scholarly journals Detection of Optically Thin Mineral Dust Aerosol Layers over the Ocean Using MODIS

2013 ◽  
Vol 30 (5) ◽  
pp. 896-916 ◽  
Author(s):  
Hyoun-Myoung Cho ◽  
Shaima L. Nasiri ◽  
Ping Yang ◽  
Istvan Laszlo ◽  
Xuepeng “Tom” Zhao
Keyword(s):  
Standard Deviation ◽  
Brightness Temperature ◽  
Ocean Surface ◽  
Training Dataset ◽  
Parameter Method ◽  
Detection Techniques ◽  
Clear Sky ◽  
Dust Detection ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract Analyses show that several existing Moderate Resolution Imaging Spectroradiometer (MODIS) dust detection techniques, including an approach based on simple brightness temperature difference thresholds, the D-parameter method, and the multichannel image (MCI) algorithm, may be more effective for detection of highly concentrated dust plumes than for thin dust layers. Using the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) cloud and aerosol classification as a reference, the sensitivities of six MODIS radiative parameters (including brightness temperature differences, and standard deviation and ratios of reflectances) to cloud, clear sky, and dust layers are examined in this paper. Reflectance ratios and the standard deviation of reflectances were confirmed to be useful in the discrimination of dust from cloud and underlying ocean surface, while brightness temperature differences alone were not sufficient to separate dust from cloud and clear sky over the ocean surface. Using a collocated MODIS and CALIPSO training dataset from 2008, visible and infrared MODIS radiative parameters from six latitude bands and four seasons were combined using linear and quadratic discriminant analyses to develop a new algorithm for the detection of optically thin dust over the ocean. The validation using collocated MODIS and CALIPSO data from 2009 shows that the present algorithm is effective in detecting thin dust layers having optical thicknesses between 0.1 and 2.0, but that it tends to misclassify optically thicker dust layers as clouds.

Test of a Modified Infrared-Only ABI Cloud Mask Algorithm for AHI Radiance Observations

2016 ◽  
Vol 55 (11) ◽  
pp. 2529-2546 ◽  
Author(s):  
X. Zhuge ◽  
X. Zou
Keyword(s):  
Near Infrared ◽  
Extended System ◽  
Clear Sky ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Cloud Mask ◽  
Very High ◽  
Lower False Alarm Rate ◽  
Infrared Channel

AbstractAssimilation of infrared channel radiances from geostationary imagers requires an algorithm that can separate cloudy radiances from clear-sky ones. An infrared-only cloud mask (CM) algorithm has been developed using the Advanced Himawari Imager (AHI) radiance observations. It consists of a new CM test for optically thin clouds, two modified Advanced Baseline Imager (ABI) CM tests, and seven other ABI CM tests. These 10 CM tests are used to generate composite CMs for AHI data, which are validated by using the Moderate Resolution Imaging Spectroradiometer (MODIS) CMs. It is shown that the probability of correct typing (PCT) of the new CM algorithm over ocean and over land is 89.73% and 90.30%, respectively and that the corresponding leakage rates (LR) are 6.11% and 4.21%, respectively. The new infrared-only CM algorithm achieves a higher PCT and a lower false-alarm rate (FAR) over ocean than does the Clouds from the Advanced Very High Resolution Radiometer (AVHRR) Extended System (CLAVR-x), which uses not only the infrared channels but also visible and near-infrared channels. A slightly higher FAR of 7.92% and LR of 6.18% occurred over land during daytime. This result requires further investigation.

scholarly journals Satellite-Based Estimation of Temporally Resolved Dust Radiative Forcing in Snow Cover

2016 ◽  
Vol 17 (7) ◽  
pp. 1999-2011 ◽  
Author(s):  
Steven D. Miller ◽  
Fang Wang ◽  
Ann B. Burgess ◽  
S. McKenzie Skiles ◽  
Matthew Rogers ◽  
...  
Keyword(s):  
Solar Irradiance ◽  
Radiative Forcing ◽  
Hydrological Cycle ◽  
Clear Sky ◽  
Operational Management ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Sky Conditions

Abstract Runoff from mountain snowpack is an important freshwater supply for many parts of the world. The deposition of aeolian dust on snow decreases snow albedo and increases the absorption of solar irradiance. This absorption accelerates melting, impacting the regional hydrological cycle in terms of timing and magnitude of runoff. The Moderate Resolution Imaging Spectroradiometer (MODIS) Dust Radiative Forcing in Snow (MODDRFS) satellite product allows estimation of the instantaneous (at time of satellite overpass) surface radiative forcing caused by dust. While such snapshots are useful, energy balance modeling requires temporally resolved radiative forcing to represent energy fluxes to the snowpack, as modulated primarily by varying cloud cover. Here, the instantaneous MODDRFS estimate is used as a tie point to calculate temporally resolved surface radiative forcing. Dust radiative forcing scenarios were considered for 1) clear-sky conditions and 2) all-sky conditions using satellite-based cloud observations. Comparisons against in situ stations in the Rocky Mountains show that accounting for the temporally resolved all-sky solar irradiance via satellite retrievals yields a more representative time series of dust radiative effects compared to the clear-sky assumption. The modeled impact of dust on enhanced snowmelt was found to be significant, accounting for nearly 50% of the total melt at the more contaminated station sites. The algorithm is applicable to regional basins worldwide, bearing relevance to both climate process research and the operational management of water resources.

