The Spatiotemporal Variability of Temperature and Precipitation Over the Upper Indus Basin: An Evaluation of 15 Year WRF Simulations

Investigating the trends in the major climatic variables over the Upper Indus Basin (UIB) region is difficult for many reasons, including highly complex terrain with heterogeneous spatial precipitation patterns and a scarcity of gauge stations. The Weather Research and Forecasting (WRF) model was applied to simulate the spatiotemporal variability of precipitation and temperature over the Indus Basin from 2000 through 2015 with boundary conditions derived from the Climate Forecast System Reanalysis (CFSR) data. The WRF model was configured with three nested domains (d01–d03) with horizontal resolutions increasing inward from 36 km to 12 km to 4 km horizontal resolution, respectively. These simulations were a continuous run with a spin-up year (i.e., 2000) to equilibrate the soil moisture, snow cover, and temperature at the beginning of the simulation. The simulations were then compared with TRMM and station data for the same time period using root mean squared error (RMSE), percentage bias (PBIAS), mean bias error (MBE), and the Pearson correlation coefficient. The results showed that the precipitation and temperature simulations were largely improved from d01 to d03. However, WRF tended to overestimate precipitation and underestimate temperature in all domains. This study presents high-resolution climatological datasets, which could be useful for the study of climate change and hydrological processes in this data-sparse region.

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Predictive Analysis of Municipal Solid Waste Generation Using an Optimized Neural Network Model

Processes ◽

10.3390/pr9112045 ◽

2021 ◽

Vol 9 (11) ◽

pp. 2045

Author(s):

Nehal Elshaboury ◽

Eslam Mohammed Abdelkader ◽

Ghasan Alfalah ◽

Abobakr Al-Sakkaf

Keyword(s):

Neural Network ◽

Waste Management ◽

Municipal Solid Waste ◽

Solid Waste ◽

Mean Squared Error ◽

Pearson Correlation ◽

Mean Bias Error ◽

Bias Error ◽

P Value ◽

Waste Generation

Developing successful municipal waste management planning strategies is crucial for implementing sustainable development. The research proposed the application of an optimized artificial neural network (ANN) to forecast quantities of waste in Poland. The neural network coupled with particle swarm optimization (PSO) algorithm is compared to the conventional neural network using five assessment metrics. The metrics are coefficient of efficiency (CE), Pearson correlation coefficient (R), Willmott’s index of agreement (WI), root mean squared error (RMSE), and mean bias error (MBE). Selected explanatory factors are incorporated in the developed models to reflect the influence of economic, demographic, and social aspects on the rate of waste generation. These factors are population, employment to population ratio, revenue per capita, number of entities by type of business activity, and number of entities enlisted in REGON per 10,000 population. According to the findings, the ANN–PSO model (CE = 0.92, R = 0.96, WI = 0.98, RMSE = 11,342.74, and MBE = 6548.55) significantly outperforms the traditional ANN model (CE = 0.11, R = 0.68, WI = 0.78, RMSE = 38,571.68, and MBE = 30,652.04). The significant level of the reported outputs is evaluated using the Wilcoxon–Mann–Whitney U-test, with a significance level of 0.05. The p-values of the pairings (ANN, observed) and (ANN, ANN–PSO) are all less than 0.05, suggesting that the models are statistically different. On the other hand, the P-value of (ANN–PSO, observed) is more than 0.05, suggesting that the difference between the models is statistically insignificant. Therefore, the proposed ANN–PSO model proves its efficiency at estimating municipal solid waste quantities and may be regarded as a cost-efficient method of developing integrated waste management systems.

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Calibration and evaluation of the DSSAT/Canegro model for sugarcane cultivars under irrigation managements

Revista Brasileira de Engenharia Agrícola e Ambiental ◽

10.1590/1807-1929/agriambi.v24n1p52-58 ◽

2020 ◽

Vol 24 (1) ◽

pp. 52-58

Author(s):

Anderson P. Coelho ◽

Alexandre B. Dalri ◽

João A. Fischer Filho ◽

Rogério T. de Faria ◽

Laércio S. Silva ◽

...

