Evaluation of global horizontal irradiance estimates from ERA5 and COSMO-REA6 reanalyses using ground and satellite-based data

Urraca, Rubén; Huld, Thomas; Gracia-Amillo, Ana; Martínez-de-Pisón, Francisco Javier; Kaspar, Frank; Sanz-García, Andrés

doi:10.1016/j.solener.2018.02.059

Cited by 310 publications

(209 citation statements)

References 38 publications

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“…After many years of research and improvements in technology, ERA5 has a higher spatial and temporal resolution (0.25 • and hourly), a greater number of vertical levels (137), a new assimilation system (IFS Cycle 41r2) and more assimilated data than the ERA-Interim dataset. These factors improve the input data for the assimilating model and better reflect the observed changes in climate forcing compared with the ERA-Interim dataset [42,43]. ERA5 covers the time period from 1979 to the present day.…”

Section: Reanalysis Datamentioning

confidence: 99%

Evaluation of Synoptic Snowfall on the Antarctic Ice Sheet Based on CloudSat, In-Situ Observations and Atmospheric Reanalysis Datasets

Liu

Hao

et al. 2019

Remote Sensing

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Snowfall data are vital in calculating the surface mass balance of the Antarctic Ice Sheet (AIS), where in-situ and satellite measurements are sparse at synoptic timescales. CloudSat data are used to construct Antarctic snowfall data at synoptic timescales to compensate for the sparseness of synoptic snowfall data on the AIS and to better understand its surface mass balance. Synoptic CloudSat snowfall data are evaluated by comparison with daily snow accumulation measurements from ten automatic weather stations (AWSs) and the fifth generation of the European Centre for Medium-Range Weather Forecasts climate reanalysis (ERA5) snowfall. Synoptic snowfall data were constructed based on the CloudSat measurements within a radius of 1.41 • . The results show that reconstructed CloudSat snowfall at daily and two-day resolutions cover about 28% and 29% of the area of the AIS, respectively. Daily CloudSat snowfall and AWS snow accumulation have similar trends at all stations. While influenced by stronger winds, >73.3% of extreme snow accumulation events correspond to snowfall at eight stations. Even if the CloudSat snowfall data have not been assimilated into the ERA5 dataset, the synoptic CloudSat snowfall data are almost identical to the daily ERA5 snowfall with only small biases (average root mean square error and mean absolute error < 3.9 mm/day). Agreement among the three datasets suggests that the CloudSat data can provide reliable synoptic snowfall data in most areas of the AIS. The ERA5 dataset captures a large number of extreme snowfall events at all AWSs, with capture rates varying from 56% to 88%. There are still high uncertainties in ERA5. Nevertheless, the result suggests that ERA5 can be used to represent actual snowfall events on the AIS at synoptic timescale.CloudSat has recently become the most useful tool for directly measuring solid precipitation (i.e., snowfall) on the AIS [9]. The cloud profiling radar (CPR) system onboard CloudSat enables the direct observation of precipitation throughout the atmosphere and can measure light precipitation [10,11]. The CloudSat snowfall retrieval products have been used to reconstruct annual and seasonal snowfall on the AIS [12][13][14][15][16]. Monthly CloudSat snowfall data have been examined by snow accumulation measurements from automatic weather stations (AWSs) [17]. However, there are few attempts to use ground-based measurements (e.g., micro-rain radar (MRR)) to examine the CloudSat snowfall over the AIS at shorter timescales [5,18,19] because there is currently no precipitation gauge network and few continuous snowfall measurements for the AIS [5,20]. CloudSat provides a possible way to solve these problems.CloudSat has a 16-day repeat period covering regions from 82 • N to 82 • S. The density of radar footprints increases with the rise of latitude. The daily overpass frequency of radar measurements for each grid box is between 0.3 and 1.3 days over continental Antarctica at a resolution of 1 • latitude × 1.5 • longitude [17]. Such a temporal resolution is enha...

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Section: Reanalysis Datamentioning

confidence: 99%

Evaluation of Synoptic Snowfall on the Antarctic Ice Sheet Based on CloudSat, In-Situ Observations and Atmospheric Reanalysis Datasets

Liu

Hao

et al. 2019

Remote Sensing

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“…Ground measurements are the first choice to describe the irradiance levels at a certain location, providing high quality data, depending on adequate maintenance of the instruments used in the measuring campaign [3]. When no on-site measurements are available, other techniques should be applied, such as empirical models based on meteorological variables like temperature or sunshine duration [3,4] or models based on reanalysis and retrospective weather prediction models [5,6]. Satellite-based models have become a very powerful tool for estimating the solar resource at high and uniform spatial resolution (typically a few kilometres) and temporal resolution (hourly or better) over large geographical areas [3,7].…”

Section: Introductionmentioning

confidence: 99%

Comparison of satellite-retrieved high-resolution solar radiation datasets for South Africa

Amillo¹,

Ntsangwane

Huld³

et al. 2018

J. energy South. Afr.

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Section: Introductionmentioning

confidence: 99%

Comparison of satellite-retrieved high-resolution solar radiation datasets for South Africa

Amillo¹,

Ntsangwane

Huld³

et al. 2018

J. energy South. Afr.

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This study compares the performance of two satellite-based solar radiation methodologies for estimating the solar resource available in South Africa. Data from thirteen stations distributed in six climatic regions were considered. More than one year of hourly values of global horizontal and beam normal irradiance were examined in the validation of the satellite-retrieved estimates at every location. The best satellite method resulted in an overall relative mean bias of 1.41% for the global horizontal irradiance corresponding to almost 3 Wm -2 and exhibited a relative mean bias of 2.85% for the beam normal irradiance estimation (about 7 Wm -2 ). This satellitebased method was implemented into a geographical information system module, which contained high-resolution terrain data and allowed the effect of the surrounding topography on the estimation of the available solar resource to be considered. These estimates can, therefore, be used as input data for further analysis or applications. As an example, maps of the potential output that could be expected in South Africa from photovoltaic systems were created.

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Evaluation of global horizontal irradiance estimates from ERA5 and COSMO-REA6 reanalyses using ground and satellite-based data

Cited by 310 publications

References 38 publications

Evaluation of Synoptic Snowfall on the Antarctic Ice Sheet Based on CloudSat, In-Situ Observations and Atmospheric Reanalysis Datasets

Evaluation of Synoptic Snowfall on the Antarctic Ice Sheet Based on CloudSat, In-Situ Observations and Atmospheric Reanalysis Datasets

Comparison of satellite-retrieved high-resolution solar radiation datasets for South Africa

Comparison of satellite-retrieved high-resolution solar radiation datasets for South Africa

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