Articles | Volume 13, issue 12
https://doi.org/10.5194/gmd-13-6285-2020
© Author(s) 2020. 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-13-6285-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
10 Dec 2020
Development and technical paper |
| 10 Dec 2020
| 10 Dec 2020
Development of a three-dimensional variational assimilation system for lidar profile data based on a size-resolved aerosol model in WRF–Chem model v3.9.1 and its application in PM2.5 forecasts across China
Yanfei Liang
Institute of Meteorology and Oceanography, National University of
Defense Technology, Nanjing, China
PLA Unit 32145, People's Liberation Army, Xinxiang, China
Zengliang Zang
CORRESPONDING AUTHOR
Institute of Meteorology and Oceanography, National University of
Defense Technology, Nanjing, China
Key Laboratory of Atmospheric Optics, Anhui Institute of Optics and
Fine Mechanics, Chinese Academy of Sciences, Hefei, China
Peng Yan
Meteorological Observation Center, Chinese Meteorological
Administration, Beijing, China
Yiwen Hu
School of Atmospheric Physics, Nanjing University of Information Science & Technology, Nanjing,
China
Yan Zhou
PLA Unit 78127, People's Liberation Army, Beijing, China
Wei You
CORRESPONDING AUTHOR
Institute of Meteorology and Oceanography, National University of
Defense Technology, Nanjing, China
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The Next-generation Emissions InVentory expansion of Akagi (NEIVA) v.1.0 is a comprehensive biomass burning emissions database that allows integration of new data and flexible querying. Data are stored in connected datasets, including recommended averages of ~1500 constituents for 14 globally relevant fire types. Individual compounds were mapped to common model species to allow better attribution of emissions in modeling studies that predict the effects of fires on air quality and climate.
Lucie Bakels, Daria Tatsii, Anne Tipka, Rona Thompson, Marina Dütsch, Michael Blaschek, Petra Seibert, Katharina Baier, Silvia Bucci, Massimo Cassiani, Sabine Eckhardt, Christine Groot Zwaaftink, Stephan Henne, Pirmin Kaufmann, Vincent Lechner, Christian Maurer, Marie D. Mulder, Ignacio Pisso, Andreas Plach, Rakesh Subramanian, Martin Vojta, and Andreas Stohl
Geosci. Model Dev., 17, 7595–7627, https://doi.org/10.5194/gmd-17-7595-2024, https://doi.org/10.5194/gmd-17-7595-2024, 2024
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Computer models are essential for improving our understanding of how gases and particles move in the atmosphere. We present an update of the atmospheric transport model FLEXPART. FLEXPART 11 is more accurate due to a reduced number of interpolations and a new scheme for wet deposition. It can simulate non-spherical aerosols and includes linear chemical reactions. It is parallelised using OpenMP and includes new user options. A new user manual details how to use FLEXPART 11.
Jaroslav Resler, Petra Bauerová, Michal Belda, Martin Bureš, Kryštof Eben, Vladimír Fuka, Jan Geletič, Radek Jareš, Jan Karel, Josef Keder, Pavel Krč, William Patiño, Jelena Radović, Hynek Řezníček, Matthias Sühring, Adriana Šindelářová, and Ondřej Vlček
Geosci. Model Dev., 17, 7513–7537, https://doi.org/10.5194/gmd-17-7513-2024, https://doi.org/10.5194/gmd-17-7513-2024, 2024
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Detailed modeling of urban air quality in stable conditions is a challenge. We show the unprecedented sensitivity of a large eddy simulation (LES) model to meteorological boundary conditions and model parameters in an urban environment under stable conditions. We demonstrate the crucial role of boundary conditions for the comparability of results with observations. The study reveals a strong sensitivity of the results to model parameters and model numerical instabilities during such conditions.
Jorge E. Pachón, Mariel A. Opazo, Pablo Lichtig, Nicolas Huneeus, Idir Bouarar, Guy Brasseur, Cathy W. Y. Li, Johannes Flemming, Laurent Menut, Camilo Menares, Laura Gallardo, Michael Gauss, Mikhail Sofiev, Rostislav Kouznetsov, Julia Palamarchuk, Andreas Uppstu, Laura Dawidowski, Nestor Y. Rojas, María de Fátima Andrade, Mario E. Gavidia-Calderón, Alejandro H. Delgado Peralta, and Daniel Schuch
Geosci. Model Dev., 17, 7467–7512, https://doi.org/10.5194/gmd-17-7467-2024, https://doi.org/10.5194/gmd-17-7467-2024, 2024
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Latin America (LAC) has some of the most populated urban areas in the world, with high levels of air pollution. Air quality management in LAC has been traditionally focused on surveillance and building emission inventories. This study performed the first intercomparison and model evaluation in LAC, with interesting and insightful findings for the region. A multiscale modeling ensemble chain was assembled as a first step towards an air quality forecasting system.
David Ho, Michał Gałkowski, Friedemann Reum, Santiago Botía, Julia Marshall, Kai Uwe Totsche, and Christoph Gerbig
Geosci. Model Dev., 17, 7401–7422, https://doi.org/10.5194/gmd-17-7401-2024, https://doi.org/10.5194/gmd-17-7401-2024, 2024
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Atmospheric model users often overlook the impact of the land–atmosphere interaction. This study accessed various setups of WRF-GHG simulations that ensure consistency between the model and driving reanalysis fields. We found that a combination of nudging and frequent re-initialization allows certain improvement by constraining the soil moisture fields and, through its impact on atmospheric mixing, improves atmospheric transport.
Phuong Loan Nguyen, Lisa V. Alexander, Marcus J. Thatcher, Son C. H. Truong, Rachael N. Isphording, and John L. McGregor
Geosci. Model Dev., 17, 7285–7315, https://doi.org/10.5194/gmd-17-7285-2024, https://doi.org/10.5194/gmd-17-7285-2024, 2024
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We use a comprehensive approach to select a subset of CMIP6 models for dynamical downscaling over Southeast Asia, taking into account model performance, model independence, data availability and the range of future climate projections. The standardised benchmarking framework is applied to assess model performance through both statistical and process-based metrics. Ultimately, we identify two independent model groups that are suitable for dynamical downscaling in the Southeast Asian region.
