Search Results

You are looking at 1 - 4 of 4 items for :

  • Decadal variability x
  • The 1st NOAA Workshop on Leveraging AI in the Exploitation of Satellite Earth Observations & Numerical Weather Prediction x
  • All content x
Clear All
Noah D. Brenowitz, Tom Beucler, Michael Pritchard, and Christopher S. Bretherton

1. Introduction Global climate models (GCMs) still cannot both explicitly resolve convective-scale motions and perform decadal or longer simulations ( IPCC 2014 ). To permit grid spacings of 25 km or larger, important physical processes operating at smaller spatial scales, such as moist atmospheric convection, must be approximated. This task is known as subgrid-scale parameterization and is one of the largest sources of uncertainty in estimating the future magnitude and spatial distribution of

Restricted access
Hanoi Medina, Di Tian, Fabio R. Marin, and Giovanni B. Chirico

-of-art technology for forecasting medium-range precipitation at daily or subdaily time step over the globe. Practically every aspect of the NWP has dramatically improved ( Hamill et al. 2013 ) over the last decades, which has led to significant increments in the skill of the model forecasts ( Bauer et al. 2015 ), and has encouraged their use in a wide range of applications. NWP has global applicability ( Bauer et al. 2015 ) and potential for improving regional precipitation, runoff, and water storage

Full access
Yaling Liu, Dongdong Chen, Soukayna Mouatadid, Xiaoliang Lu, Min Chen, Yu Cheng, Zhenghui Xie, Binghao Jia, Huan Wu, and Pierre Gentine

1. Introduction Soil moisture (SM) is an essential component of the Earth system. It affects the variability of the coupled energy (latent and sensible heat fluxes) and water fluxes (runoff and evapotranspiration) by modifying the partitioning of water and energy across the land–atmosphere interface ( Seneviratne et al. 2010 ). The effects of SM on evapotranspiration also impact temperature variability and may intrigue persistent heatwaves ( Fischer et al. 2007 ; Hirschi et al. 2011

Restricted access
Sid-Ahmed Boukabara, Vladimir Krasnopolsky, Jebb Q. Stewart, Eric S. Maddy, Narges Shahroudi, and Ross N. Hoffman

.1155/2012/649450 Krasnopolsky , V. M. , L. C. Breaker , and W. H. Gemmill , 1995 : A neural network as a nonlinear transfer function model for retrieving surface wind speeds from the special sensor microwave imager . J. Geophys. Res. , 100 , 11 033 – 11 045 , . 10.1029/95JC00857 Krasnopolsky , V. M. , M. S. Fox-Rabinovitz , and A. A. Belochitski , 2008 : Decadal climate simulations using accurate and fast neural network emulation of full, longwave and shortwave

Free access