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- Author or Editor: Hung-Lung Huang x
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Abstract
A simulation study is used to demonstrate the application of principal component analysis to both the compression of, and meteorological parameter retrieval from, high-resolution infrared spectra. The study discusses the fundamental aspects of spectral correlation, distributions, and noise; the correlation between principal components (PCs) and atmospheric-level temperature and water vapor; and how an optimal subset of PCs is selected so a good compression ratio and high retrieval accuracy are obtained.
Principal component analysis, principal component compression, and principal component regression under certain conditions are shown to provide 1) nearly full spectral information with little degradation, 2) noise reduction, 3) data compression with a compression ratio of approximately 15, and 4) tolerable loss of accuracy in temperature and water vapor retrieval. The techniques will therefore be valuable tools for data compression and the accurate retrieval of meteorological parameters from new-generation satellite instruments.
Abstract
A simulation study is used to demonstrate the application of principal component analysis to both the compression of, and meteorological parameter retrieval from, high-resolution infrared spectra. The study discusses the fundamental aspects of spectral correlation, distributions, and noise; the correlation between principal components (PCs) and atmospheric-level temperature and water vapor; and how an optimal subset of PCs is selected so a good compression ratio and high retrieval accuracy are obtained.
Principal component analysis, principal component compression, and principal component regression under certain conditions are shown to provide 1) nearly full spectral information with little degradation, 2) noise reduction, 3) data compression with a compression ratio of approximately 15, and 4) tolerable loss of accuracy in temperature and water vapor retrieval. The techniques will therefore be valuable tools for data compression and the accurate retrieval of meteorological parameters from new-generation satellite instruments.
Abstract
A new microwave algorithm, analogous to the infrared “radiance-ratioing” method (Eyre and Menzel 1989) is developed to retrieve the height and “effective” fraction (defined as the product of the emissivity times the actual physical fractional coverage) of nonprecipitating water clouds using various pairs of the 20 microwave channels planned for the Advanced Microwave Sounding Unit (AMSU), an instrument slated to fly on polar-orbiting satellites beginning in 1994. The results of a simulation study are presented to provide some insights into the potentials of this technique using different AMSU channel combinations. This study suggests that the use of the oxygen channels 3 and 5 and water vapor channels 19 and 20 will produce the most accurate retrievals of liquid water cloud parameters and the highest percentage of good-quality retrievals over a range of meteorological and cloud conditions. The use of channels 1, 2, 16, and 17, which all may have a strong surface component in their measured brightness temperature, does not give optimal results chiefly because the large uncertainties in the microwave surface temperature and emissivity obscure the brightness–temperature signatures of cloud liquid water. As with the infrared radiance ratioing method (and similar C02 slicing techniques), the best retrieval of cloud parameters is for high cloud, with poorer results for those at middle and low levels.
Abstract
A new microwave algorithm, analogous to the infrared “radiance-ratioing” method (Eyre and Menzel 1989) is developed to retrieve the height and “effective” fraction (defined as the product of the emissivity times the actual physical fractional coverage) of nonprecipitating water clouds using various pairs of the 20 microwave channels planned for the Advanced Microwave Sounding Unit (AMSU), an instrument slated to fly on polar-orbiting satellites beginning in 1994. The results of a simulation study are presented to provide some insights into the potentials of this technique using different AMSU channel combinations. This study suggests that the use of the oxygen channels 3 and 5 and water vapor channels 19 and 20 will produce the most accurate retrievals of liquid water cloud parameters and the highest percentage of good-quality retrievals over a range of meteorological and cloud conditions. The use of channels 1, 2, 16, and 17, which all may have a strong surface component in their measured brightness temperature, does not give optimal results chiefly because the large uncertainties in the microwave surface temperature and emissivity obscure the brightness–temperature signatures of cloud liquid water. As with the infrared radiance ratioing method (and similar C02 slicing techniques), the best retrieval of cloud parameters is for high cloud, with poorer results for those at middle and low levels.
