scholarly journals Retrieval of cloud properties from spectral zenith radiances observed by sky radiometers

2019 ◽  
Vol 12 (11) ◽  
pp. 6037-6047 ◽  
Author(s):  
Pradeep Khatri ◽  
Hironobu Iwabuchi ◽  
Tadahiro Hayasaka ◽  
Hitoshi Irie ◽  
Tamio Takamura ◽  
...  
Keyword(s):  
Optimal Estimation ◽  
Estimation Algorithm ◽  
Calibration Procedure ◽  
Evaluation Study ◽  
Vapor Concentration ◽  
Observation Data ◽  
Ancillary Data ◽  
Cloud Particle ◽  
Cloud Optical Depth ◽  
Cloud Properties

Abstract. An optimal estimation algorithm to retrieve the cloud optical depth (COD) and cloud particle effective radius (CER) from spectral zenith radiances observed by narrow field-of-view (FOV) ground-based sky radiometers was developed. To further address the filter degradation problem while analyzing long-term observation data, an on-site calibration procedure is proposed, which has good accuracy compared with the standard calibration transfer method. An error evaluation study conducted by assuming errors in observed transmittances and ancillary data for water vapor concentration and surface albedo suggests that the errors in input data affect retrieved CER more than COD. Except for some narrow domains that fall within a COD of < 15, the retrieval errors are small for both COD and CER. The retrieved cloud properties reproduce the broadband radiances observed by a narrow FOV radiometer more precisely than broadband irradiances observed by a wide-FOV pyranometer, justifying the quality of the retrieved product (at least of COD) and indicating the important effect of the instrument FOV in cloud remote sensing. Furthermore, CODs (CERs) from sky radiometer and satellite observations show good (poor) agreement.

scholarly journals Retrieval of cloud properties from sky radiometer observed spectral zenith radiances

2019 ◽  
Author(s):  
Pradeep Khatri ◽  
Hironobu Iwabuchi ◽  
Tadahiro Hayasaka ◽  
Hitoshi Irie ◽  
Tamio Takamura ◽  
...  
Keyword(s):  
Standard Procedure ◽  
Optimal Estimation ◽  
Calibration Procedure ◽  
Evaluation Study ◽  
Vapor Concentration ◽  
Ancillary Data ◽  
Cloud Particle ◽  
Cloud Properties ◽  
Cloud Optical Thickness ◽  
Long Term Observation

Abstract. An optimal-estimation based algorithm to retrieve cloud optical thickness (COD) and cloud-particle effective radius (CER) from spectral zenith radiances observed by a narrow field of view (FOV) ground-based sky radiometer is developed. To further address the filter-degradation problem while analyzing data of long-term observation, an on-site calibration procedure is proposed, which is found to have a very good accuracy with respect to a standard procedure, i.e., a procedure of deriving calibration constants using a master instrument. An error evaluation study conducted by assuming errors in observation-based transmittances and ancillary data of water vapor concentration and surface albedo suggests that the errors in input data can influence retrieved CER more effectively than COD. Except for some narrow domains that fall within COD 

scholarly journals Quality assessment of Second-generation Global Imager (SGLI)-observed cloud properties using SKYNET surface observation data

2021 ◽  
Author(s):  
Pradeep Khatri ◽  
Tadahiro Hayasaka ◽  
Hitoshi Irie ◽  
Husi Letu ◽  
Takashi Y. Nakajima ◽  
...  
Keyword(s):  
Second Generation ◽  
Global Climate ◽  
Observation Data ◽  
Cloud Particle ◽  
Ice Clouds ◽  
Cloud Optical Depth ◽  
Cloud Properties ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. The Second-generation Global Imager (SGLI) onboard the Global Change Observation Mission – Climate (GCOM-C) satellite launched on December 23, 2017, observes various geophysical parameters with the aim of a better understanding of the global climate system. As part of that aim, SGLI has great potential to unravel several uncertainties related to clouds by providing new cloud products along with several other atmospheric products related to cloud climatology, including aerosol products from polarization channels. However, a very little is known about the quality of the SGLI cloud products. This study uses data about clouds and global irradiances observed from the Earth’s surface using a sky radiometer and a pyranometer, respectively, to understand the quality of the two most fundamental cloud properties—cloud optical depth (COD) and cloud-particle effective radius (CER)—of both water and ice clouds. The SGLI-observed COD agrees well with values observed from the surface, although it agrees better for water clouds than for ice clouds, while the SGLI-observed CER exhibits poorer agreement than does the COD, with the SGLI values being generally higher than the sky radiometer values. These comparisons between the SGLI and sky radiometer cloud properties are found to differ for different cloud types of both the water and ice cloud phases and different solar and satellite viewing angles by agreeing better for relatively uniform and flat cloud type and for relatively low solar zenith angle. Analyses of SGLI-observed reflectance functions and values calculated by assuming plane-parallel cloud layers suggest that SGLI-retrieved cloud properties can have biases on the solar and satellite viewing angles, similar to other satellite sensors including the Moderate Resolution Imaging Spectroradiometer (MODIS). Furthermore, it is found that the SGLI-observed cloud properties reproduce global irradiances quite satisfactorily for both water and ice clouds by resembling several important features of the COD comparison, such as the better agreement for water clouds than for ice clouds and the tendency to underestimate (resp. overestimate) the COD in SGLI observations for optically thick (resp. thin) clouds.

