Atmospheric Model Development for MLRS.

1998 ◽  
Author(s):  
Oskar M. Essenwanger
Keyword(s):  
Model Development ◽  
Atmospheric Model

A new ensemble-based statistical methodology to verify changes in weather and climate models

2021 ◽  
Author(s):  
Christian Zeman ◽  
Christoph Schär
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Numerical Models ◽  
Hypothesis Test ◽  
Model Development ◽  
Atmospheric Model ◽  
Statistical Hypothesis ◽  
Model Parameters ◽  
Rounding Errors ◽  
Weather And Climate

<p>Since their first operational application in the 1950s, atmospheric numerical models have become essential tools in weather and climate prediction. As such, they are a constant subject to changes, thanks to advances in computer systems, numerical methods, and the ever increasing knowledge about the atmosphere of Earth. Many of the changes in today's models relate to seemingly unsuspicious modifications, associated with minor code rearrangements, changes in hardware infrastructure, or software upgrades. Such changes are meant to preserve the model formulation, yet the verification of such changes is challenged by the chaotic nature of our atmosphere - any small change, even rounding errors, can have a big impact on individual simulations. Overall this represents a serious challenge to a consistent model development and maintenance framework.</p><p>Here we propose a new methodology for quantifying and verifying the impacts of minor atmospheric model changes, or its underlying hardware/software system, by using ensemble simulations in combination with a statistical hypothesis test. The methodology can assess effects of model changes on almost any output variable over time, and can also be used with different hypothesis tests.</p><p>We present first applications of the methodology with the regional weather and climate model COSMO. The changes considered include a major system upgrade of the supercomputer used, the change from double to single precision floating-point representation, changes in the update frequency of the lateral boundary conditions, and tiny changes to selected model parameters. While providing very robust results, the methodology also shows a large sensitivity to more significant model changes, making it a good candidate for an automated tool to guarantee model consistency in the development cycle.</p>

scholarly journals Enhancing the Accessibility of Unified Modeling Systems: GFDL SHiELD v2021b in a Container

2021 ◽  
Author(s):  
Kai-Yuan Cheng ◽  
Lucas M. Harris ◽  
Yong Qiang Sun
Keyword(s):  
High Resolution ◽  
Model Development ◽  
Seasonal Prediction ◽  
Atmospheric Model ◽  
Regional Model ◽  
Easy Access ◽  
Unified Modeling ◽  
Modeling Systems ◽  
Cross Platform ◽  
Study Performance

Abstract. Container technology provides a pathway to facilitate easy access to unified modeling systems and opens opportunities for collaborative model development and interactive learning. In this paper, we present the implementation of software containers for the System for High‐resolution prediction on Earth‐to‐Local Domains (SHiELD), a unified atmospheric model for weather-to-seasonal prediction. The containerized SHiELD is cross-platform and easy to install. Flexibility of the containerized SHiELD is demonstrated as it can be configured as a global, a global-nest, and a regional model. Bitwise reproducibility is achieved on various x86 systems tested in this study. Performance and scalability of the containerized SHiELD are evaluated and discussed.

scholarly journals An Ensemble-Based Statistical Methodology to Detect Differences in Weather and Climate Model Executables

2021 ◽  
Author(s):  
Christian Zeman ◽  
Christoph Schär
Keyword(s):  
Climate Model ◽  
Initial Conditions ◽  
Numerical Models ◽  
Hypothesis Test ◽  
Model Development ◽  
Atmospheric Model ◽  
Great Sensitivity ◽  
Statistical Hypothesis ◽  
Statistical Hypothesis Test ◽  
Weather And Climate

Abstract. Since their first operational application in the 1950s, atmospheric numerical models have become essential tools in weather and climate prediction. As such, they are subject to continuous changes, thanks to advances in computer systems, numerical methods, more and better observations, and the ever increasing knowledge about the atmosphere of Earth. Many of the changes in today’s models relate to seemingly unsuspicious modifications, associated with minor code rearrangements, changes in hardware infrastructure, or software updates. Such changes are not supposed to significantly affect the model. However, this is difficult to verify, because our atmosphere is a chaotic system, where even a tiny change can have a big impact on individual simulations. Overall this represents a serious challenge to a consistent model development and maintenance framework. Here we propose a new methodology for quantifying and verifying the impacts of minor atmospheric model changes, or its underlying hardware/software system, by using a set of simulations with slightly different initial conditions in combination with a statistical hypothesis test. The methodology can assess effects of model changes on almost any output variable over time, and can also be used with different underlying statistical hypothesis tests. We present first applications of the methodology with a regional weather and climate model, including the verification of a major system update of the underlying supercomputer. While providing very robust results, the methodology shows a great sensitivity even to tiny changes. Results show that changes are often only detectable during the first hours, which suggests that short-term simulations (days to months) are best suited for the methodology, even when addressing long-term climate simulations. We also show that the choice of the underlying statistical hypothesis test is not of importance and that the methodology already works well for coarse resolutions, making it computationally inexpensive and therefore an ideal candidate for automated testing.

Climate Version of the SL-AV Global Atmospheric Model: Development and Preliminary Results

2019 ◽  
Vol 44 (1) ◽  
pp. 13-22 ◽  
Author(s):  
R. Yu. Fadeev ◽  
M. A. Tolstykh ◽  
E. M. Volodin
Keyword(s):  
Model Development ◽  
Atmospheric Model ◽  
Preliminary Results ◽  
Global Atmospheric Model ◽  
Climate Version

Antecedents of learners' mental model development

2008 ◽  
Author(s):  
Nicole Kohari ◽  
Robert Lord ◽  
Joelle Elicker ◽  
Steven Ash ◽  
Bryce Hruska
Keyword(s):  
Mental Model ◽  
Model Development

Theoretical Model Development in Elder Mistreatment

2011 ◽  
Author(s):  
Kerry Burnight ◽  
Laura Mosqueda
Keyword(s):  
Theoretical Model ◽  
Model Development ◽  
Elder Mistreatment
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