Microwave Sounding Unit Channel Research Articles

Development of a New Algorithm to Identify Clear Sky MSU Data Using AMSU-A Data for Verification

Observations from the microwave sounding unit (MSU), from 1978 to 2006 and its successor, the Advanced Microwave Sounding Unit-A (AMSU-A, 1998–present), and onboard the National Oceanic and Atmospheric Administration’s polar-orbiting satellites have been widely used for estimating global climate trends. The MSU has a long-term data set, but it is difficult to obtain cloud information like the cloud liquid water path (LWP) directly from this data set. To monitor and investigate the cloud effect on global upper air temperature trends using MSU observations, a cloud detection algorithm must be developed for the MSU. Considering the similar center frequencies of MSU channel 1 and AMSU-A channel 3, a new cloud detection algorithm is established based on the differences between observed and model-simulated brightness temperatures ( $O$ - $B$ - $\mu $ ( $\alpha$ )- $\mu $ ( $\phi $ )) of MSU channel 1 or AMSU-A channel 3 over oceans, where $\mu $ ( $\phi$ ) is a latitudinal dependent global mean bias and $\mu $ ( $\alpha$ ) is the global mean bias depending on scan angle. If a data point satisfies the condition of $O$ - $B$ - $\mu $ ( $\alpha$ )- $\mu (\phi) \ge 1$ K, it is removed from the clear sky data set. In order to ensure that those points that are partially affected by the clouds, such as clear and cloud mixed fields-of-views located near cloud edges and/or within optically thin clouds, all data points within the 60-km radial distance of the detected point are removed. Validated with the AMSU-A derived LWP retrievals, about 50% of the clear sky data are successively identified and 99% of the cloudy radiances are successively removed for all NOAA-15 AMSU-A data on January 15, April 15, July 15, and October 15, 2002.

Revisiting the Mystery of Recent Stratospheric Temperature Trends

Simulated stratospheric temperatures over the period 1979-2016 in models from the Chemistry-Climate Model Initiative (CCMI) are compared with recently updated and extended satellite observations. The multi-model mean global temperature trends over 1979- 2005 are -0.88 ± 0.23, -0.70 ± 0.16, and -0.50 ± 0.12 K decade-1 for the Stratospheric Sounding Unit (SSU) channels 3 (~40-50 km), 2 (~35-45 km), and 1 (~25-35 km), respectively. These are within the uncertainty bounds of the observed temperature trends from two reprocessed satellite datasets. In the lower stratosphere, the multi-model mean trend in global temperature for the Microwave Sounding Unit channel 4 (~13-22 km) is -0.25 ± 0.12 K decade-1 over 1979-2005, consistent with estimates from three versions of this satellite record. The simulated stratospheric temperature trends in CCMI models over 1979-2005 agree with the previous generation of chemistry-climate models. The models and an extended satellite dataset of SSU with the Advanced Microwave Sounding Unit-A show weaker global stratospheric cooling over 1998-2016 compared to the period of intensive ozone depletion (1979-1997). This is due to the reduction in ozone-induced cooling from the slow-down of ozone trends and the onset of ozone recovery since the late 1990s. In summary, the results show much better consistency between simulated and satellite observed stratospheric temperature trends than was reported by Thompson et al. (2012) for the previous versions of the SSU record and chemistry-climate models. The improved agreement mainly comes from updates to the satellite records; the range of simulated trends is comparable to the previous generation of models.

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

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.

Characterizing Channel Center Frequencies in AMSU-A and MSU Microwave Sounding Instruments

Abstract Passive microwave observations from the Microwave Sounding Unit (MSU) and the Advanced Microwave Sounding Unit-A (AMSU-A) have been exploited widely for numerical weather prediction (NWP), atmospheric reanalyses, and climate monitoring studies. The treatment of biases in these observations, with respect to models as well as between satellites, has been the focus of much effort in recent years. This study presents evidence that shifts, drifts, and uncertainties in pass band center frequencies are a significant contribution to these biases. Center frequencies for AMSU-A channels 6–14 and MSU channel 3 have been analyzed using NWP fields and radiative transfer models, for a series of operational satellites covering the period 1979–2012. AMSU-A channels 6 (54.40 GHz), 7 (54.94 GHz), and 8 (55.50 GHz) on several satellites exhibit significant shifts and drifts relative to nominal pass band center frequencies. No significant shifts were found for AMSU-A channels 9–14, most probably as a consequence of the active frequency locking of these channels. For MSU channel 3 (54.96 GHz) most satellites exhibit large shifts, the largest for the earliest satellites. For example, for the first MSU on the Television and Infrared Observation Satellite-N (TIROS-N), the analyzed shift is 68 MHz over the lifetime of the satellite. Taking these shifts into account in the radiative transfer modeling significantly improves the fit between model and observations, eliminates the strong seasonal cycle in the model–observation misfit, and significantly improves the bias between NWP models and observations. The study suggests that, for several channels studied, the dominant component of the model–observation bias results from these spectral errors, rather than radiometric bias due to calibration errors.

