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At least 145 records · Page 8

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NASA NTRS1993

Cloud properties from the analysis of AVHRR observations for FIRE 2

Preliminary results are presented for cloud properties from the analysis of AVHRR observations for FIRE 2. The properties were obtained from a combination of the spatial coherence method and a multispectral retrieval scheme. Geographically gritted fields for the number of cloud layers were produced. For single layered cloud systems, fractional cloud cover, cloud emission temperature, cloud emissivity, and particle size were retrieved. Statistics on the properties of upper-level clouds and the Coffeeville cloud conditions are presented.

Lin, Xijian↗

NASA NTRS2011

Major Characteristics of Southern Ocean Cloud Regimes and Their Effects on the Energy Budget

Clouds over the Southern Ocean are often poorly represented by climate models, but they make a significant contribution to the top-of-atmosphere (TOA) radiation balance, particularly in the shortwave portion of the energy spectrum. This study seeks to better quantify the organization and structure of Southern Hemisphere midlatitude clouds by combining measurements from active and passive satellite-based datasets. Geostationary and polar-orbiter satellite data from the International Satellite Cloud Climatology Project (ISCCP) are used to quantify large-scale, recurring modes of cloudiness, and active observations from CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) are used to examine vertical structure, radiative heating rates, and precipitation associated with these clouds. It is found that cloud systems are organized into eight distinct regimes and that ISCCP overestimates the midlevel cloudiness of these regimes. All regimes contain a relatively high occurrence of low cloud, with 79%of all cloud layers observed having tops below 3 km, but multiple-layered clouds systems are present in approximately 34% of observed cloud profiles. The spatial distribution of regimes varies according to season, with cloud systems being geometrically thicker, on average, during the austral winter. Those regimes found to be most closely associated with midlatitude cyclones produce precipitation the most frequently, although drizzle is extremely common in low-cloud regimes. The regimes associated with cyclones have the highest in-regime shortwave cloud radiative effect at the TOA, but the low-cloud regimes, by virtue of their high frequency of occurrence over the oceans, dominate both TOA and surface shortwave effects in this region as a whole.

Haynes, John M.↗

NASA NTRS2016

Retrievals of Cloud Droplet Size from the RSP Data: Validation Using in Situ Measurements

We present comparisons of cloud droplet size distributions retrieved from the Research Scanning Polarimeter (RSP) data with correlative in situ measurements made during the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES). This field experiment was based at St. Johns airport, Newfoundland, Canada with the latest deployment in May - June 2016. RSP was onboard the NASA C-130 aircraft together with an array of in situ and other remote sensing instrumentation. The RSP is an along-track scanner measuring polarized and total reflectances in9 spectral channels. Its unique high angular resolution allows for characterization of liquid water droplet size using the rainbow structure observed in the polarized reflectances in the scattering angle range between 135 and 165 degrees. A parametric fitting algorithm applied to the polarized reflectances provides retrievals of the droplet effective radius and variance assuming a prescribed size distribution shape (gamma distribution). In addition to this, we use a non-parametric method, Rainbow Fourier Transform (RFT), which allows us to retrieve the droplet size distribution (DSD) itself. The latter is important in the case of clouds with complex structure, which results in multi-modal DSDs. During NAAMES the aircraft performed a number of flight patterns specifically designed for comparison of remote sensing retrievals and in situ measurements. These patterns consisted of two flight segments above the same straight ground track. One of these segments was flown above clouds allowing for remote sensing measurements, while the other was at the cloud top where cloud droplets were sampled. We compare the DSDs retrieved from the RSP data with in situ measurements made by the Cloud Droplet Probe (CDP). The comparisons show generally good agreement with deviations explainable by the position of the aircraft within cloud and by presence of additional cloud layers in RSP view that do not contribute to the in situ DSDs. In the latter case the distributions retrieved from the RSP data were consistent with the multi-layer cloud structures observed in the correlative High Spectral Resolution Lidar (HSRL) profiles. The comparison results provide a rare validation of polarimetric droplet size retrieval techniques, which can be used for analysis of satellite data on global scale.

