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.

scholarly journals Marine liquid cloud geometric thickness retrieved from OCO-2's oxygen A-band spectrometer

2019 ◽  
Vol 12 (3) ◽  
pp. 1717-1737 ◽  
Author(s):  
Mark Richardson ◽  
Jussi Leinonen ◽  
Heather Q. Cronk ◽  
James McDuffie ◽  
Matthew D. Lebsock ◽  
...  
Keyword(s):  
Optical Depth ◽  
Single Layer ◽  
Cloud Droplet ◽  
Satellite Observation ◽  
Cloud Optical Depth ◽  
First Case ◽  
Phase Classification ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Cloud Top Pressure ◽  
Geometric Thickness

Abstract. This paper introduces the OCO2CLD-LIDAR-AUX product, which uses the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) lidar and the Orbiting Carbon Observatory-2 (OCO-2) hyperspectral A-band spectrometer. CALIPSO provides a prior cloud top pressure (Ptop) for an OCO-2-based retrieval of cloud optical depth, Ptop and cloud geometric thickness expressed in hPa. Measurements are of single-layer liquid clouds over oceans from September 2014 to December 2016 when collocated data are available. Retrieval performance is best for solar zenith angles <45∘ and when the cloud phase classification, which also uses OCO-2's weak CO2 band, is more confident. The highest quality optical depth retrievals agree with those from the Moderate Resolution Imaging Spectroradiometer (MODIS) with discrepancies smaller than the MODIS-reported uncertainty. Retrieved thicknesses are consistent with a substantially subadiabatic structure over marine stratocumulus regions, in which extinction is weighted towards the cloud top. Cloud top pressure in these clouds shows a 4 hPa bias compared with CALIPSO which we attribute mainly to the assumed vertical structure of cloud extinction after showing little sensitivity to the presence of CALIPSO-identified aerosol layers or assumed cloud droplet effective radius. This is the first case of success in obtaining internal cloud structure from hyperspectral A-band measurements and exploits otherwise unused OCO-2 data. This retrieval approach should provide additional constraints on satellite-based estimates of cloud droplet number concentration from visible imagery, which rely on parameterization of the cloud thickness.

scholarly journals Overview of MPLNET Version 3 Cloud Detection

2016 ◽  
Vol 33 (10) ◽  
pp. 2113-2134 ◽  
Author(s):  
Jasper R. Lewis ◽  
James R. Campbell ◽  
Ellsworth J. Welton ◽  
Sebastian A. Stewart ◽  
Phillip C. Haftings
Keyword(s):  
Optical Depth ◽  
Space Flight ◽  
Signal To Noise Ratio ◽  
Cloud Detection ◽  
Signal To Noise ◽  
Cloud Optical Depth ◽  
Goddard Space ◽  
Goddard Space Flight Center ◽  
Noise Ratio ◽  
Micro Pulse Lidar

AbstractThe National Aeronautics and Space Administration Micro Pulse Lidar Network, version 3, cloud detection algorithm is described and differences relative to the previous version are highlighted. Clouds are identified from normalized level 1 signal profiles using two complementary methods. The first method considers vertical signal derivatives for detecting low-level clouds. The second method, which detects high-level clouds like cirrus, is based on signal uncertainties necessitated by the relatively low signal-to-noise ratio exhibited in the upper troposphere by eye-safe network instruments, especially during daytime. Furthermore, a multitemporal averaging scheme is used to improve cloud detection under conditions of a weak signal-to-noise ratio. Diurnal and seasonal cycles of cloud occurrence frequency based on one year of measurements at the Goddard Space Flight Center (Greenbelt, Maryland) site are compared for the new and previous versions. The largest differences, and perceived improvement, in detection occurs for high clouds (above 5 km, above MSL), which increase in occurrence by over 5%. There is also an increase in the detection of multilayered cloud profiles from 9% to 19%. Macrophysical properties and estimates of cloud optical depth are presented for a transparent cirrus dataset. However, the limit to which the cirrus cloud optical depth could be reliably estimated occurs between 0.5 and 0.8. A comparison using collocated CALIPSO measurements at the Goddard Space Flight Center and Singapore Micro Pulse Lidar Network (MPLNET) sites indicates improvements in cloud occurrence frequencies and layer heights.

