scholarly journals Reply to “Comments on ‘A Bias in the Midtropospheric Channel Warm Target Factor on the NOAA-9 Microwave Sounding Unit’”

2013 ◽  
Vol 30 (5) ◽  
pp. 1014-1020 ◽  
Author(s):  
Stephen Po-Chedley ◽  
Qiang Fu
Keyword(s):  
Measurement Errors ◽  
Radiosonde Data ◽  
Cold Bias ◽  
Tropospheric Temperature ◽  
Microwave Sounding Unit ◽  
Before And After ◽  
Adequate Sampling ◽  
Microwave Sounding ◽  
Temperature Signal ◽  
Remote Sensing Systems

Abstract The main finding by Po-Chedley and Fu was that the University of Alabama in Huntsville (UAH) microwave sounding unit (MSU) product has a bias in its NOAA-9 midtropospheric channel (TMT) warm target factor, which leads to a cold bias in the TMT trend. This reply demonstrates that the central arguments by Christy and Spencer to challenge Po-Chedley and Fu do not stand. This reply establishes that 1) Christy and Spencer found a similar, but insignificant, bias in the UAH target factor because their radiosonde data lack adequate sampling and measurement errors were considered twice; 2) the UAH individual satellite TMT difference between NOAA-9 and NOAA-6 reveals a bias of 0.082 ± 0.011 in the UAH NOAA-9 target factor; 3) comparing the periods before and after NOAA-9 is not an adequate method to draw conclusions about NOAA-9 because of the influence of other satellites; 4) using the Christy and Spencer trend sensitivity value, UAH TMT has a cold bias of 0.035 K decade−1 given a target factor bias of 0.082; 5) similar trends from UAH and Remote Sensing Systems (RSS) for the lower tropospheric temperature product (TLT) do not indicate that the UAH TMT and TLT NOAA-9 target factor is unbiased; and 6) the NOAA-9 warm target temperature signal in UAH TMT indicates a problem with the UAH empirical algorithm to derive the target factor.

scholarly journals Estimation of Tropospheric Temperature Trends from MSU Channels 2 and 4

2006 ◽  
Vol 23 (3) ◽  
pp. 417-423 ◽  
Author(s):  
Roy W. Spencer ◽  
John R. Christy ◽  
William D. Braswell ◽  
William B. Norris
Keyword(s):  
Radiosonde Data ◽  
Training Dataset ◽  
Tropospheric Temperature ◽  
Ancillary Data ◽  
Temperature Trends ◽  
Time Period ◽  
Microwave Sounding Unit ◽  
Target Layer ◽  
Microwave Sounding ◽  
Temperature Measure

Abstract The problems inherent in the estimation of global tropospheric temperature trends from a combination of near-nadir Microwave Sounding Unit (MSU) channel-2 and -4 data are described. The authors show that insufficient overlap between those two channels’ weighting functions prevents a physical removal of the stratospheric influence on tropospheric channel 2 from the stratospheric channel 4. Instead, correlations between stratospheric and tropospheric temperature fluctuations based upon ancillary (e.g., radiosonde) information can be used to statistically estimate a correction for the stratospheric influence on MSU 2 from MSU 4. Fu et al. developed such a regression relationship from radiosonde data using the 850–300-hPa layer as the target predictand. There are large errors in the resulting fit of the two MSU channels to the tropospheric target layer, so the correlations from the ancillary data must be relied upon to provide a statistical minimization of the resulting errors. Such relationships depend upon the accuracy of the particular training dataset as well as the dataset time period and its global representativeness (i.e., temporal and spatial stationarity of the statistics). It is concluded that near-nadir MSU channels 2 and 4 cannot be combined to provide a tropospheric temperature measure without substantial uncertainty resulting from a necessary dependence on ancillary information regarding the vertical profile of temperature variations, which are, in general, not well known on a global basis.

scholarly journals Biases in Stratospheric and Tropospheric Temperature Trends Derived from Historical Radiosonde Data

2006 ◽  
Vol 19 (10) ◽  
pp. 2094-2104 ◽  
Author(s):  
William J. Randel ◽  
Fei Wu
Keyword(s):  
Lower Stratosphere ◽  
Radiosonde Data ◽  
Tropospheric Temperature ◽  
Satellite Measurements ◽  
Upper Troposphere ◽  
Step Functions ◽  
Temperature Trends ◽  
Microwave Sounding Unit ◽  
Microwave Sounding ◽  
The Tropics

Abstract Temperature trends derived from historical radiosonde data often show substantial differences compared to satellite measurements. These differences are especially large for stratospheric levels, and for data in the Tropics, where results are based on relatively few stations. Detailed comparisons of one radiosonde dataset with collocated satellite measurements from the Microwave Sounding Unit reveal time series differences that occur as step functions or jumps at many stations. These jumps occur at different times for different stations, suggesting that the differences are primarily related to problems in the radiosonde data, rather than in the satellite record. As a result of these jumps, the radiosondes exhibit systematic cooling biases relative to the satellites. A large number of the radiosonde stations in the Tropics are influenced by these biases, suggesting that cooling in the tropical lower stratosphere is substantially overestimated in these radiosonde data. Comparison of trends from stations with larger and smaller biases suggests the cooling bias extends into the tropical upper troposphere. Significant biases are observed in both daytime and nighttime radiosonde measurements.

