•We forecast irradiance with satellite imagery and cloud motion extrapolation.•Forecasts based on geostationary satellite imagery work well in the high latitudes.•We comparatively validated clear sky ...models and irradiance estimates in Finland.•Forecast performs well compared to smart persistence in changing cloud situations.
Global horizontal irradiance (GHI) estimates and forecasts are necessary for the efficient use of a naturally fluctuating energy source like solar energy. However, few forecasting methods exist for high latitudes. In this study we present the development and validation of a satellite-based GHI forecast for southern Finland, called Solis-Heliosat. The forecast is formed by combining information from the clear sky (CS) model Pvlib Solis with consecutive geostationary weather satellite imagery, using the Heliosat method. Forecasts are produced up to 4 h with a 15-min temporal resolution. The CS model, the satellite-based all-sky estimate, and the GHI forecast have been compared and validated against other methods and in situ GHI measurements. An additional comparison was made for two datasets representing a changing cloud environment.
The CS model estimates had an average rMBE (relative Mean Bias Error) of −6% to 1% and a rRMSE (relative Root Mean Square Error) of 6–10%. For the all-sky estimates the rMBE was −4% to −2%, and rRMSE 2–33%. With increasing forecast time the Solis-Heliosat rMBE descends to −9% and rRMSE reaches 50% at 4 h. Solis-Heliosat performs better than the persistence forecasts in most cases, particularly in a changing cloud environment.
Our study indicates the use of satellite-based forecasts as a viable tool for forecasting GHI for the solar energy industry also in the high latitudes. In high latitudes geostationary satellite-based methods are at their limit; however, the information they can provide will enable efficient solar energy production.
This work presents a validation of three satellite-based radiation products over an extensive network of 313 pyranometers across Europe, from 2005 to 2015. The products used have been developed by ...the Satellite Application Facility on Climate Monitoring (CM SAF) and are one geostationary climate dataset (SARAH-JRC), one polar-orbiting climate dataset (CLARA-A2) and one geostationary operational product. Further, the ERA-Interim reanalysis is also included in the comparison. The main objective is to determine the quality level of the daily means of CM SAF datasets, identifying their limitations, as well as analyzing the different factors that can interfere in the adequate validation of the products.
The quality of the pyranometer was the most critical source of uncertainty identified. In this respect, the use of records from Second Class pyranometers and silicon-based photodiodes increased the absolute error and the bias, as well as the dispersion of both metrics, preventing an adequate validation of the daily means. The best spatial estimates for the three datasets were obtained in Central Europe with a Mean Absolute Deviation (MAD) within 8–13W/m2, whereas the MAD always increased at high-latitudes, snow-covered surfaces, high mountain ranges and coastal areas. Overall, the SARAH-JRC's accuracy was demonstrated over a dense network of stations making it the most consistent dataset for climate monitoring applications. The operational dataset was comparable to SARAH-JRC in Central Europe, but lacked of the temporal stability of climate datasets, while CLARA-A2 did not achieve the same level of accuracy despite predictions obtained showed high uniformity with a small negative bias. The ERA-Interim reanalysis shows the by-far largest deviations from the surface reference measurements.
•Validation of daily means of CM-SAF satellite products over 313 ground stations•SARAH provides the best estimations of solar radiation over Europe.•Low quality pyranometers are a significant uncertainty source in the validation.