Ingrid Super, Tia Scarpelli, Arjan Droste, and Paul I. Palmer
Geosci. Model Dev., 17, 7263–7284, https://doi.org/10.5194/gmd-17-7263-2024, https://doi.org/10.5194/gmd-17-7263-2024, 2024
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Monitoring greenhouse gas emission reductions requires a combination of models and observations, as well as an initial emission estimate. Each component provides information with a certain level of certainty and is weighted to yield the most reliable estimate of actual emissions. We describe efforts for estimating the uncertainty in the initial emission estimate, which significantly impacts the outcome. Hence, a good uncertainty estimate is key for obtaining reliable information on emissions.
Álvaro González-Cervera and Luis Durán
Geosci. Model Dev., 17, 7245–7261, https://doi.org/10.5194/gmd-17-7245-2024, https://doi.org/10.5194/gmd-17-7245-2024, 2024
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RASCAL is an open-source Python tool designed for reconstructing daily climate observations, especially in regions with complex local phenomena. It merges large-scale weather patterns with local weather using the analog method. Evaluations in central Spain show that RASCAL outperforms ERA20C reanalysis in reconstructing precipitation and temperature. RASCAL offers opportunities for broad scientific applications, from short-term forecasts to local-scale climate change scenarios.
Sun-Young Park, Kyo-Sun Sunny Lim, Kwonil Kim, Gyuwon Lee, and Jason A. Milbrandt
Geosci. Model Dev., 17, 7199–7218, https://doi.org/10.5194/gmd-17-7199-2024, https://doi.org/10.5194/gmd-17-7199-2024, 2024
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We enhance the WDM6 scheme by incorporating predicted graupel density. The modification affects graupel characteristics, including fall velocity–diameter and mass–diameter relationships. Simulations highlight changes in graupel distribution and precipitation patterns, potentially influencing surface snow amounts. The study underscores the significance of integrating predicted graupel density for a more realistic portrayal of microphysical properties in weather models.
Christos I. Efstathiou, Elizabeth Adams, Carlie J. Coats, Robert Zelt, Mark Reed, John McGee, Kristen M. Foley, Fahim I. Sidi, David C. Wong, Steven Fine, and Saravanan Arunachalam
Geosci. Model Dev., 17, 7001–7027, https://doi.org/10.5194/gmd-17-7001-2024, https://doi.org/10.5194/gmd-17-7001-2024, 2024
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We present a summary of enabling high-performance computing of the Community Multiscale Air Quality Model (CMAQ) – a state-of-the-science community multiscale air quality model – on two cloud computing platforms through documenting the technologies, model performance, scaling and relative merits. This may be a new paradigm for computationally intense future model applications. We initiated this work due to a need to leverage cloud computing advances and to ease the learning curve for new users.
Peter A. Bogenschutz, Jishi Zhang, Qi Tang, and Philip Cameron-Smith
Geosci. Model Dev., 17, 7029–7050, https://doi.org/10.5194/gmd-17-7029-2024, https://doi.org/10.5194/gmd-17-7029-2024, 2024
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Using high-resolution and state-of-the-art modeling techniques we simulate five atmospheric river events for California to test the capability to represent precipitation for these events. We find that our model is able to capture the distribution of precipitation very well but suffers from overestimating the precipitation amounts over high elevation. Increasing the resolution further has no impact on reducing this bias, while increasing the domain size does have modest impacts.
Manu Anna Thomas, Klaus Wyser, Shiyu Wang, Marios Chatziparaschos, Paraskevi Georgakaki, Montserrat Costa-Surós, Maria Gonçalves Ageitos, Maria Kanakidou, Carlos Pérez García-Pando, Athanasios Nenes, Twan van Noije, Philippe Le Sager, and Abhay Devasthale
Geosci. Model Dev., 17, 6903–6927, https://doi.org/10.5194/gmd-17-6903-2024, https://doi.org/10.5194/gmd-17-6903-2024, 2024
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Aerosol–cloud interactions occur at a range of spatio-temporal scales. While evaluating recent developments in EC-Earth3-AerChem, this study aims to understand the extent to which the Twomey effect manifests itself at larger scales. We find a reduction in the warm bias over the Southern Ocean due to model improvements. While we see footprints of the Twomey effect at larger scales, the negative relationship between cloud droplet number and liquid water drives the shortwave radiative effect.
Kai Cao, Qizhong Wu, Lingling Wang, Hengliang Guo, Nan Wang, Huaqiong Cheng, Xiao Tang, Dongxing Li, Lina Liu, Dongqing Li, Hao Wu, and Lanning Wang
Geosci. Model Dev., 17, 6887–6901, https://doi.org/10.5194/gmd-17-6887-2024, https://doi.org/10.5194/gmd-17-6887-2024, 2024
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AMD’s heterogeneous-compute interface for portability was implemented to port the piecewise parabolic method solver from NVIDIA GPUs to China's GPU-like accelerators. The results show that the larger the model scale, the more acceleration effect on the GPU-like accelerator, up to 28.9 times. The multi-level parallelism achieves a speedup of 32.7 times on the heterogeneous cluster. By comparing the results, the GPU-like accelerators have more accuracy for the geoscience numerical models.
Ruyi Zhang, Limin Zhou, Shin-ichiro Shima, and Huawei Yang
Geosci. Model Dev., 17, 6761–6774, https://doi.org/10.5194/gmd-17-6761-2024, https://doi.org/10.5194/gmd-17-6761-2024, 2024
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Solar activity weakly ionises Earth's atmosphere, charging cloud droplets. Electro-coalescence is when oppositely charged droplets stick together. We introduce an analytical expression of electro-coalescence probability and use it in a warm-cumulus-cloud simulation. Results show that charge cases increase rain and droplet size, with the new method outperforming older ones. The new method requires longer computation time, but its impact on rain justifies inclusion in meteorology models.
