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Abstract
Deep-layer mean temperatures from Microwave Sounding Unit (MSU) observations have been used by scientists to study trends and interannual variations of tropospheric and lower-stratospheric temperature. The spatial resolution of MSU deep-layer mean temperatures is rather poor for studying trends in localized regions. A method is developed in which infrared observations from the High-resolution InfraRed Sounder (HIRS) is used in combination with MSU to derive deep-layer mean temperatures with improved vertical and horizontal resolution. Even though the relationship between infrared radiance and temperature is not linear, the layer associated with the mean temperature is shown to be well defined with a small airmass dependency that is similar to MSU’s airmass dependency. Preliminary validation of HIRS–MSU-derived layer mean temperatures with radiosonde layer mean temperatures show similar precision when compared to MSU-only derived temperatures.
Abstract
Deep-layer mean temperatures from Microwave Sounding Unit (MSU) observations have been used by scientists to study trends and interannual variations of tropospheric and lower-stratospheric temperature. The spatial resolution of MSU deep-layer mean temperatures is rather poor for studying trends in localized regions. A method is developed in which infrared observations from the High-resolution InfraRed Sounder (HIRS) is used in combination with MSU to derive deep-layer mean temperatures with improved vertical and horizontal resolution. Even though the relationship between infrared radiance and temperature is not linear, the layer associated with the mean temperature is shown to be well defined with a small airmass dependency that is similar to MSU’s airmass dependency. Preliminary validation of HIRS–MSU-derived layer mean temperatures with radiosonde layer mean temperatures show similar precision when compared to MSU-only derived temperatures.
Abstract
An algorithm for generating deep-layer mean temperatures from satellite-observed microwave observations is presented. Unlike traditional temperature retrieval methods, this algorithm does not require a first guess temperature of the ambient atmosphere. By eliminating the first guess a potentially systematic source of error has been removed. The algorithm is expected to yield long-term records that are suitable for detecting small changes in climate.
The atmospheric contribution to the deep-layer mean temperature is given by the averaging kernel. The algorithm computes the coefficients that will best approximate a desired averaging kernel from a linear combination of the satellite radiometer's weighting functions. The coefficients are then applied to the measurements to yield the deep-layer mean temperature. Three constraints were used in deriving the algorithm: 1) the sum of the coefficients must be one, 2) the noise of the product is minimized, and 3) the shape of the approximated averaging kernel is well behaved. Note that a trade-off between constraints 2 and 3 is unavoidable.
The algorithm can also be used to combine measurements from a future sensor [i.e., the 20-channel Advanced Microwave Sounding Unit (AMSU)] to yield the same averaging kernel as that based on an earlier sensor [i.e., the 4-channel Microwave Sounding Unit (MSU)]. This will allow a time series of deep-layer mean temperatures based on MSU measurements to be continued with AMSU measurements. The AMSU is expected to replace the MSU in 1996.
Abstract
An algorithm for generating deep-layer mean temperatures from satellite-observed microwave observations is presented. Unlike traditional temperature retrieval methods, this algorithm does not require a first guess temperature of the ambient atmosphere. By eliminating the first guess a potentially systematic source of error has been removed. The algorithm is expected to yield long-term records that are suitable for detecting small changes in climate.
The atmospheric contribution to the deep-layer mean temperature is given by the averaging kernel. The algorithm computes the coefficients that will best approximate a desired averaging kernel from a linear combination of the satellite radiometer's weighting functions. The coefficients are then applied to the measurements to yield the deep-layer mean temperature. Three constraints were used in deriving the algorithm: 1) the sum of the coefficients must be one, 2) the noise of the product is minimized, and 3) the shape of the approximated averaging kernel is well behaved. Note that a trade-off between constraints 2 and 3 is unavoidable.
The algorithm can also be used to combine measurements from a future sensor [i.e., the 20-channel Advanced Microwave Sounding Unit (AMSU)] to yield the same averaging kernel as that based on an earlier sensor [i.e., the 4-channel Microwave Sounding Unit (MSU)]. This will allow a time series of deep-layer mean temperatures based on MSU measurements to be continued with AMSU measurements. The AMSU is expected to replace the MSU in 1996.
Abstract
Layer-mean virtual temperatures retrieved from satellite measurements are more accurate than retrievals at specific pressure not only because an averaging process is involved, but also because of advantages in the retrieval process. In this note, a “retrieval efficiency” is derived to express this advantage over simple averaging as a function of layer thickness. The efficiency is examined for two common cases of retrieval initial guess: a statistical sample mean and a forecast profile obtained from a numerical prediction model. The advantage of the layer-mean retrieval clearly is demonstrated in both cases.
