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Keller CA, Knowland KE, Duncan BN, Liu J, Anderson DC, Das S, Lucchesi RA, Lundgren EW, Nicely JM, Nielsen E, Ott LE, Saunders E, Strode SA, Wales PA, Jacob DJ, Pawson S. Description of the NASA GEOS Composition Forecast Modeling System GEOS-CF v1.0. JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS 2021; 13:e2020MS002413. [PMID: 34221240 PMCID: PMC8244029 DOI: 10.1029/2020ms002413] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 02/18/2021] [Accepted: 03/16/2021] [Indexed: 05/11/2023]
Abstract
The Goddard Earth Observing System composition forecast (GEOS-CF) system is a high-resolution (0.25°) global constituent prediction system from NASA's Global Modeling and Assimilation Office (GMAO). GEOS-CF offers a new tool for atmospheric chemistry research, with the goal to supplement NASA's broad range of space-based and in-situ observations. GEOS-CF expands on the GEOS weather and aerosol modeling system by introducing the GEOS-Chem chemistry module to provide hindcasts and 5-days forecasts of atmospheric constituents including ozone (O3), carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and fine particulate matter (PM2.5). The chemistry module integrated in GEOS-CF is identical to the offline GEOS-Chem model and readily benefits from the innovations provided by the GEOS-Chem community. Evaluation of GEOS-CF against satellite, ozonesonde and surface observations for years 2018-2019 show realistic simulated concentrations of O3, NO2, and CO, with normalized mean biases of -0.1 to 0.3, normalized root mean square errors between 0.1-0.4, and correlations between 0.3-0.8. Comparisons against surface observations highlight the successful representation of air pollutants in many regions of the world and during all seasons, yet also highlight current limitations, such as a global high bias in SO2 and an overprediction of summertime O3 over the Southeast United States. GEOS-CF v1.0 generally overestimates aerosols by 20%-50% due to known issues in GEOS-Chem v12.0.1 that have been addressed in later versions. The 5-days forecasts have skill scores comparable to the 1-day hindcast. Model skills can be improved significantly by applying a bias-correction to the surface model output using a machine-learning approach.
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Affiliation(s)
- Christoph A. Keller
- NASA Goddard Space Flight CenterGreenbeltMDUSA
- Universities Space Research AssociationColumbiaMDUSA
| | - K. Emma Knowland
- NASA Goddard Space Flight CenterGreenbeltMDUSA
- Universities Space Research AssociationColumbiaMDUSA
| | | | - Junhua Liu
- NASA Goddard Space Flight CenterGreenbeltMDUSA
- Universities Space Research AssociationColumbiaMDUSA
| | - Daniel C. Anderson
- NASA Goddard Space Flight CenterGreenbeltMDUSA
- Universities Space Research AssociationColumbiaMDUSA
| | - Sampa Das
- NASA Goddard Space Flight CenterGreenbeltMDUSA
- Universities Space Research AssociationColumbiaMDUSA
| | - Robert A. Lucchesi
- NASA Goddard Space Flight CenterGreenbeltMDUSA
- Science Systems and Applications, Inc.LanhamMDUSA
| | | | - Julie M. Nicely
- NASA Goddard Space Flight CenterGreenbeltMDUSA
- Earth System Science Interdisciplinary CenterUniversity of MarylandCollege ParkLanhamMDUSA
| | - Eric Nielsen
- NASA Goddard Space Flight CenterGreenbeltMDUSA
- Science Systems and Applications, Inc.LanhamMDUSA
| | | | - Emily Saunders
- NASA Goddard Space Flight CenterGreenbeltMDUSA
- Science Systems and Applications, Inc.LanhamMDUSA
| | - Sarah A. Strode
- NASA Goddard Space Flight CenterGreenbeltMDUSA
- Universities Space Research AssociationColumbiaMDUSA
| | - Pamela A. Wales
- NASA Goddard Space Flight CenterGreenbeltMDUSA
- Universities Space Research AssociationColumbiaMDUSA
| | - Daniel J. Jacob
- School of Engineering and Applied SciencesHarvard UniversityCambridgeMAUSA
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Zhang H, Wang J, García LC, Ge C, Plessel T, Szykman J, Murphy B, Spero TL. Improving Surface PM 2.5 Forecasts in the United States Using an Ensemble of Chemical Transport Model Outputs: 1. Bias Correction With Surface Observations in Nonrural Areas. JOURNAL OF GEOPHYSICAL RESEARCH. ATMOSPHERES : JGR 2020; 125:10.1029/2019JD032293. [PMID: 33425635 PMCID: PMC7788047 DOI: 10.1029/2019jd032293] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Accepted: 05/22/2020] [Indexed: 05/29/2023]
Abstract
