Upper Air Temperature

Introduction RSS发出产品 Uncertainty Recent Updates References



Measurement Methods for Upper Air Temperatures

There are several methods available for the measurement of the upper air.

  • Radiosondes(通常称为气象气球)。这些都是小的instruments lifted aloft by helium-filled balloons. Measurements made by temperature, pressure, and humidity sensors are radioed back to the surface.

    • Advantages
      • Direct measurement of temperature
      • 高垂直分辨率
    • Disadvantages
      • Limited spatial sampling
      • Calibration problems with changing instruments and methods
  • Microwave Sounders.These are satellite-borne instruments that measure the radiance of Earth at microwave frequencies, which allows scientists to deduce the temperature of thick atmospheric layers.
    • Advantages
      • Global coverage at a high samplng rate
    • Disadvantages
      • Coarse vertical resolution
  • Infrared Sounders.These are satellite-borne instruments that measure the radiance of Earth at infrared frequencies, which allows scientists to retrieve the temperature of thick atmospheric layers using inversion algorithms.
    • Advantages
      • 全球覆盖率以高采样率
      • 适度的垂直分辨率
    • Disadvantages
      • 云和其他气溶胶污染的敏感性
  • GPS Radio Occultation.The approach uses satellite-borne GPS receivers to measure the refraction of the GPS signals by the Earth's atmosphere. This allows for the retrieval of vertical temperature and moisture profiles.
    • Advantages
      • Absolute calibration
      • 高垂直分辨率
      • Global coverage
    • Disadvantages
      • Only about 10 years of data
      • Less frequent sampling compared to satellite-borne sounders.

RSS Upper Air Temperature Products

RSS upper air temperature products are based on measurements made bymicrowave sounders。Microwave sounders are capable of retrieving vertical temperature profiles of the atmosphere by measuring the thermal emission from oxygen molecules at different frequencies. These measurements are a crucial element in the development of an accurate system for long-term monitoring of atmospheric temperature, particularly in regions with large numbers of radiosonde measurements. RSS air temperature products are assembled from measurements made by theMSUandAMSUinstruments on polar orbiting satellites. We are working toward the use of measurements from the most recent microwave sounder, ATMS.

MSUThe 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 lower stratosphere. The last MSU instrument, NOAA-14, ceased reliable operation in 2005.

AMSU一系列的后续仪器,先进的Microwave Sounding Units (AMSUs), began operation in 1998. The AMSU instruments are similar to the MSUs, but make measurements using a greater number of channels, thus sampling the atmosphere in more layers, and with smaller measurement footprints, thus increasing the spatial resolution. By using the AMSU channels that most closely match the channels in the MSU instruments, we can extended the MSU-based datasets to the present. In addition, we have completed a preliminary analysis of AMSU channels 10-14, which measure temperatures from the lower to upper stratosphere, much higher than the highest MSU channel. These AMSU-only datasets began in mid 1998 with the launch of the first AMSU on the NOAA-15 satellite. The AMSU-only datasets, now 14 years long, are beginning to be long enough for investigating long-term changes in the mid and upper stratosphere.

ATMSIn 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 data set. 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 data set can be extracted from their measurements only by careful intercalibration of the data from the MSU, AMSU and ATMS instruments.


We produce 3 single-channel MSU/AMSU data sets (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 data set 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 data sets, TTT and C25, that are constructed from weighted combinations of the single channel data sets. 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:

TLT 台湾海陆运输公司 TTT TTS TLS C10 C11 C12 C13 C14 C25
Tiros-N. 2 2 2,4 - 4. - - - - - -
NOAA-06 2 2 2,4 - 4. - - - - - -
NOAA-07 2 2 2,4 - 4. - - - - - -
NOAA-08 2 2 2,4 - 4. - - - - - -
NOAA-09 2 2 2,4 - 4. - - - - - -
NOAA-10 2 2 2,4 3. 4. - - - - - -
NOAA-11 2 2 2,4 3. 4. - - - - - -
NOAA-12 2 2 2,4 3. 4. - - - - - -
NOAA-14 2 2 2,4 3. 4. - - - - - -
NOAA-15 5. 5. 5.,9 7. 9. 10. 11. 12. 13. 14. 10-13
NOAA-16 - - - - - 10. 11. 12. 13. 14. 10-13
NOAA-17 - - - - - - - - - -
NOAA-18 5. 5. 5.,9 7. 9. 10. 11. 12. 13. 14. 10-13
METOP-A 5. 5. 5.,9 7. 9. 10. 11. 12. 13. 14. 10-13
AQUA 5. 5. 5.,9 7. 9. 10. 11. 12. 13. 14. 10-13
NOAA-19 5. 5. 5. * * * * * * * *
METOP-B 5. 5. 5. * * * * * * * *
开始的一年 1978 1978 1978 1987 1978 1998 1998 1998 1998 1998 1998
End Year Present Present Present Present Present Present Present Present Present Present Present
Maturity Stable Stable Stable Stable Stable Prelim. Prelim. Prelim. Exper. Exper.


