Results 161 to 170 of about 9,838 (274)

A new method to identify and explain sources of precipitation modification, illustrated for the western Netherlands

Quarterly Journal of the Royal Meteorological Society, EarlyView.
This study develops a method to identify the source areas of precipitation events, as illustrated for the western part of the Netherlands. Radar‐based precipitation data are traced back to their source areas and machine‐learning techniques are used to identify hypothesized causes: urban heat, surface roughness, and air pollution. We find that urban and
Jelmer van der Graaff, Gert‐Jan Steeneveld   +1 more
wiley   +1 more source

Canadian wildfires are losing their climate-cooling influence from postfire snow albedo. [PDF]

Proc Natl Acad Sci U S A
van Gerrevink MJ, Gonsamo A, Rogers BM, Potter S, Zhong Z, Veraverbeke S.   +5 more
europepmc   +1 more source

Note on Duration of Snow Cover on British Mountains

Journal of Glaciology, 1953
openaire   +1 more source

Stratospheric and tropospheric seasonality and its implications for observation requirements in numerical weather prediction

Quarterly Journal of the Royal Meteorological Society, EarlyView.
Daily time series of zonal‐mean zonal wind (m·s−1) at 10 hPa and 60° N from 1950 to 2021 from the ERA5 reanalysis. This shows huge variability in some seasons and very little in others. We provide evidence that high‐level observations, radiosonde and satellite, are more important during the extended winter season with its very large variability ...
Bruce Ingleby, Inna Polichtchouk
wiley   +1 more source

Uncovering controlling factors on rock glacier velocities in the Pamir-Karakoram-Kunlun region using explainable machine learning. [PDF]

PNAS Nexus
Sun Z, Liu L, Bolch T.
europepmc   +1 more source

Hybrid physics–data‐driven modeling for sea ice thermodynamics and transfer learning

Quarterly Journal of the Royal Meteorological Society, EarlyView.
Icepack–NN, a machine‐learning‐based hybrid version of the sea‐ice column model Icepack, is developed to correct state‐dependent forecast errors arising from misspecified snow thermodynamics, using neural networks applied online within the physical model.
G. De Cillis, A. Carrassi, J. Brajard, L. Bertino, M. Broccoli, D. Iovino, T.S. Finn, M. Bocquet   +7 more
wiley   +1 more source

Meteorology and geography, more than biological traits, drive variation in frog phenology across decades. [PDF]

Ecology
Klinges DH, Freidenburg LK, Rubinstein AD, Skelly DK.   +3 more
europepmc   +1 more source

Towards improving Arctic mixed‐phase cloud representation in the ECMWF model using MOSAiC observations

Quarterly Journal of the Royal Meteorological Society, EarlyView.
Abstract The presence of Arctic clouds plays a crucial role in the evolution of the surface temperature of Arctic sea ice. However, large biases in cloud representation remain in state‐of‐the‐art weather and climate models. In this study, we use observational data from the one‐year Arctic ship campaign Multi‐disciplinary drifting Observatory for the ...
Luise Schulte, Richard Forbes, Linus Magnusson, Vera Schemann, Jonathan Day, Susanne Crewell   +5 more
wiley   +1 more source

Reduced snow cover at the alpine treeline: resistance and recovery of saplings. [PDF]

New Phytol
Charra-Vaskou K, Charrier G, Ganthaler A, Améglio T, Mayr S.   +4 more
europepmc   +1 more source

Impact of data assimilation on Arctic sea‐ice thickness variability and its coupling with atmospheric forcing

Quarterly Journal of the Royal Meteorological Society, EarlyView.
We document for the first time how the assimilation of CS2SMOS observations improves the model representation of Arctic sea‐ice thickness (SIT) and its variability: biases are reduced (top row), while excessive variability in the Beaufort Sea and lack of variability in the ice pack are both corrected (bottom row).
Jiping Xie, Yue Ying, Richard Davy, Laurent Bertino   +3 more
wiley   +1 more source