Minimum Root Mean Square Error Research Articles

Derivation of Earth Geoid Model for Mosul City in Iraq

The quality and efficiency of geoid models have been developed rapidly to provide elevations instead of traditional surveying. The high cost and the long measurement time of surveying instrumentation cause an urgent need for deriving local Earth Geoid Model (EGM). Hundred control points are selected on the study area topographic map. The study area is located in Mosul city in Iraq. Multiple earth geoid models have been derived using different interpolation methods available in ArcGIS environment. The results of statistical analysis determine the best interpolation method. The percentage root mean square errors (RMSEs) for the adapted interpolation methods are 26.54 %, 28.43 %, 30.13 %, 34.79 %, 36.75 %, and 37.28 % in kriging, Inverse Distance Weighted (IDW), Trend, Natural Neighbor, Spline, and Topo To Raster, respectively. Kriging interpolation gives minimum standard deviation (2.226 m) and RMSE (2.654 m) so it is the preferable method. The results indicate that the geoid is departed from GPS Visualizer data by the mean value of minus (1.405 m) in the study area.

Gully erosion spatial modelling: Role of machine learning algorithms in selection of the best controlling factors and modelling process

This investigation assessed the efficacy of 10 widely used machine learning algorithms (MLA) comprising the least absolute shrinkage and selection operator (LASSO), generalized linear model (GLM), stepwise generalized linear model (SGLM), elastic net (ENET), partial least square (PLS), ridge regression, support vector machine (SVM), classification and regression trees (CART), bagged CART, and random forest (RF) for gully erosion susceptibility mapping (GESM) in Iran. The location of 462 previously existing gully erosion sites were mapped through widespread field investigations, of which 70% (323) and 30% (139) of observations were arbitrarily divided for algorithm calibration and validation. Twelve controlling factors for gully erosion, namely, soil texture, annual mean rainfall, digital elevation model (DEM), drainage density, slope, lithology, topographic wetness index (TWI), distance from rivers, aspect, distance from roads, plan curvature, and profile curvature were ranked in terms of their importance using each MLA. The MLA were compared using a training dataset for gully erosion and statistical measures such as RMSE (root mean square error), MAE (mean absolute error), and R-squared. Based on the comparisons among MLA, the RF algorithm exhibited the minimum RMSE and MAE and the maximum value of R-squared, and was therefore selected as the best model. The variable importance evaluation using the RF model revealed that distance from rivers had the highest significance in influencing the occurrence of gully erosion whereas plan curvature had the least importance. According to the GESM generated using RF, most of the study area is predicted to have a low (53.72%) or moderate (29.65%) susceptibility to gully erosion, whereas only a small area is identified to have a high (12.56%) or very high (4.07%) susceptibility. The outcome generated by RF model is validated using the ROC (Receiver Operating Characteristics) curve approach, which returned an area under the curve (AUC) of 0.985, proving the excellent forecasting ability of the model. The GESM prepared using the RF algorithm can aid decision-makers in targeting remedial actions for minimizing the damage caused by gully erosion.

Estimation of municipal waste generation of Turkey using socio-economic indicators by Bayesian optimization tuned Gaussian process regression.

Accurate estimation of municipal solid waste (MSW) generation has become a crucial task in decision-making processes for the MSW planning and management systems. In this study, the Gaussian process regression (GPR) model tuned by Bayesian optimization was used to forecast the MSW generation of Turkey. The Bayesian optimization method, which can efficiently optimize the hyperparameters of kernel functions in the machine learning algorithms, was applied to reduce the computation redundancy and enhance the estimation performance of the models. Four socio-economic indicators such as population, gross domestic product per capita, inflation rate, and the unemployment rate were used as input variables. The performance of the Bayesian GPR (BGPR) model was compared with the multiple linear regression (MLR) and Bayesian support vector regression (BSVR) models. Different performance measures such as mean absolute deviation (MAD), root mean square error (RMSE), and coefficient of determination (R2) values were used to evaluate the performance of the models. The exponential-GPR model tuned by Bayesian optimization showed superior performance with minimum MAD (0.0182), RMSE (0.0203), and high R2 (0.9914) values in the training phase and minimum MAD (0.0342), RMSE (0.0463), and high R2 (0.9841) values in the testing phase. The results of this study can help decision-makers to be aware of social-economic factors associated with waste management and ensure optimal usage of their resources in future planning.