每个产品测量厚层中大气的平均温度。这种亮度温度tB.measured by the satellite can be described as an integral over the height above Earth's surface Z of the atmospheric temperature T大气层weighted by a weighting function W(Z), plus a small contribution due to emission by Earth's Surface τεT冲浪

The exact form of the weighting function depends on the temperature, humidity, and liquid water content of the atmospheric column being measured. However, representative weighting functions based on the mean state of the atmosphere are sometimes useful. We provide weighting functions based on the U.S. Standard Atmosphere on our FTP site at/msu/weighting_functionsfor each of the MSU/AMSU products. These weighting functions are also plotted in Fig. 1 below.

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.

Single Channel Datasets (TMT, TTS, TLS, C10, C11, C12, C13, C14)

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. SeeMears等,2009afor more details. A map showing the footprints used for near-nadir products and TLT is shown in Figure 2 below. TLT, TTT, and C25 are constructed using more complicated methods.

图2. MSU仪器的两个示例扫描。卫星在南方往北方向行驶,并扫描(大致)向东扫描,每次扫描都会制作11个离散测量。万博吧manbet客户端2.0用于构造来自顶部扫描的近Nadir MSU产品(TMT,TTS,TLS)的脚印以绿色显示。每个占用量码中的数字是在构建给定扫描的平均值时分配给占用的权重。用于构建TLT的足迹在较低扫描中以红色和蓝色显示,具有红色表示负重。

TLT (Temperature Lower Troposphere)

TLT由计算加权完全不同rence between MSU2 (or AMSU5) measurements from near limb views and measurements from the same channels taken closer to nadir, as can be seen in Figure 2 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 difference 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 seeMears et al., 2009b

TTT (Temperature Total Troposphere)

TTT is a multi-channel 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 the 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傅和约翰逊,2005年

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.


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 theUAHandSTARdatasets 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.

Global and Regional Time Series

可以使用a查看全局和区域平均时间序列的时间序列的曲线图可以使用a查看每个数据集的温度异常序列time series browse tool。Several examples of the plots available are shown below.
Figure 5. Globally averaged temperature anomaly time series for the Temperature Lower Troposphere (TLT). The plot shows the warming ot the troposphere over the last 3 decades which has been attributed to human-caused global warming. (Click on the figure to go to the time series browse tool.)
Figure 6. Globally averaged temperature anomaly time series for the Temperature Lower Stratosphere (TLS). The plot shows the cooling of the lower stratosphere over the past 3 decades. This cooling is caused by a combination of ozone depletion and the increase of greenhouse gases. During the most recent decade, the rate of cooling has reduced substantially. (Click on the figure to go to the time series browse tool.)
Figure 7. Globally averaged temperature anomaly time series for the AMSU Channel 13, in the middle stratosphere. The plot shows the that middle stratosphere cooled during the most recent 15 years, even as the lower stratosphere ceased cooling. (Click on the figure to go to the time series browse tool.)

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 all MSU/AMSU datasets we produce and can be viewed using a浏览工具。趋势地图在每个通道的时间段计算每个数据集类型的每个通道的时间段计算。

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 March, 2013:

Start Time

Stop Time

# 年

Global Trend

Channel TLT



0.184 k /十年

Channel TMT



0.139 K/Decade

Channel TTT



0.179 k /十年

Channel TTS



Channel TLS



每月,全局时间序列是每个通道的亮度温度异常,以及线性适合时间序列(图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

我们还提供包含每月anom文本文件alies 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. Anomalies are computed by subtracting the a mean monthly value determined by averaging 1979 through 1998 data for each channel from the average brightness temperature for each month. The set of 12 month means for 1979 to 1998 are included in the netCDF files available on the ftp server(ftp.remss.com/msu)



Monthly maps of MSU/AMSU brightness temperatures and brightness temperature anomalies for each dataset are available for browsing using a浏览工具, or download, from our FTP server(ftp.remss.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. The references period varies from product to product due to the different dataset lengths available.