scholarly journals Coupling sky images with three-dimensional radiative transfer models: a new method to estimate cloud optical depth

2015 ◽  
Vol 8 (10) ◽  
pp. 11285-11321 ◽  
Author(s):  
F. A. Mejia ◽  
B. Kurtz ◽  
K. Murray ◽  
L. M. Hinkelman ◽  
M. Sengupta ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Optical Depth ◽  
Zenith Angle ◽  
Microwave Radiometer ◽  
Three Dimensional ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Cloud Detection ◽  
Cloud Optical Depth ◽  
Direct Normal Irradiance

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 (&amp;vartheta;s), and pixel zenith angle/view angle (&amp;vartheta;z). The effects of these parameters are described and the functions for radiance, Iλ(τc, θ0, &amp;vartheta;s, &amp;vartheta;z) and the red-blue ratio, RBR(τc, θ0, &amp;vartheta;s, &amp;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(&amp;vartheta;s, &amp;vartheta;z), in addition to RBRmeas(&amp;vartheta;s, &amp;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.

scholarly journals Combination of AIRS Dual CO2 Absorption Bands to Develop an Ice Clouds Detection Algorithm in Different Atmospheric Layers

2019 ◽  
Vol 12 (1) ◽  
pp. 6 ◽  
Author(s):  
Liwen Wang ◽  
Youfei Zheng ◽  
Chao Liu ◽  
Zeyi Niu ◽  
Jingxin Xu ◽  
...  
Keyword(s):  
Linear Relationship ◽  
Weighting Function ◽  
Detection Algorithm ◽  
Probability Of Detection ◽  
Orthogonal Polarization ◽  
Co2 Absorption ◽  
Cloud Detection ◽  
Absorption Bands ◽  
Ice Clouds ◽  
Ice Cloud

The use of infrared (IR) sensors to detect clouds in different layers of the atmosphere is a big challenge, especially for ice clouds. This study aims to improve ice cloud detection using Lin’s algorithm and apply it to Atmospheric Infrared Sounder (AIRS). To achieve these objectives, the scattering and emission characteristics of clouds as perceived by AIRS longwave infrared (LWIR, ~15 μm) and shortwave infrared (SWIR, ~4.3 μm) CO2 absorption bands are applied for ice cloud detection. Hence, the weighting function peak (WFP), cut-off pressure, and correlation coefficients between the brightness temperatures (BTs) of LWIR and SWIR channels are used to pair the LWIR and SWIR channels. After that, the linear relationship between the clear-sky BTs of the paired LWIR and SWIR channels is established by the cloud scattering and emission Index (CESI). However, the linear relationship fails in the presence of ice clouds. Comparing these results with collocated Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) observations show that the probability of detection of ice clouds for Pair-8 (WFP~330hPa), Pair-19 (WFP~555hPa), and Pair-24 (WFP~866hPa) are 0.63, 0.71, and 0.73 in the daytime and 0.46, 0.62, and 0.7 in the nighttime at a false alarm rate of 0.1 when ice clouds top pressure above 330 hPa, 555 hPa, and 866 hPa, respectively. Furthermore, the thresholds of the three pairs are 2.4 K, 3 K, and 8.7 K in the daytime and 1.7 K, 1.7 K, and 4.4 K in the nighttime at the highest Heike Skill Score (HSS). The error of HSS values based on thresholds of ice clouds is between 0.01 and 0.02 which is comparable with the ice cloud detection results in both day and night conditions. It is shown that Pair-8 (WFP~330hPa) can detect opaque and thick ice clouds above its WFP altitude over the tropical areas but it is unable to observe ice clouds over the mid-latitude while Pair-19 and Pair-24 can identify ice clouds above their WFP altitude.

