scholarly journals A high-resolution air temperature data set for the Chinese Tian Shan in 1979–2016

2018 ◽  
Vol 10 (4) ◽  
pp. 2097-2114 ◽  
Author(s):  
Lu Gao ◽  
Jianhui Wei ◽  
Lingxiao Wang ◽  
Matthias Bernhardt ◽  
Karsten Schulz ◽  
...  
Keyword(s):  
High Resolution ◽  
Air Temperature ◽  
Socioeconomic Development ◽  
Temperature Data ◽  
Efficiency Coefficient ◽  
Data Set ◽  
High Resolution Data ◽  
Local Climate ◽  
Chinese Tianshan ◽  
Tian Shan

Abstract. The Chinese Tian Shan (also known as the Chinese Tianshan Mountains, CTM) have a complex ecological environmental system. They not only have a large number of desert oases but also support many glaciers. The arid climate and the shortage of water resources are the important factors restricting the area's socioeconomic development. This study presents a unique high-resolution (1 km, 6-hourly) air temperature data set for the Chinese Tian Shan (41.1814–45.9945∘ N, 77.3484–96.9989∘ E) from 1979 to 2016 based on a robust elevation correction framework. The data set was validated by 24 meteorological stations at a daily scale. Compared to original ERA-Interim temperature, the Nash–Sutcliffe efficiency coefficient increased from 0.90 to 0.94 for all test sites. Approximately 24 % of the root-mean-square error was reduced from 3.75 to 2.85 ∘C. A skill score based on the probability density function, which was used to validate the reliability of the new data set for capturing the distributions, improved from 0.86 to 0.91 for all test sites. The data set was able to capture the warming trends compared to observations at annual and seasonal scales, except for winter. We concluded that the new high-resolution data set is generally reliable for climate change investigation over the Chinese Tian Shan. However, the new data set is expected to be further validated based on more observations. This data set will be helpful for potential users to improve local climate monitoring, modeling, and environmental studies in the Chinese Tian Shan. The data set presented in this article is published in the Network Common Data Form (NetCDF) at https://doi.org/10.1594/PANGAEA.887700. The data set includes 288 nc files and one user guidance txt file.

scholarly journals A high-resolution air temperature data set for the Chinese Tianshan Mountains in 1979–2016

2018 ◽  
Author(s):  
Lu Gao ◽  
Jianhui Wei ◽  
Lingxiao Wang ◽  
Matthias Bernhardt ◽  
Karsten Schulz ◽  
...  
Keyword(s):  
High Resolution ◽  
Air Temperature ◽  
Temperature Data ◽  
Tianshan Mountains ◽  
Efficiency Coefficient ◽  
Data Set ◽  
High Resolution Data ◽  
Local Climate ◽  
Chinese Tianshan Mountains ◽  
Chinese Tianshan

Abstract. The Chinese Tianshan Mountains has a complex ecological environment system. It not only has a large number of desert oases, but also gave birth to a large number of glaciers. The arid climate and the shortage of water resources are the important factors to restrict the socio-economic development in this area. This study presents a unique high-resolution (1 km, 6-hourly) air temperature data set for the Chinese Tianshan Mountains (41.1814–45.9945° N, 77.3484–96.9989° E) from 1979 to 2016 based on a robust statistical downscaling framework. The data set was validated by 24 meteorological stations at daily scale. Compared with original ERA-Interim temperature, the Nash-Sutcliffe efficiency coefficient increased from 0.90 to 0.94 over all test sites. Around 24 % of root-mean-square error was reduced from 3.75 to 2.85 °C. A skill score based on the probability density function, which was used to validate the reliability of the new data set for capturing the distributions, enhanced from 0.86 to 0.91 for all test sites. We conclude that the new high-resolution data set is reliable for climate change investigation over the Chinese Tianshan Mountains. This data set would be helpful for the potential users for better local climate monitoring, modelling and environmental studies in the Chinese Tianshan Mountains. The data set presented in this article is published in Network Common Data Form (NetCDF) at doi:10.1594/PANGAEA.887700. The data set includes 288 nc files and one user guidance in txt file.