cloud droplets↗

NASA NTRS1992

On the Retrieval and Analysis of Multilevel Clouds

An accurate satellite retrieval of cloud properties depends upon the detection and analysis of multilayered, overlapping cloud systems that surface observations show to be common. Multiple cloud layers are often found, for instance, in frontal situations, where cirrus overlays boundary layer convective cloud or low-to mid-level stratus cloud. Surface observers (Hahan et al., 1982) indicate that over ocean in the Northern Hemisphere between 30 deg. N and 60 deg. N, 51 percent of observations are of multilevel clouds. A satellite analysis by Coakley (1983) over the Pacific Ocean finds that more than 50 percent of 500 (250 sq km) frames exhibit evidence of multilayered cloud systems. The questions addressed in this study are the following: What error is introduced when inferring the cloud pressure from a Field-Of-View (FOV) that contains some arbitrary amount of transparent cloud overlaying a lower-level black cloud, such as stratus, by making the assumption that there is only a single cloud layer in the FOV, and what may be done to improve the cloud retrieval? The CO2 slicing methods (e.g. McCleese and Wilson, 1976; Smith and Platt, 1978; Chahine, 1974) have been shown to provide accurate means of inferring cirrus cloud altitude from passive infrared radiance measurements. The CO2 techniques have been applied to radiometric data from several instruments, notably the High Resolution Infrared Radiometric Sounder (HIRS/2, hereafter referred to as HIRS), the VISSR Atmospheric Sounder (VAS) (e.g., Menzel et al., 1983; Wylie and Menzel, 1989), and most recently to the High Resolution Interferometer Sounder (HIS) (Smith and Frey, 1990). The methods take advantage of the fact that infrared CO2 sounding channels spaced closely in wavenumber each have varying opacity to CO2, thereby causing each channel to be sensitive to a different level in the atmosphere. The techniques have been shown to be effective for single-layered, nonblack, mid- to high-level clouds such as cirrus, but are generally applied operationally to any given cloud occurrence. The CO2 slicing algorithms are most accurate for clouds than occur in a single, well-defined layer, or for multi-layered cloud cases in which the uppermost cloud layer is nearly black. Significant cloud height retrieval errors may ensue if the HIRS Field-Of-View (FOV) is cotaminated with low cloud. McCleese and Wilson (1976) have shown that the retrieved cloud height for the case of multiple cloud layers is a weighted average of the cloud heights actually present. The weight is approximately proportional to the product of the cloud heigt and the effective cloud amount. The effect of their result is that the uppermost cloud layer dominates the cloud pressure retrieval. Beyond stating that the higher cloud dominates the cloud pressure retrieval, there is no quantitative information to provide a way of estimating the errors in cloud pressure retrieval one should expect for certain common multilevel cloud situations or any suggestions on how to reduce the errors. In this paper we estimate the magnitude of the errors and use a simple algorithm to reduce the errors in optically thin cloud height retrival.

Baum, Bryan A.↗

NASA NTRS1993

Remote sensing of multilevel clouds during FIRE IFO 2

An unresolved difficulty in the remote sensing of clouds concerns the inability of the cloud retrieval algorithms to adequately recognize and analyze scenes containing overlapping cloud layers. Most cloud retrieval schemes, such as that used by the International Satellite Cloud Climatology Project (ISCCP) assume that each picture element (pixel) contains a single cloud layer. The current study begins to address the complexities of multilayered cloud property retrieval through the application of a modified multispectral, multiresolution (MSMR) method, first detailed in Baum et al. (1992), which merges 1.1 -km (at nadir) spectral data from the Advanced Very High Resolution Radiometer (AVHRR) with 17.4-km (at nadir) High Resolution Infrared Radiometer Sounder (HIRS/2, henceforth HIRS). Both instruments are flown aboard the National Oceanic and Atmospheric Administration (NOAA) polar-orbiting platforms. An ideal case study for this investigation is provided by the NOAA-11 overpass at 20:48 UTC on November 28, 1991. At this time, a large-scale cirrostratus veil overlaid a low-level stratus deck over much of the IFO region. There were both surface lidar and radar observations of the clouds as well as University of North Dakota (UND) Citation aircraft measurements. The presence of overlapping cloud layers within a HIRS FOV is determined from colocated AVHRR spectral data through the use of a fuzzy logic expert system. Conventional algorithms such as spatial coherence and CO2 slicing are used to retrieve cloud pressure and height for each identified cloud layer. The results from the satellite cloud retrieval analysis are compared to results from both surface- and aircraft-based measurements.

