scholarly journals Spectral actinic flux in the lower troposphere: measurement and 1-D simulations for cloudless, broken cloud and overcast situations

2005 ◽  
Vol 5 (2) ◽  
pp. 1421-1467 ◽  
Author(s):  
A. Kylling ◽  
A. R. Webb ◽  
R. Kift ◽  
G. P. Gobbi ◽  
L. Ammannato ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Lower Troposphere ◽  
Cloud Fraction ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Model Uncertainties ◽  
East Anglia ◽  
Actinic Flux ◽  
Cloud Properties

Abstract. In September 2002 an extensive campaign to study the influence of clouds on the spectral actinic flux in the lower troposphere was carried out in East Anglia, England. Measurements of the actinic flux, the irradiance and aerosol and cloud properties were made from four ground stations and by aircraft. For cloudless conditions the measurements of the actinic flux were reproduced by a 1-D radiative transfer model within the measurement and model uncertainties of about ±5%. For overcast days 1-D radiative transfer calculations reproduce the overall behaviour of the actinic flux measured by the aircraft. Furthermore the actinic flux is increased by between 60–100% above the cloud when compared to a cloudless sky with the largest increase for the optically thickest cloud. Similarily the below cloud actinic flux is decreased by about 55–65%. Just below the cloud top the downwelling actinic flux has a maximum which is seen in both the measurements and the model results. For broken clouds the traditional cloud fraction approximation is not able to simultaneously reproduce the measured above cloud enhancement and below cloud reduction in the actinic flux.

scholarly journals Spectral actinic flux in the lower troposphere: measurement and 1-D simulations for cloudless, broken cloud and overcast situations

2005 ◽  
Vol 5 (7) ◽  
pp. 1975-1997 ◽  
Author(s):  
A. Kylling ◽  
A. R. Webb ◽  
R. Kift ◽  
G. P. Gobbi ◽  
L. Ammannato ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Lower Troposphere ◽  
Cloud Fraction ◽  
Model Uncertainties ◽  
Ancillary Data ◽  
East Anglia ◽  
Actinic Flux ◽  
Cloud Properties ◽  
Microphysical Property ◽  
Radiation Measurements

Abstract. In September 2002, the first INSPECTRO campaign to study the influence of clouds on the spectral actinic flux in the lower troposphere was carried out in East Anglia, England. Measurements of the actinic flux, the irradiance and aerosol and cloud properties were made from four ground stations and by aircraft. The radiation measurements were modelled using the uvspec model and ancillary data. For cloudless conditions, the measurements of the actinic flux were reproduced by 1-D radiative transfer modelling within the measurement and model uncertainties of about ±10%. For overcast days, the ground-based and aircraft radiation measurements and the cloud microphysical property measurements are consistent within the framework of 1-D radiative transfer and within experimental uncertainties. Furthermore, the actinic flux is increased by between 60-100% above the cloud when compared to a cloudless sky, with the largest increase for the optically thickest cloud. Correspondingly, the below cloud actinic flux is decreased by about 55-65%. Just below the cloud top, the downwelling actinic flux has a maximum that is seen in both the measurements and the model results. For broken clouds the traditional cloud fraction approximation is not able to simultaneously reproduce the measured above-cloud enhancement and below-cloud reduction in the actinic flux.

scholarly journals Modeling the radiative effects of desert dust on weather and regional climate

2013 ◽  
Vol 13 (11) ◽  
pp. 5489-5504 ◽  
Author(s):  
C. Spyrou ◽  
G. Kallos ◽  
C. Mitsakou ◽  
P. Athanasiadis ◽  
C. Kalogeri ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Regional Climate ◽  
Lower Troposphere ◽  
Integrated Approach ◽  
Dust Particles ◽  
Radiative Transfer Model ◽  
Limited Area ◽  
Transfer Model ◽  
Radiative Effects ◽  
Desert Dust

