scholarly journals Assessing a Cloud Optical Depth Retrieval Algorithm with Model-Generated Data and the Frozen Turbulence Assumption

2004 ◽  
Vol 61 (23) ◽  
pp. 2951-2956 ◽  
Author(s):  
H. W. Barker ◽  
C. F. Pavloski ◽  
M. Ovtchinnikov ◽  
E. E. Clothiaux
Keyword(s):  
Time Series ◽  
Optical Depth ◽  
Retrieval Algorithm ◽  
Surface Site ◽  
Cloud Optical Depth ◽  
Radiation Data ◽  
Shallow Cumulus ◽  
Radiance Data ◽  
Radiation Measurements ◽  
Better Than

Abstract A cloud optical depth retrieval algorithm that utilizes time series of solar irradiance and zenith downwelling radiance data collected at a fixed surface site is assessed using model-generated cloud fields and simulated radiation measurements. To date, the retrieval algorithm has only been assessed using instantaneous cloud fields in which time series were mimicked via the frozen turbulence assumption. In this study, time series of radiation data are generated for use by the algorithm from a series of snapshots of an evolving and advecting cloud field, with values of optical depth retrieved for clouds occurring at the midpoint of the time series. This approach resembles conditions encountered in the field much better than those arising from the convenient frozen turbulence assumption. Values of optical depth are also retrieved for the same cloud field by employing the frozen turbulence approach. For the field of broken, shallow cumulus considered here, differences between the two sets of retrievals are small. This suggests that the encouraging results obtained thus far for this retrieval algorithm have not been secured falsely by the frozen turbulence assumption.

scholarly journals Application of oxygen A-band equivalent width to disambiguate downwelling radiances for cloud optical depth measurement

2016 ◽  
Vol 9 (9) ◽  
pp. 4167-4179 ◽  
Author(s):  
Edward R. Niple ◽  
Herman E. Scott ◽  
John A. Conant ◽  
Stephen H. Jones ◽  
Frank J. Iannarilli ◽  
...  
Keyword(s):  
Optical Depth ◽  
Near Infrared ◽  
Spectral Radiance ◽  
Retrieval Method ◽  
Cloud Optical Depth ◽  
Depth Measurement ◽  
The Us ◽  
Linear Fit ◽  
Radiation Measurements ◽  
Better Than

Abstract. This paper presents the three-waveband spectrally agile technique (TWST) for measuring cloud optical depth (COD). TWST is a portable field-proven sensor and retrieval method offering a unique combination of fast (1 Hz) cloud-resolving (0.5° field of view) real-time-reported COD measurements. It entails ground-based measurement of visible and near-infrared (VNIR) zenith spectral radiances much like the Aerosol Robotic Network (AERONET) cloud-mode sensors. What is novel in our approach is that we employ absorption in the oxygen A-band as a means of resolving the COD ambiguity inherent in using up-looking spectral radiances. We describe the TWST sensor and algorithm, and assess their merits by comparison to AERONET cloud-mode measurements collected during the US Department of Energy's Atmospheric Radiation Measurements (ARM) Two-Column Aerosol Project (TCAP). Spectral radiance agreement was better than 1 %, while a linear fit of COD yielded a slope of 0.905 (TWST reporting higher COD) and offset of −2.1.

scholarly journals Statistical Analyses of Satellite Cloud Object Data from CERES. Part III: Comparison with Cloud-Resolving Model Simulations of Tropical Convective Clouds

2007 ◽  
Vol 64 (3) ◽  
pp. 762-785 ◽  
Author(s):  
Yali Luo ◽  
Kuan-Man Xu ◽  
Bruce A. Wielicki ◽  
Takmeng Wong ◽  
Zachary A. Eitzen
Keyword(s):  
Physical Properties ◽  
Optical Depth ◽  
Weather Prediction ◽  
Cloud Microphysics ◽  
Retrieval Algorithm ◽  
Medium Size ◽  
Size Category ◽  
Cloud Optical Depth ◽  
Large Size ◽  
Cloud Resolving Model

