scholarly journals Ensemble Optimization Retrieval Algorithm of Hydrometeor Profiles for the Ice Cloud Imager Submillimeter‐Wave Radiometer

2018 ◽  
Vol 123 (9) ◽  
pp. 4594-4612 ◽  
Author(s):  
Yuli Liu ◽  
S. A. Buehler ◽  
M. Brath ◽  
Heguang Liu ◽  
Xiaolong Dong
Keyword(s):  
Submillimeter Wave ◽  
Retrieval Algorithm ◽  
Ice Cloud ◽  
Ensemble Optimization

scholarly journals Ice hydrometeor profile retrieval algorithm for high-frequency microwave radiometers: application to the CoSSIR instrument during TC4

2012 ◽  
Vol 5 (9) ◽  
pp. 2277-2306 ◽  
Author(s):  
K. F. Evans ◽  
J. R. Wang ◽  
D. O'C Starr ◽  
G. Heymsfield ◽  
L. Li ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Relative Humidity ◽  
A Priori ◽  
Submillimeter Wave ◽  
Monte Carlo Integration ◽  
Cumulative Distribution ◽  
Retrieval Algorithm ◽  
Hybrid Technique ◽  
Ice Cloud ◽  
Ice Water

Abstract. A Bayesian algorithm to retrieve profiles of cloud ice water content (IWC), ice particle size (Dme), and relative humidity from millimeter-wave/submillimeter-wave radiometers is presented. The first part of the algorithm prepares an a priori file with cumulative distribution functions (CDFs) and empirical orthogonal functions (EOFs) of profiles of temperature, relative humidity, three ice particle parameters (IWC, Dme, distribution width), and two liquid cloud parameters. The a priori CDFs and EOFs are derived from CloudSat radar reflectivity profiles and associated ECMWF temperature and relative humidity profiles combined with three cloud microphysical probability distributions obtained from in situ cloud probes. The second part of the algorithm uses the CDF/EOF file to perform a Bayesian retrieval with a hybrid technique that uses Monte Carlo integration (MCI) or, when too few MCI cases match the observations, uses optimization to maximize the posterior probability function. The very computationally intensive Markov chain Monte Carlo (MCMC) method also may be chosen as a solution method. The radiative transfer model assumes mixtures of several shapes of randomly oriented ice particles, and here random aggregates of spheres, dendrites, and hexagonal plates are used for tropical convection. A new physical model of stochastic dendritic snowflake aggregation is developed. The retrieval algorithm is applied to data from the Compact Scanning Submillimeter-wave Imaging Radiometer (CoSSIR) flown on the ER-2 aircraft during the Tropical Composition, Cloud and Climate Coupling (TC4) experiment in 2007. Example retrievals with error bars are shown for nadir profiles of IWC, Dme, and relative humidity, and nadir and conical scan swath retrievals of ice water path and average Dme. The ice cloud retrievals are evaluated by retrieving integrated 94 GHz backscattering from CoSSIR for comparison with the Cloud Radar System (CRS) flown on the same aircraft. The rms difference in integrated backscattering is around 3 dB over a 30 dB range. A comparison of CoSSIR retrieved and CRS measured reflectivity shows that CoSSIR has the ability to retrieve low-resolution ice cloud profiles in the upper troposphere.

scholarly journals Ice hydrometeor profile retrieval algorithm for high frequency microwave radiometers: application to the CoSSIR instrument during TC4

2012 ◽  
Vol 5 (2) ◽  
pp. 3117-3198 ◽  
Author(s):  
K. F. Evans ◽  
J. R. Wang ◽  
D. O'C Starr ◽  
G. Heymsfield ◽  
L. Li ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Relative Humidity ◽  
A Priori ◽  
Submillimeter Wave ◽  
Monte Carlo Integration ◽  
Cumulative Distribution ◽  
Retrieval Algorithm ◽  
Hybrid Technique ◽  
Ice Cloud ◽  
Ice Water

