Articles | Volume 15, issue 19
https://doi.org/10.5194/gmd-15-7397-2022
© Author(s) 2022. 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-15-7397-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
05 Oct 2022
Model evaluation paper |
| 05 Oct 2022
| 05 Oct 2022
A preliminary evaluation of FY-4A visible radiance data assimilation by the WRF (ARW v4.1.1)/DART (Manhattan release v9.8.0)-RTTOV (v12.3) system for a tropical storm case
School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, China
Precision Regional Earth Modeling and Information Center (PREMIC), Nanjing University of Information Science and Technology, Nanjing, China
Yubao Liu
CORRESPONDING AUTHOR
School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, China
Precision Regional Earth Modeling and Information Center (PREMIC), Nanjing University of Information Science and Technology, Nanjing, China
Zhaoyang Huo
School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, China
Precision Regional Earth Modeling and Information Center (PREMIC), Nanjing University of Information Science and Technology, Nanjing, China
Yang Li
School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, China
Precision Regional Earth Modeling and Information Center (PREMIC), Nanjing University of Information Science and Technology, Nanjing, China
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Different Cloud Optical Parameterizations (COPs) in RTTOV are evaluated by the comparing the observed and synthetic satellite images. The optimal COP for liquid water cloud is parameterized in terms of effective diameter (Deff) in combination with the COP for ice cloud developed by the Space Science and Engineering Center (SSEC), with the Deff for ice cloud parameterized in terms of ice water content and temperature. The findings will benefit the remote sensing and data assimilation community.
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Dario Di Santo, Cenlin He, Fei Chen, and Lorenzo Giovannini
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Robert Schoetter, Robin James Hogan, Cyril Caliot, and Valéry Masson
Geosci. Model Dev., 18, 405–431, https://doi.org/10.5194/gmd-18-405-2025, https://doi.org/10.5194/gmd-18-405-2025, 2025
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Zebediah Engberg, Roger Teoh, Tristan Abbott, Thomas Dean, Marc E. J. Stettler, and Marc L. Shapiro
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Nils Eingrüber, Alina Domm, Wolfgang Korres, and Karl Schneider
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Xuan Wang, Lei Bi, Hong Wang, Yaqiang Wang, Wei Han, Xueshun Shen, and Xiaoye Zhang
Geosci. Model Dev., 18, 117–139, https://doi.org/10.5194/gmd-18-117-2025, https://doi.org/10.5194/gmd-18-117-2025, 2025
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The Artificial-Intelligence-based Nonspherical Aerosol Optical Scheme (AI-NAOS) was developed to improve the estimation of the aerosol direct radiation effect and was coupled online with a chemical weather model. The AI-NAOS scheme considers black carbon as fractal aggregates and soil dust as super-spheroids, encapsulated with hygroscopic aerosols. Real-case simulations emphasize the necessity of accurately representing nonspherical and inhomogeneous aerosols in chemical weather models.
Lukas Pfitzenmaier, Pavlos Kollias, Nils Risse, Imke Schirmacher, Bernat Puigdomenech Treserras, and Katia Lamer
Geosci. Model Dev., 18, 101–115, https://doi.org/10.5194/gmd-18-101-2025, https://doi.org/10.5194/gmd-18-101-2025, 2025
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The Python tool Orbital-Radar transfers suborbital radar data (ground-based, airborne, and forward-simulated numerical weather prediction model) into synthetic spaceborne cloud profiling radar data, mimicking platform-specific instrument characteristics, e.g. EarthCARE or CloudSat. The tool's novelty lies in simulating characteristic errors and instrument noise. Thus, existing data sets are transferred into synthetic observations and can be used for satellite calibration–validation studies.
