A Novel Cloud Detection Algorithm Based on Simplified Radiative Transfer Model for Aerosol Retrievals: Preliminary Result on Himawari-8 Over Eastern China

Abstract. A method for retrieving cloud optical depth (τc) using a ground-based sky imager (USI) is presented. The Radiance Red-Blue Ratio (RRBR) method is motivated from the analysis of simulated images of various τc produced by a 3-D Radiative Transfer Model (3DRTM). From these images the basic parameters affecting the radiance and RBR of a pixel are identified as the solar zenith angle (θ0), τc, solar pixel angle/scattering angle (&vartheta;s), and pixel zenith angle/view angle (&vartheta;z). The effects of these parameters are described and the functions for radiance, Iλ(τc, θ0, &vartheta;s, &vartheta;z) and the red-blue ratio, RBR(τc, θ0, &vartheta;s, &vartheta;z) are retrieved from the 3DRTM results. RBR, which is commonly used for cloud detection in sky images, provides non-unique solutions for τc, where RBR increases with τc up to about τc = 1 (depending on other parameters) and then decreases. Therefore, the RRBR algorithm uses the measured Iλmeas(&vartheta;s, &vartheta;z), in addition to RBRmeas(&vartheta;s, &vartheta;z) to obtain a unique solution for τc. The RRBR method is applied to images taken by a USI at the Oklahoma Atmospheric Radiation Measurement program (ARM) site over the course of 220 days and validated against measurements from a microwave radiometer (MWR); output from the Min method for overcast skies, and τc retrieved by Beer's law from direct normal irradiance (DNI) measurements. A τc RMSE of 5.6 between the Min method and the USI are observed. The MWR and USI have an RMSE of 2.3 which is well within the uncertainty of the MWR. An RMSE of 0.95 between the USI and DNI retrieved τc is observed. The procedure developed here provides a foundation to test and develop other cloud detection algorithms.

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NWCSAF AVHRR Cloud Detection and Analysis Using Dynamic Thresholds and Radiative Transfer Modeling. Part II: Tuning and Validation

Journal of Applied Meteorology ◽

10.1175/jam-2189.1 ◽

2005 ◽

Vol 44 (1) ◽

pp. 55-71 ◽

Cited By ~ 41

Author(s):

Adam Dybbroe ◽

Karl-Göran Karlsson ◽

Anke Thoss

Keyword(s):

Radiative Transfer ◽

Forecast Model ◽

Radar Data ◽

Radiative Transfer Model ◽

Transfer Model ◽

Cloud Detection ◽

Analysis Model ◽

Cloud Type ◽

Avhrr Data ◽

Cloud Mask

Abstract Algorithms for cloud detection (cloud mask) and classification (cloud type) at high and midlatitudes using data from the Advanced Very High Resolution Radiometer (AVHRR) on board the current NOAA satellites and future polar Meteorological and Operational Weather Satellites (METOP) of the European Organisation for the Exploitation of Meteorological Satellites have been extensively validated over northern Europe and the adjacent seas. The algorithms have been described in detail in Part I and are based on a multispectral grouped threshold approach, making use of cloud-free radiative transfer model simulations. The thresholds applied in the algorithms have been validated and tuned using a database interactively built up over more than 1 yr of data from NOAA-12, -14, and -15 by experienced nephanalysts. The database contains almost 4000 rectangular (in the image data)-sized targets (typically with sides around 10 pixels), with satellite data collocated in time and space with atmospheric data from a short-range NWP forecast model, land cover characterization, elevation data, and a label identifying the given cloud or surface type as interpreted by the nephanalyst. For independent and objective validation, a large dataset of nearly 3 yr of collocated surface synoptic observation (Synop) reports, AVHRR data, and NWP model output over northern and central Europe have been collected. Furthermore, weather radar data were used to check the consistency of the cloud type. The cloud mask performs best over daytime sea and worst at twilight and night over land. As compared with Synop, the cloud cover is overestimated during night (except for completely overcast situations) and is underestimated at twilight. The algorithms have been compared with the more empirically based Swedish Meteorological and Hydrological Institute (SMHI) Cloud Analysis Model Using Digital AVHRR Data (SCANDIA), operationally run at SMHI since 1989, and results show that performance has improved significantly.

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Retrieval of gridded aerosol direct radiative forcing based on multiplatform datasets

Atmospheric Measurement Techniques ◽

10.5194/amt-13-575-2020 ◽

2020 ◽

Vol 13 (2) ◽

pp. 575-592 ◽

Cited By ~ 3

Author(s):

Yanyu Wang ◽

Rui Lyu ◽

Xin Xie ◽

Ze Meng ◽

Meijin Huang ◽

...