This work is the first of a two-part study that aims to develop a computationally efficient bias correction framework to improve surface PM2.5 forecasts in the United States. Here, an ensemble-based Kalman filter (KF) technique is developed primarily for nonrural areas with approximately 500 surface observation sites for PM2.5 and applied to three (GEOS-Chem, WRF-Chem, and WRF-CMAQ) chemical transport model (CTM) hindcast outputs for June 2012. While all CTMs underestimate daily surface PM2.5 mass concentration by 20-50%, KF correction is effective for improving each CTM forecast. Subsequently, two ensemble methods are formulated: (1) the arithmetic mean ensemble (AME) that equally weights each model and (2) the optimized ensemble (OPE) that calculates the individual model weights by minimizing the least-square errors. While the OPE shows superior performance than the AME, the combination of either the AME or the OPE with a KF performs better than the OPE alone, indicating the effectiveness of the KF technique. Overall, the combination of a KF with the OPE shows the best results. Lastly, the Successive Correction Method (SCM) was applied to spread the bias correction from model grids with surface PM2.5 observations to the grids lacking ground observations by using a radius of influence of 125 km derived from surface observations, which further improves the forecast of surface PM2.5 at the national scale. Our findings provide the foundation for the second part of this study that uses satellite-based aerosol optical depth (AOD) products to further improve the forecast of surface PM2.5 in rural areas by performing statistical analysis of model output.
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Affiliation(s)
- Huanxin Zhang
- Department of Chemical and Biochemical Engineering, The University of Iowa, Iowa City, IA, USA
- Center for Global and Regional Environmental Research, The University of Iowa, Iowa City, IA, USA
| | - Jun Wang
- Department of Chemical and Biochemical Engineering, The University of Iowa, Iowa City, IA, USA
- Center for Global and Regional Environmental Research, The University of Iowa, Iowa City, IA, USA
| | - Lorena Castro García
- Department of Chemical and Biochemical Engineering, The University of Iowa, Iowa City, IA, USA
- Center for Global and Regional Environmental Research, The University of Iowa, Iowa City, IA, USA
| | - Cui Ge
- Department of Chemical and Biochemical Engineering, The University of Iowa, Iowa City, IA, USA
- Center for Global and Regional Environmental Research, The University of Iowa, Iowa City, IA, USA
| | - Todd Plessel
- General Dynamics Information Technology, RTP, NC, USA
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Data-Driven Temporal-Spatial Model for the Prediction of AQI in Nanjing. JOURNAL OF ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING RESEARCH 2020. [DOI: 10.2478/jaiscr-2020-0017] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Abstract
Air quality data prediction in urban area is of great significance to control air pollution and protect the public health. The prediction of the air quality in the monitoring station is well studied in existing researches. However, air-quality-monitor stations are insufficient in most cities and the air quality varies from one place to another dramatically due to complex factors. A novel model is established in this paper to estimate and predict the Air Quality Index (AQI) of the areas without monitoring stations in Nanjing. The proposed model predicts AQI in a non-monitoring area both in temporal dimension and in spatial dimension respectively. The temporal dimension model is presented at first based on the enhanced k-Nearest Neighbor (KNN) algorithm to predict the AQI values among monitoring stations, the acceptability of the results achieves 92% for one-hour prediction. Meanwhile, in order to forecast the evolution of air quality in the spatial dimension, the method is utilized with the help of Back Propagation neural network (BP), which considers geographical distance. Furthermore, to improve the accuracy and adaptability of the spatial model, the similarity of topological structure is introduced. Especially, the temporal-spatial model is built and its adaptability is tested on a specific non-monitoring site, Jiulonghu Campus of Southeast University. The result demonstrates that the acceptability achieves 73.8% on average. The current paper provides strong evidence suggesting that the proposed non-parametric and data-driven approach for air quality forecasting provides promising results.
