Recharacterization of the Microwave Sounding Unit Cross-Track Asymmetry During a Spacecraft Tumble

Abstract A column EnKF, based on the Canadian global EnKF and using the RTTOV radiative transfer (RT) model, is employed to investigate issues relating to the EnKF assimilation of Advanced Microwave Sounding Unit-A (AMSU-A) radiance measurements. Experiments are performed with large and small ensembles, with and without localization. Three different descriptions of background temperature error are considered: 1) using analytical vertical modes and hypothetical spectra, 2) using the vertical modes and spectrum of a covariance matrix obtained from the global EnKF after 2 weeks of cycling, and 3) using the vertical modes and spectrum of the static background error covariance matrix employed to initiate a global data assimilation cycle. It is found that the EnKF performs well in some of the experiments with background error description 1, and yields modest error reductions with background error description 3. However, the EnKF is virtually unable to reduce the background error (even when using a large ensemble) with background error description 2. To analyze these results, the different background error descriptions are viewed through the prism of the RT model by comparing the trace of the matrix , where is the RT model and is the background error covariance matrix. Indeed, this comparison is found to explain the difference in the results obtained, which relates to the degree to which deep modes are, or are not, present in the different background error covariances. The results suggest that, after 2 weeks of cycling, the global EnKF has virtually eliminated all background error structures that can be “seen” by the AMSU-A radiances.

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Construction of the RSS V3.2 Lower-Tropospheric Temperature Dataset from the MSU and AMSU Microwave Sounders

Journal of Atmospheric and Oceanic Technology ◽

10.1175/2009jtecha1237.1 ◽

2009 ◽

Vol 26 (8) ◽

pp. 1493-1509 ◽

Cited By ~ 52

Author(s):

Carl A. Mears ◽

Frank J. Wentz

Keyword(s):

Lower Stratosphere ◽

Weighting Function ◽

Lower Troposphere ◽

Weighted Average ◽

Atmospheric Temperature ◽

Tropospheric Temperature ◽

Satellite Measurements ◽

Microwave Sounding Unit ◽

Different Types ◽

Microwave Sounding

Abstract Measurements made by microwave sounding instruments provide a multidecadal record of atmospheric temperature in several thick atmospheric layers. Satellite measurements began in late 1978 with the launch of the first Microwave Sounding Unit (MSU) and have continued to the present via the use of measurements from the follow-on series of instruments, the Advanced Microwave Sounding Unit (AMSU). The weighting function for MSU channel 2 is centered in the middle troposphere but contains significant weight in the lower stratosphere. To obtain an estimate of tropospheric temperature change that is free from stratospheric effects, a weighted average of MSU channel 2 measurements made at different local zenith angles is used to extrapolate the measurements toward the surface, which results in a measurement of changes in the lower troposphere. In this paper, a description is provided of methods that were used to extend the MSU method to the newer AMSU channel 5 measurements and to intercalibrate the results from the different types of satellites. Then, satellite measurements are compared to results from homogenized radiosonde datasets. The results are found to be in excellent agreement with the radiosonde results in the northern extratropics, where the majority of the radiosonde stations are located.

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Temperature decadal trends, and their relation to diurnal variations in the lower thermosphere, stratosphere, and mesosphere, based on measurements from SABER on TIMED

Annales Geophysicae ◽

10.5194/angeo-39-327-2021 ◽

2021 ◽

Vol 39 (2) ◽

pp. 327-339

Author(s):

Frank T. Huang ◽

Hans G. Mayr

Keyword(s):

Local Time ◽

Lower Thermosphere ◽

3D Models ◽

Diurnal Variations ◽

Local Times ◽

Temperature Trends ◽

Microwave Sounding Unit ◽

Lidar Measurements ◽

Microwave Sounding ◽

Thermal Tides

Abstract. We have derived the behavior of decadal temperature trends over the 24 h of local time, based on zonal averages of SABER data, for the years 2012 to 2014, from 20 to 100 km, within 48∘ of the Equator. Similar results have not been available previously. We find that the temperature trends, based on zonal mean measurements at a fixed local time, can be different from those based on measurements made at a different fixed local time. The trends can vary significantly in local time, even from hour to hour. This agrees with some findings based on nighttime lidar measurements. This knowledge is relevant because the large majority of temperature measurements, especially in the stratosphere, are made by instruments on sun-synchronous operational satellites which measure at only one or two fixed local times, for the duration of their missions. In these cases, the zonal mean trends derived from various satellite data are tied to the specific local times at which each instrument samples the data, and the trends are then also biased by the local time. Consequently, care is needed in comparing trends based on various measurements with each other, unless the data are all measured at the same local time. Similar caution is needed when comparing with models, since the zonal means from 3D models reflect averages over both longitude and the 24 h of local time. Consideration is also needed in merging data from various sources to produce generic, continuous, longer-term records. Diurnal variations of temperature themselves, in the form of thermal tides, are well known and are due to absorption of solar radiation. We find that at least part of the reason that temperature trends are different for different local times is that the amplitudes and phases of the tides themselves follow trends over the same time span of the data. Many of the past efforts have focused on the temperature values with local time when merging data from various sources and on the effect of unintended satellite orbital drifts, which result in drifting local times at which the temperatures are measured. However, the effect of local time on trends has not been well researched. We also derive estimates of trends by simulating the drift of local time due to drifting orbits. Our comparisons with results found by others (Advanced Microwave Sounding Unit, AMSU; lidar) are favorable and informative. They may explain, at least in part, the bridge between results based on daytime AMSU data and nighttime lidar measurements. However, these examples do not form a pattern, and more comparisons and study are needed.