Mark Buehner, Jean-Francois Caron, Ervig Lapalme, Alain Caya, Ping Du, Yves Rochon, Sergey Skachko, Maziar Bani Shahabadi, Sylvain Heilliette, Martin Deshaies-Jacques, Weiguang Chang, and Michael Sitwell
Geosci. Model Dev., 18, 1–18, https://doi.org/10.5194/gmd-18-1-2025, https://doi.org/10.5194/gmd-18-1-2025, 2025
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The Modular and Integrated Data Assimilation System (MIDAS) software is described. The flexible design of MIDAS enables both deterministic and ensemble prediction applications for the atmosphere and several other Earth system components. It is currently used for all main operational weather prediction systems in Canada and also for sea ice and sea surface temperature analysis. The use of MIDAS for multiple Earth system components will facilitate future research on coupled data assimilation.
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
Geosci. Model Dev., 17, 8885–8907, https://doi.org/10.5194/gmd-17-8885-2024, https://doi.org/10.5194/gmd-17-8885-2024, 2024
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We developed a model to simulate polycyclic aromatic hydrocarbons (PAHs) from global to regional scales. The model can reproduce PAH distribution well. 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 in BaP is lower than that in PM2.5 from 2013 to 2018. China still faces significant potential health risks posed by BaP although the Action Plan has been implemented.
Marie Taufour, Jean-Pierre Pinty, Christelle Barthe, Benoît Vié, and Chien Wang
Geosci. Model Dev., 17, 8773–8798, https://doi.org/10.5194/gmd-17-8773-2024, https://doi.org/10.5194/gmd-17-8773-2024, 2024
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We have developed a complete two-moment version of the LIMA (Liquid Ice Multiple Aerosols) microphysics scheme. We have focused on collection processes, where the hydrometeor number transfer is often estimated in proportion to the mass transfer. The impact of these parameterizations on a convective system and the prospects for more realistic estimates of secondary parameters (reflectivity, hydrometeor size) are shown in a first test on an idealized case.
Yuya Takane, Yukihiro Kikegawa, Ko Nakajima, and Hiroyuki Kusaka
Geosci. Model Dev., 17, 8639–8664, https://doi.org/10.5194/gmd-17-8639-2024, https://doi.org/10.5194/gmd-17-8639-2024, 2024
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A new parameterisation for dynamic anthropogenic heat and electricity consumption is described. The model reproduced the temporal variation in and spatial distributions of electricity consumption and temperature well in summer and winter. The partial air conditioning was the most critical factor, significantly affecting the value of anthropogenic heat emission.
Hongyi Li, Ting Yang, Lars Nerger, Dawei Zhang, Di Zhang, Guigang Tang, Haibo Wang, Yele Sun, Pingqing Fu, Hang Su, and Zifa Wang
Geosci. Model Dev., 17, 8495–8519, https://doi.org/10.5194/gmd-17-8495-2024, https://doi.org/10.5194/gmd-17-8495-2024, 2024
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To accurately characterize the spatiotemporal distribution of particulate matter <2.5 µm chemical components, we developed the Nested Air Quality Prediction Model System with the Parallel Data Assimilation Framework (NAQPMS-PDAF) v2.0 for chemical components with non-Gaussian and nonlinear properties. NAQPMS-PDAF v2.0 has better computing efficiency, excels when used with a small ensemble size, and can significantly improve the simulation performance of chemical components.
T. Nash Skipper, Christian Hogrefe, Barron H. Henderson, Rohit Mathur, Kristen M. Foley, and Armistead G. Russell
Geosci. Model Dev., 17, 8373–8397, https://doi.org/10.5194/gmd-17-8373-2024, https://doi.org/10.5194/gmd-17-8373-2024, 2024
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Chemical transport model simulations are combined with ozone observations to estimate the bias in ozone attributable to US anthropogenic sources and individual sources of US background ozone: natural sources, non-US anthropogenic sources, and stratospheric ozone. Results indicate a positive bias correlated with US anthropogenic emissions during summer in the eastern US and a negative bias correlated with stratospheric ozone during spring.