Abstract. Mineral dust aerosols exert a significant effect on both solar and terrestrial radiation. By absorbing and scattering, the solar radiation aerosols reduce the amount of energy reaching the surface. In addition, aerosols enhance the greenhouse effect by absorbing and emitting outgoing longwave radiation. Desert dust forcing exhibits large regional and temporal variability due to its short lifetime and diverse optical properties, further complicating the quantification of the direct radiative effect (DRE). The complexity of the links and feedbacks of dust on radiative transfer indicate the need for an integrated approach in order to examine these impacts. In order to examine these feedbacks, the SKIRON limited area model has been upgraded to include the RRTMG (Rapid Radiative Transfer Model – GCM) radiative transfer model that takes into consideration the aerosol radiative effects. It was run for a 6 year period. Two sets of simulations were performed, one without the effects of dust and the other including the radiative feedback. The results were first evaluated using aerosol optical depth data to examine the capabilities of the system in describing the desert dust cycle. Then the aerosol feedback on radiative transfer was quantified and the links between dust and radiation were studied. The study has revealed a strong interaction between dust particles and solar and terrestrial radiation, with several implications on the energy budget of the atmosphere. A profound effect is the increased absorption (in the shortwave and longwave) in the lower troposphere and the induced modification of the atmospheric temperature profile. These feedbacks depend strongly on the spatial distribution of dust and have more profound effects where the number of particles is greater, such as near their source.

scholarly journals Feasibility study of multi-pixel retrieval of optical thickness and droplet effective radius of inhomogeneous clouds using deep learning

2017 ◽  
Vol 10 (12) ◽  
pp. 4747-4759 ◽  
Author(s):  
Rintaro Okamura ◽  
Hironobu Iwabuchi ◽  
K. Sebastian Schmidt
Keyword(s):  
Deep Learning ◽  
Radiative Transfer ◽  
Optical Thickness ◽  
Cloud Droplet ◽  
Effective Radius ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Optical Remote Sensing ◽  
Transfer Effects ◽  
Cloud Properties

Abstract. Three-dimensional (3-D) radiative-transfer effects are a major source of retrieval errors in satellite-based optical remote sensing of clouds. The challenge is that 3-D effects manifest themselves across multiple satellite pixels, which traditional single-pixel approaches cannot capture. In this study, we present two multi-pixel retrieval approaches based on deep learning, a technique that is becoming increasingly successful for complex problems in engineering and other areas. Specifically, we use deep neural networks (DNNs) to obtain multi-pixel estimates of cloud optical thickness and column-mean cloud droplet effective radius from multispectral, multi-pixel radiances. The first DNN method corrects traditional bispectral retrievals based on the plane-parallel homogeneous cloud assumption using the reflectances at the same two wavelengths. The other DNN method uses so-called convolutional layers and retrieves cloud properties directly from the reflectances at four wavelengths. The DNN methods are trained and tested on cloud fields from large-eddy simulations used as input to a 3-D radiative-transfer model to simulate upward radiances. The second DNN-based retrieval, sidestepping the bispectral retrieval step through convolutional layers, is shown to be more accurate. It reduces 3-D radiative-transfer effects that would otherwise affect the radiance values and estimates cloud properties robustly even for optically thick clouds.

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 Assessment of cloud properties from the reanalysis with satellite observations over East Asia

2019 ◽  
Author(s):  
Bin Yao ◽  
Chao Liu ◽  
Yan Yin ◽  
Zhiquan Liu ◽  
Chunxiang Shi ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
East Asia ◽  
Deep Convection ◽  
Satellite Observation ◽  
Reanalysis Data ◽  
Satellite Observations ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
China Meteorological Administration ◽  
Cloud Properties

Abstract. Extensive observational and numerical investigations have been performed to better characterize cloud properties. However, due to the large variations of cloud spatiotemporal distributions and physical properties, quantitative depictions of clouds in different atmospheric reanalysis datasets are still highly uncertain, and cloud parameters in the models to produce those datasets remain largely unconstrained. A radiance-based evaluation approach is introduced and performed to assess the quality of cloud properties by directly comparing reanalysis-driven forward radiative transfer results with radiances from satellite observation. The newly developed China Meteorological Administration Reanalysis data (CRA), the ECMWF’s Fifth-generation Reanalysis (ERA5), and the Modern-Era Retrospective Analysis for Applications, Version 2 (MERRA-2) are considered in the present study. To avoid the unrealistic assumptions and uncertainties on satellite retrieval algorithms and products, the radiative transfer model (RTM) is used as a bridge to “translate” the reanalysis to corresponding satellite observations. The simulated reflectance and brightness temperatures (BTs) are directly compared with observations from the Advanced Himawari Imager (AHI) onboard the Himawari-8 satellite in the region from 80° E to 160° W between 60° N and 60° S, especially for results over East Asia. Comparisons of the reflectance in the solar and BTs in the infrared (IR) window channels reveal that CRA reanalysis better represents the total cloud cover than the other two reanalysis datasets. The simulated BTs for CRA and ERA5 are close to each other in many pixels, whereas the vertical distributions of cloud properties are significantly different, and ERA5 depicts a better deep convection structure than CRA reanalysis. Comparisons of the BT differences (BTDs) between the simulations and observations suggest that the water clouds are generally overestimated in ERA5 and MERRA-2, whereas the ice cloud is responsible for the overestimation over the center of cyclones in ERA5. Overall, the cloud from CRA, ERA5, and MERRA-2 show their own advantages in different aspects. The ERA5 reanalysis is found the most capability in representing the cloudy atmosphere over East Asia, and the results in CRA are close to those in ERA5.