Máté Mile, Stephanie Guedj, and Roger Randriamampianina
Geosci. Model Dev., 17, 6571–6587, https://doi.org/10.5194/gmd-17-6571-2024, https://doi.org/10.5194/gmd-17-6571-2024, 2024
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Satellite observations provide crucial information about atmospheric constituents in a global distribution that helps to better predict the weather over sparsely observed regions like the Arctic. However, the use of satellite data is usually conservative and imperfect. In this study, a better spatial representation of satellite observations is discussed and explored by a so-called footprint function or operator, highlighting its added value through a case study and diagnostics.
Lukas Pfitzenmaier, Pavlos Kollias, Nils Risse, Imke Schirmacher, Bernat Puigdomenech Treserras, and Katia Lamer
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-129, https://doi.org/10.5194/gmd-2024-129, 2024
Revised manuscript accepted for GMD
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Orbital-radar is a Python tool transferring sub-orbital radar data (ground-based, airborne, and forward-simulated NWP) into synthetical space-borne cloud profiling radar data mimicking the platform characteristics, e.g. EarthCARE or CloudSat CPR. The novelty of orbital-radar is the simulation platform characteristic noise floors and errors. By this long time data sets can be transformed into synthetic observations for Cal/Valor sensitivity studies for new or future satellite missions.
Hynek Bednář and Holger Kantz
Geosci. Model Dev., 17, 6489–6511, https://doi.org/10.5194/gmd-17-6489-2024, https://doi.org/10.5194/gmd-17-6489-2024, 2024
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The forecast error growth of atmospheric phenomena is caused by initial and model errors. When studying the initial error growth, it may turn out that small-scale phenomena, which contribute little to the forecast product, significantly affect the ability to predict this product. With a negative result, we investigate in the extended Lorenz (2005) system whether omitting these phenomena will improve predictability. A theory explaining and describing this behavior is developed.
Giorgio Veratti, Alessandro Bigi, Sergio Teggi, and Grazia Ghermandi
Geosci. Model Dev., 17, 6465–6487, https://doi.org/10.5194/gmd-17-6465-2024, https://doi.org/10.5194/gmd-17-6465-2024, 2024
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In this study, we present VERT (Vehicular Emissions from Road Traffic), an R package designed to estimate transport emissions using traffic estimates and vehicle fleet composition data. Compared to other tools available in the literature, VERT stands out for its user-friendly configuration and flexibility of user input. Case studies demonstrate its accuracy in both urban and regional contexts, making it a valuable tool for air quality management and transport scenario planning.
Sam P. Raj, Puna Ram Sinha, Rohit Srivastava, Srinivas Bikkina, and Damu Bala Subrahamanyam
Geosci. Model Dev., 17, 6379–6399, https://doi.org/10.5194/gmd-17-6379-2024, https://doi.org/10.5194/gmd-17-6379-2024, 2024
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A Python successor to the aerosol module of the OPAC model, named AeroMix, has been developed, with enhanced capabilities to better represent real atmospheric aerosol mixing scenarios. AeroMix’s performance in modeling aerosol mixing states has been evaluated against field measurements, substantiating its potential as a versatile aerosol optical model framework for next-generation algorithms to infer aerosol mixing states and chemical composition.
Angeline G. Pendergrass, Michael P. Byrne, Oliver Watt-Meyer, Penelope Maher, and Mark J. Webb
Geosci. Model Dev., 17, 6365–6378, https://doi.org/10.5194/gmd-17-6365-2024, https://doi.org/10.5194/gmd-17-6365-2024, 2024
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The width of the tropical rain belt affects many aspects of our climate, yet we do not understand what controls it. To better understand it, we present a method to change it in numerical model experiments. We show that the method works well in four different models. The behavior of the width is unexpectedly simple in some ways, such as how strong the winds are as it changes, but in other ways, it is more complicated, especially how temperature increases with carbon dioxide.
Tianning Su and Yunyan Zhang
Geosci. Model Dev., 17, 6319–6336, https://doi.org/10.5194/gmd-17-6319-2024, https://doi.org/10.5194/gmd-17-6319-2024, 2024
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Using 2 decades of field observations over the Southern Great Plains, this study developed a deep-learning model to simulate the complex dynamics of boundary layer clouds. The deep-learning model can serve as the cloud parameterization within reanalysis frameworks, offering insights into improving the simulation of low clouds. By quantifying biases due to various meteorological factors and parameterizations, this deep-learning-driven approach helps bridge the observation–modeling divide.
Siyuan Chen, Yi Zhang, Yiming Wang, Zhuang Liu, Xiaohan Li, and Wei Xue
Geosci. Model Dev., 17, 6301–6318, https://doi.org/10.5194/gmd-17-6301-2024, https://doi.org/10.5194/gmd-17-6301-2024, 2024
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This study explores strategies and techniques for implementing mixed-precision code optimization within an atmosphere model dynamical core. The coded equation terms in the governing equations that are sensitive (or insensitive) to the precision level have been identified. The performance of mixed-precision computing in weather and climate simulations was analyzed.
Sam O. Owens, Dipanjan Majumdar, Chris E. Wilson, Paul Bartholomew, and Maarten van Reeuwijk
Geosci. Model Dev., 17, 6277–6300, https://doi.org/10.5194/gmd-17-6277-2024, https://doi.org/10.5194/gmd-17-6277-2024, 2024
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Designing cities that are resilient, sustainable, and beneficial to health requires an understanding of urban climate and air quality. This article presents an upgrade to the multi-physics numerical model uDALES, which can simulate microscale airflow, heat transfer, and pollutant dispersion in urban environments. This upgrade enables it to resolve realistic urban geometries more accurately and to take advantage of the resources available on current and future high-performance computing systems.
