Spectrally Resolved Flux Derived from Collocated AIRS and CERES Observations and its Application in Model Validation

Spectrally resolved outgoing IR flux, the integrand of the outgoing longwave radiation (OLR), has its unique value in evaluating model simulations. Here we describe an algorithm of deriving such clear-sky outgoing spectral flux through the whole IR region from the collocated Atmospheric Infrared Sounder (AIRS) and the Clouds & the Earth's Radiant Energy System (CERES) measurements over the tropical oceans. Based on the scene types and corresponding angular distribution models (ADMs) used in the CERES Single Satellite Footprint (SSF) dataset, spectrally-dependent ADMs are developed and used to estimate the spectral flux at each AIRS channel. A multivariate linear prediction scheme is then used to estimate spectral fluxes at frequencies not covered by the AIRS instrument. The whole algorithm is validated using synthetic spectra as well as the CERES OLR measurements. Using the GFDL AM2 model simulation as a case study, the application of the derived clear-sky outgoing spectral flux in model evaluation is illustrated. By comparing the observed and simulated spectral flux in 2004, compensating errors in the simulated OLR from different absorption bands can be revealed, so does the errors from frequencies within a given absorption band. Discrepancies between the simulated and observed spatial distributions and seasonal evolutions of the spectral fluxes at different spectral ranges are further discussed. The methodology described in this study can be applied to other surface types as well as cloudy-sky observations and corresponding model evaluations

Spectrally resolved fluxes derived from collocated AIRS and CERES measurements and their application in model evaluation: Clear sky over the tropical oceans

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/94623/1/jgrd14292.pd

Using thermal infrared (TIR) data to characterize dust storms and their sources in the Middle East

Mineral dust and aerosols can directly and indirectly influence shortwave and longwave radiative forcing. In addition, it can cause health hazards, loss of agricultural soil, and safety hazards to aviation and motorists due to reduced visibility. Previous work utilized satellite and ground-based Thermal Infrared (TIR) data to measure aerosol content in the atmosphere. This research used TIR techniques, by creating a fine-grained (2.7-45 μm) mineral spectral library, direct laboratory emission spectroscopic analysis, and spectral and image deconvolution models, to characterize both the mineral content and particle size of dust storms affecting Kuwait. These results were validated using a combination of X-ray Diffraction (XRD) and Scanning Electron Microscopy (SEM) analyses that were performed on dust samples for three dust storms (May, July 2010, March 2011) from Kuwait. A combination of forward and backward Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) models were used to track air parcels arriving in Kuwait at the time of dust storm sample collection, thus testing the link to dust emitting areas or hotspots in eastern Syria and western Iraq. World soil maps and TIR analysis of surface deposits of these potential hotspots support this interpretation, and identified areas of high calcite concentration. This interpretation was in agreement with prior studies identifying calcite as the major mineral in dust storms affecting Kuwait. Spectral and image deconvolution models provided good tools in estimating mineral end members present in both dust samples and satellite plumes, but failed to identify the accurate particle size fractions present

Study of cloud properties from single-scattering, radiative forcing, and retrieval perspectives

This dissertation reports on three different yet related topics in light scattering computation, radiative transfer simulation, and remote sensing implementation, regarding the cloud properties and the retrieval of cloud properties from satellite-based infrared radiometric measurements. First, the errors associated with the use of circular cylinders as surrogates for hexagonal columns in computing the optical properties of pristine ice crystals at infrared (8-12 µm) wavelengths are investigated. It is found that the differences between the results for circular cylinders and hexagonal columns are on the order of a few percent at infrared wavelengths. Second, investigated in this dissertation are the outgoing broadband longwave and window channel radiances at the top-of-atmosphere under clear-sky conditions on the basis of the data acquired by the Cloud and the Earth's Radiant Energy System (CERES) instrument onboard the NASA Terra satellite platform. Based on the comparison of the observed broadband radiances with those obtained from rigorous radiative transfer simulations, it is found that the theoretical results tend to be larger than their measured counterparts. Extensive sensitivity studies regarding the uncertainties of various parameters were carried out. Within the considered uncertainties of various factors, the computed radiances are still larger than the observed radiances if thin cirrus clouds are excluded. Thus, a potential cause for the differences could be associated with the presence of thin cirrus clouds whose visible optical thickness is smaller than approximately 0.3. Third, presented in this dissertation is an illustration of the application of hyperspectral infrared channel observations to the retrieval of the cloud properties. Specifically, the hyperspectral measurements acquired from the Atmospheric Infrared Sounder (AIRS) aboard the NASA Aqua platform are used to infer cloud top pressure, effective cloud amount, cloud thermodynamic phase, cloud optical thickness, and the effective size of cloud particles. The AIRS-based retrievals are compared with the counterparts of the operational cloud products derived from the Moderate Resolution Imaging Spectroradiometer (MODIS). The two retrievals agree reasonably well except for the retrieved cloud effective particle size. Furthermore, the diurnal and seasonal contrasts of cloud properties are also investigated on the basis of the cloud properties retrieved from the AIRS data

The Assimilation of Hyperspectral Satellite Radiances in Global Numerical Weather Prediction

Hyperspectral infrared radiance data present opportunities for significant improvements in data assimilation and Numerical Weather Prediction (NWP). The increase in spectral resolution available from the Atmospheric Infrared Sounder (AIRS) sensor, for example, will make it possible to improve the accuracy of temperature and moisture fields. Improved accuracy of the NWP analyses and forecasts should result. In this thesis we incorporate these hyperspectral data, using new assimilation methods, into the National Centers for Environmental Prediction's (NCEP) operational Global Data Assimilation System/Global Forecast System (GDAS/GFS) and investigate their impact on the weather analysis and forecasts. The spatial and spectral resolution of AIRS data used by NWP centers was initially based on theoretical calculations. Synthetic data were used to determine channel selection and spatial density for real time data assimilation. Several problems were previously not fully addressed. These areas include: cloud contamination, surface related issues, dust, and temperature inversions. In this study, several improvements were made to the methods used for assimilation. Spatial resolution was increased to examine every field of view, instead of one in nine or eighteen fields of view. Improved selection criteria were developed to find the best profile for assimilation from a larger sample. New cloud and inversion tests were used to help identify the best profiles to be assimilated in the analysis. The spectral resolution was also increased from 152 to 251 channels. The channels added were mainly near the surface, in the water vapor absorption band, and in the shortwave region. The GFS was run at or near operational resolution and contained all observations available to the operational system. For each experiment the operational version of the GFS was used during that time. The use of full spatial and enhanced spectral resolution data resulted in the first demonstration of significant impact of the AIRS data in both the Northern and Southern Hemisphere. Experiments were performed to show the contribution to the improvements in global weather forecasts from the increase in spatial and spectral resolution. Both spatial and spectral resolution increases were shown to make significant contributions to forecast skill. New methods were also developed to check for clouds, inversions and for estimating surface emissivity. Overall, an improved methodology for assimilating hyperspectral AIRS data was achieved