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Raymond M Hoff

Publications and source records attributed to Raymond M Hoff.

3 recordsLinked to original sources

Validation of the Raman lidar algorithm for quantifying aerosol extinction.

To calculate aerosol extinction from Raman lidar data, it is necessary to evaluate the derivative of a molecular Raman signal with respect to range. The typical approach taken in the lidar community is to make an a priori assumption about the functional behavior of the data to calculate the derivative. It has previously been shown that the use of the chi-squared technique to determine the most likely functional behavior of the data prior to actually calculating the derivative eliminates the need for making a priori assumptions. Here that technique is validated through numerical simulation and by application to a significant body of Raman lidar measurements. In general, we show that the chi-squared approach for evaluating extinction yields lower extinction uncertainty than traditional techniques. We also use the technique to study the feasibility of developing a general characterization of the extinction uncertainty that could permit the uncertainty in Raman lidar aerosol extinction measurements to be estimated accurately without the need of the chi-squared technique.

Journal Article↗

Application of satellite remote-sensing data for source analysis of fine particulate matter transport events.

Satellite sensors have provided new datasets for monitoring regional and urban air quality. Satellite sensors provide comprehensive geospatial information on air quality with both qualitative imagery and quantitative data, such as aerosol optical depth. Yet there has been limited application of these new datasets in the study of air pollutant sources relevant to public policy. One promising approach to more directly link satellite sensor data to air quality policy is to integrate satellite sensor data with air quality parameters and models. This paper presents a visualization technique to integrate satellite sensor data, ground-based data, and back trajectory analysis relevant to a new rule concerning the transport of particulate matter across state boundaries. Overlaying satellite aerosol optical depth data and back trajectories in the days leading up to a known fine particulate matter with an aerodynamic diameter of <2.5 microm (PM2.5) event may indicate whether transport or local sources appear to be most responsible for high PM2.5 levels in a certain location at a certain time. Events in five cities in the United States are presented as case studies. This type of analysis can be used to help understand the source locations of pollutants during specific events and to support regulatory compliance decisions in cases of long distance transport.

Aerosols↗

Recommendations on the use of satellite remote-sensing data for urban air quality.

In the last 5 yr, the capabilities of earth-observing satellites and the technological tools to share and use satellite data have advanced sufficiently to consider using satellite imagery in conjunction with ground-based data for urban-scale air quality monitoring. Satellite data can add synoptic and geospatial information to ground-based air quality data and modeling. An assessment of the integrated use of ground-based and satellite data for air quality monitoring, including several short case studies, was conducted. Findings identified current U.S. satellites with potential for air quality applications, with others available internationally and several more to be launched within the next 5 yr; several of these sensors are described in this paper as illustrations. However, use of these data for air quality applications has been hindered by historical lack of collaboration between air quality and satellite scientists, difficulty accessing and understanding new data, limited resources and agency priorities to develop new techniques, ill-defined needs, and poor understanding of the potential and limitations of the data. Specialization in organizations and funding sources has limited the resources for cross-disciplinary projects. To successfully use these new data sets requires increased collaboration between organizations, streamlined access to data, and resources for project implementation.

Air Pollutants↗