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  • Author or Editor: Vincenzo Levizzani x
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Maria João Costa, Ana Maria Silva, and Vincenzo Levizzani

Abstract

A method based on the synergistic use of low earth orbit (LEO) and geostationary earth orbit (GEO) satellite data for aerosol-type characterization, as well as aerosol optical thickness (AOT) retrieval and monitoring over the ocean, is presented. These properties are used for the estimation of the direct shortwave aerosol radiative forcing at the top of the atmosphere. The synergy serves the purpose of monitoring aerosol events at the GEO time and space scales while maintaining the accuracy level achieved with LEO instruments. Aerosol optical properties representative of the atmospheric conditions are obtained from the inversion of high-spectral-resolution measurements from the Global Ozone Monitoring Experiment (GOME). The aerosol optical properties are input for radiative transfer calculations for the retrieval of the AOT from GEO visible broadband measurements, avoiding the use of fixed aerosol models available in the literature. The retrieved effective aerosol optical properties represent an essential component for the aerosol radiative forcing assessment. A sensitivity analysis is also presented to quantify the effects that changes on the aerosol model may have on modeled results of spectral reflectance, AOT, and direct shortwave aerosol radiative forcing at the top of the atmosphere. The impact on modeled values of the physical assumptions on surface reflectance and vertical profiles of ozone and water vapor are analyzed. Results show that the aerosol model is the main factor influencing the investigated radiative variables. Results of the application of the method to several significant aerosol events, as well as their validation, are presented in a companion paper.

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Maria João Costa, Vincenzo Levizzani, and Ana Maria Silva

Abstract

A method based on the synergistic use of low earth orbit and geostationary earth orbit satellite data for aerosol-type characterization and aerosol optical thickness (AOT: τ a) retrieval and monitoring over the ocean is presented in Part I of this paper. The method is now applied to a strong dust outbreak over the Atlantic Ocean in June 1997 and to two other relevant transport events of biomass burning and desert dust aerosol that occurred in 2000 over the Atlantic and Indian Oceans, respectively. The retrievals of the aerosol optical properties are checked against retrievals from sun and sky radiance measurements from the ground-based Aerosol Robotic Network (AERONET). The single-scattering albedo values obtained from AERONET are always within the error bars presented for Global Ozone Monitoring Experiment (GOME) retrievals, resulting in differences lower than 0.041. The retrieved AOT values are compared with the independent space–time-collocated measurements from the AERONET, as well as to the satellite aerosol official products of the Polarization and Directionality of the Earth Reflectances (POLDER) and the Moderate Resolution Imaging Spectroradiometer (MODIS). A first estimate of the AOT accuracy derived from comparisons with AERONET data leads to ±0.02 ± 0.22τ a when all AOT values are retained or to ±0.02 ± 0.16τ a for aerosol transport events (AOT > 0.4). The upwelling flux at the top of the atmosphere (TOA) was computed with radiative transfer calculations and used to estimate the TOA direct shortwave aerosol radiative forcing; a comparison with space–time-collocated measurements from the Clouds and the Earth's Radiant Energy System (CERES) TOA flux product was also done. It was found that more than 90% of the values differ from CERES fluxes by less than ±15%.

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Francisco J. Tapiador, Chris Kidd, Vincenzo Levizzani, and Frank S. Marzano

Abstract

The purpose of this paper is to evaluate a new operational procedure to produce half-hourly rainfall estimates at 0.1° spatial resolution. Rainfall is estimated using a neural networks (NN)–based approach utilizing passive microwave (PMW) and infrared satellite measurements. Several neural networks are tested, from multilayer perceptron to adaptative resonance theory architectures. The NN analytical selection process is explained. Half- hourly rain gauge data over Andalusia, Spain, are used for validation purposes. Several interpolation procedures are tested to transform point to areal measurements, including the maximum entropy estimation method. Rainfall estimations are also compared with Geostationary Operational Environmental Satellite precipitation index and histogram-matching results. Half-hourly rainfall estimates give ∼0.6 correlations with PMW data (∼0.2 with gauge), and average correlations of up to 0.7 and 0.6 are obtained for 0.5° and 0.1° monthly accumulated estimates, respectively.

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Gail Skofronick-Jackson, Mark Kulie, Lisa Milani, Stephen J. Munchak, Norman B. Wood, and Vincenzo Levizzani

Abstract

Retrievals of falling snow from space-based observations represent key inputs for understanding and linking Earth’s atmospheric, hydrological, and energy cycles. This work quantifies and investigates causes of differences among the first stable falling snow retrieval products from the Global Precipitation Measurement (GPM) Core Observatory satellite and CloudSat’s Cloud Profiling Radar (CPR) falling snow product. An important part of this analysis details the challenges associated with comparing the various GPM and CloudSat snow estimates arising from different snow–rain classification methods, orbits, resolutions, sampling, instrument specifications, and algorithm assumptions. After equalizing snow–rain classification methodologies and limiting latitudinal extent, CPR observes nearly 10 (3) times the occurrence (accumulation) of falling snow as GPM’s Dual-Frequency Precipitation Radar (DPR). The occurrence disparity is substantially reduced if CloudSat pixels are averaged to simulate DPR radar pixels and CPR observations are truncated below the 8-dBZ reflectivity threshold. However, even though the truncated CPR- and DPR-based data have similar falling snow occurrences, average snowfall rate from the truncated CPR record remains significantly higher (43%) than the DPR, indicating that retrieval assumptions (microphysics and snow scattering properties) are quite different. Diagnostic reflectivity (Z)–snow rate (S) relationships were therefore developed at Ku and W band using the same snow scattering properties and particle size distributions in a final effort to minimize algorithm differences. CPR–DPR snowfall amount differences were reduced to ~16% after adopting this diagnostic Z–S approach.

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