scholarly journals High-resolution daily gridded data sets of air temperature and wind speed for Europe

2016 ◽  
Vol 8 (2) ◽  
pp. 491-516 ◽  
Author(s):  
Sven Brinckmann ◽  
Stefan Krähenmann ◽  
Peter Bissolli
Keyword(s):  
High Resolution ◽  
Wind Speed ◽  
Air Temperature ◽  
Data Sets ◽  
Grid Data ◽  
Data Set ◽  
Near Surface ◽  
High Resolution Data ◽  
Station Data ◽  
Mean Wind Speed

Abstract. New high-resolution data sets for near-surface daily air temperature (minimum, maximum and mean) and daily mean wind speed for Europe (the CORDEX domain) are provided for the period 2001–2010 for the purpose of regional model validation in the framework of DecReg, a sub-project of the German MiKlip project, which aims to develop decadal climate predictions. The main input data sources are SYNOP observations, partly supplemented by station data from the ECA&D data set (http://www.ecad.eu). These data are quality tested to eliminate erroneous data. By spatial interpolation of these station observations, grid data in a resolution of 0.044° (≈ 5km) on a rotated grid with virtual North Pole at 39.25° N, 162° W are derived. For temperature interpolation a modified version of a regression kriging method developed by Krähenmann et al.(2011) is used. At first, predictor fields of altitude, continentality and zonal mean temperature are used for a regression applied to monthly station data. The residuals of the monthly regression and the deviations of the daily data from the monthly averages are interpolated using simple kriging in a second and third step. For wind speed a new method based on the concept used for temperature was developed, involving predictor fields of exposure, roughness length, coastal distance and ERA-Interim reanalysis wind speed at 850 hPa. Interpolation uncertainty is estimated by means of the kriging variance and regression uncertainties. Furthermore, to assess the quality of the final daily grid data, cross validation is performed. Variance explained by the regression ranges from 70 to 90 % for monthly temperature and from 50 to 60 % for monthly wind speed. The resulting RMSE for the final daily grid data amounts to 1–2 K and 1–1.5 ms−1 (depending on season and parameter) for daily temperature parameters and daily mean wind speed, respectively. The data sets presented in this article are published at doi:10.5676/DWD_CDC/DECREG0110v2.

scholarly journals A Global High‐Resolution Data Set of Soil Hydraulic and Thermal Properties for Land Surface Modeling

2019 ◽  
Vol 11 (9) ◽  
pp. 2996-3023 ◽  
Author(s):  
Yongjiu Dai ◽  
Qinchuan Xin ◽  
Nan Wei ◽  
Yonggen Zhang ◽  
Wei Shangguan ◽  
...  
Keyword(s):  
Thermal Properties ◽  
High Resolution ◽  
Land Surface ◽  
Surface Modeling ◽  
Data Set ◽  
High Resolution Data ◽  
Land Surface Modeling ◽  
Soil Hydraulic ◽  
Resolution Data

scholarly journals Calibration of NSRP Models from Extreme Value Distributions

Hydrology ◽  
2019 ◽  
Vol 6 (4) ◽  
pp. 89 ◽  
Author(s):  
De Luca ◽  
Galasso
Keyword(s):  
High Resolution ◽  
Probability Distributions ◽  
Mathematical Expression ◽  
Extreme Value ◽  
Extreme Value Distributions ◽  
Data Set ◽  
Maximum Rainfall ◽  
High Resolution Data ◽  
Frequency Distributions ◽  
Annual Maximum Rainfall

In this work, the authors investigated the feasibility of calibrating a model which is suitable for the generation of continuous high-resolution rainfall series, by using only data from annual maximum rainfall (AMR) series, which are usually longer than continuous high-resolution data, or they are the unique available data set for many locations. In detail, the basic version of the Neyman–Scott Rectangular Pulses (NSRP) model was considered, and numerical experiments were carried out, in order to analyze which parameters can mostly influence the extreme value frequency distributions, and whether heavy rainfall reproduction can be improved with respect to the usual calibration with continuous data. The obtained results were highly promising, as the authors found acceptable relationships among extreme value distributions and statistical properties of intensity and duration for the pulses. Moreover, the proposed procedure is flexible, and it is clearly applicable for a generic rainfall generator, in which probability distributions and shape of the pulses, and extreme value distributions can assume any mathematical expression.