Allison A. Wing, Levi G. Silvers, and Kevin A. Reed
Geosci. Model Dev., 17, 6195–6225, https://doi.org/10.5194/gmd-17-6195-2024, https://doi.org/10.5194/gmd-17-6195-2024, 2024
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This paper presents the experimental design for a model intercomparison project to study tropical clouds and climate. It is a follow-up from a prior project that used a simplified framework for tropical climate. The new project adds one new component – a specified pattern of sea surface temperatures as the lower boundary condition. We provide example results from one cloud-resolving model and one global climate model and test the sensitivity to the experimental parameters.
Astrid Kerkweg, Timo Kirfel, Doung H. Do, Sabine Griessbach, Patrick Jöckel, and Domenico Taraborrelli
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-117, https://doi.org/10.5194/gmd-2024-117, 2024
Revised manuscript accepted for GMD
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This article introduces the MESSy DWARF. Usually, the Modular Earth Submodel System (MESSy) is linked to full dynamical models to build chemistry climate models. However, due to the modular concept of MESSy, and the newly developed DWARF component, it is now possible to create simplified models containing just one or some process descriptions. This renders very useful for technical optimisation (e.g., GPU porting) and can be used to create less complex models, e.g., a chemical box model.
Philip G. Sansom and Jennifer L. Catto
Geosci. Model Dev., 17, 6137–6151, https://doi.org/10.5194/gmd-17-6137-2024, https://doi.org/10.5194/gmd-17-6137-2024, 2024
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Weather fronts bring a lot of rain and strong winds to many regions of the mid-latitudes. We have developed an updated method of identifying these fronts in gridded data that can be used on new datasets with small grid spacing. The method can be easily applied to different datasets due to the use of open-source software for its development and shows improvements over similar previous methods. We present an updated estimate of the average frequency of fronts over the past 40 years.
Kelly M. Núñez Ocasio and Zachary L. Moon
Geosci. Model Dev., 17, 6035–6049, https://doi.org/10.5194/gmd-17-6035-2024, https://doi.org/10.5194/gmd-17-6035-2024, 2024
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TAMS is an open-source Python-based package for tracking and classifying mesoscale convective systems that can be used to study observed and simulated systems. Each step of the algorithm is described in this paper with examples showing how to make use of visualization and post-processing tools within the package. A unique and valuable feature of this tracker is its support for unstructured grids in the identification stage and grid-independent tracking.
Irene C. Dedoussi, Daven K. Henze, Sebastian D. Eastham, Raymond L. Speth, and Steven R. H. Barrett
Geosci. Model Dev., 17, 5689–5703, https://doi.org/10.5194/gmd-17-5689-2024, https://doi.org/10.5194/gmd-17-5689-2024, 2024
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Atmospheric model gradients provide a meaningful tool for better understanding the underlying atmospheric processes. Adjoint modeling enables computationally efficient gradient calculations. We present the adjoint of the GEOS-Chem unified chemistry extension (UCX). With this development, the GEOS-Chem adjoint model can capture stratospheric ozone and other processes jointly with tropospheric processes. We apply it to characterize the Antarctic ozone depletion potential of active halogen species.
Sylvain Mailler, Sotirios Mallios, Arineh Cholakian, Vassilis Amiridis, Laurent Menut, and Romain Pennel
Geosci. Model Dev., 17, 5641–5655, https://doi.org/10.5194/gmd-17-5641-2024, https://doi.org/10.5194/gmd-17-5641-2024, 2024
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We propose two explicit expressions to calculate the settling speed of solid atmospheric particles with prolate spheroidal shapes. The first formulation is based on theoretical arguments only, while the second one is based on computational fluid dynamics calculations. We show that the first method is suitable for virtually all atmospheric aerosols, provided their shape can be adequately described as a prolate spheroid, and we provide an implementation of the first method in AerSett v2.0.2.
Hejun Xie, Lei Bi, and Wei Han
Geosci. Model Dev., 17, 5657–5688, https://doi.org/10.5194/gmd-17-5657-2024, https://doi.org/10.5194/gmd-17-5657-2024, 2024
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A radar operator plays a crucial role in utilizing radar observations to enhance numerical weather forecasts. However, developing an advanced radar operator is challenging due to various complexities associated with the wave scattering by non-spherical hydrometeors, radar beam propagation, and multiple platforms. In this study, we introduce a novel radar operator named the Accurate and Efficient Radar Operator developed by ZheJiang University (ZJU-AERO) which boasts several unique features.
Jonathan J. Day, Gunilla Svensson, Barbara Casati, Taneil Uttal, Siri-Jodha Khalsa, Eric Bazile, Elena Akish, Niramson Azouz, Lara Ferrighi, Helmut Frank, Michael Gallagher, Øystein Godøy, Leslie M. Hartten, Laura X. Huang, Jareth Holt, Massimo Di Stefano, Irene Suomi, Zen Mariani, Sara Morris, Ewan O'Connor, Roberta Pirazzini, Teresa Remes, Rostislav Fadeev, Amy Solomon, Johanna Tjernström, and Mikhail Tolstykh
Geosci. Model Dev., 17, 5511–5543, https://doi.org/10.5194/gmd-17-5511-2024, https://doi.org/10.5194/gmd-17-5511-2024, 2024
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The YOPP site Model Intercomparison Project (YOPPsiteMIP), which was designed to facilitate enhanced weather forecast evaluation in polar regions, is discussed here, focussing on describing the archive of forecast data and presenting a multi-model evaluation at Arctic supersites during February and March 2018. The study highlights an underestimation in boundary layer temperature variance that is common across models and a related inability to forecast cold extremes at several of the sites.
Hossain Mohammed Syedul Hoque, Kengo Sudo, Hitoshi Irie, Yanfeng He, and Md Firoz Khan
Geosci. Model Dev., 17, 5545–5571, https://doi.org/10.5194/gmd-17-5545-2024, https://doi.org/10.5194/gmd-17-5545-2024, 2024
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Using multi-platform observations, we validated global formaldehyde (HCHO) simulations from a chemistry transport model. HCHO is a crucial intermediate in the chemical catalytic cycle that governs the ozone formation in the troposphere. The model was capable of replicating the observed spatiotemporal variability in HCHO. In a few cases, the model's capability was limited. This is attributed to the uncertainties in the observations and the model parameters.