A new weighting function for estimating microwave sounding unit channel 4 temperature trends simulated by CMIP5 climate models

A new static microwave sounding unit (MSU) channel 4 weighting function is obtained from using Coupled Model Inter-comparison Project, Phase 5 (CMIP5) historical multimodel simulations as inputs into the fast Radiative Transfer Model for TOVS (RTTOV v10). For the same CMIP5 model simulations, it is demonstrated that the computed MSU channel 4 brightness temperature (T4) trends in the lower stratosphere over both the globe and the tropics using the proposed weighting function are equivalent to those calculated by RTTOV, but show more cooling than those computed using the traditional UAH (University of Alabama at Huntsville) or RSS (Remote Sensing Systems in Santa Rosa, California) static weighting functions. The new static weighting function not only reduces the computational cost, but also reveals reasons why trends using a radiative transfer model are different from those using a traditional static weighting function. This study also shows that CMIP5 model simulated T4 trends using the traditional UAH or RSS static weighting functions show less cooling than satellite observations over the globe and the tropics. Although not completely removed, this difference can be reduced using the proposed weighting function to some extent, especially over the tropics. This work aims to explore the reasons for the trend differences and to see to what extent they are related to the inaccurate weighting functions. This would also help distinguish other sources for trend errors and thus better understand the climate change in the lower stratosphere.

Uncertainty of the stratospheric/tropospheric temperature trends in 1979–2008: multiple satellite MSU, radiosonde, and reanalysis datasets

Abstract. The trends and spreads of tropospheric and stratospheric temperature are discussed in terms of three groups of datasets in 1979–2008. These datasets include (a) three satellite observations of Microwave Sounding Units (MSU) measurements, (b) five radiosonde observations and (c) five reanalysis products. The equivalent tropospheric and stratospheric temperature from radiosonde and reanalyses are calculated based on the vertical weighting function of the MSU channel 2 (CH2) and channel 4 (CH4) measurements, respectively. The results show that both cooling in the stratosphere and warming in troposphere significantly depends on the datasets and latitudes.

The annual cycle in lower stratospheric temperatures revisited

Abstract. Observed lower stratospheric temperatures show a prominent annual cycle. The cycles in the tropics and Northern Hemisphere are in phase and the cycle in the Southern Hemisphere has the opposite phase. In an elegant and influential paper, Yulaeva, Holton and Wallace (1994) explained the observed pattern as a direct consequence of hemispheric asymmetries in the dynamical forcing of the stratospheric circulation. They showed that in Microwave Sounding Unit channel 4 (weighting centered in the lower stratosphere) data the combined extratropical and the tropical temperature cycle nearly compensate and interpreted the out-of-phase temperature variations between tropics and extratropics as the temperature response to an annual cycle in the wave driven residual circulation. We show that the near-compensation of temperature variations observed by Yulaeva et al. (1994) is artefact of the weighting function of the MSU-4 channel and does not hold on individual pressure levels. We discuss in detail the conditions required that temperature variations compensate, and what insights can be obtained from analysis of tropical, extratropical and global mean temperature variations. Dynamically induced seasonal variations of lower stratospheric ozone lead to an amplification of the seasonal temperature cycle particularly in the tropics. The latitudinal structure of static stability also induces a significant deviation from compensation of tropical and combined extratropical temperature variations. In line with Yulaeva et al. (1994) we affirm that the see-saw pattern in the annual cycles of tropical and combined extratropical temperatures provides an important pointer to mechanistic models for interannual variability and trends, but additionally conclude that the feedback of dynamically induced ozone variations on temperatures and the latitudinal structure of static stability should be included as leading order processes in such models.

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

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.

