Articles | Volume 11, issue 6
https://doi.org/10.5194/gmd-11-2333-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-11-2333-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
19 Jun 2018
Model evaluation paper |
| 19 Jun 2018
The implementation of NEMS GFS Aerosol Component (NGAC) Version 2.0 for global multispecies forecasting at NOAA/NCEP – Part 2: Evaluation of aerosol optical thickness
Partha Sarathi Bhattacharjee
CORRESPONDING AUTHOR
I. M. Systems Group, INC. at NOAA/NWS National Centers for Environment Prediction, College Park, MD 20740, USA
Jun Wang
NOAA/NWS National Centers for Environment Prediction, College Park, MD 20740, USA
Cheng-Hsuan Lu
University of Albany, State University of New York, Albany, NY 12222, USA
Vijay Tallapragada
NOAA/NWS National Centers for Environment Prediction, College Park, MD 20740, USA
Related authors
Li Pan, Partha S. Bhattacharjee, Li Zhang, Raffaele Montuoro, Barry Baker, Jeff McQueen, Georg A. Grell, Stuart A. McKeen, Shobha Kondragunta, Xiaoyang Zhang, Gregory J. Frost, Fanglin Yang, and Ivanka Stajner
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-61, https://doi.org/10.5194/gmd-2023-61, 2023
Preprint under review for GMD
Short summary
Short summary
A GEFS-Aerosols simulation was conducted from September 1, 2019 to September 30, 2020 to evaluate the model performance of GEFS-Aerosols. The purpose of this study was to understand how aerosol chemical and physical processes affect ambient aerosol concentrations by placing aerosol wet deposition, dry deposition, reactions, gravitational deposition, and emissions into the aerosol mass balance equation.
Li Zhang, Raffaele Montuoro, Stuart A. McKeen, Barry Baker, Partha S. Bhattacharjee, Georg A. Grell, Judy Henderson, Li Pan, Gregory J. Frost, Jeff McQueen, Rick Saylor, Haiqin Li, Ravan Ahmadov, Jun Wang, Ivanka Stajner, Shobha Kondragunta, Xiaoyang Zhang, and Fangjun Li
Geosci. Model Dev., 15, 5337–5369, https://doi.org/10.5194/gmd-15-5337-2022, https://doi.org/10.5194/gmd-15-5337-2022, 2022
Short summary
Short summary
The NOAA’s air quality predictions contribute to protecting lives and health in the US, which requires sustainable development and improvement of forecast systems. GEFS-Aerosols v1 has been developed in a collaboration between the NOAA research laboratories for operational forecast since September 2020 in the NCEP. The predictions demonstrate substantial improvements for both composition and variability of aerosol distributions over those from the former operational system.
Jun Wang, Partha S. Bhattacharjee, Vijay Tallapragada, Cheng-Hsuan Lu, Shobha Kondragunta, Arlindo da Silva, Xiaoyang Zhang, Sheng-Po Chen, Shih-Wei Wei, Anton S. Darmenov, Jeff McQueen, Pius Lee, Prabhat Koner, and Andy Harris
Geosci. Model Dev., 11, 2315–2332, https://doi.org/10.5194/gmd-11-2315-2018, https://doi.org/10.5194/gmd-11-2315-2018, 2018
Short summary
Short summary
The NEMS GFS Aerosol Component (NGAC) version 2.0 for global multispecies aerosol forecast was developed at NCEP. Additional sea salt, sulfate, organic carbon, and black carbon aerosol species were included. This implementation advanced the global aerosol forecast capability and made a step forward toward developing a global aerosol data assimilation system. The aerosol products from this system have been provided to meet the stakeholder's needs.
Cheng-Hsuan Lu, Arlindo da Silva, Jun Wang, Shrinivas Moorthi, Mian Chin, Peter Colarco, Youhua Tang, Partha S. Bhattacharjee, Shen-Po Chen, Hui-Ya Chuang, Hann-Ming Henry Juang, Jeffery McQueen, and Mark Iredell
Geosci. Model Dev., 9, 1905–1919, https://doi.org/10.5194/gmd-9-1905-2016, https://doi.org/10.5194/gmd-9-1905-2016, 2016
Short summary
Short summary
Aerosols have an important effect on the Earth's climate and implications for public health. NASA has partnered with NOAA to transfer GOCART aerosol model to NCEP, enabling the first global aerosol forecasting system at NOAA/NCEP. This collaboration reflects an effective research-to-operation transition, paving the way for NCEP to provide global aerosol products serving a wide range of stakeholders and to allow the effects of aerosols on weather and climate prediction to be considered.
Shih-Wei Wei, Mariusz Pagowski, Arlindo da Silva, Cheng-Hsuan Lu, and Bo Huang
EGUsphere, https://doi.org/10.5194/egusphere-2023-356, https://doi.org/10.5194/egusphere-2023-356, 2023
Short summary
Short summary
This manuscript describes the modeling system and the evaluation results for the first global aerosol reanalysis product at NOAA. The reanalysis is called NOAA Aerosol ReAnalysis version 1.0 (NARA v1.0). We evaluated NARA v1.0 against AERONET observations and compared it with MERRA-2 and CAMSRA reanalyses. We further identify deficiencies of the system (both in the forecast model and in the data assimilation system) and the uncertainties that exist in our reanalysis.
Li Pan, Partha S. Bhattacharjee, Li Zhang, Raffaele Montuoro, Barry Baker, Jeff McQueen, Georg A. Grell, Stuart A. McKeen, Shobha Kondragunta, Xiaoyang Zhang, Gregory J. Frost, Fanglin Yang, and Ivanka Stajner
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-61, https://doi.org/10.5194/gmd-2023-61, 2023
Preprint under review for GMD
Short summary
Short summary
A GEFS-Aerosols simulation was conducted from September 1, 2019 to September 30, 2020 to evaluate the model performance of GEFS-Aerosols. The purpose of this study was to understand how aerosol chemical and physical processes affect ambient aerosol concentrations by placing aerosol wet deposition, dry deposition, reactions, gravitational deposition, and emissions into the aerosol mass balance equation.
Li Zhang, Raffaele Montuoro, Stuart A. McKeen, Barry Baker, Partha S. Bhattacharjee, Georg A. Grell, Judy Henderson, Li Pan, Gregory J. Frost, Jeff McQueen, Rick Saylor, Haiqin Li, Ravan Ahmadov, Jun Wang, Ivanka Stajner, Shobha Kondragunta, Xiaoyang Zhang, and Fangjun Li
Geosci. Model Dev., 15, 5337–5369, https://doi.org/10.5194/gmd-15-5337-2022, https://doi.org/10.5194/gmd-15-5337-2022, 2022
Short summary
Short summary
The NOAA’s air quality predictions contribute to protecting lives and health in the US, which requires sustainable development and improvement of forecast systems. GEFS-Aerosols v1 has been developed in a collaboration between the NOAA research laboratories for operational forecast since September 2020 in the NCEP. The predictions demonstrate substantial improvements for both composition and variability of aerosol distributions over those from the former operational system.
Dustin Francis Phillip Grogan, Cheng-Hsuan Lu, Shih-Wei Wei, and Sheng-Po Chen
Atmos. Chem. Phys., 22, 2385–2398, https://doi.org/10.5194/acp-22-2385-2022, https://doi.org/10.5194/acp-22-2385-2022, 2022
Short summary
Short summary
This study shows that incorporating aerosols into satellite radiance calculations affects the representation of African easterly waves (AEWs), and their environment, over North Africa and the eastern Atlantic in a numerical weather model. These changes are driven by radiative effects of Saharan dust captured by the aerosol-affected radiances, which modify the initial fields and can improve the forecasting of AEWs.
Cheng-Hsuan Lu, Quanhua Liu, Shih-Wei Wei, Benjamin T. Johnson, Cheng Dang, Patrick G. Stegmann, Dustin Grogan, Guoqing Ge, Ming Hu, and Michael Lueken
Geosci. Model Dev., 15, 1317–1329, https://doi.org/10.5194/gmd-15-1317-2022, https://doi.org/10.5194/gmd-15-1317-2022, 2022
Short summary
Short summary
This article is a technical note on the aerosol absorption and scattering calculations of the Community Radiative Transfer Model (CRTM) v2.2 and v2.3. It also provides guidance for prospective users of the CRTM aerosol option and Gridpoint Statistical Interpolation (GSI) aerosol-aware radiance assimilation. Scientific aspects of aerosol-affected BT in atmospheric data assimilation are also briefly discussed.
Ying-Chieh Chen, Sheng-Hsiang Wang, Qilong Min, Sarah Lu, Pay-Liam Lin, Neng-Huei Lin, Kao-Shan Chung, and Everette Joseph
Atmos. Chem. Phys., 21, 4487–4502, https://doi.org/10.5194/acp-21-4487-2021, https://doi.org/10.5194/acp-21-4487-2021, 2021
Short summary
Short summary
In this study, we integrate satellite and surface observations to statistically quantify aerosol impacts on low-level warm-cloud microphysics and drizzle over northern Taiwan. Our result provides observational evidence for aerosol indirect effects. The frequency of drizzle is reduced under polluted conditions. For light-precipitation events (≤ 1 mm h-1), however, higher aerosol concentrations drive raindrops toward smaller sizes and thus increase the appearance of the drizzle drops.
Youhua Tang, Huisheng Bian, Zhining Tao, Luke D. Oman, Daniel Tong, Pius Lee, Patrick C. Campbell, Barry Baker, Cheng-Hsuan Lu, Li Pan, Jun Wang, Jeffery McQueen, and Ivanka Stajner
Atmos. Chem. Phys., 21, 2527–2550, https://doi.org/10.5194/acp-21-2527-2021, https://doi.org/10.5194/acp-21-2527-2021, 2021
Short summary
Short summary
Chemical lateral boundary condition (CLBC) impact is essential for regional air quality prediction during intrusion events. We present a model mapping Goddard Earth Observing System (GEOS) to Community Multi-scale Air Quality (CMAQ) CB05–AERO6 (Carbon Bond 5; version 6 of the aerosol module) species. Influence depends on distance from the inflow boundary and species and their regional characteristics. We use aerosol optical thickness to derive CLBCs, achieving reasonable prediction.
