Description of MSU and AMSU Data Products

MSU and AMSU Channels

Version Notes

Introduction

RSS Analysis of MSU and AMSU Data

Decadal Trends

Zonally Averaged Monthly Anomalies

Monthly Browse Images

Monthly Binary Data Files

Figures

References

Acknowledgement

MSU and AMSU "Channels"

See channel details

Version Notes

RSS Version 3.2 Channel TLT – November, 2008
RSS Version 3.2 Channels TMT, TTS, and TLS – July, 2008

Version 3.2 simplifies and improves a number of processing steps. The most important changes are:

• Target Factors and Scene Temperature Factors are determined entirely during the merging process using monthly gridded data. In V3.0 and V3.1, the target factors were determined offline using monthly global averages, and then applied to the monthly gridded data. The new methods streamline the data processing, and result in very small changes in long-term trends.

• A more comprehensive analysis of the intersatellite differences has been performed. As a result of this study, we have identified several satellite-months of data that appear to be inconsistent with measurements from other satellites during the same time period. These typically occur near the beginning or end of a satellite's life. These data have been removed from processing.

For more details:

	Changes from RSS TLT Version 3.1 to Version 3.2
	Changes from RSS Version 3.0 to RSS Version 3.2
	Construction of the Remote Sensing Systems V3.2 atmospheric temperature records from the MSU and AMSU microwave sounders
	Construction of the RSS V3.2 lower tropospheric temperature dataset from the MSU and AMSU microwave sounders

Version 3.1 Channel TLT - January, 2008

• TLT 3.1 corrects a processing inconsistency in TLT 3.0: the production code changed between processing AMSU years 1998-2006 and year 2007. For TLT 3.1, all AMSU data have been reprocessed for full version consistency. The effect on TLT decadal trend was minor.

Version 3.0 MSU & AMSU - February, 2007

• AMSU data is now included in the TLT product.
  This allows us to extend the TLT product to the present.

• Intersatellite offsets now vary as a function of latitude.
  This leads to changes in the long-term trends as a function of latitude.

• Data from NOAA-16 AMSU are no longer used.
  NOAA-16 data appear to be drifting relative to data from earlier satellites.

• The NetCDF format is altered so that there is only 1 time dimension.

For more details: Changes from RSS Version 2.1 to RSS Version 3.0

Introduction

Satellite measurements of the Earth’s microwave emissions are a crucial element in the development of an accurate system for long-term monitoring of atmospheric temperature. Satellites provide global coverage at much higher densities than attainable with in situ observations. In situ observations also suffer from non-uniform temporal coverage and undocumented changes in the radiosonde instrumentation used that can lead to local biases and increased uncertainty. The Microwave Sounding Units (MSU) operating on NOAA polar-orbiting platforms have been the principal sources of satellite temperature profiles for the past two decades. The MSUs are cross-track scanners with measurements of microwave radiance in four channels ranging from 50.3 to 57.95 GHz on the lower shoulder of the Oxygen absorption band. These four channels measure the atmospheric temperature in four thick layers spanning the surface through the stratosphere.

Atmospheric temperature measurements extend for almost three decades, beginning in November 1978 and continuing through the present. A series of follow-on instruments, the Advanced Microwave Sounding Units (AMSUs), began operation in 1998 with the intent of extending microwave sounding measurements into the foreseeable future. The AMSU instruments are similar to the MSUs, but they make measurements using a larger number of channels, thus sampling the atmosphere in a larger number of layers. By using the AMSU channels that most closely match the channels in the MSU instruments, we can continue to extend our climate-quality dataset.

The MSU and AMSU instruments were intended for day to day operational use in weather forecasting and thus are not calibrated to the precision needed for climate studies. A climate quality dataset can be extracted from their measurements only by careful intercalibration of the distinct MSU and AMSU instruments.

RSS Analysis of MSU and AMSU Data

Remote Sensing Systems, in collaboration with Dian Seidel of the NOAA Air Resources Laboratory, is funded by the NOAA Climate and Global Change Program to perform an end-to-end analysis of the tropospheric and stratospheric data from the MSU and AMSU series of microwave sounders. The scientists working on the microwave sounding data are Carl Mears and Frank Wentz at Remote Sensing Systems. So far, we have merged the Channel 2 and 4 brightness temperature data from the nine MSU instruments, and Channel 3 brightness temperature data from NOAA-10, NOAA-11, NOAA-12, and NOAA-14, instruments into single brightness temperature datasets for each channel. The merging process requires careful adjustment of the MSU observations to account for drifts caused by orbital decay and changes in local observing time. Then, intersatellite offsets and errors caused by changes in the temperature of the calibration sources are precisely determined. Significant drifts in Channel 3 data from NOAA-6 and NOAA-9 made it impossible to accurately extend the analysis to times before December 1986 for this channel.

