22 research outputs found
A discussion about the role of the shortwave schemes on real WRF-ARW simulations. Two case studies: cloudless and cloudy sky
A wide range of approaches for radiative transfer computations leads to several parameterizations. Differences in these approximations bring about distinct results for the radiative fluxes,even under the same atmospheric conditions. Since the transfer of solar and terrestrial radiationrepresents the primordial physical process that shapes the atmospheric circulation, these deviations must have an impact on the numerical weather prediction (NWP) model performance. In this paper, an analysis of the role of shortwave schemes on the Weather Research and Forecasting (WRF-ARW) model is presented. The study compares the effect of four parameterizations(Dudhia, New Goddard, CAM and RRTMG) in two cases: i) cloudless and ii) cloudy sky situations for a domain defined over Catalonia (northeast of the Iberian Peninsula). We analyze thedirect and the indirect feedback between the dynamical aspects and the physical parameterizations driven by changes in the radiative transfer equation computation. The cumulative effect ofthese variations are studied through three simulation windows: current day (0-23 h), day-ahead(24-47 h) and two days ahead (48-71 h). These analyses are focused on several NWP model fields. From the most directly related toshortwave schemes such as global horizontal irradiance or the heating rate profile, to apparently secondary outcomes such as wind speed or cloud composition among others. The differences observed between model runs using different solar parameterizations increase with the simulation horizon, being more important in the cloudy scenario than in the cloudless sky
An analysis of the feasibility and benefits of GPU/multicore acceleration of the Weather Research and Forecasting model
There is a growing need for ever more accurate climate and weather simulations to be delivered in shorter timescales, in particular, to guard against severe weather events such as hurricanes and heavy rainfall. Due to climate change, the severity and frequency of such events – and thus the economic impact – are set to rise dramatically. Hardware acceleration using graphics processing units (GPUs) or Field-Programmable Gate Arrays (FPGAs) could potentially result in much reduced run times or higher accuracy simulations. In this paper, we present the results of a study of the Weather Research and Forecasting (WRF) model undertaken in order to assess if GPU and multicore acceleration of this type of numerical weather prediction (NWP) code is both feasible and worthwhile. The focus of this paper is on acceleration of code running on a single compute node through offloading of parts of the code to an accelerator such as a GPU. The governing equations set of the WRF model is based on the compressible, non-hydrostatic atmospheric motion with multi-physics processes. We put this work into context by discussing its more general applicability to multi-physics fluid dynamics codes: in many fluid dynamics codes, the numerical schemes of the advection terms are based on finite differences between neighboring cells, similar to the WRF code. For fluid systems including multi-physics processes, there are many calls to these advection routines. This class of numerical codes will benefit from hardware acceleration. We studied the performance of the original code of the WRF model and proposed a simple model for comparing multicore CPU and GPU performance. Based on the results of extensive profiling of representative WRF runs, we focused on the acceleration of the scalar advection module. We discuss the implementation of this module as a data-parallel kernel in both OpenCL and OpenMP. We show that our data-parallel kernel version of the scalar advection module runs up to seven times faster on the GPU compared with the original code on the CPU. However, as the data transfer cost between GPU and CPU is very high (as shown by our analysis), there is only a small speed-up (two times) for the fully integrated code. We show that it would be possible to offset the data transfer cost through GPU acceleration of a larger portion of the dynamics code. In order to carry out this research, we also developed an extensible software system for integrating OpenCL code into large Fortran code bases such as WRF. This is one of the main contributions of our work. We discuss the system to show how it allows the replacement of the sections of the original codebase with their OpenCL counterparts with minimal changes – literally only a few lines – to the original code. Our final assessment is that, even with the current system architectures, accelerating WRF – and hence also other, similar types of multi-physics fluid dynamics codes – with a factor of up to five times is definitely an achievable goal. Accelerating multi-physics fluid dynamics codes including NWP codes is vital for its application to weather forecasting, environmental pollution warning, and emergency response to the dispersion of hazardous materials. Implementing hardware acceleration capability for fluid dynamics and NWP codes is a prerequisite for up-to-date and future computer architectures
Aceleración de una herramienta para la predicción de energía solar mediante arquitecturas masivamente paralelas