Abstract The present study evaluates the ability of a cloud-resolving model (CRM) to simulate the physical properties of tropical deep convective cloud objects identified from a Clouds and the Earth’s Radiant Energy System (CERES) data product. The emphasis of this study is the comparisons among the small-, medium-, and large-size categories of cloud objects observed during March 1998 and between the large-size categories of cloud objects observed during March 1998 (strong El Niño) and March 2000 (weak La Niña). Results from the CRM simulations are analyzed in a way that is consistent with the CERES retrieval algorithm and they are averaged to match the scale of the CERES satellite footprints. Cloud physical properties are analyzed in terms of their summary histograms for each category. It is found that there is a general agreement in the overall shapes of all cloud physical properties between the simulated and observed distributions. Each cloud physical property produced by the CRM also exhibits different degrees of disagreement with observations over different ranges of the property. The simulated cloud tops are generally too high and cloud-top temperatures are too low except for the large-size category of March 1998. The probability densities of the simulated top-of-the-atmosphere (TOA) albedos for all four categories are underestimated for high albedos, while those of cloud optical depth are overestimated at its lowest bin. These disagreements are mainly related to uncertainties in the cloud microphysics parameterization and inputs such as cloud ice effective size to the radiation calculation. Summary histograms of cloud optical depth and TOA albedo from the CRM simulations of the large-size category of cloud objects do not differ significantly between the March 1998 and 2000 periods, consistent with the CERES observations. However, the CRM is unable to reproduce the significant differences in the observed cloud-top height while it overestimates the differences in the observed outgoing longwave radiation and cloud-top temperature between the two periods. Comparisons between the CRM results and the observations for most parameters in March 1998 consistently show that both the simulations and observations have larger differences between the large- and small-size categories than between the large- and medium-size, or between the medium- and small-size categories. However, the simulated cloud properties do not change as much with size as observed. These disagreements are likely related to the spatial averaging of the forcing data and the mismatch in time and space between the numerical weather prediction model from which the forcing data are produced and the CERES observed cloud systems.

scholarly journals Application of Oxygen A-band Equivalent Width for Cloud Optical Depth Measurement

2016 ◽  
Author(s):  
Edward R. Niple ◽  
Herman E. Scott ◽  
John A. Conant ◽  
Stephen H. Jones ◽  
Frank J. Iannarilli ◽  
...  
Keyword(s):  
Optical Depth ◽  
Equivalent Width ◽  
New Technique ◽  
Spectral Radiance ◽  
Cloud Optical Depth ◽  
Depth Measurement ◽  
Visible Band ◽  
A New Technique ◽  
Better Than

Abstract. This paper presents a new technique for measuring Cloud Optical Depth (COD). It is based on ground-based visible band zenith spectral radiances much like the AERONET Cloud-Mode sensors. What is novel in our approach is that we employ absorption in the oxygen A-band as a means of resolving the COD Ambiguity inherent in using up-looking spectral radiances. We describe the algorithm and a sensor that implements it, and compare its performance to AERONET Cloud-Mode measurements collected during the Two Column Aerosol Project (TCAP). Spectral radiance agreement was excellent (better than 1 %) while COD agreement was good.

scholarly journals Joint Cloud Water Path and Rain Water Path Retrievals from ORACLES Observations

2020 ◽  
Author(s):  
Andrew M. Dzambo ◽  
Tristan L'Ecuyer ◽  
Kenneth Sinclair ◽  
Bastiaan van Diedenhoven ◽  
Siddhant Gupta ◽  
...  
Keyword(s):  
Optical Depth ◽  
Liquid Water ◽  
Cloud Water ◽  
Retrieval Algorithm ◽  
Liquid Water Path ◽  
Cloud Optical Depth ◽  
Water Path ◽  
Surface Precipitation ◽  
Uncertainty Estimates ◽  
Cloud Water Path

