Estimating sampling biases and measurement uncertainties of AIRS/AMSU‐A temperature and water vapor observations using MERRA reanalysis. Issue 6 (18th March 2014)

• Record Type: Journal Article
• Authors: Hearty, Thomas J.; Savtchenko, Andrey; Tian, Baijun; Fetzer, Eric; Yung, Yuk L.; Theobald, Michael; Vollmer, Bruce; Fishbein, Evan; Won, Young‐In
• Is Part Of: Journal of geophysical research. Volume 119:Issue 6(2014:Jun.)
• Page Start: 2725
• Page End: 2741
• Publication Date: 2014-03-18
• Subjects: Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1002/2013JD021205
• Languages: English
• ISSNs: 2169-897X

Abstract

<abstract abstract-type="main" id="jgrd51224-abs-0001"> <title> <x xml:space="preserve">Abstract</x> </title> <p id="jgrd51224-para-0001">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 <italic>temporal</italic> and <italic>instrumental</italic> 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.</p> </abstract>