scholarly journals Reconstruction of Axial Tomographic High Resolution Data from Confocal Fluorescence Microscopy: A Method for Improving 3D FISH Images

2000 ◽  
Vol 20 (1) ◽  
pp. 7-15 ◽  
Author(s):  
R. Heintzmann ◽  
G. Kreth ◽  
C. Cremer
Keyword(s):  
High Resolution ◽  
Laser Scanning ◽  
Axial Direction ◽  
Laser Scanning Microscopy ◽  
Three Dimensions ◽  
Confocal Laser ◽  
Data Sets ◽  
Data Set ◽  
High Resolution Data ◽  
3D Data

Fluorescent confocal laser scanning microscopy allows an improved imaging of microscopic objects in three dimensions. However, the resolution along the axial direction is three times worse than the resolution in lateral directions. A method to overcome this axial limitation is tilting the object under the microscope, in a way that the direction of the optical axis points into different directions relative to the sample. A new technique for a simultaneous reconstruction from a number of such axial tomographic confocal data sets was developed and used for high resolution reconstruction of 3D‐data both from experimental and virtual microscopic data sets. The reconstructed images have a highly improved 3D resolution, which is comparable to the lateral resolution of a single deconvolved data set. Axial tomographic imaging in combination with simultaneous data reconstruction also opens the possibility for a more precise quantification of 3D data. The color images of this publication can be accessed from http://www.esacp.org/acp/2000/20‐1/heintzmann.htm. At this web address an interactive 3D viewer is additionally provided for browsing the 3D data. This java applet displays three orthogonal slices of the data set which are dynamically updated by user mouse clicks or keystrokes.

HRSC-A data: a new high-resolution data set with multipurpose applications in physical geography

2007 ◽  
Vol 31 (2) ◽  
pp. 179-197 ◽  
Author(s):  
J.-C. Otto ◽  
K. Kleinod ◽  
O. König ◽  
M. Krautblatter ◽  
M. Nyenhuis ◽  
...  
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Research Training ◽  
Spatial Scales ◽  
Three Dimensional ◽  
Physical Geography ◽  
Vegetation Monitoring ◽  
Data Set ◽  
High Resolution Data ◽  
Resolution Data

The analysis and interpretation of remote sensing data facilitates investigation of land surface complexity on large spatial scales. We introduce here a geometrically high-resolution data set provided by the airborne High Resolution Stereo Camera (HRSC-A). The sensor records digital multispectral and panchromatic stereo bands from which a very high-resolution ground elevation model can be produced. After introducing the basic principles of the HRSC technique and data, applications of HRSC data within the multidisciplinary Research Training Group 437 are presented. Applications include geomorphologic mapping, geomorphometric analysis, mapping of surficial grain-size distribution, rock glacier kinematic analysis, vegetation monitoring and three-dimensional landform visualization. A final evaluation of the HRSC data based on three years of multipurpose usage concludes this presentation. A combination of image and elevation data opens up various possibilities for visualization and three-dimensional analysis of the land surface, especially in geomorphology. Additionally, the multispectral imagery of the HRSC data has potential for land cover mapping and vegetation monitoring. We consider HRSC data a valuable source of high-resolution terrain information with high applicability in physical geography and earth system science.

Homogenized monthly upper-air temperature data set for Australia

2016 ◽  
Vol 37 (7) ◽  
pp. 3209-3222 ◽  
Author(s):  
Branislava Jovanovic ◽  
Robert Smalley ◽  
Bertrand Timbal ◽  
Steven Siems
Keyword(s):  
Air Temperature ◽  
Temperature Data ◽  
Data Set
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