Model Calculations and Interferometer Measurements of Ice-Cloud Characteristics

2000 ◽  
Vol 39 (5) ◽  
pp. 634-644 ◽  
Author(s):  
Sunggi Chung ◽  
Steven Ackerman ◽  
Paul F. van Delst ◽  
W. Paul Menzel
Keyword(s):  
Radiative Transfer ◽  
Spectral Resolution ◽  
Gas Absorption ◽  
High Spectral Resolution ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Model Calculations ◽  
Ice Cloud ◽  
Macrophysical Properties ◽  
Ice Water

Abstract This paper investigates the relationship between high–spectral resolution infrared (IR) radiances and the microphysical and macrophysical properties of cirrus clouds. Through use of radiosonde measurements of the atmospheric state at the Department of Energy’s Atmospheric Radiation Measurement Program site, high–spectral resolution IR radiances are calculated by combining trace gas absorption optical depths from a line-by-line radiative transfer model with the discrete ordinate radiative transfer (DISORT) method. The sensitivity of the high–spectral resolution IR radiances to particle size, ice-water path, cloud-top location, cloud thickness, and multilayered cloud conditions is estimated in a multitude of calculations. DISORT calculations and interferometer measurements of cirrus ice cloud between 700 and 1300 cm−1 are compared for three different situations. The measurements were made with the High–Spectral Resolution Interferometer Sounder mounted on a National Aeronautics and Space Administration ER-2 aircraft flying at 20-km altitude during the Subsonic Aircraft Contrail and Cloud Effects Special Study (SUCCESS).

scholarly journals Retrieval of Ice Cloud Properties from AIRS and MODIS Observations Based on a Fast High-Spectral-Resolution Radiative Transfer Model

2013 ◽  
Vol 52 (3) ◽  
pp. 710-726 ◽  
Author(s):  
Chenxi Wang ◽  
Ping Yang ◽  
Steven Platnick ◽  
Andrew K. Heidinger ◽  
Bryan A. Baum ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Spectral Resolution ◽  
Spectral Response ◽  
Thermal Infrared ◽  
High Spectral Resolution ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Retrieval Method ◽  
Ice Cloud ◽  
Cloud Properties

AbstractA computationally efficient high-spectral-resolution cloudy-sky radiative transfer model (HRTM) in the thermal infrared region (700–1300 cm−1, 0.1 cm−1 spectral resolution) is advanced for simulating the upwelling radiance at the top of atmosphere and for retrieving cloud properties. A precomputed transmittance database is generated for simulating the absorption contributed by up to seven major atmospheric absorptive gases (H2O, CO2, O3, O2, CH4, CO, and N2O) by using a rigorous line-by-line radiative transfer model (LBLRTM). Both the line absorption of individual gases and continuum absorption are included in the database. A high-spectral-resolution ice particle bulk scattering properties database is employed to simulate the radiation transfer within a vertically nonisothermal ice cloud layer. Inherent to HRTM are sensor spectral response functions that couple with high-spectral-resolution measurements in the thermal infrared regions from instruments such as the Atmospheric Infrared Sounder (AIRS) and Infrared Atmospheric Sounding Interferometer. When compared with the LBLRTM and the discrete ordinates radiative transfer model (DISORT), the root-mean-square error of HRTM-simulated single-layer cloud brightness temperatures in the thermal infrared window region is generally smaller than 0.2 K. An ice cloud optical property retrieval scheme is developed using collocated AIRS and Moderate Resolution Imaging Spectroradiometer (MODIS) data. A retrieval method is proposed to take advantage of the high-spectral-resolution instrument. On the basis of the forward model and retrieval method, a case study is presented for the simultaneous retrieval of ice cloud optical thickness τ and effective particle size Deff that includes a cloud-top-altitude self-adjustment approach to improve consistency with simulations.

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).

scholarly journals Development of a GOES-R Advanced Baseline Imager Solar Channel Radiance Simulator for Ice Clouds

2013 ◽  
Vol 52 (4) ◽  
pp. 872-888 ◽  
Author(s):  
Shouguo Ding ◽  
Ping Yang ◽  
Bryan A. Baum ◽  
Andrew Heidinger ◽  
Thomas Greenwald
Keyword(s):  
Radiative Transfer ◽  
Spectral Response ◽  
Three Dimensional ◽  
Gas Absorption ◽  
Radiative Transfer Model ◽  
Discrete Ordinates ◽  
Transfer Model ◽  
Water Vapor Absorption ◽  
Ice Clouds ◽  
Ice Cloud