Abstract. A Bayesian algorithm to retrieve profiles of cloud ice water content (IWC), ice particle size (Dme), and relative humidity from millimeter-wave/submillimeter-wave radiometers is presented. The first part of the algorithm prepares an a priori file with cumulative distribution functions (CDFs) and empirical orthogonal functions (EOFs) of profiles of temperature, relative humidity, three ice particle parameters (IWC, Dme, distribution width), and two liquid cloud parameters. The a priori CDFs and EOFs are derived from CloudSat radar reflectivity profiles and associated ECMWF temperature and relative humidity profiles combined with three cloud microphysical probability distributions obtained from in situ cloud probes. The second part of the algorithm uses the CDF/EOF file to perform a Bayesian retrieval with a hybrid technique that uses Monte Carlo integration (MCI) or, when too few MCI cases match the observations, uses optimization to maximize the posterior probability function. The very computationally intensive Markov chain Monte Carlo (MCMC) method also may be chosen as a solution method. The radiative transfer model assumes mixtures of several shapes of randomly oriented ice particles, and here random aggregates of hexagonal plates, spheres, and dendrites are used for tropical convection. A new physical model of stochastic dendritic snowflake aggregation is developed. The retrieval algorithm is applied to data from the Compact Scanning Submillimeter-wave Imaging Radiometer (CoSSIR) flown on the ER-2 aircraft during the Tropical Composition, Cloud and Climate Coupling (TC4) experiment in 2007. Example retrievals with error bars are shown for nadir profiles of IWC, Dme, and relative humidity, and nadir and conical scan swath retrievals of ice water path and average Dme. The ice cloud retrievals are evaluated by retrieving integrated 94 GHz backscattering from CoSSIR for comparison with the Cloud Radar System (CRS) flown on the same aircraft. The rms difference in integrated backscattering is around 3 dB over a 30 dB range. A comparison of CoSSIR retrieved and CRS measured reflectivity shows that CoSSIR has the ability to retrieve low-resolution ice cloud profiles in the upper troposphere.

scholarly journals Potential of Hyperspectral Thermal Infrared Spaceborne Measurements to Retrieve Ice Cloud Physical Properties: Case Study of IASI and IASI-NG

2020 ◽  
Vol 13 (1) ◽  
pp. 116
Author(s):  
Lucie Leonarski ◽  
Laurent C.-Labonnote ◽  
Mathieu Compiègne ◽  
Jérôme Vidot ◽  
Anthony J. Baran ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Signal To Noise Ratio ◽  
Thermal Infrared ◽  
Radiative Transfer Model ◽  
Retrieval Algorithm ◽  
Transfer Model ◽  
Water Path ◽  
Ice Cloud ◽  
Ice Water Path ◽  
Ice Water

The present study aims to quantify the potential of hyperspectral thermal infrared sounders such as the Infrared Atmospheric Sounding Interferometer (IASI) and the future IASI next generation (IASI-NG) for retrieving the ice cloud layer altitude and thickness together with the ice water path. We employed the radiative transfer model Radiative Transfer for TOVS (RTTOV) to simulate cloudy radiances using parameterized ice cloud optical properties. The radiances have been computed from an ice cloud profile database coming from global operational short-range forecasts at the European Center for Medium-range Weather Forecasts (ECMWF) which encloses the normal conditions, typical variability, and extremes of the atmospheric properties over one year (Eresmaa and McNally (2014)). We performed an information content analysis based on Shannon’s formalism to determine the amount and spectral distribution of the information about ice cloud properties. Based on this analysis, a retrieval algorithm has been developed and tested on the profile database. We considered the signal-to-noise ratio of each specific instrument and the non-retrieved atmospheric and surface parameter errors. This study brings evidence that the observing system provides information on the ice water path (IWP) as well as on the layer altitude and thickness with a convergence rate up to 95% and expected errors that decrease with cloud opacity until the signal saturation is reached (satisfying retrievals are achieved for clouds whose IWP is between about 1 and 300 g/m2).

