Rollenbeck, R.; Trachte, K. & Bendix, J. (2016): <b>A New Class of Quality Controls for Micrometeorological Data in Complex Tropical Environments</b>. <i>Journal of Atmospheric and Oceanic Technology</i> <b>33</b>(1), 169-183.
Resource Description
Title:
A New Class of Quality Controls for Micrometeorological Data in Complex Tropical Environments
FOR816dw ID:
1508
Publication Date:
2016-01-25
License and Usage Rights:
PAK 823-825 data user agreement. (www.tropicalmountainforest.org/dataagreementp3.do)
Resource Owner(s):
Individual:
Ruetger Rollenbeck
Contact:
email:
rollenbe <at> staff.uni-marburg.de
Laboratory for Climatology and Remote Sensing
Faculty of Geography
Philipps University of Marburg
Deutschhausstr. 10
35032 Marburg
Germany
Individual:
Katja Trachte
Contact:
email:
katja.trachte <at> b-tu.de
Atmospheric Processes
Burger Chaussee 2
Campus Nord, LG 4/3
Brandenburg University of Technology Cottbus-Senftenberg
03044 Cottbus
Germany
Individual:
Jörg Bendix
Contact:
email:
bendix <at> staff.uni-marburg.de
Faculty of Geography
Deutschhausstraße 10
Philipps University of Marburg
Laboratory for Climatology and Remote Sensing
35032 Marburg
Germany
Abstract:
Quality control is a particularly demanding problem for micrometeorological studies in complex environments. With the transition to electronic sensing and storage of climate data in high temporal resolution,<br/>
traditional approaches of homogenization are insufficient for addressing the small-scale variability and spatial<br/>
heterogeneity of the data. This problem can be successfully addressed by introducing a new class of control<br/>
procedures based on the physical and climatological relations between different climate variables. The new<br/>
approach utilizes knowledge about the interdependency of air temperature, precipitation, radiation, relative<br/>
air humidity, cloud cover, and visibility to develop empirical functions for determining the probability<br/>
margins for the co-occurrence of specific conditions in tropical mountains and deserts. It can also be applied to<br/>
other geographic settings by adjusting the parameters derived from the data itself. All procedures are integrated into a processing chain with feedback loops and combined with conventional logical and statistical<br/>
checks, which enables it to detect small errors that normally pass unnoticed. The algorithms are also adapted<br/>
to incorporate the short time steps of the original data to retain the potential for detailed process analyses.
Additional Infos:
This describes the methods used to create the Best estimate data set for ECSF climate station
email:
rollenbe <at> staff.uni-marburg.de
Laboratory for Climatology and Remote Sensing
Faculty of Geography
Philipps University of Marburg
Deutschhausstr. 10
35032 Marburg
Germany