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Improving PM2.5 Air Quality Model Forecasts in China Using a Bias-Correction Framework. ATMOSPHERE 2017. [DOI: 10.3390/atmos8080147] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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Robichaud A. Surface data assimilation of chemical compounds over North America and its impact on air quality and Air Quality Health Index (AQHI) forecasts. AIR QUALITY, ATMOSPHERE, & HEALTH 2017; 10:955-970. [PMID: 29142613 PMCID: PMC5660843 DOI: 10.1007/s11869-017-0485-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2017] [Accepted: 05/17/2017] [Indexed: 05/18/2023]
Abstract
The aim of this paper is to analyze the impact of initializing GEM-MACH, Environment and Climate Change Canada's air quality (AQ) forecast model, with multi-pollutant surface objective analyses (MPSOA). A series of 48-h air quality forecasts were launched for July 2012 (summer case) and January 2014 (winter case) for ozone, NO2, and PM2.5. In this setup, the GEM-MACH model (version 1.3.8.2) was initialized with surface analysis increments (from MPSOA) which were projected in the vertical by applying an appropriate fractional weighting in order to obtain 3D analyses in the lower troposphere. Here, we have used a methodology based on sensitivity tests to obtain the optimum vertical correlation length (VCL). Overall, results showed that for PM2.5, more specifically for sulfate and crustal materials, AQ forecasts initialized with MPSOA showed a very significant improvement compared to forecasts without data assimilation, which extended beyond 48 h in all seasons. Initializing the model with ozone analyses also had a significant impact but on a shorter time scale than that of PM2.5. Finally, assimilation of NO2 was found to have much less impact than longer-lived species. The impact of simultaneous assimilation of the three pollutants (PM2.5, ozone, and NO2) was also examined and found very significant in reducing the total error of the Air Quality Health Index (AQHI) over 48 h and beyond. We suggest that the period over which there is a significant improvement due to assimilation could be an adequate measure of the pollutant atmospheric lifetime.
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Affiliation(s)
- Alain Robichaud
- Air Quality Research Division, Environment and Climate Change Canada, 2121 Trans-Canada Highway, Dorval, QC H9P 1J3 Canada
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Ryan WF. The air quality forecast rote: Recent changes and future challenges. JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION (1995) 2016; 66:576-96. [PMID: 26889915 DOI: 10.1080/10962247.2016.1151469] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
Abstract
UNLABELLED Air quality forecasting is a recent development, with most programs initiated only in the last 20 years. During the last decade, forecast preparation procedure-the forecast rote-has changed dramatically. This paper summarizes the unique challenges posed by air quality forecasting, details the current forecast rote, and analyzes prospects for future improvements. Because air quality forecasts must diagnose and predict several pollutants and their precursors in addition to standard meteorological variables, it is, compared with weather forecasts, a higher-uncertainty forecast. Forecasters seek to contain the uncertainty by "anchoring" the forecast, using an a priori field, and then "adjusting" the forecast using additional information. The air quality a priori, or first guess, field is a blend of past, current, and near-term future observations of the pollutants of interest, on both local and regional scales, and is typically coupled with predicted air parcel trajectories. Until recently, statistical methods, based on long-term training data sets, were used to adjust the first guess. However, reductions in precursor emissions in the United States, beginning in the late 1990s and continuing to the present, eroded the stationarity assumption for the training data sets and degraded forecast skill. Beginning in the mid-2000s, output from modified numerical air quality prediction (NAQP) models, originally developed to test pollution control strategies, became available in near real time for forecast support. The current adjustment process begins with the analyses and postprocessing of individual NAQP models and their ad hoc ensembles, often in concert with new statistical techniques. The final adjustment step uses forecaster expertise to assess the impact of mesoscale features not resolved by the NAQP models. It is expected that advances in model resolution, chemical data assimilation, and the formulation of emissions fields will improve mesoscale predictions by NAQP models and drive future changes in the forecast rote. IMPLICATIONS Routine air quality forecasts are now issued for nearly all the major U.S. metropolitan areas. Methods of forecast preparation-the forecast rote-have changed significantly in the last decade. Numerical air quality models have matured and are now an indispensable part of the forecasting process. All forecasting methods, particularly statistically based models, must be continually calibrated to account for ongoing local- and regional-scale emission reductions.