AMSU-B Observations of Mixed-Phase Clouds over Land

2005 ◽  
Vol 44 (1) ◽  
pp. 72-85 ◽  
Author(s):  
M. N. Deeter ◽  
J. Vivekanandan
Keyword(s):  
Water Vapor ◽  
Radiative Transfer ◽  
Mixed Phase ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Radiative Effects ◽  
Liquid Water Path ◽  
Water Path ◽  
Cloud Properties ◽  
Mixed Phase Clouds

Abstract Measurements from passive microwave satellite instruments such as the Advanced Microwave Sounding Unit B (AMSU-B) are sensitive to both liquid and ice cloud particles. Radiative transfer modeling is exploited to simulate the response of the AMSU-B instrument to mixed-phase clouds over land. The plane-parallel radiative transfer model employed for the study accounts for scattering and absorption from cloud ice as well as absorption and emission from trace gases and cloud liquid. The radiative effects of mixed-phase clouds on AMSU-B window channels (i.e., 89 and 150 GHz) and water vapor line channels (i.e., 183 ± 1, 3, and 7 GHz) are studied. Sensitivities to noncloud parameters, including surface temperature, surface emissivity, and atmospheric temperature and water vapor profiles, are also quantified. Modeling results indicate that both cloud phases generally have significant radiative effects and that the 150- and 183 ± 7-GHz channels are typically the most sensitive channels to integrated cloud properties (i.e., liquid water path and ice water path). However, results also indicate that AMSU-B measurements alone are probably insufficient for retrieving all mixed-phase cloud properties of interest. These results are supported by comparisons of AMSU-B observations of a mixed-phase cloud over the Atmospheric Radiation Measurement (ARM) Program’s Southern Great Plains (SGP) site with corresponding calculated clear-sky values.

scholarly journals Retrieval of optical thickness and droplet effective radius of inhomogeneous clouds using deep learning

2017 ◽  
Author(s):  
Rintaro Okamura ◽  
Hironobu Iwabuchi ◽  
K. Sebastian Schmidt
Keyword(s):  
Deep Learning ◽  
Radiative Transfer ◽  
Optical Thickness ◽  
Three Dimensional ◽  
Cloud Droplet ◽  
Effective Radius ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Transfer Effects ◽  
Cloud Properties

Abstract. Three-dimensional (3D) radiative transfer effects are a major source of retrieval errors in satellite-based optical re- mote sensing of clouds. In this study, we present two retrieval methods based on deep learning. We use deep neural networks (DNNs) to retrieve multipixel estimates of cloud optical thickness and column-mean cloud droplet effective radius simultane- ously from multispectral, multipixel radiances. Cloud field data are obtained from large-eddy simulations, and a 3D radiative transfer model is employed to simulate upward radiances from clouds. The cloud and radiance data are used to train and test the DNNs. The proposed DNN-based retrieval is shown to be more accurate than the existing look-up table approach that assumes plane-parallel, homogeneous clouds. By using convolutional layers, the DNN method estimates cloud properties robustly, even for optically thick clouds, and can correct the 3D radiative transfer effects that would otherwise affect the radiance values.

scholarly journals An Algorithm for the Constraining of Radiative Transfer Calculations to CERES-Observed Broadband Top-of-Atmosphere Irradiance

2013 ◽  
Vol 30 (6) ◽  
pp. 1091-1106 ◽  
Author(s):  
Fred G. Rose ◽  
David A. Rutan ◽  
Thomas Charlock ◽  
G. Louis Smith ◽  
Seiji Kato
Keyword(s):  
Radiative Transfer ◽  
Radiant Energy ◽  
Tropical Rainfall Measuring Mission ◽  
Energy System ◽  
Atmospheric Temperature ◽  
Radiative Transfer Model ◽  
Bias Error ◽  
Transfer Model ◽  
Lagrangian Multipliers ◽  
Cloud Properties

