scholarly journals High‐resolution gridded climate data for Europe based on bias‐corrected EURO‐CORDEX: The ECLIPS dataset

2020 ◽  
Author(s):  
Debojyoti Chakraborty ◽  
Laura Dobor ◽  
Anita Zolles ◽  
Tomáš Hlásny ◽  
Silvio Schueler
Keyword(s):  
High Resolution ◽  
Climate Data ◽  
Gridded Climate Data

Listening to Inuit and Naskapi peoples in the eastern Canadian Subarctic: a quantitative comparison of local observations with gridded climate data

2017 ◽  
Vol 18 (1) ◽  
pp. 189-203 ◽  
Author(s):  
Michel Rapinski ◽  
◽  
Fanny Payette ◽  
Oliver Sonnentag ◽  
Thora Martina Herrmann ◽  
...  
Keyword(s):  
Quantitative Comparison ◽  
Climate Data ◽  
Gridded Climate Data

Aridity indices to assess desertification susceptibility: a methodological approach using gridded climate data and cartographic modeling

2022 ◽  
Author(s):  
Janaína Cassiano dos Santos ◽  
Gustavo Bastos Lyra ◽  
Marcel Carvalho Abreu ◽  
José Francisco de Oliveira-Júnior ◽  
Leonardo Bohn ◽  
...  
Keyword(s):  
Methodological Approach ◽  
Climate Data ◽  
Cartographic Modeling ◽  
Gridded Climate Data ◽  
Aridity Indices

scholarly journals A new pan-European dataset for gridded daily average wind speed based on in-situ observations

2021 ◽  
Author(s):  
Jouke de Baar ◽  
Gerard van der Schrier ◽  
Irene Garcia-Marti ◽  
Else van den Besselaar
Keyword(s):  
Spatial Distribution ◽  
High Resolution ◽  
Cross Validation ◽  
Data Sets ◽  
Optimal Prediction ◽  
Auxiliary Variables ◽  
Climate Data ◽  
Ensemble Spread ◽  
Wind Fields

<p><strong>Objective</strong></p><p>The purpose of the European Copernicus Climate Change Service (C3S) is to support society by providing information about the past, present and future climate. For the service related to <em>in-situ</em> observations, one of the objectives is to provide high-resolution (0.1x0.1 and 0.25x0.25 degrees) gridded wind speed fields. The gridded wind fields are based on ECA&D daily average station observations for the period 1970-2020.</p><p><strong>Research question</strong> </p><p>We address the following research questions: [1] How efficiently can we provide the gridded wind fields as a statistically reliable ensemble, in order to represent the uncertainty of the gridding? [2] How efficiently can we exploit high-resolution geographical auxiliary variables (e.g. digital elevation model, terrain roughness) to augment the station data from a sparse network, in order to provide gridded wind fields with high-resolution local features?</p><p><strong>Approach</strong></p><p>In our analysis, we apply greedy forward selection linear regression (FSLR) to include the high-resolution effects of the auxiliary variables on monthly-mean data. These data provide a ‘background’ for the daily estimates. We apply cross-validation to avoid FSLR over-fitting and use full-cycle bootstrapping to create FSLR ensemble members. Then, we apply Gaussian process regression (GPR) to regress the daily anomalies. We consider the effect of the spatial distribution of station locations on the GPR gridding uncertainty.</p><p>The goal of this work is to produce several decades of daily gridded wind fields, hence, computational efficiency is of utmost importance. We alleviate the computational cost of the FSLR and GPR analyses by incorporating greedy algorithms and sparse matrix algebra in the analyses.</p><p><strong>Novelty</strong>   </p><p>The gridded wind fields are calculated as a statistical ensemble of realizations. In the present analysis, the ensemble spread is based on uncertainties arising from the auxiliary variables as well as from the spatial distribution of stations.</p><p>Cross-validation is used to tune the GPR hyper parameters. Where conventional GPR hyperparameter tuning aims at an optimal prediction of the gridded mean, instead, we tune the GPR hyperparameters for optimal prediction of the gridded ensemble spread.</p><p>Building on our experience with providing similar gridded climate data sets, this set of gridded wind fields is a novel addition to the E-OBS climate data sets.</p>

scholarly journals A 40-year global dataset of visible channel remote sensing reflectances and coccolithophore bloom occurrence derived from the Advanced Very High Resolution Radiometer catalogue

2018 ◽  
Author(s):  
Benjamin R. Loveday ◽  
Timothy Smyth
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Atmospheric Correction ◽  
Global Ocean ◽  
Climate Data ◽  
Ancillary Data ◽  
Remote Sensing Reflectance ◽  
Data Files ◽  
Climate Data Record ◽  
Very High

Abstract. A consistently calibrated 40-year length dataset of visible channel remote sensing reflectance has been derived from the Advanced Very High Resolution Radiometer (AVHRR) sensor global time-series. The dataset uses as its source the Pathfinder Atmospheres – Extended (PATMOS-x) v5.3 Climate Data Record (CDR) for top-of-atmosphere (TOA) visible channel reflectances. This paper describes the theoretical basis for the atmospheric correction procedure and its subsequent implementation, including the necessary ancillary data files used and quality flags applied, in order to determine remote sensing reflectance. The resulting dataset is produced at daily, and archived at monthly, resolution, on a 0.1° × 0.1° grid at https://doi.pangaea.de/10.1594/PANGAEA.892175. The primary aim of deriving this dataset is to highlight regions of the global ocean affected by highly reflective blooms of the coccolithophorid Emiliania Huxleyi over the past 40 years.

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