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Affiliation(s)
- William F Ryan
- a Department of Meteorology , The Pennsylvania State University, University Park , Pennsylvania , USA
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7
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Pérez VA, Arasa R, Codina B, Piñón J. Enhancing Air Quality Forecasts over Catalonia (Spain) Using Model Output Statistics. ACTA ACUST UNITED AC 2015. [DOI: 10.4236/gep.2015.38002] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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Arasa R, Soler MR, Olid M. Evaluating the Performance of a Regional-Scale Photochemical Modelling System: Part I—Ozone Predictions. ACTA ACUST UNITED AC 2012. [DOI: 10.5402/2012/860234] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
We present a detailed evaluation of the seasonal performance of the Community Multiscale Air Quality (CMAQ) modelling system and the PSU/NCAR meteorological model coupled to a new Numerical Emission Model for Air Quality (MNEQA). The combined system simulates air quality at a fine resolution (3 km as horizontal resolution and 1 h as temporal resolution) in north-eastern Spain, where problems of ozone pollution are frequent. An extensive database compiled over two periods, from May to September 2009 and 2010, is used to evaluate meteorological simulations and chemical outputs. Our results indicate that the model accurately reproduces hourly and 1-h and 8-h maximum ozone surface concentrations measured at the air quality stations, as statistical values fall within the EPA and EU recommendations. However, to further improve forecast accuracy, three simple bias-adjustment techniques—mean subtraction (MS), ratio adjustment (RA), and hybrid forecast (HF)—based on 10 days of available comparisons are applied. The results show that the MS technique performed better than RA or HF, although all the bias-adjustment techniques significantly reduce the systematic errors in ozone forecasts.
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Affiliation(s)
- R. Arasa
- Department of Astronomy and Meteorology, Faculty of Physics, University of Barcelona, Avinguda Diagonal 647,
08028 Barcelona, Spain
| | - M. R. Soler
- Department of Astronomy and Meteorology, Faculty of Physics, University of Barcelona, Avinguda Diagonal 647,
08028 Barcelona, Spain
| | - M. Olid
- Department of Astronomy and Meteorology, Faculty of Physics, University of Barcelona, Avinguda Diagonal 647,
08028 Barcelona, Spain
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McKeen S, Grell G, Peckham S, Wilczak J, Djalalova I, Hsie EY, Frost G, Peischl J, Schwarz J, Spackman R, Holloway J, de Gouw J, Warneke C, Gong W, Bouchet V, Gaudreault S, Racine J, McHenry J, McQueen J, Lee P, Tang Y, Carmichael GR, Mathur R. An evaluation of real-time air quality forecasts and their urban emissions over eastern Texas during the summer of 2006 Second Texas Air Quality Study field study. ACTA ACUST UNITED AC 2009. [DOI: 10.1029/2008jd011697] [Citation(s) in RCA: 60] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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10
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Kang D, Mathur R, Rao ST, Yu S. Bias adjustment techniques for improving ozone air quality forecasts. ACTA ACUST UNITED AC 2008. [DOI: 10.1029/2008jd010151] [Citation(s) in RCA: 55] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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McKeen S, Chung SH, Wilczak J, Grell G, Djalalova I, Peckham S, Gong W, Bouchet V, Moffet R, Tang Y, Carmichael GR, Mathur R, Yu S. Evaluation of several PM2.5
forecast models using data collected during the ICARTT/NEAQS 2004 field study. ACTA ACUST UNITED AC 2007. [DOI: 10.1029/2006jd007608] [Citation(s) in RCA: 136] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- S. McKeen