Keyword(s):

Mean Squared Error ◽

Pearson Correlation ◽

Irrigation Treatment ◽

Growth And Yield ◽

Mean Bias Error ◽

Bias Error ◽

Fundamental Factor ◽

Supplementary Irrigation ◽

Sugarcane Yield ◽

Stalk Yield

ABSTRACT Model calibration is a fundamental factor to obtain high accuracy in the estimation of crop growth and yield. This study aimed to parameterize the genetic and ecotype coefficients of the DSSAT/Canegro model for five sugarcane cultivars kept under three water managements, besides evaluating the accuracy of the model in predicting sugarcane stalk yield, sugar yield and height. Experimental field data were obtained from two years (2016 and 2017) of cultivation at FCAV/Universidade Estadual Paulista, Jaboticabal, SP, Brazil. The cultivars were maintained under supplementary irrigation, deficit irrigation and no irrigation. Data of the supplementary irrigation treatment (without stress) were used for the parameterization of each cultivar. Model accuracy was assessed by Pearson correlation (r), root mean squared error (RMSE), mean bias error (MBE), index of agreement (d) and confidence coefficient (c). The DSSAT/Canegro model is highly accurate in predicting stalk and sugar yields of sugarcane grown under water regimes, presenting itself as a viable alternative in sugarcane yield simulation. For better performance of the DSSAT/Canegro model, it is necessary to parameterize the variables related to the ecotype of the cultivars, besides the specific coefficients of the cultivars.

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Energy models for cost-optimal analysis: Development and calibration of residential reference building models for Northern Cyprus

Indoor and Built Environment ◽

10.1177/1420326x211013076 ◽

2021 ◽

pp. 1420326X2110130

Author(s):

Manta Marcelinus Dakyen ◽

Mustafa Dagbasi ◽

Murat Özdenefe

Keyword(s):

Energy Efficiency ◽

Energy Consumption ◽

Mean Squared Error ◽

Residential Buildings ◽

Mean Bias Error ◽

Bias Error ◽

Sampled Data ◽

Low Carbon ◽

Building Stock ◽

Northern Cyprus

Ambitious energy efficiency goals constitute an important roadmap towards attaining a low-carbon society. Thus, various building-related stakeholders have introduced regulations targeting the energy efficiency of buildings. However, some countries still lack such policies. This paper is an effort to help bridge this gap for Northern Cyprus, a country devoid of building energy regulations that still experiences electrical energy production and distribution challenges, principally by establishing reference residential buildings which can be the cornerstone for prospective building regulations. Statistical analysis of available building stock data was performed to determine existing residential reference buildings. Five residential reference buildings with distinct configurations that constituted over 75% floor area share of the sampled data emerged, with floor areas varying from 191 to 1006 m2. EnergyPlus models were developed and calibrated for five residential reference buildings against yearly measured electricity consumption. Values of Mean Bias Error (MBE) and Cumulative Variation of Root Mean Squared Error CV(RMSE) between the models’ energy consumption and real energy consumption on monthly based analysis varied within the following ranges: (MBE)monthly from –0.12% to 2.01% and CV(RMSE)monthly from 1.35% to 2.96%. Thermal energy required to maintain the models' setpoint temperatures for cooling and heating varied from 6,134 to 11,451 kWh/year.

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Using digital image processing to characterize the Campbell–Stokes sunshine recorder and to derive high-temporal resolution direct solar irradiance

Atmospheric Measurement Techniques ◽

10.5194/amt-8-183-2015 ◽

2015 ◽

Vol 8 (1) ◽

pp. 183-194 ◽

Cited By ~ 13

Author(s):

A. Sanchez-Romero ◽

J. A. González ◽

J. Calbó ◽

A. Sanchez-Lorenzo

Keyword(s):

