A Method to Estimate Sunshine Duration Using Cloud Classification Data from a Geostationary Meteorological Satellite (FY-2D) over the Heihe River Basin

Wu, Bingfang; Liu, Shufu; Zhu, Weiwei; Yu, Mingzhou; Niu, Y.; Xing, Qiang

doi:10.3390/s16111859

Cited by 12 publications

(25 citation statements)

References 19 publications

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“…Products are distributed on data services website in HDF format (http://satellite.nsmc.org.cn/portalsite/default.aspx). Cloud data from the FY-2D satellite were used to compute the sunshine hours, instead of the interpolated meteorological sunshine hours [41,42]. AMSR-2 is a sensor aboard GCOM-W1, which can provide daily global soil moisture of the top soil with resolution of 10 km (https://gcom-w1.jaxa.jp/auth.html).…”

Section: Remote Sensing Datamentioning

confidence: 99%

Regional Daily ET Estimates Based on the Gap-Filling Method of Surface Conductance

Niu

et al. 2018

Remote Sensing

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Abstract:Remote sensing allows regional evapotranspiration (ET) values to be obtained. Surface conductance is a key variable in estimating ET and controls surface flux interactions between the underlying surface and atmosphere. Limited by the influence of clouds, ET can only be estimated on cloud-free days. In this study, a gap-filling method is proposed to acquire daily surface conductance, which was coupled into a Penman-Monteith (P-M) equation, to estimate the regional daily ET over the Hai River Basin. The gap-filling method is coupled with the canopy conductance, surface conductance and a simple time extension method, which provides more mechanisms and is more comprehensive. Field observations, including eddy covariance (EC) fluxes and meteorological elements from automatic weather station (AWS), were collected from two sites for calibration and validation. One site is located in Guantao County, which is cropped in a circular pattern with winter wheat and summer maize. The other site is located in Miyun County, which has orchard and summer maize crops. The P-M equation was inverted to the computed surface conductance at the field scale, and latent heat fluxes from EC were processed and converted to daily ET. The results show that the surface conductance model used in the gap-filling method performs well compared with the inverted surface conductance, which suggests that the model used here is reasonable. In addition, the relationship between the results estimated from the gap-filling method and EC measurements is more pronounced than that between the other method and the EC measurements. The R 2 values improve from 0.68 to 0.75 at the Guantao site and from 0.79 to 0.88 at the Miyun site. The improvement mainly occurs during the growing crop season, according to the temporal variations in the results.

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Section: Remote Sensing Datamentioning

confidence: 99%

Regional Daily ET Estimates Based on the Gap-Filling Method of Surface Conductance

Niu

et al. 2018

Remote Sensing

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“…However, these methods can only obtain sunshine duration at a site representing a limited area. When used to obtain regional sunshine duration, multiple sets of measurement equipment and higher frequency observations are required, which are time-consuming and labor-intensive and, therefore, expensive [9]. Consequently, extrapolation and interpolation methods are often used to calculate regional sunshine duration based on data from a limited number of field sites [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

“…These include the sunshine duration percentage empirical method, relative sunshine duration statistical models, and remote sensing empirical models. Sunshine duration percentage methods include the astronomical sunshine percentage method and the geographic sunshine percentage method [9,10,[15][16][17], but both overly rely on the accuracy of the assimilation of radiation data. Relative sunshine duration statistical models derive estimates from multiple variables including elevation from digital elevation models (DEMs), the amount of clouds observed at ground stations, clearness indices, air pollution indices (API), precipitation, wind speed, and surface incoming direct radiation [6,10,13,18,19].…”

Section: Introductionmentioning

confidence: 99%

Estimating Sunshine Duration Using Hourly Total Cloud Amount Data from a Geostationary Meteorological Satellite