Li Fang, Jianbing Jin, Arjo Segers, Ke Li, Ji Xia, Wei Han, Baojie Li, Hai Xiang Lin, Lei Zhu, Song Liu, and Hong Liao
Geosci. Model Dev., 17, 8267–8282, https://doi.org/10.5194/gmd-17-8267-2024, https://doi.org/10.5194/gmd-17-8267-2024, 2024
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Model evaluations against ground observations are usually unfair. The former simulates mean status over coarse grids and the latter the surrounding atmosphere. To solve this, we proposed the new land-use-based representative (LUBR) operator that considers intra-grid variance. The LUBR operator is validated to provide insights that align with satellite measurements. The results highlight the importance of considering fine-scale urban–rural differences when comparing models and observation.
Mijie Pang, Jianbing Jin, Arjo Segers, Huiya Jiang, Wei Han, Batjargal Buyantogtokh, Ji Xia, Li Fang, Jiandong Li, Hai Xiang Lin, and Hong Liao
Geosci. Model Dev., 17, 8223–8242, https://doi.org/10.5194/gmd-17-8223-2024, https://doi.org/10.5194/gmd-17-8223-2024, 2024
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The ensemble Kalman filter (EnKF) improves dust storm forecasts but faces challenges with position errors. The valid time shifting EnKF (VTS-EnKF) addresses this by adjusting for position errors, enhancing accuracy in forecasting dust storms, as proven in tests on 2021 events, even with smaller ensembles and time intervals.
Prabhakar Namdev, Maithili Sharan, Piyush Srivastava, and Saroj Kanta Mishra
Geosci. Model Dev., 17, 8093–8114, https://doi.org/10.5194/gmd-17-8093-2024, https://doi.org/10.5194/gmd-17-8093-2024, 2024
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Inadequate representation of surface–atmosphere interaction processes is a major source of uncertainty in numerical weather prediction models. Here, an effort has been made to improve the Weather Research and Forecasting (WRF) model version 4.2.2 by introducing a unique theoretical framework under convective conditions. In addition, to enhance the potential applicability of the WRF modeling system, various commonly used similarity functions under convective conditions have also been installed.
Andrew Gettelman, Richard Forbes, Roger Marchand, Chih-Chieh Chen, and Mark Fielding
Geosci. Model Dev., 17, 8069–8092, https://doi.org/10.5194/gmd-17-8069-2024, https://doi.org/10.5194/gmd-17-8069-2024, 2024
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Supercooled liquid clouds (liquid clouds colder than 0°C) are common at higher latitudes (especially over the Southern Ocean) and are critical for constraining climate projections. We compare a single-column version of a weather model to observations with two different cloud schemes and find that both the dynamical environment and atmospheric aerosols are important for reproducing observations.
Yujuan Wang, Peng Zhang, Jie Li, Yaman Liu, Yanxu Zhang, Jiawei Li, and Zhiwei Han
Geosci. Model Dev., 17, 7995–8021, https://doi.org/10.5194/gmd-17-7995-2024, https://doi.org/10.5194/gmd-17-7995-2024, 2024
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This study updates the CESM's aerosol schemes, focusing on dust, marine aerosol emissions, and secondary organic aerosol (SOA) . Dust emission modifications make deflation areas more continuous, improving results in North America and the sub-Arctic. Humidity correction to sea-salt emissions has a minor effect. Introducing marine organic aerosol emissions, coupled with ocean biogeochemical processes, and adding aqueous reactions for SOA formation advance the CESM's aerosol modelling results.
Lucas A. McMichael, Michael J. Schmidt, Robert Wood, Peter N. Blossey, and Lekha Patel
Geosci. Model Dev., 17, 7867–7888, https://doi.org/10.5194/gmd-17-7867-2024, https://doi.org/10.5194/gmd-17-7867-2024, 2024
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Marine cloud brightening (MCB) is a climate intervention technique to potentially cool the climate. Climate models used to gauge regional climate impacts associated with MCB often assume large areas of the ocean are uniformly perturbed. However, a more realistic representation of MCB application would require information about how an injected particle plume spreads. This work aims to develop such a plume-spreading model.