Baum, Bryan A.↗

NASA NTRS1996

Global Single and Multiple Cloud Classification with a Fuzzy Logic Expert System

An unresolved problem in remote sensing concerns the analysis of satellite imagery containing both single and multiple cloud layers. While cloud parameterizations are very important both in global climate models and in studies of the Earth's radiation budget, most cloud retrieval schemes, such as the bispectral method used by the International Satellite Cloud Climatology Project (ISCCP), have no way of determining whether overlapping cloud layers exist in any group of satellite pixels. Coakley (1983) used a spatial coherence method to determine whether a region contained more than one cloud layer. Baum et al. (1995) developed a scheme for detection and analysis of daytime multiple cloud layers using merged AVHRR (Advanced Very High Resolution Radiometer) and HIRS (High-resolution Infrared Radiometer Sounder) data collected during the First ISCCP Regional Experiment (FIRE) Cirrus 2 field campaign. Baum et al. (1995) explored the use of a cloud classification technique based on AVHRR data. This study examines the feasibility of applying the cloud classifier to global satellite imagery.

Welch, Ronald M.↗

NASA NTRS2013

A 19-Month Climatology of Marine Aerosol-Cloud-Radiation Properties Derived From DOE ARM AMF Deployment at the Azores: Part I: Cloud Fraction and Single-Layered MBL Cloud Properties

A 19-month record of total, and single-layered low (0-3 km), middle (3-6 km), and high (> 6 km) cloud fractions (CFs), and the single-layered marine boundary layer (MBL) cloud macrophysical and microphysical properties has been generated from ground-based measurements taken at the ARM Azores site between June 2009 and December 2010. It documents the most comprehensive and longest dataset on marine cloud fraction and MBL cloud properties to date. The annual means of total CF, and single-layered low, middle, and high CFs derived from ARM radar-lidar observations are 0.702, 0.271, 0.01 and 0.106, respectively. More total and single-layered high CFs occurred during winter, while single-layered low CFs were greatest during summer. The diurnal cycles for both total and low CFs are stronger during summer than during winter. The CFs are bimodally distributed in the vertical with a lower peak at approx. 1 km and higher one between 8 and 11 km during all seasons, except summer, when only the low peak occurs. The persistent high pressure and dry conditions produce more single-layered MBL clouds and fewer total clouds during summer, while the low pressure and moist air masses during winter generate more total and multilayered-clouds, and deep frontal clouds associated with midlatitude cyclones.

Dong, Xiquan↗

NASA NTRS2010

Detection of Multi-Layer and Vertically-Extended Clouds Using A-Train Sensors

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, 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). For the first time, we use data from the CloudSat radar to evaluate the results of a multi-layer cloud detection scheme. The cloud classification algorithms applied with different passive sensor configurations compare well with each other as well as with data from CloudSat. We compute monthly mean fractions of pixels containing multi-layer and vertically-extended clouds for January and July 2007 at the OMI spatial resolution (l2kmx24km at nadir) and at the 5kmx5km MODIS resolution used for infrared cloud retrievals. There are seasonal variations in the spatial distribution of the different cloud types. The fraction of cloudy pixels containing distinct multi-layer cloud is a strong function of the pixel size. Globally averaged, these fractions are approximately 20% and 10% 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 (approx.20% for OMI and slightly less for MODIS globally), suggesting larger spatial scales for these clouds. We also find higher fractions of vertically-extended clouds over land as compared with ocean, particularly in the tropics and summer hemisphere.