Surface albedo, the fraction of incoming solar radiation reflected hemispherically by the surface, is an essential climate variable (ECV) directly related to the energy budget of Earth. The presence ...and properties of snow cover alter surface albedo significantly, with variability in temporal scales reaching from seasonal to diurnal. The diurnal variation of snow albedo is typically parameterized with the solar zenith angle, but it cannot take into account asymmetry with respect to midday. Using the solar azimuth angle instead is suggested, since especially in the melting season the snow albedo varies highly asymmetrically during the day. To derive a general time- and latitude-independent formula, the azimuth angle values are normalized. Baseline Surface Radiation Network data are used to derive an empirical formula for the diurnal variation of snow black-sky surface albedo. The overall accuracy is on the order of 0.02, and the relative accuracy is about 3%.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Seasonal snow cover of the Northern Hemisphere (NH) greatly influences surface energy balance; hydrological cycle; and many human activities, such as tourism and agriculture. Monitoring snow cover at ...a continental scale is only possible from satellites or using reanalysis data. This study aims to analyze the time series of snow water equivalent (SWE), snow cover extent (SCE), and surface albedo in spring in ERA5 and ERA5-Land reanalysis data and to compare the time series with several satellite-based datasets. As reference data for the SWE intercomparison, we use bias-corrected SnowCCI v1 data for non-mountainous regions and the mean of Brown, MERRA-2, and Crocus v7 datasets for the mountainous regions. For surface albedo, we use the black-sky albedo datasets CLARA-A2 SAL, based on AVHRR data, and MCD43D51, based on MODIS data. Additionally, we use Rutgers and JAXA JASMES SCE products. Our study covers land areas north of 40∘ N and the period between 1982 and 2018 (spring season from March to May). The analysis shows that both ERA5 and ERA5-Land overestimate total NH SWE by 150 % to 200 % compared to the SWE reference data. ERA5-Land shows larger overestimation, which is mostly due to very high SWE values over mountainous regions. The analysis revealed a discontinuity in ERA5 around the year 2004 since adding the Interactive Multisensor Snow and Ice Mapping System (IMS) from the year 2004 onwards considerably improves SWE estimates but makes the trends less reliable. The negative NH SWE trends in ERA5 range from −249 to −236 Gt per decade in spring, which is 2 to 3 times larger than the trends detected by the other datasets (ranging from −124 to −77 Gt per decade). SCE is accurately described in ERA5-Land, whereas ERA5 shows notably larger SCE than the satellite-based datasets. Albedo estimates are more consistent between the datasets, with a slight overestimation in ERA5 and ERA5-Land. The negative trends in SCE and albedo are strongest in May, when the albedo trend varies from −0.011 to −0.006 per decade depending on the dataset. The negative SCE trend detected by ERA5 in May (-1.22×106 km2 per decade) is about twice as large as the trends detected by all other datasets (ranging from −0.66 to -0.50×106 km2 per decade). The analysis also shows that there is a large spatial variability in the trends, which is consistent with other studies.
The relationship of black-sky and white-sky albedo values of snow-covered terrain is studied using empirical measurements of six BSRN sites and the Finnish Meteorological Institute Sodankylä site, ...where albedo measurements are carried out both in an open area and above coniferous forest. In addition, a forest model was used to provide simulated albedo values to cover a wider leaf area index range. Linear regression formulas for estimating the monthly mean white-sky albedo value on the basis of the monthly statistics of the black-sky albedo were derived separately for open and forested areas. The statistical parameters used were the mean, median, standard deviation, skewness and kurtosis. In addition, the monthly mean solar zenith angle value was used as well. The mean absolute difference between the estimated monthly mean white-sky albedo and the empirical value was 0.027 for open areas and 0.015 for forested areas. The derived formulas were applied to the satellite black-sky albedo product CLARA-A2 SAL to generate white-sky albedo maps. Using the open snow area regression for the sea ice area produced values comparable to measured values both around the Antarctic and in the Arctic sea ice area.
The Greenland Ice Sheet is losing mass at a significant rate,
driven in part by increasing surface-melt-induced runoff. Because the ice
sheet's surface melt is closely connected to changes in the ...surface albedo,
studying multidecadal changes in the ice sheet's albedo offers insight into
surface melt and associated changes in its surface mass balance. Here, we
first analyse the CM SAF Cloud, Albedo and
Surface Radiation dataset from AVHRR data second edition (CLARA-A2) Surface Albedo (SAL),
covering 1982–2015, to obtain decadal albedo trends for each summer month.
We also examine the rates of albedo change during the early summer,
supported with atmospheric reanalysis data from MERRA-2 (Modern-Era Retrospective analysis for Research and Applications, version 2), to discern changes
in the intensity of early summer melt, and their likely drivers. We find
that rates of albedo decrease during summer melt have accelerated during the
2000s relative to the early 1980s and that the surface albedos now often
decrease to values typical of bare ice at elevations 50–100 m higher on
the ice sheet. The southern margins exhibit the opposite behaviour, though,
and we suggest this is due to increasing snowfall over the area. We then subtract ice discharge from the mass balance estimates observed by
the Gravity Recovery and
Climate Experiment (GRACE) satellite mission to estimate surface mass balance. The CLARA-A2 albedo changes are regressed with these data to obtain a summer-aggregated
proxy surface mass balance time series for the summer periods 1982–2015. This
proxy time series is compared with latest regional climate model estimates
from the MAR model to perform an observation-based test on the dominance of
surface runoff in the magnitude and variability of the summer surface mass
balance. We show that the proxy time series agrees with MAR through the
analysed period within the associated uncertainties of the data and methods,
demonstrating and confirming that surface runoff has dominated the rapid
surface mass loss period between the 1990s and 2010s. Finally, we extend the analysis to the drainage basin scale to examine
discharge–albedo relationships. We find little evidence of surface-melt-induced ice flow acceleration at annual timescales.
