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 Statistical Analyses of Satellite Cloud Object Data from CERES. Part II: Tropical Convective Cloud Objects during 1998 El Niño and Evidence for Supporting the Fixed Anvil Temperature Hypothesis

2007 ◽  
Vol 20 (5) ◽  
pp. 819-842 ◽  
Author(s):  
Kuan-Man Xu ◽  
Takmeng Wong ◽  
Bruce A. Wielicki ◽  
Lindsay Parker ◽  
Bing Lin ◽  
...  
Keyword(s):  
Physical Properties ◽  
Optical Depth ◽  
Convective Cloud ◽  
Tropical Pacific ◽  
Statistical Distributions ◽  
Size Category ◽  
High Cloud ◽  
Large Size ◽  
Precession Cycle ◽  
Macrophysical Properties

Abstract Characteristics of tropical deep convective cloud objects observed over the tropical Pacific during January–August 1998 are examined using the Tropical Rainfall Measuring Mission/Clouds and the Earth’s Radiant Energy System Single Scanner Footprint (SSF) data. These characteristics include the frequencies of occurrence and statistical distributions of cloud physical properties. Their variations with cloud object size, sea surface temperature (SST), and satellite precession cycle are analyzed in detail. A cloud object is defined as a contiguous patch of the earth composed of satellite footprints within a single dominant cloud-system type. It is found that statistical distributions of cloud physical properties are significantly different among three size categories of cloud objects with equivalent diameters of 100–150 (small), 150–300 (medium), and >300 km (large), except for the distributions of ice particle size. The distributions for the larger-size category of cloud objects are more skewed toward high SSTs, high cloud tops, low cloud-top temperature, large ice water path, high cloud optical depth, low outgoing longwave (LW) radiation, and high albedo than the smaller-size category. As SST varied from one satellite precession cycle to another, the changes in macrophysical properties of cloud objects over the entire tropical Pacific were small for the large-size category of cloud objects, relative to those of the small- and medium-size categories. This evidence supports the fixed anvil temperature hypothesis of Hartmann and Larson for the large-size category. Combined with the result that a higher percentage of the large-size category of cloud objects occurs during higher SST subperiods, this implies that macrophysical properties of cloud objects would be less sensitive to further warming of the climate. On the other hand, when cloud objects are classified according to SST ranges, statistical characteristics of cloud microphysical properties, optical depth, and albedo are not sensitive to the SST, but those of cloud macrophysical properties are dependent upon the SST. This result is related to larger differences in large-scale dynamics among the SST ranges than among the satellite precession cycles. Frequency distributions of vertical velocity from the European Centre for Medium-Range Weather Forecasts model that is matched to each cloud object are used to further understand some of the findings in this study.

AISI TYPE S2

Alloy Digest ◽  
1982 ◽  
Vol 31 (12) ◽  
Keyword(s):  
Physical Properties ◽  
Tensile Properties ◽  
Tool Steel ◽  
Shock Loading ◽  
Heat Treating ◽  
Medium Size ◽  
Hard Case ◽  
Large Size ◽  
Steel Mills ◽  
Aisi Type

Abstract AISI Type S2 is a water-hardening tool steel with extreme toughness and resistance to shock loading. Even at a hardness of Rockwell C 59-60, it will bend before it breaks. When hardened in medium-size and large-size pieces, it acquires a hard case and a tough core. Sizes under 3/4-inch (19mm) diameter will water harden to the center. The extreme toughness of Type S2 makes it suitable for use in many applications where no other tool steel will hold up. Its many uses include chisels, rivet busters, spike mauls, screw drivers, punches and sledges. This datasheet provides information on composition, physical properties, hardness, elasticity, and tensile properties. It also includes information on forming, heat treating, machining, and joining. Filing Code: TS-408. Producer or source: Tool steel mills.

Estimating Cloud Optical Depth from Surface Radiometric Observations: Sensitivity to Instrument Noise and Aerosol Contamination

2005 ◽  
Vol 62 (11) ◽  
pp. 4095-4104 ◽  
Author(s):  
Alain Beaulne ◽  
Howard W. Barker ◽  
Jean-Pierre Blanchet
Keyword(s):  
Optical Depth ◽  
Surface Albedo ◽  
Transport Model ◽  
Cloud Base ◽  
Spectral Difference ◽  
Cloud Optical Depth ◽  
Sensitivity Tests ◽  
Cloud Resolving Model ◽  
Model Cloud ◽  
Instrument Noise

Abstract The spectral-difference algorithm of Barker and Marshak for inferring optical depth τ of broken clouds has been shown numerically to be potentially useful. Their method estimates cloud-base reflectance and τ using spectral radiometric measurements made at the surface at two judiciously chosen wavelengths. Here it is subject to sensitivity tests that address the impacts of two ubiquitous sources of potential error: instrument noise and presence of aerosol. Experiments are conducted using a Monte Carlo photon transport model, cloud-resolving model data, and surface albedo data from satellite observations. The objective is to analyze the consistency between inherent and retrieved values of τ. Increasing instrument noise, especially if uncorrelated at both wavelengths, decreases retrieved cloud fraction and increases retrieved mean τ. As with all methods that seek to infer τ using passive radiometry, the presence of aerosol requires that threshold values be set in order to discriminate between cloudy and cloud-free columns. A technique for estimating thresholds for cloudy columns is discussed and demonstrated. Finally, it was found that surface type and mean inherent τ play major roles in defining retrieval accuracy.

