scholarly journals A kernel-driven BRDF model to inform satellite-derived visible anvil cloud detection

2020 ◽  
Vol 13 (10) ◽  
pp. 5491-5511
Author(s):  
Benjamin R. Scarino ◽  
Kristopher Bedka ◽  
Rajendra Bhatt ◽  
Konstantin Khlopenkov ◽  
David R. Doelling ◽  
...  
Keyword(s):  
Weather Forecasting ◽  
Deep Convection ◽  
Detection Methods ◽  
Cloud Detection ◽  
Bidirectional Reflectance ◽  
Cloud Optical Depth ◽  
Convective Clouds ◽  
Clear Sky ◽  
Almost All ◽  
Bidirectional Reflectance Distribution

Abstract. Satellites routinely observe deep convective clouds across the world. The cirrus outflow from deep convection, commonly referred to as anvil cloud, has a ubiquitous appearance in visible and infrared (IR) wavelength imagery. Anvil clouds appear as broad areas of highly reflective and cold pixels relative to the darker and warmer clear sky background, often with embedded textured and colder pixels that indicate updrafts and gravity waves. These characteristics would suggest that creating automated anvil cloud detection products useful for weather forecasting and research should be straightforward, yet in practice such product development can be challenging. Some anvil detection methods have used reflectance or temperature thresholding, but anvil reflectance varies significantly throughout a day as a function of combined solar illumination and satellite viewing geometry, and anvil cloud top temperature varies as a function of convective equilibrium level and tropopause height. This paper highlights a technique for facilitating anvil cloud detection based on visible observations that relies on comparative analysis with expected cloud reflectance for a given set of angles, thereby addressing limitations of previous methods. A 1-year database of anvil-identified pixels, as determined from IR observations, from several geostationary satellites was used to construct a bidirectional reflectance distribution function (BRDF) model to quantify typical anvil reflectance across almost all expected viewing, solar, and azimuth angle configurations, in addition to the reflectance uncertainty for each angular bin. Application of the BRDF model for cloud optical depth retrieval in deep convection is described as well.

scholarly journals A kernel-driven BRDF model to inform satellite-derived visible anvil cloud detection

2020 ◽  
Author(s):  
Benjamin Scarino ◽  
Kristopher Bedka ◽  
Rajendra Bhatt ◽  
Konstantin Khlopenkov ◽  
David R. Doelling ◽  
...  
Keyword(s):  
Weather Forecasting ◽  
Deep Convection ◽  
Detection Methods ◽  
Cloud Detection ◽  
Cloud Optical Depth ◽  
Convective Clouds ◽  
Clear Sky ◽  
One Year ◽  
Almost All ◽  
Cloud Top Temperature

Abstract. Satellites routinely observe deep convective clouds across the world. The cirrus outflow from deep convection, commonly referred to as anvil cloud, has a ubiquitous appearance in visible and infrared (IR) wavelength imagery. Anvil clouds appear as broad areas of highly reflective and cold pixels relative to the darker and warmer clear sky background, often with embedded textured and colder pixels that indicate updrafts and gravity waves. These characteristics would suggest that creating automated anvil cloud detection products useful for weather forecasting and research should be straightforward, yet in practice such product development can be challenging. Some anvil detection methods have used reflectance or temperature thresholding, but anvil reflectance varies significantly throughout a day as a function of combined solar illumination and satellite viewing geometry, and anvil cloud top temperature varies as a function of convective equilibrium level and tropopause height. This paper highlights a technique for facilitating anvil cloud detection based on visible observations that relies on comparative analysis with expected cloud reflectance for a given set of angles, thereby addressing limitations of previous methods. A one-year database of anvil-identified pixels, as determined from IR observations, from several geostationary satellites was used to construct a bi-directional reflectance distribution function (BRDF) model to quantify typical anvil reflectance across almost all expected viewing, solar, and azimuth angle configurations, in addition to the reflectance uncertainty for each angular bin. Application of the BRDF model for cloud optical depth retrieval in deep convection is described as well.

scholarly journals All-sky assimilation of infrared radiances sensitive to mid- and upper-tropospheric moisture and cloud

2019 ◽  
Vol 12 (9) ◽  
pp. 4903-4929 ◽  
Author(s):  
Alan J. Geer ◽  
Stefano Migliorini ◽  
Marco Matricardi
Keyword(s):  
Weather Forecasting ◽  
Deep Convection ◽  
Cloud Detection ◽  
Clear Sky ◽  
Atmospheric Sounding ◽  
Beneficial Impact ◽  
Infrared Radiances ◽  
The Tropics ◽  
The Impact ◽  
Better Than

