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Estimating sampling biases and measurement uncertainties of AIRS/AMSU‐A temperature and water vapor observations using MERRA reanalysis
Author(s) -
Hearty Thomas J.,
Savtchenko Andrey,
Tian Baijun,
Fetzer Eric,
Yung Yuk L.,
Theobald Michael,
Vollmer Bruce,
Fishbein Evan,
Won YoungIn
Publication year - 2014
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
eISSN - 2169-8996
pISSN - 2169-897X
DOI - 10.1002/2013jd021205
Subject(s) - advanced microwave sounding unit , sampling (signal processing) , environmental science , water vapor , atmospheric infrared sounder , atmospheric sciences , microwave limb sounder , sampling bias , meteorology , climatology , remote sensing , depth sounding , statistics , geology , geography , sample size determination , mathematics , oceanography , filter (signal processing) , computer science , computer vision
We use MERRA (Modern Era Retrospective‐Analysis for Research Applications) temperature and water vapor data to estimate the sampling biases of climatologies derived from the AIRS/AMSU‐A (Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit‐A) suite of instruments. We separate the total sampling bias into temporal and instrumental components. The temporal component is caused by the AIRS/AMSU‐A orbit and swath that are not able to sample all of time and space. The instrumental component is caused by scenes that prevent successful retrievals. The temporal sampling biases are generally smaller than the instrumental sampling biases except in regions with large diurnal variations, such as the boundary layer, where the temporal sampling biases of temperature can be ± 2 K and water vapor can be 10% wet. The instrumental sampling biases are the main contributor to the total sampling biases and are mainly caused by clouds. They are up to 2 K cold and > 30% dry over midlatitude storm tracks and tropical deep convective cloudy regions and up to 20% wet over stratus regions. However, other factors such as surface emissivity and temperature can also influence the instrumental sampling bias over deserts where the biases can be up to 1 K cold and 10% wet. Some instrumental sampling biases can vary seasonally and/or diurnally. We also estimate the combined measurement uncertainties of temperature and water vapor from AIRS/AMSU‐A and MERRA by comparing similarly sampled climatologies from both data sets. The measurement differences are often larger than the sampling biases and have longitudinal variations.