A Satellite-Derived Lower-Tropospheric Atmospheric Temperature Dataset Using an Optimized Adjustment for Diurnal Effects

2017 ◽  
Vol 30 (19) ◽  
pp. 7695-7718 ◽  
Author(s):  
Carl A. Mears ◽  
Frank J. Wentz
Keyword(s):  
General Circulation ◽  
Lower Troposphere ◽  
Global Scale ◽  
Radiosonde Data ◽  
Measurement Time ◽  
Tropospheric Temperature ◽  
Local Measurement ◽  
Global Trend ◽  
Microwave Sounding Unit ◽  
Microwave Sounding

Abstract Temperature sounding microwave radiometers flown on polar-orbiting weather satellites provide a long-term, global-scale record of upper-atmosphere temperatures, beginning in late 1978 and continuing to the present. The focus of this paper is a lower-tropospheric temperature product constructed using measurements made by the Microwave Sounding Unit channel 2 and the Advanced Microwave Sounding Unit channel 5. The temperature weighting functions for these channels peak in the middle to upper troposphere. By using a weighted average of measurements made at different Earth incidence angles, the effective weighting function can be lowered so that it peaks in the lower troposphere. Previous versions of this dataset used general circulation model output to remove the effects of drifting local measurement time on the measured temperatures. This paper presents a method to optimize these adjustments using information from the satellite measurements themselves. The new method finds a global-mean land diurnal cycle that peaks later in the afternoon, leading to improved agreement between measurements made by co-orbiting satellites. The changes result in global-scale warming [global trend (70°S–80°N, 1979–2016) = 0.174°C decade−1], ~30% larger than our previous version of the dataset [global trend (70°S–80°N, 1979–2016) = 0.134°C decade−1]. This change is primarily due to the changes in the adjustment for drifting local measurement time. The new dataset shows more warming than most similar datasets constructed from satellites or radiosonde data. However, comparisons with total column water vapor over the oceans suggest that the new dataset may not show enough warming in the tropics.

scholarly journals A Bias in the Midtropospheric Channel Warm Target Factor on the NOAA-9 Microwave Sounding Unit

2012 ◽  
Vol 29 (5) ◽  
pp. 646-652 ◽  
Author(s):  
Stephen Po-Chedley ◽  
Qiang Fu
Keyword(s):  
Tropospheric Temperature ◽  
University Of Alabama ◽  
Sensing Systems ◽  
Temperature Trends ◽  
Microwave Sounding Unit ◽  
Microwave Sounding ◽  
Remote Sensing Systems ◽  
Temperature Records ◽  
The University

Abstract The University of Alabama at Huntsville (UAH), Remote Sensing Systems (RSS), and the National Oceanic and Atmospheric Administration (NOAA) have constructed long-term temperature records for deep atmospheric layers using satellite Microwave Sounding Unit (MSU) and Advanced Microwave Sounding Unit (AMSU) observations. However, these groups disagree on the magnitude of global temperature trends since 1979, including the trend for the midtropospheric layer (TMT). This study evaluates the selection of the MSU TMT warm target factor for the NOAA-9 satellite using five homogenized radiosonde products as references. The analysis reveals that the UAH TMT product has a positive bias of 0.051 ± 0.031 in the warm target factor that artificially reduces the global TMT trend by 0.042 K decade−1 for 1979–2009. Accounting for this bias increases the global UAH TMT trend from 0.038 to 0.080 K decade−1, effectively eliminating the trend difference between UAH and RSS and decreasing the trend difference between UAH and NOAA by 47%. This warm target factor bias directly affects the UAH lower tropospheric (TLT) product and tropospheric temperature trends derived from a combination of TMT and lower stratospheric (TLS) channels.

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

2009 ◽  
Vol 26 (8) ◽  
pp. 1493-1509 ◽  
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.