We evaluate the accuracy of the satellite-based surface solar radiation dataset called Surface Solar Radiation Data Set - Heliosat (SARAH-E) against in situ measurements over a variety of sites in ...India between 1999 and 2014. We primarily evaluate the daily means of surface solar radiation. The results indicate that SARAH-E consistently overestimates surface solar radiation, with a mean bias of 21.9 W/m2. The results are complicated by the fact that the estimation bias is stable between 1999 and 2009 with a mean of 19.6 W/m2 but increases sharply thereafter as a result of rapidly decreasing (dimming) surface measurements of solar radiation. In addition, between 1999 and 2009, both in situ measurements and SARAH-E estimates described a statistically significant (at 95% confidence interval) trend of approximately −0.6 W/m2/year, but diverged strongly afterward. We investigated the cause of decreasing solar radiation at one site (Pune) by simulating clear-sky irradiance with local measurements of water vapor and aerosols as input to a radiative transfer model. The relationship between simulated and measured irradiance appeared to change post-2009, indicating that measured changes in the clear-sky aerosol loading are not sufficient to explain the rapid dimming in measured total irradiance. Besides instrumentation biases, possible explanations in the diverging measurements and retrievals of solar radiation may be found in the aerosol climatology used for SARAH-E generation. However, at present, we have insufficient data to conclusively identify the cause of the increasing retrieval bias. Users of the datasets are advised to be aware of the increasing bias when using the post-2009 data.
Snow cover plays a significant role in the weather and climate system by affecting the energy and mass transfer between the surface and the atmosphere. It also has far-reaching effects on ecosystems ...of snow-covered areas. Therefore, global snow-cover observations in a timely manner are needed. Satellite-based instruments can be utilized to produce snow-cover information that is suitable for these needs. Highly variable surface and snow-cover features suggest that operational snow extent algorithms may benefit from at least a partly empirical approach that is based on carefully analyzed training data. Here, a new two-phase snow-cover algorithm utilizing data from the Advanced Very High Resolution Radiometer (AVHRR) on board the MetOp satellites of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) is introduced and evaluated. This algorithm is used to produce the MetOp/AVHRR H32 snow extent product for the Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF). The algorithm aims at direct detection of snow-covered and snow-free pixels without preceding cloud masking. Pixels that cannot be classified reliably to snow or snow-free, because of clouds or other reasons, are set as unclassified. This reduces the coverage but increases the accuracy of the algorithm. More than four years of snow-depth and state-of-the-ground observations from weather stations were used to validate the product. Validation results show that the algorithm produces high-quality snow coverage data that may be suitable for numerical weather prediction, hydrological modeling, and other applications.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
•SARAH produces the best PV system simulations in Central and South Europe.•ERA5 is the best alternative to SARAH in the Nordic countries.•The bias of reanalyses got amplified due to the incorrect ...prediction of clouds.•Radiation databases with low annual bias do not assure low-biased PV simulations.
Solar radiation databases used for simulating PV systems are typically selected according to their annual bias in global horizontal irradiance (GH) because this bias propagates proportionally to plane-of-array irradiance (GPOA) and module power (PDC). However, the bias may get amplified through the simulations due to the impact of deviations in estimated irradiance on parts of the modeling chain depending on irradiance. This study quantifies these effects at 39 European locations by comparing simulations using satellite-based (SARAH) and reanalysis (COSMO-REA6 and ERA5) databases against simulations using station measurements.
SARAH showed a stable bias through the simulations producing the best PDC predictions in Central and South Europe, whereas the bias of reanalyses got substantially amplified because their deviations vary with atmospheric transmissivity due to an incorrect prediction of clouds. However, SARAH worsened at the northern locations covered by the product (55–65°N) underestimating both GPOA and PDC. On the contrary, ERA5 not only covers latitudes above 65° but it also obtained the least biased PDC estimations between 55 and 65°N, which supports its use as a complement of satellite-based databases in high latitudes. The most significant amplifications occurred through the transposition model ranging from ±1% up to +6%. Their magnitude increased linearly with the inclination angle, and they are related to the incorrect estimation of beam and diffuse irradiance. The bias increased around +1% in the PV module model because the PV conversion efficiency depends on irradiance directly, and indirectly via module temperature. The amplification of the bias was similar and occasionally greater than the bias in annual GH, so databases with the smallest bias in GH may not always provide the least biased PV simulations.
PDF