scholarly journals Improved Retrieval of Cloud Liquid Water from CloudSat and MODIS

2016 ◽  
Vol 55 (8) ◽  
pp. 1831-1844 ◽  
Author(s):  
Jussi Leinonen ◽  
Matthew D. Lebsock ◽  
Graeme L. Stephens ◽  
Kentaroh Suzuki
Keyword(s):  
Liquid Water ◽  
Optimal Estimation ◽  
Estimation Algorithm ◽  
Radar Signal ◽  
Retrieval Algorithm ◽  
Liquid Water Path ◽  
Cloud Liquid Water ◽  
Cloud Optical Depth ◽  
Cloud Water Content ◽  
Most Significant Change

AbstractA revised version of the CloudSat–MODIS cloud liquid water retrieval algorithm is presented. The new algorithm, which combines measurements of radar reflectivity and cloud optical depth, addresses issues discovered in the current CloudSat–MODIS cloud water content (CWC) product. This current product is shown to be underconstrained by observations and to be too dependent on prior information incorporated into the Bayesian optimal-estimation algorithm. The most significant change made to the algorithm in this study was decreasing the number of independent variables to allow the observations to constrain the retrieved values better. The retrieval was also reformulated for improved compliance with the mathematical assumptions of the optimal-estimation algorithm. To validate the accuracy of the revised algorithm, the path-integrated attenuation (PIA) of the CloudSat radar signal was computed from the algorithm results. These modeled values were compared with independent measurements of the PIA that were obtained using a surface reference technique. This comparison shows that the cloud liquid water retrieved by the algorithm is close to being unbiased. The revised algorithm was also found to be an improvement over the current CloudSat CWC product and, to a lesser degree, the MODIS-derived cloud liquid water path.

scholarly journals Retrieval of ice cloud properties using an optimal estimation algorithm and MODIS infrared observations: 2. Retrieval evaluation

2016 ◽  
Vol 121 (10) ◽  
pp. 5827-5845 ◽  
Author(s):  
Chenxi Wang ◽  
Steven Platnick ◽  
Zhibo Zhang ◽  
Kerry Meyer ◽  
Gala Wind ◽  
...  
Keyword(s):  
Optimal Estimation ◽  
Estimation Algorithm ◽  
Infrared Observations ◽  
Ice Cloud ◽  
Cloud Properties

scholarly journals Comparison of ISS–CATS and CALIPSO–CALIOP Characterization of High Clouds in the Tropics

2020 ◽  
Vol 12 (23) ◽  
pp. 3946
Author(s):  
Pasquale Sellitto ◽  
Silvia Bucci ◽  
Bernard Legras
Keyword(s):  
Optical Properties ◽  
Space Station ◽  
Satellite Observation ◽  
Observation Data ◽  
Upper Troposphere ◽  
Cloud Properties ◽  
Synchronous Orbit ◽  
The Tropics ◽  
High Clouds

Clouds in the tropics have an important role in the energy budget, atmospheric circulation, humidity, and composition of the tropical-to-global upper-troposphere–lower-stratosphere. Due to its non-sun-synchronous orbit, the Cloud–Aerosol Transport System (CATS) onboard the International Space Station (ISS) provided novel information on clouds from space in terms of overpass time in the period of 2015–2017. In this paper, we provide a seasonally resolved comparison of CATS characterization of high clouds (between 13 and 18 km altitude) in the tropics with well-established CALIPSO (Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation) data, both in terms of clouds’ occurrence and cloud optical properties (optical depth). Despite the fact that cloud statistics for CATS and CALIOP are generated using intrinsically different local overpass times, the characterization of high clouds occurrence and optical properties in the tropics with the two instruments is very similar. Observations from CATS underestimate clouds occurrence (up to 80%, at 18 km) and overestimate the occurrence of very thick clouds (up to 100% for optically very thick clouds, at 18 km) at higher altitudes. Thus, the description of stratospheric overshoots with CATS and CALIOP might be different. While this study hints at the consistency of CATS and CALIOP clouds characterizaton, the small differences highlighted in this work should be taken into account when using CATS for estimating cloud properties and their variability in the tropics.

scholarly journals Measurement Uncertainty Estimation for Laser Doppler Anemometer

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3847
Author(s):  
Karolina Weremijewicz ◽  
Andrzej Gajewski
Keyword(s):  
Power Plants ◽  
Thermal Power ◽  
Regression Line ◽  
Estimation Algorithm ◽  
Calibration Procedure ◽  
Uncertainty Estimation ◽  
Two Phase ◽  
Mass And Heat Transfer ◽  
Preliminary Study ◽  
Measurement Uncertainty Estimation

Twenty percent of global electricity supplied to the buildings is used for preventing air temperature increase; its consumption for this prevention will triple by 2050 up to China’s present needs. Heat removed from the thermal power plants may drive cold generation in the absorption devices where mass and heat transfer are two-phase phenomena; hence liquid film break-up into the rivulets is extensively investigated, which needs knowledge of the velocity profiles. Laminar flow in a pipe is used in the preliminary study, velocity profile of developed flow is used as a benchmark. The study account writes the applied apparatus with their calibration procedure, and the uncertainty estimation algorithm. The calibration regression line with the slope close to one and a high Pearson’s coefficient value is the final outcome. Therefore, the apparatus may be applied in the principal research.