- Chemical Sciences Division, Environmental Science Research Laboratory; NOAA; Boulder Colorado USA
| | - S. H. Chung
- Chemical Sciences Division, Environmental Science Research Laboratory; NOAA; Boulder Colorado USA
| | - J. Wilczak
- Physical Sciences Division, Environmental Science Research Laboratory; NOAA; Boulder Colorado USA
| | - G. Grell
- Global Systems Division, Environmental Science Research Laboratory; NOAA; Boulder Colorado USA
| | - I. Djalalova
- Physical Sciences Division, Environmental Science Research Laboratory; NOAA; Boulder Colorado USA
| | - S. Peckham
- Global Systems Division, Environmental Science Research Laboratory; NOAA; Boulder Colorado USA
| | - W. Gong
- Meteorological Service of Canada; Downsview, Ontario Canada
| | - V. Bouchet
- Meteorological Service of Canada; Dorval, Quebec Canada
| | - R. Moffet
- Meteorological Service of Canada; Dorval, Quebec Canada
| | - Y. Tang
- Center for Global and Regional Environmental Research; University of Iowa; Iowa City Iowa USA
| | - G. R. Carmichael
- Center for Global and Regional Environmental Research; University of Iowa; Iowa City Iowa USA
| | - R. Mathur
- Air Resources Laboratory; NOAA; Silver Spring Maryland USA
| | - S. Yu
- Science and Technology Corporation; Hampton Virginia USA
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Fehsenfeld FC, Ancellet G, Bates TS, Goldstein AH, Hardesty RM, Honrath R, Law KS, Lewis AC, Leaitch R, McKeen S, Meagher J, Parrish DD, Pszenny AAP, Russell PB, Schlager H, Seinfeld J, Talbot R, Zbinden R. International Consortium for Atmospheric Research on Transport and Transformation (ICARTT): North America to Europe-Overview of the 2004 summer field study. ACTA ACUST UNITED AC 2006. [DOI: 10.1029/2006jd007829] [Citation(s) in RCA: 205] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
| | - G. Ancellet
- Service d'Aéronomie du Centre Nationale de la Recherche Scientifique; Institut Pierre Simon Laplace/Université Pierre et Marie Curie; Paris France
| | - T. S. Bates
- Pacific Marine Environmental Laboratory; NOAA; Seattle Washington USA
| | - A. H. Goldstein
- Department of Environmental Science, Policy and Management; University of California; Berkeley California USA
| | - R. M. Hardesty
- Earth System Research Laboratory; NOAA; Boulder Colorado USA
| | - R. Honrath
- Department of Civil and Environmental Engineering; Michigan Technological University; Houghton Michigan USA
| | - K. S. Law
- Service d'Aéronomie du Centre Nationale de la Recherche Scientifique; Institut Pierre Simon Laplace/Université Pierre et Marie Curie; Paris France
| | - A. C. Lewis
- Department of Chemistry; University of York; York UK
| | - R. Leaitch
- Science and Technology Branch; Environment Canada; Toronto, Ontario Canada
| | - S. McKeen
- Earth System Research Laboratory; NOAA; Boulder Colorado USA
| | - J. Meagher
- Earth System Research Laboratory; NOAA; Boulder Colorado USA
| | - D. D. Parrish
- Earth System Research Laboratory; NOAA; Boulder Colorado USA
| | - A. A. P. Pszenny
- Institute for the Study of Earth, Oceans and Space; University of New Hampshire; Durham New Hampshire USA
| | - P. B. Russell
- NASA Ames Research Center; Moffett Field California USA
| | - H. Schlager
- Deutsches Zentrum für Luft- und Raumfahrt; Oberpfaffenhofen, Wessling Germany
| | - J. Seinfeld
- Departments of Environmental Science and Engineering and Chemical Engineering; California Institute of Technology; Pasadena California USA
| | - R. Talbot
- Institute for the Study of Earth, Oceans and Space; University of New Hampshire; Durham New Hampshire USA
| | - R. Zbinden
- Laboratoire d'Aérologie, Observatoire Midi-Pyrénées; UMR 5560, Centre Nationale de la Recherche Scientifique/Université Paul Sabatier; Toulouse France
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