Joiner, J.↗

NASA NTRS2002

Cloud Thickness from Offbeam Returns - Thor Lidar

Physical thickness of a cloud layer, and sometimes multiple cloud layers, can be estimated from the time delay of off-beam returns from a pulsed laser source illuminating one side of the cloud layer. In particular, the time delay of light returning from the outer diffuse halo of light surrounding the beam entry point, relative to the time delay at beam center, determines the cloud physical thickness. The delay combined with the pulse stretch gives the optical thickness. The halo method works best for thick cloud layers, typically optical thickness exceeding 2, and thus compliments conventional lidar which cannot penetrate thick clouds. Cloud layer top and base have been measured independently over the ARM/SGP site using conventional laser ranging (lidar) and the top minus base thickness are compared with a cloud top halo estimate obtained from the NASA/Goddard THOR System (THOR = THickness from Offbeam Returns). THOR flies on the NASA P3, and measures the halo timings from several km above cloud top, at the same time providing conventional lidar cloud top height. The ARM/SGP micropulse lidar provides cloud base height for validation.

Cahalan, R.↗

NASA NTRS2002

Cloud Thickness from Offbeam Returns (THOR) Validation Campaign on NASA's P3B Over the ARM/SGP

Physical thickness of a cloud layer, sometimes multiple cloud layers, is a crucial controller of solar heating of the Earth- atmosphere system, which drives the convective processes that produce storm systems. Yet clouds of average optical thickness are opaque to conventional lidar, so their thickness is well estimated only by combining a lidar above and another below cloud, or a radar and lidar on the same side, dual facilities not widely available. Here we report initial observations of a new airborne multiple field of view lidar, capable of determining physical thickness of cloud layers from time signatures of off-beam returns from a I kHz micropulse lidar at 540 rim. For a single layer, the time delay of light returning from the outer diffuse halo of light surrounding the beam entry point, relative to the time delay at beam center, determines the cloud physical thickness. The delay combined with the pulse stretch gives the optical thickness. This halo method requires cloud optical thickness exceeding 2, and improves with cloud thickness, thus complimenting conventional lidar, which cannot penetrate thick clouds. Results are presented from March 25, 2002, when THOR flew a butterfly pattern over the ARM site at 8.3 km, above a thin ice cloud at 5 km, and a thick boundary-layer stratus deck with top at 1.3 km, as shown by THOR channel 1, and a base at about 0.3 km as shown by the ground-based MPL. Additional information is included in the original extended abstract.

Cahalan, R. F.↗

NASA NTRS1994

H2O-H2SO4 system in Venus' clouds and OCS, CO, and H2SO4 profiles in Venus' troposphere

A coupled problem of diffusion and condensation is solved for the H2SO4-H2O system in Venus' cloud layer. The position of the lower cloud boundary and profiles of the H2O and H2SO4 vapor mixing ratios and of the H2O/H2SO4 ratio of sulfuric acid aerosol and its flux are calculated as functions of the column photochemical production rate of sulfuric acid, Phi(sub H2SO4). Variations of the lower cloud boundary are considered. Our basic model, which is constrained to yield f(sub H2O) (30 km) = 30 ppm (Pollack et al. 1993), predicts the position of the lower cloud boundary at 48.4 km coinciding with the mean Pioneer Venus value, the peak H2SO4 mixing ratio of 5.4 ppm, and the H2SO4 production rate Phi(sub H2SO4) = 2.2 x 10(exp 12)/sq cm/s. The sulfur to sulfuric acid mass flux ratio in the clouds is 1:27 in this model, and the mass loading ratio may be larger than this value if sulfur particles are smaller than those of sulfuric acid. The model suggests that the extinction coefficient of sulfuric acid particles with radius 3.7 micrometers (mode 3) is equal to 0.3/km in the middle cloud layer. The downward flux of CO is equal to 1.7 x 10(exp 12)/sq cm/s in this model. Our second model, which is constrained to yield f(sub H2O) = 10 ppm at the lower cloud boundary, close to the value measured by the Magellan radiooccultations, predicts the position of this boundary to be at 46.5 km, which agrees with the Magellan data; f(sub H2O) (30 km) = 90 ppm, close to the data of Moroz et al. (1983) at this altitude; Phi(sub H2SO4) = 6.4 x 10(exp 12)/sq cm/s; and Phi(sub co) = 4.2 x 10(exp 12)/sq cm/s. The S/H2SO4 flux mass ratio is 1:18, and the extinction coefficient of the mode 3 sulfuric acid particles is equal to 0.9 km in the middle cloud layer. A strong gradient of the H2SO4 vapor mixing ratio near the bottom of the cloud layer drives a large upward flux of H2SO4, which condenses and forms the excessive downward flux of liquid sulfuric acid, which is larger by a factor of 4-7 than the flux in the middle cloud layer. This is the mechanism of formation of the lower cloud layer. Variations of the lower cloud layer are discussed. Our modeling of the OCS and CO profiles in the lower atmosphere measured by Pollack et al. (1993) provides a reasonable explanation of these data and shows that the rate coefficient of the reaction SO3 + CO yields CO2 + SO2 is equal to 10(exp -11) exp(-(13,100 +/- 1000)/T) cc/s. The main channel of the reaction between SO3 and OCS is CO2 + (SO)2, and its rate coefficient is equal to 10(exp -11) exp(-(8900 +/- 500)/T) cc/s. In the conditions of Venus' lower atmosphere, (SO)2 is removed by the reaction (SO)2 + OCS yields CO + S2 + SO2. The model predicts an OCS mixing ratio of 28 ppm near the surface.