Error Structure and Atmospheric Temperature Trends in Observations from the Microwave Sounding Unit

Abstract The Microwave Sounding Unit (MSU) onboard the National Oceanic and Atmospheric Administration polar-orbiting satellites measures the atmospheric temperature from the surface to the lower stratosphere under all weather conditions, excluding precipitation. Although designed primarily for monitoring weather processes, the MSU observations have been extensively used for detecting climate trends, and calibration errors are a major source of uncertainty. To reduce this uncertainty, an intercalibration method based on the simultaneous nadir overpass (SNO) matchups for the MSU instruments on satellites NOAA-10, -11, -12, and -14 was developed. Due to orbital geometry, the SNO matchups are confined to the polar regions, where the brightness temperature range is slightly smaller than the global range. Nevertheless, the resulting calibration coefficients are applied globally to the entire life cycle of an MSU satellite. Such intercalibration reduces intersatellite biases by an order of magnitude compared to prelaunch calibration and, thus, results in well-merged time series for the MSU channels 2, 3, and 4, which respectively represent the deep layer temperature of the midtroposphere (T2), tropopause (T3), and lower stratosphere (T4). Focusing on the global atmosphere over ocean surfaces, trends for the SNO-calibrated T2, T3, and T4 are, respectively, 0.21 ± 0.07, 0.08 ± 0.08, and −0.38 ± 0.27 K decade−1 from 1987 to 2006. These trends are independent of the number of limb-corrected footprints used in the dataset, and trend differences are marginal for varying bias correction techniques for merging the overlapping satellites on top of the SNO calibration. The spatial pattern of the trends reveals the tropical midtroposphere to have warmed at a rate of 0.28 ± 0.19 K decade−1, while the Arctic atmosphere warmed 2 to 3 times faster than the global average. The troposphere and lower stratosphere, however, cooled across the southern Indian and Atlantic Oceans adjacent to the Antarctic continent. To remove the stratospheric cooling effect in T2, channel trends from T2 and T3 (T23) and T2 and T4 (T24) were combined. The trend patterns for T23 and T24 are in close agreement, suggesting internal consistencies for the trend patterns of the three channels.

Recalibration of microwave sounding unit for climate studies using simultaneous nadir overpasses

The measurements from microwave sounding unit (MSU) on board different NOAA polar‐orbiting satellites have been extensively used for detecting atmospheric temperature trend during the last several decades. However, temperature trends derived from these measurements are under significant debate, mostly caused by calibration errors. This study recalibrates the MSU channel 2 observations at level 0 using the postlaunch simultaneous nadir overpass (SNO) matchups and then provides a well‐merged new MSU 1b data set for climate studies. The calibration algorithm consists of a dominant linear response of the MSU raw counts to the Earth‐view radiance plus a smaller quadratic term. Uncertainties are represented by a constant offset and errors in the coefficient for the nonlinear quadratic term. A SNO matchup data set for nadir pixels with criteria of simultaneity of less than 100 s and within a ground distance of 111 km is generated for all overlaps of NOAA satellites. The simultaneous nature of these matchups eliminates the impact of orbital drifts on the calibration. A radiance error model for the SNO pairs is developed and then used to determine the offsets and nonlinear coefficients through regressions of the SNO matchups. It is found that the SNO matchups can accurately determine the differences of the offsets as well as the nonlinear coefficients between satellite pairs, thus providing a strong constraint to link calibration coefficients of different satellites together. However, SNO matchups alone cannot determine the absolute values of the coefficients because there is a high degree of colinearity between satellite SNO observations. Absolute values of calibration coefficients are obtained through sensitivity experiments, in which the percentage of variance in the brightness temperature difference time series that can be explained by the warm target temperatures of overlapping satellites is a function of the calibration coefficient. By minimizing these percentages of variance for overlapping observations, a new set of calibration coefficients is obtained from the SNO regressions. These new coefficients are significantly different from the prelaunch calibration values, but they result in bias‐free SNO matchups and near‐zero contaminations by the warm target temperatures in terms of the calibrated brightness temperature. Applying the new calibration coefficients to the Level 0 MSU observations, a well‐merged MSU pentad data set is generated for climate trend studies. To avoid errors caused by small SNO samplings between NOAA 10 and 9, observations only from and after NOAA 10 are used. In addition, only ocean averages are investigated so that diurnal cycle effect can be ignored. The global ocean‐averaged intersatellite biases for the pentad data set are between 0.05 and 0.1 K, which is an order of magnitude smaller than that obtained when using the unadjusted calibration algorithm. The ocean‐only anomaly trend for the combined MSU channel 2 brightness temperature is found to be 0.198 K decade−1 during 1987–2003.

Robustness of Tropospheric Temperature Trends from MSU Channels 2 and 4

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.