Abstract
Layer-mean virtual temperatures retrieved from satellite measurements are more accurate than retrievals at specific pressure not only because an averaging process is involved, but also because of advantages in the retrieval process. In this note, a “retrieval efficiency” is derived to express this advantage over simple averaging as a function of layer thickness. The efficiency is examined for two common cases of retrieval initial guess: a statistical sample mean and a forecast profile obtained from a numerical prediction model. The advantage of the layer-mean retrieval clearly is demonstrated in both cases.
Abstract
A method for deriving a water vapor index is presented. An important feature of the index is the fact that it does not rely on radiosondes. Thus, it is not influenced by problems associated with radiosondes and the extent to which the horizontal variability of moisture invalidates the extrapolations from radiosonde measurements to satellite measurements. The index is derived by using channels that are insensitive to changes in moisture to predict a brightness temperature for one of the moisture channels and then by subtracting this predicted value from the observation. The predicted value represents the moisture value expected for the given temperature profile, and the difference between the predicted and measured values is the index. The subtraction removes the variability due to changes in atmospheric temperature from the moisture signal. This separation greatly enhances the ability to monitor atmospheric moisture patterns, especially near the ground and at high latitudes where some alternative methods have difficulties. The ability of the indices to display moisture patterns at all levels and latitudes is demonstrated.
Abstract
A method for deriving a water vapor index is presented. An important feature of the index is the fact that it does not rely on radiosondes. Thus, it is not influenced by problems associated with radiosondes and the extent to which the horizontal variability of moisture invalidates the extrapolations from radiosonde measurements to satellite measurements. The index is derived by using channels that are insensitive to changes in moisture to predict a brightness temperature for one of the moisture channels and then by subtracting this predicted value from the observation. The predicted value represents the moisture value expected for the given temperature profile, and the difference between the predicted and measured values is the index. The subtraction removes the variability due to changes in atmospheric temperature from the moisture signal. This separation greatly enhances the ability to monitor atmospheric moisture patterns, especially near the ground and at high latitudes where some alternative methods have difficulties. The ability of the indices to display moisture patterns at all levels and latitudes is demonstrated.
Abstract
Two Pattern recognition procedures are developed to provide improvements to first-guess fields for satellite temperature retrievals. The first is a technique whereby a radiometer measurement may be used to select one or more historical radiosonde temperature profiles as analog estimates of ambient thermal structure. The vertical scales of the analogs are those of radiosondes—the vertical resolving power of the satellite radiometer being relevant only to a decision process. The analog selection process is shown to be much more effective if implemented in an orthogonalized space of measurement information. The second procedure is one which partitions a priori dependent data into shape-coherent pattern libraries using structure information inherent in the data itself. This is an alternative to traditional partitioning schemes whereby proxy classifiers such as season, location and surface type are used.
These pattern recognition techniques are shown to be capable of reducing first-guess profile errors by nearly 50%, in an independent test of about 800 diverse retrievals. The impact of pattern recognition on temperature retrieval error is assessed using regression and physical-iterative retrieval algorithms. The influence of improved first-guess fields is markedly different on these two types of algorithms. Pattern recognition is shown to have a strong, positive impact on the physical-iterative method but little significant impact on regression when evaluated in an overall batch sense. A case study suggests that a small number of very poor retrievals may particularly mask the potential benefits of pattern recognition on both methods.
Abstract
Two Pattern recognition procedures are developed to provide improvements to first-guess fields for satellite temperature retrievals. The first is a technique whereby a radiometer measurement may be used to select one or more historical radiosonde temperature profiles as analog estimates of ambient thermal structure. The vertical scales of the analogs are those of radiosondes—the vertical resolving power of the satellite radiometer being relevant only to a decision process. The analog selection process is shown to be much more effective if implemented in an orthogonalized space of measurement information. The second procedure is one which partitions a priori dependent data into shape-coherent pattern libraries using structure information inherent in the data itself. This is an alternative to traditional partitioning schemes whereby proxy classifiers such as season, location and surface type are used.
These pattern recognition techniques are shown to be capable of reducing first-guess profile errors by nearly 50%, in an independent test of about 800 diverse retrievals. The impact of pattern recognition on temperature retrieval error is assessed using regression and physical-iterative retrieval algorithms. The influence of improved first-guess fields is markedly different on these two types of algorithms. Pattern recognition is shown to have a strong, positive impact on the physical-iterative method but little significant impact on regression when evaluated in an overall batch sense. A case study suggests that a small number of very poor retrievals may particularly mask the potential benefits of pattern recognition on both methods.