Abstract. This study presents a new algorithm that combines W-band reflectivity measurements from the Airborne Precipitation Radar-3rd generation (APR-3), passive radiometric cloud optical depth and effective radius retrievals from the Research Scanning Polarimeter (RSP) to estimate total liquid water path in warm clouds and identify the contributions from cloud water path (CWP) and rainwater path (RWP). The resulting CWP estimates are primarily determined by the optical depth input, although reflectivity measurements contribute ~ 10–50 % of the uncertainty due to attenuation through the profile. Uncertainties in CWP estimates across all conditions are 25 % to 35 %, while RWP uncertainty estimates frequently exceed 100 %. Two thirds of all radar-detected clouds observed during the ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) campaign that took place from 2016–2018 over the southeast Atlantic Ocean have CWP between 41 and 168 g m−2 and almost all CWPs (99 %) between 6 to 445 g m−2. RWP, by contrast, typically makes up a much smaller fraction of total liquid water path (LWP) with more than 70 % of raining clouds having less than 10 g m−2 of rainwater. In heavier warm rain (i.e. rain rate exceeding 40 mm h−1 or 1000 mm d−1), however, RWP is observed to exceed 2500 g m−2. CWP (RWP) is found to be approximately 30 g m−2 (7 g m−2) larger in unstable environments compared to stable environments. Surface precipitation is also more than twice as likely in unstable environments. Comparisons against in-situ cloud microphysical probe data spanning the range of thermodynamic stability and meteorological conditions encountered across the southeast Atlantic basin demonstrate that the combined APR-3 and RSP dataset enable a robust joint cloud-precipitation retrieval algorithm to support future ORACLES precipitation susceptibility and cloud–aerosol–precipitation interaction studies.

scholarly journals Thin ice clouds in the Arctic: Cloud optical depth and particle size retrieved from ground-based thermal infrared radiometry

2016 ◽  
Author(s):  
Yann Blanchard ◽  
Alain Royer ◽  
Norman T. O'Neill ◽  
David D. Turner ◽  
Edwin W. Eloranta
Keyword(s):  
Optical Depth ◽  
Spectral Resolution ◽  
Particle Diameter ◽  
Thermal Infrared ◽  
High Spectral Resolution ◽  
The Arctic ◽  
Retrieval Algorithm ◽  
Ice Clouds ◽  
Cloud Optical Depth ◽  
Effective Particle

Abstract. Multi-band thermal measurements of zenith sky radiance, along with height profile information, were used in a retrieval algorithm, to estimate cloud optical depth and effective particle diameter of thin ice clouds in the Canadian high-Arctic. Ground-based thermal infrared (IR) radiances for 150 semi-transparent ice clouds cases were acquired at the Polar Environment Atmospheric Research Laboratory (PEARL) in Eureka, Nunavut, Canada (80° N, 86° W). We analyzed and quantified the sensitivity of thermal radiance to several cloud parameters including optical depth, effective particle diameter and shape, water vapor content, thickness and bottom altitude. A look up table retrieval method was used to successfully extract, through an optimal estimation method, cloud optical depth up to values of 2.6 and to separate thin ice clouds into two classes: 1) TIC1 clouds characterized by small crystals (effective particle diameter ≤ 30 μm), and 2) TIC2 clouds characterized by large ice crystals (effective particle diameter > 30 μm). The retrieval technique was validated using data from the Arctic High Spectral Resolution Lidar (AHSRL) and Millimeter Wave Cloud Radar (MMCR). Inversions were performed across three polar winters and results showed a significant correlation (R2 = 0.95) for cloud optical depth retrievals and an overall accuracy of 83 % for the classification of TIC1 and TIC2 clouds. A partial validation relative to an algorithm based on infrared radiance measurements at high spectral resolution between 8 and 21 μm was also carried out. It confirms the robustness of the optical depth retrieval and the fact that the radiometer retrieval was sensitive to small particle (TIC1) sizes.

scholarly journals The Potential for Improved Boundary Layer Cloud Optical Depth Retrievals from the Multiple Directions of MISR

2008 ◽  
Vol 65 (10) ◽  
pp. 3179-3196 ◽  
Author(s):  
K. Franklin Evans ◽  
Alexander Marshak ◽  
Tamás Várnai
Keyword(s):  
Boundary Layer ◽  
Standard Deviation ◽  
Optical Depth ◽  
Retrieval Algorithm ◽  
Depth Information ◽  
Cloud Optical Depth ◽  
Retrieval Error ◽  
The Mean ◽  
Boundary Layer Cloud ◽  
Layer Cloud