Abstract
Contemporary and future high spectral resolution sounders represent a significant technical advancement for environmental and meteorological prediction and monitoring. Given their large volume of spectral observations, the use of robust data compression techniques will be beneficial to data transmission and storage. In this paper, a novel adaptive vector quantization (VQ)-based linear prediction (AVQLP) method for lossless compression of high spectral resolution sounder data is proposed. The AVQLP method optimally adjusts the quantization codebook sizes to yield the maximum compression on prediction residuals and side information. The method outperforms the state-of-the-art compression methods [Joint Photographic Experts Group (JPEG)-LS, JPEG2000 Parts 1 and 2, Consultative Committee for Space Data Systems (CCSDS) Image Data Compression (IDC) 5/3, Context-Based Adaptive Lossless Image Coding (CALIC), and 3D Set Partitioning in Hierarchical Trees (SPIHT)] and achieves a new high in lossless compression for the standard test set of 10 NASA Atmospheric Infrared Sounder (AIRS) granules. It also compares favorably in terms of computational efficiency and compression gain to recently reported adaptive clustering methods for lossless compression of high spectral resolution data. Given its superior compression performance, the AVQLP method is well suited to ground operation of high spectral resolution satellite data compression for rebroadcast and archiving purposes.
Abstract
Contemporary and future high spectral resolution sounders represent a significant technical advancement for environmental and meteorological prediction and monitoring. Given their large volume of spectral observations, the use of robust data compression techniques will be beneficial to data transmission and storage. In this paper, a novel adaptive vector quantization (VQ)-based linear prediction (AVQLP) method for lossless compression of high spectral resolution sounder data is proposed. The AVQLP method optimally adjusts the quantization codebook sizes to yield the maximum compression on prediction residuals and side information. The method outperforms the state-of-the-art compression methods [Joint Photographic Experts Group (JPEG)-LS, JPEG2000 Parts 1 and 2, Consultative Committee for Space Data Systems (CCSDS) Image Data Compression (IDC) 5/3, Context-Based Adaptive Lossless Image Coding (CALIC), and 3D Set Partitioning in Hierarchical Trees (SPIHT)] and achieves a new high in lossless compression for the standard test set of 10 NASA Atmospheric Infrared Sounder (AIRS) granules. It also compares favorably in terms of computational efficiency and compression gain to recently reported adaptive clustering methods for lossless compression of high spectral resolution data. Given its superior compression performance, the AVQLP method is well suited to ground operation of high spectral resolution satellite data compression for rebroadcast and archiving purposes.
Abstract
A theoretical analysis is performed to evaluate the accuracy and vertical resolution of atmospheric profiles obtained with the HIRS/2, GOES I/M, and HIS instruments. In addition, a linear simultaneous retrieval algorithm is used with aircraft observations to validate the theoretical predictions. Both theoretical and observational results clearly indicate that the accuracy and vertical resolution of the retrieval profile would be improved by high spectral resolution and broad spectral coverage of infrared radiance measurements.
The HIS is found to possess the equivalent of 11 pieces of temperature-and 9 pieces of water vapor-independent precise measurements. The characteristics for temperature include a vertical resolution of 1–6 km with an accuracy of 1 K and for water vapor a vertical resolution of 0.5–3.0 km with an accuracy of 3 K in dewpoint temperature. The HIS is a factor of 2–3 times better in vertical resolution and a factor of 2 times better in accuracy than the GOES 1/M and HIRS/2 filter radiometers.
Abstract
A theoretical analysis is performed to evaluate the accuracy and vertical resolution of atmospheric profiles obtained with the HIRS/2, GOES I/M, and HIS instruments. In addition, a linear simultaneous retrieval algorithm is used with aircraft observations to validate the theoretical predictions. Both theoretical and observational results clearly indicate that the accuracy and vertical resolution of the retrieval profile would be improved by high spectral resolution and broad spectral coverage of infrared radiance measurements.
