Quality Check Procedures and Statistics for the Indian SRRA Solar Radiation Measurement Network

Schwandt, Marko; Chhatbar, Kaushal; Meyer, Richard; Mitra, Indradip; Vashistha, Ramadhan; Giridhar, G.; Gomathinayagam, S.; Kumar, Ashok

doi:10.1016/j.egypro.2014.10.112

Cited by 13 publications

(7 citation statements)

References 1 publication

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“…This is because of that the TOACs test depends on TOA (Equation (1)) and BSRN depends on increased TOA (Equation (2), Figure 5). This result of each test is in agreement with published studies [1,16,21,22,24,30] that have applied each test separately.…”

Section: Discussionsupporting

confidence: 91%

“…The application of the sets of QC tests to the GHI station data of both station types in northeastern Iraq has shown high data quality. The data gaps are generally very limited at the tower stations, whereas all automatic stations have a rate of missing values, which are similar to previously published studies [16,44]. The results of QC of GHI show slight differences between the BSRN and TOACs set of tests.…”

Section: Discussionsupporting

confidence: 86%

“…In contrast, many data values are flagged by the TOACs test (flag 5) (Tables 5 and 6, Figure 3). This might be owing to the full or partial shading of the sensor, dirt on the sensor or malfunction of the sensor [11,16,26]. The lower limit of the BSRN test can be useful when checking day-and night-time data in cold regions.…”

Section: Discussionmentioning

confidence: 99%

“…Detecting missing hourly values in the time series and setting them to NA is important to show missing observations which can later be used for different purposes, such as comparing two observations in the time series or comparing ground data with satellite data to avoid inappropriate comparisons [1,16,27]. We automatically checked all hourly time series data for any gaps or unreliable filled values such as 999 and (///), and we set them to NA.…”

Section: Missing Value (Na) Detectionmentioning

confidence: 99%

“…In addition, there are operational and installation errors from miscalibration, a lack of regular cleaning of dust, snow, water droplets and bird droppings, and shadows cast on the equipment by nearby trees and buildings [12]. These factors cause systematic and random errors in the data, which have been reported in the literature [13][14][15][16][17]. Therefore, sensors that are measuring SR are unlike other meteorological instruments in that they need high maintenance to sustain performance and collection of high quality data, and their data recording needs to be checked regularly before using in scientific studies [1,11,18].…”

Section: Introductionmentioning

confidence: 98%

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Quality Control of Global Horizontal Irradiance Estimates through BSRN, TOACs and Air Temperature/Sunshine Duration Test Procedures

Ameen

Balzter

Jarvis

2018

Climate

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Solar Radiation (SR) data are required for many disciplines and applications. The ground measurement of SR data is hampered by technical and operational errors. Therefore, several approaches have been developed to detect these errors. This study aimed to compare two quality tests of hourly Global Horizontal Irradiance (GHI) estimates through the Baseline Surface Radiation Network (BSRN) of the World Meteorological Organization (WMO) and Top of Atmosphere irradiance and Clear sky (TOACs) on a horizontal plane. Each of these tests has a threshold to pass data, which leads to different results. A newly developed quality test method is presented that uses Sunshine Duration (SD) and Air Temperature (AT) to check hourly GHI and is applied to data from 20 meteorological stations in northeast Iraq. The new method was validated using independent high quality data from six stations in various regions with the same climate regime. The method consists of several tests that compare ground data with upper and lower limits of radiation at the top of the atmosphere, using a clear sky radiation model and the relation between SD and AT with SR to determine data values of dubious quality. The rate of error flags generally range from 1% to 27%. The findings show that SD and AT can be used to support other quality tests and to detect nearly 2% additional dubious data values compared to BSRN and TOACs tests. The SD test tends to work like a consistency check but AT does not work like that according to the validation result. However, AT can be used to test the plausibility of data. The argument for using AT in this study may be impractical for other climate conditions. The results suggest that a combination of tests can lead to a better quality of ground data, especially when the components of SR are unviable. Using climate variables for further checks is another possibility.

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