The Multiangle Imaging Spectroradiometer (MISR) views the earth with nine cameras, ranging from a 70° zenith angle viewing forward through nadir to 70° viewing aft. MISR does not have an operational cloud optical depth retrieval algorithm, but previous research has hinted that solar reflection measured in multiple directions might improve cloud optical depth retrievals. This study explores the optical depth information content of MISR’s multiple angles using a retrieval simulation approach. Hundreds of realistic boundary-layer cloud fields are generated with large-eddy simulation (LES) models for stratocumulus, small trade cumulus, and land surface–forced fair-weather cumulus. Reflectances in MISR directions are computed with three-dimensional radiative transfer from the LES cloud fields over an ocean surface and averaged to MISR resolution and sampled at MISR 275-m pixel spacing. Neural networks are trained to retrieve the mean and standard deviation of optical depth over different size pixel patches from the mean and standard deviation of simulated MISR reflectances. Various configurations of MISR cameras are input to the retrieval, and the rms retrieval errors are compared. For 5 × 5 pixel patches the already low mean optical depth retrieval error for stratocumulus decreases 41% and 23% (for 25° and 45° solar zenith angles, respectively) from using only the nadir camera to using seven MISR cameras. For cumulus, however, the much higher normalized optical depth retrieval error only decreases around 14%. These small improvements suggest that measurements of solar reflection in multiple directions do not contribute substantially to more accurate optical depth retrievals for small cumulus clouds. The 3D statistical retrievals, however, even with only the nadir camera, are much more accurate for small cumulus than standard nadir plane-parallel retrievals; therefore, this approach may be worth pursuing.

scholarly journals Joint cloud water path and rainwater path retrievals from airborne ORACLES observations

2021 ◽  
Vol 21 (7) ◽  
pp. 5513-5532
Author(s):  
Andrew M. Dzambo ◽  
Tristan L'Ecuyer ◽  
Kenneth Sinclair ◽  
Bastiaan van Diedenhoven ◽  
Siddhant Gupta ◽  
...  
Keyword(s):  
Optical Depth ◽  
Liquid Water ◽  
Cloud Water ◽  
Retrieval Algorithm ◽  
Liquid Water Path ◽  
Cloud Optical Depth ◽  
Water Path ◽  
Surface Precipitation ◽  
Uncertainty Estimates ◽  
Cloud Water Path

Abstract. This study presents a new algorithm that combines W-band reflectivity measurements from the Airborne Precipitation Radar – third generation (APR-3) passive radiometric cloud optical depth and effective radius retrievals from the Research Scanning Polarimeter (RSP) to estimate total liquid water path in warm clouds and identify the contributions from cloud water path (CWP) and rainwater path (RWP). The resulting CWP estimates are primarily determined by the optical depth input, although reflectivity measurements contribute ∼10 %–50 % of the uncertainty due to attenuation through the profile. Uncertainties in CWP estimates across all conditions are 25 % to 35 %, while RWP uncertainty estimates frequently exceed 100 %. Two-thirds of all radar-detected clouds observed during the ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) campaign that took place from 2016–2018 over the southeast Atlantic Ocean have CWP between 41 and 168 g m−2 and almost all CWPs (99 %) between 6 to 445 g m−2. RWP, by contrast, typically makes up a much smaller fraction of total liquid water path (LWP), with more than 70 % of raining clouds having less than 10 g m−2 of rainwater. In heavier warm rain (i.e., rain rate exceeding 40 mm h−1 or 1000 mm d−1), however, RWP is observed to exceed 2500 g m−2. CWP (RWP) is found to be approximately 30 g m−2 (7 g m−2) larger in unstable environments compared to stable environments. Surface precipitation is also more than twice as likely in unstable environments. Comparisons against in situ cloud microphysical probe data spanning the range of thermodynamic stability and meteorological conditions encountered across the southeast Atlantic basin demonstrate that the combined APR-3 and RSP dataset enable a robust joint cloud–precipitation retrieval algorithm to support future ORACLES precipitation susceptibility and cloud–aerosol–precipitation interaction studies.

scholarly journals Aerosol optical depth retrievals at the Izaña Atmospheric Observatory from 1941 to 2013 by using artificial neural networks

