Description of MSU and AMSU Data Products

Introduction

Microwave Sounding Data Products from RSS

Microwave Sounding Data Products from Other Research Groups

Decadal Trends

Zonally Averaged Monthly Anomalies

Monthly Browse Images

Monthly Binary Data Files

Version Notes

References

Acknowledgement

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 were the principal sources of satellite temperature profiles from late 1978 to the early 2000's. The MSUs were cross-track scanners that made 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 measured the atmospheric temperature in four thick layers spanning the surface through the stratosphere. The last MSU instrument, NOAA-14, ceased reliable operation in 2005.

A series of follow-on instruments, the Advanced Microwave Sounding Units (AMSUs), began operation in 1998. The AMSU instruments are similar to the MSUs, but 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 have extended our climate-quality dataset to the present. In addition, we have completed a preliminary analysis of AMSU channels 10-14, which measure temperatures higher in the stratosphere than the highest MSU channel, MSU channel 4. These datasets begin in mid 1998 with the launch of the first AMSU on the NOAA-15 satellite. These datasets, now 14 years long, are beginning to be long enough to become interesting for investigating long-term changes in the mid and upper stratosphere.

In the future, the AMSU instruments will be phased out, and replaced with the Advanced Technology Microwave Sounder (ATMS). The first ATMS was launched on October 28, 2011. Measurements made by the ATMS are not yet used in our dataset. We are working to cross-calibrate ATMS with AMSU so that ATMS measurements can be included in the future.

All microwave sounding instruments were developed for day to day operational use in weather forecasting and thus are typically 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 data from the MSU, AMSU and ATMS instruments.

Microwave Sounding Data Products from RSS

We produce 3 single-channel MSU/AMSU datasets (TMT, TTS, and TLS) that extend back to late 1978, and 5 single channel AMSU-only datasets (C10, C11, C12, C13, and C14) that begin in mid 1998. TLT is a more complex dataset constructed by calculating a weighted difference between measurements made at different Earth incidence angles to extrapolate MSU channel 2 and AMSU channel 5 measurements lower in the atmosphere. In addition, there are 2 multi-channel datasets, TTT and C25, that are constructed from weighted combinations of the single channel datasets. The satellites and channels used in each RSS data product, as well as the weighting functions for each product are shown below. The AMSU-only datasets (C10-C14, C25) are relatively early in their development process and should be considered preliminary, or in the case of C13 and C14, experimental.

Channels and Satellites used in RSS Atmospheric Temperature Products:

Figure 1. Weighting function for each RSS product. 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 /msu/weighting_functions.

The single channel datasets are mostly constructed by calculating an average of near-nadir views (central 5 views for MSU, central 12 views for AMSU). The exception to this is TLS from AMSU, which uses a set of off-nadir views to match the measurements from MSU channel 4 more closely. See Mears et al, 2009a for more details. A map showing the footprints use for near-nadir products and TLT is shown in Figure 2 below. TLT, TTT, and C25 are constructed using more complicated methods.

Figure 2. Two example scans for the MSU instrument. The satellite is traveling in the South to North direction, and scanning (roughly) West to East, making 11 discrete measurements in each scan. Footprints used to construct the near-nadir MSU products (TMT, TTS, TLS) from the top scan are shown in green. The numbers in each footprint are the weights assigned to the footprint when constructing the average for a given scan. The footprints used to construct TLT are shown in red and blue in the lower scan, with red denoting negative weight.

TLT (Temperature Lower Troposphere)

TLT is contructed by calculating a weighted difference between MSU2 (or AMSU 5) measurements from near limb views and measurements from the same channels taken closer to nadir, as can be seen in Figure 1 for the case of MSU. This has the effect of extrapolating the MSU2 (or AMSU5) measurements lower in the troposphere, and removing most of the stratospheric influence. Because of the differences involves measurements made at different locations, and because of the large absolute values of the weights used, additional noise is added by this process, increasing the uncertainty in the final results. For more details see Mears et al., 2009b.

TTT (Temperature Total Troposphere)

TTT is a multichannel combined product made by calculating a linear combination of TMT and TLS. TTT = 1.1*TMT - 0.1*TLS. This combination has the effect of reducing the influence of lower stratosphere, as shown Figure 3. In the simpler TMT product, about 10% of the weight is from the lower stratosphere. Because the lower stratosphere is cooling at most locations, this causes the decadal trends in TMT to be less than the trends in the mid and upper troposphere. TTT was proposed by Fu and Johanson, 2005.