Krasnopolsky, V. A.↗

DOE OSTI2022

Environmental effects on aerosol–cloud interaction in non-precipitating marine boundary layer (MBL) clouds over the eastern North Atlantic

Abstract. Over the eastern North Atlantic (ENA) ocean, a total of 20 non-precipitating single-layer marine boundary layer (MBL) stratus and stratocumulus cloud cases are selected to investigate the impacts of the environmental variables on the aerosol–cloud interaction (ACIr) using the ground-based measurements from the Department of Energy Atmospheric Radiation Measurement (ARM) facility at the ENA site during 2016–2018. The ACIr represents the relative change in cloud droplet effective radius re with respect to the relative change in cloud condensation nuclei (CCN) number concentration at 0.2 % supersaturation (NCCN,0.2 %) in the stratified water vapor environment. The ACIr values vary from −0.01 to 0.22 with increasing sub-cloud boundary layer precipitable water vapor (PWVBL) conditions, indicating that re is more sensitive to the CCN loading under sufficient water vapor supply, owing to the combined effect of enhanced condensational growth and coalescence processes associated with higher Nc and PWVBL. The principal component analysis shows that the most pronounced pattern during the selected cases is the co-variations in the MBL conditions characterized by the vertical component of turbulence kinetic energy (TKEw), the decoupling index (Di), and PWVBL. The environmental effects on ACIr emerge after the data are stratified into different TKEw regimes. The ACIr values, under both lower and higher PWVBL conditions, more than double from the low-TKEw to high-TKEw regime. This can be explained by the fact that stronger boundary layer turbulence maintains a well-mixed MBL, strengthening the connection between cloud microphysical properties and the below-cloud CCN and moisture sources. With sufficient water vapor and low CCN loading, the active coalescence process broadens the cloud droplet size spectra and consequently results in an enlargement of re. The enhanced activation of CCN and the cloud droplet condensational growth induced by the higher below-cloud CCN loading can effectively decrease re, which jointly presents as the increased ACIr. This study examines the importance of environmental effects on the ACIr assessments and provides observational constraints to future model evaluations of aerosol–cloud interactions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

An Overview of Atmospheric Features Over the Western North Atlantic Ocean and North American East Coast—Part 2: Circulation, Boundary Layer, and Clouds