Leonardo Olivetti and Gabriele Messori
Geosci. Model Dev., 17, 7915–7962, https://doi.org/10.5194/gmd-17-7915-2024, https://doi.org/10.5194/gmd-17-7915-2024, 2024
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Data-driven models are becoming a viable alternative to physics-based models for weather forecasting up to 15 d into the future. However, it is unclear whether they are as reliable as physics-based models when forecasting weather extremes. We evaluate their performance in forecasting near-surface cold, hot, and windy extremes globally. We find that data-driven models can compete with physics-based models and that the choice of the best model mainly depends on the region and type of extreme.
David C. Wong, Jeff Willison, Jonathan E. Pleim, Golam Sarwar, James Beidler, Russ Bullock, Jerold A. Herwehe, Rob Gilliam, Daiwen Kang, Christian Hogrefe, George Pouliot, and Hosein Foroutan
Geosci. Model Dev., 17, 7855–7866, https://doi.org/10.5194/gmd-17-7855-2024, https://doi.org/10.5194/gmd-17-7855-2024, 2024
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This work describe how we linked the meteorological Model for Prediction Across Scales – Atmosphere (MPAS-A) with the Community Multiscale Air Quality (CMAQ) air quality model to form a coupled modelling system. This could be used to study air quality or climate and air quality interaction at a global scale. This new model scales well in high-performance computing environments and performs well with respect to ground surface networks in terms of ozone and PM2.5.
Markus Kunze, Christoph Zülicke, Tarique Adnan Siddiqui, Claudia Christine Stephan, Yosuke Yamazaki, Claudia Stolle, Sebastian Borchert, and Hauke Schmidt
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-191, https://doi.org/10.5194/gmd-2024-191, 2024
Revised manuscript accepted for GMD
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We present the Icosahedral Nonhydrostatic (ICON) general circulation model with upper atmosphere extension with the physics package for numerical weather prediction (UA-ICON(NWP)). The parameters for the gravity wave parameterizations were optimized, and realistic modelling of the thermal and dynamic state of the mesopause regions was achieved. UA-ICON(NWP) now shows a realistic frequency of major sudden stratospheric warmings and well-represented solar tides in temperature.
Giulio Mandorli and Claudia J. Stubenrauch
Geosci. Model Dev., 17, 7795–7813, https://doi.org/10.5194/gmd-17-7795-2024, https://doi.org/10.5194/gmd-17-7795-2024, 2024
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In recent years, several studies focused their attention on the disposition of convection. Lots of methods, called indices, have been developed to quantify the amount of convection clustering. These indices are evaluated in this study by defining criteria that must be satisfied and then evaluating the indices against these standards. None of the indices meet all criteria, with some only partially meeting them.
Kerry Anderson, Jack Chen, Peter Englefield, Debora Griffin, Paul A. Makar, and Dan Thompson
Geosci. Model Dev., 17, 7713–7749, https://doi.org/10.5194/gmd-17-7713-2024, https://doi.org/10.5194/gmd-17-7713-2024, 2024
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The Global Forest Fire Emissions Prediction System (GFFEPS) is a model that predicts smoke and carbon emissions from wildland fires. The model calculates emissions from the ground up based on satellite-detected fires, modelled weather and fire characteristics. Unlike other global models, GFFEPS uses daily weather conditions to capture changing burning conditions on a day-to-day basis. GFFEPS produced lower carbon emissions due to the changing weather not captured by the other models.
Jianyu Lin, Tie Dai, Lifang Sheng, Weihang Zhang, Shangfei Hai, and Yawen Kong
EGUsphere, https://doi.org/10.5194/egusphere-2024-3321, https://doi.org/10.5194/egusphere-2024-3321, 2024
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The effectiveness of assimilation system and its sensitivity to ensemble member size and length of assimilation window have been investigated. This study advances our understanding about the selection of basic parameters in the four-dimension local ensemble transform Kalman filter assimilation system and the performance of ensemble simulation in a particulate matter polluted environment.
