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Synergistic Retrievals of Ice in High Clouds from Elastic Backscatter Lidar, Ku-Band Radar, and Submillimeter Wave Radiometer ObservationsAbstract
In this study, we investigate the synergy of elastic backscatter lidar, Ku-band radar, and submillimeter-wave radiometer measurements in the retrieval of ice from satellite observations. The synergy is analyzed through the generation of a large dataset of ice water content (IWC) profiles and simulated lidar, radar and radiometer observations. The characteristics of the instruments (frequencies, sensitivities, etc.) are set based on the expected characteristics of instruments of the Atmosphere Observing System (AOS) mission. A hold-out validation methodology is used to assess the accuracy of the IWC profiles retrieved from various combinations of observations from the three instruments. Specifically, the IWC and associated observations are randomly divided into two datasets, one for training and the other for evaluation. The training dataset is used to train the retrieval algorithm, while the evaluation dataset is used to assess the retrieval performance. The dataset of IWC profiles is derived from CloudSat reflectivity and CALIOP lidar observations. The retrieval of the ice water content IWC profiles from the computed observations is achieved in two steps. In the first step, a class, of 18 potential classes characterized by different vertical distribution of IWC, is estimated from the observations. The 18 classes are predetermined based on the k-means clustering algorithm. In the second step, the IWC profile is estimated using an ensemble Kalman smoother algorithm that uses the estimated class as a priori information. The results of the study show that the synergy of lidar, radar, and radiometer observations is significant in the retrieval of the IWC profiles. Nevertheless, it should be mentioned that this synergy was found under idealized conditions, and additional work might be required to materialize it in practice. The inclusion of the lidar backscatter observations in the retrieval process has a larger impact on the retrieval performance than the inclusion of the radar observations. As ice clouds have a significant impact on atmospheric radiative processes, this work is relevant to ongoing efforts to reduce uncertainties in climate analyses and projections.
Abstract
In this study, we investigate the synergy of elastic backscatter lidar, Ku-band radar, and submillimeter-wave radiometer measurements in the retrieval of ice from satellite observations. The synergy is analyzed through the generation of a large dataset of ice water content (IWC) profiles and simulated lidar, radar and radiometer observations. The characteristics of the instruments (frequencies, sensitivities, etc.) are set based on the expected characteristics of instruments of the Atmosphere Observing System (AOS) mission. A hold-out validation methodology is used to assess the accuracy of the IWC profiles retrieved from various combinations of observations from the three instruments. Specifically, the IWC and associated observations are randomly divided into two datasets, one for training and the other for evaluation. The training dataset is used to train the retrieval algorithm, while the evaluation dataset is used to assess the retrieval performance. The dataset of IWC profiles is derived from CloudSat reflectivity and CALIOP lidar observations. The retrieval of the ice water content IWC profiles from the computed observations is achieved in two steps. In the first step, a class, of 18 potential classes characterized by different vertical distribution of IWC, is estimated from the observations. The 18 classes are predetermined based on the k-means clustering algorithm. In the second step, the IWC profile is estimated using an ensemble Kalman smoother algorithm that uses the estimated class as a priori information. The results of the study show that the synergy of lidar, radar, and radiometer observations is significant in the retrieval of the IWC profiles. Nevertheless, it should be mentioned that this synergy was found under idealized conditions, and additional work might be required to materialize it in practice. The inclusion of the lidar backscatter observations in the retrieval process has a larger impact on the retrieval performance than the inclusion of the radar observations. As ice clouds have a significant impact on atmospheric radiative processes, this work is relevant to ongoing efforts to reduce uncertainties in climate analyses and projections.
The NASA GSFC 94-GHz Airborne Solid-State Cloud Radar System (CRS)Abstract
The NASA Goddard Space Flight Center’s (GSFC’s) W-band (94 GHz) Cloud Radar System (CRS) has been comprehensively updated to modern solid-state and digital technology. This W-band (94 GHz) radar flies in nadir-pointing mode on the NASA ER-2 high-altitude aircraft, providing polarimetric reflectivity and Doppler measurements of clouds and precipitation. This paper describes the design and signal processing of the upgraded CRS. It includes details on the hardware upgrades [solid-state power amplifier (SSPA) transmitter, antenna, and digital receiver] including a new reflectarray antenna and solid-state transmitter. It also includes algorithms, including internal loop-back calibration, external calibration using a direct relationship between volume reflectivity and the range-integrated backscatter of the ocean, and a modified staggered–pulse repetition frequency (PRF) Doppler algorithm that is highly resistant to unfolding errors. Data samples obtained by upgraded CRS through recent NASA airborne science missions are provided.
Abstract
The NASA Goddard Space Flight Center’s (GSFC’s) W-band (94 GHz) Cloud Radar System (CRS) has been comprehensively updated to modern solid-state and digital technology. This W-band (94 GHz) radar flies in nadir-pointing mode on the NASA ER-2 high-altitude aircraft, providing polarimetric reflectivity and Doppler measurements of clouds and precipitation. This paper describes the design and signal processing of the upgraded CRS. It includes details on the hardware upgrades [solid-state power amplifier (SSPA) transmitter, antenna, and digital receiver] including a new reflectarray antenna and solid-state transmitter. It also includes algorithms, including internal loop-back calibration, external calibration using a direct relationship between volume reflectivity and the range-integrated backscatter of the ocean, and a modified staggered–pulse repetition frequency (PRF) Doppler algorithm that is highly resistant to unfolding errors. Data samples obtained by upgraded CRS through recent NASA airborne science missions are provided.