The Western North Atlantic Ocean (WNAO) is a complex land-ocean-atmosphere system that experiences a wide range of atmospheric phenomena, which in turn drive unique aerosol transport pathways, cloud morphologies, and boundary layer variability. This work, Part 2 of a 2-part paper series, provides an overview of the atmospheric circulation, boundary layer variability, three-dimensional cloud structure, and precipitation over the WNAO; the companion paper (Part 1) focused on chemical characterization of aerosol, gas, and wet deposition. Seasonal changes in the circulation and sea surface temperature explain a clear transition in cloud morphologies from small shallow cumulus clouds, convective clouds, and tropical storms in summer, to stratus/stratocumulus and multi-layer cloud systems associated with winter storms. Synoptic variability in cloud fields is estimated using satellite-based weather states, and the role of postfrontal conditions (cold-air outbreaks) in the development of stratiform clouds is further analyzed. Precipitation is persistent over the ocean, with a regional peak over the Gulf Stream path, where offshore sea surface temperature gradients are large and surface fluxes reach a regional peak. Satellite data show a clear annual cycle in cloud droplet number concentration along the coast in winter, and minima in summer, suggesting a marked annual cycle in aerosol-cloud interactions. The performance of four climate models are assessed against satellite cloud retrievals. The models qualitatively reproduce the annual cycle in cloud cover and liquid water path, but with large discrepancies across models, especially in the extra-tropics. The paper concludes with a summary of outstanding issues and recommendations for future work.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRSDate not supplied

CERES Single Scanner Satellite Footprint, TOA, Surface Fluxes and Clouds (SSF) data in HDF (CER_SSF_TRMM-PFM-VIRS_Edition2A-TransOps)

The Single Scanner Footprint TOA/Surface Fluxes and Clouds (SSF) product contains one hour of instantaneous Clouds and the Earth's Radiant Energy System (CERES) data for a single scanner instrument. The SSF combines instantaneous CERES data with scene information from a higher-resolution imager such as Visible/Infrared Scanner (VIRS) on TRMM or Moderate-Resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua. Scene identification and cloud properties are defined at the higher imager resolution and these data are averaged over the larger CERES footprint. For each CERES footprint, the SSF contains the number of cloud layers and for each layer the cloud amount, height, temperature, pressure, optical depth, emissivity, ice and liquid water path, and water particle size. The SSF also contains the CERES filtered radiances for the total, shortwave (SW), and window (WN) channels and the unfiltered SW, longwave (LW), and WN radiances. The SW, LW, and WN radiances at spacecraft altitude are converted to Top-of-the-Atmosphere (TOA) fluxes based on the imager defined scene. These TOA fluxes are used to estimate surface fluxes. Only footprints with adequate imager coverage are included on CER_SSF_TRMM-PFM-VIRS_Subset_Edition1the SSF which is much less than the full set of footprints on the CERES ES-8 product. The following CERES SSF data sets are currently available: CER_SSF_TRMM-PFM-VIRS_Edition1 CER_SSF_TRMM-PFM-VIRS_Subset_Edition1 CER_SSF_TRMM-PFM-VIRS_Edition2A CER_SSF_TRMM-SIM-VIRS_Edition2_VIRSonly CER_SSF_TRMM-PFM-VIRS_Edition2A-TransOps CER_SSF_TRMM-PFM-VIRS_Edition2B-TransOps CER_SSF_TRMM-PFM-VIRS_Edition2B CER_SSF_Terra-FM1-MODIS_Edition1A CER_SSF_Terra-FM1-MODIS_Edition1A CER_SSF_Terra-FM1-MODIS_Edition2A CER_SSF_Terra-FM2-MODIS_Edition2A CER_SSF_Terra-FM1-MODIS_Edition2B CER_SSF_Terra-FM2-MODIS_Edition2B CER_SSF_Aqua-FM4-MODIS_Beta1 CER_SSF_Aqua-FM3-MODIS_Beta2 CER_SSF_Aqua-FM4-MODIS_Beta2. [Location=GLOBAL] [Temporal_Coverage: Start_Date=1998-01-01; Stop_Date=1999-07-31] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180] [Data_Resolution: Temporal_Resolution=1 hour; Temporal_Resolution_Range=Hourly - < Daily].