scholarly journals A Supplementary Clear-Sky Snow and Ice Recognition Technique for CERES Level 2 Products

2013 ◽  
Vol 30 (3) ◽  
pp. 557-568 ◽  
Author(s):  
Alexander Radkevich ◽  
Konstantin Khlopenkov ◽  
David Rutan ◽  
Seiji Kato
Keyword(s):  
Confidence Intervals ◽  
Radiant Energy ◽  
Energy System ◽  
Radiation Budget ◽  
Data Series ◽  
Clear Sky ◽  
Multiple Thresholds ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Snow And Ice

Abstract Identification of clear-sky snow and ice is an important step in the production of cryosphere radiation budget products, which are used in the derivation of long-term data series for climate research. In this paper, a new method of clear-sky snow/ice identification for Moderate Resolution Imaging Spectroradiometer (MODIS) is presented. The algorithm’s goal is to enhance the identification of snow and ice within the Clouds and the Earth’s Radiant Energy System (CERES) data after application of the standard CERES scene identification scheme. The input of the algorithm uses spectral radiances from five MODIS bands and surface skin temperature available in the CERES Single Scanner Footprint (SSF) product. The algorithm produces a cryosphere rating from an aggregated test: a higher rating corresponds to a more certain identification of the clear-sky snow/ice-covered scene. Empirical analysis of regions of interest representing distinctive targets such as snow, ice, ice and water clouds, open waters, and snow-free land selected from a number of MODIS images shows that the cryosphere rating of snow/ice targets falls into 95% confidence intervals lying above the same confidence intervals of all other targets. This enables recognition of clear-sky cryosphere by using a single threshold applied to the rating, which makes this technique different from traditional branching techniques based on multiple thresholds. Limited tests show that the established threshold clearly separates the cryosphere rating values computed for the cryosphere from those computed for noncryosphere scenes, whereas individual tests applied consequently cannot reliably identify the cryosphere for complex scenes.

scholarly journals Automated Discrimination of Certain Brightness Fronts in RADARSAT-2 Images of the Ocean Surface

2013 ◽  
Vol 30 (9) ◽  
pp. 2203-2215 ◽  
Author(s):  
Chris T. Jones ◽  
Todd D. Sikora ◽  
Paris W. Vachon ◽  
John Wolfe ◽  
Brendan DeTracey
Keyword(s):  
Ocean Surface ◽  
Difficult Problem ◽  
Horizontal Wind ◽  
Automated Classification ◽  
Sar Images ◽  
Contextual Features ◽  
Moderate Resolution ◽  
Canny Edge ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Occurrence Matrix

Abstract Automated classification of the signatures of atmospheric and oceanic processes in synthetic aperture radar (SAR) images of the ocean surface has been a difficult problem, partly because different processes can produce signatures that are very similar in appearance. For example, brightness fronts that are the signatures of horizontal wind shear caused by atmospheric processes that occur independently of properties of the ocean (WIN herein) often appear very similar to brightness fronts that are signatures of sea surface temperature (SST) fronts (SST herein). Using Moderate Resolution Imaging Spectroradiometer (MODIS)-derived SST for validation, 302 SAR SST and 193 SAR WIN signatures were collected from over 250 RADARSAT-2 images of the Gulf Stream region using a Canny edge detector. A vector consisting of textural and contextual features was extracted from each signature and used to train and test logistic regression, maximum likelihood, and binary tree classifiers. Following methods proven effective in the analysis of SAR images of sea ice, textural features included those computed from the gray-level co-occurrence matrix for regions along and astride each signature. Contextual features consisted of summaries of the wind vector field near each signature. Results indicate that signatures labeled SST can be automatically discriminated from signatures labeled WIN using the mean wind direction with respect to a brightness front with an accuracy of between 80% and 90%.

scholarly journals Assessment of MISR Cloud Motion Vectors (CMVs) Relative to GOES and MODIS Atmospheric Motion Vectors (AMVs)

2017 ◽  
Vol 56 (3) ◽  
pp. 555-572 ◽  
Author(s):  
Kevin J. Mueller ◽  
Dong L. Wu ◽  
Ákos Horváth ◽  
Veljko M. Jovanovic ◽  
Jan-Peter Muller ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Motion Vector ◽  
Motion Vectors ◽  
Cloud Motion ◽  
Modis Imagery ◽  
Moderate Resolution ◽  
Atmospheric Motion ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Atmospheric Motion Vectors ◽  
Along Track

