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  • Author or Editor: Georg A. Grell x
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Fotini Katopodes Chow
,
Katelyn A. Yu
,
Alexander Young
,
Eric James
,
Georg A. Grell
,
Ivan Csiszar
,
Marina Tsidulko
,
Saulo Freitas
,
Gabriel Pereira
,
Louis Giglio
,
Mariel D. Friberg
, and
Ravan Ahmadov

Abstract

Smoke from the 2018 Camp Fire in Northern California blanketed a large part of the region for 2 weeks, creating poor air quality in the “unhealthy” range for millions of people. The NOAA Global System Laboratory’s HRRR-Smoke model was operating experimentally in real time during the Camp Fire. Here, output from the HRRR-Smoke model is compared to surface observations of PM2.5 from AQS and PurpleAir sensors as well as satellite observation data. The HRRR-Smoke model at 3-km resolution successfully simulated the evolution of the plume during the initial phase of the fire (8–10 November 2018). Stereoscopic satellite plume height retrievals were used to compare with model output (for the first time, to the authors’ knowledge), showing that HRRR-Smoke is able to represent the complex 3D distribution of the smoke plume over complex terrain. On 15–16 November, HRRR-Smoke was able to capture the intensification of PM2.5 pollution due to a high pressure system and subsidence that trapped smoke close to the surface; however, HRRR-Smoke later underpredicted PM2.5 levels due to likely underestimates of the fire radiative power (FRP) derived from satellite observations. The intensity of the Camp Fire smoke event and the resulting pollution during the stagnation episodes make it an excellent test case for HRRR-Smoke in predicting PM2.5 levels, which were so high from this single fire event that the usual anthropogenic pollution sources became insignificant. The HRRR-Smoke model was implemented operationally at NOAA/NCEP in December 2020, now providing essential support for smoke forecasting as the impact of U.S. wildfires continues to increase in scope and magnitude.

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Jerome D. Fast
,
William I. Gustafson Jr.
,
Elaine G. Chapman
,
Richard C. Easter
,
Jeremy P. Rishel
,
Rahul A. Zaveri
,
Georg A. Grell
, and
Mary C. Barth

Abstract

The current paradigm of developing and testing new aerosol process modules is haphazard and slow. Aerosol modules are often tested for short simulation periods using limited data so that their overall performance over a wide range of meteorological conditions is not thoroughly evaluated. Although several model intercomparison studies quantify the differences among aerosol modules, the range of answers provides little insight on how to best improve aerosol predictions. Understanding the true impact of an aerosol process module is also complicated by the fact that other processes—such as emissions, meteorology, and chemistry—are often treated differently. To address this issue, the authors have developed an Aerosol Modeling Testbed (AMT) with the objective of providing a new approach to test and evaluate new aerosol process modules. The AMT consists of a more modular version of the Weather Research and Forecasting model (WRF) and a suite of tools to evaluate the performance of aerosol process modules via comparison with a wide range of field measurements. Their approach systematically targets specific aerosol process modules, whereas all the other processes are treated the same. The suite of evaluation tools will streamline the process of quantifying model performance and eliminate redundant work performed among various scientists working on the same problem. Both the performance and computational expense will be quantified over time. The use of a test bed to foster collaborations among the aerosol scientific community is an important aspect of the AMT; consequently, the longterm development and use of the AMT needs to be guided by users.

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Gabriele G. Pfister
,
Sebastian D. Eastham
,
Avelino F. Arellano
,
Bernard Aumont
,
Kelley C. Barsanti
,
Mary C. Barth
,
Andrew Conley
,
Nicholas A. Davis
,
Louisa K. Emmons
,
Jerome D. Fast
,
Arlene M. Fiore
,
Benjamin Gaubert
,
Steve Goldhaber
,
Claire Granier
,
Georg A. Grell
,
Marc Guevara
,
Daven K. Henze
,
Alma Hodzic
,
Xiaohong Liu
,
Daniel R. Marsh
,
John J. Orlando
,
John M. C. Plane
,
Lorenzo M. Polvani
,
Karen H. Rosenlof
,
Allison L. Steiner
,
Daniel J. Jacob
, and
Guy P. Brasseur