Abstract
The Advanced Microwave Sounding Unit-A (AMSU-A) is the first of a new generation of polar-orbiting cross-track microwave sounders operated by the National Oceanic and Atmospheric Administration. A feature of a cross-track sounder is that the measurements vary with scan angle because of the change in the optical pathlength between the earth and the satellite. This feature is called the limb effect and can be as much as 30 K. One approach to this problem is to limb adjust the measurements to a fixed view angle. This approach was used for the older series of Microwave Sounding Units. Limb adjusting is important for climate applications and regression retrieval algorithms. This paper describes and evaluates several limb adjustment procedures. The recommended procedure uses a combined physical and statistical technique. The limb adjusted measurements were compared with computed radiances from radiosondes and National Centers for Environmental Prediction models. The model error was found to be less than the instrument noise for most of the temperature sounding channels. The error in the window channels was small relative to the observed range of these channels. Limb adjusted fields appear to be smooth. Statistical tests of the distributions of the adjusted measurements at each scan angle show them to be very similar.
Abstract
The Advanced Microwave Sounding Unit-A (AMSU-A) is the first of a new generation of polar-orbiting cross-track microwave sounders operated by the National Oceanic and Atmospheric Administration. A feature of a cross-track sounder is that the measurements vary with scan angle because of the change in the optical pathlength between the earth and the satellite. This feature is called the limb effect and can be as much as 30 K. One approach to this problem is to limb adjust the measurements to a fixed view angle. This approach was used for the older series of Microwave Sounding Units. Limb adjusting is important for climate applications and regression retrieval algorithms. This paper describes and evaluates several limb adjustment procedures. The recommended procedure uses a combined physical and statistical technique. The limb adjusted measurements were compared with computed radiances from radiosondes and National Centers for Environmental Prediction models. The model error was found to be less than the instrument noise for most of the temperature sounding channels. The error in the window channels was small relative to the observed range of these channels. Limb adjusted fields appear to be smooth. Statistical tests of the distributions of the adjusted measurements at each scan angle show them to be very similar.
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.
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.
Abstract
To maximize the contribution of the Cross-track Infrared Sounder (CrIS) measurements to the global weather forecasting, we attempt to choose the CrIS channels to be assimilated in the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS). From preselected 431 CrIS channels, 207 channels are newly selected using a one-dimensional variational (1D-Var) approach where the channel score index (CSI) is used as a figure of merit. Newly selected 207 channels comprise 85 temperature, 49 water vapor, and 73 surface channels, respectively. In addition, to examine how the channels are selected if the forecast error covariance is differently defined depending on the latitudinal regions (i.e., Northern and Southern Hemispheres, and tropics), the same selection process is carried out repeatedly using three regional forecast error covariances. From three regional channel sets, two-channel sets are made for the global data assimilation. One channel set is made with 134 channels overlapped between three regional channel sets. Another channel set consists of 277 channels that is the sum of 3 regional channel sets. In the global trial experiments, the global CrIS 207 channels have a significant positive forecast impact in terms of the improvement of GFS global forecasting, as compared with the forecasts with the operational 100 channels as well as the overlapped 134 and the union 277 channel sets. The improved forecast is mainly due to the additional temperature/water vapor channels of the global CrIS 207 channels that are selected optimally based on the global forecast error of operational GFS.
Abstract
To maximize the contribution of the Cross-track Infrared Sounder (CrIS) measurements to the global weather forecasting, we attempt to choose the CrIS channels to be assimilated in the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS). From preselected 431 CrIS channels, 207 channels are newly selected using a one-dimensional variational (1D-Var) approach where the channel score index (CSI) is used as a figure of merit. Newly selected 207 channels comprise 85 temperature, 49 water vapor, and 73 surface channels, respectively. In addition, to examine how the channels are selected if the forecast error covariance is differently defined depending on the latitudinal regions (i.e., Northern and Southern Hemispheres, and tropics), the same selection process is carried out repeatedly using three regional forecast error covariances. From three regional channel sets, two-channel sets are made for the global data assimilation. One channel set is made with 134 channels overlapped between three regional channel sets. Another channel set consists of 277 channels that is the sum of 3 regional channel sets. In the global trial experiments, the global CrIS 207 channels have a significant positive forecast impact in terms of the improvement of GFS global forecasting, as compared with the forecasts with the operational 100 channels as well as the overlapped 134 and the union 277 channel sets. The improved forecast is mainly due to the additional temperature/water vapor channels of the global CrIS 207 channels that are selected optimally based on the global forecast error of operational GFS.
Abstract
To provide global coverage for the hyperspectral infrared (IR) and microwave (MW) sounders, the low-Earth-orbiting (LEO) satellite constellation is in operation in three temporally well-spaced sun-synchronous orbits. However, the satellite program can be altered as a result of aging satellites needing to deorbit and/or termination of the legacy program, resulting in less spatiotemporal coverage. In this study, to stress the contribution of IR and MW sounder observations from the LEO satellite constellation on numerical weather prediction (NWP) system performance, the change of the analysis impact is assessed under two assumptions: 1) the loss of the IR and MW sounder observations in each of three sun-synchronous orbits and 2) the loss of the secondary LEO satellite in two orbits, using a 2017 version of the National Centers for Environmental Prediction Global Forecast System (GFS). In the analysis verification, it is found that the analysis field is degraded due to the loss of the IR and MW sounders in each of the three primary orbits. In particular, the satellites in the afternoon orbit significantly contribute to improving the analysis as compared with the satellites in the other two orbits. In addition, the loss of the secondary satellite results in significant degradation of the analysis, resulting from reduced spatial coverage by the IR and MW sounders. These results suggest that the LEO satellite constellation, consisting of the LEO satellites in three primary sun-synchronous orbits, should be maintained in terms of the contribution to the NWP.