Estimation of Tropospheric Temperature Trends from MSU Channels 2 and 4

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.

Stratospheric Influences on MSU-Derived Tropospheric Temperature Trends: A Direct Error Analysis

Abstract Retrievals of tropospheric temperature trends from data of the Microwave Sounding Unit (MSU) are subject to biases related to the strong cooling of the stratosphere during the past few decades. The magnitude of this stratospheric contamination in various retrievals is estimated using stratospheric temperature trend profiles based on observations. It is found that from 1979 to 2001 the stratospheric contribution to the trend of MSU channel-2 brightness temperature is about −0.08 K decade−1, which is consistent with the findings of Fu et al. In the retrieval method developed by Fu et al. based on a linear combination of MSU channels 2 and 4, the stratospheric influence is largely removed, leaving a residual influence of less than ±0.01 K decade−1. This method is also found to be more accurate than the angular scanning retrieval technique of Spencer and Christy to remove the stratospheric contamination.

Tropospheric temperature series from satellites

There has been considerable debate about changes in the temperature of the troposphere measured using the Microwave Sounding Unit (MSU) instrument or radiosondes. Fu et al. linearly combine time series from two MSU channels to estimate vertically integrated 850-300-hPa temperatures and claim consistency between surface and free-troposphere warming for one MSU record. We believe that their approach overfits the data, produces trends that overestimate warming and gives overly optimistic uncertainty estimates. There still remain large differences between observed tropospheric temperature trends and those simulated by a climate model.

A Reanalysis of the MSU Channel 2 Tropospheric Temperature Record

Over the period from 1979 to 2001, tropospheric trends derived from a widely cited analysis of the Microwave Sounding Unit (MSU) temperature record show little or no warming, while surface temperature trends based on in situ observations show a pronounced warming of ∼0.2 K decade−1. This discrepancy between trends at the surface and in the upper atmosphere has been a source of significant debate. Model predictions of amplification of warming with height in the troposphere are clearly inconsistent with the available observations, leading some researchers to question the adequacy of their representation of the water vapor greenhouse feedback. A reanalysis of the MSU channel 2 dataset, with the objective of providing a second independent source of these data, is described in this paper. Results presented herein show a global trend of 0.097 ± 0.020 K decade−1, generally agreeing with the work of Prabhakara et al. but in disagreement with the MSU analysis of Christy and Spencer, which shows significantly less (∼0.09 K decade−1) warming. Differences in the various methodologies are discussed and it is demonstrated that the principal source of these discrepancies is in the treatment of errors due to variations in the temperature of the MSU hot calibration target.

A satellite radiance‐bias correction scheme for data assimilation

AbstractRecently, much progress has been made in the direct assimilation of satellite radiance measurements in numerical weather‐prediction systems. In order to use radiances from the TIROS Operational Vertical Sounder (TOVS), biases between the observed radiances and those simulated from the model first guess must be corrected. The original scheme for TOVS radiance‐bias correction at the European Centre for Medium‐Range Weather Forecasts utilized a global scan correction, and a linear air‐mass correction, with the observed radiances from the Microwave Sounding Unit channels 2, 3 and 4 as predictors. The new scheme differs in two fundamental ways. Analysis of radiance data shows a significant residual scan bias which depends strongly on latitude for some channels. The new scheme applies a latitudinally dependent scan correction to take this into account. The air‐mass predictors are now computed from the background field, since the background field contains a more consistent representation of the air mass and surface characteristics than the observed microwave radiances. Four new predictors are used, 1000–300 hPa thickness, 200–50 hPa thickness, model surface skin temperature and total precipitable water. In particular, the skin‐temperature predictor is able to differentiate between ocean and sea‐ice, performing much better than the old scheme in the winter hemisphere. The use of model predictors is a change in philosophy away from correction of the observations to correction of the computed forward radiances. This leads to a natural extension where the gradient of the bias correction can be taken into account in variational retrieval schemes.