Abstract. All-sky assimilation of infrared (IR) radiances has not yet become operational at any weather forecasting centre, but it promises to bring new observations in sensitive areas and avoid the need for cloud detection. A new all-sky IR configuration gives results comparable to (and in some areas better than) clear-sky assimilation of the same data, meaning that operational implementation is now feasible. The impact of seven upper-tropospheric water vapour (WV) sounding channels from the Infrared Atmospheric Sounding Interferometer (IASI) is evaluated in both all-sky and clear-sky approaches. All-sky radiative transfer simulations (and the forecast model's cloud fields) are now sufficiently accurate that systematic errors are comparable to those of clear-sky assimilation outside of a few difficult areas such as deep convection. All-sky assimilation brings 65 % more data than clear-sky assimilation globally, with the biggest increases in midlatitude storm tracks and tropical convective areas. However, all-sky gives slightly less weight to any one observation than in the clear-sky approach. In the midlatitudes, all-sky and clear-sky assimilation have similarly beneficial impact on mid- and upper-tropospheric dynamical forecast fields. Here the addition of data in cloudy areas is offset by the slightly lower weight given to the observations. But in the tropics, all-sky assimilation is significantly more beneficial than clear-sky assimilation, with improved dynamical short-range forecasts throughout the troposphere and stratosphere.

scholarly journals All-sky assimilation of infrared radiances sensitive to mid- and upper-tropospheric moisture and cloud

2019 ◽  
Author(s):  
Alan J. Geer ◽  
Stefano Migliorini ◽  
Marco Matricardi
Keyword(s):  
Weather Forecasting ◽  
Deep Convection ◽  
Cloud Detection ◽  
Clear Sky ◽  
Atmospheric Sounding ◽  
Beneficial Impact ◽  
Infrared Radiances ◽  
The Tropics ◽  
The Impact ◽  
Better Than

Abstract. All-sky assimilation of infrared (IR) radiances has not yet become operational at any weather forecasting centre but it promises to bring new observations in sensitive areas and it avoids the need for cloud detection. A new all-sky IR configuration gives results comparable to (and in some areas better than) clear-sky assimilation of the same data, meaning that operational implementation is now feasible. The impact of 7 upper-tropospheric water vapour (WV) sounding channels from the Infrared Atmospheric Sounding Interferometer (IASI) is evaluated in both all-sky and clear-sky approaches. All-sky radiative transfer simulations (and the forecast model’s cloud fields) are now sufficiently accurate that systematic errors are comparable to those of clear-sky assimilation outside of a few difficult areas such as deep-convection. All-sky assimilation brings 65 % more data than clear-sky assimilation globally, with the biggest increases in midlatitude storm tracks and tropical convective areas. However all-sky gives slightly less weight to any one observation than in the clear-sky approach. In the midlatitudes, all-sky and clear-sky assimilation have similarly beneficial impact on mid- and upper-tropospheric dynamical forecast fields. Here the addition of data in cloudy areas is offset by the slightly lower weight given to the observations. But in the tropics, all-sky assimilation is significantly more beneficial than clear-sky assimilation, with improved dynamical short-range forecasts throughout the troposphere and stratosphere.

scholarly journals Development of CO2 Band-Based Cloud Emission and Scattering Indices and Their Applications to FY-3D Hyperspectral Infrared Atmospheric Sounder

2020 ◽  
Vol 12 (24) ◽  
pp. 4171
Author(s):  
Xinlu Xia ◽  
Xiaolei Zou
Keyword(s):  
Brightness Temperature ◽  
Optical Depth ◽  
Co2 Absorption ◽  
Cloud Detection ◽  
Detection Scheme ◽  
Cloud Optical Depth ◽  
Ice Cloud ◽  
Clear Sky ◽  
Temperature Observations ◽  
Cloud Top Pressure

The Hyperspectral Infrared Atmospheric Sounder (HIRAS) onboard the Feng Yun-3D (FY-3D) satellite is the first Chinese hyperspectral infrared instrument. In this study, an improved cloud detection scheme using brightness temperature observations from paired HIRAS long-wave infrared (LWIR) and short-wave infrared (SWIR) channels at CO2 absorption bands (15-μm and 4.3-μm) is developed. The weighting function broadness and a set of height-dependent thresholds of cloud-sensitive-level differences are incorporated into pairing LWIR and SWIR channels. HIRAS brightness temperature observations made under clear-sky conditions during a training period are used to develop a set of linear regression equations between paired LWIR and SWIR channels. Moderate-resolution Imaging Spectroradiometer (MODIS) cloud mask data are used for selecting HIRAS clear-sky observations. Cloud Emission and Scattering Indices (CESIs) are defined as the differences in SWIR channels between HIRAS observations and regression simulations from LWIR observations. The cloud retrieval products of ice cloud optical depth and cloud-top pressure from the Atmospheric Infrared Sounder (AIRS) are used to illustrate the effectiveness of the proposed cloud detection scheme for FY-3D HIRAS observations. Results show that the distributions of modified CESIs at different altitudes can capture features in the distributions of AIRS-retrieved ice cloud optical depth and cloud-top pressure better than the CESIs obtained by the original method.