Image Processing ◽

Temporal Resolution ◽

Solar Irradiance ◽

Atmospheric Aerosol ◽

Mean Squared Error ◽

Sunshine Duration ◽

Strong Relationship ◽

Mean Bias Error ◽

Bias Error ◽

High Temporal Resolution

Abstract. The Campbell–Stokes sunshine recorder (CSSR) has been one of the most commonly used instruments for measuring sunshine duration (SD) through the burn length of a given CSSR card. Many authors have used SD to obtain information about cloudiness and solar radiation (by using Ångström–Prescott type formulas), but the burn width has not been used systematically. In principle, the burn width increases for increasing direct beam irradiance. The aim of this research is to show the relationship between burn width and direct solar irradiance (DSI) and to prove whether this relationship depends on the type of CSSR and burning card. A method of analysis based on image processing of digital scanned images of burned cards is used. With this method, the temporal evolution of the burn width with 1 min resolution can be obtained. From this, SD is easily calculated and compared with the traditional (i.e., visual) determination. The method tends to slightly overestimate SD, but the thresholds that are used in the image processing could be adjusted to obtain an improved estimation. Regarding the burn width, experimental results show that there is a high correlation between two different models of CSSRs, as well as a strong relationship between burn widths and DSI at a high-temporal resolution. Thus, for example, hourly DSI may be estimated from the burn width with higher accuracy than based on burn length (for one of the CSSR, relative root mean squared error is 24 and 30%, respectively; mean bias error is −0.6 and −30.0 W m−2, respectively). The method offers a practical way to exploit long-term sets of CSSR cards to create long time series of DSI. Since DSI is affected by atmospheric aerosol content, CSSR records may also become a proxy measurement for turbidity and atmospheric aerosol loading.

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Cloud Detection with Historical Geostationary Satellite Sensors for Climate Applications

Remote Sensing ◽

10.3390/rs11091052 ◽

2019 ◽

Vol 11 (9) ◽

pp. 1052 ◽

Cited By ~ 3

Author(s):

Reto Stöckli ◽

Jędrzej S. Bojanowski ◽

Viju O. John ◽

Anke Duguay-Tetzlaff ◽

Quentin Bourgeois ◽

...

Keyword(s):

Mean Squared Error ◽

Sensor Data ◽

Mean Bias Error ◽

Retrieval Algorithm ◽

Bias Error ◽

Cloud Detection ◽

Climate Data ◽

Detection Algorithms ◽

Fractional Cover ◽

Spatio Temporal

Can we build stable Climate Data Records (CDRs) spanning several satellite generations? This study outlines how the ClOud Fractional Cover dataset from METeosat First and Second Generation (COMET) of the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF) was created for the 25-year period 1991–2015. Modern multi-spectral cloud detection algorithms cannot be used for historical Geostationary (GEO) sensors due to their limited spectral resolution. We document the innovation needed to create a retrieval algorithm from scratch to provide the required accuracy and stability over several decades. It builds on inter-calibrated radiances now available for historical GEO sensors. It uses spatio-temporal information and a robust clear-sky retrieval. The real strength of GEO observations—the diurnal cycle of reflectance and brightness temperature—is fully exploited instead of just accounting for single “imagery”. The commonly-used naive Bayesian classifier is extended with covariance information of cloud state and variability. The resulting cloud fractional cover CDR has a bias of 1% Mean Bias Error (MBE), a precision of 7% bias-corrected Root-Mean-Squared-Error (bcRMSE) for monthly means, and a decadal stability of 1%. Our experience can serve as motivation for CDR developers to explore novel concepts to exploit historical sensor data.

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Using digital image processing to characterize the Campbell–Stokes sunshine recorder and to derive high-temporal resolution direct solar irradiance

Atmospheric Measurement Techniques Discussions ◽

10.5194/amtd-7-9537-2014 ◽

2014 ◽

Vol 7 (9) ◽

pp. 9537-9571

Author(s):

A. Sanchez-Romero ◽

J. A. González ◽

J. Calbó ◽

A. Sanchez-Lorenzo

Keyword(s):

