The impact of gravity waves and cloud nucleation threshold on stratospheric water and tropical tropospheric cloud fraction
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Mark Schoeberl↗
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Results for “Cloud fraction”
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CALIPSO observations of near‐cloud aerosol properties as a function of cloud fraction
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Weidong Yang↗
Improving Low‐Cloud Fraction Prediction Through Machine Learning
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54 ENVIRONMENTAL SCIENCES↗
Cloud fields retrieved from analysis of HIRS2/MSU sounding data
The methods used to determine effective cloud fraction (cloud fraction times cloud emissivity at 11-14 microns) and cloud top pressure from analysis of HIRS2/MSU sounding data are described. Identical procedures are used day and night so as to allow for meaningful day-night difference fields. Results are shown for June 1979. The monthly mean effective cloud fraction is 43.4 percent, resulting from a 45.2 percent value at 0300 LT and 41.6 percent at 1500 LT. The retrieved single-day cloud field for June 11 shows good agreement with high spatial resolution visible and infrared imagery.
Susskind, J.↗
Top-of-Atmosphere Shortwave Broadband Observed Radiance and Estimated Irradiance over Polar Regions from Clouds and the Earth's Radiant Energy System (CERES) Instruments on Terra
Empirical angular distribution models for estimating top-of-atmosphere shortwave irradiances from radiance measurements over permanent snow, fresh snow and sea ice are developed using CERES measurements on Terra. Permanent snow angular distribution models depend on cloud fraction, cloud optical thickness, and snow brightness. Fresh snow and sea ice angular distribution models depend on snow and sea ice fraction, cloud fraction, cloud optical thickness, and snow and ice brightness. These classifications lead to 10 scene types for permanent snow and 25 scene types for fresh snow and sea ice. The average radiance over clear-sky permanent snow is more isotropic with satellite viewing geometry than that over overcast permanent snow. On average, the albedo of clear-sky permanent snow varies from 0.65 to 0.68 for solar zenith angles between 60$logical and\circ$ and 80 deg, while the corresponding albedo of overcast scenes varies from 0.70 to 0.73. Clear-sky permanent snow albedos over Antarctica estimated from two independent angular distribution models are consistent to within 0.6%, on average. Despite significant variability in sea ice optical properties with season, the estimated mean relative albedo error is -1 % for very dark sea ice and 0.1% for very bright sea ice when albedos derived from different viewing angles are averaged. The estimated regional root-mean-square (RMS) relative albedo error is 5.6% and 2.6% when the sea ice angular distribution models are applied to a region that contains very dark and very bright sea ice, respectively. Similarly, the estimated relative albedo bias error for fresh snow is -0.1% for very dark snow.
Kato, S.↗
Validation of AIRS/AMSU Cloud Retrievals Using MODIS Cloud Analyses
The AIRS/AMSU (flying on the EOS-AQUA satellite) sounding retrieval methodology allows for the retrieval of key atmospheric/surface parameters under partially cloudy conditions (Susskind et al.). In addition, cloud parameters are also derived from the AIRS/AMSU observations. Within each AIRS footprint, cloud parameters at up to 2 cloud layers are determined with differing cloud top pressures and effective (product of infrared emissivity at 11 microns and physical cloud fraction) cloud fractions. However, so far the AIRS cloud product has not been rigorously evaluated/validated. Fortunately, collocated/coincident radiances measured by MODIS/AQUA (at a much lower spectral resolution but roughly an order of-magnitude higher spatial resolution than that of AIRS) are used to determine analogous cloud products from MODIS. This allows us for a rather rare and interesting possibility: the intercomparisons and mutual validation of imager vs. sounder-based cloud products obtained from the same satellite positions. First, we present results of small-scale (granules) instantaneous intercomparisons. Next, we will evaluate differences of temporally averaged (monthly) means as well as the representation of inter-annual variability of cloud parameters as presented by the two cloud data sets. In particular, we present statistical differences in the retrieved parameters of cloud fraction and cloud top pressure. We will investigate what type of cloud systems are retrieved most consistently (if any) with both retrieval schemes, and attempt to assess reasons behind statistically significant differences.
