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Mrinal K. Biswas
,
Jun A. Zhang
,
Evelyn Grell
,
Evan Kalina
,
Kathryn Newman
,
Ligia Bernardet
,
Laurie Carson
,
James Frimel
, and
Georg Grell

Abstract

The Developmental Testbed Center (DTC) tested two convective parameterization schemes in the Hurricane Weather Research and Forecasting (HWRF) Model and compared them in terms of performance of forecasting tropical cyclones (TCs). Several TC forecasts were conducted with the scale-aware Simplified Arakawa Schubert (SAS) and Grell–Freitas (GF) convective schemes over the Atlantic basin. For this sample of over 100 cases, the storm track and intensity forecasts were superior for the GF scheme compared to SAS. A case study showed improved storm structure for GF when compared with radar observations. The GF run had increased inflow in the boundary layer, which resulted in higher angular momentum. An angular momentum budget analysis shows that the difference in the contribution of the eddy transport to the total angular momentum tendency is small between the two forecasts. The main difference is in the mean transport term, especially in the boundary layer. The temperature tendencies indicate higher contribution from the microphysics and cumulus heating above the boundary layer in the GF run. A temperature budget analysis indicated that both the temperature advection and diabatic heating were the dominant terms and they were larger near the storm center in the GF run than in the SAS run. The above results support the superior performance of the GF scheme for TC intensity forecast.

Free access
Partha S. Bhattacharjee
,
Li Zhang
,
Barry Baker
,
Li Pan
,
Raffaele Montuoro
,
Georg A. Grell
, and
Jeffery T. McQueen

Abstract

The NWS/NCEP recently implemented a new global deterministic aerosol forecast model named the Global Ensemble Forecast Systems Aerosols (GEFS-Aerosols), which is based on the Finite Volume version 3 GFS (FV3GFS). It replaced the operational NOAA Environmental Modeling System (NEMS) GFS Aerosol Component version 2 (NGACv2), which was based on a global spectral model (GSM). GEFS-Aerosols uses aerosol modules from the GOCART previously integrated in the WRF Model with Chemistry (WRF-Chem), FENGSHA dust scheme, and several other updates. In this study, we have extensively evaluated aerosol optical depth (AOD) forecasts from GEFS-Aerosols against various observations over a timespan longer than one year (2019–20). The total AOD improvement (in terms of seasonal mean) in GEFS-Aerosols is about 40% compared to NGACv2 in the fall and winter season of 2019. In terms of aerosol species, the biggest improvement came from the enhanced representation of biomass burning aerosol species as GEFS-Aerosols is able to capture more fire events in southern Africa, South America, and Asia than its predecessor. Dust AODs reproduce the seasonal variation over Africa and the Middle East. We have found that correlation of total AOD over large regions of the globe remains consistent for forecast days 3–5. However, we have found that GEFS-Aerosols generates some systematic positive biases for organic carbon AOD near biomass burning regions and sulfate AOD over prediction over East Asia. The addition of a data assimilation capability to GEFS-Aerosols in the near future is expected to address these biases and provide a positive impact to aerosol forecasts by the model.

Significance Statement

The purpose of this study is to quantify improvements associated with the newly implemented global aerosol forecast model at NWS/NCEP. The monthly and seasonal variations of AOD forecasts of various aerosol regimes are overall consistent with the observations. Our results provide a guide to downstream regional air quality models like CMAQ that will use GEFS-Aerosols to provide lateral boundary conditions.

Free access
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.

Full access
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.

Full access
Stanley G. Benjamin
,
Dezsö Dévényi
,
Stephen S. Weygandt
,
Kevin J. Brundage
,
John M. Brown
,
Georg A. Grell
,
Dongsoo Kim
,
Barry E. Schwartz
,
Tatiana G. Smirnova
,
Tracy Lorraine Smith
, and
Geoffrey S. Manikin

Abstract

The Rapid Update Cycle (RUC), an operational regional analysis–forecast system among the suite of models at the National Centers for Environmental Prediction (NCEP), is distinctive in two primary aspects: its hourly assimilation cycle and its use of a hybrid isentropic–sigma vertical coordinate. The use of a quasi-isentropic coordinate for the analysis increment allows the influence of observations to be adaptively shaped by the potential temperature structure around the observation, while the hourly update cycle allows for a very current analysis and short-range forecast. Herein, the RUC analysis framework in the hybrid coordinate is described, and some considerations for high-frequency cycling are discussed.

A 20-km 50-level hourly version of the RUC was implemented into operations at NCEP in April 2002. This followed an initial implementation with 60-km horizontal grid spacing and a 3-h cycle in 1994 and a major upgrade including 40-km horizontal grid spacing in 1998. Verification of forecasts from the latest 20-km version is presented using rawinsonde and surface observations. These verification statistics show that the hourly RUC assimilation cycle improves short-range forecasts (compared to longer-range forecasts valid at the same time) even down to the 1-h projection.

Full access
Stanley G. Benjamin
,
Stephen S. Weygandt
,
John M. Brown
,
Ming Hu
,
Curtis R. Alexander
,
Tatiana G. Smirnova
,
Joseph B. Olson
,
Eric P. James
,
David C. Dowell
,
Georg A. Grell
,
Haidao Lin
,
Steven E. Peckham
,
Tracy Lorraine Smith
,
William R. Moninger
,
Jaymes S. Kenyon
, and
Geoffrey S. Manikin

Abstract

The Rapid Refresh (RAP), an hourly updated assimilation and model forecast system, replaced the Rapid Update Cycle (RUC) as an operational regional analysis and forecast system among the suite of models at the NOAA/National Centers for Environmental Prediction (NCEP) in 2012. The need for an effective hourly updated assimilation and modeling system for the United States for situational awareness and related decision-making has continued to increase for various applications including aviation (and transportation in general), severe weather, and energy. The RAP is distinct from the previous RUC in three primary aspects: a larger geographical domain (covering North America), use of the community-based Advanced Research version of the Weather Research and Forecasting (WRF) Model (ARW) replacing the RUC forecast model, and use of the Gridpoint Statistical Interpolation analysis system (GSI) instead of the RUC three-dimensional variational data assimilation (3DVar). As part of the RAP development, modifications have been made to the community ARW model (especially in model physics) and GSI assimilation systems, some based on previous model and assimilation design innovations developed initially with the RUC. Upper-air comparison is included for forecast verification against both rawinsondes and aircraft reports, the latter allowing hourly verification. In general, the RAP produces superior forecasts to those from the RUC, and its skill has continued to increase from 2012 up to RAP version 3 as of 2015. In addition, the RAP can improve on persistence forecasts for the 1–3-h forecast range for surface, upper-air, and ceiling forecasts.

Full 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

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