相关论文: Microburst nowcasting applications of GOES

Moment-based estimation filters have successfully aided spacecraft navigation for decades. However, future missions plan to venture into deep-space regimes with significant round-trip light-time telecommunication delays, operate in…

The next-generation (3G/XG) ground-based gravitational-wave (GW) detectors such as Einstein Telescope (ET) and Cosmic Explorer (CE) will begin observing in the next decade. Due to the extremely high sensitivity of these detectors, the…

Characterizing the polarized dust emission from our Galaxy will be decisive for the quest for the Cosmic Microwave Background (CMB) primordial $B$-modes. The incomplete modelling of its potentially complex spectral properties could lead to…

Groundbreaking applications such as ChatGPT have heightened research interest in generative artificial intelligence (GAI). Essentially, GAI excels not only in content generation but also in signal processing, offering support for wireless…

The Global Navigation Satellite System (GNSS) has been a very powerful and important contributor to all scientific questions related to precise positioning on Earth's surface, particularly as a mature technique in geodesy and geosciences.…

This paper presents a deep learning architecture for nowcasting of precipitation almost globally every 30 min with a 4-hour lead time. The architecture fuses a U-Net and a convolutional long short-term memory (LSTM) neural network and is…

In September 1996, a dust/debris detector: GORID was launched into the geostationary (GEO) region as a piggyback instrument on the Russian Express-2 telecommunications spacecraft. The instrument began its normal operation in April 1997 and…

Precipitation nowcasting, critical for flood emergency and river management, has remained challenging for decades, although recent developments in deep generative modeling (DGM) suggest the possibility of improvements. River management…

In the search for small exoplanets orbiting cool stars whose spectral energy distributions peak in the near infrared, the strong absorption of radiation in this region due to water vapour in the atmosphere is a particularly adverse effect…

Deep learning models for meteorological forecasting often fail in rare but high-impact events such as typhoons, where relevant data is scarce. Existing fine-tuning methods typically face a trade-off between overlooking these extreme events…

It is tested whether past abrupt climate changes support the validity of statistical early-warning signals (EWS) as predictor of future climate tipping points. EWS are expected increases in amplitude and correlation of fluctuations driven…

This study evaluated the probability and uncertainty forecasts of five recently proposed Bayesian deep learning methods relative to a deterministic residual neural network (ResNet) baseline for 0-1 h convective initiation (CI) nowcasting…

It has been established that Gamma-Ray Bursts (GRB) can produce Very High Energy radiation (E > 100 GeV), opening a new window on the investigation of particle acceleration and radiation properties in the most energetic domain. We expect…

This study investigates the environments and characteristics of Gamma-Ray Bursts (GRBs) exhibiting very high energy (VHE) emission. Recent detections of VHE emission, up to TeV energies, challenge synchrotron-only emission models and…

Climate change necessitates rapid expansion of renewable energy, with wind energy offering a scalable and low-impact solution. However, accurate prediction of wind loads and power generation remains challenging due to uncertainties in wind…

Power outages caused by tropical cyclones (TCs) pose serious risks to electric power systems and the communities they serve. Accurate, high-resolution outage forecasting is essential for enabling both proactive mitigation planning and…

We present an enhanced method for the application of Gaussian Mixture Modelling (GMM) to the coherent WaveBurst (cWB) algorithm in the search for short-duration gravitational wave (GW) transients. The supervised Machine Learning method of…

Kinetic inductance detectors are widely used in millimeter- and submillimeter-wave astronomy, benefiting from their fast response and relative ease of fabrication. The GroundBIRD telescope employs microwave kinetic inductance detectors at…

Data reconstruction of rotating turbulent snapshots is investigated utilizing data-driven tools. This problem is crucial for numerous geophysical applications and fundamental aspects, given the concurrent effects of direct and inverse…

Geocasting is the delivery of packets to nodes within a certain geographic area. For many applications in wireless ad hoc and sensor networks, geocasting is an important and frequent communication service. The challenging problem in…