Molnar, Gyula I.↗
Surface Radiation Budget (SRB) Release 2.5 Longwave cloud props 3 hourly Data in Native Format (SRB_REL2.5_LW_CLDPROPS_3HRLY)
The Surface Radiation Budget (SRB) data set contain 47 surface and cloud properties including cloud fraction, cloud visible optical depth, cloud top temperature, cloud top pressure, cloud base pressure, and cloud water and ice content for each of 5 cloud types and in total. In addition to these cloud properties, the parameters also include surface pressure, total column ozone, day/night flag, and precipitable water. All perameters were measured at three hourly intervals for each day for the entire globe between 07/01/1983 and 06/30/2005 and serve as inputs to the Global Energy and Water-Cycle Experiment (GEWEX) longwave algorithm (GLW). [Location=GLOBAL] [Temporal_Coverage: Start_Date=1998-01-01; Stop_Date=2005-06-30] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180] [Data_Resolution: Latitude_Resolution=1 degree; Longitude_Resolution=Ranges from 1 degree (tropics and subtropics) to 120 degrees (the poles).; Temporal_Resolution=3 hourly; Temporal_Resolution_Range=3 hourly].
CLOUD TOP TEMPERATURE↗
The Footprints of 16 Year Trends of Arctic Springtime Cloud and Radiation Properties on September Sea Ice Retreat
The most prominent September Arctic sea ice decline over the period of 2000–2015 occurs over the Siberian Sea, Laptev Sea, and Kara Sea. The satellite observed and retrieved sea ice concentration (SIC) and cloud/radiation properties over the Arctic (70°–90°N) have been used to investigate the impact of springtime cloud and radiation properties on September SIC variation. Positive trends of cloud fractions, cloud water paths, and surface downward longwave flux at the surface over the September sea ice retreat areas are found over the period of 1 March to 14 May, while negative trends are found over the period of 15 May to 28 June. The spatial distributions of correlations between springtime cloud/radiation properties and September SIC have been calculated, indicating that increasing cloud fractions and downward longwave flux during springtime tend to enhance sea ice melting due to strong cloud warming effect. Surface downward and upward shortwave fluxes play an important role from May to June when the onset of sea ice melting occurs. The comparison between linearly detrended and nondetrended of each parameter indicates that significant impact of cloud and radiation properties on September sea ice retreat occurs over the Chukchi/Beaufort Sea at interannual time scale, especially over the period of 31 March to 29 April, while strongest climatological trends are found over the Laptev/Siberian Sea.
Arctic sea ice trend↗
Top-of-Atmosphere Albedo Estimation from Angular Distribution Models using Scene Identification from Satellite Cloud Property Retrievals
The next generation of Earth radiation budget satellite instruments will routinely merge estimates of global top-of-atmosphere radiative fluxes with cloud properties. This information will offer many new opportunities for validating radiative transfer models and cloud parameterizations in climate models. In this study, five months of POLarization and Directionality of the Earth's Reflectances (POLDER) 670 nm radiance measurements are considered in order to examine how satellite cloud property retrievals can be used to define empirical Angular Distribution Models (ADMs) for estimating top-of-atmosphere (TOA) albedo. ADMs are defined for 19 scene types defined by satellite retrievals of cloud fraction and cloud optical depth. Two approaches are used to define the ADM scene types: The first assumes there are no biases in the retrieved cloud properties and defines ADMs for fixed discrete intervals of cloud fraction and cloud optical depth (fixed-tau approach). The second approach involves the same cloud fraction intervals, but uses percentile intervals of cloud optical depth instead (percentile-tau approach). Albedos generated using these methods are compared with albedos inferred directly from the mean observed reflectance field. Albedos based on ADMs that assume cloud properties are unbiased (fixed-tau approach) show a strong systematic dependence on viewing geometry. This dependence becomes more pronounced with increasing solar zenith angle, reaching approximately equals 12% (relative) between near-nadir and oblique viewing zenith angles for solar zenith angles between 60 deg and 70 deg. The cause for this bias is shown to be due to biases in the cloud optical depth retrievals. In contrast, albedos based on ADMs built using percentile intervals of cloud optical depth (percentile-tau approach) show very little viewing zenith angle dependence and are in good agreement with albedos obtained by direct integration of the mean observed reflectance field (less than 1% relative error). When the ADMs are applied separately to populations consisting of only liquid water and ice clouds, significant biases in albedo with viewing geometry are observed (particularly at low sun elevations), highlighting the need to account for cloud phase both in cloud optical depth retrievals and in defining ADM scene types. ADM-derived monthly mean albedos determined for all 5 deg x 5 deg latitude/longitude regions over ocean are in good agreement (regional RMS relative errors less than 2%) with those obtained by direct integration when ADM albedos inferred from specific angular bins are averaged together. Albedos inferred from near-nadir and oblique viewing zenith angles are the least accurate, with regional RMS errors reaching approximately 5-10% (relative). Compared to an earlier study involving ERBE ADMs, regional mean albedos based on the 19 scene types considered here show a factor of 4 reduction in bias error and a factor of 3 reduction in RMS error.