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 (or south of 70S for TLT) due to difficulties in merging measurements in these regions.

Monthly Data Files

Each data product is available in 3 formats, netCDF, binary, and text. (See links at top left of this page to access the data). We prefer that users work with the netCDF versions, but will continue to provide the earlier formats for the time being. As of February 2016, TMT and TTT are Version 4.0 - all other products are Version 3.3.


File Name Format


RSS_TB_MAPS_CH _ ### _ v3_3.nc

Average monthly brightness temperature in degrees K.


Average monthly brightness temperature anomalies in degrees K. The reference period 1978-1998 for TLT, TMT, TTT, TTS and TLS. The reference period is 1999-2008 for AMSU only channels C10 thru C14. The reference period data are provided in the netCDF files.


Average monthly brightness temperature in degrees K. These files are identical to the versions archived at NCDC, except that they are in netCDF3 instead of netCDF4.


平均每月亮度温度异常值为K.这些文件与在NCDC上存档的版本相同,除了它们在NetCDF3而不是NetCDF4中。这些文件还包含1279-1998(TLT,TMT,TTT,TTT和TTS和TLS)或1999-2008(C10 THRU C14)的平均图用于确定异常。

B.inary Files

Each binary data file located on our MSU FTP site consists of a 144 x 72 xnumber_of_monthsarray of 4 byte real numbers. Thenumber_of_monthsis currently set to 420, but grows by 12 as each new year of data is begun. 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. As of February 2016, TMT and TTT are Version 4.0 - all other products are Version 3.3.

File Name Format



Average monthly brightness temperature


Average monthly brightness temperature anomalies in degrees K. The reference period 1978-1998 for TLT, TTT, TMT, TTS and TLS. The reference period is 1999-2008 for AMSU only channels C10 thru C14

Text formatted data.


阅读例程written in Fortran, C, IDL and Matlab are available in the /msu/support directory(ftp.remss.com/msu/support).

These data are also available in netCDF4 format with CF-compliant metadata from the国家气候数据中心, where they are calledMean Layer Temperatures - RSS.

Uncertainty in our MSU/AMSU atmospheric temperature datasets

(NOTE: the discussion below only directly applies to the V3.3 versions of the RSS datasets. We have not yet completed the uncertainty analysis for V4.0 datasets)

Why study the uncertainty?

  • Without realistic uncertainty estimates we are not doing science!
  • In the past, numerous conclusions have been drawn from MSU/AMSU data with little regard to the long term uncertainty in the data.
  • MSU / AMSU数据集的最先前错误分析集中在一起decadal-scaletrends inglobal-scale means, while in contrast, many applications are focused on shorter time scales and smaller spatial scales.
  • Here we describe a comprehensive analysis of the uncertainty in the RSS MSU/AMSU products. The results can be used to evaluate the estimated uncertainty on all relevant temporal and spatial scales.


Our MSU/AMSU products use data from 14 different satellites. The data need to be intercalibrated before being merged together. This is a complex process, as shown in the flow chart below.

  • 首先,对局部测量时间(昼夜调节)和地球入射角的变化进行调整。
  • Then, intercalibration is performed by comparing measurements from co-orbiting satellites, yielding a set of “merging parameters”.
  • Uncertainty that arises earlier in the process (e.g. from the adjustments for local measurement time) can cause uncertainty in the merging parameters, which adds to the uncertainty in the final results.

B.ecause of the complex nature of the errors, they are difficult to calculate and describe using simple statistical methods. Instead, we use a Monte Carlo technique to produce a large number of possible realizations of the errors that are consistent with the sources of error that we have studied.

A detailed description of the methods used to generate the uncertainty ensembles, and a summary of the results is given inMears et al, 2011

Available Uncertainty Information and Recommendations

We have constructed a 100 realization uncertainty ensemble for each of the MSU/AMSU products we produce. (No uncertainty analysis has yet been performed on the AMSU-only stratospheric channels.) These提供不确定性的实现在NetCDF中,表单与基线温度数据完全相同。我们建议,研究人员通过使用不确定性集万博网址是什么合的每个成员重新运行他们的分析,从而使用我们的MSU / AMSU数据获得的任何结果中的不确定性,然后在获得的结果分布中评估扩散。

Recent Updates

RSS Version 4.0 Channel TLT product released June 28, 2016. The TTS and TLS products remain Version 3.3.