AbstractThis paper describes the development of an ice cloud radiance simulator for the anticipated Geostationary Operational Environmental Satellite R (GOES-R) Advanced Baseline Imager (ABI) solar channels. The simulator is based on the discrete ordinates radiative transfer (DISORT) model. A set of correlated k-distribution (CKD) models is developed for the ABI solar channels to account for atmospheric trace gas absorption. The CKD models are based on the ABI spectral response functions and also consider when multiple gases have overlapping absorption. The related errors of the transmittance profile are estimated on the basis of the exact line-by-line results, and it is found that errors in transmittance are less than 0.2% for all but one of the ABI solar channels. The exception is for the 1.378-μm channel, centered near a strong water vapor absorption band, for which the errors are less than 2%. For ice clouds, the band-averaged bulk-scattering properties for each ABI [and corresponding Moderate Resolution Imaging Spectroradiometer (MODIS)] solar channel are derived using an updated single-scattering property database of both smooth and severely roughened ice particles, which include droxtals, hexagonal plates, hexagonal hollow and solid columns, three-dimensional hollow and solid bullet rosettes, and several types of aggregates. The comparison shows close agreement between the radiance simulator and the benchmark model, the line-by-line radiative transfer model (LBLRTM)+DISORT model. The radiances of the ABI and corresponding MODIS measurements are compared. The results show that the radiance differences between the ABI and MODIS channels under ice cloud conditions are likely due to the different band-averaged imaginary indices of refraction.

scholarly journals A synthetic data set of high-spectral resolution infrared spectra for the Arctic atmosphere

2016 ◽  
Author(s):  
C. J. Cox ◽  
P. M. Rowe ◽  
S. P. Neshyba ◽  
V. P. Walden
Keyword(s):  
Remote Sensing ◽  
Radiative Transfer ◽  
Infrared Spectra ◽  
Spectral Resolution ◽  
High Spectral Resolution ◽  
The Arctic ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Data Set ◽  
Atmospheric State

Abstract. Retrievals of cloud microphysical and macrophysical properties from ground-based and satellite-based infrared remote sensing instruments are critical for understanding clouds. However, retrieval uncertainties are difficult to quantify without a standard for comparison. This is particularly true over the polar regions where surface-based data for a cloud climatology are sparse, yet clouds represent a major source of uncertainty in weather and climate models. We describe a synthetic high-spectral resolution infrared data set that is designed to facilitate validation and development of cloud retrieval algorithms for surface- and satellite-based remote sensing instruments. Since the data set is calculated using pre-defined cloudy atmospheres, the properties of the cloud and atmospheric state are known a priori. The atmospheric state used for the simulations is drawn from radiosonde measurements made at the North Slope of Alaska (NSA) Atmospheric Radiation Measurement (ARM) site at Barrow, Alaska (71.325° N, 156.615° W), a location that is generally representative of the western Arctic. The cloud properties for each simulation are selected from statistical distributions derived from past field measurements. Upwelling (at 60 km) and downwelling (at the surface) infrared spectra are simulated for 222 cloudy cases from 50–3000 cm−1 (3.3 to 200 μm) at monochromatic (line-by-line) resolution at a spacing of ~ 0.01 cm−1 using the Line-by-line Radiative Transfer Model (LBLRTM) and the discrete-ordinate-method radiative transfer code (DISORT). These spectra are freely available for interested researchers from the ACADIS data repository (doi:10.5065/D61J97TT).

scholarly journals Measuring Stratospheric H2O with an Airborne Spectrometer

2014 ◽  
Vol 31 (7) ◽  
pp. 1502-1515 ◽  
Author(s):  
Maziar Bani Shahabadi ◽  
Yi Huang
Keyword(s):  
Water Vapor ◽  
Radiative Transfer ◽  
Spectral Resolution ◽  
High Sensitivity ◽  
Far Infrared ◽  
High Spectral Resolution ◽  
Radiative Transfer Model ◽  
Small Scale ◽  
Transfer Model ◽  
Infrared Spectral

Abstract This study examines the ability of an infrared spectral sensor flying at the tropopause level for retrieving stratospheric H2O. Synthetic downwelling radiance spectra simulated by the line-by-line radiative transfer model are used for this examination. The potential of high-sensitivity water vapor retrieval is demonstrated by an ideal sensor with low detector noise, high spectral resolution, and full infrared coverage. A suite of hypothetical sensors with varying specifications is then examined to determine the technological requirements for a satisfactory retrieval. This study finds that including far infrared in the sensor’s spectral coverage is essential for achieving accurate H2O retrieval with an accuracy of 0.4 ppmv (1-sigma). The uncertainties in other gas species such as CH4, N2O, O3, and CO2 do not significantly affect the H2O retrieval. Such a hyperspectral instrument may afford an advantageous tool, especially for detecting small-scale lower-stratospheric moistening events.