En la última década, Uruguay ha comenzado a incorporar fuertemente la energía eólica y solar a su matriz energética. La inclusión de este tipo de fuentes de energía para abastecer la red eléctrica presenta un gran desafío al momento de administrar su uso, principalmente debido a su flujo de carácter fluctuante. Considerando esta situación, y con el objetivo de simplificar el trabajo de despacho de carga (que se encarga de administrar eficientemente los recursos energéticos presentes en la matriz), desde la Facultad de Ingeniería se ha desarrollado una herramienta capaz de predecir la generación de energía solar fotovoltaica en el país para un horizonte de tiempo de 96 horas. Uno de los principales inconvenientes de dicha herramienta es su elevado costo computacional, lo que resulta en tiempos de ejecución restrictivos. Esta tesis aborda el estudio de la herramienta mencionada, haciendo foco especialmente en la componente que más tiempo y recursos requiere, el modelo numérico de circulación general de la atmósfera Weather Research and Forecasting (WRF). En una primera fase de este trabajo se analiza el tiempo de ejecución de dicho modelo, concluyendo que una de las etapas más costosa es el cómputo de la radiación solar, debido, entre otras cosas, a la precisión numérica que se requiere en estos cálculos. A partir de esta situación, en el presente trabajo se propone una nueva arquitectura de software asincrónica que permita desacoplar y calcular de forma paralela la radiación solar con el resto de las propiedades atmosféricas presentes en el WRF, siguiendo un patrón de paralelismo de tipo pipeline. Adicionalmente, se aborda el portado de una porción del cálculo de la radiación a un coprocesador masivamente paralelo, concretamente una GPU (Graphics Processing Unit) y/o un procesador XeonPhi, con el objetivo de disminuir la demanda de cómputo sobre la CPU. La evaluación experimental de esta propuesta en un escenario de doce plantas fotovoltaicas en el territorio uruguayo permite concluir que la arquitectura asincrónica logra disminuir los tiempos de ejecución del modelo original en un 10 % aproximadamente, cuando se consideran equipos multicore con una gran cantidad de núcleos. Adicionalmente, la extensión de esta arquitectura permite incorporar exitosamente la capacidad de cómputo de un coprocesador (GPU o Xeon-Phi), alcanzando mejoras de entre un 25 % a un 30 % en el tiempo total del modelo cuando se combinan ambas estrategias (asincronismo y uso de dispositivos de cómputo secundario).Over the last decade, Uruguay has begun a strong incorporation of eolic and solar energy to its energy matrix. The inclusion of this type of energy sources to supply the power grid poses a significant challenge at the moment of managing its use, mainly because of its variable flux. Considering this situation, and in order to simplify the power dispatch task (which efficiently manages the energy resources in the matrix), the Facultad de Ingeniería has developed a tool capable of predicting the generation of photovoltaic solar energy in the country for a 96-hour time horizon. One of the main drawbacks of said tool is its high computational cost, which results in restrictive runtimes. This thesis addresses the study of the aforementioned tool, focusing especially on the most resource- and time-consuming component, the numerical weather prediction model Weather Research and Forecasting (WRF). In a first stage of this work, the runtime of said model is assessed, concluding that one of the most expensive steps is the solar radiation computation, because of, inter alia, the numerical precision required in these calculations. Starting from this situation, this work proposes a new asynchronous software architecture which enables decoupling computation of solar radiation and its parallel calculation with the remaining atmospheric properties in the WRF, following a pipeline parallel strategy. Additionally, offloading of a portion of the radiation calculation to a co-processor is addressed, specifically a GPU (Graphics Processing Unit) and/or a Xeon-Phi processor, in order to decrease the computation load on the CPU. Experimental assessment of this proposal in a twelve-photovoltaic-facility scenario in Uruguayan land makes it possible to conclude that asynchronous architecture decreases runtimes of the original model by approximately 10 %, when considering multicore equipment with a large amount of cores. Furthermore, the extent of this architecture enables the successful incorporation of the computation ability of a co-processor (GPU o Xeon-Phi), reaching improvements of between 25 % and 30 % in the total execution time of the model when both strategies are combined (asynchronism and use of secondary computation devices)
Radiation-Based Analytic Approaches to Investigate the Earth’s Atmosphere
Radiation, propagating through Earth’s atmosphere, plays an important role in the Earth system. Solar radiation is the major source of energy, followed by thermal infrared radiation emitted by the Earth. The total radiative energy budget affects dynamic, thermodynamics, photochemical and biological processes. In addition, by measuring the reflected and emitted radiation at a distance (e.g., satellite or aircraft), we can detect and monitor the physical characteristics of a region which can help researchers get a better understanding of Earth’s atmosphere. Therefore, radiation-based analytic approaches are powerful tools in Earth Science. This thesis focuses on using radiation-based analytic tools to study the Earth’s atmosphere and to understand human impacts on the Earth system.