High-precision VTEC derivation with GEONET

This paper proposes a new technique, namely Phase bias-based Small Grid Model (PSGM), to derive absolute ionospheric vertical total electron content (VTEC) with observations of Global Navigation Satellite System Earth Observation Network of Japan (GEONET). The proposed technique deals with the phase observations alone without handling the pseudoranges, which reduces the noise in VTEC estimation. A new parameter, the arc bias (Barc), is introduced to combine the phase ambiguities and differential phase biases. To solve Barc, equations are constructed under the assumption that the VTEC is identical in the same 0.1° × 0.1° grid. The performance of PSGM is evaluated with the observations in solar maximum year 2014. The root mean square error (RMSE) of PSGM is 0.40 TECU in average, the maximum RMSE is 0.73 TECU and the minimum RMSE is 0.26 TECU. The fitting accuracy of the VTEC results is improved compared with most of the existing methods.

Support vector machine based prediction of photovoltaic module and power station parameters

ABSTRACTThe uncertainty in the output power of the photovoltaic (PV) power generation station due to variation in meteorological parameters is of serious concern. An accurate output power prediction of a PV system helps in better design and planning. The present study is carried out for the prediction of output power of PV generating station by using Support Vector Machines. Two cases are considered in the present study for prediction. Case-I deals with the prediction of PV module parameters such as Voc, Ish, Rs, Rsh, Imax, Vmax, Pmax, and case-II deals with the prediction of power generation parameters such as PDC, PAC, and system efficiency. Historical data of PV power station with an installed capacity of 10 MW and weather information are used as input to develop four different seasons-based SVM models for all parameters. The performance results of the models are presented in terms of Mean Relative Error (MRE) and Root Mean Square Error (RMSE). Additionally, the performance results obtained with polynomial and Radial Based Function kernel are also compared to show that which kernel has better prediction accuracy, and practicability. The result shows that the minimum average RMSE and MRE for case-I with Radial Based Function kernel are 0.034%, 0.055%, 0.002%, 1.726%, 0.044%, 0.047%, 2.342%, and 0.005%, 0.014%, 0.079%, 0.885%, 0.005%, 0.007%, 0.013%, and for case-II with poly kernel are 0.014%, 0.016%, 0.149% and 0.011%, 0.0175, 1.03%, respectively. The present study will be helpful to provide technical guidance to the prediction of the PV power System.

Integration of DInSAR and SBAS Techniques to Determine Mining-Related Deformations Using Sentinel-1 Data: The Case Study of Rydułtowy Mine in Poland