Ping Zhu, Bryce Tyner, Jun A. Zhang, Eric Aligo, Sundararaman Gopalakrishnan, Frank D. Marks, Avichal Mehra, and Vijay Tallapragada
Atmos. Chem. Phys., 19, 14289–14310, https://doi.org/10.5194/acp-19-14289-2019, https://doi.org/10.5194/acp-19-14289-2019, 2019
Short summary
Short summary
Producing timely and accurate intensity forecasts of tropical cyclones (TCs) continues to be one of the most difficult challenges in numerical weather prediction. The difficulty stems from the fact that TC intensification is not only modulated by environmental conditions but also largely depends on TC internal dynamics. The study shows that asymmetric eyewall and rainband eddy forcing above the boundary layer plays an important role in spinning up a TC vortex including rapid intensification.
Keren Rosado, Bin Liu, Vernon Morris, Vijay Tallapragada, and Lin Zhu
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-139, https://doi.org/10.5194/gmd-2019-139, 2019
Publication in GMD not foreseen
Short summary
Short summary
The operational Hurricane Weather Research and Forecast (HWRF) model has been used to investigate the role of lightning diagnostics in the life cycle of tropical cyclones (TC). A lightning parameterization was implemented into HWRF with the motivation of using lightning forecast as a proxy for TC intensity changes. Results from this investigation show mixed results in terms of correlating lightning forecast and TC intensity forecast.
Jun Wang, Partha S. Bhattacharjee, Vijay Tallapragada, Cheng-Hsuan Lu, Shobha Kondragunta, Arlindo da Silva, Xiaoyang Zhang, Sheng-Po Chen, Shih-Wei Wei, Anton S. Darmenov, Jeff McQueen, Pius Lee, Prabhat Koner, and Andy Harris
Geosci. Model Dev., 11, 2315–2332, https://doi.org/10.5194/gmd-11-2315-2018, https://doi.org/10.5194/gmd-11-2315-2018, 2018
Short summary
Short summary
The NEMS GFS Aerosol Component (NGAC) version 2.0 for global multispecies aerosol forecast was developed at NCEP. Additional sea salt, sulfate, organic carbon, and black carbon aerosol species were included. This implementation advanced the global aerosol forecast capability and made a step forward toward developing a global aerosol data assimilation system. The aerosol products from this system have been provided to meet the stakeholder's needs.
Cheng-Hsuan Lu, Arlindo da Silva, Jun Wang, Shrinivas Moorthi, Mian Chin, Peter Colarco, Youhua Tang, Partha S. Bhattacharjee, Shen-Po Chen, Hui-Ya Chuang, Hann-Ming Henry Juang, Jeffery McQueen, and Mark Iredell
Geosci. Model Dev., 9, 1905–1919, https://doi.org/10.5194/gmd-9-1905-2016, https://doi.org/10.5194/gmd-9-1905-2016, 2016
Short summary
Short summary
Aerosols have an important effect on the Earth's climate and implications for public health. NASA has partnered with NOAA to transfer GOCART aerosol model to NCEP, enabling the first global aerosol forecasting system at NOAA/NCEP. This collaboration reflects an effective research-to-operation transition, paving the way for NCEP to provide global aerosol products serving a wide range of stakeholders and to allow the effects of aerosols on weather and climate prediction to be considered.
Related subject area
Atmospheric sciences
Modelling concentration heterogeneities in streets using the street-network model MUNICH
Simulation model of Reactive Nitrogen Species in an Urban Atmosphere using a Deep Neural Network: RNDv1.0
J-GAIN v1.1: a flexible tool to incorporate aerosol formation rates obtained by molecular models into large-scale models
Metrics for evaluating the quality in linear atmospheric inverse problems: a case study of a trace gas inversion
Improved representation of volcanic sulfur dioxide depletion in Lagrangian transport simulations: a case study with MPTRAC v2.4
Use of threshold parameter variation for tropical cyclone tracking
Passive-tracer modelling at super-resolution with Weather Research and Forecasting – Advanced Research WRF (WRF-ARW) to assess mass-balance schemes
The High-resolution Intermediate Complexity Atmospheric Research (HICAR v1.1) model enables fast dynamic downscaling to the hectometer scale
A gridded air quality forecast through fusing site-available machine learning predictions from RFSML v1.0 and chemical transport model results from GEOS-Chem v13.1.0 using the ensemble Kalman filter
Plume detection and emission estimate for biomass burning plumes from TROPOMI carbon monoxide observations using APE v1.1
CHEEREIO 1.0: a versatile and user-friendly ensemble-based chemical data assimilation and emissions inversion platform for the GEOS-Chem chemical transport model
A method to derive Fourier–wavelet spectra for the characterization of global-scale waves in the mesosphere and lower thermosphere and its MATLAB and Python software (fourierwavelet v1.1)
Dynamic Meteorology-induced Emissions Coupler (MetEmis) development in the Community Multiscale Air Quality (CMAQ): CMAQ-MetEmis
Visual analysis of model parameter sensitivities along warm conveyor belt trajectories using Met.3D (1.6.0-multivar1)
Simulating heat and CO2 fluxes in Beijing using SUEWS V2020b: sensitivity to vegetation phenology and maximum conductance
A Python library for computing individual and merged non-CO2 algorithmic climate change functions: CLIMaCCF V1.0
The three-dimensional structure of fronts in mid-latitude weather systems in numerical weather prediction models
The development and validation of the Inhomogeneous Wind Scheme for Urban Street (IWSUS-v1)
GPU-HADVPPM V1.0: a high-efficiency parallel GPU design of the piecewise parabolic method (PPM) for horizontal advection in an air quality model (CAMx V6.10)
Variability and combination as an ensemble of mineral dust forecasts during the 2021 CADDIWA experiment using the WRF 3.7.1 and CHIMERE v2020r3 models
Breakups are complicated: an efficient representation of collisional breakup in the superdroplet method
An optimized semi-empirical physical approach for satellite-based PM2.5 retrieval: embedding machine learning to simulate complex physical parameters
Sensitivity of tropospheric ozone to halogen chemistry in the chemistry–climate model LMDZ-INCA vNMHC
Segmentation of XCO2 images with deep learning: application to synthetic plumes from cities and power plants
Evaluating precipitation distributions at regional scales: a benchmarking framework and application to CMIP5 and 6 models
The Fire Inventory from NCAR version 2.5: an updated global fire emissions model for climate and chemistry applications
An approach to refining the ground meteorological observation stations for improving PM2.5 forecasts in the Beijing–Tianjin–Hebei region
Assessment of WRF (v 4.2.1) dynamically downscaled precipitation on subdaily and daily timescales over CONUS
Convective-gust nowcasting based on radar reflectivity and a deep learning algorithm
Self-nested large-eddy simulations in PALM model system v21.10 for offshore wind prediction under different atmospheric stability conditions
How does cloud-radiative heating over the North Atlantic change with grid spacing, convective parameterization, and microphysics scheme in ICON version 2.1.00?
Simulations of idealised 3D atmospheric flows on terrestrial planets using LFRic-Atmosphere
Updated isoprene and terpene emission factors for the Interactive BVOC (iBVOC) emission scheme in the United Kingdom Earth System Model (UKESM1.0)
Technical descriptions of the experimental dynamical downscaling simulations over North America by the CAM–MPAS variable-resolution model
Evaluating WRF-GC v2.0 predictions of boundary layer and vertical ozone profiles during the 2021 TRACER-AQ campaign in Houston, Texas
Intercomparison of the weather and climate physics suites of a unified forecast–climate model system (GRIST-A22.7.28) based on single-column modeling
A robust error correction method for numerical weather prediction wind speed based on Bayesian optimization, Variational Mode Decomposition, Principal Component Analysis, and Random Forest: VMD-PCA-RF (version 1.0.0)
Halogen chemistry in volcanic plumes: a 1D framework based on MOCAGE 1D (version R1.18.1) preparing 3D global chemistry modelling
PyFLEXTRKR: a flexible feature tracking Python software for convective cloud analysis
CLGAN: a generative adversarial network (GAN)-based video prediction model for precipitation nowcasting
Long-term evaluation of surface air pollution in CAMSRA and MERRA-2 global reanalyses over Europe (2003–2020)
A simplified non-linear chemistry-transport model for analyzing NO2 column observations
Evaluating Three Decades of Precipitation in the Upper Colorado River Basin from a High-Resolution Regional Climate Model
Emulating aerosol optics with randomly generated neural networks
Development of an ecophysiology module in the GEOS-Chem chemical transport model version 12.2.0 to represent biosphere–atmosphere fluxes relevant for ozone air quality
Application of the Multi-Scale Infrastructure for Chemistry and Aerosols version 0 (MUSICAv0) for air quality in Africa
Comparison of ozone formation attribution techniques in the northeastern United States
Description and performance of the CARMA sectional aerosol microphysical model in CESM2
Improving trajectory calculations by FLEXPART 10.4+ using single-image super-resolution
Data fusion uncertainty-enabled methods to map street-scale hourly NO2 in Barcelona: a case study with CALIOPE-Urban v1.0
Thibaud Sarica, Alice Maison, Yelva Roustan, Matthias Ketzel, Steen Solvang Jensen, Youngseob Kim, Christophe Chaillou, and Karine Sartelet
Geosci. Model Dev., 16, 5281–5303, https://doi.org/10.5194/gmd-16-5281-2023, https://doi.org/10.5194/gmd-16-5281-2023, 2023
Short summary
Short summary
A new version of the Model of Urban Network of Intersecting Canyons and Highways (MUNICH) is developed to represent heterogeneities of concentrations in streets. The street volume is discretized vertically and horizontally to limit the artificial dilution of emissions and concentrations. This new version is applied to street networks in Copenhagen and Paris. The comparisons to observations are improved, with higher concentrations of pollutants emitted by traffic at the bottom of the street.
