Learning earth system models from observations: machine learning or data assimilation?
AJ Geer - … Transactions of the Royal Society A, 2021 - royalsocietypublishing.org
Recent progress in machine learning (ML) inspires the idea of improving (or learning) earth
system models directly from the observations. Earth sciences already use data assimilation …
system models directly from the observations. Earth sciences already use data assimilation …
Detecting climate signals using explainable AI with single‐forcing large ensembles
It remains difficult to disentangle the relative influences of aerosols and greenhouse gases
on regional surface temperature trends in the context of global climate change. To address …
on regional surface temperature trends in the context of global climate change. To address …
Latent space data assimilation by using deep learning
Performing data assimilation (DA) at low cost is of prime concern in Earth system modeling,
particularly in the era of Big Data, where huge quantities of observations are available …
particularly in the era of Big Data, where huge quantities of observations are available …
A new cumulative anomaly-based model for the detection of heavy precipitation using GNSS-derived tropospheric products
In recent years, tropospheric products obtained from ground-based global navigation
satellite system (GNSS) measurements, especially the zenith total delay (ZTD) and …
satellite system (GNSS) measurements, especially the zenith total delay (ZTD) and …
The Earth-Observing Satellite Constellation: A review from a meteorological perspective of a complex, interconnected global system with extensive applications
SA Boukabara, J Eyre, RA Anthes… - … and Remote Sensing …, 2021 - ieeexplore.ieee.org
The global Earth-observing satellite constellation (EOSC) is a major international asset that
has developed since 1960 with a dramatic growth in size and complexity in the recent past …
has developed since 1960 with a dramatic growth in size and complexity in the recent past …
A neural network-based approach for the detection of heavy precipitation using GNSS observations and surface meteorological data
Recent years have witnessed a growing interest in using GNSS observations to detect
heavy precipitation. In this study, a neural network-based (NN-based) approach taking …
heavy precipitation. In this study, a neural network-based (NN-based) approach taking …
Improved weather forecasting using neural network emulation for radiation parameterization
In this study, a neural network (NN) emulator for radiation parameterization was developed
to use in an operational weather forecasting model in the Korea Meteorological …
to use in an operational weather forecasting model in the Korea Meteorological …
Prediction of river water temperature using machine learning algorithms: a tropical river system of India
Abstract Machine learning (ML) has been increasingly adopted due to its ability to model
complex and non-linearities between river water temperature (RWT) and its predictors (eg …
complex and non-linearities between river water temperature (RWT) and its predictors (eg …
[HTML][HTML] Combining optical and radar satellite imagery to investigate the surface properties and evolution of the Lordsburg Playa, New Mexico, USA
Driven by erodible soil, hydrological stresses, land use/land cover (LULC) changes, and
meteorological parameters, windblown dust events initiated from Lordsburg Playa, New …
meteorological parameters, windblown dust events initiated from Lordsburg Playa, New …
Polar vortex multi-day intensity prediction relying on new deep learning model: A combined convolution neural network with long short-term memory based on …
K Peng, X Cao, B Liu, Y Guo, C Xiao, W Tian - Entropy, 2021 - mdpi.com
The variation of polar vortex intensity is a significant factor affecting the atmospheric
conditions and weather in the Northern Hemisphere (NH) and even the world. However …
conditions and weather in the Northern Hemisphere (NH) and even the world. However …