Yi-Ning Shi, Jun Yang, Wei Han, Lujie Han, Jiajia Mao, Wanlin Kan, and Fuzhong Weng
EGUsphere, https://doi.org/10.5194/egusphere-2024-2884, https://doi.org/10.5194/egusphere-2024-2884, 2024
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Assimilating Ground-based microwave radiometers' observations into numerical weather prediction models holds significant promise for enhancing forecast accuracy. Radiative transfer models (RTM) are crucial for direct data assimilation. We propose a new RTM capable of simulating brightness temperatures observed by GMRs and their Jacobians. Several improvements are introduced to achieve higher accuracy.The RTM align with RTTOV-gb well and can achieve smaller STD in water vapor absorption channels.
Samiha Binte Shahid, Forrest G. Lacey, Christine Wiedinmyer, Robert J. Yokelson, and Kelley C. Barsanti
Geosci. Model Dev., 17, 7679–7711, https://doi.org/10.5194/gmd-17-7679-2024, https://doi.org/10.5194/gmd-17-7679-2024, 2024
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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.
Bjarke Tobias Eisensøe Olsen, Andrea Noemi Hahmann, Nicolás González Alonso-de-Linaje, Mark Žagar, and Martin Dörenkämper
EGUsphere, https://doi.org/10.5194/egusphere-2024-3123, https://doi.org/10.5194/egusphere-2024-3123, 2024
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Low-level jets (LLJs) are strong winds in the lower atmosphere, important for wind energy as turbines get taller. This study compares a weather model (WRF) with real data across five North and Baltic Sea sites. Adjusting the model improved accuracy over the widely-used ERA5. In key offshore regions, LLJs occur 10–15 % of the time and significantly boost wind power, especially in spring and summer, contributing up to 30 % of total capacity in some areas.
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.
Matthieu Dabrowski, José Mennesson, Jérôme Riedi, Chaabane Djeraba, and Pierre Nabat
EGUsphere, https://doi.org/10.5194/egusphere-2024-2676, https://doi.org/10.5194/egusphere-2024-2676, 2024
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This work focuses on the prediction of aerosol concentration values at ground level, which are a strong indicator of air quality, using Artificial Neural Networks. A study of different variables and their efficiency as inputs for these models is also proposed, and reveals that the best results are obtained when using all of them. Comparison of networks architectures and information fusion methods allows the extraction of knowledge on the most efficient methods in the context of this study.
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.
Jie Gao, Yi Huang, Jonathon S. Wright, Ke Li, Tao Geng, and Qiurun Yu
EGUsphere, https://doi.org/10.5194/egusphere-2024-2815, https://doi.org/10.5194/egusphere-2024-2815, 2024
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The aerosol in the upper troposphere and stratosphere is highly variable, and its radiative effect is poorly understood. To estimate that effect, the radiative kernel is constructed and applied. The results show that the kernels can reproduce aerosol radiative effects and are expected to simulate stratospheric aerosol radiative effects. This approach reduces computational expense and consists well with radiative model calculations and can be applied to atmospheric models with speed requirements.
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.
Juan Escobar, Philippe Wautelet, Joris Pianezze, Florian Pantillon, Thibaut Dauhut, Christelle Barthe, and Jean-Pierre Chaboureau
EGUsphere, https://doi.org/10.5194/egusphere-2024-2879, https://doi.org/10.5194/egusphere-2024-2879, 2024
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The Meso-NH weather research code is adapted for GPUs using OpenACC, leading to significant performance and energy efficiency improvements. Called MESONH-v55-OpenACC, it includes enhanced memory management, communication optimizations, and a new solver. On the AMD MI250X Adastra platform, it achieved up to 6× speedup and 2.3× energy efficiency gain compared to CPUs. Storm simulations at 100 m resolution show successful results , positioning the code for future use on exascale supercomputers.