Junsu Gil, Meehye Lee, Jeonghwan Kim, Gangwoong Lee, Joonyoung Ahn, and Cheol-Hee Kim
Geosci. Model Dev., 16, 5251–5263, https://doi.org/10.5194/gmd-16-5251-2023, https://doi.org/10.5194/gmd-16-5251-2023, 2023
Short summary
Short summary
In this study, the framework for calculating reactive nitrogen species using a deep neural network (RND) was developed. It works through simple Python codes and provides high-accuracy reactive nitrogen oxide data. In the first version (RNDv1.0), the model calculates the nitrous acid (HONO) in urban areas, which has an important role in producing O3 and fine aerosol.
Daniel Yazgi and Tinja Olenius
Geosci. Model Dev., 16, 5237–5249, https://doi.org/10.5194/gmd-16-5237-2023, https://doi.org/10.5194/gmd-16-5237-2023, 2023
Short summary
Short summary
We present flexible tools to implement aerosol formation rate predictions in climate and chemical transport models. New-particle formation is a significant but uncertain factor affecting aerosol numbers and an active field within molecular modeling which provides data for assessing formation rates for different chemical species. We introduce tools to generate and interpolate formation rate lookup tables for user-defined data, thus enabling the easy inclusion and testing of formation schemes.
Vineet Yadav, Subhomoy Ghosh, and Charles E. Miller
Geosci. Model Dev., 16, 5219–5236, https://doi.org/10.5194/gmd-16-5219-2023, https://doi.org/10.5194/gmd-16-5219-2023, 2023
Short summary
Short summary
Measuring the performance of inversions in linear Bayesian problems is crucial in real-life applications. In this work, we provide analytical forms of the local and global sensitivities of the estimated fluxes with respect to various inputs. We provide methods to uniquely map the observational signal to spatiotemporal domains. Utilizing this, we also show techniques to assess correlations between the Jacobians that naturally translate to nonstationary covariance matrix components.
Mingzhao Liu, Lars Hoffmann, Sabine Griessbach, Zhongyin Cai, Yi Heng, and Xue Wu
Geosci. Model Dev., 16, 5197–5217, https://doi.org/10.5194/gmd-16-5197-2023, https://doi.org/10.5194/gmd-16-5197-2023, 2023
Short summary
Short summary
We introduce new and revised chemistry and physics modules in the Massive-Parallel Trajectory Calculations (MPTRAC) Lagrangian transport model aiming to improve the representation of volcanic SO2 transport and depletion. We test these modules in a case study of the Ambae eruption in July 2018 in which the SO2 plume underwent wet removal and convection. The lifetime of SO2 shows highly variable and complex dependencies on the atmospheric conditions at different release heights.
Bernhard M. Enz, Jan P. Engelmann, and Ulrike Lohmann
Geosci. Model Dev., 16, 5093–5112, https://doi.org/10.5194/gmd-16-5093-2023, https://doi.org/10.5194/gmd-16-5093-2023, 2023
Short summary
Short summary
An algorithm to track tropical cyclones in model simulation data has been developed. The algorithm uses many combinations of varying parameter thresholds to detect weaker phases of tropical cyclones while still being resilient to false positives. It is shown that the algorithm performs well and adequately represents the tropical cyclone activity of the underlying simulation data. The impact of false positives on overall tropical cyclone activity is shown to be insignificant.
Sepehr Fathi, Mark Gordon, and Yongsheng Chen
Geosci. Model Dev., 16, 5069–5091, https://doi.org/10.5194/gmd-16-5069-2023, https://doi.org/10.5194/gmd-16-5069-2023, 2023
Short summary
Short summary
We have combined various capabilities within a WRF model to generate simulations of atmospheric pollutant dispersion at 50 m resolution. The study objective was to resolve transport processes at the scale of measurements to assess and optimize aircraft-based emission rate retrievals. Model performance evaluation resulted in agreement within 5 % of observed meteorological and within 1–2 standard deviations of observed wind fields. Mass was conserved in the model within 5 % of input emissions.
Dylan Reynolds, Ethan Gutmann, Bert Kruyt, Michael Haugeneder, Tobias Jonas, Franziska Gerber, Michael Lehning, and Rebecca Mott
Geosci. Model Dev., 16, 5049–5068, https://doi.org/10.5194/gmd-16-5049-2023, https://doi.org/10.5194/gmd-16-5049-2023, 2023
Short summary
Short summary
The challenge of running geophysical models is often compounded by the question of where to obtain appropriate data to give as input to a model. Here we present the HICAR model, a simplified atmospheric model capable of running at spatial resolutions of hectometers for long time series or over large domains. This makes physically consistent atmospheric data available at the spatial and temporal scales needed for some terrestrial modeling applications, for example seasonal snow forecasting.
Li Fang, Jianbing Jin, Arjo Segers, Hong Liao, Ke Li, Bufan Xu, Wei Han, Mijie Pang, and Hai Xiang Lin
Geosci. Model Dev., 16, 4867–4882, https://doi.org/10.5194/gmd-16-4867-2023, https://doi.org/10.5194/gmd-16-4867-2023, 2023
Short summary
Short summary
Machine learning models have gained great popularity in air quality prediction. However, they are only available at air quality monitoring stations. In contrast, chemical transport models (CTM) provide predictions that are continuous in the 3D field. Owing to complex error sources, they are typically biased. In this study, we proposed a gridded prediction with high accuracy by fusing predictions from our regional feature selection machine learning prediction (RFSML v1.0) and a CTM prediction.
Manu Goudar, Juliëtte C. S. Anema, Rajesh Kumar, Tobias Borsdorff, and Jochen Landgraf
Geosci. Model Dev., 16, 4835–4852, https://doi.org/10.5194/gmd-16-4835-2023, https://doi.org/10.5194/gmd-16-4835-2023, 2023
Short summary
Short summary
A framework was developed to automatically detect plumes and compute emission estimates with cross-sectional flux method (CFM) for biomass burning events in TROPOMI CO datasets using Visible Infrared Imaging Radiometer Suite active fire data. The emissions were more reliable when changing plume height in downwind direction was used instead of constant injection height. The CFM had uncertainty even when the meteorological conditions were accurate; thus there is a need for better inversion models.
Drew C. Pendergrass, Daniel J. Jacob, Hannah Nesser, Daniel J. Varon, Melissa Sulprizio, Kazuyuki Miyazaki, and Kevin W. Bowman
Geosci. Model Dev., 16, 4793–4810, https://doi.org/10.5194/gmd-16-4793-2023, https://doi.org/10.5194/gmd-16-4793-2023, 2023
Short summary
Short summary
We have built a tool called CHEEREIO that allows scientists to use observations of pollutants or gases in the atmosphere, such as from satellites or surface stations, to update supercomputer models that simulate the Earth. CHEEREIO uses the difference between the model simulations of the atmosphere and real-world observations to come up with a good guess for the actual composition of our atmosphere, the true emissions of various pollutants, and whatever else they may want to study.
Yosuke Yamazaki
Geosci. Model Dev., 16, 4749–4766, https://doi.org/10.5194/gmd-16-4749-2023, https://doi.org/10.5194/gmd-16-4749-2023, 2023
Short summary
Short summary
The Earth's atmosphere can support various types of global-scale waves. Some waves propagate eastward and others westward, and they can have different zonal wavenumbers. The Fourier–wavelet analysis is a useful technique for identifying different components of global-scale waves and their temporal variability. This paper introduces an easy-to-implement method to derive Fourier–wavelet spectra from 2-D space–time data. Application examples are presented using atmospheric models.
Bok H. Baek, Carlie Coats, Siqi Ma, Chi-Tsan Wang, Yunyao Li, Jia Xing, Daniel Tong, Soontae Kim, and Jung-Hun Woo
Geosci. Model Dev., 16, 4659–4676, https://doi.org/10.5194/gmd-16-4659-2023, https://doi.org/10.5194/gmd-16-4659-2023, 2023
Short summary
Short summary
To enable the direct feedback effects of aerosols and local meteorology in an air quality modeling system without any computational bottleneck, we have developed an inline meteorology-induced emissions coupler module within the U.S. Environmental Protection Agency’s Community Multiscale Air Quality modeling system to dynamically model the complex MOtor Vehicle Emission Simulator (MOVES) on-road mobile emissions inline without a separate dedicated emissions processing model like SMOKE.
Christoph Neuhauser, Maicon Hieronymus, Michael Kern, Marc Rautenhaus, Annika Oertel, and Rüdiger Westermann
Geosci. Model Dev., 16, 4617–4638, https://doi.org/10.5194/gmd-16-4617-2023, https://doi.org/10.5194/gmd-16-4617-2023, 2023
Short summary
Short summary
Numerical weather prediction models rely on parameterizations for sub-grid-scale processes, which are a source of uncertainty. We present novel visual analytics solutions to analyze interactively the sensitivities of a selected prognostic variable to multiple model parameters along trajectories regarding similarities in temporal development and spatiotemporal relationships. The proposed workflow is applied to cloud microphysical sensitivities along coherent strongly ascending trajectories.
Yingqi Zheng, Minttu Havu, Huizhi Liu, Xueling Cheng, Yifan Wen, Hei Shing Lee, Joyson Ahongshangbam, and Leena Järvi
Geosci. Model Dev., 16, 4551–4579, https://doi.org/10.5194/gmd-16-4551-2023, https://doi.org/10.5194/gmd-16-4551-2023, 2023
Short summary
Short summary
The performance of the Surface Urban Energy and Water Balance Scheme (SUEWS) is evaluated against the observed surface exchanges (fluxes) of heat and carbon dioxide in a densely built neighborhood in Beijing. The heat flux modeling is noticeably improved by using the observed maximum conductance and by optimizing the vegetation phenology modeling. SUEWS also performs well in simulating carbon dioxide flux.
