Solar radiation based calibration of a short-wave ir radiometer and a comparison of exoatmospheric solar spectral irradiance data sets

2005 ◽  
Author(s):  
Edward Zalewski ◽  
Christopher Cattrall
Keyword(s):  
Solar Radiation ◽  
Short Wave ◽  
Spectral Irradiance ◽  
Data Sets ◽  
Solar Spectral Irradiance ◽  
Irradiance Data ◽  
Ir Radiometer

scholarly journals Creation of the GSFCSSI2 Composite Solar Spectral Irradiance Data Set

2019 ◽  
Vol 6 (7) ◽  
pp. 1284-1298 ◽  
Author(s):  
Matthew T. DeLand ◽  
Linton E. Floyd ◽  
Sergey Marchenko ◽  
Ramaswamy Tiruchirapalli
Keyword(s):  
Spectral Irradiance ◽  
Data Set ◽  
Solar Spectral Irradiance ◽  
Irradiance Data

scholarly journals Monte Carlo method for determining uncertainty of total ozone derived from direct solar irradiance spectra: Application to Izaña results

2018 ◽  
Author(s):  
Anna Vaskuri ◽  
Petri Kärhä ◽  
Luca Egli ◽  
Julian Gröbner ◽  
Erkki Ikonen
Keyword(s):  
Monte Carlo ◽  
Total Ozone ◽  
Monte Carlo Model ◽  
Spectral Irradiance ◽  
Total Column Ozone ◽  
Solar Spectral Irradiance ◽  
Irradiance Data ◽  
Column Ozone ◽  
Ozone Column ◽  
Total Column

Abstract. We demonstrate a Monte Carlo model to calculate the uncertainties of total ozone column, TOC, derived from ground-based directional solar spectral irradiance measurements. The model takes into account effects that correlations in the spectral irradiance data may have on the results. The model is tested with spectral data measured with three different spectroradiometers at an intercomparison campaign of the research project Traceability for atmospheric total column ozone at Izaña, Tenerife on 17 September 2016. The TOC values derived at noon have expanded uncertainties of 1.3 % for a high-end scanning spectroradiometer, 1.3 % for a high-end array spectroradiometer, and 3.3 % for a roughly adopted instrument based on commercially available components and an array spectroradiometer. The level of TOC measured with reference Brewer spectrophotometer #183 is of the order of 282 DU during the analysed day and in agreement with the results of the two former instruments.

scholarly journals How long do satellites need to overlap? Evaluation of climate data stability from overlapping satellite records

2017 ◽  
Vol 17 (24) ◽  
pp. 15069-15093 ◽  
Author(s):  
Elizabeth C. Weatherhead ◽  
Jerald Harder ◽  
Eduardo A. Araujo-Pradere ◽  
Greg Bodeker ◽  
Jason M. English ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Spectral Irradiance ◽  
Data Sets ◽  
Climate Data ◽  
Data Set ◽  
Long Term Stability ◽  
Earth Observations ◽  
Earth’S Climate ◽  
The Stability

Abstract. Sensors on satellites provide unprecedented understanding of the Earth's climate system by measuring incoming solar radiation, as well as both passive and active observations of the entire Earth with outstanding spatial and temporal coverage. A common challenge with satellite observations is to quantify their ability to provide well-calibrated, long-term, stable records of the parameters they measure. Ground-based intercomparisons offer some insight, while reference observations and internal calibrations give further assistance for understanding long-term stability. A valuable tool for evaluating and developing long-term records from satellites is the examination of data from overlapping satellite missions. This paper addresses how the length of overlap affects the ability to identify an offset or a drift in the overlap of data between two sensors. Ozone and temperature data sets are used as examples showing that overlap data can differ by latitude and can change over time. New results are presented for the general case of sensor overlap by using Solar Radiation and Climate Experiment (SORCE) Spectral Irradiance Monitor (SIM) and Solar Stellar Irradiance Comparison Experiment (SOLSTICE) solar irradiance data as an example. To achieve a 1 % uncertainty in estimating the offset for these two instruments' measurement of the Mg II core (280 nm) requires approximately 5 months of overlap. For relative drift to be identified within 0.1 % yr−1 uncertainty (0.00008 W m−2 nm−1 yr−1), the overlap for these two satellites would need to be 2.5 years. Additional overlap of satellite measurements is needed if, as is the case for solar monitoring, unexpected jumps occur adding uncertainty to both offsets and drifts; the additional length of time needed to account for a single jump in the overlap data may be as large as 50 % of the original overlap period in order to achieve the same desired confidence in the stability of the merged data set. Results presented here are directly applicable to satellite Earth observations. Approaches for Earth observations offer additional challenges due to the complexity of the observations, but Earth observations may also benefit from ancillary observations taken from ground-based and in situ sources. Difficult choices need to be made when monitoring approaches are considered; we outline some attempts at optimizing networks based on economic principles. The careful evaluation of monitoring overlap is important to the appropriate application of observational resources and to the usefulness of current and future observations.

