Preface

This book covers comprehensive text and reference work on atmospheric models for methods of numerical modeling and important related areas of data assimilation and predictability. It incorporates various aspects of environmental computer modeling including an historical overview of the subject, approximations to land surface and atmospheric physics and dynamics, radiative transfer and applications in satellite remote sensing, and data assimilation. With individual chapters authored by eminent professionals in their respective topics, Advanced Topics in application of atmospheric models try to provide in-depth guidance on some of the key applied in atmospheric models for scientists and modelers.

> **Dr. Ismail Yucel**  Middle East Technical University, Civil Engineering Department, Water Resources Lab, Ankara, Turkey

**1. Introduction**

global models.

environment.

interactions between scales.

Numerical models have been used extensively in the last decades to understand and predict weather phenomena and the climate. In general, models are classified according to their operation domain: global (entire Earth) and regional (country, state, etc). Global models have spatial resolution of about 0.2 to 1.5 degrees of latitude and therefore cannot represent very well the scale of regional weather phenomena. Their main limitation is computing power. On the other hand, regional models have higher resolution but are restricted to limited area domains. Forecasting on limited domain demands the knowledge of future atmospheric conditions at domain's borders. Therefore, regional models require previous execution of

**Performance on a Multi-Core Cluster System** 

Pablo Grunmann1, Pedro L. Silva Dias1, Francieli Boito2, Rodrigo Kassick2, Laércio Pilla2, Philippe Navaux2, Claudio Schepke2, Nicolas Maillard2,

**Improving Atmospheric Model** 

Carla Osthoff1, Roberto Pinto Souto1, Fabrício Vilasbôas1,

Jairo Panetta3, Pedro Pais Lopes3 and Robert Walko4 *1Laboratório Nacional de Computação Científica (LNCC) 2Universidade Federal do Rio Grande do Sul (UFRGS) 3Instituto Nacional de Pesquisas Espaciais (INPE)* 

*4University of Miami* 

*1,2,3Brazil 4USA* 

**1**

OLAM (Ocean-Land-Atmosphere Model), initially developed at Duke University (Walko & Avissar, 2008), tries to combine these two approaches to provide a global grid that can be locally refined, forming a single grid. This feature allows simultaneous representation (and forecasting) of both the global and the local scale phenomena, as well as bi-directional

Due to the large computational demands and execution time constraints, these models rely on parallel processing. They are executed on clusters or grids in order to benefit from the architecture's parallelism and divide the simulation load. On the other hand, over the next decade the degree of on-chip parallelism will significantly increase and processors will contain tens and even hundreds of cores, increasing the impact of levels of parallelism on clusters. In this scenario, it is imperative to investigate the scale of programs on multilevel parallelism
