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Sundar A. Christopher and Jianglong Zhang

Abstract

Hourly Geostationary Operational Environmental Satellite-8 (GOES-8) imager data (1344–1944 UTC) from 20 July–31 August 1998 were used to study the daytime variation of shortwave direct radiative forcing (SWARF) of smoke aerosols over biomass burning regions in South America (4°–16°S, 51°–65°W). Vicarious calibration procedures were used to adjust the GOES visible channel reflectance values for the degradation in signal response. Using Mie theory and discrete ordinate radiative transfer (DISORT) calculations, smoke aerosol optical thickness (AOT) was estimated at 0.67 μm. The GOES-retrieved AOT was then compared against ground-based AOT retrieved values. Using the retrieved GOES-8 AOT, a four-stream broadband radiative transfer model was used to compute shortwave fluxes for smoke aerosols at the top of the atmosphere (TOA). The daytime variation of smoke AOT and SWARF was examined for the study area. For selected days, the Clouds and the Earth's Radiant Energy System (CERES) TOA shortwave (SW) fluxes are compared against the model-derived SW fluxes.

Results of this study show that the GOES-derived AOT is in excellent agreement with Aerosol Robotic Network (AERONET)-derived AOT values with linear correlation coefficient of 0.97. The TOA CERES-estimated SW fluxes compare well with the model-calculated SW fluxes with linear correlation coefficient of 0.94. For August 1998 the daytime diurnally averaged AOT and SWARF for the study area is 0.63 ± 0.39 and −45.8 ± 18.8 W m−2, respectively. This is among the first studies to estimate the daytime diurnal variation of SWARF of smoke aerosols using satellite data.

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Natalie Midzak, John E. Yorks, Jianglong Zhang, Bastiaan van Diedenhoven, Sarah Woods, and Matthew McGill

Abstract

Using collocated NASA Cloud Physics Lidar (CPL) and Research Scanning Polarimeter (RSP) data from the Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) campaign, a new observational-based method was developed which uses a K-means clustering technique to classify ice crystal habit types into seven categories: column, plates, rosettes, spheroids, and three different type of irregulars. Intercompared with the collocated SPEC, Inc., Cloud Particle Imager (CPI) data, the frequency of the detected ice crystal habits from the proposed method presented in the study agrees within 5% with the CPI-reported values for columns, irregulars, rosettes, and spheroids, with more disagreement for plates. This study suggests that a detailed ice crystal habit retrieval could be applied to combined space-based lidar and polarimeter observations such as CALIPSO and POLDER in addition to future missions such as the Aerosols, Clouds, Convection, and Precipitation (A-CCP).

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Jared W. Marquis, Alec S. Bogdanoff, James R. Campbell, James A. Cummings, Douglas L. Westphal, Nathaniel J. Smith, and Jianglong Zhang

Abstract

Passive longwave infrared radiometric satellite–based retrievals of sea surface temperature (SST) at instrument nadir are investigated for cold bias caused by unscreened optically thin cirrus (OTC) clouds [cloud optical depth (COD) ≤ 0.3]. Level 2 nonlinear SST (NLSST) retrievals over tropical oceans (30°S–30°N) from Moderate Resolution Imaging Spectroradiometer (MODIS) radiances collected aboard the NASA Aqua satellite (Aqua-MODIS) are collocated with cloud profiles from the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. OTC clouds are present in approximately 25% of tropical quality-assured (QA) Aqua-MODIS Level 2 data, representing over 99% of all contaminating cirrus found. Cold-biased NLSST (MODIS, AVHRR, and VIIRS) and triple-window (AVHRR and VIIRS only) SST retrievals are modeled based on operational algorithms using radiative transfer model simulations conducted with a hypothetical 1.5-km-thick OTC cloud placed incrementally from 10.0 to 18.0 km above mean sea level for cloud optical depths between 0.0 and 0.3. Corresponding cold bias estimates for each sensor are estimated using relative Aqua-MODIS cloud contamination frequencies as a function of cloud-top height and COD (assuming they are consistent across each platform) integrated within each corresponding modeled cold bias matrix. NLSST relative OTC cold biases, for any single observation, range from 0.33° to 0.55°C for the three sensors, with an absolute (bulk mean) bias between 0.09° and 0.14°C. Triple-window retrievals are more resilient, ranging from 0.08° to 0.14°C relative and from 0.02° to 0.04°C absolute. Cold biases are constant across the Pacific and Indian Oceans. Absolute bias is lower over the Atlantic but relative bias is higher, indicating that this issue persists globally.

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Jared W. Marquis, Mayra I. Oyola, James R. Campbell, Benjamin C. Ruston, Carmen Córdoba-Jabonero, Emilio Cuevas, Jasper R. Lewis, Travis D. Toth, and Jianglong Zhang

Abstract

Numerical weather prediction systems depend on Hyperspectral Infrared Sounder (HIS) data, yet the impacts of dust-contaminated HIS radiances on weather forecasts has not been quantified. To determine the impact of dust aerosol on HIS radiance assimilation, we use a modified radiance assimilation system employing a one-dimensional variational assimilation system (1DVAR) developed under the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Numerical Weather Prediction–Satellite Application Facility (NWP-SAF) project, which uses the Radiative Transfer for TOVS (RTTOV). Dust aerosol impacts on analyzed temperature and moisture fields are quantified using synthetic HIS observations from rawinsonde, Micropulse Lidar Network (MPLNET), and Aerosol Robotic Network (AERONET). Specifically, a unit dust aerosol optical depth (AOD) contamination at 550 nm can introduce larger than 2.4 and 8.6 K peak biases in analyzed temperature and dewpoint, respectively, over our test domain. We hypothesize that aerosol observations, or even possibly forecasts from aerosol predication models, may be used operationally to mitigate dust induced temperature and moisture analysis biases through forward radiative transfer modeling.

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