Advancing Maritime Transparent Cirrus Detection Using the Advanced Baseline Imager “Cirrus” Band

2021 ◽  
Author(s):  
Theodore M. McHardy ◽  
James R. Campbell ◽  
David A. Peterson ◽  
Simone Lolli ◽  
Richard L. Bankert ◽  
...  
Keyword(s):  
Precipitable Water ◽  
Detection Algorithm ◽  
Machine Learning Techniques ◽  
Orthogonal Polarization ◽  
Cirrus Cloud ◽  
Cloud Detection ◽  
Cloud Optical Depth ◽  
Clear Sky ◽  
Learning Techniques ◽  
Microphysical Property

AbstractWe describe a quantitative evaluation of maritime transparent cirrus cloud detection, which is based on Geostationary Operational Environmental Satellite – 16 (GOES-16) and developed with collocated Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) profiling. The detection algorithm is developed using one month of collocated GOES-16 Advanced Baseline Imager (ABI) Channel 4 (1.378 μm) radiance and CALIOP 0.532 μm column-integrated cloud optical depth (COD). First, the relationships between the clear-sky 1.378 μm radiance, viewing/solar geometry, and precipitable water vapor (PWV) are characterized. Using machine learning techniques, it is shown that the total atmospheric pathlength, proxied by airmass factor (AMF), is a suitable replacement for viewing zenith and solar zenith angles alone, and that PWV is not a significant problem over ocean. Detection thresholds are computed using the Ch. 4 radiance as a function of AMF. The algorithm detects nearly 50% of sub-visual cirrus (COD < 0.03), 80% of transparent cirrus (0.03 < COD < 0.3), and 90% of opaque cirrus (COD > 0.3). Using a conservative radiance threshold results in 84% of cloudy pixels being correctly identified and 4% of clear-sky pixels being misidentified as cirrus. A semi-quantitative COD retrieval is developed for GOES ABI based on the observed relationship between CALIOP COD and 1.378 μm radiance. This study lays the groundwork for a more complex, operational GOES transparent cirrus detection algorithm. Future expansion includes an over-land algorithm, a more robust COD retrieval that is suitable for assimilation purposes, and downstream GOES products such as cirrus cloud microphysical property retrieval based on ABI infrared channels.

scholarly journals A total sky cloud detection method using real clear sky background

2015 ◽  
Vol 8 (12) ◽  
pp. 13073-13098 ◽  
Author(s):  
J. Yang ◽  
Q. Min ◽  
W. Lu ◽  
Y. Ma ◽  
W. Yao ◽  
...  
Keyword(s):  
Detection Algorithm ◽  
Brightness Distribution ◽  
Detection Methods ◽  
Cloud Detection ◽  
Image Brightness ◽  
Clear Sky ◽  
Sensitivity Tests ◽  
Thin Cloud ◽  
Distribution Sensitivity ◽  
Solar Position

Abstract. The brightness distribution of sky background is usually non-uniform, which creates many problems for traditional cloud detection methods including the failure of thin cloud detection in total sky images and significantly reducing retrieval accuracy in the circumsolar and near-horizon regions. This paper describes the development of a new cloud detection algorithm, named "clear sky background differencing (CSBD)", which is accomplished by differencing the original image and the corresponding clear sky background image using the images' green channel. First, a library of clear sky background images with a variety of solar elevation angles needs to be developed. The image rotation and image brightness adjustment algorithms are applied to ensure the two images being differenced have the same solar position and similar brightness distribution. Sensitivity tests show, as long as the positions of the sun in the two images are the same, the cloud detection results are satisfactory. Several experimental cases show that the CSBD algorithm obtains good cloud recognition results visually, especially for thin clouds.

scholarly journals MODIS Collection 6 MAIAC algorithm

2018 ◽  
Vol 11 (10) ◽  
pp. 5741-5765 ◽  
Author(s):  
Alexei Lyapustin ◽  
Yujie Wang ◽  
Sergey Korkin ◽  
Dong Huang
Keyword(s):  
Missing Values ◽  
Atmospheric Correction ◽  
Global Processing ◽  
Cloud Detection ◽  
Bidirectional Reflectance ◽  
Time Series Approach ◽  
Fine Mode ◽  
Spectral Regression ◽  
Cloud Mask ◽  
Bidirectional Reflectance Distribution

