Microwave Sounding Unit temperature measurements refers to temperature measurement using the Microwave Sounding Unit instrument and is one of several methods of measuring Earth atmospheric temperature from satellites. Microwave measurements have been obtained from the troposphere since 1979, when they were included within NOAA weather satellites, starting with TIROS-N. By comparison, the usable balloon (radiosonde) record begins in 1958 but has less geographic coverage and is less uniform.
Microwave brightness measurements do not directly measure temperature. They measure radiances in various wavelength bands, which must then be mathematically inverted to obtain indirect inferences of temperature. The resulting temperature profiles depend on details of the methods that are used to obtain temperatures from radiances. As a result, different groups that have analyzed the satellite data have obtained different temperature trends. Among these groups are Remote Sensing Systems (RSS) and the University of Alabama in Huntsville (UAH). The satellite series is not fully homogeneous – the record is constructed from a series of satellites with similar but not identical instrumentation. The sensors deteriorate over time, and corrections are necessary for satellite drift in orbit. Particularly large differences between reconstructed temperature series occur at the few times when there is little temporal overlap between successive satellites, making intercalibration difficult.
Creation of the satellite temperature record
From 1979 to 2005 the microwave sounding units (MSUs) and since 1998 the Advanced Microwave Sounding Units on NOAA polar orbiting satellites have measured the intensity of upwelling microwave radiation from atmospheric oxygen. The intensity is proportional to the temperature of broad vertical layers of the atmosphere, as demonstrated by theory and direct comparisons with atmospheric temperatures from radiosonde (balloon) profiles.
Different frequencies sample a different weighted range of the atmosphere, depending on the absorption depth (i.e., optical depth) of the microwaves through the atmosphere.Mears, C. A. and Wentz, F. J. Upper Air Temperature, Remote Sensing Systems. Retrieved 3 Feb. 2020 To derive data of the temperature profile at lower altitudes and remove the stratospheric influence, researchers have developed synthetic products by subtracting signals at different altitude and view angles; such as "2LT", which has a maximum at about 650 hPa. However this process amplifies noise, increases inter-satellite calibration biases and enhances surface contamination.
Records have been created by merging data from nine different MSUs and AMSU data, each with peculiarities that must be calculated and removed because they can have substantial impacts on the resulting trend.The Satellite Temperature Records: Parts 1 and 2 May 1996 The process of constructing a temperature record from a radiance record is difficult and some of the required corrections are as large as the trend itself:
Analysis technique
Upwelling radiance is measured at different frequencies; these different frequency bands sample a different weighted range of the atmosphere. Since the atmosphere is partially but not completely opaque, the brightness measured is an average across a band of atmosphere, depending on the penetration depth of the microwaves.Dudhia, A. (2015). "Temperature Soundings," Encyclopedia of Atmospheric Sciences (Second Edition). Retrieved 12 January 2019. The brightness temperature (TB) measured by satellite is given by:
where is the surface weight, and are the temperatures at the surface and at the atmospheric level and is the atmospheric weighting function.
Both the surface and atmospheric weights are dependent on the surface emissivity , the absorption coefficient and the Earth incidence angle ; the surface weight is the product of and an attenuation factor:
where the secant theta term accounts for the dependence of optical path length on the vertical angle, and is the optical depth:
The atmospheric weighting functions can be written as:
The first term in this equation is related to the radiation emitted upward from the level and attenuated along the path to the top of the atmosphere (∞), the second include the radiation emitted downward from the level z to the surface (0) and the radiation reflected back by the surface (proportional to ) to the top of the atmosphere, the exact form of is dependent upon the temperature, water vapor and liquid water content of the atmosphere.
Channels
MSU Channel 1 is not used to monitor atmospheric temperature because it's too much sensitive to the emission from the surface, furthermore it is heavily contaminated by water vapor/liquid water in the lowermost troposphere.
Channel 2 or TMT is broadly representative of the troposphere, albeit with a significant overlap with the lower stratosphere; the weighting function has its maximum at 350 hPa (corresponding to about 8 km altitude)American Vacuum Society, Atmospheric Pressure at Different Altitudes. Retrieved 11 Jan. 2020. and half-power at about 40 and 800 hPa (roughly 2–22 km).
Figure 3 (right) shows the atmospheric levels sampled by different wavelength from the satellite measurements, where TLS, TTS, and TTT represent three different wavelengths. Note that the lowest measurement, TTT, includes brightness from both atmospheric and surface emission. TMT and TLT represent the altitude range computed lower troposphere temperature calculated using an atmospheric model as discussed below.
The T4 or TLS channel in representative of the temperature in the lower stratosphere with a peak weighting function at around 17 km above the Earth surface.