Simone Dietmüller, Sigrun Matthes, Katrin Dahlmann, Hiroshi Yamashita, Abolfazl Simorgh, Manuel Soler, Florian Linke, Benjamin Lührs, Maximilian M. Meuser, Christian Weder, Volker Grewe, Feijia Yin, and Federica Castino
Geosci. Model Dev., 16, 4405–4425, https://doi.org/10.5194/gmd-16-4405-2023, https://doi.org/10.5194/gmd-16-4405-2023, 2023
Short summary
Short summary
Climate-optimized aircraft trajectories avoid atmospheric regions with a large climate impact due to aviation emissions. This requires spatially and temporally resolved information on aviation's climate impact. We propose using algorithmic climate change functions (aCCFs) for CO2 and non-CO2 effects (ozone, methane, water vapor, contrail cirrus). Merged aCCFs combine individual aCCFs by assuming aircraft-specific parameters and climate metrics. Technically this is done with a Python library.
Andreas A. Beckert, Lea Eisenstein, Annika Oertel, Tim Hewson, George C. Craig, and Marc Rautenhaus
Geosci. Model Dev., 16, 4427–4450, https://doi.org/10.5194/gmd-16-4427-2023, https://doi.org/10.5194/gmd-16-4427-2023, 2023
Short summary
Short summary
We investigate the benefit of objective 3-D front detection with modern interactive visual analysis techniques for case studies of extra-tropical cyclones and comparisons of frontal structures between different numerical weather prediction models. The 3-D frontal structures show agreement with 2-D fronts from surface analysis charts and augment them in the vertical dimension. We see great potential for more complex studies of atmospheric dynamics and for operational weather forecasting.
Zhenxin Liu, Yuanhao Chen, Yuhang Wang, Cheng Liu, Shuhua Liu, and Hong Liao
Geosci. Model Dev., 16, 4385–4403, https://doi.org/10.5194/gmd-16-4385-2023, https://doi.org/10.5194/gmd-16-4385-2023, 2023
Short summary
Short summary
The heterogeneous layout of urban buildings leads to the complex wind field in and over the urban canopy. Large discrepancies between the observations and the current simulations result from misunderstanding the character of the wind field. The Inhomogeneous Wind Scheme in Urban Street (IWSUS) was developed to simulate the heterogeneity of the wind speed in a typical street and then improve the simulated energy budget in the lower atmospheric layer over the urban canopy.
Kai Cao, Qizhong Wu, Lingling Wang, Nan Wang, Huaqiong Cheng, Xiao Tang, Dongqing Li, and Lanning Wang
Geosci. Model Dev., 16, 4367–4383, https://doi.org/10.5194/gmd-16-4367-2023, https://doi.org/10.5194/gmd-16-4367-2023, 2023
Short summary
Short summary
Offline performance experiment results show that the GPU-HADVPPM on a V100 GPU can achieve up to 1113.6 × speedups to its original version on an E5-2682 v4 CPU. A series of optimization measures are taken, and the CAMx-CUDA model improves the computing efficiency by 128.4 × on a single V100 GPU card. A parallel architecture with an MPI plus CUDA hybrid paradigm is presented, and it can achieve up to 4.5 × speedup when launching eight CPU cores and eight GPU cards.
Laurent Menut
Geosci. Model Dev., 16, 4265–4281, https://doi.org/10.5194/gmd-16-4265-2023, https://doi.org/10.5194/gmd-16-4265-2023, 2023
Short summary
Short summary
This study analyzes forecasts that were made in 2021 to help trigger measurements during the CADDIWA experiment. The WRF and CHIMERE models were run each day, and the first goal is to quantify the variability of the forecast as a function of forecast leads and forecast location. The possibility of using the different leads as an ensemble is also tested. For some locations, the correlation scores are better with this approach. This could be tested on operational forecast chains in the future.
Emily de Jong, John Ben Mackay, Oleksii Bulenok, Anna Jaruga, and Sylwester Arabas
Geosci. Model Dev., 16, 4193–4211, https://doi.org/10.5194/gmd-16-4193-2023, https://doi.org/10.5194/gmd-16-4193-2023, 2023
Short summary
Short summary
In clouds, collisional breakup occurs when two colliding droplets splinter into new, smaller fragments. Particle-based modeling approaches often do not represent breakup because of the computational demands of creating new droplets. We present a particle-based breakup method that preserves the computational efficiency of these methods. In a series of simple demonstrations, we show that this representation alters cloud processes in reasonable and expected ways.
Caiyi Jin, Qiangqiang Yuan, Tongwen Li, Yuan Wang, and Liangpei Zhang
Geosci. Model Dev., 16, 4137–4154, https://doi.org/10.5194/gmd-16-4137-2023, https://doi.org/10.5194/gmd-16-4137-2023, 2023
Short summary
Short summary
The semi-empirical physical approach derives PM2.5 with strong physical significance. However, due to the complex optical characteristic, the physical parameters are difficult to express accurately. Thus, combining the atmospheric physical mechanism and machine learning, we propose an optimized model. It creatively embeds the random forest model into the physical PM2.5 remote sensing approach to simulate a physical parameter. Our method shows great optimized performance in the validations.
Cyril Caram, Sophie Szopa, Anne Cozic, Slimane Bekki, Carlos A. Cuevas, and Alfonso Saiz-Lopez
Geosci. Model Dev., 16, 4041–4062, https://doi.org/10.5194/gmd-16-4041-2023, https://doi.org/10.5194/gmd-16-4041-2023, 2023
Short summary
Short summary
We studied the role of halogenated compounds (containing chlorine, bromine and iodine), emitted by natural processes (mainly above the oceans), in the chemistry of the lower layers of the atmosphere. We introduced this relatively new chemistry in a three-dimensional climate–chemistry model and looked at how this chemistry will disrupt the ozone. We showed that the concentration of ozone decreases by 22 % worldwide and that of the atmospheric detergent, OH, by 8 %.
Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Marc Bocquet, Jinghui Lian, Grégoire Broquet, Gerrit Kuhlmann, Alexandre Danjou, and Thomas Lauvaux
Geosci. Model Dev., 16, 3997–4016, https://doi.org/10.5194/gmd-16-3997-2023, https://doi.org/10.5194/gmd-16-3997-2023, 2023
Short summary
Short summary
Monitoring of CO2 emissions is key to the development of reduction policies. Local emissions, from cities or power plants, may be estimated from CO2 plumes detected in satellite images. CO2 plumes generally have a weak signal and are partially concealed by highly variable background concentrations and instrument errors, which hampers their detection. To address this problem, we propose and apply deep learning methods to detect the contour of a plume in simulated CO2 satellite images.
Min-Seop Ahn, Paul A. Ullrich, Peter J. Gleckler, Jiwoo Lee, Ana C. Ordonez, and Angeline G. Pendergrass
Geosci. Model Dev., 16, 3927–3951, https://doi.org/10.5194/gmd-16-3927-2023, https://doi.org/10.5194/gmd-16-3927-2023, 2023
Short summary
Short summary
We introduce a framework for regional-scale evaluation of simulated precipitation distributions with 62 climate reference regions and 10 metrics and apply it to evaluate CMIP5 and CMIP6 models against multiple satellite-based precipitation products. The common model biases identified in this study are mainly associated with the overestimated light precipitation and underestimated heavy precipitation. These biases persist from earlier-generation models and have been slightly improved in CMIP6.
Christine Wiedinmyer, Yosuke Kimura, Elena C. McDonald-Buller, Louisa K. Emmons, Rebecca R. Buchholz, Wenfu Tang, Keenan Seto, Maxwell B. Joseph, Kelley C. Barsanti, Annmarie G. Carlton, and Robert Yokelson
Geosci. Model Dev., 16, 3873–3891, https://doi.org/10.5194/gmd-16-3873-2023, https://doi.org/10.5194/gmd-16-3873-2023, 2023
Short summary
Short summary
The Fire INventory from NCAR (FINN) provides daily global estimates of emissions from open fires based on satellite detections of hot spots. This version has been updated to apply MODIS and VIIRS satellite fire detection and better represents both large and small fires. FINNv2.5 generates more emissions than FINNv1 and is in general agreement with other fire emissions inventories. The new estimates are consistent with satellite observations, but uncertainties remain regionally and by pollutant.
Lichao Yang, Wansuo Duan, and Zifa Wang
Geosci. Model Dev., 16, 3827–3848, https://doi.org/10.5194/gmd-16-3827-2023, https://doi.org/10.5194/gmd-16-3827-2023, 2023
Short summary
Short summary
An approach is proposed to refine a ground meteorological observation network to improve the PM2.5 forecasts in the Beijing–Tianjin–Hebei region. A cost-effective observation network is obtained and makes the relevant PM2.5 forecasts assimilate fewer observations but achieve the forecasting skill comparable to or higher than that obtained by assimilating all ground station observations, suggesting that many of the current ground stations can be greatly scattered to avoid much unnecessary work.
Abhishekh Kumar Srivastava, Paul Aaron Ullrich, Deeksha Rastogi, Pouya Vahmani, Andrew Jones, and Richard Grotjahn
Geosci. Model Dev., 16, 3699–3722, https://doi.org/10.5194/gmd-16-3699-2023, https://doi.org/10.5194/gmd-16-3699-2023, 2023
Short summary
Short summary
Stakeholders need high-resolution regional climate data for applications such as assessing water availability and mountain snowpack. This study examines 3 h and 24 h historical precipitation over the contiguous United States in the 12 km WRF version 4.2.1-based dynamical downscaling of the ERA5 reanalysis. WRF improves precipitation characteristics such as the annual cycle and distribution of the precipitation maxima, but it also displays regionally and seasonally varying precipitation biases.
