Use of USGS-Provided Data to Improve Weather and Climate Simulations

1997 ◽  
Vol 7 (1) ◽  
pp. 3 ◽  
Author(s):  
R. A. Pielke ◽  
T. J. Lee ◽  
J. H. Copeland ◽  
J. L. Eastman ◽  
C. L. Ziegler ◽  
...  
Keyword(s):  
Climate Simulations ◽  
Weather And Climate

scholarly journals Improving weather and climate predictions by training of supermodels

2019 ◽  
Author(s):  
Francine Schevenhoven ◽  
Frank Selten ◽  
Alberto Carrassi ◽  
Noel Keenlyside
Keyword(s):  
Time Derivative ◽  
Learning Rule ◽  
Training Methods ◽  
Model Errors ◽  
Climate Simulations ◽  
Weather And Climate ◽  
Present Context ◽  
Training Schemes ◽  
The Individual ◽  
Derivatives Of

Abstract. Recent studies demonstrate that weather and climate predictions potentially improve by dynamically combining different models into a so called "supermodel". Here we focus on the weighted supermodel – the supermodel's time derivative is a weighted superposition of the time-derivatives of the imperfect models, referred to as weighted supermodeling. A crucial step is to train the weights of the supermodel on the basis of historical observations. Here we apply two different training methods to a supermodel of up to four different versions of the global atmosphere-ocean-land model SPEEDO. The standard version is regarded as truth. The first training method is based on an idea called Cross Pollination in Time (CPT), where models exchange states during the training. The second method is a synchronization based learning rule, originally developed for parameter estimation. We demonstrate that both training methods yield climate simulations and weather predictions of superior quality as compared to the individual model versions. Supermodel predictions also outperform predictions based on the commonly used Multi-Model Ensemble (MME) mean. Furthermore we find evidence that negative weights can improve predictions in cases where model errors do not cancel (for instance all models are warm with respect to the truth). In principle the proposed training schemes are applicable to state-of-the-art models and historical observations. A prime advantage of the proposed training schemes is that in the present context relatively short training periods suffice to find good solutions. Additional work needs to be done to assess the limitations due to incomplete and noisy data, to combine models that are structurally different (different resolution and state representation for instance) and to evaluate cases for which the truth falls outside of the model class.

scholarly journals A multi-model ensemble method that combines imperfect models through learning

2011 ◽  
Vol 2 (1) ◽  
pp. 161-177 ◽  
Author(s):  
L. A. van den Berge ◽  
F. M. Selten ◽  
W. Wiegerinck ◽  
G. S. Duane
Keyword(s):  
Climate Model ◽  
Model Ensemble ◽  
Promising Strategy ◽  
Climate Simulations ◽  
Weather And Climate ◽  
Standard Parameter ◽  
Climate Model Simulations ◽  
Model Equations ◽  
Parameter Values ◽  
Better Than

Abstract. In the current multi-model ensemble approach climate model simulations are combined a posteriori. In the method of this study the models in the ensemble exchange information during simulations and learn from historical observations to combine their strengths into a best representation of the observed climate. The method is developed and tested in the context of small chaotic dynamical systems, like the Lorenz 63 system. Imperfect models are created by perturbing the standard parameter values. Three imperfect models are combined into one super-model, through the introduction of connections between the model equations. The connection coefficients are learned from data from the unperturbed model, that is regarded as the truth. The main result of this study is that after learning the super-model is a very good approximation to the truth, much better than each imperfect model separately. These illustrative examples suggest that the super-modeling approach is a promising strategy to improve weather and climate simulations.