Image Processing ◽

Temporal Resolution ◽

Solar Irradiance ◽

Atmospheric Aerosol ◽

Mean Squared Error ◽

Sunshine Duration ◽

Mean Bias Error ◽

Unbiased Estimation ◽

Bias Error ◽

High Temporal Resolution

Abstract. The Campbell–Stokes sunshine recorder (CSSR) has been one of the most commonly used instruments for measuring sunshine duration (SD) through the burn length of a given CSSR card. Many authors have used SD to obtain information about cloudiness and solar radiation (by using Ångström–Prescott type formulas). Contrarily, the burn width has not been used systematically. In principle, the burn width increases for increasing direct beam irradiance. The aim of this research is to show the relationship between burn width and direct solar irradiance (DSI), and to prove whether this relationship depends on the type of CSSR and burning card. A semi-automatic method based on image processing of digital scanned images of burnt cards is presented. With this method, the temporal evolution of the burn width with 1 min resolution can be obtained. From this, SD is easily calculated and compared with the traditional (i.e. visual) determination. The method tends to slightly overestimate SD but the thresholds that are used in the image processing could be adjusted to obtain an unbiased estimation. Regarding the burn width, results show that there is a high correlation between two different models of CSSRs, as well as a strong relationship between burn widths and DSI at a high-temporal resolution. Thus, for example, hourly DSI may be estimated from the burn width with higher accuracy than based on burn length (for one of the CSSR, relative root mean squared error 24 and 30% respectively; mean bias error −0.6 and −30.0 W m−2 respectively). The method offers a practical way to exploit long-term sets of CSSR cards to create long time series of DSI. Since DSI is affected by atmospheric aerosol content, CSSR records may also become a proxy measurement for turbidity and atmospheric aerosol loading.

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Observed Trends and Variability of Temperature and Precipitation and Their Global Teleconnections in the Upper Indus Basin, Hindukush-Karakoram-Himalaya

Atmosphere ◽

10.3390/atmos12080973 ◽

2021 ◽

Vol 12 (8) ◽

pp. 973

Author(s):

Azfar Hussain ◽

Jianhua Cao ◽

Ishtiaq Hussain ◽

Saira Begum ◽

Mobeen Akhtar ◽

...

Keyword(s):

Climate Change ◽

Scientific Basis ◽

Indus Basin ◽

Spatiotemporal Variability ◽

Maximum Temperature ◽

Local Climate ◽

Upper Indus Basin ◽

Temperature And Precipitation ◽

Increasing Trend ◽

Increasing Trends

Having an extreme topography and heterogeneous climate, the Upper Indus Basin (UIB) is more likely to be affected by climate change and it is a crucial area for climatological studies. Based on the monthly minimum temperature (Tmin), maximum temperature (Tmax) and precipitation from nine meteorological stations, the spatiotemporal variability of temperature and precipitation were analyzed on monthly, seasonal, and annual scales. Results show a widespread significant increasing trend of 0.14 °C/decade for Tmax, but a significant decreasing trend of −0.08 °C/decade for Tmin annually, during 1955–2016 for the UIB. Seasonally, warming in Tmax is stronger in winter and spring, while the cooling in Tmin is greater in summer and autumn. Results of seasonal Tmax indicate increasing trends in winter, spring and autumn at rates of 0.38, 0.35 and 0.05 °C/decade, respectively, while decreasing in summer with −0.14 °C/decade. Moreover, seasonal Tmin results indicate increasing trends in winter and spring at rates of 0.09 and 0.08 °C/decade, respectively, while decreasing significantly in summer and autumn at rates of −0.21 and −0.22 °C/decade respectively for the whole the UIB. Precipitation exhibits an increasing trend of 2.74 mm/decade annually, while, increasing in winter, summer and autumn at rates of 1.18, 2.06 and 0.62 mm/decade respectively. The warming in Tmax and an increase in precipitation have been more distinct since the mid-1990s, while the cooling in Tmin is observed in the UIB since the mid-1980s. Warming in the middle and higher altitude (1500–2800 m and >2800 m) are much stronger, and the increase is more obvious in regions with elevation >2800 m. The wavelet analysis illustrated sporadic inter-annual covariance of seasonal Tmax, Tmin and precipitation with ENSO, NAO, IOD and PDO in the UIB. The periodicities were usually constant over short timescales and discontinuous over longer timescales. This study offers a better understanding of the local climate characteristics and provides a scientific basis for government policymakers.