Abstract. This paper describes the latest version of the algorithm MAIAC used for processing the MODIS Collection 6 data record. Since initial publication in 2011–2012, MAIAC has changed considerably to adapt to global processing and improve cloud/snow detection, aerosol retrievals and atmospheric correction of MODIS data. The main changes include (1) transition from a 25 to 1 km scale for retrieval of the spectral regression coefficient (SRC) which helped to remove occasional blockiness at 25 km scale in the aerosol optical depth (AOD) and in the surface reflectance, (2) continuous improvements of cloud detection, (3) introduction of smoke and dust tests to discriminate absorbing fine- and coarse-mode aerosols, (4) adding over-water processing, (5) general optimization of the LUT-based radiative transfer for the global processing, and others. MAIAC provides an interdisciplinary suite of atmospheric and land products, including cloud mask (CM), column water vapor (CWV), AOD at 0.47 and 0.55 µm, aerosol type (background, smoke or dust) and fine-mode fraction over water; spectral bidirectional reflectance factors (BRF), parameters of Ross-thick Li-sparse (RTLS) bidirectional reflectance distribution function (BRDF) model and instantaneous albedo. For snow-covered surfaces, we provide subpixel snow fraction and snow grain size. All products come in standard HDF4 format at 1 km resolution, except for BRF, which is also provided at 500 m resolution on a sinusoidal grid adopted by the MODIS Land team. All products are provided on per-observation basis in daily files except for the BRDF/Albedo product, which is reported every 8 days. Because MAIAC uses a time series approach, BRDF/Albedo is naturally gap-filled over land where missing values are filled-in with results from the previous retrieval. While the BRDF model is reported for MODIS Land bands 1–7 and ocean band 8, BRF is reported for both land and ocean bands 1–12. This paper focuses on MAIAC cloud detection, aerosol retrievals and atmospheric correction and describes MCD19 data products and quality assurance (QA) flags.

scholarly journals A total sky cloud detection method using real clear sky background

2016 ◽  
Vol 9 (2) ◽  
pp. 587-597 ◽  
Author(s):  
Jun Yang ◽  
Qilong Min ◽  
Weitao Lu ◽  
Ying Ma ◽  
Wen Yao ◽  
...  
Keyword(s):  
Detection Algorithm ◽  
Brightness Distribution ◽  
Detection Methods ◽  
Cloud Detection ◽  
Image Brightness ◽  
Clear Sky ◽  
Sensitivity Tests ◽  
Thin Cloud ◽  
Distribution Sensitivity ◽  
Solar Position

Abstract. The brightness distribution of sky background is usually non-uniform, which creates many problems for traditional cloud detection methods, including the failure of thin cloud detection in total sky images and significantly reducing retrieval accuracy in the circumsolar and near-horizon regions. This paper describes the development of a new cloud detection algorithm, named "clear sky background differencing (CSBD)", which is accomplished by differencing the original image and the corresponding clear sky background image using the images' green channel. First, a library of clear sky background images with a variety of solar elevation angles needs to be developed. The image rotation and image brightness adjustment algorithms are applied to ensure the two images being differenced have the same solar position and similar brightness distribution. Sensitivity tests show that the cloud detection results are satisfactory when the two images have the same solar positions. Several experimental cases show that the CSBD algorithm obtains good cloud recognition results visually, especially for thin clouds.

scholarly journals An improved cloud index for estimating downwelling surface solar irradiance from various satellite imagers in the framework of a Heliosat-V method

2021 ◽  
Author(s):  
Benoît Tournadre ◽  
Benoît Gschwind ◽  
Yves-Marie Saint-Drenan ◽  
Philippe Blanc
Keyword(s):  
Solar Irradiance ◽  
Surface Radiation ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Bidirectional Reflectance ◽  
Clear Sky ◽  
Derived Data ◽  
Bidirectional Reflectance Distribution ◽  
Visible Imagery

Abstract. We develop a new way to retrieve the cloud index from a large variety of satellite instruments sensitive to reflected solar radiation, embedded on geostationary as non geostationary platforms. The cloud index is a widely used proxy for the effective cloud transmissivity, also called clear-sky index. This study is in the framework of the development of the Heliosat-V method for estimating downwelling solar irradiance at the surface of the Earth (DSSI) from satellite imagery. To reach its versatility, the method uses simulations from a fast radiative transfer model to estimate overcast (cloudy) and clear-sky (cloud-free) satellite scenes of the Earth’s reflectances. Simulations consider the anisotropy of the reflectances caused by both surface and atmosphere, and are adapted to the spectral sensitivity of the sensor. The anisotropy of ground reflectances is described by a bidirectional reflectance distribution function model and external satellite-derived data. An implementation of the method is applied to the visible imagery from a Meteosat Second Generation satellite, for 11 locations where high quality in situ measurements of DSSI are available from the Baseline Surface Radiation Network. Results from our preliminary implementation of Heliosat-V and ground-based measurements show a correlation coefficient reaching 0.948, for 15-minute means of DSSI, similar to operational and corrected satellite-based data products (0.950 for HelioClim3 version 5 and 0.937 for CAMS Radiation Service).

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