Ice Cloud Retrievals and Analysis with the Compact Scanning Submillimeter Imaging Radiometer and the Cloud Radar System during CRYSTAL FACE

2005 ◽  
Vol 44 (6) ◽  
pp. 839-859 ◽  
Author(s):  
K. Franklin Evans ◽  
James R. Wang ◽  
Paul E. Racette ◽  
Gerald Heymsfield ◽  
Lihua Li
Keyword(s):  
Particle Diameter ◽  
Submillimeter Wave ◽  
Radar System ◽  
Regional Study ◽  
Bayesian Algorithm ◽  
Ice Clouds ◽  
Ice Cloud ◽  
Cloud Radar ◽  
Brightness Temperatures ◽  
Ice Water

Abstract Submillimeter-wave radiometry is a new technique for determining ice water path (IWP) and particle size in upper-tropospheric ice clouds. The first brightness temperatures images of ice clouds above 340 GHz were measured by the Compact Scanning Submillimeter Imaging Radiometer (CoSSIR) during the Cirrus Regional Study of Tropical Anvils and Cirrus Layers (CRYSTAL) Florida Area Cirrus Experiment (FACE) campaign in July 2002. CoSSIR operated with 12 channels from receivers at 183, 220, 380, 487, and 640 GHz. CoSSIR and the nadir-viewing 94-GHz Cloud Radar System (CRS) flew on the NASA ER-2 airplane based out of Key West, Florida. A qualitative comparison of the CoSSIR brightness temperatures demonstrates that the submillimeter-wave frequencies are more sensitive to anvil ice cloud particles than are the lower frequencies. A Bayesian algorithm, with a priori microphysical information from in situ cloud probes, is used to retrieve the IWP and median mass equivalent sphere particle diameter (Dme). Microwave scattering properties of random aggregates of plates and aggregates of frozen droplets are computed with the discrete dipole approximation (DDA) and an effective medium approximation tuned to DDA results. As a test of the retrievals, the vertically integrated 94-GHz radar backscattering is also retrieved from the CoSSIR data and compared with that measured by the CRS. The integrated backscattering typically agrees within 1–2 dB for IWP from 1000 to 10 000 g m−2, and while the disagreement increases for smaller IWP, it is typically within the Bayesian error bars. Retrievals made with only the three 183- and one 220-GHz channel are generally as good or better than those including 380 ± 6.2 and 640 GHz, because the CoSSIR submillimeter-wave channels were much noisier than expected. An algorithm to retrieve profiles of ice water content and Dme from CRS and CoSSIR data was developed. This Bayesian algorithm also retrieves the coefficients of an IWC–radar reflectivity power-law relation and could be used to evaluate radar-only ice cloud retrieval algorithms.

scholarly journals Submillimeter Wave Instrument radiometry of the Jovian icy moons

2020 ◽  
Vol 644 ◽  
pp. A24
Author(s):  
Y. A. Ilyushin ◽  
P. Hartogh
Keyword(s):  
Single Scattering ◽  
Submillimeter Wave ◽  
Skin Depth ◽  
Single Scattering Albedo ◽  
Wave Parameter ◽  
Retrieval Algorithm ◽  
Physical Parameters ◽  
Model Parameters ◽  
Icy Moons ◽  
Wide Range

Context. We address the issue of remote sensing of the surfaces of Galilean icy moons. We investigate the prospects for retrieval of the physical parameters of the surface of the Jovian icy moons from submillimeter wave radiometry data. Aims. We show that the model parameters could not be completely retrieved from the polarized radiometry data, but some of their combinations can be effectively constrained. Methods. The polarized radiative transfer in lossy porous ice was numerically simulated. A Bayesian maximum likelihood retrieval algorithm was developed and tested on the simulated data in a wide range of variation of the model parameters. The uncertainty of the retrievals was evaluated with the Cramer-Rao bounds. We established the combinations of model parameters that can be effectively constrained from the measured data. Results. We reveal that the effective scatterer size can be reliably constrained for a range of values where the scattering asymmetry parameter uniquely depends on the wave parameter, and for relatively high values of the single scattering albedo, for which the scattering in the medium is significant. Similarly, the domains of reliable retrieval of the single scattering albedo and thermal skin depth are established.