Next-Generation Time Integration targeting Weather and Climate Simulations

2021 ◽  
Author(s):  
Martin Schreiber
Keyword(s):  
High Performance ◽  
Time Integration ◽  
Time Frame ◽  
Time Dimension ◽  
Weather Forecasts ◽  
Climate Simulations ◽  
Weather And Climate ◽  
Super Computer ◽  
Strong Scaling ◽  
High Performance Computers

<p>Running simulations on high-performance computers faces new challenges due to e.g. the stagnating or even decreasing per-core speed. This poses new restrictions and therefore challenges on solving PDEs within a particular time frame in the strong scaling case. Here, disruptive mathematical reformulations, which e.g. exploit additional degrees of parallelism also along the time dimension, gained increasing interest over the last two decades.</p><p>This talk will cover various examples of our current research on (parallel-in-)time integration methods in the context of weather and climate simulations such as rational approximation of exponential integrators, multi-level time integration of spectral deferred correction (PFASST) as well as other methods.</p><p>These methods are realized and studied with numerics similar to the ones used by the European Centre for Medium-Range Weather Forecasts (ECMWF). Our results motivate further investigation for operational weather/climate systems in order to cope with the hardware imposed restrictions of future super computer architectures.</p><p>I gratefully acknowledge contributions and more from Jed Brown, Francois Hamon, Terry S. Haut, Richard Loft, Michael L. Minion, Pedro S. Peixoto, Nathanaël Schaeffer, Raphael Schilling</p>

Reflecting on the Goal and Baseline for Exascale Computing: A Roadmap Based on Weather and Climate Simulations

2019 ◽  
Vol 21 (1) ◽  
pp. 30-41 ◽  
Author(s):  
Thomas C. Schulthess ◽  
Peter Bauer ◽  
Nils Wedi ◽  
Oliver Fuhrer ◽  
Torsten Hoefler ◽  
...  
Keyword(s):  
Exascale Computing ◽  
Climate Simulations ◽  
Weather And Climate

scholarly journals Mechanism of tropical low-cloud response to surface warming using weather and climate simulations

2013 ◽  
Vol 40 (10) ◽  
pp. 2427-2432 ◽  
Author(s):  
Satoru Demoto ◽  
Masahiro Watanabe ◽  
Youichi Kamae
Keyword(s):  
Surface Warming ◽  
Climate Simulations ◽  
Weather And Climate ◽  
Low Cloud ◽  
Cloud Response

scholarly journals Single Precision in the Dynamical Core of a Nonhydrostatic Global Atmospheric Model: Evaluation Using a Baroclinic Wave Test Case

2018 ◽  
Vol 146 (2) ◽  
pp. 409-416 ◽  
Author(s):  
Masuo Nakano ◽  
Hisashi Yashiro ◽  
Chihiro Kodama ◽  
Hirofumi Tomita
Keyword(s):  
Finite Volume Method ◽  
Finite Volume ◽  
Double Precision ◽  
Baroclinic Wave ◽  
Single Precision ◽  
Dynamical Core ◽  
Wave Growth ◽  
Climate Simulations ◽  
Weather And Climate ◽  
Volume Method

Abstract Reducing the computational cost of weather and climate simulations would lower electric energy consumption. From the standpoint of reducing costs, the use of reduced precision arithmetic has become an active area of research. Here the impact of using single-precision arithmetic on simulation accuracy is examined by conducting Jablonowski and Williamson’s baroclinic wave tests using the dynamical core of a global fully compressible nonhydrostatic model. The model employs a finite-volume method discretized on an icosahedral grid system and its mesh size is set to 220, 56, 14, and 3.5 km. When double-precision arithmetic is fully replaced by single-precision arithmetic, a spurious wavenumber-5 structure becomes dominant in both hemispheres, rather than the expected baroclinic wave growth only in the Northern Hemisphere. It was found that this spurious wave growth comes from errors in the calculation of gridcell geometrics. Therefore, an additional simulation was conducted using double precision for calculations that only need to be performed for model setup, including calculation of gridcell geometrics, and single precision everywhere else, meaning that all calculations performed each time step used single precision. In this case, the model successfully simulated the growth of the baroclinic wave with only small errors and a 46% reduction in runtime. These results suggest that the use of single-precision arithmetic will allow significant reduction of computational costs in next-generation weather and climate simulations using a fully compressible nonhydrostatic global model with the finite-volume method.

Sign in / Sign up

Export Citation Format

Share Document