The most important differences between the previous version (Version 3.3) and the new version (Version 4.0) are:

  • The method used to make adjustments for drifting satellite measurement time was changed. In the new method, the model based diurnal cycle climatology used for these adjustments was optimized so that differences between satellites making measurements are different times of day are more accurately removed.
  • Intersatellite offsets are now calculated separately for land and ocean scenes. This prevents possible errors over land, where the adjustment for changing measurement times are large, from adversely affecting measurements over the ocean, where the diurnal cycle is close to zero.
  • Several periods of suspect data were removed (see the paper for more details).
  • Two new satellites, NOAA-19 and METOP-B are now included in the processing. This serves to reduce sampling error and any remaining errors due to the diurnal adjustment during the last part of the record.



This change represents a major upgrade. There are 4 important changes to the methods used to construct the new products.

  • 用于对漂移卫星测量时间进行调整的方法,“昼夜调整”改变。在新方法中,优化了用于这些调整的基于模型的昼夜周期气候学,使其更准确地消除由于漂移的局部测量时间而导致的差距差异。这是最重要的变化,在1999 - 2005年期间,当NOAA-15卫星迅速漂流时,在1999 - 2005年期间导致更加变暖。
  • Intersatellite offsets are now calculated separately for land and ocean scenes. This prevents errors in the much larger land measurement time adjustments from adversely affecting the ocean measurements, where the adjustments for measurement time are much smaller.
  • More fields of view are now included in the dataset. The previous version used the central 5 (out of 11 total) fields of view for MSU, and the central 12 (out of 30) fields of view for AMSU. This new version uses the central 9 MSU fields of view, and the central 24 AMSU fields of view. This reduces the size of the gaps between satellite swaths, and serves to reduce spatial noise in the monthly mean maps.
  • 添加到DataSet中,两个新的卫星NoaA-19(2009年的数据开始)和MetoP-B(2012年的数据开始)。

For more details on the new version, refer to the recently accepted paper describing this upgrade in the Journal of Climate. An early online edition of the paper isavailable online

For more details and information about earlier version changes, see ourrss_msu_amsu_version_notes.


Mears, C. A. and F. J. Wentz, (2017)A satellite-derived lower tropospheric atmospheric temperature dataset using an optimized adjustment for diurnal effects, Journal of Climate, 30(19), 7695-7718, doi: 10.1175/jcli-d-16-0768.1.

Mears,C. A.和F. J.Ventz,(2016)Sensitivity of satellite-derived tropospheric temperature trends to the diurnal cycle adjustment,气候杂志,29(10),3629-3646,DOI:DOI:10.1175 / JCLI-D-15-0744.1。

MEARS,C. A.,F. J.Vedz和P. W. Thorne(2012年)Assessing the Value of Microwave Sounding Unit-Radiosonde Comparisons in Ascertaining Errors in Climate Data Records of Tropospheric Temperatures, J. Geophys. Res., 117(D19), D19103, doi:10.1029/2012JD017710.

Mears, C. A., F. J. Wentz, P. Thorne and D. Bernie, (2011)Assessing Uncertainty in Estimates of Atmospheric Temperature Changes From MSU and AMSU Using a Monte-Carlo Estimation Technique, J. Geophys. Res., 116, D08112, doi:10.1029/2010JD014954.

Mears, C. A. and F. 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.

Mears, C. A. and F. J. Wentz, (2009)Construction of the Remote Sensing Systems V3.2 Atmospheric Temperature Records From the MSU and AMSU Microwave Sounders,大气和海洋科技,26,1040-1056。

Mears, C. A. and F. J. Wentz, (2005)The Effect of Drifting Measurement Time on Satellite-Derived Lower Tropospheric Temperature, Science, 309, 1548-1551.

Mears, C. A., M. C. Schabel and F. J. Wentz, (2003)A Reanalysis of the MSU Channel 2 Tropospheric Temperature Record, Journal of Climate, 16(22), 3650-3664.

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.

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"地球物理研究杂志万博网址是什么,111,D03106,2005。

Prabhakara,C.,R. Iaacovazzi,J. M. Yoo和G. Dalu。“全球变暖:来自卫星观察的证据”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.

J. R. Christy, R. W. Spencer, W. D. Braswell. "MSU Tropospheric Temperatures: Dataset Construction and Radiosonde Comparisons"大气和海洋技术学报,卷。17,pp。1153-1170,2000。


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.

How to Cite These Data




TTS and TLS:

Uncertainty Estimates:

Radiosonde Comparisons: