<title>Overview of ASTER instrument and ASTER data product</title>

ABSTRACT We perform a joint BAO and RSD analysis using the eBOSS DR16 LRG and ELG samples in the redshift range of z ∈ [0.6, 1.1], and detect an RSD signal from the cross-power spectrum at a ∼4σ confidence level, i.e., fσ8 = 0.317 ± 0.080 at zeff = 0.77. Based on the chained power spectrum, which is a new development in this work to mitigate the angular systematics, we measure the BAO distances and growth rate simultaneously at two effective redshifts, namely, DM/rd (z = 0.70) = 17.96 ± 0.51, DH/rd (z = 0.70) = 21.22 ± 1.20, fσ8 (z = 0.70) = 0.43 ± 0.05, and DM/rd (z = 0.845) = 18.90 ± 0.78, DH/rd (z = 0.845) = 20.91 ± 2.86, fσ8 (z = 0.845) = 0.30 ± 0.08. Combined with BAO measurements including those from the eBOSS DR16 QSO and Lyman-α sample, our measurement has raised the significance level of a non-zero ΩΛ to ∼11σ. The data product of this work is publicly available at https://github.com/icosmology/eBOSS_DR16_LRGxELG and https://www.sdss.org/science/final-bao-and-rsd-measurements/.

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Research on Data Product Quality Evaluation Model Based on AHP and TOPSIS

Proceedings of the 4th International Conference on Computer Science and Application Engineering ◽

10.1145/3424978.3425087 ◽

2020 ◽

Author(s):

Yaqing Si ◽

Qingjun Xiao ◽

Jing Su ◽

Sen Zeng ◽

Xiao Hong

Keyword(s):

Product Quality ◽

Quality Evaluation ◽

Evaluation Model ◽

Model Based ◽

Data Product ◽

Quality Evaluation Model

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New Approach for Bias Correction and Stochastic Downscaling of Future Projections for Daily Mean Temperatures to a High-Resolution Grid

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-19-0086.1 ◽

2019 ◽

Vol 58 (12) ◽

pp. 2617-2632 ◽

Cited By ~ 1

Author(s):

Qifen Yuan ◽

Thordis L. Thorarinsdottir ◽

Stein Beldring ◽

Wai Kwok Wong ◽

Shaochun Huang ◽

...

Keyword(s):

High Resolution ◽

Bias Correction ◽

Climate Models ◽

Regional Climate ◽

Regional Climate Models ◽

Climate Change Signal ◽

Climate Projections ◽

Data Product ◽

Temporal Features ◽

Stochastic Downscaling

AbstractIn applications of climate information, coarse-resolution climate projections commonly need to be downscaled to a finer grid. One challenge of this requirement is the modeling of subgrid variability and the spatial and temporal dependence at the finer scale. Here, a postprocessing procedure for temperature projections is proposed that addresses this challenge. The procedure employs statistical bias correction and stochastic downscaling in two steps. In the first step, errors that are related to spatial and temporal features of the first two moments of the temperature distribution at model scale are identified and corrected. Second, residual space–time dependence at the finer scale is analyzed using a statistical model, from which realizations are generated and then combined with an appropriate climate change signal to form the downscaled projection fields. Using a high-resolution observational gridded data product, the proposed approach is applied in a case study in which projections of two regional climate models from the Coordinated Downscaling Experiment–European Domain (EURO-CORDEX) ensemble are bias corrected and downscaled to a 1 km × 1 km grid in the Trøndelag area of Norway. A cross-validation study shows that the proposed procedure generates results that better reflect the marginal distributional properties of the data product and have better consistency in space and time when compared with empirical quantile mapping.

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