Abstract NASA’s Clouds and the Earth’s Radiant Energy System (CERES) project is responsible for operation and data processing of observations from scanning radiometers on board the Tropical Rainfall Measuring Mission (TRMM), Terra, Aqua, and Suomi National Polar-Orbiting Partnership (NPP) satellites. The clouds and radiative swath (CRS) CERES data product contains irradiances computed using a radiative transfer model for nearly all CERES footprints in addition to top-of-atmosphere (TOA) irradiances derived from observed radiances by CERES instruments. This paper describes a method to constrain computed irradiances by CERES-derived TOA irradiances using Lagrangian multipliers. Radiative transfer model inputs include profiles of atmospheric temperature, humidity, aerosols and ozone, surface temperature and albedo, and up to two sets of cloud properties for a CERES footprint. Those inputs are adjusted depending on predefined uncertainties to match computed TOA and CERES-derived TOA irradiance. Because CERES instantaneous irradiances for an individual footprint also include uncertainties, primarily due to the conversion of radiance to irradiance using anisotropic directional models, the degree of the constraint depends on CERES-derived TOA irradiance as well. As a result of adjustment, TOA computed-minus-observed standard deviations are reduced from 8 to 4 W m−2 for longwave irradiance and from 15 to 6 W m−2 for shortwave irradiance. While agreement of computed TOA with CERES-derived irradiances improves, comparisons with surface observations show that model constrainment to the TOA does not reduce computation bias error at the surface. After constrainment, shortwave down at the surface has an increased bias (standard deviation) of 1% (0.5%) and longwave increases by 0.2% (0.1%). Clear-sky changes are negligible.

Shortwave Radiative Impacts from Aerosol Effects on Marine Shallow Cumuli

2008 ◽  
Vol 65 (6) ◽  
pp. 1979-1990 ◽  
Author(s):  
Paquita Zuidema ◽  
Huiwen Xue ◽  
Graham Feingold
Keyword(s):  
Radiative Transfer ◽  
Liquid Water ◽  
Three Dimensional ◽  
Cloud Fraction ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Liquid Water Path ◽  
Cloud Albedo ◽  
Aerosol Loading ◽  
Radiative Impact

Abstract The net shortwave radiative impact of aerosol on simulations of two shallow marine cloud cases is investigated using a Monte Carlo radiative transfer model. For a shallow cumulus case, increased aerosol concentrations are associated not only with smaller droplet sizes but also reduced cloud fractions and cloud dimensions, a result of evaporation-induced mixing and a lack of precipitation. Three-dimensional radiative transfer (3DRT) effects alter the fluxes by 10%–20% from values calculated using the independent column approximation for these simulations. The first (Twomey) aerosol indirect effect is dominant but the decreased cloud fraction reduces the magnitude of the shortwave cloud forcing substantially. The 3DRT effects slightly decrease the sensitivity of the cloud albedo to changes in droplet size under an overhead sun for the two ranges of cloud liquid water paths examined, but not strongly so. A popular two-stream radiative transfer approximation to the cloud susceptibility overestimates the more directly calculated values for the low liquid-water-path clouds within pristine aerosol conditions by a factor of 2 despite performing well otherwise, suggesting caution in its application to the cloud albedos within broken cloud fields. An evaluation of the influence of cloud susceptibility and cloud fraction changes to a “domain” area-weighted cloud susceptibility found that the domain cloud albedo is more likely to increase under aerosol loading at intermediate aerosol concentrations than under the most pristine conditions, contrary to traditional expectations. The second simulation (cumulus penetrating into stratus) is characterized by higher cloud fractions and more precipitation. This case has two regimes: a clean, precipitating regime where cloud fraction increases with increasing aerosol, and a more polluted regime where cloud fraction decreases with increasing aerosol. For this case the domain-mean cloud albedo increases steadily with aerosol loading under clean conditions, but increases only slightly after the cloud coverage decreases. Three-dimensional radiative transfer effects are mostly negligible for this case. Both sets of simulations suggest that aerosol-induced cloud fraction changes must be considered in tandem with the Twomey effect for clouds of small dimensions when assessing the net radiative impact, because both effects are drop size dependent and radiatively significant.

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