A posteriori clear-sky identification methods in solar irradiance time series: Review and preliminary validation using sky imagers
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The third generation of the Sloan Digital Sky Survey (SDSS-III) took data from 2008 to 2014 using the original SDSS wide-field imager, the original and an upgraded multi-object fiber-fed optical spectrograph, a new near-infrared high-resolution spectrograph, and a novel optical interferometer. All of the data from SDSS-III are now made public. In particular, this paper describes Data Release 11 (DR11) including all data acquired through 2013 July, and Data Release 12 (DR12) adding data acquired through 2014 July (including all data included in previous data releases), marking the end of SDSS-III observing. Relative to our previous public release (DR10), DR12 adds one million new spectra of galaxies and quasars from the Baryon Oscillation Spectroscopic Survey (BOSS) over an additional 3000 deg 2 of sky, more than triples the number of H-band spectra of stars as part of the Apache Point Observatory (APO) Galactic Evolution Experiment (APOGEE), and includes repeated accurate radial velocity measurements of 5500 stars from the Multi-object APO Radial Velocity Exoplanet Large-area Survey (MARVELS). The APOGEE outputs now include the measured abundances of 15 different elements for each star. In total, SDSS-III added 5200 deg 2 of ugriz imaging; 155,520 spectra of 138,099 stars as part of the Sloan Exploration of Galactic Understanding and Evolution 2 (SEGUE-2) survey; 2,497,484 BOSS spectra of 1,372,737 galaxies, 294,512 quasars, and 247,216 stars over 9376 deg 2 ; 618,080 APOGEE spectra of 156,593 stars; and 197,040 MARVELS spectra of 5513 stars. Since its first light in 1998, SDSS has imaged over 1/3 of the Celestial sphere in five bands and obtained over five million astronomical spectra.
Meeting the science goals for many current and future ground-based optical large-area sky surveys requires that the calibrated broadband photometry is both stable in time and uniform over the sky to 1% precision or better. Past and current surveys have achieved photometric precision of 1%–2% by calibrating the survey’s stellar photometry with repeated measurements of a large number of stars observed in multiple epochs. The calibration techniques employed by these surveys only consider the relative frame-by-frame photometric zeropoint offset and the focal plane position-dependent illumination corrections, which are independent of the source color. However, variations in the wavelength dependence of the atmospheric transmission and the instrumental throughput induce source color-dependent systematic errors. These systematic errors must also be considered to achieve the most precise photometric measurements. In this paper, we examine such systematic chromatic errors (SCEs) using photometry from the Dark Energy Survey (DES) as an example. We first define a natural magnitude system for DES and calculate the systematic errors on stellar magnitudes when the atmospheric transmission and instrumental throughput deviate from the natural system. We conclude that the SCEs caused by the change of airmass in each exposure, the change of the precipitable water vapor and aerosol in the atmosphere over time, and the non-uniformity of instrumental throughput over the focal plane can be up to 2% in some bandpasses. We then compare the calculated SCEs with the observed DES data. For the test sample data, we correct these errors using measurements of the atmospheric transmission and instrumental throughput from auxiliary calibration systems. The residual after correction is less than 0.3%. Moreover, we calculate such SCEs for Type Ia supernovae and elliptical galaxies and find that the chromatic errors for non-stellar objects are redshift-dependent and can be larger than those for stars at certain redshifts.
The use of high-quality simulated sky catalogs is essential for the success of cosmological surveys. The catalogs have diverse applications, such as investigating signatures of fundamental physics in cosmological observables, understanding the effect of systematic uncertainties on measured signals and testing mitigation strategies for reducing these uncertainties, aiding analysis pipeline development and testing, and survey strategy optimization. The list of applications is growing with improvements in the quality of the catalogs and the details that they can provide. Given the importance of simulated catalogs, it is critical to provide rigorous validation protocols that enable both catalog providers and users to assess the quality of the catalogs in a straightforward and comprehensive way. For this purpose, we have developed the DESCQA framework for the Large Synoptic Survey Telescope Dark Energy Science Collaboration as well as for the broader community. The goal of DESCQA is to enable the inspection, validation, and comparison of an inhomogeneous set of synthetic catalogs via the provision of a common interface within an automated framework. Here in this paper, we present the design concept and first implementation of DESCQA. In order to establish and demonstrate its full functionality we use a set of interim catalogs and validation tests. We highlight several important aspects, both technical and scientific, that require thoughtful consideration when designing a validation framework, including validation metrics and how these metrics impose requirements on the synthetic sky catalogs.
The Sloan Digital Sky Survey (SDSS) has been in operation since 2000 April. This paper presents the Tenth Public Data Release (DR10) from its current incarnation, SDSS-III. This data release includes the first spectroscopic data from the Apache Point Observatory Galaxy Evolution Experiment (APOGEE), along with spectroscopic data from the Baryon Oscillation Spectroscopic Survey (BOSS) taken through 2012 July. The APOGEE instrument is a near-infrared R ∼ 22,500 300 fiber spectrograph covering 1.514-1.696 μm. The APOGEE survey is studying the chemical abundances and radial velocities of roughly 100,000 red giant star candidates in the bulge, bar, disk, and halo of the Milky Way. DR10 includes 178,397 spectra of 57,454 stars, each typically observed three or more times, from APOGEE. Derived quantities from these spectra (radial velocities, effective temperatures, surface gravities, and metallicities) are also included. DR10 also roughly doubles the number of BOSS spectra over those included in the Ninth Data Release. DR10 includes a total of 1,507,954 BOSS spectra comprising 927,844 galaxy spectra, 182,009 quasar spectra, and 159,327 stellar spectra selected over 6373.2 deg{sup 2}.
When the sky gets smoke in it, it can be more or less bright (white) than before. This is because the small smoke bits can change whether sun light gets through the sky to the ground, can change how much white sky water there is, and can change how the sky air moves around. If the smoke is blacker, it can make the sky warmer and if the smoke is whiter, it can make the sky (and the ground) cooler because the sun light doesn't get through. So we wanted to know whether the smoke is blacker or whiter over the big water far away from here, near where there are a lot of fires on the land.We went to the big water place and watched the sky, the air, the sky smoke, and the white sky water. We did this by flying in the sky with our computers. We watched the smoke in a lot of different ways: we can look at the smoke from near the water and see what sun light gets through the sky, or from above and see how the sun light comes back up to space, or go right in the smoke with our flying computer eyes. Then we looked at whether the different ways of seeing showed that the same smoke was different (blacker or whiter) or the same. A lot of time they said it was the same, at least in some ways, but sometimes the smoke looked different and it changed a lot in different places over the big water.We hope other people can put these answers into their computer studies, or into their computers that fly REALLY high, and then we can better understand how the sky gets warmer or cooler from smoke.
Sky models in daylight simulations represent the luminance variation across the sky-dome for different locations, dates, times and weather conditions, but skies are typically modelled as colourless. Recent studies explore techniques for incorporating the spectral content of daylighting in simulations. This paper provides an evaluation of the existing spectral sky models in lighting simulation software. The comparisons are made between the available mathematical sky models and naturally occurring skies that were recorded using high dynamic range photography and spectrophotometric measurements. The results show that recently developed sky models present progress compared to colourless sky models, but further research is needed to accurately simulate daylight spectra.
All-sky imagers (ASIs) have significant promise as scalable sensors for short-term solar irradiance forecasting. Many of the current computational techniques that use ASIs for this purpose rely on collections of clear-sky images indexed by time of day, solar angle, or both, called clear-sky dictionaries (CSDs). These CSDs act as baselines against which images can be compared to locate and classify clouds within the image frame. CSDs are often compiled by hand, where individuals visually inspect collections of images one at a time to find clear-sky images. This process is not scalable, and it is prone to error. This paper proposes an automated, nonparametric alternative that uses the principles of digital image processing to find clear-sky images within a set of images taken over several days. We use ground-truth measurements of the clearness index to assess the performance of our method, and we show that the images it selects accurately correspond to clear-sky images. We also compare our proposal, which is nonparametric, with a state-of-the-art parametric method. The numerical results indicate that the performance of the method proposed here is superior.
The solar energy industry often uses individual steps to empirically compute plane-of-array (POA) irradiance from horizontal irradiance and decompose it to narrow-wavelength bands. Conventional radiative transfer models designed for meteorological applications requires significant computing efforts in practice; however, they provide a physics-based solution of radiance and therefore are capable of computing spectral POA irradiances in a single step. In this study, we integrate the advantages of the current models and develop an innovative radiative transfer model, the Fast All-sky Radiation Model for Solar applications with Narrowband Irradiances on Tilted surfaces (FARMS-NIT), to efficiently compute irradiances on inclined photovoltaics (PV) panels for 2002 narrow-wavelength bands from 0.28 to 4.0 um. This study is reported in two parts. Part I presents the methodology and performance evaluation of the new model under clear-sky conditions. The Simple Model of the Atmospheric Radiative Transfer of Sunshine (SMARTS), which was designed to compute clear-sky irradiances, is employed to rapidly provide the optical properties of a given clear-sky atmosphere. The clear-sky radiances in the narrow-wavelength bands are computed by considering three paths of photon transmission and solving the radiative transfer equation with the single-scattering approximation. The Bi-directional Transmittance Distribution Function (BTDF) of aerosols is given by their single-scattering phase function with a correction using a two-stream approximation. The validation analysis confirms that FARMS-NIT has improved accuracy compared to TMYSPEC as evaluated by both surface observations and a state-of-the-art radiative transfer model. This model substantially improves computational efficiency compared to other radiative transfer models though it uses slightly more computing time than TMYSPEC. Part II of this study addresses the model in cloud-sky conditions and will be published as a companion paper.
The objectives of this task in the AITT (Airborne Instrument Technology Transition) Program are to (1) upgrade the NASA 4STAR (Spectrometer for Sky-Scanning, Sun-Tracking Atmospheric Research) instrument to its full science capability of measuring (a) direct-beam sun transmission to derive aerosol optical depth spectra, (b) sky radiance vs scattering angle to retrieve aerosol absorption and type (via complex refractive index spectra, shape, and mode-resolved size distribution), (c) zenith radiance for cloud properties, and (d) hyperspectral signals for trace gas retrievals, and (2) demonstrate its suitability for deployment in challenging NASA airborne multiinstrument campaigns. 4STAR combines airborne sun tracking, sky scanning, and zenith pointing with diffraction spectroscopy to improve knowledge of atmospheric constituents and their links to air pollution, radiant energy budgets (hence climate), and remote measurements of Earth's surfaces. Direct beam hyperspectral measurement of optical depth improves retrievals of gas constituents and determination of aerosol properties. Sky scanning enhances retrievals of aerosol type and size distribution. 4STAR measurements are intended to tighten the closure between satellite and ground-based measurements. 4STAR incorporates a modular sun-tracking/sky-scanning optical head with fiber optic signal transmission to rack mounted spectrometers, permitting miniaturization of the external optical head, and future detector evolution. 4STAR test flights, as well as science flights in the 2012-13 TCAP (Two-Column Aerosol Project) and 2013 SEAC4RS (Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys) have demonstrated that the following are essential for 4STAR to achieve its full science potential: (1) Calibration stability for both direct-beam irradiance and sky radiance, (2) Improved light collection and usage, and (3) Improved flight operability and reliability. A particular challenge for the AITT-4STAR project has been conducting it simultaneously with preparations for, and execution of, ARISE (Arctic Radiation - IceBridge Sea&Ice Experiment), a NASA airborne science deployment (unplanned when AITT-4STAR was selected for funding) in which 4STAR will deploy to Thule, Greenland, and Fairbanks, Alaska, on the NASA C- 130. This presentation describes progress to date in accomplishing AITT-4STAR goals, and plans for project completion.
The Fast All-sky Radiation Model for Solar applications with Narrowband Irradiances on Tilted surfaces (FARMS-NIT) reported in Part I of this report is enhanced to include the requirements for cloudy-sky conditions. Surface radiances in 2002 narrow-wavelength bands from 0.28 to 4.0 um are analytically computed by solving the radiative transfer equation for five independent photon paths accounting for clear-sky absorption, Rayleigh scattering, and cloud absorption and scattering. The Simple Model of the Atmospheric Radiative Transfer of Sunshine (SMARTS) is used to show the optical thickness of the clear-sky atmosphere. Unlike Part I, which approximates the computation of aerosol scattering using the single-scattering phase function, the cloud transmittance and reflectance are efficiently retrieved from a comprehensive look-up table pre-computed by a 32-stream DIScrete Ordinates Radiative Transfer (DISORT) model for possible cloud conditions as well as solar and viewing geometries. A resolution assessment is performed to observe the optimal balance between the computational efficiency and accuracy in the development of the look-up table. Model simulations by DISORT and TMYSPEC are used to evaluate the performance of FARMS-NIT under cloudy-sky conditions. Compared to DISORT, FARMS-NIT yields 2-3% uncertainties on average, but it substantially reduces the computational time because of the independent computation of cloud properties and the implementation of the look-up table. In contrast to TMYSPEC, which uses successive steps to empirically compute plane-of-array (POA) irradiances and spectral irradiances, FARMS-NIT directly solves spectral radiances from the radiative transfer equation, which profoundly increases the accuracy in surface irradiances, especially over inclined photovoltaics (PV) panels.
The summit of the Antarctic plateau, Dome A, is proving to be an excellent site for optical, near-infrared, and terahertz astronomical observations. Gattini is a wide-field camera installed on the PLATO instrument module as part of the Chinese-led traverse to Dome A in 2009 January. We present here the measurements of sky brightness with the Gattini ultra-large field of view (90{sup ∘}×90{sup ∘}) in the photometric B-, V-, and R-bands; cloud cover statistics measured during the 2009 winter season; and an estimate of the sky transparency. A cumulative probability distribution indicates that the darkest 10% of the nights at Dome A have sky brightness of S {sub B} = 22.98, S {sub V} = 21.86, and S {sub R} = 21.68 mag arcsec{sup −2}. These values were obtained during the year 2009 with minimum aurora, and they are comparable to the faintest sky brightness at Maunakea and the best sites of northern Chile. Since every filter includes strong auroral lines that effectively contaminate the sky brightness measurements, for instruments working around the auroral lines, either with custom filters or with high spectral resolution instruments, these values could be easily obtained on a more routine basis. In addition, we present example light curves for bright targets to emphasize the unprecedented observational window function available from this ground-based site. These light curves will be published in a future paper.
The Fast All-sky Radiation Model for Solar applications with Narrowband Irradiances on Tilted surfaces (FARMSNIT) reported in Part I of this study is enhanced to include the requirements for cloudy-sky conditions. Surface radiances in 2002 narrow-wavelength bands from 0.28 to 4.0 μm are analytically computed by solving the radiative transfer equation for five independent photon paths accounting for clear-sky absorption, Rayleigh scattering, and cloud absorption and scattering. The Simple Model of the Atmospheric Radiative Transfer of Sunshine (SMARTS) is used to provide the optical thickness of the clear-sky atmosphere. Unlike Part I, which approximates the computation of aerosol scattering using the single-scattering phase function, the cloud transmittance and reflectance are efficiently retrieved from a comprehensive look-up table pre-computed by a 32-stream DIScrete Ordinates Radiative Transfer (DISORT) model for possible cloud conditions as well as solar and viewing geometries. A resolution analysis is performed to assess the optimal balance between the computational efficiency and accuracy in the development of the look-up table. Model simulations by DISORT and TMYSPEC are used to evaluate the performance of FARMS-NIT under cloudy-sky conditions. Compared to DISORT, FARMS-NIT yields 2–3% uncertainties on average, but it substantially reduces the computational time because of the independent computation of cloud properties and the implementation of the look-up table. In contrast to TMYSPEC, which uses successive steps to empirically compute plane-of-array (POA) irradiances and spectral irradiances, FARMS-NIT directly solves spectral radiances from the radiative transfer equation, which profoundly increases the accuracy in surface irradiances, especially over inclined photovoltaics (PV) panels.
We present details of the construction and characterization of the coaddition of the Sloan Digital Sky Survey (SDSS) Stripe 82 ugriz imaging data. This survey consists of 275 deg{sup 2} of repeated scanning by the SDSS camera over –50° ≤ α ≤ 60° and –1.°25 ≤ δ ≤ +1.°25 centered on the Celestial Equator. Each piece of sky has ∼20 runs contributing and thus reaches ∼2 mag fainter than the SDSS single pass data, i.e., to r ∼ 23.5 for galaxies. We discuss the image processing of the coaddition, the modeling of the point-spread function (PSF), the calibration, and the production of standard SDSS catalogs. The data have an r-band median seeing of 1.''1 and are calibrated to ≤1%. Star color-color, number counts, and PSF size versus modeled size plots show that the modeling of the PSF is good enough for precision five-band photometry. Structure in the PSF model versus magnitude plot indicates minor PSF modeling errors, leading to misclassification of stars as galaxies, as verified using VVDS spectroscopy. There are a variety of uses for this wide-angle deep imaging data, including galactic structure, photometric redshift computation, cluster finding and cross wavelength measurements, weak lensing cluster mass calibrations, and cosmic shear measurements.
