Dataset Information

Estimation of hourly average illuminance under clear sky conditions in Chongqing.

ABSTRACT: Satellite-based methods are proposed for the estimation of clear day average hourly illuminance from satellite data under local climate conditions. First, aerosol optical depth (AOD) data collected using a ground-based sun photometer were used to calibrate the satellite remote sensing AOD data. Next, we screened for the factors affecting the illuminance of clear sky and detected three important factors, namely the sine of the solar altitude angle, aerosol optical thickness, and atmospheric precise water content. Finally, based on the AOD data of satellite remote sensing, combined with the local illumination data and meteorological data, a clear sky average hourly illumination model in Chongqing was established via the regression method. There was good agreement between the calculated and the measured values of clear day average hourly illuminance, with a root mean square difference and mean bias difference of 22% and -0.05%, respectively. The model was used to map clear day annual, quarterly, and monthly average hourly illuminance. The maps show the clear day annual, seasonal, and monthly variations of average hourly illuminance in Chongqing.

SUBMITTER: He Y

PROVIDER: S-EPMC7446841 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Estimation of hourly average illuminance under clear sky conditions in Chongqing.

He Ying Y Zhang Xinshuo X Quan Li L

PloS one 20200824 8

Satellite-based methods are proposed for the estimation of clear day average hourly illuminance from satellite data under local climate conditions. First, aerosol optical depth (AOD) data collected using a ground-based sun photometer were used to calibrate the satellite remote sensing AOD data. Next, we screened for the factors affecting the illuminance of clear sky and detected three important factors, namely the sine of the solar altitude angle, aerosol optical thickness, and atmospheric preci ...[more]

PMID: 32833977

Similar Datasets

Project description:Abstract Sensors that measure the attenuated backscatter coefficient (e.g., automatic lidars and ceilometers [ALCs]) provide information on aerosols that can impact urban climate and human health. To design an observational network of ALC sensors for supporting data assimilation and to improve prediction of urban weather and air quality, a methodology is needed. In this study, spatio‐temporal patterns of aerosol‐attenuated backscatter coefficient are modelled using Met Office numerical weather prediction (NWP) models at two resolutions, 1.5 km (UKV) and 300 m (London Model [LM]), for 28 clear‐sky days and nights. Initially, attenuated backscatter coefficient data are analysed using S‐mode principal component analysis (PCA) with varimax rotation. Four to seven empirical orthogonal functions (EOFs) are produced for each model level, with common EOFs found across different heights (day and night) for both NWP models. EOFs relate strongly to orography, wind, and emissions source location, highlighting these as critical controls of attenuated backscatter coefficient spatial variability across the megacity. Urban–rural differences are largest when wind speeds are low and vertical boundary‐layer dynamics can more effectively distribute near‐surface aerosol emissions vertically. In several night‐time EOFs, gravity‐wave features are found for both NWP models. Increasing the horizontal resolution of native ancillaries (model input parameters) and improving the urban surface scheme in the LM may enhance the urban signal in the EOFs. PCA output, with agglomerative Ward cluster analysis (CA), minimises intra‐group variance. The UKV and LM CA shape and size results are similar and strongly related to orography. PCA‐CA is a simple, but adaptable methodology, allowing close alignment with observation network design goals. Here, CA is used with wind roses to suggest the optimised ALC deployment is one in the city to observe the urban plume and others surrounding the city, with priority given to cluster size and frequency of upwind advection. Principal component analysis and cluster analysis are used with forward‐modelled aerosol attenuated backscatter from two numerical weather prediction models over Greater London. This identified regions with common spatial patterns in backscatter variability, which can subsequently help inform lidar deployment in an urban network. Common patterns strongly relate to relative aerosol emission locations, winds, and orography, factors that should be considered in future instrument network deployments.