Zijun Liu, Li Dong, Zongxu Qiu, Xingrong Li, Huiling Yuan, Dongmei Meng, Xiaobin Qiu, Dingyuan Liang, and Yafei Wang
Geosci. Model Dev., 17, 5477–5496, https://doi.org/10.5194/gmd-17-5477-2024, https://doi.org/10.5194/gmd-17-5477-2024, 2024
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In this study, we completed a series of simulations with MPAS-Atmosphere (version 7.3) to study the extreme precipitation event of Henan, China, during 20–22 July 2021. We found the different performance of two built-in parameterization scheme suites (mesoscale and convection-permitting suites) with global quasi-uniform and variable-resolution meshes. This study holds significant implications for advancing the understanding of the scale-aware capability of MPAS-Atmosphere.
Laurent Menut, Arineh Cholakian, Romain Pennel, Guillaume Siour, Sylvain Mailler, Myrto Valari, Lya Lugon, and Yann Meurdesoif
Geosci. Model Dev., 17, 5431–5457, https://doi.org/10.5194/gmd-17-5431-2024, https://doi.org/10.5194/gmd-17-5431-2024, 2024
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A new version of the CHIMERE model is presented. This version contains both computational and physico-chemical changes. The computational changes make it easy to choose the variables to be extracted as a result, including values of maximum sub-hourly concentrations. Performance tests show that the model is 1.5 to 2 times faster than the previous version for the same setup. Processes such as turbulence, transport schemes and dry deposition have been modified and updated.
G. Alexander Sokolowsky, Sean W. Freeman, William K. Jones, Julia Kukulies, Fabian Senf, Peter J. Marinescu, Max Heikenfeld, Kelcy N. Brunner, Eric C. Bruning, Scott M. Collis, Robert C. Jackson, Gabrielle R. Leung, Nils Pfeifer, Bhupendra A. Raut, Stephen M. Saleeby, Philip Stier, and Susan C. van den Heever
Geosci. Model Dev., 17, 5309–5330, https://doi.org/10.5194/gmd-17-5309-2024, https://doi.org/10.5194/gmd-17-5309-2024, 2024
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Building on previous analysis tools developed for atmospheric science, the original release of the Tracking and Object-Based Analysis (tobac) Python package, v1.2, was open-source, modular, and insensitive to the type of gridded input data. Here, we present the latest version of tobac, v1.5, which substantially improves scientific capabilities and computational efficiency from the previous version. These enhancements permit new uses for tobac in atmospheric science and potentially other fields.
Taneil Uttal, Leslie M. Hartten, Siri Jodha Khalsa, Barbara Casati, Gunilla Svensson, Jonathan Day, Jareth Holt, Elena Akish, Sara Morris, Ewan O'Connor, Roberta Pirazzini, Laura X. Huang, Robert Crawford, Zen Mariani, Øystein Godøy, Johanna A. K. Tjernström, Giri Prakash, Nicki Hickmon, Marion Maturilli, and Christopher J. Cox
Geosci. Model Dev., 17, 5225–5247, https://doi.org/10.5194/gmd-17-5225-2024, https://doi.org/10.5194/gmd-17-5225-2024, 2024
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A Merged Observatory Data File (MODF) format to systematically collate complex atmosphere, ocean, and terrestrial data sets collected by multiple instruments during field campaigns is presented. The MODF format is also designed to be applied to model output data, yielding format-matching Merged Model Data Files (MMDFs). MODFs plus MMDFs will augment and accelerate the synergistic use of model results with observational data to increase understanding and predictive skill.
Chongzhi Yin, Shin-ichiro Shima, Lulin Xue, and Chunsong Lu
Geosci. Model Dev., 17, 5167–5189, https://doi.org/10.5194/gmd-17-5167-2024, https://doi.org/10.5194/gmd-17-5167-2024, 2024
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We investigate numerical convergence properties of a particle-based numerical cloud microphysics model (SDM) and a double-moment bulk scheme for simulating a marine stratocumulus case, compare their results with model intercomparison project results, and present possible explanations for the different results of the SDM and the bulk scheme. Aerosol processes can be accurately simulated using SDM, and this may be an important factor affecting the behavior and morphology of marine stratocumulus.
Zichen Wu, Xueshun Chen, Zifa Wang, Huansheng Chen, Zhe Wang, Qing Mu, Lin Wu, Wending Wang, Xiao Tang, Jie Li, Ying Li, Qizhong Wu, Yang Wang, Zhiyin Zou, and Zijian Jiang
EGUsphere, https://doi.org/10.5194/egusphere-2024-1437, https://doi.org/10.5194/egusphere-2024-1437, 2024
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We developed a model to simulate polycyclic aromatic hydrocarbons (PAHs) from global to regional scales. The model can well reproduce the distribution of PAHs. The concentration of BaP (indicator species for PAHs) could exceed the target values of 1 ng m-3 over some areas (e.g., in central Europe, India, and eastern China). The change of BaP is less than PM2.5 from 2013 to 2018. China still faces significant potential health risks posed by BaP although "the Action Plan" has been implemented.
Cited articles
Bannister, R. N.: A review of operational methods of variational and
ensemble-variational data assimilation, Q. J. Roy. Meteor. Soc., 143,
607–633, https://doi.org/10.1002/qj.2982, 2017.
Baraskar, A., Bhushan, M., Venkataraman, C., and Cherian, R.: An offline
constrained data assimilation technique for aerosols: Improving GCM
simulations over South Asia using observations from two satellite sensors,
Atmos. Environ., 132, 36–48, https://doi.org/10.1016/j.atmosenv.2016.02.026, 2016.
Benedetti, A., Morcrette, J. J., Boucher, Dethof, O., Engelen, R. J.,
Fisher, M., Flentje, H., Huneeus, N., Jones, L., Kaiser, J. W., Kinne,S.,
Mangold, A., Razinger, M., Simmons, A. J., and Suttie, M.: Aerosol analysis and
forecast in the European Centre for Medium-Range Weather Forecasts
Integrated Forecast System: 2. Data assimilation, J. Geophys. Res., 114,
D13205, https://doi.org/10.1029/2008JD011115, 2009.
