High-resolution Climate Data From an Improved GIS-based Regression Technique for South Korea

2018 ◽  
Vol 22 (12) ◽  
pp. 5215-5228 ◽  
Author(s):  
Hyung-Il Eum ◽  
Jong Pil Kim ◽  
Jaepil Cho
Keyword(s):  
High Resolution ◽  
South Korea ◽  
Regression Technique ◽  
Climate Data

scholarly journals Visualizing High-Resolution Climate Data

2001 ◽  
pp. 212-220
Author(s):  
Sheri A. Voelz ◽  
John Taylor
Keyword(s):  
High Resolution ◽  
Climate Data

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.

High-Resolution Seismic Stratigraphy Offshore Haeundae Beach in Busan, South Korea

2020 ◽  
Vol 102 (sp1) ◽  
Author(s):  
Dong-Lim Choi ◽  
Dong-Hyeok Shin ◽  
Jae-Youll Jin ◽  
Yong-Kuk Lee ◽  
Byung-Cheol Kum
Keyword(s):  
High Resolution ◽  
South Korea ◽  
Seismic Stratigraphy

scholarly journals Evaluation of Rainfall Forecasts with Heavy Rain Types in the High-Resolution Unified Model over South Korea

2019 ◽  
Vol 34 (5) ◽  
pp. 1277-1293 ◽  
Author(s):  
Hwan-Jin Song ◽  
Byunghwan Lim ◽  
Sangwon Joo
Keyword(s):  
High Resolution ◽  
South Korea ◽  
Korean Peninsula ◽  
Heavy Rainfall ◽  
Rainfall Intensity ◽  
Heavy Rain ◽  
Unified Model ◽  
Moderate Intensity ◽  
Local Data ◽  
Total Rainfall

Abstract Heavy rainfall events account for most socioeconomic damages caused by natural disasters in South Korea. However, the microphysical understanding of heavy rain is still lacking, leading to uncertainties in quantitative rainfall prediction. This study is aimed at evaluating rainfall forecasts in the Local Data Assimilation and Prediction System (LDAPS), a high-resolution configuration of the Unified Model over the Korean Peninsula. The rainfall of LDAPS forecasts was evaluated with observations based on two types of heavy rain events classified from K-means clustering for the relationship between surface rainfall intensity and cloud-top height. LDAPS forecasts were characterized by more heavy rain cases with high cloud-top heights (cold-type heavy rain) in contrast to observations showing frequent moderate-intensity rain systems with relatively lower cloud-top heights (warm-type heavy rain) over South Korea. The observed cold-type and warm-type events accounted for 32.7% and 67.3% of total rainfall, whereas LDAPS forecasts accounted for 65.3% and 34.7%, respectively. This indicates severe overestimation and underestimation of total rainfall for the cold-type and warm-type forecast events, respectively. The overestimation of cold-type heavy rainfall was mainly due to its frequent occurrence, whereas the underestimation of warm-type heavy rainfall was affected by both its low occurrence and weak intensity. The rainfall forecast skill for the warm-type events was much lower than for the cold-type events, due to the lower rainfall intensity and smaller rain area of the warm-type. Therefore, cloud parameterizations for warm-type heavy rain should be improved to enhance rainfall forecasts over the Korean Peninsula.

High resolution climate data for Austria in the period 2008-2040 from a statistical climate change model

2012 ◽  
Vol 33 (2) ◽  
pp. 430-443 ◽  
Author(s):  
F. Strauss ◽  
H. Formayer ◽  
E. Schmid
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Change Model ◽  
Climate Data ◽  
Climate Change Model

Climate change projections for Tamil Nadu, India: deriving high-resolution climate data by a downscaling approach using PRECIS

2015 ◽  
Vol 123 (3-4) ◽  
pp. 523-535 ◽  
Author(s):  
Prasanta Kumar Bal ◽  
A. Ramachandran ◽  
R. Geetha ◽  
B. Bhaskaran ◽  
P. Thirumurugan ◽  
...  
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Tamil Nadu ◽  
Climate Data ◽  
Climate Change Projections

scholarly journals TopoSCALE: deriving surface fluxes from gridded climate data

2013 ◽  
Vol 6 (2) ◽  
pp. 3381-3426 ◽  
Author(s):  
J. Fiddes ◽  
S. Gruber
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Climate Models ◽  
Good Description ◽  
Longwave Radiation ◽  
Surface Fluxes ◽  
Swiss Alps ◽  
Climate Data ◽  
Heterogeneous Terrain ◽  
Reference Methods

Abstract. Simulation of land surface processes is problematic in heterogeneous terrain due to the the high resolution required of model grids to capture strong lateral variability caused by e.g. topography and the lack of accurate meteorological forcing data at the site or scale it is required. Gridded data products produced by atmospheric models can fill this gap, however, often not at an appropriate spatial resolution to drive land-surface simulations. In this study we describe a method that leverages the good description of the atmospheric column provided by climate models, together with high resolution DEM's, to derive a consistent topography-based, scaling of coarse grid climate variables to fine-scale. We test the method together with unscaled grid-level data and a set of reference methods, against a large evaluation dataset (up to 210 stations per variable) in the Swiss Alps. We demonstrate that the method can be used to derive meteorological inputs in complex terrain, with most significant improvements (with respect to reference methods) seen in variables derived from pressure-levels: air temperature, relative humidity, wind speed and incoming longwave radiation. It is expected that this method can be used to improve inputs to numerical simulations in complex and/or remote terrain especially when statistical methods are not possible due to lack of observations i.e. remote areas or future periods.

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