Keyword(s):

Radiative Transfer ◽

Radiative Forcing ◽

Eastern China ◽

Radiant Energy ◽

Radiative Transfer Model ◽

Transfer Model ◽

Rapid Urbanization ◽

Radiative Fluxes ◽

Direct Radiative Forcing

Abstract. Atmospheric aerosols play a crucial role in regional radiative budgets. Previous studies on clear-sky aerosol direct radiative forcing (ADRF) have mainly been limited to site-scale observations or model simulations for short-term cases, and long-term distributions of ADRF in China have not been portrayed yet. In this study, an accurate fine-resolution ADRF estimate at the surface was proposed. Multiplatform datasets, including satellite (MODIS aboard Terra and Aqua) and reanalysis datasets, served as inputs to the Santa Barbara Discrete Atmospheric Radiative Transfer (SBDART) model for ADRF simulation with consideration of the aerosol vertical profile over eastern China during 2000–2016. Specifically, single-scattering albedo (SSA) from the Modern-Era Retrospective Analysis for Research and Application, Version 2 (MERRA-2) was validated with sun photometers over eastern China. The gridded asymmetry parameter (ASY) was then simulated by matching the calculated top-of-atmosphere (TOA) radiative fluxes from the radiative transfer model with satellite observations (Clouds and the Earth's Radiant Energy System, CERES). The high correlation and small discrepancy (6–8 W m−2) between simulated and observed radiative fluxes at three sites (Baoshan, Fuzhou, and Yong'an) indicated that ADRF retrieval is feasible and has high accuracy over eastern China. Then this method was applied in each grid of eastern China, and the overall picture of ADRF distributions over eastern China during 2000–2016 was displayed. ADRF ranges from −220 to −20 W m−2, and annual mean ADRF is −100.21 W m−2, implying that aerosols have a strong cooling effect at the surface in eastern China. With the economic development and rapid urbanization, the spatiotemporal changes of ADRF during the past 17 years are mainly attributed to the changes of anthropogenic emissions in eastern China. Our method provides the long-term ADRF distribution over eastern China for the first time, highlighting the importance of aerosol radiative impact under climate change.

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Coupling sky images with radiative transfer models: a new method to estimate cloud optical depth

Atmospheric Measurement Techniques ◽

10.5194/amt-9-4151-2016 ◽

2016 ◽

Vol 9 (8) ◽

pp. 4151-4165 ◽

Cited By ~ 8

Author(s):

Felipe A. Mejia ◽

Ben Kurtz ◽

Keenan Murray ◽

Laura M. Hinkelman ◽

Manajit Sengupta ◽

...

Keyword(s):

Radiative Transfer ◽

Optical Depth ◽

Zenith Angle ◽

Microwave Radiometer ◽

Radiative Transfer Model ◽

Transfer Model ◽

Cloud Detection ◽

Cloud Optical Depth ◽

Angle Scattering ◽

Atmospheric Radiation Measurement

Abstract. A method for retrieving cloud optical depth (τc) using a UCSD developed ground-based sky imager (USI) is presented. The radiance red–blue ratio (RRBR) method is motivated from the analysis of simulated images of various τc produced by a radiative transfer model (RTM). From these images the basic parameters affecting the radiance and red–blue ratio (RBR) of a pixel are identified as the solar zenith angle (θ0), τc, solar pixel angle/scattering angle (ϑs), and pixel zenith angle/view angle (ϑz). The effects of these parameters are described and the functions for radiance, Iλτc, θ0, ϑs, ϑz, and RBRτc, θ0, ϑs, ϑz are retrieved from the RTM results. RBR, which is commonly used for cloud detection in sky images, provides non-unique solutions for τc, where RBR increases with τc up to about τc = 1 (depending on other parameters) and then decreases. Therefore, the RRBR algorithm uses the measured Iλmeasϑs, ϑz, in addition to RBRmeasϑs, ϑz, to obtain a unique solution for τc. The RRBR method is applied to images of liquid water clouds taken by a USI at the Oklahoma Atmospheric Radiation Measurement (ARM) program site over the course of 220 days and compared against measurements from a microwave radiometer (MWR) and output from the Min et al. (2003) method for overcast skies. τc values ranged from 0 to 80 with values over 80, being capped and registered as 80. A τc RMSE of 2.5 between the Min et al. (2003) method and the USI are observed. The MWR and USI have an RMSE of 2.2, which is well within the uncertainty of the MWR. The procedure developed here provides a foundation to test and develop other cloud detection algorithms.

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A Comparison of Multi-Angle Implementation of Atmospheric Correction and MOD09 Daily Surface Reflectance Products From MODIS

Frontiers in Remote Sensing ◽

10.3389/frsen.2021.712093 ◽

2021 ◽

Vol 2 ◽

Author(s):

Alexei Lyapustin ◽

Feng Zhao ◽

Yujie Wang

Keyword(s):

