scholarly journals Primary Evaluation of the GCOM-C Aerosol Products at 380 nm Using Ground-Based Sky Radiometer Observations

2020 ◽  
Vol 12 (16) ◽  
pp. 2661
Author(s):  
Hossain Mohammed Syedul Hoque ◽  
Hitoshi Irie ◽  
Alessandro Damiani ◽  
Masahiro Momoi
Keyword(s):  
Dry Season ◽  
Diurnal Variations ◽  
Mean Bias Error ◽  
Optical Absorption Spectroscopy ◽  
Bias Error ◽  
Ozone Monitoring Instrument ◽  
Rural Sites ◽  
Primary Evaluation ◽  
Absorbing Aerosols ◽  
Good Agreement

The Global Change Observation Mission-Climate (GCOM-C) is currently the only satellite sensor providing aerosol optical thickness (AOT) in the ultraviolet (UV) region during the morning overpass time. The observations in the UV region are important to detect the presence of absorbing aerosols in the atmosphere. The recently available GCOM-C dataset of AOT at 380 nm for January to September 2019 were evaluated using ground-based SKYNET sky radiometer measurements at Chiba, Japan (35.62° N, 140.10° E) and Phimai, central Thailand (15.18° N, 102.56° E), representing urban and rural sites, respectively. AOT retrieved from sky radiometer observations in Chiba and Phimai was compared with coincident AERONET and multi-axis differential optical absorption spectroscopy (MAX-DOAS) AOT values, respectively. Under clear sky conditions, the datasets showed good agreement. The sky radiometer and GCOM-C AOT values showed a positive correlation (R) of ~0.73 for both sites, and agreement between the datasets was mostly within ±0.2 (the number of coincident points at both sites was less than 50 for the coincidence criterion of ≤30 km). At Chiba, greater differences in the AOT values were primarily related to cloud screening in the datasets. The mean bias error (MBE) (GCOM-C – sky radiometer) for the Chiba site was −0.02 for a coincidence criterion of ≤10 km. For a similar coincidence criterion, the MBE values were higher for observations at the Phimai site. This difference was potentially related to the strong influence of biomass burning during the dry season (Jan–Apr). The diurnal variations in AOT, inferred from the combination of GCOM-C and ozone monitoring instrument (OMI) observations, showed good agreement with the sky radiometer data, despite the differences in the absolute AOT values. Over Phimai, the AOT diurnal variations from the satellite and sky radiometer observations were different, likely due to the large differences in the AOT values during the dry season.

scholarly journals Comparison between Energy Simulation and Monitoring Data in an Office Building

Energies ◽  
2021 ◽  
Vol 15 (1) ◽  
pp. 239
Author(s):  
Koldobika Martin-Escudero ◽  
Garazi Atxalandabaso ◽  
Aitor Erkoreka ◽  
Amaia Uriarte ◽  
Matteo Porta
Keyword(s):  
Building Energy ◽  
Energy Performance ◽  
Building Envelope ◽  
Energy Simulation ◽  
Office Building ◽  
Mean Bias Error ◽  
Bias Error ◽  
Data Sets ◽  
Hourly Basis ◽  
Good Agreement

One of the most important steps in the retrofitting process of a building is to understand its pre-retrofitting stage energy performance. The best choice for carrying this out is by means of a calibrated building energy simulation (BES) model. Then, the testing of different retrofitting solutions in the validated model allows for quantifying the improvements that may be obtained, in order to choose the most suitable solution. In this work, based on the available detailed building drawings, constructive details, building operational data and the data sets obtained on a minute basis (for a whole year) from a dedicated energy monitoring system, the calibration of an in-use office building energy model has been carried out. It has been possible to construct a detailed white box model based on Design Builder software. Then, comparing the model output for indoor air temperature, lighting consumption and heating consumption against the monitored data, some of the building envelope parameters and inner building inertia of the model were fine tuned to obtain fits fulfilling the ASHRAE criteria. Problems found during this fitting process and how they are solved are explained in detail. The model calibration is firstly performed on an hourly basis for a typical winter and summer week; then, the whole year results of the simulation are compared against the monitored data. The results show a good agreement for indoor temperature, lighting and heating consumption compared with the ASHRAE criteria for the mean bias error (MBE).

scholarly journals INVESTIGATION ON THE VALIANTZAS’ EVAPOTRANSPIRATION MODELS FOR PENINSULAR MALAYSIA

2019 ◽  
pp. 174-182
Author(s):  
Nor Farah Atiqah Binti Ahmad ◽  
Sobri Harun ◽  
Haza Nuzly Abdull Hamed ◽  
Muhamad Askari ◽  
Zulkiflee Ibrahim ◽  
...  
Keyword(s):  
Accurate Method ◽  
Peninsular Malaysia ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
Mean Square ◽  
The World ◽  
Good Agreement ◽  
Agricultural Organization