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The Effect of Hydrometeors on MSU/AMSU Temperature Observations over the Tropical Ocean

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-17-0190.1 ◽

2018 ◽

Vol 35 (5) ◽

pp. 1141-1150 ◽

Cited By ~ 1

Author(s):

Hamid A. Pahlavan ◽

Qiang Fu ◽

John M. Wallace

Keyword(s):

Regression Coefficient ◽

Temperature Fields ◽

Tropospheric Temperature ◽

Tropical Ocean ◽

Microwave Sounding Unit ◽

Mean Fields ◽

Microwave Sounding ◽

Temperature Observations ◽

The Difference ◽

Grid Points

AbstractThe temperature of Earth’s atmosphere has been monitored continuously since late 1978 by the Microwave Sounding Unit (MSU) and the Advanced Microwave Sounding Unit (AMSU) flown on polar-orbiting weather satellites. It is well known that these measurements are affected by the scattering and emission from hydrometeors, including cloud water, precipitation, and ice particles. In this study the hydrometeor effects on MSU/AMSU temperature observations are investigated by comparing satellite-observed temperature of the middle troposphere (TMT) with synthetic TMT constructed using temperature fields from ECMWF Interim [ERA-Interim (ERA-I)]. Precipitation data have been used to estimate how much of the difference between these two TMT fields is due to hydrometeor contamination effects. It is shown that there exists a robust linear proportionality between TMT deficit (i.e., the measured TMT minus the synthetic TMT) and precipitation at individual grid points in monthly mean fields. The linear correlation is even stronger in the annual mean and seasonally varying climatology and also in the spatial pattern of ENSO-related anomalies. The linear regression coefficient obtained in all of these analyses is virtually identical: −0.042 K (mm day−1)−1. The channel that senses lower-tropospheric temperature (TLT) is more sensitive to precipitation than the TMT channel: the regression coefficient is −0.059 K (mm day−1)−1. It is shown that correcting the TMT or TLT monthly anomalies by removing the hydrometeor contamination does not significantly influence estimates of tropical mean temperature trends, but it could affect the pattern of temperature trend over the tropical oceans.

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Development of a precipitation-retrieval scheme for cross-track passive microwave sounding instruments

10.1016/b978-0-12-822973-6.00009-3 ◽

2022 ◽

pp. 287-304

Author(s):

Chris Kidd ◽

Toshihisa Matsui ◽

Sarah Ringerud

Keyword(s):

Passive Microwave ◽

Retrieval Scheme ◽

Microwave Sounding ◽

Cross Track

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Connecting the Time Series of Microwave Sounding Observations from AMSU to ATMS for Long-Term Monitoring of Climate

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-13-00232.1 ◽

2014 ◽

Vol 31 (10) ◽

pp. 2206-2222 ◽

Cited By ~ 5

Author(s):

Xiaolei Zou ◽

Fuzhong Weng ◽

H. Yang

Keyword(s):

Time Series ◽

Atmospheric Temperature ◽

Advanced Technology ◽

Central Frequency ◽

Brightness Temperatures ◽

Microwave Sounding Unit ◽

Microwave Sounding ◽

Cross Calibration ◽

Term Monitoring

Abstract The measurements from the Microwave Sounding Unit (MSU) and the Advanced Microwave Sounding Unit-A (AMSU-A) on board NOAA polar-orbiting satellites have been extensively utilized for detecting atmospheric temperature trend during the last several decades. After the launch of the Suomi National Polar-orbiting Partnership (Suomi-NPP) satellite on 28 October 2011, MSU and AMSU-A time series will be overlapping with the Advanced Technology Microwave Sounder (ATMS) measurements. While ATMS inherited the central frequency and bandpass from most of AMSU-A sounding channels, its spatial resolution and noise features are, however, distinctly different from those of AMSU. In this study, the Backus–Gilbert method is used to optimally resample the ATMS data to AMSU-A fields of view (FOVs). The differences between the original and resampled ATMS data are demonstrated. By using the simultaneous nadir overpass (SNO) method, ATMS-resampled observations are collocated in space and time with AMSU-A data. The intersensor biases are then derived for each pair of ATMS–AMSU-A channels. It is shown that the brightness temperatures from ATMS now fall well within the AMSU data family after resampling and SNO cross calibration. Thus, the MSU–AMSU time series can be extended into future decades for more climate applications.

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