Underground coal exploitation often results in land-surface subsidence, the rate of which depends on geological characteristics, the mechanical properties of the rocks, and the applied extraction technology. Since mining-related subsidence is characterized by “fast” displacement and high nonlinearity, monitoring this process by using Interferometric Synthetic Aperture Radar (InSAR) is very challenging. The Small BAseline Subset (SBAS) approach needs to predefine an a priori deformation model to properly estimate an interferometric component related to displacements. As a consequence, there is a lack of distributed scatterers (DS) when the selected a priori deformation model deviates from the real deformation. The conventional differential SAR interferometry (DInSAR) approach does not have this limitation, since it does not need any deformation model. However, the accuracy of this technique is limited by factors related to spatial and temporal decorrelation, signal delays due to the atmospheric artifacts, and orbital or topographic errors. Therefore, this study presents the integration of DInSAR and SBAS techniques in order to leverage the advantages and overcome the disadvantages of both methods and to retrieve the complete deformation pattern over the investigated study area. The obtained results were evaluated internally and externally with leveling data. Results indicated that the Kriging-based integration method of DInSAR and SBAS can be effectively applied to monitor mining-related subsidence. The root-mean-square Error (RMSE) between modeled and measured deformation by InSAR was found to be 11 and 13 mm for vertical and horizontal displacements, respectively. Moreover, DInSAR technique as a cost-effective and complementary method to conventional geodetic techniques can be applied for effective monitoring fast mining subsidence. The minimum and maximum RMSE between DInSAR displacement and specific leveling profiles were found to be 0.9 and 3.2 cm, respectively. Since the SBAS processing failed in subsidence estimation in the area of maximum deformation rate, the deformation estimates outside the maximum rate could only be compared. In these areas, the good agreement between SBAS and DInSAR indicates that the SBAS technique could be reliable for monitoring the residual subsidence that surrounds the subsidence trough. Using the proposed approach, we detected subsidence of up to −1 m and planar displacements (east–west) of up to 0.24 m.

Riesz fractional order derivative in Fractional Fourier Transform domain: An insight

This paper presents a novel closed-form analytical expression for Riesz fractional order derivative in the Fractional Fourier domain. The expression is obtained in the terms of higher transcendental functions such as Parabolic Cylinder Function as well as Confluent Hypergeometric Function. The presented work is analyzed in the discrete domain by using the properties of Discrete Fractional Fourier Transform (DFrFT). The proposed algorithm is capable of preserving the texture and edge information without any phase distortion. The design example discussed in the paper shows the efficacy of the proposed algorithm for a signal with high frequency chirp noise. The design flexibility of the proposed approach is confirmed due to the fact that it provides an optimal value of performance metrics such as Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) corresponding to the variation of the fractional order of Riesz derivative and fractional parameter in the rotation angle of Fractional Fourier Transform (FrFT). The proposed algorithm provides better results in terms of minimum RMSE of 0.115136 and MAE of 0.094223 for the optimal fractional order of 0.43 at a rotation angle of 0.45π.

Continuous monitoring of land disturbance based on Landsat time series

We developed a new algorithm for COntinuous monitoring of Land Disturbance (COLD) using Landsat time series. COLD can detect many kinds of land disturbance continuously as new images are collected and provide historical land disturbance maps retrospectively. To better detect land disturbance, we tested different kinds of input data and explored many time series analysis techniques. We have several major observations as follows. First, time series of surface reflectance provides much better detection results than time series of Top-Of-Atmosphere (TOA) reflectance, and with some adjustments to the temporal density, time series from Landsat Analysis Ready Data (ARD) is better than it is from the same Landsat scene. Second, the combined use of spectral bands is always better than using a single spectral band or index, and if all the essential spectral bands have been employed, the inclusion of other indices does not further improve the algorithm performance. Third, the remaining outliers in the time series can be removed based on their deviation from model predicted values based on probability-based thresholds derived from normal or chi-squared distributions. Fourth, model initialization is pivotal for monitoring land disturbance, and a good initialization stability test can influence algorithm performance substantially. Fifth, time series model estimation with eight coefficients model, updated for every single observation, based on all available clear observations achieves the best result. Sixth, a change probability of 0.99 (chi-squared distribution) with six consecutive anomaly observations and a mean included angle < 45° to confirm a change provide the best results, and the combined use of temporally-adjusted Root Mean Square Error (RMSE) and minimum RMSE is recommended. Finally, spectral changes (or “breaks”) contributed from vegetation regrowth should be excluded from land disturbance maps. The COLD algorithm was developed and calibrated based on all these lessons learned above. The accuracy assessment shows that COLD results were accurate for detecting land disturbance, with an omission error of 27% and a commission error of 28%.