The search for an accurate evapotranspiration (ET) continues when the world has responsibility to cope with the water scarcity issue, population outgrown and uncertain change of weather. Measuring actual evapotranspiration (ETa) can be tedious and requires a lot of time and cost. Therefore, numbers of empirical ET models have been developed to overcome this problem. The Valiantzas’ models are quite familiar to the hydrologist community as it has been developed based on Penman evaporation equation. This paper presents the evaluation on the selected six Valiantzas’ models by comparing to Food and Agricultural Organization Penman-Montieth (FAO-PM) empirical model in estimating ET in the Peninsular Malaysia. Seventeen meteorological stations around Peninsular Malaysia with data gathered from 1987 till 2003 were tested. The performance for each model was evaluated by root mean square error (RMSE), coefficient of determination (R2), percentage error (PE) and mean bias error (MBE). All the six models showed good agreement to FAO-PM with R2> 0.90. The PETval2 model which gave R2 of 0.97 was the best performer with the lowest RMSE, PE and MBE of 0.26, 5.5% and 0.14, respectively. The good and sensible performance on the ET estimation displayed by Valiantzas’ model may promise an accurate method for calculation on the water management for irrigation and catchment studies.

scholarly journals Observation of slant column NO<sub>2</sub> using the super-zoom mode of AURA-OMI

2011 ◽  
Vol 4 (2) ◽  
pp. 1989-2005 ◽  
Author(s):  
L. C. Valin ◽  
A. R. Russell ◽  
E. J. Bucsela ◽  
J. P. Veefkind ◽  
R. C. Cohen
Keyword(s):  
United Arab Emirates ◽  
Spatial Resolution ◽  
Signal To Noise Ratio ◽  
Near Field ◽  
Optical Absorption Spectroscopy ◽  
Ozone Monitoring Instrument ◽  
Spatial Response ◽  
Close Proximity ◽  
Individual Detector ◽  
Good Agreement

Abstract. We retrieve slant column NO2 from the super-zoom mode of the Ozone Monitoring Instrument (OMI) to explore its utility for understanding NOx emissions and removal. Slant column NO2 is operationally retrieved from OMI at 13 × 24 km2, a nadir footprint resulting from the on-board average of eight detector elements. For 85 orbits in late 2004, OMI reported observations from individual "super-zoom" detector elements (spaced at 13 × 3 km2 at nadir). We assess the spatial response of these individual detector elements in-flight and determine an upper-bound on spatial resolution of 9 km, in good agreement with on-ground calibration (7 km FWHM). We retrieve slant column NO2 from these super-zoom observations over Sarni, India (19 November), Seoul, South Korea (21 November), Dubai, United Arab Emirates (21 November) and the Rihand Reservoir in India (23 November) using differential optical absorption spectroscopy. Comparison of super-zoom and operational-scale retrievals highlights the capacity of the super-zoom mode to distinguish NOx sources in close proximity. The 1-σ signal to noise ratio (SNR) for these retrievals is as high as 25 and is greater than 5 over the observed enhancements indicating that instrumental noise is not the limitation to obtaining high spatial resolution NO2 maps. We show that these high resolution observations provide constraints on NO2 gradients providing a direct measure of the NOx lifetime in the near field of large plumes.

scholarly journals Observation of slant column NO<sub>2</sub> using the super-zoom mode of AURA-OMI

2011 ◽  
Vol 4 (9) ◽  
pp. 1929-1935 ◽  
Author(s):  
L. C. Valin ◽  
A. R. Russell ◽  
E. J. Bucsela ◽  
J. P. Veefkind ◽  
R. C. Cohen
Keyword(s):  
United Arab Emirates ◽  
Random Noise ◽  
Point Sources ◽  
Optical Absorption Spectroscopy ◽  
Ozone Monitoring Instrument ◽  
Spatial Response ◽  
Individual Detector ◽  
Instrumental Precision ◽  
Good Agreement ◽  
Ground Calibration

Abstract. We retrieve slant column NO2 from the super-zoom mode of the Ozone Monitoring Instrument (OMI) to explore its utility for understanding NOx emissions and variability. Slant column NO2 is operationally retrieved from OMI (Boersma et al., 2007; Bucsela et al., 2006) with a nadir footprint of 13 × 24 km2, the result of averaging eight detector elements on board the instrument. For 85 orbits in late 2004, OMI reported observations from individual "super-zoom" detector elements (spaced at 13 × 3 km2 at nadir). We assess the spatial response of these individual detector elements in-flight and determine an upper-bound on spatial resolution of 9 km, in good agreement with on-ground calibration (7 km FWHM). We determine the precision of the super-zoom mode to be 2.1 × 1015 molecules cm−2, approximately a factor of √8 lower than an identical retrieval at operational scale as expected if random noise dominates the uncertainty. We retrieve slant column NO2 over the Satpura power plant in India; Seoul, South Korea; Dubai, United Arab Emirates; and a set of large point sources on the Rihand Reservoir in India using differential optical absorption spectroscopy (DOAS). Over these sources, the super-zoom mode of OMI observes variation in slant column NO2 of up to 30 × the instrumental precision within one operational footprint.

