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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Ground and aerial imaging techniques to detect tree damage caused by bark beetles in forested areas
Multispectral sensing techniques to detect ponderosa pine trees under stress from insect or diseases
Multispectral sensing techniques for ground and airborne detection of Ponderosa pine trees under stress from insect or pathogenic organisms
Application of multispectral sensors to detect insect and disease infestation of ponderosa pine trees
Previsual detection of vigor loss and mortality signs in ponderosa pine trees subject to bark beetle attack
Remote spectral reflectance measurements on trees for soil anomaly detection
Optimum realizable linear filter role in some communication problems, considering detection and continuous estimation
Detection, estimation and linear modulation theory, Part 1, covering random processes, signal detection in noise and continuous waveforms
Logic hazards detection in threshold gate networks, discussing hazard free network synthesis based on Boolean functions and tree method
Differential equation technique of representing dynamic systems for optimal control problems, and fundamental role of optimum linear filter
Equalization of dispersive channels using decision feedback, and state variable estimation of sonar or seismic data in presence of pure delay
Spaceborne passive microwave (PMW) radiometry is central to global precipitation monitoring, yet retrieval uncertainties remain substantial, particularly for cross-track sounders whose variable footprints and channel configurations are optimized for atmospheric temperature and moisture profiling rather than precipitation. Consequently, existing operational products often exhibit angular-dependent biases, limited effective swath utilization, unrealistic rainfall probability distributions, and systematic misclassification of precipitation phase. These limitations are further compounded by the scarcity of globally accurate and representative precipitation observations, as training data from the Dual-frequency Precipitation Radar (DPR) and the Cloud Profiling Radar (CPR) are spatially sparse, lack uniform global coverage, and exhibit heterogeneous error characteristics across precipitation regimes. To address these challenges, this study presents a supervised retrieval algorithm that incrementally trains an ensemble of extreme gradient-boosted decision trees by augmenting base learners with pre-training on reanalysis data and post-training on coincident DPR and CPR observations matched with the Advanced Technology Microwave Sounder (ATMS). By transferring prior information from reanalysis to posterior constraints from radar observations and adopting a sequential detection–estimation strategy for precipitation phase and rate retrieval, the proposed approach yields retrievals across the full ATMS swath that are largely free from persistent deficiencies in current Global Precipitation Measurement (GPM) passive microwave operational products. In particular, the method resolves bimodal artifacts in rainfall retrievals and mitigates systematic high-latitude snowfall biases, including overestimation across the Arctic and underestimation across the Antarctic. Validation against independent Multi-Radar Multi-Sensor (MRMS) data over the Contiguous United States (CONUS) further demonstrates improved performance in precipitation phase detection and rate estimation relative to both reanalysis and current GPM PMW products.