scholarly journals Recent variability of the solar spectral irradiance and its impact on climate modelling

2013 ◽  
Vol 13 (8) ◽  
pp. 3945-3977 ◽  
Author(s):  
I. Ermolli ◽  
K. Matthes ◽  
T. Dudok de Wit ◽  
N. A. Krivova ◽  
K. Tourpali ◽  
...  
Keyword(s):  
Solar Cycle ◽  
Solar Radiation ◽  
Spectral Range ◽  
Solar Irradiance ◽  
Spectral Irradiance ◽  
Climate Modelling ◽  
Earth’S Atmosphere ◽  
Solar Spectral Irradiance ◽  
Earth's Atmosphere ◽  
Earth’S Climate

Abstract. The lack of long and reliable time series of solar spectral irradiance (SSI) measurements makes an accurate quantification of solar contributions to recent climate change difficult. Whereas earlier SSI observations and models provided a qualitatively consistent picture of the SSI variability, recent measurements by the SORCE (SOlar Radiation and Climate Experiment) satellite suggest a significantly stronger variability in the ultraviolet (UV) spectral range and changes in the visible and near-infrared (NIR) bands in anti-phase with the solar cycle. A number of recent chemistry-climate model (CCM) simulations have shown that this might have significant implications on the Earth's atmosphere. Motivated by these results, we summarize here our current knowledge of SSI variability and its impact on Earth's climate. We present a detailed overview of existing SSI measurements and provide thorough comparison of models available to date. SSI changes influence the Earth's atmosphere, both directly, through changes in shortwave (SW) heating and therefore, temperature and ozone distributions in the stratosphere, and indirectly, through dynamical feedbacks. We investigate these direct and indirect effects using several state-of-the art CCM simulations forced with measured and modelled SSI changes. A unique asset of this study is the use of a common comprehensive approach for an issue that is usually addressed separately by different communities. We show that the SORCE measurements are difficult to reconcile with earlier observations and with SSI models. Of the five SSI models discussed here, specifically NRLSSI (Naval Research Laboratory Solar Spectral Irradiance), SATIRE-S (Spectral And Total Irradiance REconstructions for the Satellite era), COSI (COde for Solar Irradiance), SRPM (Solar Radiation Physical Modelling), and OAR (Osservatorio Astronomico di Roma), only one shows a behaviour of the UV and visible irradiance qualitatively resembling that of the recent SORCE measurements. However, the integral of the SSI computed with this model over the entire spectral range does not reproduce the measured cyclical changes of the total solar irradiance, which is an essential requisite for realistic evaluations of solar effects on the Earth's climate in CCMs. We show that within the range provided by the recent SSI observations and semi-empirical models discussed here, the NRLSSI model and SORCE observations represent the lower and upper limits in the magnitude of the SSI solar cycle variation. The results of the CCM simulations, forced with the SSI solar cycle variations estimated from the NRLSSI model and from SORCE measurements, show that the direct solar response in the stratosphere is larger for the SORCE than for the NRLSSI data. Correspondingly, larger UV forcing also leads to a larger surface response. Finally, we discuss the reliability of the available data and we propose additional coordinated work, first to build composite SSI data sets out of scattered observations and to refine current SSI models, and second, to run coordinated CCM experiments.

Solar infrared radiation towards building energy efficiency: measurement, data, and modeling

2020 ◽  
Vol 28 (4) ◽  
pp. 457-465
Author(s):  
Yuhui Song ◽  
Qiuhua Duan ◽  
Yanxiao Feng ◽  
Enhe Zhang ◽  
Julian Wang ◽  
...  
Keyword(s):  
Solar Irradiance ◽  
Infrared Radiation ◽  
Measurement Data ◽  
Building Energy ◽  
Energy Performance ◽  
Spectral Irradiance ◽  
Solar Irradiation ◽  
Solar Spectral Irradiance ◽  
Modeling Techniques ◽  
Irradiance Data