Project description:Background People usually spend most of their time indoors, so indoor fine particulate matter (PM2.5) concentrations are crucial for refining individual PM2.5 exposure evaluation. The development of indoor PM2.5 concentration prediction models is essential for the health risk assessment of PM2.5 in epidemiological studies involving large populations. Methods In this study, based on the monitoring data of multiple types of places, the classical multiple linear regression (MLR) method and random forest regression (RFR) algorithm of machine learning were used to develop hourly average indoor PM2.5 concentration prediction models. Indoor PM2.5 concentration data, which included 11,712 records from five types of places, were obtained by on-site monitoring. Moreover, the potential predictor variable data were derived from outdoor monitoring stations and meteorological databases. A ten-fold cross-validation was conducted to examine the performance of all proposed models. Results The final predictor variables incorporated in the MLR model were outdoor PM2.5 concentration, type of place, season, wind direction, surface wind speed, hour, precipitation, air pressure, and relative humidity. The ten-fold cross-validation results indicated that both models constructed had good predictive performance, with the determination coefficients (R2) of RFR and MLR were 72.20 and 60.35%, respectively. Generally, the RFR model had better predictive performance than the MLR model (RFR model developed using the same predictor variables as the MLR model, R2 = 71.86%). In terms of predictors, the importance results of predictor variables for both types of models suggested that outdoor PM2.5 concentration, type of place, season, hour, wind direction, and surface wind speed were the most important predictor variables. Conclusion In this research, hourly average indoor PM2.5 concentration prediction models based on multiple types of places were developed for the first time. Both the MLR and RFR models based on easily accessible indicators displayed promising predictive performance, in which the machine learning domain RFR model outperformed the classical MLR model, and this result suggests the potential application of RFR algorithms for indoor air pollutant concentration prediction.

Project description:According to Thorkild Ramskou's theory proposed in 1967, under overcast and foggy skies, Viking seafarers might have used skylight polarization analysed with special crystals called sunstones to determine the position of the invisible Sun. After finding the occluded Sun with sunstones, its elevation angle had to be measured and its shadow had to be projected onto the horizontal surface of a sun compass. According to Ramskou's theory, these sunstones might have been birefringent calcite or dichroic cordierite or tourmaline crystals working as polarizers. It has frequently been claimed that this method might have been suitable for navigation even in cloudy weather. This hypothesis has been accepted and frequently cited for decades without any experimental support. In this work, we determined the accuracy of this hypothetical sky-polarimetric Viking navigation for 1080 different sky situations characterized by solar elevation ? and cloudiness ?, the sky polarization patterns of which were measured by full-sky imaging polarimetry. We used the earlier measured uncertainty functions of the navigation steps 1, 2 and 3 for calcite, cordierite and tourmaline sunstone crystals, respectively, and the newly measured uncertainty function of step 4 presented here. As a result, we revealed the meteorological conditions under which Vikings could have used this hypothetical navigation method. We determined the solar elevations at which the navigation uncertainties are minimal at summer solstice and spring equinox for all three sunstone types. On average, calcite sunstone ensures a more accurate sky-polarimetric navigation than tourmaline and cordierite. However, in some special cases (generally at 35°???????40°, 1 okta???????6 oktas for summer solstice, and at 20°???????25°, 0 okta???????4 oktas for spring equinox), the use of tourmaline and cordierite results in smaller navigation uncertainties than that of calcite. Generally, under clear or less cloudy skies, the sky-polarimetric navigation is more accurate, but at low solar elevations its accuracy remains relatively large even at high cloudiness. For a given ?, the absolute value of averaged peak North uncertainties dramatically decreases with increasing ? until the sign (±) change of these uncertainties. For a given ?, this absolute value can either decrease or increase with increasing ?. The most advantageous sky situations for this navigation method are at summer solstice when the solar elevation and cloudiness are 35°???????40° and 2 oktas???????3 oktas.

Dataset Information

Estimation of hourly average illuminance under clear sky conditions in Chongqing.

Publications

Estimation of hourly average illuminance under clear sky conditions in Chongqing.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets