scholarly journals Identification of Small Ice Cloud Particles Using Passive Radiometric Observations

2010 ◽  
Vol 49 (11) ◽  
pp. 2334-2347 ◽  
Author(s):  
Steven J. Cooper ◽  
Timothy J. Garrett
Keyword(s):  
Near Infrared ◽  
Cirrus Clouds ◽  
Ice Crystals ◽  
Regional Study ◽  
Ice Cloud ◽  
Window Technique ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Tropics ◽  
The Impact

Abstract There is currently significant uncertainty about the extent to which cirrus clouds are composed of “small” ice crystals smaller than about 20-μm effective radius. This is due in part to concerns that in situ measurements from aircraft are plagued by ice particle shattering on instrument inlets, artificially negatively biasing effective radii. Here, space-based measurements are applied to the problem. It is found that a space-based infrared split-window technique is less sensitive but more accurate than a visible-near-infrared technique for confident assessment of whether thin cirrus clouds have small effective radii, independent of a normal range of retrieval assumptions. Because of the sensitivities of the infrared split-window technique, however, this method can only accurately determine the presence of small particles for ice clouds with optical depths between roughly 0.5 and 3.0. Applied to Moderate Resolution Imaging Spectroradiometer (MODIS) data, it is found that a very conservative minimum of 15%–20% of such thin cirrus globally are composed of small ice crystals, but that the actual value could be as high as 40%, and even higher for cold clouds or those in the tropics. Retrievals are found to be in good agreement with airborne probe measurements from the Cirrus Regional Study of Tropical Anvils and Cirrus Layers–Florida-Area Cirrus Experiment (CRYSTAL-FACE) field campaign, implying that, for the cases examined, the impact of inlet shattering on measurements must have been limited.

Distribution and Radiative Forcing of Tropical Thin Cirrus Clouds

2009 ◽  
Vol 66 (12) ◽  
pp. 3721-3731 ◽  
Author(s):  
Joonsuk Lee ◽  
Ping Yang ◽  
Andrew E. Dessler ◽  
Bo-Cai Gao ◽  
Steven Platnick
Keyword(s):  
Radiative Forcing ◽  
Cirrus Clouds ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Atmospheric Infrared Sounder ◽  
Ice Cloud ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Autumn And Winter ◽  
Cloud Mask

Abstract To understand the radiative impact of tropical thin cirrus clouds, the frequency of occurrence and optical depths of these clouds have been derived. “Thin” cirrus clouds are defined here as being those that are not detected by the operational Moderate Resolution Imaging Spectroradiometer (MODIS) cloud mask, corresponding to an optical depth value of approximately 0.3 or smaller, but that are detectable in terms of the cirrus reflectance product based on the MODIS 1.375-μm channel. With such a definition, thin cirrus clouds were present in more than 40% of the pixels flagged as “clear sky” by the operational MODIS cloud mask algorithm. It is shown that these thin cirrus clouds are frequently observed in deep convective regions in the western Pacific. Thin cirrus optical depths were derived from the cirrus reflectance product. Regions of significant cloud fraction and large optical depths were observed in the Northern Hemisphere during the boreal spring and summer and moved southward during the boreal autumn and winter. The radiative effects of tropical thin cirrus clouds were studied on the basis of the retrieved cirrus optical depths, the atmospheric profiles derived from the Atmospheric Infrared Sounder (AIRS) observations, and a radiative transfer model in conjunction with a parameterization of ice cloud spectral optical properties. To understand how these clouds regulate the radiation field in the atmosphere, the instantaneous net fluxes at the top of the atmosphere (TOA) and at the surface were calculated. The present study shows positive and negative net forcings at the TOA and at the surface, respectively. The positive (negative) net forcing at the TOA (surface) is due to the dominance of longwave (shortwave) forcing. Both the TOA and surface forcings are in a range of 0–20 W m−2, depending on the optical depths of thin cirrus clouds.

scholarly journals Signature of tropical fires in the diurnal cycle of tropospheric CO as seen from Metop-A/IASI

2015 ◽  
Vol 15 (22) ◽  
pp. 13041-13057 ◽  
Author(s):  
T. Thonat ◽  
C. Crevoisier ◽  
N. A. Scott ◽  
A. Chédin ◽  
R. Armante ◽  
...  
Keyword(s):  
Quantitative Relation ◽  
Burned Area ◽  
Night Time ◽  
Fire Emissions ◽  
Diurnal Difference ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Area Product ◽  
The Tropics ◽  
The Impact

Abstract. Five years (July 2007 to June 2012) of CO tropospheric columns derived from the hyperspectral Infrared Atmospheric Sounding Interferometer (IASI) on-board Metop-A are used to study the impact of fires on the concentrations of CO in the troposphere. Following Chédin et al. (2005, 2008), who found a quantitative relation between the daily tropospheric excess of CO2 and fire emissions, we show that tropospheric CO also displays a diurnal signal with a seasonality that agrees well with the seasonal evolution of fires given by Global Fire Emission Database version 3 (GFED3.1) and Global Fire Assimilation System version 1 (GFAS1.0) emissions and Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 5 burned area product. Unlike day- or night-time CO fields, which mix local emissions with nearby emissions transported to the region of study, the day–night difference of CO allows to highlight the CO signal due to local fire emissions. A linear relationship between CO fire emissions from the GFED3.1 and GFAS1.0 inventories and the diurnal difference of IASI CO was found over various regions in the tropics, with a better agreement with GFAS1.0 (correlation coefficient of R2 ∼ 0.7) than GFED3.1 (R2 ∼ 0.6). Based on the specificity of the two main phases of the combustion (flaming vs. smoldering) and on the vertical sensitivity of the sounder to CO, the following mechanism is proposed to explain such a CO diurnal signal: at night, after the passing of IASI at 21:30 local time (LT), a large amount of CO emissions from the smoldering phase is trapped in the boundary layer before being uplifted the next morning by natural and pyroconvection up to the free troposphere, where it is seen by IASI at 09:30 LT. The results presented here highlight the need to take into account the specificity of both the flaming and smoldering phases of fire emissions in order to fully take advantage of CO observations.

scholarly journals Investigation of GOSAT TANSO-CAI Cloud Screening Ability through an Intersatellite Comparison

2011 ◽  
Vol 50 (7) ◽  
pp. 1571-1586 ◽  
Author(s):  
Haruma Ishida ◽  
Takashi Y. Nakjima ◽  
Tatsuya Yokota ◽  
Nobuyuki Kikuchi ◽  
Hiroshi Watanabe
Keyword(s):  
Near Infrared ◽  
Cirrus Clouds ◽  
Threshold Test ◽  
Four Seasons ◽  
Screening Algorithm ◽  
Moderate Resolution ◽  
Satellite Sensors ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Individual ◽  
Cloud Screening

AbstractIn this work, the Greenhouse Gases Observing Satellite (GOSAT) Thermal and Near-infrared Sensor for Carbon Observation–Cloud and Aerosol Imager (TANSO-CAI) cloud screening results, which are necessary for the retrieval of carbon dioxide (CO2) and methane (CH4) gas amounts from GOSAT TANSO–Fourier Transform Spectrometer (FTS) observations, are compared with results from Aqua/Moderate Resolution Imaging Spectroradiometer (MODIS) in four seasons. A large number of pixels, acquired from both satellites with nearly coincident locations and times, are extracted for statistical comparisons. The same cloud screening algorithm was applied to both satellite datasets to focus on the performance of the individual satellite sensors, without concern for differences in algorithms. The comparisons suggest that CAI is capable of discriminating between clear and cloudy areas over water without sun glint and also may be capable of identifying thin cirrus clouds, which are generally difficult to detect without thermal infrared or near-infrared bands. On the other hand, cloud screening over land by CAI resulted in greater cloudy discrimination than that by MODIS, whereas detection of thin cirrus clouds tended to be more difficult over land than water, resulting in incorrect identification of thin cirrus as clear. The amount of missed thin cirrus had a seasonal variation, with the maximum occurring in summer. The cloudy tendency of CAI over half vegetation is caused by lack of an effective threshold test that can be applied to MODIS. The statistical results of the comparison clarified the important points to consider when using the results of CAI cloud screening.

Daytime Cloud Overlap Detection from AVHRR and VIIRS

2004 ◽  
Vol 43 (5) ◽  
pp. 762-778 ◽  
Author(s):  
Michael J. Pavolonis ◽  
Andrew K. Heidinger
Keyword(s):  
Optical Depth ◽  
Near Infrared ◽  
Radar Data ◽  
Cloud Particle ◽  
Ice Cloud ◽  
Infrared Imager ◽  
Moderate Resolution ◽  
Sensitivity Studies ◽  
Water Cloud ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract Two algorithms for detecting multilayered cloud systems with satellite data are presented. The first algorithm utilizes data in the 0.65-, 11-, and 12-μm regions of the spectrum that are available on the Advanced Very High Resolution Radiometer (AVHRR). The second algorithm incorporates two different techniques to detect cloud overlap: the same technique used in the first algorithm and an additional series of spectral tests that now include data from the 1.38- and 1.65-μm near-infrared regions that are available on the Moderate Resolution Imaging Spectroradiometer (MODIS) and will be available on the Visible/Infrared Imager/Radiometer Suite (VIIRS). VIIRS is the imager that will replace the AVHRR on the next generation of polar-orbiting satellites. Both algorithms were derived assuming that a scene with cloud overlap consists of a semitransparent ice cloud that overlaps a cloud composed of liquid water droplets. Each algorithm was tested on three different MODIS scenes. In all three cases, the second (VIIRS) algorithm was able to detect more cloud overlap than the first (AVHRR) algorithm. Radiative transfer calculations indicate that the VIIRS algorithm will be more effective than the AVHRR algorithm when the visible optical depth of the ice cloud is greater than 3. Both algorithms will work best when the visible optical depth of the water cloud is greater than 5. Model sensitivity studies were also performed to assess the sensitivity of each algorithm to various parameters. It was found that the AVHRR algorithm is most sensitive to cloud particle size and the VIIRS near-infrared test is most sensitive to cloud vertical location. When validating each algorithm using cloud radar data, the VIIRS algorithm was shown to be more effective at detecting cloud overlap than the AVHRR algorithm; however, the VIIRS algorithm was slightly more prone to false cloud overlap detection.

scholarly journals Detection of multi-layer and vertically-extended clouds using A-train sensors

2009 ◽  
Vol 2 (5) ◽  
pp. 2707-2748 ◽  
Author(s):  
J. Joiner ◽  
A. P. Vasilkov ◽  
P. K. Bhartia ◽  
G. Wind ◽  
S. Platnick ◽  
...  
Keyword(s):  
Spatial Scales ◽  
Simple Algorithm ◽  
Ozone Monitoring Instrument ◽  
Infrared Observations ◽  
Summer Hemisphere ◽  
Moderate Resolution ◽  
Cloud Optical Thickness ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Tropics ◽  
Cloud Top Pressure

Abstract. The detection of multiple cloud layers using satellite observations is important for retrieval algorithms as well as climate applications. In this paper, we describe a relatively simple algorithm to detect multiple cloud layers and distinguish them from vertically-extended clouds. The algorithm can be applied to coincident passive sensors that derive both cloud-top pressure from the thermal infrared observations and an estimate of solar photon pathlength from UV, visible, or near-IR measurements. Here, we use data from the A-train afternoon constellation of satellites: cloud-top pressure, cloud optical thickness, and the multi-layer flag from the Aqua MODerate-resolution Imaging Spectroradiometer (MODIS) and the optical centroid cloud pressure from the Aura Ozone Monitoring Instrument (OMI). The cloud classification algorithms applied with different passive sensor configurations compare well with each other as well as with data from the A-train CloudSat radar. We compute monthly mean fractions of pixels containing multi-layer and vertically-extended clouds for January and July 2007 at the OMI spatial resolution (12 km×24 km at nadir) and at the 5 km×5 km MODIS resolution for infrared cloud retrievals. There are seasonal variations in the spatial distribution of the different cloud types. The fraction of pixels containing distinct multi-layer cloud is a strong function of the pixel size. Globally averaged, these fractions are approximately 20% and 5% for OMI and MODIS, respectively. These fractions may be significantly higher or lower depending upon location. There is a much smaller resolution dependence for fractions of pixels containing vertically-extended clouds (~20% for OMI and slightly less for MODIS globally), suggesting larger spatial scales for these clouds. We also find significantly higher fractions of vertically-extended clouds over land as compared with ocean, particularly in the tropics and summer hemisphere.

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.

Understanding the Importance of Microphysics and Macrophysics for Warm Rain in Marine Low Clouds. Part I: Satellite Observations

2009 ◽  
Vol 66 (10) ◽  
pp. 2953-2972 ◽  
Author(s):  
Terence L. Kubar ◽  
Dennis L. Hartmann ◽  
Robert Wood
Keyword(s):  
Gulf Of Mexico ◽  
Radar Data ◽  
Droplet Radius ◽  
Liquid Water Path ◽  
Cloud Radar ◽  
Warm Rain ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Cloud Tops ◽  
The Tropics

Abstract The importance of macrophysical variables [cloud thickness, liquid water path (LWP)] and microphysical variables (effective radius re, effective droplet concentration Neff) on warm drizzle intensity and frequency across the tropics and subtropics is studied. In this first part of a two-part study, Moderate Resolution Imaging Spectroradiometer (MODIS) optical and CloudSat cloud radar data are used to understand warm rain in marine clouds. Part II uses simple heuristic models. Cloud-top height and LWP substantially increase as drizzle intensity increases. Droplet radius estimated from MODIS also increases with cloud radar reflectivity (dBZ) but levels off as dBZ > 0, except where the influence of continental pollution is present, in which case a monotonic increase of re with drizzle intensity occurs. Off the Asian coast and over the Gulf of Mexico, re values are smaller (by several μm) and Neff values are larger compared to more remote marine regions. For heavy drizzle intensity, both re and Neff values off the Asian coast and over the Gulf of Mexico approach re and Neff values in more remote marine regions. Drizzle frequency, defined as profiles in which maximum dBZ > −15, increases dramatically and nearly uniformly when cloud tops grow from 1 to 2 km. Drizzle frequencies exceed 90% in all regions when LWPs exceed 250 g m−2 and Neff values are below 50 cm−3, even in regions where drizzle occurs infrequently on the whole. The fact that the relationship among drizzle frequency, LWP, and Neff is essentially the same for all regions suggests a near universality among tropical and subtropical regions.

scholarly journals Identifying drought events in sugarcane using drought indices derived from Modis sensor

2017 ◽  
Vol 52 (11) ◽  
pp. 1063-1071 ◽  
Author(s):  
Michelle Cristina Araujo Picoli ◽  
Daniel Garbellini Duft ◽  
Pedro Gerber Machado
Keyword(s):  
Near Infrared ◽  
Sensor Data ◽  
Drought Indices ◽  
Spectral Indices ◽  
Seasonal Fluctuations ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Aqua Satellite ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Shortwave Infrared

Abstract: The objective of this work was to evaluate the potential of several spectral indices, used on moderate resolution imaging spectroradiometer (Modis) images, in identifying drought events in sugarcane. Images of Terra and Aqua satellites were used to calculate the spectral indices, using visible (red), near infrared, and shortwave infrared bands, and eight indices were selected: NDVI, EVI2, GVMI, NDI6, NDI7, NDWI, SRWI, and MSI. The indices were calculated using images between October and April of the crop years 2007/08, 2008/09, 2009/10, and 2013/14. These indices were then correlated with the standardized precipitation-evapotranspiration index (SPEI), calculated for 1, 3, and 6 months. Four of them had significant correlations with SPEI: GVMI, MSI, NDI7, and NDWI. Spectral indices from Modis sensor on board the Aqua satellite (MYD) were more suited for drought detection, and March provided the most relevant indices for that purpose. Drought indices calculated from Modis sensor data are effective for detecting sugarcane drought events, besides being able to indicate seasonal fluctuations.

scholarly journals Determination of snow-covered area in different land covers in central Alaska, U.S.A., from aircraft data — April 1995

1998 ◽  
Vol 26 ◽  
pp. 149-155 ◽  
Author(s):  
Dorothy K. Hall ◽  
James L. Foster ◽  
Alfred T. C. Chang ◽  
Carl S. Benson ◽  
Janet Y. L. Chien
Keyword(s):  
Snow Cover ◽  
Vegetation Cover ◽  
Near Infrared ◽  
Mixed Forest ◽  
Mixed Forests ◽  
Mapping Algorithm ◽  
Covered Area ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Cover Density

During April 1995, a field and aircraft experiment was conducted in central Alaska in support of the Moderate Resolution Imaging Spectroradiometer (MODIS) snow-mapping project. The MODIS Airborne Simulator (MAS), a 50 channel spectroradiometer, was flown on board the NASA ER-2 aircraft. An objective of the mission was to determine the accuracy of mapping snow in different surface covers using an algorithm designed to map global snow cover after the launch of MODIS in 1998. The surface cover in this area of central Alaska is typically spruce, birch, aspen, mixed forest and muskeg. Integrated reflectance, Ri was calculated from the visible/near-infrared channels of the MAS sensor. The Ri was used to estimate different vegetation-cover densities because there is an inverse relationship between vegetation-cover density and albedo in snow-covered terrain. A vegetation-cover density map was constructed using MAS data acquired on 13 April 1995 over central Alaska. In the part of the scene that was mapped as having a vegetation-cover density of < 50%, the snow-mapping algorithm mapped 96.41% snow cover. These areas are generally composed of muskeg and mixed forests and include frozen lake. In the part of the scene that was estimated to have a vegetation-cover density of ≥50%, the snow-mapping algorithm mapped 71.23% snow cover. These areas are generally composed of dense coniferous or deciduous forests. Overall, the accuracy of the snow-mapping algorithm is > 87.41% for a 13 April MAS scene with a variety of surface covers (coniferous and deciduous and mixed forests, muskeg, tundra and frozen lake).

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