The HIS is found to possess the equivalent of 11 pieces of temperature-and 9 pieces of water vapor-independent precise measurements. The characteristics for temperature include a vertical resolution of 1–6 km with an accuracy of 1 K and for water vapor a vertical resolution of 0.5–3.0 km with an accuracy of 3 K in dewpoint temperature. The HIS is a factor of 2–3 times better in vertical resolution and a factor of 2 times better in accuracy than the GOES 1/M and HIRS/2 filter radiometers.
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
A global database of infrared (IR) land surface emissivity is introduced to support more accurate retrievals of atmospheric properties such as temperature and moisture profiles from multispectral satellite radiance measurements. Emissivity is derived using input from the Moderate Resolution Imaging Spectroradiometer (MODIS) operational land surface emissivity product (MOD11). The baseline fit method, based on a conceptual model developed from laboratory measurements of surface emissivity, is applied to fill in the spectral gaps between the six emissivity wavelengths available in MOD11. The six available MOD11 wavelengths span only three spectral regions (3.8–4, 8.6, and 11–12 μm), while the retrievals of atmospheric temperature and moisture from satellite IR sounder radiances require surface emissivity at higher spectral resolution. Emissivity in the database presented here is available globally at 10 wavelengths (3.6, 4.3, 5.0, 5.8, 7.6, 8.3, 9.3, 10.8, 12.1, and 14.3 μm) with 0.05° spatial resolution. The wavelengths in the database were chosen as hinge points to capture as much of the shape of the higher-resolution emissivity spectra as possible between 3.6 and 14.3 μm. The surface emissivity from this database is applied to the IR regression retrieval of atmospheric moisture profiles using radiances from MODIS, and improvement is shown over retrievals made with the typical assumption of constant emissivity.
Abstract
A global database of infrared (IR) land surface emissivity is introduced to support more accurate retrievals of atmospheric properties such as temperature and moisture profiles from multispectral satellite radiance measurements. Emissivity is derived using input from the Moderate Resolution Imaging Spectroradiometer (MODIS) operational land surface emissivity product (MOD11). The baseline fit method, based on a conceptual model developed from laboratory measurements of surface emissivity, is applied to fill in the spectral gaps between the six emissivity wavelengths available in MOD11. The six available MOD11 wavelengths span only three spectral regions (3.8–4, 8.6, and 11–12 μm), while the retrievals of atmospheric temperature and moisture from satellite IR sounder radiances require surface emissivity at higher spectral resolution. Emissivity in the database presented here is available globally at 10 wavelengths (3.6, 4.3, 5.0, 5.8, 7.6, 8.3, 9.3, 10.8, 12.1, and 14.3 μm) with 0.05° spatial resolution. The wavelengths in the database were chosen as hinge points to capture as much of the shape of the higher-resolution emissivity spectra as possible between 3.6 and 14.3 μm. The surface emissivity from this database is applied to the IR regression retrieval of atmospheric moisture profiles using radiances from MODIS, and improvement is shown over retrievals made with the typical assumption of constant emissivity.
Abstract
In this study, the accuracy of a simulated infrared brightness temperature dataset derived from a unique large-scale, high-resolution Weather Research and Forecasting (WRF) Model simulation is evaluated through a comparison with Spinning Enhanced Visible and Infrared Imager (SEVIRI) observations. Overall, the analysis revealed that the simulated brightness temperatures realistically depict many of the observed features, although several large discrepancies were also identified. The similar shapes of the simulated and observed probability distributions calculated for each infrared band indicate that the model simulation realistically depicted the cloud morphology and relative proportion of clear and cloudy pixels. A traditional error analysis showed that the largest model errors occurred over central Africa because of a general mismatch in the locations of deep tropical convection and intervening regions of clear skies and low-level cloud cover. A detailed inspection of instantaneous brightness temperature difference (BTD) imagery showed that the modeling system realistically depicted the radiative properties associated with various cloud types. For instance, thin cirrus clouds along the edges of deep tropical convection and within midlatitude cloud shields were characterized by much larger 10.8 − 12.0-μm BTD than optically thicker clouds. Simulated ice clouds were effectively discriminated from liquid clouds and clear pixels by the close relationship between positive 8.7 − 10.8-μm BTD and the coldest 10.8-μm brightness temperatures. Comparison of the simulated and observed BTD probability distributions revealed that the liquid and mixed-phase cloud-top properties were consistent with the observations, whereas the narrower BTD distributions for the colder 10.8-μm brightness temperatures indicated that the microphysics scheme was unable to simulate the full dynamic range of ice clouds.