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 Estimating nocturnal opaque ice cloud optical depth from MODIS multispectral infrared radiances using a neural network method

2016 ◽  
Vol 121 (9) ◽  
pp. 4907-4932 ◽  
Author(s):  
Patrick Minnis ◽  
Gang Hong ◽  
Szedung Sun‐Mack ◽  
William L. Smith ◽  
Yan Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Optical Depth ◽  
Cloud Optical Depth ◽  
Neural Network Method ◽  
Ice Cloud ◽  
Network Method ◽  
Infrared Radiances

scholarly journals An Initial Study on Climate Change Fingerprinting Using the Reflected Solar Spectra

2016 ◽  
Vol 29 (8) ◽  
pp. 2781-2796 ◽  
Author(s):  
Zhonghai Jin ◽  
Moguo Sun
Keyword(s):  
Climate Change ◽  
Optical Depth ◽  
A Priori ◽  
Initial Study ◽  
Spectral Variation ◽  
Climate Variables ◽  
Cloud Optical Depth ◽  
Spatial And Temporal Scales ◽  
Retrieval Accuracy ◽  
Clear Sky

Abstract Attribution of averaged spectral variation over large spatial and temporal scales to different climate variables is central to climate change fingerprinting. Using 10 years of satellite data for simulation, the authors generate a group of observation-based spectral fingerprints and a time series of monthly mean reflectance spectra over the ocean in five large latitude regions and globally. Next, these fingerprints and the interannual variation spectra are used to retrieve the interannual changes in the relevant climate variables to test the concept of using the spectral fingerprinting approach for climate change attribution. Comparing the fingerprinting retrieval of climate variable change to the actual underlying variable change, the RMS differences between the two are less than twice as large as the monthly variability for all variables in all regions. Instances where larger errors are observed correspond to those variables with large nonlinear radiative response, such as the cloud optical depth and the ice particle size. Using the linear fingerprinting approach and accounting for the nonlinear radiative error in fingerprints results in significantly higher retrieval accuracy; the RMS errors are reduced to less than the monthly variability for nearly all variables, indicating the profound impact of the nonlinear error on fingerprinting retrieval. Another important finding is that if the cloud fraction is known a priori, the retrieval accuracy in cloud optical depth would be improved substantially. Moreover, a better retrieval for the water vapor amount and aerosol optical depth can be achieved from the clear-sky data only. The test results demonstrate that climate change fingerprinting based on reflected solar benchmark spectra is possible.

scholarly journals Assessment of cloud-related fine-mode AOD enhancements based on AERONET SDA product

2017 ◽  
Vol 17 (9) ◽  
pp. 5991-6001 ◽  
Author(s):  
Antti Arola ◽  
Thomas F. Eck ◽  
Harri Kokkola ◽  
Mikko R. A. Pitkänen ◽  
Sami Romakkaniemi
Keyword(s):  
Optical Depth ◽  
Relative Role ◽  
Hygroscopic Growth ◽  
Cloud Optical Depth ◽  
Cloud Processing ◽  
Clear Sky ◽  
Coarse Mode ◽  
Fine Mode ◽  
L1 And L2 ◽  
Sky Conditions

