AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
Microwave scatterometers are capable of acquiring land surface backscattering coefficients day and night under all-weather conditions, offering advantages for snow cover monitoring. However, the relatively low spatial resolution of traditional scatterometer data limits their application in the fine-scale monitoring of...
It is shown that high-resolution sea ice topography can be reconstructed directly from optical satellite imagery using a conditional diffusion framework, providing a scalable pathway for high-resolution sea ice mapping and roughness estimation in data-sparse environments.
Quantifying the spatiotemporal distribution of mountain snowpack is critical for hydrology, as it represents a vital freshwater resource for over a billion people. While C-band synthetic aperture radar (SAR) from Sentinel-1 provides high-resolution estimates over mountains, its use for daily snow monitoring is hindered...
A deep learning–based emulator is developed to reproduce SWE simulated by Noah-MP using a Convolutional Long Short-Term Memory (ConvLSTM) network trained on meteorological forcings from the Weather Research and Forecasting model and augmented with static physiographic variables and remotely sensed snow cover and leaf a...
Accurately estimating Snow Water Equivalent (SWE) in glacierized regions is critical for glacier modeling and mass balance analysis yet remains challenging due to sparse observations and uncertainties in precipitation products. The objectives are twofold: (a) assess how well three widely used precipitation datasets—Sta...
Lake ice and snow cover on the Tibetan Plateau play a critical role in regulating lake-surface radiative, thermodynamic, and hydrological processes, yet accurate monitoring remains challenging at moderate spatial resolutions. Over frozen lakes, widely used snow products (e.g., MOD10A1) frequently misclassify lake ice a...
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