Equivalent Sensor Radiance Generation and Remote Sensing from Model Parameters

In this paper we describe a general procedure for calculating equivalent sensor radiances from variables output from a global atmospheric forecast model. In order to take proper account of the discrepancies between model resolution and sensor footprint the algorithm takes explicit account of the model subgrid variability, in particular its description of the probably density function of total water (vapor and cloud condensate.) The equivalent sensor radiances are then substituted into an operational remote sensing algorithm processing chain to produce a variety of remote sensing products that would normally be produced from actual sensor output. This output can then be used for a wide variety of purposes such as model parameter verification, remote sensing algorithm validation, testing of new retrieval methods and future sensor studies. We show a specific implementation using the GEOS-5 model, the MODIS instrument and the MODIS Adaptive Processing System (MODAPS) Data Collection 5.1 operational remote sensing cloud algorithm processing chain (including the cloud mask, cloud top properties and cloud optical and microphysical properties products.) We focus on clouds and cloud/aerosol interactions, because they are very important to model development and improvement

Multi-sensor Cloud Retrieval Simulator and Remote Sensing from Model Parameters

In this paper we describe a general procedure for calculating synthetic sensor radiances from variable output from a global atmospheric forecast model. In order to take proper account of the discrepancies between model resolution and sensor footprint, the algorithm takes explicit account of the model subgrid variability, in particular its description of the probability density function of total water (vapor and cloud condensate.) The simulated sensor radiances are then substituted into an operational remote sensing algorithm processing chain to produce a variety of remote sensing products that would normally be produced from actual sensor output. This output can then be used for a wide variety of purposes such as model parameter verification, remote sensing algorithm validation, testing of new retrieval methods and future sensor studies.We show a specific implementation using the GEOS-5 model, the MODIS instrument and the MODIS Adaptive Processing System (MODAPS) Data Collection 5.1 operational remote sensing cloud algorithm processing chain (including the cloud mask, cloud top properties and cloud optical and microphysical properties products). We focus on clouds because they are very important to model development and improvement

The effect of ice crystal surface roughness on the retrieval of ice cloud microphysical and optical properties

The effect of the surface roughness of ice crystals is not routinely accounted for in current cloud retrieval algorithms that are based on pre-computed lookup libraries. In this study, we investigate the effect of ice crystal surface roughness on the retrieval of ice cloud effective particle size, optical thickness and cloud-top temperature. Three particle surface conditions, smooth, moderately rough and deeply rough, are considered in the visible and near-infrared channels (0.65 and 3.75 õm). The discrete ordinates radiative transfer (DISORT) model is used to compute the radiances for a set of optical thicknesses, particle effective sizes, viewing and illumination angles, and cloud temperatures. A parameterization of cloud bi-directional reflectances and effective emittances is then developed from a variety of particle surface conditions. This parameterization is applied in a 3-channel retrieval method for Moderate Resolution Imaging Spectroradiometer (MODIS) data at 0.65, 3.75, and 10.8 õm. Cloud optical properties are derived iteratively for each pixel that contains ice clouds. The impact of ice crystal surface roughness on the cloud parameter retrievals is examined by comparing the results for particles with smooth surfaces and rough surfaces. Retrieval results from two granules of MODIS data indicate that the retrieved cloud optical thickness is significantly reduced if the parameterization for roughened particles is used, as compared with the case of smooth particles. For the retrieval of cloud effective particle size, the inclusion of the effect of surface roughness tends to decrease the retrieved effective particle size if ice crystals are small. The reversed result is noticed for large ice crystals. It is also found that surface roughness has a very minor effect on the retrieval of cloud-top temperatures

Clouds and the Earth's Radiant Energy System (CERES) Algorithm Theoretical Basis Document

The theoretical bases for the Release 1 algorithms that will be used to process satellite data for investigation of the Clouds and Earth's Radiant Energy System (CERES) are described. The architecture for software implementation of the methodologies is outlined. Volume 3 details the advanced CERES methods for performing scene identification and inverting each CERES scanner radiance to a top-of-the-atmosphere (TOA) flux. CERES determines cloud fraction, height, phase, effective particle size, layering, and thickness from high-resolution, multispectral imager data. CERES derives cloud properties for each pixel of the Tropical Rainfall Measuring Mission (TRMM) visible and infrared scanner and the Earth Observing System (EOS) moderate-resolution imaging spectroradiometer. Cloud properties for each imager pixel are convolved with the CERES footprint point spread function to produce average cloud properties for each CERES scanner radiance. The mean cloud properties are used to determine an angular distribution model (ADM) to convert each CERES radiance to a TOA flux. The TOA fluxes are used in simple parameterization to derive surface radiative fluxes. This state-of-the-art cloud-radiation product will be used to substantially improve our understanding of the complex relationship between clouds and the radiation budget of the Earth-atmosphere system

Innovative Techniques for the Retrieval of Earth’s Surface and Atmosphere Geophysical Parameters: Spaceborne Infrared/Microwave Combined Analyses