Zhu

Niu

et al. 2019

Atmosphere

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Sunshine duration is an important indicator of the amount of solar radiation received in a region and an important input parameter for the study of atmospheric energy balance, climate change, ecosystem evolution, and social sustainability. Currently, extrapolation and interpolation of data from meteorological stations are the most common methods used to calculate sunshine duration on a regional scale. However, it is difficult to obtain high precision sunshine duration in areas lacking ground observation or where sunshine duration is highly heterogeneous on the ground. In this paper, a new method is proposed to estimate sunshine duration with hourly total cloud amount (CTA) data from sunrise to sunset derived from the Fengyun-2G geostationary meteorological satellite (FY-2G). This method constructs a new index known as daytime mean total cloud coverage amount and provides quadratic equations relating daytime mean total cloud coverage amount to relative sunshine duration in different seasons. The method was validated with ground observation data for 2016 from 18 meteorological stations in the Three-River Headwaters Region of Qinghai Province, China. For individual stations, the coefficient of determination (R2) between estimated and measured sunshine was at least 0.894, the RMSE (root mean square error) was 0.977 h/day or less, the MAE (mean absolute error) was 0.824 h/day or less, the RE (relative error) was 0.150 or lower, and the value of d was 0.963 or greater, which validated that the proposed method can effectively predict daily sunshine duration. These equations can also provide higher precision estimates of regional-scale sunshine duration. This was demonstrated by comparing, for the entire study region, the spatial distribution of sunshine duration estimated from season-based equations with results from three different interpolation methods based on ground observations. Overall, the study confirms that total cloud amount measures from a geostationary satellite can be used to successfully estimate sunshine duration.

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“…Both approaches were tested for one year of MSG-based data. Wu et al [11] used cloud classification data from geostationary satellite data to estimate sunshine duration for a region in China.…”

Section: Introductionmentioning

confidence: 99%

“…Kothe et al [9] derived sunshine duration for the whole of Europe, but just for one year. Methods by Kandirmaz [3], Shamim et al [5] or Wu et al [11] additionally used station data from a limited region for training or regression. Thus, none of these studies could answer the question of the applicability of these approaches to regions on continental scale and on climatological time scales at the same time.…”

Section: Introductionmentioning

confidence: 99%

A Satellite-Based Sunshine Duration Climate Data Record for Europe and Africa

et al. 2017

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Besides 2 m temperature and precipitation, sunshine duration is one of the most important and commonly used parameter in climatology, with measured time series of partly more than 100 years in length. EUMETSAT's Satellite Application Facility on Climate Monitoring (CM SAF) presents a climate data record for daily and monthly sunshine duration (SDU) for Europe and Africa. Basis for the advanced retrieval is a highly resolved satellite product of the direct solar radiation from measurements by Meteosat satellites 2 to 10. The data record covers the time period 1983 to 2015 with a spatial resolution of 0.05 • × 0.05 • . The comparison against ground-based data shows high agreement but also some regional differences. Sunshine duration is overestimated by the satellite-based data in many regions, compared to surface data. In West and Central Africa, low clouds seem to be the reason for a stronger overestimation of sunshine duration in this region (up to 20% for monthly sums). For most stations, the overestimation is low, with a bias below 7.5 h for monthly sums and below 0.4 h for daily sums. A high correlation of 0.91 for daily SDU and 0.96 for monthly SDU also proved the high agreement with station data. As SDU is based on a stable and homogeneous climate data record of more than 30 years length, it is highly suitable for climate applications, such as trend estimates.

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A Method to Estimate Sunshine Duration Using Cloud Classification Data from a Geostationary Meteorological Satellite (FY-2D) over the Heihe River Basin

Cited by 12 publications

References 19 publications

Regional Daily ET Estimates Based on the Gap-Filling Method of Surface Conductance

Regional Daily ET Estimates Based on the Gap-Filling Method of Surface Conductance

Estimating Sunshine Duration Using Hourly Total Cloud Amount Data from a Geostationary Meteorological Satellite

A Satellite-Based Sunshine Duration Climate Data Record for Europe and Africa

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