Matti Niskanen, Aku Seppänen, Henri Oikarinen, Miska Olin, Panu Karjalainen, Santtu Mikkonen, and Kari Lehtinen
EGUsphere, https://doi.org/10.5194/egusphere-2024-1898, https://doi.org/10.5194/egusphere-2024-1898, 2024
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Particle size is a key factor determining the properties of aerosol particles, which have a major influence on the climate and on human health. When measuring the particle sizes, however, sometimes the sampling lines that transfer the aerosol to the measurement device distort the size distribution, making the measurement unreliable. We propose a method to correct for the distortions and estimate the true particle sizes, improving measurement accuracy.
Cited articles
Albers, S., Saleeby, S. M., Kreidenweis, S., Bian, Q., Xian, P., Toth, Z., Ahmadov, R., James, E., and Miller, S. D.: A fast visible-wavelength 3D radiative transfer model for numerical weather prediction visualization and forward modeling, Atmos. Meas. Tech., 13, 3235–3261, https://doi.org/10.5194/amt-13-3235-2020, 2020.
Anderson, J., Hoar, T., Raeder, K., Liu, H., Collins, N., Torn, R., and Avellano, A.: The Data Assimilation Research Testbed: A Community Facility, B. Am. Meteorol. Soc., 90, 1283–1296, https://doi.org/10.1175/2009BAMS2618.1, 2009.
Anderson, J. L.: An Ensemble Adjustment Kalman Filter for Data Assimilation, Mon. Weather Rev., 129, 2884–2903, https://doi.org/10.1175/1520-0493(2001)129<2884:AEAKFF>2.0.CO;2, 2001.
Anderson, J. L.: An adaptive covariance inflation error correction algorithm for ensemble filters, Tellus A, 59, 210–224, https://doi.org/10.1111/j.1600-0870.2006.00216.x, 2007.
Anderson, J. L.: Spatially and temporally varying adaptive covariance inflation for ensemble filters, Tellus A, 61, 72–83, https://doi.org/10.1111/j.1600-0870.2008.00361.x, 2009 (code available at: https://github.com/NCAR/DART/archive/refs/tags/v9.8.0.tar.gz, last access: 23 November 2019).
Anderson, J. L.: A Non-Gaussian Ensemble Filter Update for Data Assimilation, Mon. Weather Rev., 138, 4186–4198, https://doi.org/10.1175/2010MWR3253.1, 2010.
Anderson, J. L.: Localization and Sampling Error Correction in Ensemble Kalman Filter Data Assimilation, Mon. Weather Rev., 140, 2359–2371, https://doi.org/10.1175/MWR-D-11-00013.1, 2012.
Baren, A. J., Cotton, R., Furtado, K., Havemann, S., Labonnote, L.-C., Marenco, F., Smith, A., and Thelen. J.-C.: A self-consistent scatteringmodel for cirrus. II: The high and low frequencies, Q. J. Roy. Meteor. Soc., 140, 1039–1057, https://doi.org/10.1002/qj.2193, 2014.
Bauer, P., Geer, J. A., Lopez, P., and Salmond, D.: Direct 4D-Var assimilation of all-sky radiances. Part I: Implementation, Q. J. Roy. Meteor. Soc., 136, 1868–1885, https://doi.org/10.1002/qj.659, 2010.
Bauer, P., Ohring, G., Kummerow, C., and Auligne, T.: Assimilating satellite observations of clouds and precipitation into NWP models, B. Am. Meteorol. Soc., 92, ES25–ES28, https://doi.org/10.1175/2011BAMS3182.1, 2011.
Bretherton, C. S. and Park, S.: A New Moist Turbulence Parameterization in the Community Atmosphere Model, J. Clim., 22, 3422–3448, https://doi.org/10.1175/2008JCLI2556.1, 2009.