Comparison of Sea Surface Variation Derived from Global Navigation Satellite System (GNSS) and Co-tidal in Java Sea

Tide represents the vertical variation of sea surface. This parameter plays important rules in bathymetric survey. The conventional method to observe the sea surface variation is by using tide pole. Nowdays, a Global Navigation Satellite System (GNSS) can be used as a means to measure the variation of sea surface as it provides high accuracy coordinates. In this research, the vertical component of GNSS was utilized to analyze the variation of sea surface. The distance between tidal stations and the survey area can be a constrain to the depth reduction because its tidal zoning. The traditional tidal zoning is a discrete model. This can be minimalized using a co-tidal chart. In this research, the vertical variation of sea surface from GNSS and co-tidal chart approachs were examined and compared to the conventional method. The comparative analysis was performed with Root Mean Square Error (RMSE). The maximum and minimum RMSE during 3 days period between the GNSS and conventional approach are 0.246 m and 0.051 m, respectively. Whereas, the maximum and minimum RMSE between the co-tidal chart model and the conventional approach at the same time are 0.286 m and 0.109 m.

Estimating Stand Density in a Tropical Broadleaf Forest Using Airborne LiDAR Data

Forest-related statistics, including forest biomass, carbon sink, and the prevention of forest fires, can be obtained by estimating stand density. In this study, a dataset with the laser pulse density of 225.5 pulses/m2 was obtained using airborne laser scanning in a tropical broadleaf forest. Three digital surface models (DSMs) were generated using first-echo, last-echo, and highest first-echo data. Three canopy height models (CHMs) were obtained by deducting the digital elevation model from the three DSMs. The cell sizes (Csizes) of the CHMs were 1, 0.5, and 0.2 m. In addition, stand density was estimated using CHM data and following the local maximum method. The stand density of 35 sample regions was acquired via in-situ measurement. The results indicated that the root-mean-square error ( R M S E ) ranged between 1.68 and 2.43; the R M S E difference was only 0.78, indicating that stand density was effectively estimated in both cases. Furthermore, regression models were used to correct the error in stand density estimations; the R M S E after correction was called R M S E ′ . A comparison of the R M S E and R M S E ′ showed that the average value decreased from 12.35 to 2.66, meaning that the regression model could effectively reduce the error. Finally, a comparison of the effects of different laser pulse densities on the R M S E value showed that, in order to obtain the minimum R M S E for stand density, the laser pulse density must be greater than 10, 30, and 125 pulses/m2 at Csizes of 1, 0.5, and 0.2 m, respectively.

Evaluating the Effectiveness of DART® Buoy Networks Based on Forecast Accuracy

A performance measure for a DART® tsunami buoy network has been developed. DART® buoys are used to detect tsunamis, but the full potential of the data they collect is realized through accurate forecasts of inundations caused by the tsunamis. The performance measure assesses how well the network achieves its full potential through a statistical analysis of simulated forecasts of wave amplitudes outside an impact site and a consideration of how much the forecasts are degraded in accuracy when one or more buoys are inoperative. The analysis uses simulated tsunami amplitude time series collected at each buoy from selected source segments in the Short-term Inundation Forecast for Tsunamis database and involves a set for 1000 forecasts for each buoy/segment pair at sites just offshore of selected impact communities. Random error-producing scatter in the time series is induced by uncertainties in the source location, addition of real oceanic noise, and imperfect tidal removal. Comparison with an error-free standard leads to root-mean-square errors (RMSEs) for DART® buoys located near a subduction zone. The RMSEs indicate which buoy provides the best forecast (lowest RMSE) for sections of the zone, under a warning-time constraint for the forecasts of 3 h. The analysis also shows how the forecasts are degraded (larger minimum RMSE among the remaining buoys) when one or more buoys become inoperative. The RMSEs provide a way to assess array augmentation or redesign such as moving buoys to more optimal locations. Examples are shown for buoys off the Aleutian Islands and off the West Coast of South America for impact sites at Hilo HI and along the US West Coast (Crescent City CA and Port San Luis CA, USA). A simple measure (coded green, yellow or red) of the current status of the network’s ability to deliver accurate forecasts is proposed to flag the urgency of buoy repair.

