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David Landgrebe, Professor of Electrical Engineering and Director of the Purdue University Laboratory for Applications of Remote Sensing (LARS), was a primary innovator in the field of digital image analysis and remote sensing of the environment. He and his LARS colleagues, along with a selected few other researchers at institutions including the University of Michigan, University of California Berkley, NASA Goddard Space Flight Center, and NASA Johnson Space Center, defined and developed remote sensing technology to monitor the Earth's terrestrial environment. This research led to the Landsat program, which has continued to monitor the Earth's land areas for a half century. These technologies have defined new fields of scientific query in digital image analysis, biophysical remote sensing, as well as remote sensing science and applications. Dr. Landgrebe's contributions to these research areas were substantial and profound. Understanding the early evolution of work is critical to understanding how this technology is still advancing today. The authors hope that current and future students of these fields will benefit from understanding how this all began.

Abstract A new configuration of the Community Earth System Model (CESM)/Community Atmosphere Model with full chemistry (CAM‐chem) supporting the capability of horizontal mesh refinement through the use of the spectral element (SE) dynamical core is developed and called CESM/CAM‐chem‐SE. Horizontal mesh refinement in CESM/CAM‐chem‐SE is unique and novel in that pollutants such as ozone are accurately represented at human exposure relevant scales while also directly including global feedbacks. CESM/CAM‐chem‐SE with mesh refinement down to ∼14 km over the conterminous US (CONUS) is the beginning of the Multi‐Scale Infrastructure for Chemistry and Aerosols (MUSICAv0). Here, MUSICAv0 is evaluated and used to better understand how horizontal resolution and chemical complexity impact ozone and ozone precursors over CONUS as compared to measurements from five aircraft campaigns, which occurred in 2013. This field campaign analysis demonstrates the importance of using finer horizontal resolution to accurately simulate ozone precursors such as nitrogen oxides and carbon monoxide. In general, the impact of using more complex chemistry on ozone and other oxidation products is more pronounced when using finer horizontal resolution where a larger number of chemical regimes are resolved. Large model biases for ozone near the surface remain in the Southeast US as compared to the aircraft observations even with updated chemistry and finer horizontal resolution. This suggests a need for adding the capability of replacing sections of global emission inventories with regional inventories, increasing the vertical resolution in the planetary boundary layer, and reducing model biases in meteorological variables such as temperature and clouds.

We have initiated a mutational analysis of pathogenicity in the rice blast fungus, Magnaporthe grisea, in which hygromycin-resistant transformants, most generated by restriction enzyme-mediated integration (REMI), were screened for the ability to infect plants. A rapid primary infection assay facilitated screening of 5,538 transformants. Twenty-seven mutants were obtained that showed a reproducible pathogenicity defect, and 18 of these contained mutations that cosegregated with the hygromycin resistance marker. Analysis of eight mutants has resulted in the cloning of seven PTH genes that play a role in pathogenicity on barley, weeping lovegrass, and rice. Two independent mutants identified the same gene, PTH2, suggesting nonrandom insertion of the transforming DNA. These first 7 cloned PTH genes are described.

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Accurate representation of fire emissions is critical for modeling the in-plume, near-source, and remote effects of biomass burning (BB) on atmospheric composition, air quality, and climate. In recent years application of advanced instrumentation has significantly improved knowledge of the compounds emitted from fires, which, coupled with a large number of recent laboratory and field campaigns, has facilitated the emergence of new emission factor (EF) compilations. The Next-generation Emissions InVentory expansion of Akagi (NEIVA) version 1.0 is one such compilation in which the EFs for 14 globally relevant fuel and fire types have been updated to include data from recent studies, with a focus on gaseous non-methane organic compounds (NMOC_g). The data are stored in a series of connected tables that facilitate flexible querying from the individual study level to recommended averages of all laboratory and field data by fire type. The querying features are enabled by assignment of unique identifiers to all compounds and constituents, including thousands of NMOC_g. NEIVA also includes chemical and physical property data and model surrogate assignments for three widely used chemical mechanisms for each NMOC_g. NEIVA EF datasets are compared with recent publications and other EF compilations at the individual compound level and in the context of overall volatility distributions and hydroxyl (OH) reactivity (OHR) estimates. The NMOC_g in NEIVA include ∼4 –8 times more compounds with improved representation of intermediate volatility organic compounds, resulting in much lower overall volatility (lowest-volatility bin shifted by as much as 3 orders of magnitude) and significantly higher OHR (up to 90 %) than other compilations. These updates can strongly impact model predictions of the effects of BB on atmospheric composition and chemistry. [ABSTRACT FROM AUTHOR]