Observations included in RSS analysis:

The brightness temperature for each channel corresponds to an average temperature of the atmosphere averaged over that channel's weighting function. In the case of channel TMT, most of the signal is from a thick layer in the middle troposphere at altitudes from 4 to 7 km, with smaller contributions from both the surface and the stratosphere. Channel TLT uses a weighted average between the near-limb and nadir views to extrapolate the data to lower altitude, thus removing almost all of the stratospheric influence. For each channel, the brightness temperature can be thought of as the averaged temperature over a thick atmospheric layer.

Previous work on long time series of MSU channel 2 brightness temperatures has been performed by Christy and Spencer; and Prabhakara, et al. This previous work has played a controversial and high-profile role in the debate over the existence and extent of anthropogenic global climate change. One of the goals of our research is to provide a complete and independent analysis as a check of these important results. We have found that the temperature of the middle troposphere is warming by approximately 0.090 K/decade . We calculate that MSU channel TMT data published by Christy and Spencer (vortex.nsstc.uah.edu/data/msu/t2/) contains a smaller warming trend of approximately 0.054 K/decade .

Christy and Spencer also developed the first version of the TLT dataset. For a global average extending from 70S to 82.5N, we find a warming trend of 0.154 K/decade , while Christy and Spencer (version 5.2) find a warming trend of 0.147 K/decade .

A global map of 31-year MSU/AMSU channel TMT trends shows large regions of significant warming over eastern and central Asia, and northern Canada, cooling over the southern oceans, with moderate warming over most other regions. A map of channel TLT trends shows a very similar pattern, but with more pronounced mid-latitude warming.

These results are discussed in the following papers:

	Correcting the MSU Middle Tropospheric Temperature for Diurnal Drifts (2002)
	Stable Long-Term Retrieval of Tropospheric Temperature Time Series from the Microwave Sounding Unit (2002)
	A reanalysis of the MSU Channel 2 Tropospheric Temperature Record (2003)
	The Effect of Drifting Measurement Time on Satellite-Derived Lower Tropospheric Temperature (2005)
	Construction of the Remote Sensing Systems V3.2 atmospheric temperature records from the MSU and AMSU microwave sounders - Accepted for publication by the Journal of Atmospheric and Oceanic Technology, December 2008.
	Construction of the RSS V3.2 lower tropospheric temperature dataset from the MSU and AMSU microwave sounders - Submitted to the Journal of Atmospheric and Oceanic Technology, September 2008.

The articles are in PDF format. You will need a PDF viewer, such as the free Adobe Acrobat Reader to view the files.

Decadal Trends

Long term trends are useful for detecting global climate change, and for comparing these measured results with the output from climate models.

Maps of global trend on a 2.5-degree scale have been made for MSU channel TLT, MSU/AMSU channel TMT, MSU/AMSU channel TTS, and MSU/AMSU channel TLS. Trend maps are computed over the time period for each channel that contains complete years of valid data.

Globally averaged trends computed over latitudes from 82.5S to 82.5N (70S to 82.5N for channel TLT) are shown in the table below, and include data through :

	Start Time	Stop Time	# Years	Global Trend
Channel TLT	1979		30+	0.154 K/decade
Channel TMT	1979		30+	0.090 K/decade
Channel TTS	1987		22+	-0.020 K/decade
Channel TLS	1979		30+	-0.324 K/decade

See the monthly, global time series of brightness temperature anomalies for each channel, as well as linear fits to the time series (Figure 7). Anomalies are computed by subtracting the mean monthly value (averaged from 1979 through 1998 for each channel) from the average brightness temperature for each month.

Zonally Averaged Monthly Anomalies

For your convenience, we provide text files containing monthly anomalies of each MSU/AMSU channel averaged over a number of zonal bands. In addition, these averages are performed over land, ocean, and land+ocean spacial subsets.

Zonally Averaged Monthly Anomalies are available here in text format.

Monthly Browse Images

Monthly maps of MSU brightness temperatures and brightness temperature anomalies for channels TLT, TMT, TTS and TLS are available on this website, and from our FTP server (ftp.ssmi.com/msu). Each monthly map is a 144 x 72 (2.5 degree resolution) gridded dataset of brightness temperatures. Brightness temperatures are adjusted to correspond to a local time of midnight using our monthly diurnal cycle climatology. Brightness temperature anomalies are the difference between the monthly brightness temperatures and the average value for that month (found by averaging that month from 1979 through 1998).

Each monthly image consists of the average brightness temperature or brightness temperature anomaly. The scale for each map is located at the bottom of the map for reference. Missing data are shown in grey. We do not provide monthly means poleward of 82.5 degrees due to difficulties in merging measurements in these regions, and because these regions are not sampled by all central fields of view.

Monthly Binary Data Files

Each binary data file located on our MSU FTP site consists of a 144 x 72 x 372 array of 4 byte real numbers. The first two indices correspond to longitude and latitude (at 2.5 degree resolution), and the last index is the month number, starting in January 1978. The first 10 months contain no valid data, but are included so that the first month corresponds to the first month of the year. The files are also padded with empty data to fill in months through the end of the current year.