Performance of MIDAS Over East African Longitude Sector: Case Study During 4–14 March 2012 Quiet to Disturbed Geomagnetic Conditions

AbstractThis study investigates the performance of a tomographic algorithm, Multi‐Instrument and Data Analysis System (MIDAS), during an extended period of 4–14 March 2012, containing moderate and strong geomagnetic storms conditions, over an understudied and data scarce Eastern African longitude sector. Nonetheless, a relatively better distribution of Global Navigation Satellite Systems stations exists along a narrow longitude sector between 30°E and 44°E and latitude range of 30°S and 36°N that spans the equatorial, middle‐, and low‐latitude ionosphere. Then results are compared with independent global models such as International Reference Ionosphere 2012 (IRI‐2012) and global ionosphere map (GIM). MIDAS performance was better than that of the IRI‐2012 and GIM models in terms of capturing the diurnal trends as well as the short temporal total electron content (TEC) structures, with least root‐mean‐square errors (RMSEs). Overall, MIDAS results showed better agreement with the observation vertical TEC (VTEC) with computed maximum correlation coefficient (r) of 0.99 and minimum root‐mean‐square error (RMSE) of 2.91 TEC unit (1 TECU = 1,016 el m−2 over all the test stations and the validation days. Conversely, for the IRI‐2012 and GIM TEC estimates, the corresponding maximum computed r values were 0.93 and 0.99, respectively, while the minimum RMSEs were 13.03 TECU and 6.52 TECU, respectively, for all the test stations and the validation days.

Prediction of ultimate strength of shale using artificial neural network

A rock failure criterion is very important for prediction of the ultimate strength in rock mechanics and geotechnics; it is determined for rock mechanics studies in mining, civil, and oil wellborn drilling operations. Also shales are among the most difficult to treat formations. Therefore, in this research work, using the artificial neural network (ANN), a model was built to predict the ultimate strength of shale, and comparison was made with support vector machine (SVM), multiple linear regression models, and the widely used conventional polyaxial failure criteria in the stability analysis of rock structures, Drucker-Prager, and Mogi-Coulomb. For building the model, the corresponding results of triaxial and polyaxial tests have been performed on shales by various researchers. They were collected from reliable published articles. The results obtained showed that a feed forward back propagation multi-layer perceptron (MLP) was used and trained using the Levenberg–Marquardt algorithm, and the 2-4-1 architecture with root-mean-square-error (RMSE) of 24.41 exhibits a better performance in predicting the ultimate strength of shale in comparison with the investigated models. Also for further validation, triaxial tests were performed on the deep shale specimens. They were prepared from the Ramshire oilfield in SW Iran. The results obtained were compared with ANN, SVM, multiple linear regression models, and the conventional failure criterion prediction. They showed that the ANN model predicted ultimate strength with a minimum error and RMSE being equal to 43.81. Then the model was used for prediction of the threshold broken pressure shale layer in the Gachsaran oilfield in Iran. For this, a vertical and horizontal stress was calculated based on a depth of shale layer. The threshold broken pressure was calculated for the beginning and ending of a shale layer to be 154.21 and 167.98 Mpa, respectively.

Nonlinear single-input single-output model-based estimation of cardiac output for normal and depressed cases