Abstract
In this study, the accuracy of a simulated infrared brightness temperature dataset derived from a unique large-scale, high-resolution Weather Research and Forecasting (WRF) Model simulation is evaluated through a comparison with Spinning Enhanced Visible and Infrared Imager (SEVIRI) observations. Overall, the analysis revealed that the simulated brightness temperatures realistically depict many of the observed features, although several large discrepancies were also identified. The similar shapes of the simulated and observed probability distributions calculated for each infrared band indicate that the model simulation realistically depicted the cloud morphology and relative proportion of clear and cloudy pixels. A traditional error analysis showed that the largest model errors occurred over central Africa because of a general mismatch in the locations of deep tropical convection and intervening regions of clear skies and low-level cloud cover. A detailed inspection of instantaneous brightness temperature difference (BTD) imagery showed that the modeling system realistically depicted the radiative properties associated with various cloud types. For instance, thin cirrus clouds along the edges of deep tropical convection and within midlatitude cloud shields were characterized by much larger 10.8 − 12.0-μm BTD than optically thicker clouds. Simulated ice clouds were effectively discriminated from liquid clouds and clear pixels by the close relationship between positive 8.7 − 10.8-μm BTD and the coldest 10.8-μm brightness temperatures. Comparison of the simulated and observed BTD probability distributions revealed that the liquid and mixed-phase cloud-top properties were consistent with the observations, whereas the narrower BTD distributions for the colder 10.8-μm brightness temperatures indicated that the microphysics scheme was unable to simulate the full dynamic range of ice clouds.
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
This paper describes the theory and application of the minimum local emissivity variance (MLEV) technique for simultaneous retrieval of cloud pressure level and effective spectral emissivity from high-spectral-resolution radiances, for the case of single-layer clouds. This technique, which has become feasible only with the recent development of high-spectral-resolution satellite and airborne instruments, is shown to provide reliable cloud spectral emissivity and pressure level under a wide range of atmospheric conditions. The MLEV algorithm uses a physical approach in which the local variances of spectral cloud emissivity are calculated for a number of assumed or first-guess cloud pressure levels. The optimal solution for the single-layer cloud emissivity spectrum is that having the “minimum local emissivity variance” among the retrieved emissivity spectra associated with different first-guess cloud pressure levels. This is due to the fact that the absorption, reflection, and scattering processes of clouds exhibit relatively limited localized spectral emissivity structure in the infrared 10–15-μm longwave region. In this simulation study it is shown that the MLEV cloud pressure root-mean-square errors for a single level with effective cloud emissivity greater than 0.1 are ∼30, ∼10, and ∼50 hPa, for high (200– 300 hPa), middle (500 hPa), and low (850 hPa) clouds, respectively. The associated cloud emissivity root-mean-square errors in the 900 cm−1 spectral channel are less than 0.05, 0.04, and 0.25 for high, middle, and low clouds, respectively.