Haixia Xiao, Yaqiang Wang, Yu Zheng, Yuanyuan Zheng, Xiaoran Zhuang, Hongyan Wang, and Mei Gao
Geosci. Model Dev., 16, 3611–3628, https://doi.org/10.5194/gmd-16-3611-2023, https://doi.org/10.5194/gmd-16-3611-2023, 2023
Short summary
Short summary
Due to the small-scale and nonstationary nature of convective wind gusts (CGs), reliable CG nowcasting has remained unattainable. Here, we developed a deep learning model — namely CGsNet — for 0—2 h of quantitative CG nowcasting, first achieving minute—kilometer-level forecasts. Based on the CGsNet model, the average surface wind speed (ASWS) and peak wind gust speed (PWGS) predictions are obtained. Experiments indicate that CGsNet exhibits higher accuracy than the traditional method.
Maria Krutova, Mostafa Bakhoday-Paskyabi, Joachim Reuder, and Finn Gunnar Nielsen
Geosci. Model Dev., 16, 3553–3564, https://doi.org/10.5194/gmd-16-3553-2023, https://doi.org/10.5194/gmd-16-3553-2023, 2023
Short summary
Short summary
Local refinement of the grid is a powerful method allowing us to reduce the computational time while preserving the accuracy in the area of interest. Depending on the implementation, the local refinement may introduce unwanted numerical effects into the results. We study the wind speed common to the wind turbine operational speeds and confirm strong alteration of the result when the heat fluxes are present, except for the specific refinement scheme used.
Sylvia Sullivan, Behrooz Keshtgar, Nicole Albern, Elzina Bala, Christoph Braun, Anubhav Choudhary, Johannes Hörner, Hilke Lentink, Georgios Papavasileiou, and Aiko Voigt
Geosci. Model Dev., 16, 3535–3551, https://doi.org/10.5194/gmd-16-3535-2023, https://doi.org/10.5194/gmd-16-3535-2023, 2023
Short summary
Short summary
Clouds absorb and re-emit infrared radiation from Earth's surface and absorb and reflect incoming solar radiation. As a result, they change atmospheric temperature gradients that drive large-scale circulation. To better simulate this circulation, we study how the radiative heating and cooling from clouds depends on model settings like grid spacing; whether we describe convection approximately or exactly; and the level of detail used to describe small-scale processes, or microphysics, in clouds.
Denis E. Sergeev, Nathan J. Mayne, Thomas Bendall, Ian A. Boutle, Alex Brown, Iva Kavcic, James Kent, Krisztian Kohary, James Manners, Thomas Melvin, Enrico Olivier, Lokesh K. Ragta, Ben J. Shipway, Jon Wakelin, Nigel Wood, and Mohamed Zerroukat
EGUsphere, https://doi.org/10.5194/egusphere-2023-647, https://doi.org/10.5194/egusphere-2023-647, 2023
Short summary
Short summary
3D climate models are one of the best tools we have to study planetary atmospheres. Here, we apply LFRic-Atmosphere, a new model developed by the Met Office, to seven different scenarios for terrestrial planetary climates, including four for the exoplanet TRAPPIST-1e, a primary target for future observations. LFRic-Atmosphere reproduces these scenarios within the spread of the existing models across a range of key climatic variables, justifying its use in future exoplanet studies.
James Weber, James A. King, Katerina Sindelarova, and Maria Val Martin
Geosci. Model Dev., 16, 3083–3101, https://doi.org/10.5194/gmd-16-3083-2023, https://doi.org/10.5194/gmd-16-3083-2023, 2023
Short summary
Short summary
The emissions of volatile organic compounds from vegetation (BVOCs) influence atmospheric composition and contribute to certain gases and aerosols (tiny airborne particles) which play a role in climate change. BVOC emissions are likely to change in the future due to changes in climate and land use. Therefore, accurate simulation of BVOC emission is important, and this study describes an update to the simulation of BVOC emissions in the United Kingdom Earth System Model (UKESM).
Koichi Sakaguchi, L. Ruby Leung, Colin M. Zarzycki, Jihyeon Jang, Seth McGinnis, Bryce E. Harrop, William C. Skamarock, Andrew Gettelman, Chun Zhao, William J. Gutowski, Stephen Leak, and Linda Mearns
Geosci. Model Dev., 16, 3029–3081, https://doi.org/10.5194/gmd-16-3029-2023, https://doi.org/10.5194/gmd-16-3029-2023, 2023
Short summary
Short summary
We document details of the regional climate downscaling dataset produced by a global variable-resolution model. The experiment is unique in that it follows a standard protocol designed for coordinated experiments of regional models. We found negligible influence of post-processing on statistical analysis, importance of simulation quality outside of the target region, and computational challenges that our model code faced due to rapidly changing super computer systems.
Xueying Liu, Yuxuan Wang, Shailaja Wasti, Wei Li, Ehsan Soleimanian, James Flynn, Travis Griggs, Sergio Alvarez, John T. Sullivan, Maurice Roots, Laurence Twigg, Guillaume Gronoff, Timothy Berkoff, Paul Walter, Mark Estes, Johnathan W. Hair, Taylor Shingler, Amy Jo Scarino, Marta Fenn, and Laura Judd
EGUsphere, https://doi.org/10.5194/egusphere-2023-892, https://doi.org/10.5194/egusphere-2023-892, 2023
Short summary
Short summary
With a comprehensive suite of ground-based and airborne remote sensing measurements during the 2021 Tracking Aerosol Convection Experiment Air Quality (TRACER-AQ) campaign in Houston, this study evaluates the simulation of the planetary boundary layer (PBL) height and the ozone vertical profile by a high-resolution (1.33 km) 3-D photochemical model Weather Research and Forecasting-driven GEOS-Chem (WRF-GC).
Xiaohan Li, Yi Zhang, Xindong Peng, Baiquan Zhou, Jian Li, and Yiming Wang
Geosci. Model Dev., 16, 2975–2993, https://doi.org/10.5194/gmd-16-2975-2023, https://doi.org/10.5194/gmd-16-2975-2023, 2023
Short summary
Short summary
The weather and climate physics suites used in GRIST-A22.7.28 are compared using single-column modeling. The source of their discrepancies in terms of modeling cloud and precipitation is explored. Convective parameterization is found to be a key factor responsible for the differences. The two suites also have intrinsic differences in the interaction between microphysics and other processes, resulting in different cloud features and time step sensitivities.
Shaohui Zhou, Yuchao Gao, Zexia Duan, Xingya Xi, and Yubin Li
EGUsphere, https://doi.org/10.5194/egusphere-2023-945, https://doi.org/10.5194/egusphere-2023-945, 2023
Short summary
Short summary
The proposed wind speed correction model (VMD-PCA-RF) demonstrates the highest prediction accuracy and stability in the five southern provinces in nearly a year and at different heights. VMD-PCA-RF evaluation indexes for 10 months remain relatively stable: accuracy rate FA is above 85 %. In future research, the proposed VMD-PCA-RF algorithm can be extrapolated to the 3 km grid points of the five southern provinces to generate a 3 km grid-corrected wind speed product.
Virginie Marécal, Ronan Voisin-Plessis, Tjarda Jane Roberts, Alessandro Aiuppa, Herizo Narivelo, Paul David Hamer, Béatrice Josse, Jonathan Guth, Luke Surl, and Lisa Grellier
Geosci. Model Dev., 16, 2873–2898, https://doi.org/10.5194/gmd-16-2873-2023, https://doi.org/10.5194/gmd-16-2873-2023, 2023
Short summary
Short summary
We implemented a halogen volcanic chemistry scheme in a one-dimensional modelling framework preparing for further use in a three-dimensional global chemistry-transport model. The results of the simulations for an eruption of Mt Etna in 2008, including various sensitivity tests, show a good consistency with previous modelling studies.
Zhe Feng, Joseph Hardin, Hannah C. Barnes, Jianfeng Li, L. Ruby Leung, Adam Varble, and Zhixiao Zhang
Geosci. Model Dev., 16, 2753–2776, https://doi.org/10.5194/gmd-16-2753-2023, https://doi.org/10.5194/gmd-16-2753-2023, 2023
Short summary
Short summary
PyFLEXTRKR is a flexible atmospheric feature tracking framework with specific capabilities to track convective clouds from a variety of observations and model simulations. The package has a collection of multi-object identification algorithms and has been optimized for large datasets. This paper describes the algorithms and demonstrates applications for tracking deep convective cells and mesoscale convective systems from observations and model simulations at a wide range of scales.
Yan Ji, Bing Gong, Michael Langguth, Amirpasha Mozaffari, and Xiefei Zhi
Geosci. Model Dev., 16, 2737–2752, https://doi.org/10.5194/gmd-16-2737-2023, https://doi.org/10.5194/gmd-16-2737-2023, 2023
Short summary
Short summary
Formulating short-term precipitation forecasting as a video prediction task, a novel deep learning architecture (convolutional long short-term memory generative adversarial network, CLGAN) is proposed. A benchmark dataset is built on minute-level precipitation measurements. Results show that with the GAN component the model generates predictions sharing statistical properties with observations, resulting in it outperforming the baseline in dichotomous and spatial scores for heavy precipitation.
Aleksander Lacima, Hervé Petetin, Albert Soret, Dene Bowdalo, Oriol Jorba, Zhaoyue Chen, Raúl F. Méndez Turrubiates, Hicham Achebak, Joan Ballester, and Carlos Pérez García-Pando
Geosci. Model Dev., 16, 2689–2718, https://doi.org/10.5194/gmd-16-2689-2023, https://doi.org/10.5194/gmd-16-2689-2023, 2023
Short summary
Short summary
Understanding how air pollution varies across space and time is of key importance for the safeguarding of human health. This work arose in the context of the project EARLY-ADAPT, for which the Barcelona Supercomputing Center developed an air pollution database covering all of Europe. Through different statistical methods, we compared two global pollution models against measurements from ground stations and found significant discrepancies between the observed and the modeled surface pollution.
Dien Wu, Joshua L. Laughner, Junjie Liu, Paul I. Palmer, John C. Lin, and Paul O. Wennberg
EGUsphere, https://doi.org/10.5194/egusphere-2023-876, https://doi.org/10.5194/egusphere-2023-876, 2023
Short summary
Short summary
To balance computational expenses and chemical complexity in extracting emission signals from tropospheric NO2 columns, we propose a simplified non-linear Lagrangian chemistry transport model and evaluate modeled results against TROPOMI v2 over multiple power plants and cities. Using this model, we then discuss how NOx chemistry affects the relationship between NOx and CO2 emissions and how studying NO2 columns helps quantify modeled biases in wind direction and prior emissions.