AbstractCloud motion vector (CMV) winds retrieved from the Multiangle Imaging SpectroRadiometer (MISR) instrument on the polar-orbiting Terra satellite from 2003 to 2008 are compared with collocated atmospheric motion vectors (AMVs) retrieved from Geostationary Operational Environmental Satellite (GOES) imagery over the tropics and midlatitudes and from Moderate Resolution Imaging Spectroradiometer (MODIS) imagery near the poles. MISR imagery from multiple view angles is exploited to jointly retrieve stereoscopic cloud heights and motions, showing advantages over the AMV heights assigned by radiometric means, particularly at low heights (<3 km) that account for over 95% of MISR CMV sampling. MISR–GOES wind differences exhibit a standard deviation ranging with increasing height from 3.3 to 4.5 m s−1 for a high-quality [quality indicator (QI) ≥ 80] subset where height differences are <1.5 km. Much of the observed difference can be attributed to the less accurately retrieved component of CMV motion along the direction of satellite motion. MISR CMV retrieval is subject to correlation between error in retrieval of this along-track component and of height. This manifests as along-track bias varying with height to magnitudes as large as 2.5 m s−1. The cross-track component of MISR CMVs shows small (<0.5 m s−1) bias and standard deviation of differences (1.7 m s−1) relative to GOES AMVs. Larger differences relative to MODIS are attributed to the tracking of cloud features at heights lower than MODIS in multilayer cloud scenes.

scholarly journals Observed HIRS and MODIS High-Cloud Frequencies in the 2000s

2019 ◽  
Vol 58 (11) ◽  
pp. 2469-2478
Author(s):  
Richard A. Frey ◽  
W. Paul Menzel
Keyword(s):  
Cloud Detection ◽  
High Cloud ◽  
Clear Sky ◽  
Spectral Bands ◽  
Moderate Resolution ◽  
Summer Maximum ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Cloud Parameter ◽  
Cloud Top Pressure

AbstractThis paper compares the cloud parameter data records derived from High Resolution Infrared Radiation Sounder (HIRS) and Moderate Resolution Imaging Spectroradiometer (MODIS) measurements from the years 2003 through 2013. Cloud-top pressure (CTP) and effective emissivity (εf; cloud emissivity multiplied by cloud fraction) are derived using the 15-μm spectral bands in the CO2 absorption band and implementing the CO2-slicing technique; the approach is robust for high semitransparent clouds but weak for low clouds with little thermal contrast from clear-sky radiances. The high-cloud (HiCld; with CTP less than 440 hPa) seasonal cycles of HIRS and MODIS observations are found to be in sync, but the HIRS frequency of detection is about 10% higher than that of MODIS (which is attributed to a lower threshold for cloud detection in the HIRS CO2 bands). Differences are largest during nighttime and at the beginning of the time series (2003–06). Both show Northern Hemisphere (NH) and Southern Hemisphere (SH) seasonal HiClds are out of phase and both agree within 2% on NH–SH HiCld differences. During the summer, maximum HiCld frequency averages 5% more in the NH.

scholarly journals Radiative and Microphysical Properties of Cirrus Cloud Inferred from Infrared Measurements Made by the Moderate Resolution Imaging Spectroradiometer (MODIS). Part I: Retrieval Method

2014 ◽  
Vol 53 (5) ◽  
pp. 1297-1316 ◽  
Author(s):  
Hironobu Iwabuchi ◽  
Soichiro Yamada ◽  
Shuichiro Katagiri ◽  
Ping Yang ◽  
Hajime Okamoto
Keyword(s):  
Brightness Temperature ◽  
Optical Thickness ◽  
Optimal Estimation ◽  
Particle Radius ◽  
Cirrus Cloud ◽  
Microphysical Properties ◽  
Effective Particle ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

AbstractAn optimal estimation–based algorithm is developed to infer the global-scale distribution of cirrus cloud radiative and microphysical properties from the measurements made by the Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) at three infrared (IR) window bands centered at 8.5, 11, and 12 μm. Cloud-top and underlying surface temperatures, as a priori information, are obtained from the MODIS operational products. A fast-forward model based on semianalytical equations for the brightness temperature is used. The modeling errors in brightness temperature are mainly from the uncertainties in model parameters including surface emissivity, precipitable water, and cloud-base temperature. The total measurement–model errors are well correlated for the three bands, which are considered in the retrieval. The most important factors for the accurate retrieval of cloud optical thickness and the effective particle radius are cloud-top and surface temperatures, whereas model parameter uncertainties constitute a moderately significant error source. The three-band IR method is suitable for retrieving optical thickness and effective radius for cloud optical thicknesses within a range of 0.5–6, where the typical root-mean-square error is less than 20% in optical thickness and less than 40% in effective particle radius. A tropical-region case study demonstrates the advantages of the method—in particular, the ability to be applied to more pixels in optically thin cirrus in comparison with a solar-reflection-based method—and the ability of the optimal estimation framework to produce useful diagnostics of the retrieval quality. Collocated comparisons with spaceborne active remote sensing data exhibit reasonable consistency with respect to retrieved particle size.

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