Related papers: AODisaggregation: toward global aerosol vertical p…
Detecting out-of-distribution (OOD) data is crucial for ensuring the safe deployment of machine learning models in real-world applications. However, existing OOD detection approaches primarily rely on the feature maps or the full gradient…
Aero-optical beam control relies on the development of low-latency forecasting techniques to quickly predict wavefronts aberrated by the Turbulent Boundary Layer (TBL) around an airborne optical system, and its study applies to a…
Ambient fine particulate matter less than 2.5 $\mu$m in aerodynamic diameter (PM$_{2.5}$) has been linked to various adverse health outcomes and has, therefore, gained interest in public health. However, the sparsity of air quality monitors…
Out-of-distribution (OOD) detection represents a critical challenge in remote sensing applications, where reliable identification of novel or anomalous patterns is essential for autonomous monitoring, disaster response, and environmental…
We present a radiative transfer analysis of latitudinally resolved H (1.487-1.783 micron) and K (2.028-2.364 micron) band spectra of Uranus, from which we infer the distributions of aerosols and methane in the planet's atmosphere. Data were…
The large majority of extinction sight lines in our Galaxy obey a simple relation depending on one parameter, the total-to-selective extinction coefficient, Rv. Different values of Rv are able to match the whole extinction curve through…
Detecting out-of-distribution (OOD) samples is important for deploying machine learning models in safety-critical applications such as autonomous driving and robot-assisted surgery. Existing research has mainly focused on unimodal scenarios…
Out-of-distribution (OOD) detection is essential to prevent anomalous inputs from causing a model to fail during deployment. While improved OOD detection methods have emerged, they often rely on the final layer outputs and require a full…
Trajectory prediction is central to the safe and seamless operation of autonomous vehicles (AVs). In deployment, however, prediction models inevitably face distribution shifts between training data and real-world conditions, where rare or…
One key challenge in Out-of-Distribution (OOD) detection is the absence of ground-truth OOD samples during training. One principled approach to address this issue is to use samples from external datasets as outliers (i.e., pseudo OOD…
Satellite aerosol products have been widely used to retrieve ground PM2.5 concentration because of its wide coverage and continuously spatial distribution. While more and more studies focus on the retrieval algorithm, we find that the…
A simple model which can explain the observed vertical distribution and size spectrum of atmospheric aerosol has been proposed. The model is based on a new physical hypothesis for the vertical mass exchange between the troposphere and the…
The AErosol RObotic NETwork (AERONET), established in 1993 with limited global sites, has grown to over 900 locations, providing three decades of continuous aerosol data. While earlier studies based on shorter time periods (10-12 years) and…
Atmospheric structure, represented by backscatter coefficients (BC) recovered from satellite LiDAR attenuated backscatter (ATB), provides a volumetric view of clouds, aerosols, and molecules, playing a critical role in human activities,…
LiDAR-based 3D object detection plays a critical role for reliable and safe autonomous driving systems. However, existing detectors often produce overly confident predictions for objects not belonging to known categories, posing significant…
Owing to the recent, rapid development of computer technology, the resolution of atmospheric numerical models has increased substantially. With the use of next-generation supercomputers, atmospheric simulations using horizontal grid…
Anomaly-diffusing energy balance models (AD-EBM) are routinely employed to analyze and emulate the warming response of both observed and simulated Earth systems. We demonstrate a deficiency in common multi-layer as well as…
Out-of-distribution (OOD) detection is crucial for the reliable deployment of machine learning models in real-world scenarios, enabling the identification of unknown samples or objects. A prominent approach to enhance OOD detection…
Detecting out-of-distribution (OOD) instances is crucial for the reliable deployment of machine learning models in real-world scenarios. OOD inputs are commonly expected to cause a more uncertain prediction in the primary task; however,…
We present, for the first time, spectral behaviour of aerosol optical depths (AODs) over Manora Peak, Nainital located at an altitude of $\sim$ 2 km in the Shivalik ranges of central Himalayas. The observations were carried out using a…