CLOUD PARTICLE PHASE↗

NASA NTRSDate not supplied

CERES Single Scanner Satellite Footprint, TOA, Surface Fluxes and Clouds (SSF) data in HDF (CER_SSF_TRMM-PFM-VIRS_Edition2A-TransOps)

LONGWAVE UNFILTERED RADIANCE↗

NASA NTRSDate not supplied

CERES Single Scanner Satellite Footprint, TOA, Surface Fluxes and Clouds (SSF) data in HDF (CER_SSF_Terra-FM1-MODIS_Edition1A)

The Single Scanner Footprint TOA/Surface Fluxes and Clouds (SSF) product contains one hour of instantaneous Clouds and the Earth's Radiant Energy System (CERES) data for a single scanner instrument. The SSF combines instantaneous CERES data with scene information from a higher-resolution imager such as Visible/Infrared Scanner (VIRS) on TRMM or Moderate-Resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua. Scene identification and cloud properties are defined at the higher imager resolution and these data are averaged over the larger CERES footprint. For each CERES footprint, the SSF contains the number of cloud layers and for each layer the cloud amount, height, temperature, pressure, optical depth, emissivity, ice and liquid water path, and water particle size. The SSF also contains the CERES filtered radiances for the total, shortwave (SW), and window (WN) channels and the unfiltered SW, longwave (LW), and WN radiances. The SW, LW, and WN radiances at spacecraft altitude are converted to Top-of-the-Atmosphere (TOA) fluxes based on the imager defined scene. These TOA fluxes are used to estimate surface fluxes. Only footprints with adequate imager coverage are included on CER_SSF_TRMM-PFM-VIRS_Subset_Edition1the SSF which is much less than the full set of footprints on the CERES ES-8 product. The following CERES SSF data sets are currently available: CER_SSF_TRMM-PFM-VIRS_Edition1 CER_SSF_TRMM-PFM-VIRS_Subset_Edition1 CER_SSF_TRMM-PFM-VIRS_Edition2A CER_SSF_TRMM-SIM-VIRS_Edition2_VIRSonly CER_SSF_TRMM-PFM-VIRS_Edition2A-TransOps CER_SSF_TRMM-PFM-VIRS_Edition2B-TransOps CER_SSF_TRMM-PFM-VIRS_Edition2B CER_SSF_Terra-FM1-MODIS_Edition1A CER_SSF_Terra-FM1-MODIS_Edition1A CER_SSF_Terra-FM1-MODIS_Edition2A CER_SSF_Terra-FM2-MODIS_Edition2A CER_SSF_Terra-FM1-MODIS_Edition2B CER_SSF_Terra-FM2-MODIS_Edition2B CER_SSF_Aqua-FM4-MODIS_Beta1 CER_SSF_Aqua-FM3-MODIS_Beta2 CER_SSF_Aqua-FM4-MODIS_Beta2. [Location=GLOBAL] [Temporal_Coverage: Start_Date=1998-01-01; Stop_Date=2002-10-31] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180] [Data_Resolution: Temporal_Resolution=1 hour; Temporal_Resolution_Range=Hourly - < Daily].

CLOUD BASE PRESSURE↗

NASA NTRSDate not supplied

CERES Single Scanner Satellite Footprint, TOA, Surface Fluxes and Clouds (SSF) data in HDF (CER_SSF_Aqua-FM3-MODIS_Edition1B)

The Single Scanner Footprint TOA/Surface Fluxes and Clouds (SSF) product contains one hour of instantaneous Clouds and the Earth's Radiant Energy System (CERES) data for a single scanner instrument. The SSF combines instantaneous CERES data with scene information from a higher-resolution imager such as Visible/Infrared Scanner (VIRS) on TRMM or Moderate-Resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua. Scene identification and cloud properties are defined at the higher imager resolution and these data are averaged over the larger CERES footprint. For each CERES footprint, the SSF contains the number of cloud layers and for each layer the cloud amount, height, temperature, pressure, optical depth, emissivity, ice and liquid water path, and water particle size. The SSF also contains the CERES filtered radiances for the total, shortwave (SW), and window (WN) channels and the unfiltered SW, longwave (LW), and WN radiances. The SW, LW, and WN radiances at spacecraft altitude are converted to Top-of-the-Atmosphere (TOA) fluxes based on the imager defined scene. These TOA fluxes are used to estimate surface fluxes. Only footprints with adequate imager coverage are included on CER_SSF_TRMM-PFM-VIRS_Subset_Edition1the SSF which is much less than the full set of footprints on the CERES ES-8 product. The following CERES SSF data sets are currently available: CER_SSF_TRMM-PFM-VIRS_Edition1 CER_SSF_TRMM-PFM-VIRS_Subset_Edition1 CER_SSF_TRMM-PFM-VIRS_Edition2A CER_SSF_TRMM-SIM-VIRS_Edition2_VIRSonly CER_SSF_TRMM-PFM-VIRS_Edition2A-TransOps CER_SSF_TRMM-PFM-VIRS_Edition2B-TransOps CER_SSF_TRMM-PFM-VIRS_Edition2B CER_SSF_Terra-FM1-MODIS_Edition1A CER_SSF_Terra-FM1-MODIS_Edition1A CER_SSF_Terra-FM1-MODIS_Edition2A CER_SSF_Terra-FM2-MODIS_Edition2A CER_SSF_Terra-FM1-MODIS_Edition2B CER_SSF_Terra-FM2-MODIS_Edition2B CER_SSF_Aqua-FM4-MODIS_Beta1 CER_SSF_Aqua-FM3-MODIS_Beta2 CER_SSF_Aqua-FM4-MODIS_Beta2. [Location=GLOBAL] [Temporal_Coverage: Start_Date=1998-01-01; Stop_Date=2005-03-31] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180] [Data_Resolution: Temporal_Resolution=1 hour; Temporal_Resolution_Range=Hourly - < Daily].

