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Vanessa Caicedo, Ruben Delgado, Ricardo Sakai, Travis Knepp, David Williams, Kevin Cavender, Barry Lefer, and James Szykman

Abstract

A unique automated planetary boundary layer (PBL) retrieval algorithm is proposed as a common cross-platform method for use with commercially available ceilometers for implementation under the redesigned U.S. Environmental Protection Agency Photochemical Assessment Monitoring Stations program. This algorithm addresses instrument signal quality and screens for precipitation and cloud layers before the implementation of the retrieval method using the Haar wavelet covariance transform. Layer attribution for the PBL height is supported with the use of continuation and time-tracking parameters, and uncertainties are calculated for individual PBL height retrievals. Commercial ceilometer retrievals are tested against radiosonde PBL height and cloud-base height during morning and late-afternoon transition times, critical to air quality model prediction and when retrieval algorithms struggle to identify PBL heights. A total of 58 radiosonde profiles were used, and retrievals for nocturnal stable layers, residual layers, and mixing layers were assessed. Overall good agreement was found for all comparisons, with one system showing limitations for the cases of nighttime surface stable layers and daytime mixing layer. It is recommended that nighttime shallow stable-layer retrievals be performed with a recommended minimum height or with additional verification. Retrievals of residual-layer heights and mixing-layer comparisons revealed overall good correlations with radiosonde heights (square of correlation coefficients r 2 ranging from 0.89 to 0.96, and bias ranging from approximately −131 to +63 m for the residual layer and r 2 from 0.88 to 0.97 and bias from −119 to +101 m for the mixing layer).

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Jassim Al-Saadi, James Szykman, R. Bradley Pierce, Chieko Kittaka, Doreen Neil, D. Allen Chu, Lorraine Remer, Liam Gumley, Elaine Prins, Lewis Weinstock, Clinton MacDonald, Richard Wayland, Fred Dimmick, and Jack Fishman

Accurate air quality forecasts can allow for mitigation of the health risks associated with high levels of air pollution. During September 2003, a team of NASA, NOAA, and EPA researchers demonstrated a prototype tool for improving fine particulate matter (PM2.5) air quality forecasts using satellite aerosol observations. Daily forecast products were generated from a near-real-time fusion of multiple input data products, including aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS)/Earth Observing System (EOS) instrument on the NASA Terra satellite, PM2.5 concentration from over 300 state/local/national surface monitoring stations, meteorological fields from the NOAA/NCEP Eta Model, and fire locations from the NOAA/National Environmental Satellite, Data, and Information Service (NESDIS) Geostationary Operational Environmental Satellite (GOES) Wildfire Automated Biomass Burning Algorithm (WF_ABBA) product. The products were disseminated via a Web interface to a small group of forecasters representing state and local air management agencies and the EPA. The MODIS data improved forecaster knowledge of synoptic-scale air pollution events, particularly over oceans and in regions devoid of surface monitors. Forecast trajectories initialized in regions of high AOD offered guidance for identifying potential episodes of poor air quality. The capability of this approach was illustrated with a case study showing that aerosol resulting from wildfires in the northwestern United States and southwestern Canada is transported across the continent to influence air quality in the Great Lakes region a few days later. The timing of this demonstration was selected to help improve the accuracy of the EPA's AIRNow (www.epa.gov/airnow/) next-day PM2.5 air quality index forecast, which began on 1 October 2003. Based on the positive response from air quality managers and forecasters, this prototype was expanded and transitioned to an operational provider during the summer of 2004.

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