Loeb, N. G.↗
Savannah River Site Climatology for Research Activities Conducted Onsite and in P-Area (1994-2023)
This report presents a climatology of meteorological variables at the Savannah River Site (SRS) using measurements from the P-Area and Central Climatology meteorological towers to support research activities at the Savannah River Site. Key values included in this report are wind, surface pressure, temperature, relative humidity, soil moisture, cloud fraction, cloud base, lightning, atmospheric stability, and boundary layer parameters. The Central Climatology tower is useful in that it is instrumented at four levels, allowing for an examination of height dependence on some variables.
54 ENVIRONMENTAL SCIENCES↗
Savannah River Site Climatology for Research Activities Conducted Onsite and in P-Area (1994-2023)
Executive Summary This report presents a climatology of meteorological variables at the Savannah River Site (SRS) using measurements from the P-Area and Central Climatology meteorological towers to support research activities at the Savannah River Site. Key values included in this report are wind, surface pressure, temperature, relative humidity, soil moisture, cloud fraction, cloud base, lightning, atmospheric stability, and boundary layer parameters. The Central Climatology tower is useful in that it is instrumented at four levels, allowing for an examination of height dependence on some variables.
54 ENVIRONMENTAL SCIENCES↗
An Improved Retrieval of Tropospheric Nitrogen Dioxide from GOME
We present a retrieval of tropospheric nitrogen dioxide (NO2) columns from the Global Ozone Monitoring Experiment (GOME) satellite instrument that improves in several ways over previous retrievals, especially in the accounting of Rayleigh and cloud scattering. Slant columns, which are directly fitted without low-pass filtering or spectral smoothing, are corrected for an artificial offset likely induced by spectral structure on the diffuser plate of the GOME instrument. The stratospheric column is determined from NO2 columns over the remote Pacific Ocean to minimize contamination from tropospheric NO2. The air mass factor (AMF) used to convert slant columns to vertical columns is calculated from the integral of the relative vertical NO2 distribution from a global 3-D model of tropospheric chemistry driven by assimilated meteorological data (Global Earth Observing System (GEOS)-CHEM), weighted by altitude dependent scattering weights computed with a radiative transfer model (Linearized Discrete Ordinate Radiative Transfer), using local surface albedos determined from GOME observations at NO2 wavelengths. The AMF calculation accounts for cloud scattering using cloud fraction, cloud top pressure, and cloud optical thickness from a cloud retrieval algorithm (GOME Cloud Retrieval Algorithm). Over continental regions with high surface emissions, clouds decrease the AMT by 20- 30% relative to clear sky. GOME is almost twice as sensitive to tropospheric NO2 columns over ocean than over land. Comparison of the retrieved tropospheric NO2 columns for July 1996 with GEOS-CHEM values tests both the retrieval and the nitrogen oxide radical (NOx) emissions inventories used in GEOS-CHEM. Retrieved tropospheric NO2 columns over the United States, where NOx emissions are particularly well known, are within 18% of GEOS-CHEM columns and are strongly spatially correlated (r = 0.78, n = 288, p less than 0.005). Retrieved columns show more NO2 than GEOS-CHEM columns over the Transvaal region of South Africa and industrial regions of the northeast United States and Europe. They are lower over Houston, India, eastern Asia, and the biomass burning region of central Africa, possibly because of biases from absorbing aerosols.
Martin, Randall V.↗
Comparison of Cloud Resolving Model Simulations to Remote Sensing Data
The purpose of this research was to evaluate the ability of a cloud-resolving model (CRM) to simulate the dynamical, radiative, and microphysical properties of deep convective cloud objects identified using CERES (Clouds and the Earth s Radiant Energy System) on board the Tropical Rainfall Measuring Mission (TRMM) satellite platform, for many cases. A deep convective cloud object is a contiguous region that is composed of satellite footprints that fulfill the following selection criteria: 100% cloud fraction, cloud optical depth > 10, and a cloud top height of at least 10 km. Selection criteria have also been formed for different types of boundary-layer clouds, as described in Xu et al. (2005). The purpose of the cloud object approach is to identify specific areas of where the cloud properties simulated by the CRM systematically differ from the observed cloud properties. Where these systematic differences exist, concrete steps can be made to improve the CRM s simulation of an entire class of clouds, rather than by tuning the model to correctly simulate a single case study, as is often done. Additional information regarding detailed approaches and findings are presented.