Significance Statement
Hyperspectral infrared (IR) and microwave (MW) sounders from low-Earth-orbiting (LEO) satellites significantly contribute to improving numerical weather forecasting. Nevertheless, the resiliency of the LEO satellite programs, operating in three sun-synchronous orbits, can be compromised by aging satellites needing to deorbit and/or termination of legacy satellite systems. Thus, to highlight the importance of the IR and MW sounder observations from LEO satellites in terms of numerical weather forecasting, we assessed the analysis impact of these observations with diverse satellite data availability scenarios. In the trial experiments, it is demonstrated that analysis performance is significantly degraded if the IR and MW sounders are lost, suggesting that the satellite programs carrying the IR and MW sounders should be maintained seamlessly in the future.
Abstract
To provide global coverage for the hyperspectral infrared (IR) and microwave (MW) sounders, the low-Earth-orbiting (LEO) satellite constellation is in operation in three temporally well-spaced sun-synchronous orbits. However, the satellite program can be altered as a result of aging satellites needing to deorbit and/or termination of the legacy program, resulting in less spatiotemporal coverage. In this study, to stress the contribution of IR and MW sounder observations from the LEO satellite constellation on numerical weather prediction (NWP) system performance, the change of the analysis impact is assessed under two assumptions: 1) the loss of the IR and MW sounder observations in each of three sun-synchronous orbits and 2) the loss of the secondary LEO satellite in two orbits, using a 2017 version of the National Centers for Environmental Prediction Global Forecast System (GFS). In the analysis verification, it is found that the analysis field is degraded due to the loss of the IR and MW sounders in each of the three primary orbits. In particular, the satellites in the afternoon orbit significantly contribute to improving the analysis as compared with the satellites in the other two orbits. In addition, the loss of the secondary satellite results in significant degradation of the analysis, resulting from reduced spatial coverage by the IR and MW sounders. These results suggest that the LEO satellite constellation, consisting of the LEO satellites in three primary sun-synchronous orbits, should be maintained in terms of the contribution to the NWP.
Significance Statement
Hyperspectral infrared (IR) and microwave (MW) sounders from low-Earth-orbiting (LEO) satellites significantly contribute to improving numerical weather forecasting. Nevertheless, the resiliency of the LEO satellite programs, operating in three sun-synchronous orbits, can be compromised by aging satellites needing to deorbit and/or termination of legacy satellite systems. Thus, to highlight the importance of the IR and MW sounder observations from LEO satellites in terms of numerical weather forecasting, we assessed the analysis impact of these observations with diverse satellite data availability scenarios. In the trial experiments, it is demonstrated that analysis performance is significantly degraded if the IR and MW sounders are lost, suggesting that the satellite programs carrying the IR and MW sounders should be maintained seamlessly in the future.
Abstract
The Advanced Microwave Sounding Unit (AMSU) has better horizontal resolution and vertical temperature sounding abilities than its predecessor, the Microwave Sounding Unit (MSU). Those improved capabilities are demonstrated with observations of two cyclonic weather systems located in the South Pacific Ocean on 1 March 1999. These weather systems appear quite similar in conventional infrared satellite imagery, suggesting that they are comparable in structure and intensity. However, an analysis using temperature retrievals created from the AMSU shows that their vertical thermal structure is quite different.
This is just one example of an application highlighting the improved sounding capabilities available with the AMSU instrument suite. A preliminary look at what the AMSU can provide in data-void regions and a discussion of future plans to create AMSU-based products to better diagnose synoptic-scale weather systems are presented.
Abstract
The Advanced Microwave Sounding Unit (AMSU) has better horizontal resolution and vertical temperature sounding abilities than its predecessor, the Microwave Sounding Unit (MSU). Those improved capabilities are demonstrated with observations of two cyclonic weather systems located in the South Pacific Ocean on 1 March 1999. These weather systems appear quite similar in conventional infrared satellite imagery, suggesting that they are comparable in structure and intensity. However, an analysis using temperature retrievals created from the AMSU shows that their vertical thermal structure is quite different.
This is just one example of an application highlighting the improved sounding capabilities available with the AMSU instrument suite. A preliminary look at what the AMSU can provide in data-void regions and a discussion of future plans to create AMSU-based products to better diagnose synoptic-scale weather systems are presented.