With the advent of the first satellites for Earth Observation: Landsat-1 in July 1972 and ERS-1 in May 1991, the discipline of environmental remote sensing has become, over time, increasingly fundamental for the study of phenomena characterizing the planet Earth. The goal of environmental remote sensing is to perform detailed analyses and to monitor the temporal evolution of different physical phenomena, exploiting the mechanisms of interaction between the objects that are present in an observed scene and the electromagnetic radiation detected by sensors, placed at a distance from the scene, operating at different frequencies. The analyzed physical phenomena are those related to climate change, weather forecasts, global ocean circulation, greenhouse gas profiling, earthquakes, volcanic eruptions, soil subsidence, and the effects of rapid urbanization processes. Generally, remote sensing sensors are of two primary types: active and passive. Active sensors use their own source of electromagnetic radiation to illuminate and analyze an area of interest. An active sensor emits radiation in the direction of the area to be investigated and then detects and measures the radiation that is backscattered from the objects contained in that area. Passive sensors, on the other hand, detect natural electromagnetic radiation (e.g., from the Sun in the visible band and the Earth in the infrared and microwave bands) emitted or reflected by the object contained in the observed scene. The scientific community has dedicated many resources to developing techniques to estimate, study and analyze Earth’s geophysical parameters. These techniques differ for active and passive sensors because they depend strictly on the type of the measured physical quantity. In my P.h.D. work, inversion techniques for estimating Earth’s surface and atmosphere geophysical parameters will be addressed, emphasizing methods based on machine learning (ML). In particular, the study of cloud microphysics and the characterization of Earth’s surface changes phenomenon are the critical points of this work

Effect of vertical profile of aerosols on the local shortwave radiative forcing estimation

In this work, the effect of the aerosol vertical distribution on the local shortwave aerosol radiative forcing is studied. We computed the radiative forcing at the top and bottom of the atmosphere between 0.2 and 4 microns using the libRadTran package and compared the results with those provided by AERONET (AErosol RObotic NETwork). Lidar measurements were employed to characterize the aerosol vertical profile, and collocated AERONET measurements provided aerosol optical parameters required to calculate its radiative forcing. A good correlation between the calculated radiative forcings and those provide by AERONET, with differences smaller than 1 W m-2 (15% of estimated radiative forcing), is obtained when a gaussian vertical aerosol profile is assumed. Notwithstanding, when a measured aerosol profile is inserted into the model, differences between radiative forcings can vary up to 6.54Wm-2 (15%), with a mean of differences =-0.74±3.06W m-2 at BOA and -3.69Wm-2 (13%), with a mean of differences = -0.27±1.32Wm-2 at TOA due to multiple aerosol layers and aerosol types. These results indicate that accurate information about aerosol vertical distribution must be incorporated in the radiative forcing calculation in order to reduce its uncertainties.This research was funded by European Union’s Horizon 2020 research and innovation programme through project ACTRIS-2 (grant 654109), the Spanish Ministry of Economy and Competitivity (CRISOL, CGL2017-85344-R and ACTRIS-ESPAÑA, CGL2017-90884-REDT) and Madrid Regional Government (TIGAS-CM, Y2018/EMT-5177)

Characterization of the Earth\u27s surface and atmosphere for multispectral and hyperspectral thermal imagery

The goal of this research was to develop a new approach to solve the inverse problem of thermal remote sensing of the Earth. This problem falls under a large class of inverse problems that are ill-conditioned because there are many more unknowns than observations. The approach is based on a multivariate analysis technique known as Canonical Correlation Analysis (CCA). By collecting two ensembles of observations, it is possible to find the latent dimensionality where the data are maximally correlated. This produces a reduced and orthogonal space where the problem is not ill-conditioned. In this research, CCA was used to extract atmospheric physical parameters such as temperature and water vapor profiles from multispectral and hyperspectral thermal imagery. CCA was also used to infer atmospheric optical properties such as spectral transmission, upwelled radiance, and downwelled radiance. These properties were used to compensate images for atmospheric effects and retrieve surface temperature and emissivity. Results obtained from MODTRAN simulations, the MODerate resolution Imaging Spectrometer (MODIS) Airborne Sensor (MAS), and the MODIS and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) (MASTER) airborne sensor show that it is feasible to retrieve land surface temperature and emissivity with 1.0 K and 0.01 accuracies, respectively

Validation of the GRAPE single view aerosol retrieval for ATSR-2 and insights into the long term global AOD trend over the ocean

The Global Retrieval of ATSR Cloud Parameters and Evaluation (GRAPE) project has produced a global data-set of cloud and aerosol properties from the Along Track Scanning Radiometer-2 (ATSR-2) instrument, covering the time period 1995�2001. This paper presents the validation of aerosol optical depths (AODs) over the ocean from this product against AERONET sun-photometer measurements, as well as a comparison to the Advanced Very High Resolution Radiometer (AVHRR) optical depth product produced by the Global Aerosol Climatology Project (GACP). The GRAPE AOD over ocean is found to be in good agreement with AERONET measurements, with a Pearson's correlation coefficient of 0.79 and a best-fit slope of 1.0±0.1, but with a positive bias of 0.08±0.04. Although the GRAPE and GACP datasets show reasonable agreement, there are significant differences. These discrepancies are explored, and suggest that the downward trend in AOD reported by GACP may arise from changes in sampling due to the orbital drift of the AVHRR instruments

On the properties of cirrus clouds over the tropical West Pacific

June 2002.Includes bibliographical references.Sponsored by DOE/ARM DE-FG03-94ER61748.Sponsored by DOE/ARM DE-FG03-98ER62569