With the recent discoveries and engineering solutions emerging in nanomaterials and nanostructures, independent band modulation of solar radiation on building envelopes, including glazing systems, has become increasingly viable as a potential means of improving building energy savings and indoor visual comfort. However, when it comes to the prediction of these new materials’ potential energy performance in buildings, most studies utilize a simple solar irradiance (e.g., global horizontal solar irradiance, direct beam solar irradiance) or a rough estimation of solar infrared (e.g., 50% solar irradiance) as input, which may cause significant errors. Consequently, there is a pressing need for reliable performance estimations of the solar infrared control and response at the building’s scale. To assess this, we need a solar spectral irradiance model, or at least a wideband (visible or infrared) solar irradiance model, as input. To develop this new type of model, one needs to understand the modeling-related key elements, including available solar spectral irradiance datasets, data collection methods, and modeling techniques. As such, this paper reviews the current major measurement methods and tools used in collecting solar spectral irradiance data with a focus on the solar infrared region, identifies the available related resources and datasets that particularly encompass the solar spectral irradiance data with a sufficient wavelength range, and studies existing solar irradiation modeling techniques for building simulations. These investigations will then form the background and backbone for a study scheme of solar infrared radiation modeling and indicate future research paths and opportunities.

scholarly journals Impact of UV–Vis light on the oxidation of bitumen in correlation to solar spectral irradiance data

2022 ◽  
Vol 316 ◽  
pp. 125816
Author(s):  
Johannes Mirwald ◽  
Drilon Nura ◽  
Lukas Eberhardsteiner ◽  
Bernhard Hofko
Keyword(s):  
Spectral Irradiance ◽  
Solar Spectral Irradiance ◽  
Irradiance Data

Global and Regional Satellite-based Surface Solar Radiation data sets provided by the CM SAF

2021 ◽  
Author(s):  
Jörg Trentmann ◽  
Uwe Pfeifroth ◽  
Jaqueline Drücke ◽  
Roswitha Cremer
Keyword(s):  
Solar Radiation ◽  
Global Climate ◽  
Surface Radiation ◽  
Data Sets ◽  
Climate Data ◽  
Surface Solar Radiation ◽  
Climate Monitoring ◽  
Data Record ◽  
Irradiance Data ◽  
Climate Data Record

<p>The incoming surface solar radiation has been defined as an essential climate variable by GCOS. Long term monitoring of this part of the earth’s energy budget is required to gain insights on the state and variability of the climate system. In addition, climate data sets of surface solar radiation have received increased attention over the recent years as an important source of information for solar energy assessments, for crop modeling, and for the validation of climate and weather models.</p><p>The EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF) is deriving climate data records (CDRs) from geostationary and polar-orbiting satellite instruments. Within the CM SAF these CDRs are accompanied by operational data at a short time latency to be used for climate monitoring. All data from the CM SAF are freely available via www.cmsaf.eu.</p><p>Here we present the regional and global climate data records of surface solar radiation from the CM SAF. The regional SARAH-2.1 climate data record (Surface Solar Radiation Dataset – Heliosat, doi: 10.5676/EUM_SAF_CM/SARAH/V002_01) is based on observations from the series of Meteosat satellites. SARAH-2.1 provides high resolution data (temporal and spatial) of the surface solar radiation (global and direct) and the sunshine duration from 1983 to 2017 for the full view of the Meteosat satellite (i.e, Europe, Africa, parts of South America, and the Atlantic ocean). The global climate data record CLARA (CM SAF Clouds, Albedo and Radiation dataset from AVHRR data, doi: 10.5676/EUM_SAF_CM/CLARA_AVHRR/V002_01) is based on observations from the series of AVHRR instruments onboard polar-orbiting satellites. CLARA provides daily- and monthly-averaged global data of the solar irradiance (SIS) from January 1982 to June 2019 with a spatial resolution of 0.25°. In addition to the solar surface radiation, also the longwave surface radiation as well as surface albedo and numerous cloud properties are provided in CLARA. The high accuracy and stability of these data record allows the assessment of the spatial and temporal variability and trends as well as a number of other applications that require high-resolution surface irradiance data.</p><p>Both Thematic Climate Data Records (TCDR) are accompanied and temporally-extended by consistent data records, so-called Interim Climate Data Records (ICDR), which are provided with a latency of 5 days to support applications that require more recent surface irradiance data, e.g., operational climate monitoring.</p><p>In late 2021 / early 2022 new versions of both data records, SARAH and CLARA, will be provided by the CM SAF. The quality of these data records will be improved, e.g, by a better treatment of snow-covered surfaces, and temporally extended to cover the WMO climate reference period 1991 to 2020. Here, first results of the updated data records and their improvements will be presented.</p>

Sign in / Sign up

Export Citation Format

Share Document