Results 101 to 110 of about 78,507 (299)

An objective Bayesian method for including parameter uncertainty in ensemble model output statistics

Quarterly Journal of the Royal Meteorological Society, EarlyView.
Conventional model output statistics and ensemble model output statistics methods for calibrating ensemble forecasts lead to severe underestimation of the probabilities of ensemble extremes (in blue). This is because they ignore statistical parameter uncertainty.
Stephen Jewson, Leonardo Olivetti, Gabriele Messori, Paul J. Northrop, Trevor Sweeting   +4 more
wiley   +1 more source

Trophic amplification: A model intercomparison of climate driven changes in marine food webs. [PDF]

PLoS One, 2023
Guibourd de Luzinais V, du Pontavice H, Reygondeau G, Barrier N, Blanchard JL, Bornarel V, Büchner M, Cheung WWL, Eddy TD, Everett JD, Guiet J, Harrison CS, Maury O, Novaglio C, Petrik CM, Steenbeek J, Tittensor DP, Gascuel D.   +17 more
europepmc   +1 more source

Formaldehyde over the central Pacific during PEM-Tropics B [PDF]

, 2001
Formaldehyde, CH2O, mixing ratios are reported for the central Pacific troposphere from a series of 41 flights, which took place in March-April 1999 as part of the NASA Pacific Exploratory Mission (PEM) -Tropics B mission.
Blake, D, Blake, N, Chen, G, Crawford, J, Davis, D, Egli, P, Heikes, B, O'Sullivan, D, Olson, J, Snow, J   +9 more
core   +1 more source

Comparison of two numerical weather prediction models in simulating south foehn in the Alpine Rhine Valley

Quarterly Journal of the Royal Meteorological Society, EarlyView.
ICON outperforms COSMO in simulating a south foehn event in the Alpine Rhine Valley, with improved accuracy in temperature, wind speed, and foehn timing. This enhanced performance likely results from a more accurate representation of surface energy balance and gravity wave pattern on the lee side of the mountain.
Yue Tian, Julian Quimbayo Duarte, Shweta Singh, Juerg Schmidli   +3 more
wiley   +1 more source

Quantifying Process Connectivity With Transfer Entropy in Hydrologic Models

Water Resources Research, 2019
Quantifying the behavior and performance of hydrologic models is an important aspect of understanding the underlying hydrologic systems. We argue that classical error measures do not offer a complete picture for building this understanding.
Andrew Bennett, Bart Nijssen, Gengxin Ou, Martyn Clark, Grey Nearing   +4 more
doaj   +1 more source

How Modelers Model: the Overlooked Social and Human Dimensions in Model Intercomparison Studies. [PDF]

Environ Sci Technol, 2022
Albanito F, McBey D, Harrison M, Smith P, Ehrhardt F, Bhatia A, Bellocchi G, Brilli L, Carozzi M, Christie K, Doltra J, Dorich C, Doro L, Grace P, Grant B, Léonard J, Liebig M, Ludemann C, Martin R, Meier E, Meyer R, De Antoni Migliorati M, Myrgiotis V, Recous S, Sándor R, Snow V, Soussana JF, Smith WN, Fitton N.   +28 more
europepmc   +1 more source

Underestimating Internal Variability Leads to Narrow Estimates of Climate System Properties [PDF]

, 2019
Probabilistic estimates of climate system properties often rely on the comparison of model simulations to observed temperature records and an estimate of the internal climate variability.
Forest, CE, Libardoni, AG, Monier, E, Sokolov, AP   +3 more
core   +1 more source

Quantifying driving ensemble influence on operational convection‐permitting ensemble spread

Quarterly Journal of the Royal Meteorological Society, EarlyView.
By comparing statistics of precipitation patterns between a convection‐permitting ensemble and the global ensemble used to drive it, we investigate the conditions under which the convection‐permitting ensemble diverges from the evolution of the driving ensemble.
Adam Gainford, Suzanne L. Gray, Thomas H. A. Frame, Aurore N. Porson, Marco Milan   +4 more
wiley   +1 more source

A Model Intercomparison Study of Aerosol‐Cloud‐Turbulence Interactions in a Cloud Chamber: 1. Model Results

Journal of Advances in Modeling Earth Systems
This study presents the first model intercomparison of aerosol‐cloud‐turbulence interactions in a controlled cloudy Rayleigh‐Bénard Convection chamber environment, utilizing the Pi Chamber at Michigan Technological University. We analyzed simulated cloud
Sisi Chen, Steven K. Krueger, Piotr Dziekan, Kotaro Enokido, Theodore MacMillan, David Richter, Silvio Schmalfuß, Shin‐ichiro Shima, Fan Yang, Jesse C. Anderson, Will Cantrell, Dennis Niedermeier, Raymond A. Shaw, Frank Stratmann   +13 more
doaj   +1 more source

Scientific data from precipitation driver response model intercomparison project. [PDF]

Sci Data, 2022
Myhre G, Samset B, Forster PM, Hodnebrog Ø, Sandstad M, Mohr CW, Sillmann J, Stjern CW, Andrews T, Boucher O, Faluvegi G, Iversen T, Lamarque JF, Kasoar M, Kirkevåg A, Kramer R, Liu L, Mülmenstädt J, Olivié D, Quaas J, Richardson TB, Shawki D, Shindell D, Smith C, Stier P, Tang T, Takemura T, Voulgarakis A, Watson-Parris D.   +28 more
europepmc   +1 more source