Abstract
This paper describes the theory and application of the minimum local emissivity variance (MLEV) technique for simultaneous retrieval of cloud pressure level and effective spectral emissivity from high-spectral-resolution radiances, for the case of single-layer clouds. This technique, which has become feasible only with the recent development of high-spectral-resolution satellite and airborne instruments, is shown to provide reliable cloud spectral emissivity and pressure level under a wide range of atmospheric conditions. The MLEV algorithm uses a physical approach in which the local variances of spectral cloud emissivity are calculated for a number of assumed or first-guess cloud pressure levels. The optimal solution for the single-layer cloud emissivity spectrum is that having the “minimum local emissivity variance” among the retrieved emissivity spectra associated with different first-guess cloud pressure levels. This is due to the fact that the absorption, reflection, and scattering processes of clouds exhibit relatively limited localized spectral emissivity structure in the infrared 10–15-μm longwave region. In this simulation study it is shown that the MLEV cloud pressure root-mean-square errors for a single level with effective cloud emissivity greater than 0.1 are ∼30, ∼10, and ∼50 hPa, for high (200– 300 hPa), middle (500 hPa), and low (850 hPa) clouds, respectively. The associated cloud emissivity root-mean-square errors in the 900 cm−1 spectral channel are less than 0.05, 0.04, and 0.25 for high, middle, and low clouds, respectively.
Abstract
Bullet rosette particles are common in ice clouds, and the bullets may often be hollow. Here the single-scattering properties of randomly oriented hollow bullet rosette ice particles are investigated. A bullet, which is an individual branch of a rosette, is defined as a hexagonal column attached to a hexagonal pyramidal tip. For this study, a hollow structure is included at the end of the columnar part of each bullet branch and the shape of the hollow structure is defined as a hexagonal pyramid. A hollow bullet rosette may have between 2 and 12 branches. An improved geometric optics method is used to solve for the scattering of light in the particle. The primary optical effect of incorporating a hollow end in each of the bullets is to decrease the magnitude of backscattering. In terms of the angular distribution of scattered energy, the hollow bullets increase the scattering phase function values within the forward scattering angle region from 1° to 20° but decrease the phase function values at side- and backscattering angles of 60°–180°. As a result, the presence of hollow bullets tends to increase the asymmetry factor. In addition to the scattering phase function, the other elements of the phase matrix are also discussed. The backscattering depolarization ratios for hollow and solid bullet rosettes are found to be very different. This may have an implication for active remote sensing of ice clouds, such as from polarimetric lidar measurements. In a comparison of solid and hollow bullet rosettes, the effect of the differences on the retrieval of both the ice cloud effective particle size and optical thickness is also discussed. It is found that the presence of hollow bullet rosettes acts to decrease the inferred effective particle size and to increase the optical thickness in comparison with the use of solid bullet rosettes.
Abstract
Bullet rosette particles are common in ice clouds, and the bullets may often be hollow. Here the single-scattering properties of randomly oriented hollow bullet rosette ice particles are investigated. A bullet, which is an individual branch of a rosette, is defined as a hexagonal column attached to a hexagonal pyramidal tip. For this study, a hollow structure is included at the end of the columnar part of each bullet branch and the shape of the hollow structure is defined as a hexagonal pyramid. A hollow bullet rosette may have between 2 and 12 branches. An improved geometric optics method is used to solve for the scattering of light in the particle. The primary optical effect of incorporating a hollow end in each of the bullets is to decrease the magnitude of backscattering. In terms of the angular distribution of scattered energy, the hollow bullets increase the scattering phase function values within the forward scattering angle region from 1° to 20° but decrease the phase function values at side- and backscattering angles of 60°–180°. As a result, the presence of hollow bullets tends to increase the asymmetry factor. In addition to the scattering phase function, the other elements of the phase matrix are also discussed. The backscattering depolarization ratios for hollow and solid bullet rosettes are found to be very different. This may have an implication for active remote sensing of ice clouds, such as from polarimetric lidar measurements. In a comparison of solid and hollow bullet rosettes, the effect of the differences on the retrieval of both the ice cloud effective particle size and optical thickness is also discussed. It is found that the presence of hollow bullet rosettes acts to decrease the inferred effective particle size and to increase the optical thickness in comparison with the use of solid bullet rosettes.