CLOUD LAYER AREA↗

NASA NTRSDate not supplied

CERES Single Satellite Footprint, TOA and Surface Fluxes, Clouds (SSF) data in HDF (CER_SSF_Aqua-FM4-MODIS_Ed2A-NoSW)

The Single Scanner Footprint TOA/Surface Fluxes and Clouds (SSF) product contains one hour of instantaneous Clouds and the Earth's Radiant Energy System (CERES) data for a single scanner instrument. The SSF combines instantaneous CERES data with scene information from a higher-resolution imager such as Visible/Infrared Scanner (VIRS) on TRMM or Moderate-Resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua. Scene identification and cloud properties are defined at the higher imager resolution and these data are averaged over the larger CERES footprint. For each CERES footprint, the SSF contains the number of cloud layers and for each layer the cloud amount, height, temperature, pressure, optical depth, emissivity, ice and liquid water path, and water particle size. The SSF also contains the CERES filtered radiances for the total, shortwave (SW), and window (WN) channels and the unfiltered SW, longwave (LW), and WN radiances. The SW, LW, and WN radiances at spacecraft altitude are converted to Top-of-the-Atmosphere (TOA) fluxes based on the imager defined scene. These TOA fluxes are used to estimate surface fluxes. Only footprints with adequate imager coverage are included on CER_SSF_TRMM-PFM-VIRS_Subset_Edition1the SSF which is much less than the full set of footprints on the CERES ES-8 product. The following CERES SSF data sets are currently available: CER_SSF_TRMM-PFM-VIRS_Edition1 CER_SSF_TRMM-PFM-VIRS_Subset_Edition1 CER_SSF_TRMM-PFM-VIRS_Edition2A CER_SSF_TRMM-SIM-VIRS_Edition2_VIRSonly CER_SSF_TRMM-PFM-VIRS_Edition2A-TransOps CER_SSF_TRMM-PFM-VIRS_Edition2B-TransOps CER_SSF_TRMM-PFM-VIRS_Edition2B CER_SSF_Terra-FM1-MODIS_Edition1A CER_SSF_Terra-FM1-MODIS_Edition1A CER_SSF_Terra-FM1-MODIS_Edition2A CER_SSF_Terra-FM2-MODIS_Edition2A CER_SSF_Terra-FM1-MODIS_Edition2B CER_SSF_Terra-FM2-MODIS_Edition2B CER_SSF_Aqua-FM4-MODIS_Beta1 CER_SSF_Aqua-FM3-MODIS_Beta2 CER_SSF_Aqua-FM4-MODIS_Beta2. [Location=GLOBAL] [Temporal_Coverage: Start_Date=1998-01-01; Stop_Date=2006-01-01] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180] [Data_Resolution: Temporal_Resolution=1 hour; Temporal_Resolution_Range=Hourly - < Daily].

LIQUID WATER PATH↗