Randall, David A.↗
Top-of-the-Atmosphere Shortwave Flux Estimation from UV Observations: An Empirical Approach
Measurements of top of the atmosphere (TOA) radiation are essential to the understanding of Earth's climate. Clouds, aerosols, and ozone (0,) are among the most important agents impacting the Earth's short-wave (SW) radiation budget. There are several sensors in orbit that provide independent information related to the Earth's SW radiation budget. Having coincident information from these sensors is important for understanding their potential contributions. The A-train constellation of satellites provides a unique opportunity to analyze near-simultaneous data from several of these sensors. They include the Ozone Monitoring Instrument (OMI), on the NASA Aura satellite, that makes TOA hyper-spectral measurements from ultraviolet (UV) to visible wavelengths, and Clouds and the Earth's Radiant Energy System (CERES) instrument, on the NASA Aqua satellite, that makes broadband measurements in both the long- and short-wave. OMI measurements have been successfully utilized to derive the information on trace gases (e.g., 0 1, NO" and SO,), clouds, and absorbing aerosols. TOA SW fluxes are estimated using a combination of data from CERES and the Aqua MODerate-resolution Imaging Spectroradiometer (MODIS). In this paper, OMI retrievals of cloud/aerosol parameters and 0 1 have been collocated with CERES TOA SW flux retrievals. We use this collocated data to develop a neural network that estimates TOA shortwave flux globally over ocean using data from OMI and meteorological analyses. This input data include the effective cloud fraction, cloud optical centroid pressure (OCP), total-column 0" and sun-satellite viewing geometry from OMI as well as wind speed and water vapor from the Goddard Earth Observing System 5 Modern Era Retrospective-analysis for Research and Applications (GEOS-5 MERRA) along with a climatology of chlorophyll content. We train the neural network using a subset of CERES retrievals of TOA SW flux as the target output (truth) and withhold a different subset of the CERES data to be used for validation.
Gupta, P.↗
Spectral Dependence of MODIS Cloud Droplet Effective Radius Retrievals for Marine Boundary Layer Clouds
Low-level warm marine boundary layer (MBL) clouds cover large regions of Earth's surface. They have a significant role in Earth's radiative energy balance and hydrological cycle. Despite the fundamental role of low-level warm water clouds in climate, our understanding of these clouds is still limited. In particular, connections between their properties (e.g. cloud fraction, cloud water path, and cloud droplet size) and environmental factors such as aerosol loading and meteorological conditions continue to be uncertain or unknown. Modeling these clouds in climate models remains a challenging problem. As a result, the influence of aerosols on these clouds in the past and future, and the potential impacts of these clouds on global warming remain open questions leading to substantial uncertainty in climate projections. To improve our understanding of these clouds, we need continuous observations of cloud properties on both a global scale and over a long enough timescale for climate studies. At present, satellite-based remote sensing is the only means of providing such observations.
Cloud physics↗
The Atmospheric Measurements of ICESat-2
ICESat-2 is an exciting new multi-beam, photon-counting, satellite lidar designed to acquire simultaneous high-resolution measurements of the earth's surface and atmospheric structure. Launched in September, 2018 the Advanced Topographic Lidar Altimeter System (ATLAS) on board ICESat-2 has been acquiring data continuously for nearly 1 year. The 3 laser beams comprising the atmospheric channel are providing excellent back scatter measurements that enable the retrieval of global cloud fraction, cloud and aerosol layer height, column optical depth, and blowing snow properties. Public release of the data products began with version 1in May, 2019 and continues with a significantly improved version 2 in October, 2019. This presentation will give an overview of the ICESat-2 atmospheric channel and show examples of cloud, aerosol and blowings now properties as well as comparisons of ATLAS and CALIOP measurements.