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 An Estimate of Low-Cloud Feedbacks from Variations of Cloud Radiative and Physical Properties with Sea Surface Temperature on Interannual Time Scales

2011 ◽  
Vol 24 (4) ◽  
pp. 1106-1121 ◽  
Author(s):  
Zachary A. Eitzen ◽  
Kuan-Man Xu ◽  
Takmeng Wong
Keyword(s):  
Physical Properties ◽  
Optical Depth ◽  
Cloud Amount ◽  
Cloud Fraction ◽  
Radiative Properties ◽  
Radiative Effect ◽  
Cloud Optical Depth ◽  
Cloud Radiative Effect ◽  
Low Cloud ◽  
Sst Anomaly

Abstract Simulations of climate change have yet to reach a consensus on the sign and magnitude of the changes in physical properties of marine boundary layer clouds. In this study, the authors analyze how cloud and radiative properties vary with SST anomaly in low-cloud regions, based on five years (March 2000–February 2005) of Clouds and the Earth’s Radiant Energy System (CERES)–Terra monthly gridded data and matched European Centre for Medium-Range Weather Forecasts (ECMWF) meteorological reanalaysis data. In particular, this study focuses on the changes in cloud radiative effect, cloud fraction, and cloud optical depth with SST anomaly. The major findings are as follows. First, the low-cloud amount (−1.9% to −3.4% K−1) and the logarithm of low-cloud optical depth (−0.085 to −0.100 K−1) tend to decrease while the net cloud radiative effect (3.86 W m−2 K−1) becomes less negative as SST anomalies increase. These results are broadly consistent with previous observational studies. Second, after the changes in cloud and radiative properties with SST anomaly are separated into dynamic, thermodynamic, and residual components, changes in the dynamic component (taken as the vertical velocity at 700 hPa) have relatively little effect on cloud and radiative properties. However, the estimated inversion strength decreases with increasing SST, accounting for a large portion of the measured decreases in cloud fraction and cloud optical depth. The residual positive change in net cloud radiative effect (1.48 W m−2 K−1) and small changes in low-cloud amount (−0.81% to 0.22% K−1) and decrease in the logarithm of optical depth (–0.035 to –0.046 K−1) with SST are interpreted as a positive cloud feedback, with cloud optical depth feedback being the dominant contributor. Last, the magnitudes of the residual changes differ greatly among the six low-cloud regions examined in this study, with the largest positive feedbacks (∼4 W m−2 K−1) in the southeast and northeast Atlantic regions and a slightly negative feedback (−0.2 W m−2 K−1) in the south-central Pacific region. Because the retrievals of cloud optical depth and/or cloud fraction are difficult in the presence of aerosols, the transport of heavy African continental aerosols may contribute to the large magnitudes of estimated cloud feedback in the two Atlantic regions.

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 Evaluation of Cloud Physical Properties of ECMWF Analysis and Re-Analysis (ERA) against CERES Tropical Deep Convective Cloud Object Observations

2009 ◽  
Vol 137 (1) ◽  
pp. 207-223 ◽  
Author(s):  
Kuan-Man Xu
Keyword(s):  
Physical Properties ◽  
Optical Depth ◽  
Large Scale ◽  
Grid Cell ◽  
Convective Cloud ◽  
Radiative Properties ◽  
Detection Rates ◽  
Cloud Optical Depth ◽  
Object A ◽  
Deep Convective Cloud

Abstract This study presents an approach that converts the vertical profiles of grid-averaged cloud properties from large-scale models to probability density functions (pdfs) of subgrid-cell cloud physical properties measured at satellite footprints. Cloud physical and radiative properties, rather than just cloud and precipitation occurrences, of assimilated cloud systems by the European Centre for Medium-Range Weather Forecasts (ECMWF) operational analysis (EOA) and 40-yr ECMWF Re-Analysis (ERA-40) are validated against those obtained from Earth Observing System satellite cloud object data for the January–August 1998 and March 2000 periods. These properties include the ice water path (IWP), cloud-top height and temperature, cloud optical depth, and solar and infrared radiative fluxes. Each cloud object, a contiguous region with similar cloud physical properties, is temporally and spatially matched with EOA and ERA-40 data. Results indicate that most pdfs of EOA and ERA-40 cloud physical and radiative properties agree with those of satellite observations of the tropical deep convective cloud object type for the January–August 1998 period. There are, however, significant discrepancies in selected ranges of the cloud property pdfs such as the upper range of EOA cloud-top height. A major discrepancy is that the dependence of the pdfs on the cloud object size for both EOA and ERA-40 is not as strong as in the observations. Modifications to the cloud parameterization in ECMWF that occurred in October 1999 eliminate the clouds near the tropopause but shift power of the pdf to lower cloud-top heights and greatly reduce the ranges of IWP and cloud optical depth pdfs. These features persist in ERA-40 due to the use of the same cloud parameterizations. The less sophisticated data assimilation technique and the lack of snow water content information in ERA-40, not the larger horizontal grid spacing, are also responsible for the disagreements with observed pdfs of cloud physical properties, although the detection rates of cloud object occurrence are improved for small-size categories. A possible improvement to the convective parameterization is to introduce a stronger dependence of updraft penetration heights on grid-cell dynamics.

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 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.

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