Fast satellite selection algorithm for GNSS multisystems based on the Sherman-Morrison formula

2021 ◽  
Author(s):  
Junpeng SHI ◽  
Kezhao LI ◽  
Lin CHAI ◽  
Lingfeng LIANG ◽  
Chengdong TIAN ◽  
...  
Keyword(s):  
Optimal Estimation ◽  
Observation Data ◽  
Selection Algorithm ◽  
Maximum Volume ◽  
Adaptive Function ◽  
Satellite Selection ◽  
Geometric Dilution Of Precision ◽  
Value Decomposition ◽  
Selection Algorithms ◽  
Usage Efficiency

Abstract The usage efficiency of GNSS multisystem observation data can be greatly improved by applying rational satellite selection algorithms. Such algorithms can also improve the real-time reliability and accuracy of navigation. By combining the Sherman-Morrison formula and singular value decomposition (SVD), a smaller geometric dilution of precision (GDOP) value method with increasing number of visible satellites is proposed. Moreover, by combining this smaller GDOP value method with the maximum volume of tetrahedron method, a new rapid satellite selection algorithm based on the Sherman-Morrison formula for GNSS multisystems is proposed. The basic idea of the algorithm is as follows: first, the maximum volume of tetrahedron method is used to obtain four initial reference satellites; then, the visible satellites are co-selected by using the smaller GDOP value method to reduce the GDOP value and improve the accuracy of the overall algorithm. By setting a reasonable precise threshold, the satellite selection algorithm can be autonomously run without intervention. The experimental results based on measured data indicate that (1) the GDOP values in most epochs over the whole period obtained with the satellite selection algorithm based on the Sherman-Morrison formula are less than 2. Furthermore, compared with the optimal estimation results of the GDOP for all visible satellites, the results of this algorithm can meet the requirements of high-precision navigation and positioning when the corresponding number of selected satellites reaches 13. Moreover, as the number of selected satellites continues to increase, the calculation time increases, but the decrease in the GDOP value is not obvious. (2) The algorithm includes an adaptive function based on the end indicator of the satellite selection calculation and the reasonable threshold. When the reasonable precise threshold is set to 0.01, the selected number of satellites in most epochs is less than 13. Furthermore, when the number of selected satellites reaches 13, the GDOP value is less than 2, and the corresponding probability is 93.54%. These findings verify that the proposed satellite selection algorithm based on the Sherman-Morrison formula provides autonomous functionality and high-accuracy results.

scholarly journals The interdependence of continental warm cloud properties derived from unexploited solar background signals in ground-based lidar measurements

2014 ◽  
Vol 14 (16) ◽  
pp. 8389-8401 ◽  
Author(s):  
J. C. Chiu ◽  
J. A. Holmes ◽  
R. J. Hogan ◽  
E. J. O'Connor
Keyword(s):  
Optical Depth ◽  
Effective Radius ◽  
Liquid Water Path ◽  
Cloud Optical Depth ◽  
Cloud Properties ◽  
Warm Cloud ◽  
Lidar Measurements ◽  
Atmospheric Radiation Measurement ◽  
Ground Based Lidar ◽  
Geometric Thickness

Abstract. We have extensively analysed the interdependence between cloud optical depth, droplet effective radius, liquid water path (LWP) and geometric thickness for stratiform warm clouds using ground-based observations. In particular, this analysis uses cloud optical depths retrieved from untapped solar background signals that are previously unwanted and need to be removed in most lidar applications. Combining these new optical depth retrievals with radar and microwave observations at the Atmospheric Radiation Measurement (ARM) Climate Research Facility in Oklahoma during 2005–2007, we have found that LWP and geometric thickness increase and follow a power-law relationship with cloud optical depth regardless of the presence of drizzle; LWP and geometric thickness in drizzling clouds can be generally 20–40% and at least 10% higher than those in non-drizzling clouds, respectively. In contrast, droplet effective radius shows a negative correlation with optical depth in drizzling clouds and a positive correlation in non-drizzling clouds, where, for large optical depths, it asymptotes to 10 μm. This asymptotic behaviour in non-drizzling clouds is found in both the droplet effective radius and optical depth, making it possible to use simple thresholds of optical depth, droplet size, or a combination of these two variables for drizzle delineation. This paper demonstrates a new way to enhance ground-based cloud observations and drizzle delineations using existing lidar networks.

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