Results 61 to 70 of about 2,350 (166)

A Deep Learning Model of Lightning Stroke Density

Journal of Geophysical Research: Atmospheres, Volume 131, Issue 15, 16 August 2026.
Abstract Lightning plays a crucial role in the Earth's climate system; however, existing parameterizations for use in forecasting and earth system models show room for improvement in capturing spatial and temporal variations in its frequency. This study develops deep learning‐based parameterizations of lightning stroke density using meteorological ...
Randall Jones II, Joel A. Thornton, Chris J. Wright, Robert A. Holzworth   +3 more
wiley   +1 more source

DSGE Model Forecasting: Rational Expectations Versus Adaptive Learning

Journal of Forecasting, Volume 45, Issue 5, Page 2565-2586, August 2026.
ABSTRACT This paper compares within‐sample and out‐of‐sample fit of a DSGE model with rational expectations to a model with adaptive learning. The Galí, Smets, and Wouters model is the chosen laboratory using quarterly real‐time euro area data vintages, covering 2001Q1–2019Q4.
Anders Warne
wiley   +1 more source

A Strategy to set up Test Dataset and Evaluation Benchmark for Radar Nowcasting of Precipitation in Italy

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Prediction of extreme precipitation with high spatial resolution on short time scales (i.e., nowcasting) is still challenging, and data-driven approaches such as artificial intelligence tools are increasingly being used.
Clizia Annella, Edoardo Raparelli, Gianfranco Vulpiani, Vincenzo Capozzi, Elisa Adirosi, Luca Baldini, Giorgio Budillon, Raffaele Lidori, Mario Montopoli   +8 more
doaj   +1 more source

Empirical Assessment of Storm‐Time Thermospheric Density Inversion Methods From LEO POD Data

Earth and Space Science, Volume 13, Issue 8, August 2026.
Abstract Thermospheric mass density is one of the largest sources of operational uncertainty for spacecraft in low Earth orbit, particularly during geomagnetic storms. The growing population of Global Navigation Satellite System‐equipped satellites presents a data set of opportunity: precise orbit determination (POD) data streams can be used to ...
Charles Constant, Indigo Brownhall, Anasuya Aruliah, Marek Ziebart, Santosh Bhattarai   +4 more
wiley   +1 more source

Increasing Frequency of Coastal Erosion Indicated by a Hindcast Model of Storm‐Driven Forcing Calibrated With Beach Stratigraphy

Earth's Future, Volume 14, Issue 8, August 2026.
Abstract Beach stratigraphy at North Beach, Sandy Hook, New Jersey is used to calibrate a model of coastal erosion, establish a model‐based erosion threshold, and evaluate how often threshold conditions have been exceeded since 1979 through hindcast analysis.
W. John Schmelz, Kenneth G. Miller, Jonathan Liu, J. N. Stanley, James V. Browning   +4 more
wiley   +1 more source

Convcast: An embedded convolutional LSTM based architecture for precipitation nowcasting using satellite data.

PLoS ONE, 2020
Nowcasting of precipitation is a difficult spatiotemporal task because of the non-uniform characterization of meteorological structures over time. Recently, convolutional LSTM has been shown to be successful in solving various complex spatiotemporal ...
Ashutosh Kumar, Tanvir Islam, Yoshihide Sekimoto, Chris Mattmann, Brian Wilson   +4 more
doaj   +1 more source

Observation‐Driven Correction of Numerical Weather Prediction for Marine Winds

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 4, August 2026.
Abstract Accurate marine wind forecasts are essential for safe navigation, ship routing, and energy operations, yet they remain challenging because observations over the ocean are sparse, heterogeneous, and temporally variable. We present an observation‐informed correction approach for global numerical weather prediction (NWP) of marine winds.
Matteo Peduto, Qidong Yang, Jonathan Giezendanner, Devis Tuia, Sherrie Wang   +4 more
wiley   +1 more source

The ASTE‐BGC Data‐Assimilative Regional Ocean Biogeochemical Model

Journal of Advances in Modeling Earth Systems, Volume 18, Issue 8, August 2026.
Abstract We present a data‐assimilative regional ocean biogeochemical model, ASTE‐BGC, which simulates the physical and biogeochemical state of the North Atlantic Ocean from 2002 to 2017. Model physics are provided by a physical state estimate (ASTE), which assimilates O(109) in situ and satellite‐based observations over the model domain and time ...
L. A. Moseley, G. A. McKinley, D. Carroll, D. Menemenlis, R. Dussin, A. T. Nguyen   +5 more
wiley   +1 more source

MAUSAM: An Observations‐Focused Assessment of Global AI Weather Prediction Models During the South Asian Monsoon

Journal of Advances in Modeling Earth Systems, Volume 18, Issue 8, August 2026.
Abstract Past evaluation of artificial intelligence (AI) weather prediction has primarily relied on reanalyses, which can obscure important deficiencies due to prevailing biases in reanalyses themselves. Here, we present MAUSAM (Measuring AI Uncertainty during South Asian Monsoon), an evaluation of seven leading AI‐based prediction systems—FourCastNet,
Aman Gupta, Aditi Sheshadri, Dhruv Suri
wiley   +1 more source

Radar based rainfall nowcasting and its characteristic prediction based on spatially correlated random field, normalized duration line and Kalman filter algorithm

MATEC Web of Conferences, 2018
Rainfall is not only one of the most natural processes on the earth, but also an important factor of flood generation. Precise rainfall nowcasting can give an effective warning before hazards occur. This paper presented an ensemble nowcasting methodology
He Ting, Zhang Chao, Zhang Yi
doaj   +1 more source