scholarly journals A synthetic data set of high-spectral-resolution infrared spectra for the Arctic atmosphere

2016 ◽  
Vol 8 (1) ◽  
pp. 199-211 ◽  
Author(s):  
Christopher J. Cox ◽  
Penny M. Rowe ◽  
Steven P. Neshyba ◽  
Von P. Walden
Keyword(s):  
Remote Sensing ◽  
Radiative Transfer ◽  
Infrared Spectra ◽  
Spectral Resolution ◽  
High Spectral Resolution ◽  
The Arctic ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Data Set ◽  
Atmospheric State

Abstract. Cloud microphysical and macrophysical properties are critical for understanding the role of clouds in climate. These properties are commonly retrieved from ground-based and satellite-based infrared remote sensing instruments. However, retrieval uncertainties are difficult to quantify without a standard for comparison. This is particularly true over the polar regions, where surface-based data for a cloud climatology are sparse, yet clouds represent a major source of uncertainty in weather and climate models. We describe a synthetic high-spectral-resolution infrared data set that is designed to facilitate validation and development of cloud retrieval algorithms for surface- and satellite-based remote sensing instruments. Since the data set is calculated using pre-defined cloudy atmospheres, the properties of the cloud and atmospheric state are known a priori. The atmospheric state used for the simulations is drawn from radiosonde measurements made at the North Slope of Alaska (NSA) Atmospheric Radiation Measurement (ARM) site at Barrow, Alaska (71.325° N, 156.615° W), a location that is generally representative of the western Arctic. The cloud properties for each simulation are selected from statistical distributions derived from past field measurements. Upwelling (at 60 km) and downwelling (at the surface) infrared spectra are simulated for 260 cloudy cases from 50 to 3000 cm−1 (3.3 to 200 µm) at monochromatic (line-by-line) resolution at a spacing of  ∼  0.01 cm−1 using the Line-by-line Radiative Transfer Model (LBLRTM) and the discrete-ordinate-method radiative transfer code (DISORT). These spectra are freely available for interested researchers from the NSF Arctic Data Center data repository (doi:10.5065/D61J97TT).

scholarly journals A High-Spectral-Resolution Radiative Transfer Model for Simulating Multilayered Clouds and Aerosols in the Infrared Spectral Region

2015 ◽  
Vol 72 (2) ◽  
pp. 926-942 ◽  
Author(s):  
Chenxi Wang ◽  
Ping Yang ◽  
Xu Liu
Keyword(s):  
Radiative Transfer ◽  
Spectral Resolution ◽  
Present Model ◽  
Reference Model ◽  
Mineral Dust ◽  
High Spectral Resolution ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Multiple User ◽  
Infrared Measurements

Abstract A fast and flexible model is developed to simulate the transfer of thermal infrared radiation at wavenumbers from 700 to 1300 cm−1 with a spectral resolution of 0.1 cm−1 for scattering–absorbing atmospheres. In a single run and at multiple user-defined levels, the present model simulates radiances at different viewing angles and fluxes. Furthermore, the model takes into account complicated and realistic scenes in which ice cloud, water cloud, and mineral dust layers may coexist within an atmospheric column. The present model is compared to a rigorous reference model, the 32-stream Discrete Ordinate Radiative Transfer model (DISORT) code. For an atmosphere with three scattering layers (water, ice, and mineral dust), the root-mean-square error of the simulated brightness temperatures at the top of the atmosphere is approximately 0.05 K, and the relative flux errors at the boundary and internal levels are much smaller than 1%. Within the same computing environment, the fast model runs more than 10 000, 6000, and 4000 times faster than DISORT under single-layer, two-layer, and three-layer cloud–aerosol conditions, respectively. With its computational efficiency and accuracy, the present model may optimally facilitate the forward radiative transfer simulations involved in remote sensing implementations based on high-spectral-resolution and narrowband infrared measurements and in the data assimilation applications of the weather forecasting system. The selected 0.1-cm−1 spectral resolution is an obstacle to extending the present model to strongly absorptive bands (e.g., 600–700 cm−1). However, the present clear-sky module can be substituted by a more accurate model for specific applications involving spectral bands with strong absorption.