Palm, Stephen↗
Using Two-Stream Theory to Capture Fluctuations of Satellite-Perceived TOA SW Radiances Reflected from Clouds Over Ocean
Shortwave (SW) fluxes estimated from broadband radiometry rely on empirically gathered and hemispherically resolved fields of outgoing top-of-atmosphere (TOA) radiances. This study aims to provide more accurate and precise fields of TOA SW radiances reflected from clouds over ocean by introducing a novel semiphysical model predicting radiances per narrow sun-observer geometry. This model was statistically trained using CERES-measured radiances paired with MODIS-retrieved cloud parameters as well as reanalysis-based geophysical parameters. By using radiative transfer approximations as a framework to ingest the above parameters, the new approach incorporates cloud-top effective radius and above-cloud water vapor in addition to traditionally used cloud optical depth, cloud fraction, cloud phase, and surface wind speed. A two-stream cloud albedo – serving to statistically incorporate cloud optical thickness and cloud-top effective radius – and Cox–Munk ocean reflectance were used to describe an albedo over each CERES footprint. Effective-radius-dependent asymmetry parameters were obtained empirically and separately for each viewing-illumination geometry. A simple equation of radiative transfer, with this albedo and attenuating above-cloud water vapor as inputs, was used in its log-linear form to allow for statistical optimization. We identified the two-stream functional form that minimized radiance residuals calculated against CERES observations and outperformed the state-of-the-art approach for most observer geometries outside the sun-glint and solar zenith angles between 20 and 70∘, reducing the median SD of radiance residuals per solar geometry by up to 13.2 % for liquid clouds, 1.9 % for ice clouds, and 35.8 % for footprints containing both cloud phases. Geometries affected by sun glint (constituting between 10 % and 1 % of the discretized upward hemisphere for solar zenith angles of 20 and 70∘, respectively), however, often showed weaker performance when handled with the new approach and had increased residuals by as much as 60 % compared to the state-of-the-art approach. Overall, uncertainties were reduced for liquid-phase and mixed-phase footprints by 5.76 % and 10.81 %, respectively, while uncertainties for ice-phase footprints increased by 0.34 %. Tested for a variety of scenes, we further demonstrated the plausibility of scene-wise predicted radiance fields. This new approach may prove useful when employed in angular distribution models and may result in improved flux estimates, in particular dealing with clouds characterized by small or large droplet/crystal sizes.
Two- stream theory↗
Impacts of Aerosol and Cloud Variability on Satellite-Derived All-skies Direct Aerosol Radiative Effects (DARE) over the North and Southeast Atlantic
The Top-of-Atmosphere (TOA) Shortwave (SW) Direct Aerosol Radiative Effect (DARE) in all-sky conditions (i.e., aerosols in clear skies and aerosols above and below all types of clouds) is the global change in upwelling radiative flux due to aerosols. It is one of the strongest indicators of global climate change due to aerosols. SW DARE at TOA depends on the Earth’s surface albedo, cloud fraction, cloud optical properties, and aerosol optical properties, which are all challenging to accurately characterize from space. The overarching goals of our project are to provide state-of-the-art observational all-sky TOA SW DARE, along with guidance on which aerosol and/or cloud properties are the most important to measure, and at which spatio-temporal scales, for accurate DARE observations. We compute all-sky DARE based on state-of-the-art cloud and aerosol retrieval algorithms from CALIOP (Cloud–Aerosol Lidar with Orthogonal Polarization) and MODIS (Moderate Resolution Imaging spectroradiometer) satellite sensors, as well as aerosol intensive properties from MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, version 2) simulations over three specific regions of the Atlantic Ocean from 2012 to 2016. In this symposium, we present a characterization of the cloud and aerosol optical and physical properties and the observational TOA SW DARE along three individual satellite tracks in the Southeast Atlantic region -- on September 18 and 20, 2016 and August 13, 2017. We then quantify the impact of assuming homogeneous cloud or aerosol fields in space-based TOA SW DARE calculations. And, finally, the resulting space-based all-skies aerosol vertical distribution and DARE calculations are evaluated using remote sensing observations from coincident suborbital flights from the NASA ORACLES (the ObseRvations of Aerosols above CLouds and their intEractionS, ORACLES field campaign) field campaign. The NASA Atmospheric Observing System (AOS) mission addresses the NASA Aerosol, Cloud, Convection and Precipitation (ACCP) designated observables and proposes, as one of its aerosol objectives, to reduce uncertainties in estimates of global mean all-sky SW DARE at TOA. Well characterizing clouds, aerosol vertical distributions, aerosol types and associated all-skies DARE over the Atlantic Ocean will inform the AOS community on where, when, how, and how often the satellite retrievals should be performed to estimate DARE and reduce all-skies DARE uncertainties most accurately. These comprehensive characterizations will also identify the key regions and times when the AOS (or other ACCP-related) suborbital missions should be conducted to evaluate and improve the AOS space-based observations and retrievals.
aerosol↗