File Name Format	Contents
channel_###_tb_v03_x.dat	Average monthly brightness temperature
channel_###_tb_anom_v03_x.dat	Brightness temperature anomalies: Monthly brightness temperature minus the average values for that month, averaged from 1979 through 1998. (e.g. the average of January, 1984 minus the average of every January from 1979 through 1998, inclusive.)

Monthly binary data are available in the /msu/data directory of our FTP Server (ftp.ssmi.com/msu/data).

Read routines written in Fortran, C, IDL and Matlab are available in the /msu/support directory (ftp.ssmi.com/msu/support).

Figures

Figure 2. Vertical relative weighting functions for each of the channels discussed on this website. The vertical weighting function describes the relative contribution that microwave radiation emitted by a layer in the atmosphere makes to the total intensity measured above the atmosphere by the satellite. The weighting functions are available on our FTP site at ftp.ssmi.com/msu/weighting_functions

Figure 3. Color coded map of decadal trends in MSU channel TLT (1979 - 2009). Data poleward of 82.5° North and 70° South, as well as areas with land or ice elevations above 3000 meters, are not available and are shown in white.

Figure 4. Color coded map of decadal trends in MSU/AMSU channel TMT (1979 - 2009). Data poleward of 82.5° are not available and are shown in white.

Figure 5. Color coded map of decadal trends in MSU/AMSU channel TTS (1987 - 2009). Data poleward of 82.5° are not available and are shown in white. This channel is affected by both tropospheric warming, and stratospheric cooling.

Figure 6. Color coded map of decadal trends in MSU/AMSU channel TLS (1979 - 2009). Data poleward of 82.5° are not available and are shown in white. This channel is dominated by stratospheric cooling.

Ch. TLT Ch. TMT Ch. TTS Ch. TLS

Figure 7. Global, monthly time series of brightness temperature anomaly for channels TLT, TMT, TTS, and TLS. For Channel TLT (Lower Troposphere) and Channel TMT (Middle Troposphere), the anomaly time series is dominated by ENSO events and slow tropospheric warming. The three primary El Niños during the past 20 years are clearly evident as peaks in the time series occurring during 1982-83, 1987-88, and 1997-98, with the most recent one being the largest. Channel TLS (Lower Stratosphere) is dominated by stratospheric cooling, punctuated by dramatic warming events caused by the eruptions of El Chichon (1982) and Mt Pinatubo (1991). Channel TTS (Troposhere / Stratosphere) appears to be a mixture of both effects.

Figure 8.

Figure 9.

Figure 10.

Figure 11.

References

J. R. Christy, R. W. Spencer, W. D. Braswell.
"MSU Tropospheric Temperatures: Dataset Construction and Radiosonde Comparisons"
Journal of Atmospheric and Oceanic Technology, vol. 17, pp. 1153-1170, 2000.

Associated Website:

NASA GHCC Global Temperature Anomaly Data ( http://www.ghcc.msfc.nasa.gov/temperature/ )

Carl A. Mears, Matthias Schabel, Frank J. Wentz.
"A reanalysis of the MSU Channel 2 Tropospheric Temperature Record"
Journal of Climate, Volume 16, pg. 3650-3664, November, 2003.

Carl A. Mears and Frank J. Wentz.
“Construction of climate-quality atmospheric temperature records from the MSU and AMSU microwave sounders”
submitted to the Journal of Atmospheric and Oceanic Technology, May 2008.

Carl A. Mears and Frank J. Wentz.
"The Effect of Drifting Measurement Time on Satellite-Derived Lower Tropospheric Temperature"
Science, published online 11 August 2005; 10.1126/science.1114772.

Carl A. Mears, Matthias Schabel, Frank J. Wentz, Benjamin D. Santer, Bala Govindasamy.
"Correcting the MSU Middle Tropospheric Temperature for Diurnal Drifts"
Proceedings of the International Geophysics and Remote Sensing Symposium, Volume III, pg. 1839-1841, 2002.

Prabhakara, C., R. Iacovazzi Jr, J.-M. Yoo, G. Dalu.
"Global warming: Estimation from satellite observations"
Geophysical Research Letters, Vol. 27(21), 3517-3520, 2000.

Matthias C. Schabel, Carl A. Mears, Frank J. Wentz.
"Stable Long-Term Retrieval of Tropospheric Temperature Time Series from the Microwave Sounding Unit,"
Proceedings of the International Geophysics and Remote Sensing Symposium, Volume III, pg. 1845-1847, 2002.

Acknowledgement

MSU/AMSU data are produced by Remote Sensing Systems and sponsored by the NOAA Climate and Global Change Program. Data are available at www.remss.com.

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Last updated: February 04, 2009