Mental depression is associated with an increased risk of cardiovascular mortality, thus provisioning generic simple nonlinear mathematical models for normal and depressed cases using only heart rate (HR) or stroke volume (SV) as a single input to produce cardiac output (CO) as a single output instead of using both HR and SV as two inputs. The proposed models could be in the future an effective tool to investigate the effect of neuroleptic medication, especially depression, and it reduces the time of processing. Seventy-four depressed cases, 74 normal peers and autoregressive considered as a main role in the nonlinear discrete system identification are chosen to lie under investigation on the way to produce four simple nonlinear models. The first generic model using only HR as an input which generated from the depressed case number 62 produced minimum root-mean-square error (RMSE) of 0.0018 and when it is applied to the 74 depressed cases it produced average RMSE equal to 0.1978. Second, generic model using only HR as an input created from the normal case number 55 produced minimum RMSE of 0.0008 and average RMSE equal to 0.0572. The third generic model using only SV as an input which generated from the depressed case number 16 produced minimum RMSE of 0.0027 and when it is applied to the 74 depressed cases it produced average RMSE equal to 0.9405. Fourth generic model using only SV as an input created from the normal case number 58 produced minimum RMSE of 0.0019 and average RMSE equal to 1.0833. The four simple nonlinear models for depression and normal cases are succeeded to determine CO by using only one input such as HR or SV and could be a good contribution in the future to neuroleptic medications field especially depression while HR showed the minimum average RMSE.

Retrieval of outgoing longwave radiation at top-of-atmosphere using Himawari-8 AHI data

This study proposes an algorithm to retrieve the outgoing longwave radiation at the top of the atmosphere (TOA OLR) using the advanced Himawari imager (AHI) unit that is a narrowband sensor installed in Himawari-8, a geostationary satellite providing window and water vapor imagery. The outgoing longwave radiation at the top of the atmosphere (TOA OLR) was retrieved by an empirical method where a radiative transfer model (RTM) was used to simulate various atmospheric conditions, such as the surface temperature, water vapor, and cloud characteristics (e.g., cloud optical thickness and cloud height). The results of an algorithm using a single channel (OLR12.4) and two channels (OLR6.2+12.4) were converted into Terra Cloud and Earth's Radiant Energy System (CERES) TOA OLR for the daytime (0105–0135 UTC) and nighttime (1325–1355 UTC) cases of a scene analysis (typhoon Goni on August 18, 2015). Long-term analysis (August 2015–July 2016) and statistical analysis (i.e., mean, bias, root mean square error (RMSE), and correlation coefficient) were conducted. The analysis results showed that the largest error was produced in the cloudy areas (overcast areas), and the minimum RMSE and a high correlation coefficient were observed over an ocean area. In the scene analysis, however, the daytime and nighttime average RMSE of the CERES TOA OLR and OLR6.2+12.4 was 12.21Wm−2 and showed the correlation coefficient of 0.971. It was an improved result over the RMSE (12.33Wm−2) and correlation coefficient (0.967) shown in the analysis of OLR12.4. In the long-term analysis, the average RMSE was 11.83Wm−2 and showed a correlation coefficient of 0.972 (OLR12.4 showed 12.17Wm−2 and 0.969). Furthermore, to minimize the difference in the cloud area that had the largest error, the study analyzed TOA OLR at the significance level of 95% (±2 RMSE). As a result, the scene analysis showed an RMSE of 10.54Wm−2 and correlation coefficient of 0.977, and the long-term data showed an RMSE of 10.23Wm−2 and correlation coefficient of 0.979.

A hybrid solar radiation modeling approach using wavelet multiresolution analysis and artificial neural networks

Assessment of solar potential over a location of interest is an important step towards the successful planning of renewable energy projects. However, solar data are not available for every point of interest due to the absence of meteorological stations and sophisticated solar sensors, so solar radiation has to be estimated using models. This paper presents a hybrid technique to improve the performance of a widely used modeling technique i.e. artificial neural network (ANN). Four different architectures of ANN, namely: multilayer perceptron (MLP), Adaptive neuro-fuzzy inference system (ANFIS), Nonlinear autoregressive recurrent exogenous neural network (NARX), and generalized regression neural networks (GRNN), are used in this study. A wavelet multiresolution analysis is applied to decompose the complex meteorological signals into relatively simple parts, wavelet sub-series, using discrete wavelet transformation (DWT) algorithm. The wavelet sub-series are modeled by the ANN models and reconstructed to estimate the original signal. Hence, enhancing the learning process of these models. Four meteorological parameters, namely: temperature (T), relative humidity (RH), wind speed (WS), and sunshine duration (SSD), are used to mode the global horizontal irradiation (GHI) over Abu Dhabi, the United Arab Emirates. The proposed approach is compared to standalone ANN models and validated using well-known statistical validation metrics including coefficient of determination (R2), root mean square error (RMSE), mean bias error (MBE), mean absolute percentage error (MAPE), and t-statistics. In addition, wavelet cross spectrum (WCS) is used as a visual indicator of the model performance in time, frequency, and phase domains. The results show that using the proposed strategy considerably improves the modeling performance of the ANN with a maximum improvement of 6.84% in R2 for MLP. In addition, minimum RMSE of 2.78% is observed for GRNN.