ABSTRACT

To explore the various couplings across space and time and between ecosystems in a consistent manner, atmospheric modeling is moving away from the fractured limited-scale modeling strategy of the past toward a unification of the range of scales inherent in the Earth system. This paper describes the forward-looking Multi-Scale Infrastructure for Chemistry and Aerosols (MUSICA), which is intended to become the next-generation community infrastructure for research involving atmospheric chemistry and aerosols. MUSICA will be developed collaboratively by the National Center for Atmospheric Research (NCAR) and university and government researchers, with the goal of serving the international research and applications communities. The capability of unifying various spatiotemporal scales, coupling to other Earth system components, and process-level modularization will allow advances in both fundamental and applied research in atmospheric composition, air quality, and climate and is also envisioned to become a platform that addresses the needs of policy makers and stakeholders.

Free access
Gabriele G. Pfister
,
Sebastian D. Eastham
,
Avelino F. Arellano
,
Bernard Aumont
,
Kelley C. Barsanti
,
Mary C. Barth
,
Andrew Conley
,
Nicholas A. Davis
,
Louisa K. Emmons
,
Jerome D. Fast
,
Arlene M. Fiore
,
Benjamin Gaubert
,
Steve Goldhaber
,
Claire Granier
,
Georg A. Grell
,
Marc Guevara
,
Daven K. Henze
,
Alma Hodzic
,
Xiaohong Liu
,
Daniel R. Marsh
,
John J. Orlando
,
John M. C. Plane
,
Lorenzo M. Polvani
,
Karen H. Rosenlof
,
Allison L. Steiner
,
Daniel J. Jacob
, and
Guy P. Brasseur
Full access
Jordan G. Powers
,
Joseph B. Klemp
,
William C. Skamarock
,
Christopher A. Davis
,
Jimy Dudhia
,
David O. Gill
,
Janice L. Coen
,
David J. Gochis
,
Ravan Ahmadov
,
Steven E. Peckham
,
Georg A. Grell
,
John Michalakes
,
Samuel Trahan
,
Stanley G. Benjamin
,
Curtis R. Alexander
,
Geoffrey J. Dimego
,
Wei Wang
,
Craig S. Schwartz
,
Glen S. Romine
,
Zhiquan Liu
,
Chris Snyder
,
Fei Chen
,
Michael J. Barlage
,
Wei Yu
, and
Michael G. Duda

Abstract

Since its initial release in 2000, the Weather Research and Forecasting (WRF) Model has become one of the world’s most widely used numerical weather prediction models. Designed to serve both research and operational needs, it has grown to offer a spectrum of options and capabilities for a wide range of applications. In addition, it underlies a number of tailored systems that address Earth system modeling beyond weather. While the WRF Model has a centralized support effort, it has become a truly community model, driven by the developments and contributions of an active worldwide user base. The WRF Model sees significant use for operational forecasting, and its research implementations are pushing the boundaries of finescale atmospheric simulation. Future model directions include developments in physics, exploiting emerging compute technologies, and ever-innovative applications. From its contributions to research, forecasting, educational, and commercial efforts worldwide, the WRF Model has made a significant mark on numerical weather prediction and atmospheric science.

Full access
Ariane Frassoni
,
Dayana Castilho
,
Michel Rixen
,
Enver Ramirez
,
João Gerd Z. de Mattos
,
Paulo Kubota
,
Alan James Peixoto Calheiros
,
Kevin A. Reed
,
Maria Assunção F. da Silva Dias
,
Pedro L. da Silva Dias
,
Haroldo Fraga de Campos Velho
,
Stephan R. de Roode
,
Francisco Doblas-Reyes
,
Denis Eiras
,
Michael Ek
,
Silvio N. Figueroa
,
Richard Forbes
,
Saulo R. Freitas
,
Georg A. Grell
,
Dirceu L. Herdies
,
Peter H. Lauritzen
,
Luiz Augusto T. Machado
,
Antonio O. Manzi
,
Guilherme Martins
,
Gilvan S. Oliveira
,
Nilton E. Rosário
,
Domingo C. Sales
,
Nils Wedi
, and
Bárbara Yamada
Full access