Eastern China ◽

Spectral Characteristics ◽

Atmospheric Correction ◽

Radiative Transfer Model ◽

Surface Model ◽

Transfer Model ◽

Surface Reflectance ◽

Cloud Detection ◽

Wide Range ◽

North Eastern

This study presents the first systematic comparison of MAIAC Collection 6 MCD19A1 daily surface reflectance (SR) product with standard MODIS SR (MOD/MYD09). The study was limited to four tiles located in mid-Atlantic United States (H11V05), Canada (H12V03), central Amazon (H11V09), and North-Eastern China (H27V05) and used over 5000 MODIS granules in 2018. Overall, there is a remarkable agreement between the best quality pixels of the two products, in particular in the Red and NIR bands. Over selected tiles, the evaluation found that MAIAC provides from 4 to 25% more high-quality retrievals than MOD09 annually, with the largest difference in tropical regions, confirming results of the previous studies. The comparison of spectral characteristics showed a systematic MAIAC-MOD09 difference increasing from NIR to Blue, typical of biases of a Lambertian assumption in MOD09 algorithm. Over the North-Eastern China, MCD19A1 SR is found more stable at wide range of aerosol optical depth (AOD) variations, whereas MOD09 SR shows a consistent positive bias increasing with AOD and at shorter wavelengths. The observed SR differences can be attributed to differences in cloud detection, aerosol retrieval and in atmospheric correction which is performed using an accurate BRDF-coupled radiative transfer model in MAIAC and a Lambertian surface model in MOD09. While this study is not representative of the global performance because of its limited geographical coverage, it should help the land community to better understand the differences between the two products.

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NWCSAF AVHRR Cloud Detection and Analysis Using Dynamic Thresholds and Radiative Transfer Modeling. Part I: Algorithm Description

Journal of Applied Meteorology ◽

10.1175/jam-2188.1 ◽

2005 ◽

Vol 44 (1) ◽

pp. 39-54 ◽

Cited By ~ 87

Author(s):

Adam Dybbroe ◽

Karl-Göran Karlsson ◽

Anke Thoss

Keyword(s):

Radiative Transfer ◽

Land Surface ◽

Short Range ◽

Radiative Transfer Model ◽

Actual State ◽

Transfer Model ◽

Cloud Detection ◽

Cloud Type ◽

Wide Range ◽

Type Classification

Abstract New methods and software for cloud detection and classification at high and midlatitudes using Advanced Very High Resolution Radiometer (AVHRR) data are developed for use in a wide range of meteorological, climatological, land surface, and oceanic applications within the Satellite Application Facilities (SAFs) of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), including the SAF for Nowcasting and Very Short Range Forecasting Applications (NWCSAF) project. The cloud mask employs smoothly varying (dynamic) thresholds that separate fully cloudy or cloud-contaminated fields of view from cloud-free conditions. Thresholds are adapted to the actual state of the atmosphere and surface and the sun–satellite viewing geometry using cloud-free radiative transfer model simulations. Both the cloud masking and the cloud-type classification are done using sequences of grouped threshold tests that employ both spectral and textural features. The cloud-type classification divides the cloudy pixels into 10 different categories: 5 opaque cloud types, 4 semitransparent clouds, and 1 subpixel cloud category. The threshold method is fuzzy in the sense that the distances in feature space to the thresholds are stored and are used to determine whether to stop or to continue testing. They are also used as a quality indicator of the final output. The atmospheric state should preferably be taken from a short-range NWP model, but the algorithms can also run with climatological fields as input.

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A quality control procedure for Fengyun-3A microwave temperature sounder with emphasis on a new cloud detection algorithm

Journal of Southern Hemisphere Earth System Science ◽

10.1071/es16004 ◽

2016 ◽

Vol 66 (1) ◽

pp. 19

Author(s):

Xiang Wang ◽

Yifang Ren ◽

Gang Li

Keyword(s):

Quality Control ◽

Liquid Water ◽

Detection Algorithm ◽

Radiative Transfer Model ◽

Transfer Model ◽

Control Procedure ◽

Cloud Detection ◽

Liquid Water Path ◽

Model Simulations ◽

Water Path

A cloud detection algorithm for satellite radiance from the microwave temperature sounder (MWTS) on board the first satellite of the Chinese polar-orbiting Fengyun-three series (FY-3A) is proposed based on the measurements at the frequencies of 50.3 and 53.6 GHz. The cloud liquid water path index (LWP index) is calculated using the brightness temperature at these two channels. Analysis of one case carried out in January2010shows the great consistency be-tween this new algorithm result and the available liquid water path product from the Meteorological Operational satellite A (MetOp-A).In general, about 60% of the global MWTS data are considered to be contaminated by cloud by virtue of the new cloud detection algorithm. A quality control (QC) procedure is applied to MWTS measurements with emphasis on the cloud detection. The QC steps are composed of (i) channel 2 over sea ice, land and coastal field of views (FOVs); (ii) channels 2 and 3 over cloudy FOVs; and (iii) outliers with large differences between observations and model simulations. After QC, MWTS measurements of channels 2–4 agree very well with the model simulations using the National Centres for Environmental Prediction (NCEP) forecast data as radiative transfer model input; the scan biases are reduced significantly, especially at the edges of the swath; and the frequency distributions of the differences between observations and model simulations become more Gaussian-like.

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