The spatial and temporal behaviour of the lower stratospheric temperature over the Southern Hemisphere: the MSU view. Part I: data, methodology and temporal behaviour

AbstractThe lower stratosphere monthly temperature anomalies over the Southern Hemisphere derived from soundings made by the Microwave Sounding Unit (MSU) between 1979 and 1997 are analysed. Specifically MSU channel 4 temperature retrievals are considered. Principal component (PC) analysis with the S‐mode approach is used in order to isolate grid points that covary in a similar manner and to determine the main features of their temporal behaviour. The first six PCs explain 81.3% of the variance and represent the different time variability patterns observed over the Southern Hemisphere for the ten area clusters determined by the method. The most important feature is common to all the PC score pattern–time series and corresponds to a negative linear trend present in almost all the Southern Hemisphere except over New Zealand and surrounding areas. The negative trend is largest over Antarctica. The remaining features of the temporal variability are different for each PC score and therefore for each cluster region over the Southern Hemisphere. The first PC score pattern shows the impact of the Chichón and Mt Pinatubo eruptions that each produced a 2‐year warming over the tropical and sub‐tropical lower stratosphere. This variability is orthogonal with the behaviour present over Antarctica. There are different anomalies between 1987 (El Niño) and 1988 (La Niña). This second PC does not show any evidence whatsoever of the volcanic eruptions. The semi‐annual wave is present in the anomaly recurrence at mid to high latitudes. Over very low latitudes, close to the Equator, the Quasi‐Biennial Oscillation (QBO) band of frequency is also present. Copyright © 2001 Royal Meteorological Society

An Estimation of Typhoon Intensity and the Prediction of Its Track by Using MSU Data

This study uses both airborne and NOAA (National Oceanic and Atmospheric Administration) MSU (Microwave Sounding Unit) observational data, covering the Taiwan area during the 1990 TATEX (Taiwan Area Typhoon Experiment) period to develop a typhoon intensity estimation model. Although some previous studies have shown that the temperature difference of the typhoon center and its environment estimated by the MSU at 250-mb level has a positive relationship with the maximum wind speed, the results in this paper show that they are not as satisfactory as expected. When using only the 250-mb temperature anomaly, the value of the correlation is roughly about 0.6, and the RMSE (Root Mean Square) being 19kts. According to the sensor readings of the weighting function, this outcome suggests that the maximum typhoon wind speed is strongly related to the brightness temperature, which is contributed throughout each layer of the typhoon. Thus, the wind speed information is not complete when only the upper levels of the temperature information are considered. Because the weighting function of the third MSU channel extends downward all the way to the 700-mb level, additional information can be extracted for the retrieval of the intensity of a typhoon. The result shows an improvement in predicting the typhoon intensity accuracy with the correlation and RMSE equaling 0.7 and 16.5kts, respectively when both the 250 and 700-mb temperature anomalies are considered simultaneously. We also used a 700-mb cold-core location in predicting the path a typhoon follows. The results showed a value of 0.9 R-squared and an RMSE of 13.1 degrees in angle. Generally speaking, the results were rather impressive and pave the way for further exploration of the method's potential application in monitoring a typhoon's track.

Global warming: Evidence from satellite observations

Observations made in Channel 2 (53.74 GHz) of the Microwave Sounding Unit (MSU) radiometer, flown on‐board sequential, sun‐synchronous, polar‐orbiting NOAA operational satellites, indicate that the mean temperature of the atmosphere over the globe increased during the period 1980 to 1999. In this study, we have minimized systematic errors in the time series introduced by satellite orbital drift in an objective manner. This is done with the help of the onboard warm‐blackbody temperature, which is used in the calibration of the MSU radiometer. The corrected MSU Channel 2 observations of the NOAA satellite series reveal that the vertically‐weighted global‐mean temperature of the atmosphere, with a peak weight near the mid troposphere, warmed at the rate of 0.13±0.05 Kdecade−1 during 1980 to 1999. The global warming deduced from conventional meteorological data that have been corrected for urbanization effects agrees reasonably with this satellite‐deduced result.

Reanalyses-Based Tropospheric Temperature Estimates: Uncertainties in the Context of Global Climate Change Detection

Abstract Uncertainties in estimates of tropospheric mean temperature were investigated in the context of climate change detection through comparisons of the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) 40-yr reanalysis (1958–97), the National Aeronautics and Space Administration Data Assimilation Office (NASA/DAO) 14-yr reanalysis (1980–93), the European Centre for Medium-Range Weather Forecasts Reanalysis Project (ERA) 15-yr reanalysis (1979–94), and the satellite microwave sounding unit channel 2 (MSU Ch2) (1979–97) temperature data. The maximum overlap period for comparison among these datasets is the 14 full years January 1980 to December 1993. This study documents similar shifts in the relative bias between the MSU Ch2 and the ERA and the NCEP–NCAR reanalyses in the 1991–97 period suggesting changes in the satellite analysis. However, the intercomparisons were not able to rule out the changes in the reanalysis systems and/or the input data on whic...