We present and release co-added images of the Sloan Digital Sky Survey (SDSS) Stripe 82. Stripe 82 covers an area of ∼300 deg{sup 2} on the celestial equator, and has been repeatedly scanned 70-90 times in the ugriz bands by the SDSS imaging survey. By making use of all available data in the SDSS archive, our co-added images are optimized for depth. Input single-epoch frames were properly processed and weighted based on seeing, sky transparency, and background noise before co-addition. The resultant products are co-added science images and their associated weight images that record relative weights at individual pixels. The depths of the co-adds, measured as the 5σ detection limits of the aperture (3.''2 diameter) magnitudes for point sources, are roughly 23.9, 25.1, 24.6, 24.1, and 22.8 AB magnitudes in the five bands, respectively. They are 1.9-2.2 mag deeper than the best SDSS single-epoch data. The co-added images have good image quality, with an average point-spread function FWHM of ∼1'' in the r, i, and z bands. We also release object catalogs that were made with SExtractor. These co-added products have many potential uses for studies of galaxies, quasars, and Galactic structure. We further present and release near-IR J-band images that cover ∼90 deg{sup 2} of Stripe 82. These images were obtained using the NEWFIRM camera on the NOAO 4 m Mayall telescope, and have a depth of about 20.0-20.5 Vega magnitudes (also 5σ detection limits for point sources)
Here, the Sloan Digital Sky Survey has surveyed 14,555 square degrees of the sky, and delivered over a trillion pixels of imaging data. We present the large-scale clustering of 1.6 million quasars between z=0.5 and z=2.5 that have been classified from this imaging, representing the highest density of quasars ever studied for clustering measurements. This data set spans 0~ 11,00 square degrees and probes a volume of 80 h –3 Gpc 3 . In principle, such a large volume and medium density of tracers should facilitate high-precision cosmological constraints. We measure the angular clustering of photometrically classified quasars using an optimal quadratic estimator in four redshift slices with an accuracy of ~ 25% over a bin width of δ l ~ 10–15 on scales corresponding to matter-radiation equality and larger (0ℓ ~ 2–3).
Frequency-domain multiplexing (fMux) is an established technique for the readout of large arrays of transition-edge sensor (TES) bolometers. Each TES in a multiplexing module has a unique AC voltage bias that is selected by a resonant filter. This scheme provides for the operation and readout of multiple bolometers on a single pair of wires, reducing thermal loading onto sub-Kelvin stages. The current receiver on the South Pole Telescope, SPT-3G, uses a 68x fMux system to operate its large-format camera of ~16,000 TES bolometers. We present here the successful implementation and performance of the SPT-3G readout as measured on-sky. Characterization of the noise reveals a median pair-differenced 1/f knee frequency of 33 mHz, indicating that low-frequency noise in the readout will not limit SPT-3G’s measurements of sky power on large angular scales. Measurements further show that the median readout white noise level in each of the SPT-3G observing bands is below the expectation for photon noise, demonstrating that SPT-3G is operating in the photon-noise-dominated regime.
We present three maps of the millimeter-wave sky created by combining data from the South Pole Telescope (SPT) and the Planck satellite. We use data from the SPT-SZ survey, a survey of 2540 deg2 of the the sky with arcminute resolution in three bands centered at 95, 150, and 220 GHz, and the full-mission Planck temperature data in the 100, 143, and 217 GHz bands. A linear combination of the SPT-SZ and Planck data is computed in spherical harmonic space, with weights derived from the noise of both instruments. This weighting scheme results in Planck data providing most of the large-angular-scale information in the combined maps, with the smaller-scale information coming from SPT-SZ data. A number of tests have been done on the maps. We find their angular power spectra to agree very well with theoretically predicted spectra and previously published results.