Abstract. AERONET (AErosol RObotic NETwork), which is a network of ground-based sun photometers, produces a data product called the aerosol spectral deconvolution algorithm (SDA) that utilizes spectral total aerosol optical depth (AOD) data to infer the component fine- and coarse-mode optical depths at 500 nm. Based on its assumptions, SDA identifies cloud optical depth as the coarse-mode AOD component and therefore effectively computes the fine-mode AOD also in mixed cloud–aerosol observations. Therefore, it can be argued that the more representative AOD for fine-mode fraction should be based on all direct sun measurements and not only on those cloud screened for clear-sky conditions, i.e., on those from level 1 (L1) instead of level 2 (L2) in AERONET. The objective of our study was to assess, including all the available AERONET sites, how the fine-mode AOD is enhanced in cloudy conditions, contrasting SDA L1 and L2 in our analysis. Assuming that the cloud screening correctly separates the cloudy and clear-sky conditions, then the increases in fine-mode AOD can be due to various cloud-related processes, mainly by the strong hygroscopic growth of particles in the vicinity of clouds and in-cloud processing leading to growth of accumulation mode particles. We estimated these cloud-related enhancements in fine-mode AOD seasonally and found, for instance, that in June–August season the average over all the AERONET sites was 0.011, when total fine-mode AOD from L2 data was 0.154; therefore, the relative enhancement was 7 %. The enhancements were largest, both absolutely and relatively, in East Asia; for example, in June–August season the absolute and relative differences in fine-mode AOD, between L1 and L2 measurements, were 0.022 and 10 %, respectively. Corresponding values in North America and Europe were about 0.01 and 6–7 %. In some highly polluted areas, the enhancement is greater than these regional averages, e.g., in Beijing region and in June–July–August (JJA) season the corresponding absolute values were about 0.1. It is difficult to separate the fine-mode AOD enhancements due to in-cloud processing and hygroscopic growth, but we attempted to get some understanding by conducting a similar analysis for SDA-based fine-mode Ångström exponent (AE) patterns. Moreover, we exploited a cloud parcel model, in order to understand in detail the relative role of different processes. We found that in marine conditions, were aerosol concentration are low and cloud scavenging is efficient, the AE changes in opposite direction than in the more polluted conditions, were hygroscopic growth of particles leads to a negative AE change.

scholarly journals Assimilation of AIRS Radiances Affected by Mid- to Low-Level Clouds

2009 ◽  
Vol 137 (12) ◽  
pp. 4276-4292 ◽  
Author(s):  
Thomas Pangaud ◽  
Nadia Fourrie ◽  
Vincent Guidard ◽  
Mohamed Dahoui ◽  
Florence Rabier
Keyword(s):  
Weather Prediction ◽  
Numerical Weather Prediction Model ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Cloud Detection ◽  
Southeastern Part ◽  
Pressure System ◽  
Detection Scheme ◽  
Cloud Top Pressure

Abstract An approach to make use of Atmospheric Infrared Sounder (AIRS) cloud-affected infrared radiances has been developed at Météo-France in the context of the global numerical weather prediction model. The method is based on (i) the detection and the characterization of clouds by the CO2-slicing algorithm and (ii) the identification of clear–cloudy channels using the ECMWF cloud-detection scheme. Once a hypothetical cloud-affected pixel is detected by the CO2-slicing scheme, the cloud-top pressure and the effective cloud fraction are provided to the radiative transfer model simultaneously with other atmospheric variables to simulate cloud-affected radiances. Furthermore, the ECMWF scheme flags each channel of the pixel as clear or cloudy. In the current configuration of the assimilation scheme, channels affected by clouds whose cloud-top pressure ranges between 600 and 950 hPa are assimilated over sea in addition to clear channels. Results of assimilation experiments are presented. On average, 3.5% of additional pixels are assimilated over the globe but additional assimilated channels are much more numerous for mid- to high latitudes (10% of additional assimilated channels on average). Encouraging results are found in the quality of the analyses: background departures of AIRS observations are reduced, especially for surface channels, which are globally 4 times smaller, and the analysis better fits some conventional and satellite data. Global forecasts are slightly improved for the geopotential field. These improvements are significant up to the 72-h forecast range. Predictability improvements have been obtained for a case study: a low pressure system that affected the southeastern part of Italy in September 2006. The trajectory, intensity, and the whole development of the cyclogenesis are better predicted, whatever the forecast range, for this case study.

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