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

2018 ◽  
Vol 35 (5) ◽  
pp. 1141-1150 ◽  
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.

scholarly journals Robustness of Tropospheric Temperature Trends from MSU Channels 2 and 4

2006 ◽  
Vol 19 (17) ◽  
pp. 4234-4242 ◽  
Author(s):  
Celeste M. Johanson ◽  
Qiang Fu
Keyword(s):  
Training Dataset ◽  
Retrieval Algorithm ◽  
Tropospheric Temperature ◽  
Temperature Trends ◽  
Combining Data ◽  
Microwave Sounding Unit ◽  
Microwave Sounding ◽  
The Mean ◽  
Stratospheric Cooling ◽  
Global Mean

Abstract Tropospheric temperature trends based on Microwave Sounding Unit (MSU) channel 2 data are susceptible to contamination from strong stratospheric cooling. Recently, Fu et al. devised a method of removing the stratospheric contamination by linearly combining data from MSU channels 2 and 4. In this study the sensitivity of the weights of the two channels in the retrieval algorithm for the tropospheric temperatures to the choice of period of record used in the analysis and to the choice of training dataset is examined. The weights derived using monthly temperature anomalies are within about 10% of those obtained by Fu et al. irrespective of the choice of analysis period or training dataset. The trend errors in the retrieved global-mean tropospheric temperatures tested using two independent radiosonde datasets are less than about 0.01 K decade−1 for all time periods of 25 yr or longer with different starting and ending years during 1958–2004. It is found that the retrievals are more robust if they are interpreted in terms of the layer-mean temperature for the entire troposphere, rather than the mean of the 850–300-hPa layer. Because large spurious jumps remain in the reanalyses, especially prior to 1979, one should be cautious when using them as training datasets and in testing the trend errors.

Removing Diurnal Cycle Contamination in Satellite-Derived Tropospheric Temperatures: Understanding Tropical Tropospheric Trend Discrepancies

2015 ◽  
Vol 28 (6) ◽  
pp. 2274-2290 ◽  
Author(s):  
Stephen Po-Chedley ◽  
Tyler J. Thorsen ◽  
Qiang Fu
Keyword(s):  
Diurnal Cycle ◽  
General Circulation ◽  
Circulation Model ◽  
Tropospheric Temperature ◽  
University Of Alabama ◽  
Temperature Trends ◽  
Microwave Sounding Unit ◽  
Microwave Sounding ◽  
The University ◽  
Diurnal Correction

Abstract Independent research teams have constructed long-term tropical time series of the temperature of the middle troposphere (TMT) using satellite Microwave Sounding Unit (MSU) and Advanced MSU (AMSU) measurements. Despite careful efforts to homogenize the MSU/AMSU measurements, tropical TMT trends beginning in 1979 disagree by more than a factor of 3. Previous studies suggest that the discrepancy in tropical TMT trends is caused by differences in both the NOAA-9 warm target factor and diurnal drift corrections. This work introduces a new observationally based method for removing biases related to satellite diurnal drift. Over land, the derived diurnal correction is similar to a general circulation model (GCM) diurnal cycle. Over ocean, the diurnal corrections have a negligible effect on TMT trends, indicating that oceanic biases are small. It is demonstrated that this method is effective at removing biases between coorbiting satellites and biases between nodes of individual satellites. Using a homogenized TMT dataset, the ratio of tropical tropospheric temperature trends relative to surface temperature trends is in accord with the ratio from GCMs. It is shown that bias corrections for diurnal drift based on a GCM produce tropical trends very similar to those from the observationally based correction, with a trend difference smaller than 0.02 K decade−1. Differences between various TMT datasets are explored further. Large differences in tropical TMT trends between this work and that of the University of Alabama in Huntsville (UAH) are attributed to differences in the treatment of the NOAA-9 target factor and the diurnal cycle correction.

scholarly journals Retrieval of upper tropospheric water vapor and upper tropospheric humidity from AMSU radiances

2005 ◽  
Vol 5 (8) ◽  
pp. 2019-2028 ◽  
Author(s):  
A. Houshangpour ◽  
V. O. John ◽  
S. A. Buehler
Keyword(s):  
Water Vapor ◽  
Synthetic Data ◽  
Weighted Average ◽  
Radiosonde Data ◽  
Atmospheric Conditions ◽  
Data Set ◽  
Microwave Sounding Unit ◽  
Upper Tropospheric Humidity ◽  
Microwave Sounding ◽  
Average Relative Humidity

Abstract. A regression method was developed to retrieve upper tropospheric water vapor (UTWV in kg/m2) and upper tropospheric humidity (UTH in % RH) from radiances measured by the Advanced Microwave Sounding Unit (AMSU). In contrast to other UTH retrieval methods, UTH is defined as the average relative humidity between 500 and 200hPa, not as a Jacobian weighted average, which has the advantage that the UTH altitude does not depend on the atmospheric conditions. The method uses AMSU channels 6-10, 18, and 19, and should achieve an accuracy of 0.48 kg/m2 for UTWV and 6.3% RH for UTH, according to a test against an independent synthetic data set. This performance was confirmed for northern mid-latitudes by a comparison against radiosonde data from station Lindenberg in Germany, which yielded errors of 0.23 kg/m2 for UTWV and 6.1% RH for UTH.

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