Burton, S. P., Ferrare, R. A., Hostetler, C. A., Hair, J. W., Kittaka, C.,
Vaughan, M. A., Obland, M. D., Rogers, R. R., Cook, A. L., Harper, D. B.,
and Remer, L. A.: Using airborne high spectral resolution lidar data to evaluate
combined active plus passive retrievals of aerosol extinction profiles, J.
Geophys. Res.-Atmos., 115, D00H15, https://doi.org/10.1029/2009JD012130, 2010.
Cao, J. J., Wang, Q. Y., Chow, J. C., Watson, J. G., Tie, X. X., Shen, Z.
X., Wang, P., and An, Z. S.: Impacts of aerosol compositions on visibility
impairment in Xi'an, China, Atmos. Environ., 59, 559–566,
https://doi.org/10.1016/j.atmosenv.2012.05.036, 2012a.
Cao, J. J., Shen, Z. X., Chow, J. C., Watson, J. G., Lee, S. C., Tie, X. X.,
Ho, K. F., Wang, G. H., and Han, Y. M.: Winter and Summer PM2.5 Chemical
Compositions in Fourteen Chinese Cities, J. Air. Waste. Manage., 62,
1214–1226, https://doi.org/10.1080/10962247.2012.701193, 2012b.
Carmichael, G. R., Sandu, A., Chai, T., Daescu, D. N., Constantinescu, E.
M., and Tang, Y.: Predicting air quality: Improvements through advanced methods
to integrate models and measurements, J. Comput. Phys., 227, 3540–3571,
https://doi.org/10.1016/j.jcp.2007.02.024, 2008.
Cheng, X. H., Liu, Y. L., Xu, X. D., You, W., Zang, Z. Z., Gao, L. N., Chen,
Y. B., Su, D. B., and Yan, P.: Lidar data assimilation method based on CRTM and
WRF-Chem models and its application in PM2.5 forecasts in Beijing, Sci.
Total. Environ., 682, 541–552,
https://doi.org/10.1016/j.scitotenv.2019.05.186, 2019.
Chen, Y., Li, F. F., Shao, N., Wang, X. P., Wang, Y. M., Hu, X. Y., and Wang,
X.: Aerosol Lidar Intercomparison in the Framework of the MEMO Project. 1.
Lidar Self Calibration and 1st Comparison Observation Calibration Based on
Statistical Analysis Method, in: 2019 International Conference on
Meteorology Observations (ICMO), Chengdu, China, 28–31 December 2019, 1–5,
https://doi.org/10.1109/ICMO49322.2019.9026086, 2019.
Chen, Z. J., Zhang, J. S., Zhang, T. S., Liu, W. Q., and Liu, J. G.: Haze
observations by simultaneous lidar and WPS in Beijing before and during
APEC, 2014, Sci. China. Chem., 58, 1385–1392,
https://doi.org/10.1007/s11426-015-5467-x, 2015.
Ganguly, D., Ginoux, P., Ramaswamy, V., Dubovik, O., Welton, J., Reid, E.
A., and Holben, B. N.: Inferring the composition and concentration of aerosols
by combining AERONET and MPLNET data: Comparison with other measurements and
utilization to evaluate GCM output, J. Geophys. Res.-Atmos. 114, D16203,
https://doi.org/10.1029/2009JD011895, 2009.
Fernald, F. G.: Analysis of atmospheric lidar observations: some comments,
Appl. Optics., 23, 652–653, https://doi.org/10.1364/AO.23.000652, 1984.
Gordon T. D., Prenni A. J., Renfro J. R., McClure, E., Hicks, B., Onasch, T.
B., Freedman, A., McMeeking, G. R., and Chen, P.: Open-path, closed-path and
reconstructed aerosol extinction at a rural site, J. Air. Waste. Manage.,
68, 824–835, https://doi.org/10.1080/10962247.2018.1452801, 2018.
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/10.1029/1999RG00:0078, 2000.
Jiang, Z. Q., Liu, Z. Q., Wang, T. J., Schwartz, C. S., Lin, H. C., and Jiang,
F.: Probing into the impact of 3-DVAR assimilation of surface PM10
observations over China using process analysis, J. Geophys. Res.-Atmos.,
118, 6738–6749, https://doi.org/10.1002/jgrd.50495, 2013.
Kahnert, M.: Variational data analysis of aerosol species in a regional CTM:
background error covariance constraint and aerosol optical observation
operators, Tellus B, 60, 753–770,
https://doi.org/10.1111/j.1600-0889.2008.00377.x, 2008.
Kim, Y. J., Kim, K. W., Kim, S. D., Lee, B. K., and Han, J. S.: Fine particulate
matter characteristics and its impact on visibility impairment at two urban
sites in Korea: Seoul and Incheon, Atmos. Environ., 40, 593–605,
https://doi.org/10.1016/j.atmosenv.2005.11.076, 2006.
Li, Z., Zang, Z., Li, Q. B., Chao, Y., Chen, D., Ye, Z., Liu, Y., and Liou, K. N.: A three-dimensional variational data assimilation system for multiple aerosol species with WRF/Chem and an application to PM2.5 prediction, Atmos. Chem. Phys., 13, 4265–4278, https://doi.org/10.5194/acp-13-4265-2013, 2013.
Liu, Q. H. and Weng, F. Z.: Advanced doubling-adding method for radiative
transfer in planetary atmosphere, J. Atmos. Sci., 63, 3459–3465,
https://doi.org/10.1175/JAS3808.1, 2006.
Liu, Z., Q. Liu, Q. H., Lin, H. C., Schwartz, C. S., Lee, Y. H., and Wang, T.
J.: Three-dimensional variational assimilationof MODIS aerosol optical
depth: Implementation and application to a dust storm over East Asia, J.