A New Model for Computing Daily Global Radiation of Lanzhou, China

2012 ◽  
Vol 608-609 ◽  
pp. 42-45
Author(s):  
Xiao Jun Cheng ◽  
Ying Ni Jiang ◽  
Yue Hua Liu
Keyword(s):  
Sunshine Duration ◽  
Global Radiation ◽  
Statistical Error ◽  
Global Solar Radiation ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
Daily Sunshine ◽  
Predicted Values ◽  
Good Agreement

Models for computing daily global radiation for Lanzhou of China were developed. The development of the models was based on the analysis of global and daily sunshine duration data during 10 years. Estimated values were compared with measured values in terms of statistical error tests such as mean percentage error (MPE), mean bias error (MBE), root mean square error (RMSE). The model performing best was selected. The global solar radiation estimated from the best model was compared with measured values. It was determined that the predicted values have good agreement with the measured values at high daily global radiation.

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

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 281
Author(s):  
Stuart L. Joy ◽  
José L. Chávez
Keyword(s):  
Heat Flux ◽  
Eddy Covariance ◽  
Agricultural Land ◽  
Flux Measurement ◽  
Source Area ◽  
Mean Bias Error ◽  
Bias Error ◽  
Crop Water ◽  
The Past ◽  
Measurement Results

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%.

scholarly journals CO2 Flux over the Contiguous United States in 2016 Inverted by WRF-Chem/DART from OCO-2 XCO2 Retrievals

2021 ◽  
Vol 13 (15) ◽  
pp. 2996
Author(s):  
Qinwei Zhang ◽  
Mingqi Li ◽  
Maohua Wang ◽  
Arthur Paul Mizzi ◽  
Yongjian Huang ◽  
...  
Keyword(s):  
United States ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Co2 Flux ◽  
Total Carbon ◽  
Mean Bias Error ◽  
Bias Error ◽  
Parallel Experiment ◽  
Concentration Measurements ◽  
Global Stocktake

High spatial resolution carbon dioxide (CO2) flux inversion systems are needed to support the global stocktake required by the Paris Agreement and to complement the bottom-up emission inventories. Based on the work of Zhang, a regional CO2 flux inversion system capable of assimilating the column-averaged dry air mole fractions of CO2 (XCO2) retrieved from Orbiting Carbon Observatory-2 (OCO-2) observations had been developed. To evaluate the system, under the constraints of the initial state and boundary conditions extracted from the CarbonTracker 2017 product (CT2017), the annual CO2 flux over the contiguous United States in 2016 was inverted (1.08 Pg C yr−1) and compared with the corresponding posterior CO2 fluxes extracted from OCO-2 model intercomparison project (OCO-2 MIP) (mean: 0.76 Pg C yr−1, standard deviation: 0.29 Pg C yr−1, 9 models in total) and CT2017 (1.19 Pg C yr−1). The uncertainty of the inverted CO2 flux was reduced by 14.71% compared to the prior flux. The annual mean XCO2 estimated by the inversion system was 403.67 ppm, which was 0.11 ppm smaller than the result (403.78 ppm) simulated by a parallel experiment without assimilating the OCO-2 retrievals and closer to the result of CT2017 (403.29 ppm). Independent CO2 flux and concentration measurements from towers, aircraft, and Total Carbon Column Observing Network (TCCON) were used to evaluate the results. Mean bias error (MBE) between the inverted CO2 flux and flux measurements was 0.73 g C m−2 d−1, was reduced by 22.34% and 28.43% compared to those of the prior flux and CT2017, respectively. MBEs between the CO2 concentrations estimated by the inversion system and concentration measurements from TCCON, towers, and aircraft were reduced by 52.78%, 96.45%, and 75%, respectively, compared to those of the parallel experiment. The experiment proved that CO2 emission hotspots indicated by the inverted annual CO2 flux with a relatively high spatial resolution of 50 km consisted well with the locations of most major metropolitan/urban areas in the contiguous United States, which demonstrated the potential of combing satellite observations with high spatial resolution CO2 flux inversion system in supporting the global stocktake.

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

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.

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

Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1207
Author(s):  
Gonçalo C. Rodrigues ◽  
Ricardo P. Braga
Keyword(s):  
Wind Speed ◽  
Bias Correction ◽  
Goodness Of Fit ◽  
Mediterranean Climate ◽  
Weather Data ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Bias Error ◽  
Weather Variables ◽  
Weather Stations

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.

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

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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