First, we develop novel machine learning methods for hyperspectral radiative transfer simulations. Hyperspectral technique is one of the most popular and powerful methods for atmospheric remote sensing and is widely used for temperature, gas, aerosol, and cloud retrievals. However, accurate forward radiative transfer simulations are computationally expensive since they require a larger number of monochromatic radiative transfer calculations. We, therefore explore the feasibility of machine learning techniques for fast hyperspectral radiative transfer simulations that perform calculations at a small fraction of hyperspectral wavelengths and extend them across the entire spectral range. The machine learning-based approach achieves better performance than the traditional principal component analysis (PCA) method.
Second, we evaluate modeled hyperspectral infrared spectra against satellite all-sky observations. The national weather centers obtain data from hyperspectral infrared sounders on a global scale. The cloudless scenario of this data is used to initialize weather forecasts, including temperature, water vapor, water cloud, and ice cloud profiles on a global grid. Although the data from these satellites are sensitive to the vertical distribution of ice and liquid water in the clouds, this information is not fully utilized. In this study, we evaluate how well the modeled spectra compare to AIRS observations using different cloud overlap models. We hope that this information can be used to verify clouds in the National Meteorological Center model and to initialize forecasts in the future.
In the last chapter, we use radiation-based analytic approaches to study human impacts on the Earth system. In the first study case, we show that the radiative forcing due to geospatially redistributed anthropogenic aerosols mainly determined the spatial variations of winter extreme weather in the Northern Hemisphere during 1970-2005, which is a unique transition period for global aerosol forcing. In the second case, we review satellite and ground-based observations and conduct state-of-art atmospheric model simulations during the COVID-19 lockdown period. The halted human activities during the COVID-19 pandemic in China provided a unique experiment to assess the efficiency of air-pollution mitigation.</p
Energy and Water Cycles in the Third Pole
As the most prominent and complicated terrain on the globe, the Tibetan Plateau (TP) is often called the “Roof of the World”, “Third Pole” or “Asian Water Tower”. The energy and water cycles in the Third Pole have great impacts on the atmospheric circulation, Asian monsoon system and global climate change. On the other hand, the TP and the surrounding higher elevation area are also experiencing evident and rapid environmental changes under the background of global warming. As the headwater area of major rivers in Asia, the TP’s environmental changes—such as glacial retreat, snow melting, lake expanding and permafrost degradation—pose potential long-term threats to water resources of the local and surrounding regions. To promote quantitative understanding of energy and water cycles of the TP, several field campaigns, including GAME/Tibet, CAMP/Tibet and TORP, have been carried out. A large amount of data have been collected to gain a better understanding of the atmospheric boundary layer structure, turbulent heat fluxes and their coupling with atmospheric circulation and hydrological processes. The focus of this reprint is to present recent advances in quantifying land–atmosphere interactions, the water cycle and its components, energy balance components, climate change and hydrological feedbacks by in situ measurements, remote sensing or numerical modelling approaches in the “Third Pole” region
Proceedings Of The 18th Annual Meeting Of The Asia Oceania Geosciences Society (Aogs 2021)
The 18th Annual Meeting of the Asia Oceania Geosciences Society (AOGS 2021) was held from 1st to 6th August 2021. This proceedings volume includes selected extended abstracts from a challenging array of presentations at this conference. The AOGS Annual Meeting is a leading venue for professional interaction among researchers and practitioners, covering diverse disciplines of geosciences