Geophys. Res.-Atmos., 116, D23206, https://doi.org/10.1029/2011JD016159,
2011.
Lowenthal, D. H. and Kumar, N.: PM2.5 Mass and Light Extinction Reconstruction
in IMPROVE, J. Air. Waste. Manage., 53, 1109–1120,
https://doi.org/10.1080/10473289.2003.10466264, 2003.
Matthias, V., Freudenthaler, V., Amodeo, A., Balin, I., Balis, D.,
Bösenberg, J., Chaikovsky, A., Chourdakis, G., Comeron, A., Delaval, A.,
Tomasi, F. D., Eixmann, R., Hågård, A., Komguem, L., Kreipl, S.,
Matthey, R., Rizi, V., Rodrigues, J. A., Wandinger, U., and Wang, X.:
Aerosol lidar intercomparison in the framework of the EARLINET project. 1.
Instruments, Appl. Optics., 43, 961–976,
https://doi.org/10.1364/AO.43.000961, 2004.
Milroy, C., Martucci, G., Lolli, S., Loaec, S., Sauvage, L., Xueref-Remy, I., Lavrič, J. V., Ciais, P., and O'Dowd, C. D.: On the ability of pseudo-operational ground-based light detection and ranging (LIDAR) sensors to determine boundary-layer structure: intercomparison and comparison with in-situ radiosounding, Atmos. Meas. Tech. Discuss., 4, 563–597, https://doi.org/10.5194/amtd-4-563-2011, 2011.
Niu, T., Gong, S. L., Zhu, G. F., Liu, H. L., Hu, X. Q., Zhou, C. H., and Wang, Y. Q.: Data assimilation of dust aerosol observations for the CUACE/dust forecasting system, Atmos. Chem. Phys., 8, 3473–3482, https://doi.org/10.5194/acp-8-3473-2008, 2008.
Parrish, D. F. and Derber, J. C.: The national meteorological center's
spectral statistical-interpolation analysis system, Mon. Wea. Rev., 120,
1747–1763, https://doi.org/10.1175/1520-0493(1992)120<1747:TNMCSS>2.0.co;2, 1992.
Peng, Z., Liu, Z., Chen, D., and Ban, J.: Improving PM2.5 forecast over China by the joint adjustment of initial conditions and source emissions with an ensemble Kalman filter, Atmos. Chem. Phys., 17, 4837–4855, https://doi.org/10.5194/acp-17-4837-2017, 2017.
Pitchford, M., Maim, W., Schichtel, B., Kumar, N., Lowenthal, D., and Hand, J.:
Revised algorithm for estimating light extinction from IMPROVE particle
speciation data, J. Air. Waste. Manage., 57, 1326–1336,
https://doi.org/10.3155/1047-3289.57.11.1326, 2007.
Raut, J.-C. and Chazette, P.: Assessment of vertically-resolved PM10 from mobile lidar observations, Atmos. Chem. Phys., 9, 8617–8638, https://doi.org/10.5194/acp-9-8617-2009, 2009.
Roy, B., Mathur, R., Gilliland, A. B., and Howard, S. C.: A comparison of
CMAQ-based aerosol properties with IMPROVE, MODIS, and AERONET data, J.
Geophys. Res., 112, D14301, https://doi.org/10.1029/2006JD008085, 2007.
Ryan, P. A., Lowenthal, D., and Kumar, N.: Improved Light Extinction
Reconstruction in Interagency Monitoring of Protected Visual Environments,
J. Air. Waste. Manage., 55, 1751–1759, https://doi.org/10.1080/10473289.2005.10464768, 2005.
Saide, P. E., Carmichael, G. R., Liu, Z., Schwartz, C. S., Lin, H. C., da Silva, A. M., and Hyer, E.: Aerosol optical depth assimilation for a size-resolved sectional model: impacts of observationally constrained, multi-wavelength and fine mode retrievals on regional scale analyses and forecasts, Atmos. Chem. Phys., 13, 10425–10444, https://doi.org/10.5194/acp-13-10425-2013, 2013.
Sandu, A. and Chai, T.: Chemical Data Assimilation – An Overview,
Atmos., 2, 426–463, https://doi.org/10.3390/atmos2030426, 2011.
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.
Schwartz, C. S., Liu, Z. Q., Lin, H. C., and McKeen, S. A.: Simultaneous
three-dimensional variational assimilation of surface fine particulate
matter and MODIS aerosol optical depth, J. Geophys. Res.-Atmos., 117, D13202, https://doi.org/10.1029/2011JD017383, 2012.
Sugimoto, N., Matsui, I., Shimizu, A., Nishizawa, T., and Yoon, S. C.: Lidar
network observations of tropospheric aerosols, Proc. SPIE, 7153,
https://doi.org/10.1117/12.806540, 2008.
Sugimoto, N. and Huang Z. W.: Lidar methods for observing mineral dust, J.
Meteor. Res., 28, 173–184, https://doi.org/10.1007/s13351-014-3068-9, 2014.
Tang, Y., Pagowski, M., Chai, T., Pan, L., Lee, P., Baker, B., Kumar, R., Delle Monache, L., Tong, D., and Kim, H.-C.: A case study of aerosol data assimilation with the Community Multi-scale Air Quality Model over the contiguous United States using 3D-Var and optimal interpolation methods, Geosci. Model Dev., 10, 4743–4758, https://doi.org/10.5194/gmd-10-4743-2017, 2017.
Tao, J., Ho, K. F., Chen, L., Zhu, L., Han, J., and Xu, Z.: Effect of chemical
composition of PM2.5 on visibility in Guangzhou, China, 2007 spring,
Particuology, 7, 68–75, https://doi.org/10.1016/j.partic.2008.11.002,
2009.
Tao, J., Cao J. J., Zhang, R. J., Zhu, L. H., Zang, T., Shi, S., Chan, C.
Y.: Reconstructed light extinction coefficients using chemical compositions
of PM2.5 in winter in urban Guangzhou, China, Adv. Atmos. Sci., 29, 359–368,
https://doi.org/10.1007/s00376-011-1045-0, 2012.