William Rudisill, Alejandro Flores, and Rosemary Carroll
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-69, https://doi.org/10.5194/gmd-2023-69, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
It's important to know how well atmospheric models do in the mountains, but there aren't very many weather stations. We evaluate rain and snow from a model from 1987–2020 in the Upper Colorado river basin against the data that's available. The model works pretty well but, there are still some uncertainties in remote locations. We then use snow maps collected by aircraft, streamflow measurements, and some advanced statistics to help identify how well the model works in ways we couldn't before.
Andrew Geiss, Po-Lun Ma, Balwinder Singh, and Joseph C. Hardin
Geosci. Model Dev., 16, 2355–2370, https://doi.org/10.5194/gmd-16-2355-2023, https://doi.org/10.5194/gmd-16-2355-2023, 2023
Short summary
Short summary
Atmospheric aerosols play a critical role in Earth's climate, but it is too computationally expensive to directly model their interaction with radiation in climate simulations. This work develops a new neural-network-based parameterization of aerosol optical properties for use in the Energy Exascale Earth System Model that is much more accurate than the current one; it also introduces a unique model optimization method that involves randomly generating neural network architectures.
Joey C. Y. Lam, Amos P. K. Tai, Jason A. Ducker, and Christopher D. Holmes
Geosci. Model Dev., 16, 2323–2342, https://doi.org/10.5194/gmd-16-2323-2023, https://doi.org/10.5194/gmd-16-2323-2023, 2023
Short summary
Short summary
We developed a new component within an atmospheric chemistry model to better simulate plant ecophysiological processes relevant for ozone air quality. We showed that it reduces simulated biases in plant uptake of ozone in prior models. The new model enables us to explore how future climatic changes affect air quality via affecting plants, examine ozone–vegetation interactions and feedbacks, and evaluate the impacts of changing atmospheric chemistry and climate on vegetation productivity.
Wenfu Tang, Louisa K. Emmons, Helen M. Worden, Rajesh Kumar, Cenlin He, Benjamin Gaubert, Zhonghua Zheng, Simone Tilmes, Rebecca R. Buchholz, Sara-Eva Martinez-Alonso, Claire Granier, Antonin Soulie, Kathryn McKain, Bruce Daube, Jeff Peischl, Chelsea Thompson, and Pieternel Levelt
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-50, https://doi.org/10.5194/gmd-2023-50, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
The new MUSICAv0 model enables the study of atmospheric chemistry across all relevant scales. We develop a MUSICAv0 grid for Africa. We evaluate MUSICAv0 with observations, and compare it with a previously used model – WRF-Chem. Overall, the performance of MUSICAv0 is comparable to WRF-Chem. Based on model-satellite discrepancies, we find that future field campaigns in an East African region (30° E – 45° E, 5° S – 5° N) could substantially improve the predictive skill of air quality models.
Qian Shu, Sergey L. Napelenok, William T. Hutzell, Kirk R. Baker, Barron H. Henderson, Benjamin N. Murphy, and Christian Hogrefe
Geosci. Model Dev., 16, 2303–2322, https://doi.org/10.5194/gmd-16-2303-2023, https://doi.org/10.5194/gmd-16-2303-2023, 2023
Short summary
Short summary
Source attribution methods are generally used to determine culpability of precursor emission sources to ambient pollutant concentrations. However, source attribution of secondarily formed pollutants such as ozone and its precursors cannot be explicitly measured, making evaluation of source apportionment methods challenging. In this study, multiple apportionment approach comparisons show common features but still reveal wide variations in predicted sector contribution and species dependency.
Simone Tilmes, Michael J. Mills, Yunqian Zhu, Charles G. Bardeen, Francis Vitt, Pengfei Yu, David Fillmore, Xiaohong Liu, Brian Toon, and Terry Deshler
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-79, https://doi.org/10.5194/gmd-2023-79, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
We implemented an alternative aerosol scheme in the high and low-top model versions of the Community Earth System Model Version 2 (CESM2) with a more detailed description of tropospheric and stratospheric aerosol size distributions than the existing aerosol model. The development enables the comparison of different aerosol schemes with different complexity in the same model framework and identifies improvements in comparison to a range of observations in both the troposphere and stratosphere.
Rüdiger Brecht, Lucie Bakels, Alex Bihlo, and Andreas Stohl
Geosci. Model Dev., 16, 2181–2192, https://doi.org/10.5194/gmd-16-2181-2023, https://doi.org/10.5194/gmd-16-2181-2023, 2023
Short summary
Short summary
We use neural-network-based single-image super-resolution to improve the upscaling of meteorological wind fields to be used for particle dispersion models. This deep-learning-based methodology improves the standard linear interpolation typically used in particle dispersion models. The improvement of wind fields leads to substantial improvement in the computed trajectories of the particles.
Alvaro Criado, Jan Mateu Armengol, Hervé Petetin, Daniel Rodriguez-Rey, Jaime Benavides, Marc Guevara, Carlos Pérez García-Pando, Albert Soret, and Oriol Jorba
Geosci. Model Dev., 16, 2193–2213, https://doi.org/10.5194/gmd-16-2193-2023, https://doi.org/10.5194/gmd-16-2193-2023, 2023
Short summary
Short summary
This work aims to derive and evaluate a general statistical post-processing tool specifically designed for the street scale that can be applied to any urban air quality system. Our data fusion methodology corrects NO2 fields based on continuous hourly observations and experimental campaigns. This study enables us to obtain exceedance probability maps of air quality standards. In 2019, 13 % of the Barcelona area had a 70 % or higher probability of exceeding the annual legal NO2 limit of 40 µg/m3.
Cited articles
Ben-Ami, Y., Koren, I., and Altaratz, O.: Patterns of North African dust
transport over the Atlantic: winter vs. summer, based on CALIPSO first year
data, Atmos. Chem. Phys., 9, 7867–7875, https://doi.org/10.5194/acp-9-7867-2009, 2009.
Benedetti, A., Morcrette, J.-J., Boucher, O., Dethof, A., Engelen, R. J.,
Fisher, M., Flentje, H., Huneeus, N., Jones, L., Kaiser, J. W., Kinne, S.,
Mangold, A., Razinger, M., Simmons, A. J., and Suttieet, M.: Aerosol analysis
and forecast in the European Centre for Medium Range Forecasts Integrated
Forecast System: 2. Data assimilation, J. Geophys. Res., 114, D13205,
https://doi.org/10.1029/2008JD011115, 2009.
Bhawar, R. L., Lee, W.-S., and Rahul, P. R. C.: Aerosol types and radiative
forcing estimates over East Asia, Atmos. Environ., 141, 532–541, 2016.
Campbell, J. R., Tackett, J. L., Reid, J. S., Zhang, J., Curtis, C. A., Hyer,
E. J., Sessions, W. R., Westphal, D. L., Prospero, J. M., Welton, E. J.,
Omar, A. H., Vaughan, M. A., and Winker, D. M.: Evaluating nighttime CALIOP
0.532 µm aerosol optical depth and extinction coefficient
retrievals, Atmos. Meas. Tech., 5, 2143–2160, https://doi.org/10.5194/amt-5-2143-2012,
2012.
Cesnulyte, V., Lindfors, A. V., Pitkänen, M. R. A., Lehtinen, K. E. J.,
Morcrette, J.-J., and Arola, A.: Comparing ECMWF AOD with AERONET
observations at visible and UV wavelengths, Atmos. Chem. Phys., 14, 593–608,
https://doi.org/10.5194/acp-14-593-2014, 2014.
Chin, M., Ginoux, P., Kinne, S., Torres, O., Holben, B. N., Duncan, B. N.,
Martin, R. V., Logan, J. A., Higurashi, A., and Nakajima, T.: Tropospheric
aerosol optical thickness from the GOCART model and comparisons with
satellite and sunphotometer measurements, J. Atmos. Sci., 59, 461–483, 2002.
Chin, M., Diehl, T., Ginoux, P., and Malm, W.: Intercontinental transport of
pollution and dust aerosols: implications for regional air quality, Atmos.
Chem. Phys., 7, 5501–5517, https://doi.org/10.5194/acp-7-5501-2007, 2007.
Ciren, P., Liu, H., Kondragunta, S., and Laszlo, I.: Adapting MODIS Dust Mask
Algorithm toSuomi NPP VIIRS for Air Quality Applications, AGU Fall Meeting
Abstracts, San Francisco, 11–16 December 2012.
Colarco, P., da Silva, A., Chin, M., and Diehl, T.: Online simulations of
global aerosol distributions in the NASA GEOS-4 model and comparisons to
satellite and ground-based aerosol optical depth, J. Geophys. Res., 115,
D14207, https://doi.org/10.1029/2009JD012820, 2010.
Darmenov, A. and Da Sila, A. M.: The Quick Fire Emissions Dataset (QFED) –
Documentation of versions 2.1, 2.2 and 2.4, NASA Technical Report Series on
Global Modeling and Data Assimilation, NASA/TM-2015-104606, Vol. 18, 211 pp.,
available at: http://gmao.gsfc.nasa.gov/pubs/tm/ (last access: November
2017), 2015.
Eck, T. F., Holben, B. N., Reid, J. S., Dubovik, O., Smirnov, A., O'Neill, N.
T., Slutsker, I., and Kinne, S.: The wavelength dependence of the optical
depth of biomass burning, urban and desert dust aerosols, J. Geophys. Res.,
104, 31333–31350, 1999.
Eskes, H., Huijnen, V., Arola, A., Benedictow, A., Blechschmidt, A.-M.,
Botek, E., Boucher, O., Bouarar, I., Chabrillat, S., Cuevas, E., Engelen, R.,
Flentje, H., Gaudel, A., Griesfeller, J., Jones, L., Kapsomenakis, J.,
Katragkou, E., Kinne, S., Langerock, B., Razinger, M., Richter, A., Schultz,
M., Schulz, M., Sudarchikova, N., Thouret, V., Vrekoussis, M., Wagner, A.,
and Zerefos, C.: Validation of reactive gases and aerosols in the MACC global
analysis and forecast system, Geosci. Model Dev., 8, 3523–3543,
https://doi.org/10.5194/gmd-8-3523-2015, 2015.
Field, R. D., van der Werf, G. R., Fanin, T., Fetzer, E. J., Fuller, R.,
Jethva, H., Levy, R., Livesey, N. J., Luo, M., Torres, O., and Worden, H. M.:
Indonesian fire activity and smoke pollution in 2015 show persistent
nonlinear sensitivity to El-Nino induced drought, P. Natl. Acad. Sci. USA,
113, 9204–9209, 2016.
Fordham, D. A., Wigley, T. M., Watts, M. J., and Brook, B. W.: Strengthening
forecasts of climate change impacts with multi-model ensemble averaged
projections using MAGICC/SCENGEN 5.3, Ecography, 35, 4–8, 2012.
Forster, P., Ramaswamy, V., Artaxo, P., Berntsen, T., Betts, R., Fahey, D.
W., Haywood, J., Lean, J., Lowe, D. C., Myhre, G., Nganga, J., Prinn, R.,
Raga, G., Schultz, M., and Van Dorland, R.: Changes in Atmospheric
Constituents and in Radiative Forcing, in: Climate Change 2007: The Physical
Science Basis. Contribution of Working Group I to the Fourth Assessment
Report of the Intergovernmental Panel on Climate Change, edited by: Solomon,
S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K. B., Tignor, M.,
and Miller, H. L., Cambridge University Press, Cambridge, United Kingdom and
New York, NY, USA, 2007.
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs,
L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K.,
Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., da
Silva, A. M., Gu, W., Kim, G., Koster, R., Lucchesi, D. Merkova, J. E.,
Nielsen, G. Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert, S.
D., Sienkiewicz, M., and Zhao, B.: The Modern-Era Retrospective Analysis for
Research and Applications, Version 2 (MERRA-2), J. Climate, 30, 5419–5454,
https://doi.org/10.1175/JCLI-D-16-0758.1, 2017.
Generoso, S., Bréon, F.-M., Balkanski, Y., Boucher, O., and Schulz, M.:
Improving the seasonal cycle and interannual variations of biomass burning
aerosol sources, Atmos. Chem. Phys., 3, 1211–1222,
https://doi.org/10.5194/acp-3-1211-2003, 2003.
Ghan, S. J., Liu, X., Easter, R. C., Zaveri, R., Rasch, P. J., Yoon, J., and
Eaton, B.: Toward a minimal representation of aerosols in climate models:
Comparative decomposition of aerosol direct, semidirect and indirect
radiative forcing, J. Climate, 25, 6461–6476, 2012.
Ginoux, P., Chin, M., Tegen, I., Prospero, J. M., Holben, B., Dubovik, O.,
and Lin, S.-J.: Sources and global distributions of dust aerosols simulated
with the GOCART model, J. Geophys. Res., 106, 20255–20273, 2001.
Hansen, J., Sato, M., and Ruedy, R.: Radiative forcing and climate response,
J. Geophys. Res., 102, 6831–6864, https://doi.org/10.1029/96JD03436, 1997.
Haywood, J. and Boucher, O.: Estimates of the direct and indirect radiative
forcing due to tropospheric aerosols: A review, Rev. Geophys., 38, 513–543,
https://doi.org/10.1029/1999RG000078, 2000.
Haywood, J. M., Allan, R. P., Culverwell, I., Slingo, T., Milton, S.,
Edwards, J., and Clerbaux, N.: Can desert dust explain the outgoing longwave
radiation anomaly over the Sahara during July 2003?, J. Geophys. Res., 110,
D05105, https://doi.org/10.1029/2004JD005232, 2005.
Holben, B. N., Eck, T. F., Slutsker, I., Tanré, D., Buis, J. P., Setzer,
A., Vermote, E., Reagan, J. A., Kaufman, Y. J., Nakajima, T., Lavenu, F.,
Jankowiak, I., and Smirnov, A.: AERONET – A Federated instrument network and
data archive for aerosol characterization, Remote Sens. Environ., 66, 1–16,
1998.
Hsu, N. C., Jeong, M.-J., Bettenhausen, C., Sayer, A. M., Hansell, R.,
Seftor, C. S., Huang, J., and Tsay, S.-C.: Enhanced Deep Blue aerosol
retrieval algorithm: The second generation, J. Geophys. Res.-Atmos., 118,
9296–9315, https://doi.org/10.1002/jgrd.50712, 2013.
Jackson, J., Liu, H., Laszlo, I., Kondragunta, S., Remer, L. A., Huang, J.,
and Huang, H.-C.: Suomi-NPP VIIRS Aerosol Algorithms and Data Products, J.
Geophys. Res., 118, 12673–12689, https://doi.org/10.1002/2013jd020449, 2013.
Karyampudi, V.: Validation of the Saharan dust plume conceptual model using
Lidar, Meteosat, and ECMWF Data, B. Am. Meteorol. Soc., 80, 1045–1075, 1999.
Kaufman, Y. J., Koren, I., Remer, L. A., Tanré, D., Ginoux, P., and Fan,
S.: Dust transport and deposition observed from the Terra-Moderate Resolution
Imaging Spectroradiometer (MODIS) spacecraft ovet the Atlantic Ocean,
J. Geophs. Res., 110, D10S12, https://doi.org/10.1029/2003JD004436, 2005.
Kedia, S., Ramachandran, S., Holben, B. N., and Tripathi, S. N.:
Quantification of aerosol type, and sources of aerosols over the
Indo-Gangetic Plain, Atmos. Environ., 98, 607–619, 2014.
Kroll, J. H. and Seinfeld, J. H.: Chemistry of secondary organic aerosol:
formation and evolution of low-volatility organics in the atmosphere, Atmos.
Environ., 42, 3293–3624, 2008.
Laszlo, I. and Liu, H.: EPS Aerosol Optical Depth (AOD) Algorithm Theoretical
Basis Document, VIIRS ATBD, JPSS internal note, available at:
https://www.star.nesdis.noaa.gov/jpss/documents/ATBD/ATBD_EPS_Aerosol_AOD_v3.0.1.pdf
(last access: March 2018), 2016.
Levy, R. C., Mattoo, S., Munchak, L. A., Remer, L. A., Sayer, A. M., Patadia,
F., and Hsu, N. C.: The Collection 6 MODIS aerosol products over land and
ocean, Atmos. Meas. Tech., 6, 2989–3034, https://doi.org/10.5194/amt-6-2989-2013,
2013.
Lee, P., McQueen, J., Stajner, I., Huang, J., Pan, L., Tong, D., Kim, H.,
Tang, Y., Kondragunta, S., Ruminski, M., Lu, S., Rogers, E., Saylor, R.,
Shafran, P., Huang, H., Gorline, J., Upadhayay, S., and Artz, R.: NAQFC
developmental forecast guidance for Fine particulate matter (PM2.5),
Weather Forecast., 32, 407–421, 2017.
Liu, H., Remer, L. A., Huang, J., Huang, H.-C., Kondragunta, S., Laszlo, I.,
Oo, M., and Jackson, J. M.: Preliminary Evaluation of Suomi-NPP VIIRS Aerosol
Optical Thickness, J. Geophys. Res., 119, 3942–3962,
https://doi.org/10.1002/2013jd020360, 2013.
Lohmann, U. and Feichter, J.: Global indirect aerosol effects: a review,
Atmos. Chem. Phys., 5, 715–737, https://doi.org/10.5194/acp-5-715-2005, 2005.
Lu, C.-H., da Silva, A., Wang, J., Moorthi, S., Chin, M., Colarco, P., Tang,
Y., Bhattacharjee, P. S., Chen, S.-P., Chuang, H.-Y., Juang, H.-M. H.,
McQueen, J., and Iredell, M.: The implementation of NEMS GFS Aerosol
Component (NGAC) Version 1.0 for global dust forecasting at NOAA/NCEP,
Geosci. Model Dev., 9, 1905–1919, https://doi.org/10.5194/gmd-9-1905-2016, 2016.
Lu, C.-H., Wei, S.-W., Zhang, X., Kondragunta, S., Chen, S.-P., Zhao, Q.,
Wang, J., Bhattacharjee, P., and McQueen, J. T.: The utilization of satellite
observations for improving Global aerosol forecasting, Asia Oceania Geosc.
Soc. Conference, Singapore, 11 August 2017.
Lynch, P., Reid, J. S., Westphal, D. L., Zhang, J., Hogan, T. F., Hyer, E.
J., Curtis, C. A., Hegg, D. A., Shi, Y., Campbell, J. R., Rubin, J. I.,
Sessions, W. R., Turk, F. J., and Walker, A. L.: An 11-year global gridded
aerosol optical thickness reanalysis (v1.0) for atmospheric and climate
sciences, Geosci. Model Dev., 9, 1489–1522, https://doi.org/10.5194/gmd-9-1489-2016,
2016.
Mallet, M., Dubovik, O., Nabat, P., Dulac, F., Kahn, R., Sciare, J., Paronis,
D., and Léon, J. F.: Absorption properties of Mediterranean aerosols
obtained from multi-year ground-based remote sensing observations, Atmos.
Chem. Phys., 13, 9195–9210, https://doi.org/10.5194/acp-13-9195-2013, 2013.
Mangold, A., De Backer, H., De Paepe, B., Dewitte, S., Chiapello, I.,
Derimian, Y., Kacenelenbogen, M., Léon, J.-F., Huneeus, N., Schulz, M.,
Ceburnis, D., O'Dowd, C., Flentje, H., Kinne, S., Benedetti, A., Morcrette,
J.-J., and Boucher, O.: Aerosol analysis and forecast in the European Centre
for Medium Range Weather Forecasts Integrated Forecast System: 3. Evaluation
by means of case studies, J. Geophys. Res., 116, D03302,
https://doi.org/10.1029/2010JD014864, 2011.