2016 ◽  
Vol 9 (1) ◽  
pp. 53-62 ◽  
Author(s):  
R. D. García ◽  
O. E. García ◽  
E. Cuevas ◽  
V. E. Cachorro ◽  
A. Barreto ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Mineral Dust ◽  
Dust Particles ◽  
Filter Radiometer ◽  
Artificial Neural

Abstract. This paper presents the reconstruction of a 73-year time series of the aerosol optical depth (AOD) at 500 nm at the subtropical high-mountain Izaña Atmospheric Observatory (IZO) located in Tenerife (Canary Islands, Spain). For this purpose, we have combined AOD estimates from artificial neural networks (ANNs) from 1941 to 2001 and AOD measurements directly obtained with a Precision Filter Radiometer (PFR) between 2003 and 2013. The analysis is limited to summer months (July–August–September), when the largest aerosol load is observed at IZO (Saharan mineral dust particles). The ANN AOD time series has been comprehensively validated against coincident AOD measurements performed with a solar spectrometer Mark-I (1984–2009) and AERONET (AErosol RObotic NETwork) CIMEL photometers (2004–2009) at IZO, obtaining a rather good agreement on a daily basis: Pearson coefficient, R, of 0.97 between AERONET and ANN AOD, and 0.93 between Mark-I and ANN AOD estimates. In addition, we have analysed the long-term consistency between ANN AOD time series and long-term meteorological records identifying Saharan mineral dust events at IZO (synoptical observations and local wind records). Both analyses provide consistent results, with correlations  >  85 %. Therefore, we can conclude that the reconstructed AOD time series captures well the AOD variations and dust-laden Saharan air mass outbreaks on short-term and long-term timescales and, thus, it is suitable to be used in climate analysis.

scholarly journals Sea Ice Concentration Products over Polar Regions with Chinese FY3C/MWRI Data

2021 ◽  
Vol 13 (11) ◽  
pp. 2174
Author(s):  
Lijian Shi ◽  
Sen Liu ◽  
Yingni Shi ◽  
Xue Ao ◽  
Bin Zou ◽  
...  
Keyword(s):  
Time Series ◽  
Sea Ice ◽  
Ocean Circulation ◽  
The Arctic ◽  
Retrieval Algorithm ◽  
Atmospheric Effects ◽  
Observation Data ◽  
Long Time Series ◽  
Ice Concentration ◽  
Long Time

Polar sea ice affects atmospheric and ocean circulation and plays an important role in global climate change. Long time series sea ice concentrations (SIC) are an important parameter for climate research. This study presents an SIC retrieval algorithm based on brightness temperature (Tb) data from the FY3C Microwave Radiation Imager (MWRI) over the polar region. With the Tb data of Special Sensor Microwave Imager/Sounder (SSMIS) as a reference, monthly calibration models were established based on time–space matching and linear regression. After calibration, the correlation between the Tb of F17/SSMIS and FY3C/MWRI at different channels was improved. Then, SIC products over the Arctic and Antarctic in 2016–2019 were retrieved with the NASA team (NT) method. Atmospheric effects were reduced using two weather filters and a sea ice mask. A minimum ice concentration array used in the procedure reduced the land-to-ocean spillover effect. Compared with the SIC product of National Snow and Ice Data Center (NSIDC), the average relative difference of sea ice extent of the Arctic and Antarctic was found to be acceptable, with values of −0.27 ± 1.85 and 0.53 ± 1.50, respectively. To decrease the SIC error with fixed tie points (FTPs), the SIC was retrieved by the NT method with dynamic tie points (DTPs) based on the original Tb of FY3C/MWRI. The different SIC products were evaluated with ship observation data, synthetic aperture radar (SAR) sea ice cover products, and the Round Robin Data Package (RRDP). In comparison with the ship observation data, the SIC bias of FY3C with DTP is 4% and is much better than that of FY3C with FTP (9%). Evaluation results with SAR SIC data and closed ice data from RRDP show a similar trend between FY3C SIC with FTPs and FY3C SIC with DTPs. Using DTPs to present the Tb seasonal change of different types of sea ice improved the SIC accuracy, especially for the sea ice melting season. This study lays a foundation for the release of long time series operational SIC products with Chinese FY3 series satellites.

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