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Estimation of Room-Level Cooling Energy in Hot/Arid Climate by Machine Learning-Based Approaches

ASME 2021 Verification and Validation Symposium ◽

10.1115/vvs2021-65272 ◽

2021 ◽

Author(s):

Bingyan Jia ◽

Danlin Hou ◽

Liangzhu (Leon) Wang ◽

Ibrahim Galal Hassan

Keyword(s):

Machine Learning ◽

Power Density ◽

Mean Squared Error ◽

Building Energy ◽

Energy Performance ◽

Mean Bias Error ◽

Support Vector ◽

Bias Error ◽

Model Parameters ◽

Meta Model

Abstract Building energy models (BEM) are developed for understanding a building’s energy performance. A meta-model of the whole building energy analysis is often used for the BEM calibration and energy prediction. The literature review shows that studies with a focus on the development of room-level meta-models are missing. This study aims to address this research gap through a case study of a residential building with 138 apartments in Doha, Qatar. Five parameters, including cooling setpoint, number of occupants, lighting power density, equipment power density, and interior solar reflectance, are selected as input parameters to create ninety-six different scenarios. Three machine-learning models are used as meta-models to generalize the relationship between cooling energy and the model parameters, including Multiple Linear Regression, Support Vector Regression, and Artificial Neural Networks. The three meta-models’ prediction accuracies are evaluated by the Normalized Mean Bias Error (NMBE), Coefficient of Variation of the Root Mean Squared Error CV (RMSE), and R square (R2). The results show that the ANN model performs best. A new generic BEM is then established to validate the meta-model. The results indicate that the proposed meta-model is accurate and efficient in predicting the cooling energy in summer and transitional months for a building with a similar floor configuration.

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

10.5194/amt-2017-9 ◽

2017 ◽

Author(s):

Stefano Federico ◽

Rosa Claudia Torcasio ◽

Paolo Sanò ◽

Daniele Casella ◽

Monica Campanelli ◽

...

Keyword(s):

Mesoscale Model ◽

Horizontal Resolution ◽

Climatic Conditions ◽

Atmospheric Modeling ◽

Mean Bias Error ◽

Bias Error ◽

Hourly Data ◽

Sky Conditions ◽

The Impact ◽

Rams Model

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 one-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 performance of MSG-GHI estimate and RAMS-GHI one-day forecast are evaluated for one year (1 June 2013–31 May 2014) against data of twelve ground based pyranometers over Italy spanning a range of climatic conditions, i.e. from maritime Mediterranean to Alpine climate. Statistics on 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 analysis. Results on hourly data show an evident dependence on the sky conditions, with the Root Mean Square Error (RMSE) increasing from clear to contaminated, and to overcast conditions. The RMSE increases substantially 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 for the RAMS model, the RMSE ranges from 152 W/m2 (31 %) obtained for Cozzo Spadaro, a maritime station, to 287 W/m2 (82 %) for Aosta, an Alpine site. Considering the yearly statistics for MSG-GHI, the minimum RMSE is for Cozzo Spadaro (71 W/m2 , 14 %), while the maximum is for Aosta (181 W/m2 , 51 %). The Mean Bias Error (MBE) shows the tendency of RAMS to over forecast the GHI, while no specific tendency if found for MSG-GHI. Results for daily integrated GHI show a reduction of the RMSE of at least 10 %, compared to hourly GHI evaluation, for both RAMS-GHI one-day forecast and MSG-GHI estimate. 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 hourly and daily integrated GHI. The application of MOS shows an improvement for RAMS-GHI up to 24 %, depending on the site considered, while the impact of MOS on MSG-GHI RMSE is small (2–3 %).

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

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-7-221-2010 ◽

2010 ◽

Vol 7 (1) ◽

pp. 221-267 ◽

Cited By ~ 5

Author(s):

W. Terink ◽

R. T. W. L. Hurkmans ◽

P. J. J. F. Torfs ◽

R. Uijlenhoet

Keyword(s):

Bias Correction ◽

Meteorological Data ◽

Correction Method ◽

Climate Impact ◽

Reanalysis Data ◽

Mean Bias Error ◽

Bias Error ◽

Rhine Basin ◽

The Mean ◽

Precipitation And Temperature

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 is 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 the mean and coefficient of variation (CV) of the observations. Temperature is corrected by fitting 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, and for September and October for precipitation only. 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.

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