Results 71 to 80 of about 935 (210)

A Spectrum‐Reconstruction Based I/Q Series Simulator for Dual‐Polarization Weather Radar

IET Radar, Sonar &Navigation, Volume 20, Issue 1, January/December 2026.
This paper proposes a simulator capable of generating arbitrarily shaped echo spectra by reconstructing the power spectra from meteorological particle microphysical information. It offers a physically consistent simulation framework for developing and testing non‐Gaussian spectra processors in severe convective weather and supports flexible radar ...
Xichao Dong, Shuo Li, Teng Yu, Zewei Zhao, Kai Cui, Cheng Hu, Yubao Chen, Shuai Zhang   +7 more
wiley   +1 more source

Soil moisture–precipitation coupling: observations from the Oklahoma Mesonet and underlying physical mechanisms [PDF]

Hydrology and Earth System Sciences, 2015
Interactions between soil moisture and the atmosphere are driven by the partitioning of sensible and latent heating, through which soil moisture has been connected to atmospheric modifications that could potentially lead to the initiation of convective ...
T. W. Ford, A. D. Rapp, S. M. Quiring, J. Blake   +3 more
doaj   +1 more source

A Method for Calculating Radar Echo‐Top Height Based on Signals From the Same Horizontal Location

Advances in Meteorology, Volume 2026, Issue 1, 2026.
In traditional methods, the radar echo‐top (ET) height is in fact calculated from radar signals at the same slant range but different horizontal locations, which may result in deviations. To address this problem, a new method for calculating ET height has been presented in this article, involving a three‐step process: first, the raw 3D polar coordinate
Shiqi Zhu, Haibo Zou, Jinfang Yin, Jiaxin Wang, Landi Zhong, Mingjun Ding, Pramita Mishra   +6 more
wiley   +1 more source

Enhancing Machine Learning Models for Nowcasting and Short‐Term Forecasting of Precipitation With a Novel Probability‐Matching Loss Function

Geophysical Research Letters, Volume 52, Issue 24, 28 December 2025.
Abstract Machine learning (ML) precipitation forecasting models typically employ a mean squared error (MSE) loss function for optimization. However, due to the well‐known “double penalty” effect, MSE‐based losses often lead to overly smoothed prediction fields and a systematic underestimation of the frequency of heavy rainfall.
Yuan Cao, Shuaiyi Li, Jie Feng, Lei Chen, Hao Li, Yijun Zhang   +5 more
wiley   +1 more source

Tuning Extreme NEXRAD and CMORPH Precipitation Estimates

Journal of Hydrometeorology, 2014
AbstractHigh-resolution satellite precipitation estimates, such as the Climate Prediction Center morphing technique (CMORPH), provide alternative sources of precipitation data for hydrological applications, especially in regions where adequate ground-based instruments are unavailable. These estimates are, however, subject to large errors, especially at
Jonathan Woody, Robert Lund, Mekonnen Gebremichael   +2 more
openaire   +1 more source

A Radar Radial Velocity Dealiasing Algorithm for Radar Data Assimilation and its Evaluation with Observations from Multiple Radar Networks

Remote Sensing, 2019
Automated and accurate radar dealiasing algorithms are very important for their assimilation into operational numerical weather forecasting models. A radar radial velocity dealiasing algorithm aimed at radar data assimilation is introduced and assessed ...
Guangxin He, Juanzhen Sun, Zhuming Ying, Lejian Zhang   +3 more
doaj   +1 more source

BATS: Bat‐aggregated time series—A python‐based toolkit for landscape‐level monitoring of free‐tailed bats via weather radar

Methods in Ecology and Evolution
The US operates a system of 160 S‐band Doppler weather radars known as NEXRAD (NEXt generation weather RADar) that continuously monitors the airspace around the majority of the United States and outlying territories.
Brian Lee, Alex Rich, Robert H. Diehl, Ashley Larsen   +3 more
doaj   +1 more source

Artificial Intelligence Weather Radar

Radio Science, Volume 60, Issue 12, December 2025.
Abstract The integration of artificial intelligence (AI) with weather radar systems marks a transformative advancement in remote sensing. This study introduces an AI‐powered radar system that utilizes Long Short‐Term Memory (LSTM) neural networks to predict the in‐phase (I) and quadrature (Q) components of radar signals, enabling faster, and more ...
Jothiram Vivekanandan, Gwo‐Jong Huang
wiley   +1 more source

A Network of Portable, Low-Cost, X-Band Radars [PDF]

, 2012
Radar is a unique tool to get an overview on the weather situation, given its high spatio- temporal resolution. Over 60 years, researchers have been investigating ways for obtaining the best use of radar.
Allegretti, Marco, Andrea Prato, Marco Allegretti, Giovanni Pero, Prato, Andrea, Lucianaz, Claudio, Perona, Giovanni Emilio, Riccardo Notarpietro, Claudio Lucianaz, Marco Gabella, Notarpietro, Riccardo, Silvano Bertoldo, Oscar Rorato, Giovanni Perona, Gabella, Marco, Bertoldo, Silvano, Rorato, Oscar   +16 more
core   +1 more source

Impact of Urbanization on Convection, Lightning, and Precipitation Over the Houston Metropolitan Area: Case Study Simulation From the TRACER Campaign

Journal of Advances in Modeling Earth Systems, Volume 17, Issue 11, November 2025.
Abstract This study investigates the effects of urbanization, specifically land use change and anthropogenic emissions (AE), on convection, lightning, and surface precipitation for a case of summertime sea‐breeze convection observed over the Houston metropolitan area.
T. Iguchi, Z. Tao, J. Yoo, E. C. Bruning, E. R. Mansell, T. Matsui, M. van Lier‐Walqui, M. Chin, P. Lawston‐Parker, J. A. Santanello, J. M. Shepherd   +10 more
wiley   +1 more source