Tao, J., Zhang, L. M., Ho, K. F., Zhang, R. J., Lin, Z. J., Zhang, Z. S.,
Lin, M., Cao, J. J., Liu, S. X., and Wang, G. H.: Impact of PM2.5 chemcial
compositions on aerosol light scattering in Guangzhou-the largest megacity
in South China, Atmos. Res., 135–136, 48–58,
https://doi.org/10.1016/j.atmosres.2013.08.015, 2014.
Tesche, M., Ansmann, A., Müller, D., Althausen, D., and Engelman, R.:
Particle backscatter, extinction, and lidar ratio profiling with Raman lidar
in south and north China, Appl. Optics., 46, 6302–6308,
https://doi.org/10.1364/AO.46.006302, 2007.
Tombette, M., Mallet, V., and Sportisse, B.: PM10 data assimilation over Europe with the optimal interpolation method, Atmos. Chem. Phys., 9, 57–70, https://doi.org/10.5194/acp-9-57-2009, 2009.
Wang, D., You, W., Zang, Z., Pan, X., He, H., and Liang, Y.: A three-dimensional
variational data assimilation system for a size-resolved aerosol model:
Implementation and application for particulate matter and gaseous pollutant
forecasts across China, Sci. China. Earth. Sci., 63, 1366–1380,
https://doi.org/10.1007/s11430-019-9601-4, 2020.
Wang, Y., Sartelet, K. N., Bocquet, M., and Chazette, P.: Assimilation of ground versus lidar observations for PM10 forecasting, Atmos. Chem. Phys., 13, 269–283, https://doi.org/10.5194/acp-13-269-2013, 2013.
Wang, Y., Sartelet, K. N., Bocquet, M., and Chazette, P.: Modelling and assimilation of lidar signals over Greater Paris during the MEGAPOLI summer campaign, Atmos. Chem. Phys., 14, 3511–3532, https://doi.org/10.5194/acp-14-3511-2014, 2014a.
Wang, Y., Sartelet, K. N., Bocquet, M., Chazette, P., Sicard, M., D'Amico, G., Léon, J. F., Alados-Arboledas, L., Amodeo, A., Augustin, P., Bach, J., Belegante, L., Binietoglou, I., Bush, X., Comerón, A., Delbarre, H., García-Vízcaino, D., Guerrero-Rascado, J. L., Hervo, M., Iarlori, M., Kokkalis, P., Lange, D., Molero, F., Montoux, N., Muñoz, A., Muñoz, C., Nicolae, D., Papayannis, A., Pappalardo, G., Preissler, J., Rizi, V., Rocadenbosch, F., Sellegri, K., Wagner, F., and Dulac, F.: Assimilation of lidar signals: application to aerosol forecasting in the western Mediterranean basin, Atmos. Chem. Phys., 14, 12031–12053, https://doi.org/10.5194/acp-14-12031-2014, 2014b.
Wiscombe, W. J.: Improved MIE scattering algorithms, Appl. Opt.,
19, 1505–1509, https://doi.org/10.1364/AO.19.001505, 1980.
Wu, J. B., Xu, J. M., Pagowski, M., Geng, F. H., Gu, S. Q., Zhou, G. Q.,
Xie, Y., and Yu, Z. Q.: Modeling study of a severe aerosol pollution event in
December 2013 over Shanghai China:An application of chemical data
assimilation, Particuology, 20, 41–51, https://doi.org/10.1016/j.partic.2014.10.008, 2015.
Xia, X. L., Min, J. Z., Shen, F. F., Wang, Y. B., and Yang, C.: Aerosol data
assimilation using data from Fengyun-3A and MODIS: application to a dust
storm over East Asia in 2011, Adv. Atmos. Sci., 36, 1–14,
https://doi.org/10.1007/s00376-018-8075-9, 2019.
Young, S. A. and Vaughan, M. A.: The retrieval of profiles of particulate
extinction from Cloud-Aerosol Lidar Infrared Pathfinder Satellite
Observations (CALIPSO) data: Algorithm description, J. Atmos. Ocean. Tech.,
26, 1105–1119, https://doi.org/10.1175/2008JTECHA1221.1, 2009.
Yumimoto, K., Murakami, H., Tanaka, T. Y., Sekiyama, T. T., Ogi, A., and Maki,
T.: Forecasting of Asian dust storm that occurred on May 10–13, 2011, using
an ensemble-based data assimilation system, Particuology, 28, 121–130,
https://doi.org/10.1016/j.partic.2015.09.001, 2015.
Yumimoto, K., Nagao, T. M., Kikuchi, M., Sekiyama, T. T., Murakami, H.,
Tanaka, T. Y., Ogi, A., Irie, H., Khatri, P., Okumura, H., Arai, K., Morino,
I., Uchino, O., and Maki, T.: Aerosol data assimilation using data from
Himawari-8, a next-generation geostationary meteorological satellite,
Geophys. Res. Lett., 43, 5886–5894, https://doi.org/10.1002/2016GL069298,
2016.
Zhang, S., Zhou, Z. M., Ye, C. L., Shi, J. B., Wang, P., and Liu, D.: Analysis
of a Pollution Transmission Process in Hefei City Based on Mobile Lidar, in:
EPJ Web Conf., 237, 02006, https://doi.org/10.1051/epjconf/202023702006,
2020.
Zang, Z. Z., Li, Z. J., Pan, X. B., Hao, Z. L., and You, W.: Aerosol data
assimilation and forecasting experiments using aircraft and surface
observations during CalNex, Tellus B, 68, 1–14,
https://doi.org/10.3402/tellusb.v68.29812, 2016.
Zou, X., Vandenberghe, F., Pondeca, M., and Kuo, Y. H.: Introduction to Adjoint
Techniques and the MM5 Adjoint Modeling System, NCAR Technical Note
NCAR/TN-435-STR 122, https://doi.org/10.5065/D6F18WNM, 1997.