scholarly journals First Odin sub-mm retrievals in the tropical upper troposphere: ice cloud properties

2006 ◽  
Vol 6 (5) ◽  
pp. 8681-8712
Author(s):  
P. Eriksson ◽  
M. Ekström ◽  
B. Rydberg ◽  
D. P. Murtagh
Keyword(s):  
Dynamic Range ◽  
Detection Threshold ◽  
High Dynamic Range ◽  
Retrieval Algorithm ◽  
Atmospheric Models ◽  
Ice Cloud ◽  
Cloud Properties ◽  
Retrieval Scheme ◽  
Cloud Ice ◽  
Further Development

Abstract. There exists today no established satellite technique for measuring the amount of ice in thicker clouds. Sub-mm radiometry is a promising technique for the task, and a retrieval scheme for the first such instrument in space, Odin-SMR, is presented. Several advantages of sub-mm observations are confirmed, such as low influence of particle shape and orientation, and a high dynamic range of the retrievals. In the case of Odin-SMR, cloud ice amounts above ~12.5 km can be determined. The presented retrieval scheme gives a detection threshold of ~4 g/m2 without saturation even for thickest observed clouds. The main retrieval uncertainty is the assumed particle size distribution. Initial results are found to be consistent with similar Aura MLS retrievals. It is then shown that important differences compared to atmospheric models exist. This first retrieval algorithm is limited to lowermost Odin-SMR tangent altitudes, and further development should improve the detection threshold and the vertical resolution.

scholarly journals A Comparison among Four Different Retrieval Methods for Ice-Cloud Properties Using Data from CloudSat, CALIPSO, and MODIS

2011 ◽  
Vol 50 (9) ◽  
pp. 1952-1969 ◽  
Author(s):  
Thorwald H. M. Stein ◽  
Julien Delanoë ◽  
Robin J. Hogan
Keyword(s):  
Water Content ◽  
Effective Radius ◽  
Retrieval Algorithm ◽  
Empirical Formulas ◽  
Mean Values ◽  
Ice Cloud ◽  
Microphysical Properties ◽  
Ice Water Content ◽  
The Mean ◽  
Ice Water

AbstractThe A-Train constellation of satellites provides a new capability to measure vertical cloud profiles that leads to more detailed information on ice-cloud microphysical properties than has been possible up to now. A variational radar–lidar ice-cloud retrieval algorithm (VarCloud) takes advantage of the complementary nature of the CloudSat radar and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) lidar to provide a seamless retrieval of ice water content, effective radius, and extinction coefficient from the thinnest cirrus (seen only by the lidar) to the thickest ice cloud (penetrated only by the radar). In this paper, several versions of the VarCloud retrieval are compared with the CloudSat standard ice-only retrieval of ice water content, two empirical formulas that derive ice water content from radar reflectivity and temperature, and retrievals of vertically integrated properties from the Moderate Resolution Imaging Spectroradiometer (MODIS) radiometer. The retrieved variables typically agree to within a factor of 2, on average, and most of the differences can be explained by the different microphysical assumptions. For example, the ice water content comparison illustrates the sensitivity of the retrievals to assumed ice particle shape. If ice particles are modeled as oblate spheroids rather than spheres for radar scattering then the retrieved ice water content is reduced by on average 50% in clouds with a reflectivity factor larger than 0 dBZ. VarCloud retrieves optical depths that are on average a factor-of-2 lower than those from MODIS, which can be explained by the different assumptions on particle mass and area; if VarCloud mimics the MODIS assumptions then better agreement is found in effective radius and optical depth is overestimated. MODIS predicts the mean vertically integrated ice water content to be around a factor-of-3 lower than that from VarCloud for the same retrievals, however, because the MODIS algorithm assumes that its retrieved effective radius (which is mostly representative of cloud top) is constant throughout the depth of the cloud. These comparisons highlight the need to refine microphysical assumptions in all retrieval algorithms and also for future studies to compare not only the mean values but also the full probability density function.