Figure 3. The left panel shows the weighted versions of the TMT and TLS weighting functions. The right panel shows the weighting function for TTT = 1.1*TMT – 0.1*TLS in blue, with the unmodified TMT weighting function shown in black.

C25

As shown in Figure 4, C25 is constructed using a linear combination of AMSU channels 10,11,12, and 13.

C25 = 0.258*C10 + 0.215*C11 + 0.409*C12 + 0.122*C13

The weighting function of this channel closely matches the weighting function of Channel 25 (sometimes called Channel 1) of the stratospheric sounding unit (SSU), and this product is intended to be used to extend the existing SSU channel 25.

Figure 4. The left panel shows the weighted versions of the C10 through C13 weighting functions. The right panel shows the weighting function for C25 in black, with the weighting function for SSU channel 25 (sometimes called SSU channel 1) in blue.

Microwave Sounding Data Products From Other Research Groups

A number of other research groups have produced datasets from the MSU and AMSU instruments. Of these, only the UAH and STAR datasets are currently being updated. Other previous work was performed by Prabhakara, et al. and Vinnikov et al., but these datasets are not currently being updated and do not extend to the present.

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+	0.130 K/decade
Channel TMT	1979		30+	0.079 K/decade
Channel TTS	1987		22+	-0.001 K/decade
Channel TLS	1979		30+	-0.296 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).

Version Notes

RSS Version 3.3 Channel TLT, TMT, TTS, and TLS – January, 2011

Change from 3.2 to 3.3:

• Additional satellites are now included in the merge. Version 3.2 only used data from one AMSU instrument, NOAA-15. For TLT, TMT, and TLS, Version 3.3 includes data from the AMSU instruments on NOAA-15, AQUA, NOAA-18, and METOP-A. AMSU channel 7 exhibits unexplained drifts in METOP-A, so for TTS, data from METOP-A is not used.

• Comparisons with other AMSU satellites are now used to detemine the AMSU merging coefficients.

• When merging MSU and AMSU together, the data for each generation of satellites is weighted by the number of satellites with valid data for that month. This has the effect of de-emphasizing MSU data after the advent of the AQUA satellite in June 2002. Since the 2002-2004 period is when there is an unexplained warming drift in MSU channel 2 data from NOAA-14 relative to AMSU data, this change has the effect of lowering the overall warming in TMT and TLT during the post 2002 period.

• The changes also result in a reduction of sampling noise and “orbital striping” for periods when data from more satellites is used.

• Data from NOAA-16 is not used because all 3 channels show unexplained drift throughout it’s lifetime. NOAA-17 was only operational for a short period of time, thus it’s data is of little use for climate studies. We plan to begin including data from NOAA-19 after 3 years of operation.

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 3.2 to Version 3.3
	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

References

Mears, C. A., F. J. Wentz, P. Thorne, and D. Bernie
Assessing uncertainty in estimates of atmospheric temperature changes from MSU and AMSU using a Monte-Carlo estimation technique
J. Geophys. Res., doi:10.1029/2010JD014954, in press, 2011.

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.

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, 2009,
“Construction of the RSS V3.2 lower tropospheric dataset from the MSU and AMSU microwave sounders”
Journal of Atmospheric and Oceanic Technology, 26, 1493-1509.

Carl A. Mears and Frank J. Wentz, 2009,
“Construction of the Remote Sensing Systems V3.2 atmospheric temperature records from the MSU and AMSU microwave sounders”
Journal of Atmospheric and Oceanic Technology, 26, 1040-1056.

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.

Vinnikov, K. Y., N. C. Grody, A. Robock, R. J. Stouffer, P. D. Jones, and M. D. Goldberg.
"Temperature Trends at the Surface and in the Troposphere"
Journal of Geophysical Research, 111, D03106, 2005.

Prabhakara, C., R. Iaacovazzi, J. M. Yoo, and G. Dalu.
"Global Warming: Evidence From Satellite Observations"
Geophysical Research Letters, 27, 3517-3520, 2000.

Fu, Q. and C. M. Johanson.
"Satellite-Derived Vertical Dependence of Tropospheric Temperature Trends"
Geophysical Research Letters, 32, L10703, 2005.

Acknowledgement

MSU/AMSU data are produced by Remote Sensing Systems. Over the years, we have received support for the development of this dataset from a number of sources, including NOAA's Office of Global Programs, NOAA's Climate Program Office, and NOAA's Climate Data Record Program. Production of the current dataset (version 3.3) is supported by NOAA's Climate Data Record Program, while improvements to the methods used to produce the dataset are currently supported by NASA's Earth Science Division, which is part of the Science Mission Directorate.

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Last updated: August 17, 2012