Bryce Nordgren

Aerosol optical depth (AOD) and Angstrom exponent of aerosols observed by the Chinese Sun Hazemeter Network from August 2004 to September 2005

500, and 650 nm were analyzed for the period of August 2004 to September 2005. The smallest mean AOD (0.15) was found in the Tibetan Plateau where a showed the largest range in value (0.06‐0.9). The remote northeast corner of China was the next cleanest region with AODs ranging from 0.19 to 0.21 and with the largest a (1.16‐1.79), indicating the presence of fine aerosol particles. The forested sites exhibited moderate values of AOD (0.19‐0.51) and a (0.97‐1.47). A surprising finding was that the AOD measured at a few desert sites in northern China were relatively low, ranging from 0.24 to 0.36, and that a ranged from 0.42 to 0.99, presumably because of several dustblowing episodes during the observation period. The AOD observed over agricultural areas ranges from 0.38 to 0.90; a ranges from 0.55 to 1.11. These values do not differ much from those observed at the inland urban and suburban sites where AOD ranges from 0.50 to 0.69 and a ranges from 0.90 to 1.48. Given the geographic heterogeneity and the rapid increase in urbanization in China, much longer and more extensive observations are required.

Comparison of aerosol optical thickness measurements by MODIS, AERONET sun photometers, and Forest Service handheld sun photometers in southern Africa during the SAFARI 2000 campaign

The Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on the NASA Terra satellite has been used to monitor aerosol optical thickness (AOT, τ) daily at 10 km×10 km resolution worldwide since August 2000. This information, together with the locations of active fires detected by the MODIS instrument, is essential for understanding the seasonal trends and interannual variability of fires and their impacts on air pollution, atmospheric chemistry, and global climate. We compared aerosol optical thickness derived from MODIS, five automated sun photometers of the Aerosol Robotic Network (AERONET), and 38 Forest Service (FS) handheld sun photometers in western Zambia from 20 August to 20 September 2000. Aerosol optical thicknesses derived from AERONET sun photometers and FS sun photometers were also compared in the same region between mid‐June and late September 2000. Our objectives were to validate the AOT measurements by MODIS and to investigate the factors that affect AOT measurements. We demonstrated that in the regions of intense biomass burning, MODIS aerosol optical thickness was consistently 40–50% lower at 470, 550, and 660 nm compared with ground‐based AOT measurements by automated and handheld sun photometers and airborne measurements by NASA Ames Airborne Tracking 14‐channel Sunphotometers (AATS‐14). The satellite look angles can influence the MODIS AOT values, with the actual MODIS AOT values being as much as 0.06 higher than model‐calculated MODIS AOT values on the right edge of the MODIS scene. This phenomenon may be due to error in the assumed aerosol scattering phase function or surface directional properties. Density of vegetation cover can also affect MODIS measurements of aerosol optical thickness.

Laboratory investigation of fire radiative energy and smoke aerosol emissions

[1] Fuel biomass samples from southern Africa and the United States were burned in a laboratory combustion chamber while measuring the biomass consumption rate, the fire radiative energy (FRE) release rate (Rfre), and the smoke concentrations of carbon monoxide (CO), carbon dioxide (CO2), and particulate matter (PM). The PM mass emission rate (RPM) was quantified from aerosol optical thickness (AOT) derived from smoke extinction measurements using a custom-made laser transmissometer. The RPM and Rfre time series for each fire were integrated to total PM mass and FRE, respectively, the ratio of which represents its FRE-based PM emission coefficient (Ce ). A strong correlation (r 2 = 0.82) was found between the total FRE and total PM mass, from which an average Ce value of 0.03 kg MJ �1 was calculated. This value agrees with those derived similarly from satellite-borne measurements of Rfre and AOT acquired over large-scale wildfires.

Supporting Interoperable Geospatial Data Fusion by adopting OGC and ISO TC 211 standards

Geospatial data fusion refers to the ability to process (fuse) data from a variety of sources which capture and/or model earth-related phenomena in order to produce added-value information. This paper provides an overview of the latest advancements in standardization for interoperability in the geographical information community achieved by the International Organization for Standardization (ISO) and the Open Geospatial Consortium (OGC). It also provides guidelines and suggestions for designing superior architectures to support geospatial data fusion by employing OGC/ISO specifications

Improved methodology for the retrieval of the particulate extinction coefficient and the lidar ratio from the lidar multiangle measurement

An improved methodology for processing scanning lidar data is considered. We demonstrate a new principle of determining vertical profiles of the particulate extinction coefficient and the lidar ratio with the Kano-Hamilton multiangle solution. This technique, which is also applicable to combined elastic/inelastic lidar measurements, computes the extinction coefficient from the backscatter term rather than from optical depth, thus avoiding numerical differentiation. The inversion is based on determining the stepwise column-integrated lidar ratios that provide the best matching of the initial profile of the optical depth to that obtained after the inversion. We explore two approaches concerning the division of the column-integrated lidar ratio into different ranges: in the first case, divisions between ranges are uniformly distributed; in the second case, divisions are located using estimated uncertainty boundaries in the inverted optical depth. The inversion method was used to process the experimental data obtained in the vicinity of large wildfires with the Fire Sciences Laboratory lidar. Examples of the simulated and experimental data are presented, which illustrate the specifics and prospects of this data-processing methodology.