Menon, S., Hansen, J., Nazarenko, L., and Luo, Y.: Climate effects of black
carbon aerosols in China and India, Science, 297, 2250–2253,
https://doi.org/10.1126/science.1075159, 2002.
Meehl, G. A., Covey, C., Delworth, T., Latif, M., McAvaney, B., Mitchell, J.
F., Stouffer, R. J., and Taylor, K. E.: The WCRP CMIP3 multimodel dataset –
A new era in climate change research, B. Am. Meteorol. Soc, 88, 1383–1394,
2007.
Morcrette, J.-J., Boucher, O., Jones, L., Salmond, D., Bechtold, P.,
Beljaars, A., Benedetti, A., Bonet, A., Kaiser, J. W., Razinger, M., Schulz,
M., Serrar, S., Simmons, A. J., Sofiev, M., Suttie, M., Tompkins, A. M., and
Untch, A.: Aerosol analysis and forecast in the European centre for
medium-range weather forecasts integrated forecast system: forward modeling,
J. Geophys. Res., 114, D06206, https://doi.org/10.1029/2008JD011235, 2009.
Mulcahy, J. P., Walters, D. N., Bellouin, N., and Milton, S. F.: Impacts of
increasing the aerosol complexity in the Met Office global numerical weather
prediction model, Atmos. Chem. Phys., 14, 4749–4778,
https://doi.org/10.5194/acp-14-4749-2014, 2014.
Ramanathan, V., Crutzen, P. J., Lelieveld, J., Mitra, A. P., Althausen, D.,
Andersons, J., Andreae, M. O., Cantrell, W., Cass, G. R., Chung, C. E.,
Clarke, A. D., Coakley, J. A., Collins, W. D., Conant, W. C., Dulac, F.,
Heintzenberg, J., Heymsfield, A. J., Holben, B., Howell, S., Hudson, J.,
Jayaraman, A., Kiehl, J. T., Krishnamurti, T. N., Lubin, D., McFarquhar, G.,
Novakov, T., Ogren, J. A., Podgorny, I. A., Prather, K., Priestley, K.,
Prospero, J. M., Quinn, P. K., Rajeev, K., Rasch, P., Rupert, S., Sadourny,
R., Satheesh, S. K., Shaw, G. E., Sheridan, P., and Valero, F. P. J.: Indian
Ocean Experiment: An integrated analysis of the climate forcing and effects
of the great Indo-Asian haze, J. Geophys. Res., 106, 28371–28398,
https://doi.org/10.1029/2001JD900133, 2001.
Rappold, A. G., Stone, S. L., Cascio, W. E., Neas, L. M., Kilaru, V. J.,
Carraway, M. S., Szykman, J. J., Ising, A., Cleve, W. E., Meredith, J. T.,
Vaughan-Batten, H., Deyneka, L., and Devlin, R. B.: Peat bog wildfire smoke
exposure in rural North Carolina is associated with cardiopulmonary emergency
department visits assessed through syndromic surveillance, Environ. Health
Persp., 119, 1415–1420, 2011.
Reid, J., Benedetti, A., Colarco, P. R., and Hansen, J. A.: International
operational aerosol observability work-shop, B. Am. Meteorol. Soc., 92,
ES21–ES24, https://doi.org/10.1175/2010BAMS3183.1, 2011.
Sayer, A. M., Hsu, N. C., Bettenhausen, C., and Jeong, M.-J.: Validation and
uncertainty estimates for MODIS Collection 6 “Deep Blue” aerosol data,
J. Geophys. Res.-Atmos., 118, 7864–7872, https://doi.org/10.1002/jgrd.50600, 2013.
Sayer, A. M., L. A. Munchak, N. C. Hsu, R. C. Levy, C. Bettenhausen, and
M.-J. Jeong: MODIS Collection 6 aerosol products: Comparison between Aqua's
e-Deep blue, dark target and “merged” data sets and usage recommendations,
J. Geophys. Res.-Atmos., 119, 13965–989, 2014.
Sekiyama, T. T., Tanaka, T. Y., Shimizu, A., and Miyoshi, T.: Data
assimilation of CALIPSO aerosol observations, Atmos. Chem. Phys., 10, 39–49,
https://doi.org/10.5194/acp-10-39-2010, 2010.
Sessions, W. R., Reid, J. S., Benedetti, A., Colarco, P. R., da Silva, A.,
Lu, S., Sekiyama, T., Tanaka, T. Y., Baldasano, J. M., Basart, S., Brooks, M.
E., Eck, T. F., Iredell, M., Hansen, J. A., Jorba, O. C., Juang, H.-M. H.,
Lynch, P., Morcrette, J.-J., Moorthi, S., Mulcahy, J., Pradhan, Y., Razinger,
M., Sampson, C. B., Wang, J., and Westphal, D. L.: Development towards a
global operational aerosol consensus: basic climatological characteristics of
the International Cooperative for Aerosol Prediction Multi-Model Ensemble
(ICAP-MME), Atmos. Chem. Phys., 15, 335–362, https://doi.org/10.5194/acp-15-335-2015,
2015.
Shalaby, A., Rappenglueck, B., and Eltahir, E. A. B.: The climatology of dust
aerosol over the arabian peninsula, Atmos. Chem. Phys. Discuss., 15,
1523–1571, https://doi.org/10.5194/acpd-15-1523-2015, 2015.
Smirnov, A., Holben, B. N., Eck, T. F., Dubovik, O., and Slutsker, I.: Cloud
Screening and quality control algorithms for the AERONET database, Remote
Sens. Environ., 73, 337–349, 2000.
Takemura, T., Okamoto, H., Maruyama, Y., Numaguti, A., Higurashi, A., and
Nakajima, T.: Global three-dimensional simulation of aerosol optical
thickness distribution of various origins, J. Geophys. Res., 105,
17853–17873, 2000.
Tanaka, T. Y., Orito, K., Sekiyama, T. T., Shibata, K., Chiba, M., and
Tanaka, H.: MASINGAR, a global tropospheric aerosol chemical transport model
coupled with MRI/JMA98 GCM: model description, Pap. Meteorol. Geophys., 53,
119–138, 2003.
Taylor, K. E.: Summarizing multiple aspects of model performance in a single
diagram, J. Geophys. Res., 106, 7183–7192, https://doi.org/10.1029/2000JD900719, 2001.
Wang, J., Bhattacharjee, P. S., Tallapragada, V., Lu, C.-H., Kondragunta, S.,
da Silva, A., Zhang, X., Chen, S.-P., Wei, S.-W., Darmenov, A. S., McQueen,
J., Lee, P., Koner, P., and Harris, A.: The implementation of NEMS GFS
Aerosol Component (NGAC) Version 2.0 for global multispecies forecasting at
NOAA/NCEP – Part 1: Model descriptions, Geosci. Model Dev., 11, 2315–2332,
https://doi.org/10.5194/gmd-11-2315-2018, 2018.
Wei, S.-W., Zhao, Q., Chen, S. P., Wang, J., Bhattacharjee, P. S.,
Kondragunta, S., McQueen, J.., and Lu, S.: Improving NCEP global aerosol
forecasts by data assimilation of VIIRS aerosol products, Fifth AMS Symposium
on the Joint Center for Satellite Data Assimilation, Seattle, WA, USA, 23–26
January 2017, American Meteorological Society, 2017.
Westphal, D. L., Curtis, C. A., Liu, M., and Walker, A. L.: Operational
aerosol and dust storm forecasting, in: WMO/GEO expert meeting on an
international sand and dust storm warning system, IOP conference series:
Earth and Environmental Science, 7, 012007,
https://doi.org/10.1088/1755-1307/7/1/012007, 2009.
Yoo, H., Li, Z., Hou, Y.-T., Lord, S., Weng, F., and Barker, H. W.: Diagnosis
and testing of low-level cloud parameterizations for the NCEP/GFS model using
satellite and ground-based measurements, Clim. Dynam., 41, 1595–1613, 2013.
Yu, H., Chin, M., Yuan, T., Bian, H., Remer, L. A., Prospero, J. M., Omar,
A., Winker, D., Yang, Y., Zhang, Y., Zhang, Z., and Zhao, C.: The fertilizing
role of African dust in the Amazon rainforest: A first multiyear assessment
based on data from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite
Observations, Geophys. Res. Lett., 42, 1984–1991,
https://doi.org/10.1002/2015GL063040, 2015.
Zhang, J., Reid, J. S., Westphal, D. L., Baker, N. L., and Hyer, E. J.: A
system for operational aerosol optical depth data assimilation over global
oceans, J. Geophys. Res., 113, D10208, https://doi.org/10.1029/2007JD009065, 2008.
Zhang, X., Kondragunta, S., Ram, J., Schmidt, C., and Huang, H.-C.:
Near-real-time global biomass burning emissions product from geostationary
satellite constellation, J. Geophys. Res., 117, D14201,
https://doi.org/10.1029/2012JD017459, 2012.
Zhao, T. X., Stowe, L. L., Smirnov, A., Crosby, D., Sapper, J., and McClain,
C. R.: Development of a global validation package for satellite oceanic
aerosol optical thickness retrieval based on AERONET observations and its
application to NOAA/NESDIS operational aerosol retrievals, J. Atmos. Sci.,
59, 294–312, 2002.
Short summary
National Center for Environmental Prediction (NCEP) at NOAA recently upgraded their operational global aerosol forecast model from dust-only in version 1 to five species (dust, sea salt, black and organic carbon) of aerosols in version 2. In this work, we have validated the newly implemented aerosol model (NGACv2) which forecast at every 3 h up to 5 days against ground and satellite observations and other available model simulations.
National Center for Environmental Prediction (NCEP) at NOAA recently upgraded their operational...