Modeling soil erosion by data-driven methods using limited input variables

Abstract Soil is one of the main elements of natural resources. Accurate estimation of soil erosion is very important in optimum soil resources development and management. Analyzing soil erosion by water on cultivated lands is an important task due to the numerous problems caused by erosion. In this study, the performance of three different data-driven approaches, e.g. multilayer perceptron artificial neural network (ANN), grid partitioning (GP), and subtractive neuro-fuzzy (NF) models were evaluated for estimating soil erosion. Land use, slope, soil and upland erosion amount were used as input parameters of the applied models and the erosion values obtained by MPSIAC method were considered as the benchmark for evaluating the ANN and NF models. The applied models were assessed using the coefficient of determination (R2), the root mean square error (RMSE), the BIAS, and the variance accounted for (VAF) indices. The results showed that the subtractive NF model presented the most accurate results with the minimum RMSE value (3.775) and GP, NF and ANN models were ranked successively.

Assessment of Chlorophyll-a Algorithms Considering Different Trophic Statuses and Optimal Bands

Numerous algorithms have been proposed to retrieve chlorophyll-a concentrations in Case 2 waters; however, the retrieval accuracy is far from satisfactory. In this research, seven algorithms are assessed with different band combinations of multispectral and hyperspectral bands using linear (LN), quadratic polynomial (QP) and power (PW) regression approaches, resulting in altogether 43 algorithmic combinations. These algorithms are evaluated by using simulated and measured datasets to understand the strengths and limitations of these algorithms. Two simulated datasets comprising 500,000 reflectance spectra each, both based on wide ranges of inherent optical properties (IOPs), are generated for the calibration and validation stages. Results reveal that the regression approach (i.e., LN, QP, and PW) has more influence on the simulated dataset than on the measured one. The algorithms that incorporated linear regression provide the highest retrieval accuracy for the simulated dataset. Results from simulated datasets reveal that the 3-band (3b) algorithm that incorporate 665-nm and 680-nm bands and band tuning selection approach outperformed other algorithms with root mean square error (RMSE) of 15.87 mg·m−3, 16.25 mg·m−3, and 19.05 mg·m−3, respectively. The spatial distribution of the best performing algorithms, for various combinations of chlorophyll-a (Chla) and non-algal particles (NAP) concentrations, show that the 3b_tuning_QP and 3b_680_QP outperform other algorithms in terms of minimum RMSE frequency of 33.19% and 60.52%, respectively. However, the two algorithms failed to accurately retrieve Chla for many combinations of Chla and NAP, particularly for low Chla and NAP concentrations. In addition, the spatial distribution emphasizes that no single algorithm can provide outstanding accuracy for Chla retrieval and that multi-algorithms should be included to reduce the error. Comparing the results of the measured and simulated datasets reveal that the algorithms that incorporate the 665-nm band outperform other algorithms for measured dataset (RMSE = 36.84 mg·m−3), while algorithms that incorporate the band tuning approach provide the highest retrieval accuracy for the simulated dataset (RMSE = 25.05 mg·m−3).

