the grid during the monitored period. The expected outcomes of the solar PV plant are assessed using PVGIS, PV Watts, and PV Syst simulation tools. We observed an average mean bias error (MBE) of 5.33% (PVGIS), 12.33% (PV Watts) and 30.64% (PV Syst) and average normalized mean bias error (NMBE) of 2.954% (PVGIS), 7.88% (PV watts) and 22.75% (PV Syst). Overall, it is observed that there is a deviation between the simulated and monitored energy performance.

Application of a Dynamic Clustered Bayesian Model Averaging (DCBA) Algorithm for Merging Multisatellite Precipitation Products over Pakistan

AbstractMerged multisatellite precipitation datasets (MMPDs) combine the advantages of individual satellite precipitation products (SPPs), have a tendency to reduce uncertainties, and provide higher potentials to hydrological applications. This study applied a dynamic clustered Bayesian model averaging (DCBA) algorithm to merge four SPPs across Pakistan. The DCBA algorithm produced dynamic weights to different SPPs varying both spatially and temporally to accommodate the spatiotemporal differences of SPP performances. The MMPD is developed at daily temporal scale from 2000 to 2015 with spatial resolution of 0.25° using extensively evaluated SPPs and a global atmospheric reanalysis–precipitation dataset: Tropical Rainfall Measurement Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42V7, Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR), Climate Prediction Center morphing technique (CMORPH), and ERA-Interim. The DCBA algorithm is evaluated across four distinct climate regions of Pakistan over 102 ground precipitation gauges (GPGs). DCBA forecasting outperformed all four SPPs with average Theil’s U of 0.49, 0.38, 0.37, and 0.36 in glacial, humid, arid, and hyperarid regions, respectively. The average mean bias error (MBE), mean error (MAE), root-mean-square error (RMSE), correlation coefficient (CC), and standard deviation (SD) of DCBA over all of Pakistan are 0.54, 1.40, 4.94, 0.77, and 5.17 mm day−1, respectively. Seasonal evaluation revealed a dependency of DCBA performance on precipitation magnitude/intensity and elevation. Relatively poor DCBA performance is observed in premonsoon/monsoon seasons and at high/mild elevated regions. Average improvements of DCBA in comparison with TMPA are 59.56% (MBE), 49.37% (MAE), 45.89% (RMSE), 19.48% (CC), 46.7% (SD), and 18.66% (Theil’s U). Furthermore, DCBA efficiently captured extreme precipitation trends (premonsoon/monsoon seasons).

Determination of the current–voltage characteristics of concentrator systems by using different adapted conventional techniques

The modelling of the current–voltage characteristics of HCPV (high concentrator photovoltaic) modules is fundamental for the design, monitoring and energy prediction of HCPV systems and power plants. However, the modelling of these devices is inherently different and more complex than that of conventional PV (photovoltaic) modules. Because of this, considerable efforts have been done to develop models tailored to the specific features of this technology. However, there is still a lack of studies and techniques concerning the modelling of the whole I–V curve of HCPV modules. In the present work, the possibility of obtaining the I–V curve of a HCPV module by applying common methods exploited in conventional PV technology by using the effective irradiance and cell temperature is analysed. In particular, the studied methods are: the single exponential model, the Blasser's method and the bilinear interpolation method. Every method has been adapted to be entirely function of the effective irradiance and cell temperature of the concentrator. Results show that all the methods present a good performance in the estimation of the I–V curve of a concentrator, with an average RMSE (root mean square error) ranging from 1.15% to 5.23%, and an average MBE (mean bias error) close to 0%.

Temperature-based approaches for estimating monthly reference evapotranspiration based on MODIS data over North China

Reference evapotranspiration (ETo) maps play an important role in distributed hydrological modeling and are particularly useful for regional agricultural and water resource management. In the Three-North Shelter Forest Program, water requirements (i.e., ETo) of different land use types are important preconditions for afforestation program management. The Food and Agriculture Organization Penman–Monteith (FAO-PM) method is the most common method for estimating ETo, but it requires many different types of meteorological data, and few stations with adequate meteorological resources exist in the Three-North regions. In addition, the spatial distribution of ordinary meteorological stations is limited. This study employed two temperature-based ETo methods, Hargreaves and Thornthwaite. The monthly mean, maximum, and minimum air temperatures were estimated using moderate resolution imaging spectroradiometer (MODIS) data. The original coefficients of Hargreaves and mean temperatures of Thornthwaite were modified for regional calibration (with the FAO-PM method as the standard). In the comparison between the original/adjusted Hargreaves and the original/adjusted Thornthwaite methods, the adjusted Hargreaves method was appropriate for estimating ETo in the Three-North regions. The average mean bias error (MBE) was −0.21 mm, the relatively root mean square error (RRMSE) was 13.44 %, the correlation coefficient (R 2) was 0.85, and the slope (b) was 1.00 for the monthly ETo. While the MBE was 2.32 mm, the RRMSE was 7.07 %, the R 2 was 0.90, and the b was 1.00 for the annual ETo. Therefore, it is possible to estimate monthly and annual ETo values for other parts of the country or the world using adjusted Hargreaves with the estimated air temperature data instead of using the FAO-PM with observed data.

Novel approach in developing hourly global solar radiation model based on peak sunshine hours

The present study explores a novel approach to derive the hourly global solar radiation (HGSR) for any given latitude based on the peak sunshine hours (PSHs). The proposed analytical model describes a relationship between the HGSR and the day length and the PSHs. The applicability of this model is evaluated by comparing the actual and derived values of HGSR for two cities Chennai (13°04′N, 80°17′E) and New Delhi (29°06′N, 77°22′E). To judge the goodness of the proposed model a set of error metrics has been developed by evaluating the variation of actual HGSR from the simulated value for a given day over 12 months. The overall average mean bias error for one year is 1.015% and 1.08% for Chennai and New Delhi, respectively. The agreement between the actual and the simulated values is generally good, with an appreciable correlation of 95%. In particular, unlike the other models this approach requires only two inputs which are easily available for any location. The proposed technique is useful for any solar application designer for deriving the hourly solar radiation values for a given day of any location with less available climate data.

Evaluation of a bias correction method applied to downscaled precipitation and temperature reanalysis data for the Rhine basin

Abstract. In many climate impact studies hydrological models are forced with meteorological data without an attempt to assess the quality of these data. The objective of this study was to compare downscaled ERA15 (ECMWF-reanalysis data) precipitation and temperature with observed precipitation and temperature and apply a bias correction to these forcing variables. Precipitation is corrected by fitting it to the mean and coefficient of variation (CV) of the observations. Temperature is corrected by fitting it to the mean and standard deviation of the observations. It appears that the uncorrected ERA15 is too warm and too wet for most of the Rhine basin. The bias correction leads to satisfactory results, precipitation and temperature differences decreased significantly, although there are a few years for which the correction of precipitation is less satisfying. Corrections were largest during summer for both precipitation and temperature. For precipitation alone large corrections were applied during September and October as well. Besides the statistics the correction method was intended to correct for, it is also found to improve the correlations for the fraction of wet days and lag-1 autocorrelations between ERA15 and the observations. For the validation period temperature is corrected very well, but for precipitation the RMSE of the daily difference between modeled and observed precipitation has increased for the corrected situation. When taking random years for calibration, and the remaining years for validation, the spread in the mean bias error (MBE) becomes larger for the corrected precipitation during validation, but the overal average MBE has decreased.