Impact of Ice Cloud Microphysics on Satellite Cloud Retrievals and Broadband Flux Radiative Transfer Model Calculations

2018 ◽  
Vol 31 (5) ◽  
pp. 1851-1864 ◽  
Author(s):  
Norman G. Loeb ◽  
Ping Yang ◽  
Fred G. Rose ◽  
Gang Hong ◽  
Sunny Sun-Mack ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Single Scattering ◽  
Cloud Fraction ◽  
Radiative Transfer Model ◽  
Particle Model ◽  
Transfer Model ◽  
Model Calculations ◽  
Ice Cloud ◽  
Hexagonal Ice ◽  
Thermodynamic Phase

Ice cloud particles exhibit a range of shapes and sizes affecting a cloud’s single-scattering properties. Because they cannot be inferred from passive visible/infrared imager measurements, assumptions about the bulk single-scattering properties of ice clouds are fundamental to satellite cloud retrievals and broadband radiative flux calculations. To examine the sensitivity to ice particle model assumptions, three sets of models are used in satellite imager retrievals of ice cloud fraction, thermodynamic phase, optical depth, effective height, and particle size, and in top-of-atmosphere (TOA) and surface broadband radiative flux calculations. The three ice particle models include smooth hexagonal ice columns (SMOOTH), roughened hexagonal ice columns, and a two-habit model (THM) comprising an ensemble of hexagonal columns and 20-element aggregates. While the choice of ice particle model has a negligible impact on daytime cloud fraction and thermodynamic phase, the global mean ice cloud optical depth retrieved from THM is smaller than from SMOOTH by 2.3 (28%), and the regional root-mean-square difference (RMSD) is 2.8 (32%). Effective radii derived from THM are 3.9 μm (16%) smaller than SMOOTH values and the RMSD is 5.2 μm (21%). In contrast, the regional RMSD in TOA and surface flux between THM and SMOOTH is only 1% in the shortwave and 0.3% in the longwave when a consistent ice particle model is assumed in the cloud property retrievals and forward radiative transfer model calculations. Consequently, radiative fluxes derived using a consistent ice particle model assumption throughout provide a more robust reference for climate model evaluation compared to ice cloud property retrievals.

scholarly journals A Spectral Data Compression (SDCOMP) Radiative Transfer Model for High-Spectral-Resolution Radiation Simulations

2020 ◽  
Vol 77 (6) ◽  
pp. 2055-2066 ◽  
Author(s):  
Chao Liu ◽  
Bin Yao ◽  
Vijay Natraj ◽  
Pushkar Kopparla ◽  
Fuzhong Weng ◽  
...  
Keyword(s):  
Optical Property ◽  
Radiative Transfer ◽  
Data Compression ◽  
Spectral Data ◽  
Spectral Resolution ◽  
General Circulation ◽  
General Circulation Models ◽  
High Spectral Resolution ◽  
Radiative Transfer Model ◽  
Transfer Model

Abstract With the increasing use of satellite and ground-based high-spectral-resolution (HSR) measurements for weather and climate applications, accurate and efficient radiative transfer (RT) models have become essential for accurate atmospheric retrievals, for instrument calibration, and to provide benchmark RT solutions. This study develops a spectral data compression (SDCOMP) RT model to simulate HSR radiances in both solar and infrared spectral regions. The SDCOMP approach “compresses” the spectral data in the optical property and radiance domains, utilizing principal component analysis (PCA) twice to alleviate the computational burden. First, an optical-property-based PCA is performed for a given atmospheric scenario (atmospheric, trace gas, and aerosol profiles) to simulate relatively low-spectral-resolution radiances at a small number of representative wavelengths. Second, by using precalculated principal components from an accurate radiance dataset computed for a large number of atmospheric scenarios, a radiance-based PCA is carried out to extend the low-spectral-resolution results to desired HSR results at all wavelengths. This procedure ensures both that individual monochromatic RT calculations are efficiently performed and that the number of such computations is optimized. SDCOMP is approximately three orders of magnitude faster than numerically exact RT calculations. The resulting monochromatic radiance has relative errors less than 0.2% in the solar region and brightness temperature differences less than 0.1 K for over 95% of the cases in the infrared region. The efficiency and accuracy of SDCOMP not only make it useful for analysis of HSR measurements, but also hint at the potential for utilizing this model to perform RT simulations in mesoscale numerical weather and general circulation models.

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