Open AccessAssessing uncertainty in estimates of atmospheric temperature changes from MSU and AMSU using a Monte‐Carlo estimation technique
Author(s) -
Mears Carl A.,
Wentz Frank J.,
Thorne Peter,
Bernie Dan
Publication year - 2011
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.67
H-Index - 298
eISSN - 2156-2202
pISSN - 0148-0227
DOI - 10.1029/2010jd014954
Subject(s) - advanced microwave sounding unit , radiosonde , environmental science , monte carlo method , sampling (signal processing) , meteorology , stratosphere , uncertainty analysis , satellite , troposphere , depth sounding , climatology , remote sensing , statistics , mathematics , computer science , geography , geology , cartography , filter (signal processing) , aerospace engineering , engineering , computer vision
Measurements made by the Microwave Sounding Unit (MSU) and the Advanced Microwave Sounding Unit (AMSU) provide a multidecadal record of global atmospheric temperature change, which have been used by several groups to produce long‐term temperature records of thick layers of the atmosphere from the lower troposphere to the lower stratosphere. Here we present an internal uncertainty estimate for the Remote Sensing Systems data sets made using a Monte Carlo approach that includes contributions to the total uncertainty from sampling error, premerge adjustments to each individual satellite, and the merging procedure. The results can be used to estimate uncertainties in this product at all space and time scales of interest to any specific application. On small space and time scales sampling effects dominate. On the longer time scales intersatellite merging is important at all levels and the diurnal adjustment is a critical uncertainty for the two layers that have a significant surface component, particularly over land. A comparison of trends for the globe, tropics, and extratropics between the best estimate data set along with these error estimates and homogenized radiosonde estimates and available MSU/AMSU estimates from other groups is undertaken. This shows consistency between our product and those produced by others within the stated uncertainty for many regions and layers. In almost as many cases, however, the interdata set differences of the estimated trends are too large be accounted for by the internal uncertainty estimates derived herein.