scholarly journals Towards an operational Ice Cloud Imager (ICI) retrieval product

2019 ◽  
Author(s):  
Patrick Eriksson ◽  
Bengt Rydberg ◽  
Vinia Mattioli ◽  
Anke Thoss ◽  
Christophe Accadia ◽  
...  
Keyword(s):  
Degrees Of Freedom ◽  
Monte Carlo Integration ◽  
Retrieval Algorithm ◽  
Posterior Probability Distribution ◽  
Ice Cloud ◽  
Ice Water Path ◽  
Measurement Series ◽  
Bayesian Monte Carlo ◽  
Gaussian Statistics ◽  
Ice Water

Abstract. The second generation of the EUMETSAT Polar System (EPS-SG) will include the Ice Cloud Imager (ICI), the first operational sensor covering sub-millimetre wavelengths. Three copies of ICI will be launched that together will give a measurement series exceeding 20 years. Due to the novelty of ICI, preparing the data processing is especially important and challenging. This paper focuses on activities related to the operational product planned, but also presents basic technical characteristics of the instrument. A retrieval algorithm based on Bayesian Monte Carlo integration has been developed. The main retrieval quantities are ice water path (IWP), mean mass height (Zm) and mean mass diameter (Dm). A novel part of the algorithm is to fully present the inversion as a description of the posterior probability distribution. This is to prefer for ICI as its retrieval errors not always follow Gaussian statistics. A state-of-the-art retrieval database is used to test the algorithm and to give an updated estimate of the retrieval performance. The degrees of freedom in measured radiances, and consequently the retrieval precision, vary with cloud situation. According to present simulations, IWP, Zm and Dm can be determined with 90 % confidence at best inside 50 %, 700 m and 50 um, respectively. The retrieval requires that the data from the thirteen channels of ICI are remapped to a common footprint. First estimates of the errors introduced by this remapping are also presented.

scholarly journals Towards an operational Ice Cloud Imager (ICI) retrieval product

2020 ◽  
Vol 13 (1) ◽  
pp. 53-71 ◽  
Author(s):  
Patrick Eriksson ◽  
Bengt Rydberg ◽  
Vinia Mattioli ◽  
Anke Thoss ◽  
Christophe Accadia ◽  
...  
Keyword(s):  
Degrees Of Freedom ◽  
Monte Carlo Integration ◽  
Retrieval Algorithm ◽  
Retrieval Performance ◽  
Posterior Probability Distribution ◽  
Ice Cloud ◽  
Ice Water Path ◽  
Bayesian Monte Carlo ◽  
Gaussian Statistics ◽  
Ice Water

Abstract. The second generation of the EUMETSAT Polar System (EPS-SG) will include the Ice Cloud Imager (ICI), the first operational sensor covering sub-millimetre wavelengths. Three copies of ICI will be launched that together will give a measurement time series exceeding 20 years. Due to the novelty of ICI, preparing the data processing is especially important and challenging. This paper focuses on activities related to the operational product planned, but also presents basic technical characteristics of the instrument. A retrieval algorithm based on Bayesian Monte Carlo integration has been developed. The main retrieval quantities are ice water path (IWP), mean mass height (Zm) and mean mass diameter (Dm). A novel part of the algorithm is that it fully presents the inversion as a description of the posterior probability distribution. This is preferred for ICI as its retrieval errors do not always follow Gaussian statistics. A state-of-the-art retrieval database is used to test the algorithm and to give an updated estimate of the retrieval performance. The degrees of freedom in measured radiances, and consequently the retrieval precision, vary with cloud situation. According to present simulations, IWP, Zm and Dm can be determined with 90 % confidence at best inside 50 %, 700 m and 50 µm, respectively. The retrieval requires that the data from the 13 channels of ICI are remapped to a common footprint. First estimates of the errors introduced by this remapping are also presented.

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