Comparison of hourly surface downwelling solar radiation estimated from MSG–SEVIRI and forecast by the RAMS model with pyranometers over Italy

Abstract. In this paper, we evaluate the performance of two global horizontal solar irradiance (GHI) estimates, one derived from Meteosat Second Generation (MSG) and another from the 1-day forecast of the Regional Atmospheric Modeling System (RAMS) mesoscale model. The horizontal resolution of the MSG-GHI is 3 × 5 km2 over Italy, which is the focus area of this study. For this paper, RAMS has the horizontal resolution of 4 km.The performances of the MSG-GHI estimate and RAMS-GHI 1-day forecast are evaluated for 1 year (1 June 2013–31 May 2014) against data of 12 ground-based pyranometers over Italy spanning a range of climatic conditions, i.e. from maritime Mediterranean to Alpine climate.Statistics for hourly GHI and daily integrated GHI are presented for the four seasons and the whole year for all the measurement sites. Different sky conditions are considered in the analysisResults for hourly data show an evident dependence on the sky conditions, with the root mean square error (RMSE) increasing from clear to cloudy conditions. The RMSE is substantially higher for Alpine stations in all the seasons, mainly because of the increase of the cloud coverage for these stations, which is not well represented at the satellite and model resolutions. Considering the yearly statistics computed from hourly data for the RAMS model, the RMSE ranges from 152 W m−2 (31 %) obtained for Cozzo Spadaro, a maritime station, to 287 W m−2 (82 %) for Aosta, an Alpine site. Considering the yearly statistics computed from hourly data for MSG-GHI, the minimum RMSE is for Cozzo Spadaro (71 W m−2, 14 %), while the maximum is for Aosta (181 W m−2, 51 %). The mean bias error (MBE) shows the tendency of RAMS to over-forecast the GHI, while no specific behaviour is found for MSG-GHI.Results for daily integrated GHI show a lower RMSE compared to hourly GHI evaluation for both RAMS-GHI 1-day forecast and MSG-GHI estimate. Considering the yearly evaluation, the RMSE of daily integrated GHI is at least 9 % lower (in percentage units, from 31 to 22 % for RAMS in Cozzo Spadaro) than the RMSE computed for hourly data for each station. A partial compensation of underestimation and overestimation of the GHI contributes to the RMSE reduction. Furthermore, a post-processing technique, namely model output statistics (MOS), is applied to improve the GHI forecast at hourly and daily temporal scales. The application of MOS shows an improvement of RAMS-GHI forecast, which depends on the site considered, while the impact of MOS on MSG-GHI RMSE is small.

The role of topography and the north Indian monsoon on mean monthly climate interpolation within the Himalayan Kingdom of Bhutan

ABSTRACTSpatial climate datasets currently available for Bhutan are limited by weather station data availability, spatial resolution or interpolation methodology. This article presents new datasets for monthly maximum temperature, minimum temperature, precipitation and vapour pressure climate normals interpolated for the 1986–2015 reference period using trivariate smoothing splines. The inclusion of standardized day time Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) values as partial spline dependencies reduced cross validated root mean square error (RMSE) for maximum temperature by up to 16.0% and was most effective between March and September. Using both a topographic index of relative elevation and standardized night time MODIS LST values as partial spline dependencies reduced monthly mean minimum temperature RMSE by up to 23.4%. Neither variable was effective for minimum temperature interpolation between June and September. High humidity, extensive cloud cover and heavy precipitation occur during these months, which are likely to suppress the formation of temperature inversions that typically form under clear, calm conditions. These new temperature and precipitation surfaces show distinct differences from the WorldClim and CRU CL 2.0 datasets, which do not use weather stations within Bhutan for calibration. New precipitation surfaces better describe the heavy rainfall experienced in the southern foothills while retaining the effect of orography throughout the central valleys and ranges. The development of vapour pressure surfaces also allow for the calculation of ecologically important variables such as vapour pressure deficit, and may also be useful for solar radiation modelling in the region. The different datasets presented in this article will facilitate ecological and agricultural research in Bhutan and provide high quality surfaces needed for future climate change scenarios.