Calculation of lower troposphere temperature
In an attempt to derive data for lower altitudes and remove the stratospheric influence, several researchers have developed synthetic products that subtract the higher-altitude values from the lowest altitude (TMT) measurement. Such a data-analysis technique is dependent on modeling the effect of altitude on temperature. However, this process amplifies noise, increases inter-satellite calibration biases and enhances surface contamination. Spencer and Christy developed the synthetic "2LT" (later renamed "TLT") product by subtracting signals at different view angles; this has a maximum at about 650 hPa. The 2LT product has gone through numerous versions as various corrections have been applied. Another such methodology has been developed by Fu and Johanson, the TTT(Total Troposphere Temperature) channel is a linear combination of the TMT and TLS channel: TTT=1.156*TMT-0.153*TLS for the global average and TTT=1.12*TMT-0.11*TLS at tropical latitudes
Measurement corrections
Diurnal sampling
All the MSU instruments and to a lesser extent AMSU drift slowly from the Sun-synchronous equatorial crossing time changing the local time observed by the instrument, therefore the natural diurnal cycle may be aliased into the long term trend. The diurnal sampling correction is in the order of a few hundredths °C/decade for TLT and TMT.
Orbit decay
All polar orbiting satellite lose height after launch, the orbital decay is stronger during period of elevated solar activity when the enhanced ultraviolet radiation warm the upper atmosphere and increase the frictional drag over the spacecraft.
The orbital decay change the instrument view angle relative to the surface and thus the observed microwave emissivity, furthermore the long-term time-series is constructed by sequential merging of the inter-calibrated satellite data so that the error is summed up over time, the required correction is in the order of 0.1 °C/decade for TLT.
Calibration changes
Once every Earth scan MSU instrument use the deep space (2.7K) and on-board warm targets to make calibration measures, however as the spacecraft drifts through the diurnal cycle the calibration target temperature may change due to varying solar shadowing effect, the correction is in the order of 0.1 °C/decade for TLT and TMT.
One widely reported satellite temperature record is that developed by Roy Spencer and John Christy at the University of Alabama in Huntsville (UAH). The record comes from a succession of different satellites and problems with inter-calibration between the satellites are important, especially NOAA-9, which accounts for most of the difference between the RSS and UAH analyses.Remote Sensing Systems NOAA-11 played a significant role in a 2005 study by Mears et al. identifying an error in the diurnal correction that leads to the 40% jump in Spencer and Christy's trend from version 5.1 to 5.2.
Trends
Records have been created by merging data from nine different MSUs, each with peculiarities (e.g., time drift of the spacecraft relative to the local solar time) that must be calculated and removed because they can have substantial impacts on the resulting trend.
The process of constructing a temperature record from a radiance record is difficult. The satellite temperature record comes from a succession of different satellites and problems with inter-calibration between the satellites are important, especially NOAA-9, which accounts for most of the difference between various analyses.Remote Sensing Systems NOAA-11 played a significant role in a 2005 study by Mears et al. identifying an error in the diurnal correction that leads to the 40% jump in Spencer and Christy's trend from version 5.1 to 5.2. There are ongoing efforts to resolve differences in satellite temperature datasets.
Comparison with surface trends
To compare the MSU retrievals to the trend from the surface temperature record it is most appropriate to derive trends for the part of the atmosphere nearest the surface, i.e., the lower troposphere. As discussed earlier, the lowest of the temperature retrievals, TLT, is not a direct measurement, but a value calculated by subtracting higher altitude brightness temperature from the lower measurements. The trends found from the UAH and the RSS groups, shown in the table below, are calculated by slightly different methods, and result in different values for the trends.
Using the T2 or TMT channel (which include significant contributions from the stratosphere, which has cooled), Mears et al. of Remote Sensing Systems (RSS) find (through January 2017) a trend of +0.140 °C/decade. Spencer and Christy of the University of Alabama in Huntsville (UAH), find a smaller trend of +0.08 °C/decade.
In comparing these measurements to surface temperature models, resulting values for the lower troposphere measurements taken by the MSU is a weighted average of temperatures over multiple altitudes (roughly 0 to 12 km), and not a surface temperature (see TLT in figure 3 above). The results are thus not precisely comparable to surface temperature models.
Trends from the record
Channel Start End date RSS v4.0 Global Trend (70S – 82.5N)(K/decade) UAH v6.0 Global Trend(90S – 90N) (K/decade) STAR v3.0 Global Trend(K/decade) UW UAH Global Trend (ºK/decade) UW RSS Global Trend (ºK/decade) TLT 1979 2017-05 0.184 0.12 TTT 1979 2017-01 0.180 0.13 0.14 TMT 1979 2017-01 0.140 0.08 0.129 TLS 1979 2017-01 −0.260 −0.31−0.263
Another satellite temperature analysis is provided by NOAA/NESDIS STAR Center for Satellite Application and Research and use simultaneous nadir overpasses (SNO)
to remove satellite intercalibration biases yielding more accurate temperature trends. The STAR-NOAA analysis finds a 1979–2016 trend of +0.129 °C/decade for TMT channel.
Using an alternative adjustment to remove the stratospheric contamination, 1979–2011 trends of +0.14 °C/decade when applied to the RSS data set and +0.11 °C/decade when applied to the UAH data set were found.
A University of Washington analysis finds 1979–2012 trends of +0.13 °C/decade when applied to the RSS data set and +0.10 °C/decade when applied to the UAH data set.
Combined surface and satellite data