Recalibration of microwave sounding unit for climate studies using simultaneous nadir overpasses

Abstract

[1] The measurements from microwave sounding unit (MSU) on board different NOAA polar-orbiting satellites have been extensively used for detecting atmospheric temperature trend during the last several decades. However, temperature trends derived from these measurements are under significant debate, mostly caused by calibration errors. This study recalibrates the MSU channel 2 observations at level 0 using the postlaunch simultaneous nadir overpass (SNO) matchups and then provides a well-merged new MSU 1b data set for climate studies. The calibration algorithm consists of a dominant linear response of the MSU raw counts to the Earth-view radiance plus a smaller quadratic term. Uncertainties are represented by a constant offset and errors in the coefficient for the nonlinear quadratic term. A SNO matchup data set for nadir pixels with criteria of simultaneity of less than 100 s and within a ground distance of 111 km is generated for all overlaps of NOAA satellites. The simultaneous nature of these matchups eliminates the impact of orbital drifts on the calibration. A radiance error model for the SNO pairs is developed and then used to determine the offsets and nonlinear coefficients through regressions of the SNO matchups. It is found that the SNO matchups can accurately determine the differences of the offsets as well as the nonlinear coefficients between satellite pairs, thus providing a strong constraint to link calibration coefficients of different satellites together. However, SNO matchups alone cannot determine the absolute values of the coefficients because there is a high degree of colinearity between satellite SNO observations. Absolute values of calibration coefficients are obtained through sensitivity experiments, in which the percentage of variance in the brightness temperature difference time series that can be explained by the warm target temperatures of overlapping satellites is a function of the calibration coefficient. By minimizing these percentages of variance for overlapping observations, a new set of calibration coefficients is obtained from the SNO regressions. These new coefficients are significantly different from the prelaunch calibration values, but they result in bias-free SNO matchups and near-zero contaminations by the warm target temperatures in terms of the calibrated brightness temperature. Applying the new calibration coefficients to the Level 0 MSU observations, a well-merged MSU pentad data set is generated for climate trend studies. To avoid errors caused by small SNO samplings between NOAA 10 and 9, observations only from and after NOAA 10 are used. In addition, only ocean averages are investigated so that diurnal cycle effect can be ignored. The global ocean-averaged intersatellite biases for the pentad data set are between 0.05 and 0.1 K, which is an order of magnitude smaller than that obtained when using the unadjusted calibration algorithm. The ocean-only anomaly trend for the combined MSU channel 2 brightness temperature is found to be 0.

Melting of major Glaciers in the western Himalayas: evidence of climatic changes from long term MSU derived tropospheric temperature trend (1979–2008)

“…The MSU data suffer from a number of inter-calibration issues, time and diurnal biases. Several groups (Remote Sensing Systems (RSS), the University of Maryland (UMD), the University of Alabama at Huntsville (UAH), the NOAA National Environmental Satellite, Data, and Information Service (NOAA/NESDIS)) have merged the data available from these MSU/AMSU instruments to produce a long-term atmospheric temperature record (Christy et al, 2000(Christy et al, , 2003Grody et al, 2004;Mears et al, 2002Mears et al, , 2003Mears and Wentz, 2009;Prabhakara et al, 2000;Vinnikov et al, 2005;Zou et al, 2006). We have used the atmospheric temperature (microwave lower and mid tropospheric) records from the MSU and AMSU-derived temperature data that are available from Remote Sensing Systems (RSS).…”

Recent slowdown of tropical upper tropospheric warming associated with Pacific climate variability

“…We also used the satellite microwave sounding unit (MSU) and its successor, the advanced MSU (AMSU) data set version 3.0 [Zou et al, 2006[Zou et al, , 2009, developed by the Center for Satellite Applications and Research of the National Oceanic and Atmospheric Administration (NOAA). An intercalibration method, based on simultaneous nadir overpass matchups, was applied to reduce biases in the intersatellite MSU instruments [Zou et al, 2006[Zou et al, , 2009.…”

“…An intercalibration method, based on simultaneous nadir overpass matchups, was applied to reduce biases in the intersatellite MSU instruments [Zou et al, 2006[Zou et al, , 2009. We used the temperatures of the troposphere and stratosphere corresponding to measurements from MSU channels 2 and 4 (T 2 and T 4 ) covering the vertical layers of 0-15 and 12-26 km (with their weighting functions peaking near 550 and 100 hPa), respectively [Zou and Li, 2014].…”

Identifying human influences on atmospheric temperature

“…The key model uncertainties are in the anthropogenic and natural external forcings (20), the climate responses to these forcings, and the estimates of internal variability (17)(18)(19). Uncertainties in observations of atmospheric temperature change arise because of the different choices analysts make in adjusting raw measurements for the effects of nonclimatic influences (21)(22)(23)(24)(25)(26)(27)(28).…”