Buehner, M., Houtekamer, P. L., Charette, C., Mitchell, H. L., and He, B.: Intercomparison of Variational Data Assimilation and the Ensemble Kalman Filter for Global Deterministic NWP. Part I: Description and Single-Observation Experiments, Mon. Weather Rev., 138, 1550–1566, https://doi.org/10.1175/2009MWR3157.1, 2013.
Carminati, F. and Migliorini, S.: All-sky Data Assimilation of MWTS-2 and MWHS-2 in the Met Office Global NWP System, Adv. Atmos. Sci., 38, 1682–1694, https://doi.org/10.1007/s00376-021-1071-5, 2021.
Di, Z., Gong, W., Gan, Y., Shen, C., and Duan, Q.: Combinatorial Optimization for WRF Physical Parameterization Schemes: A Case Study of Three-Day Typhoon Simulations over the Northwest Pacific Ocean, Atmosphere, 10, 233, https://doi.org/10.3390/atmos10050233, 2019.
Dowell, D. C., Wicker, L. J., and Snyder, C.: Ensemble Kalman Filter Assimilation of Radar Observations of the 8 May 2003 Oklahoma City Supercell: Influences of Reflectivity Observations on Storm-Scale Analyses, Mon. Weather Rev., 139, 272–294, https://doi.org/10.1175/2010MWR3438.1, 2011.
Dudhia, J.: A Multi-layer Soil Temperature Model for MM5, Preprints, in: Sixth PSU/NCAR Mesoscale Model Users' Workshop, Boulder, USA, 22–24 July 1996, 49–50, https://www2.mmm.ucar.edu/mm5/lsm/soil.pdf (last access: 23 September 2022), 1996.
Evans, K. F.: SHDOMPPDA: A radiative transfer model for cloudy sky data assimilation, J. Atmos. Sci., 64, 3858–3868, https://doi.org/10.1175/2006JAS2047.1, 2007.
Field, P. R. and Heymsfield, A. J.: Importance of snow to global precipitation, Geophys. Res. Lett., 42, 9512–9520, https://doi.org/10.1002/2015GL065497, 2015.
Gao, J. D., Xue, M., and Stensrud, D. J.: The Development of a Hybrid EnKF-3DVAR Algorithm for Storm-Scale Data Assimilation, Adv. Meteorol., 2013, 512656, https://doi.org/10.1155/2013/512656, 2013.
Geer, A. J., Bauer, P., and O'Dell, C. W.: A revised cloud overlap scheme for fast microwave radiative transfer in rain and cloud, J. Appl. Meteorol. Clim., 48, 2257–2270, https://doi.org/10.1175/2009JAMC2170.1, 2009.
Geer, A. J., Lonitz, K., Weston, P., Kazumori, M., Okamoto, K., Zhu, Y., Liu, H. E., Collard, A., Bell, W., Migliorini, S., Chambon, P., Fourrié, N., Kim, M.-J., Köpken-Watts, C., and Schraff, C.: All-sky satellite data assimilation at operational weather forecasting centres, Q. J. Roy. Meteor. Soc., 144, 1191–1217, https://doi.org/10.1002/qj.3202, 2017.
Geer, A. J., Migliorini, S., and Matricardi, M.: All-sky assimilation of infrared radiances sensitive to mid- and upper-tropospheric moisture and cloud, Atmos. Meas. Tech., 12, 4903–4929, https://doi.org/10.5194/amt-12-4903-2019, 2019.
Hasselmann, K., Barnett, T. P., Bouws, E., Carlson, H., Cartwright, D. E., Enke, K., Ewing, J. A., Gienapp, H., Hasselmann, D. E., Kruseman, P., Meerburg, A., Müller, P., Olbers, D. J., Richter, K., Sell, W., and Walden, H.: Measurements of wind-wave growth and swell during the Joint North Sea Wave Project (JONSWAP), Dtsch. Hydrogr. Z., 8, 1–95, http://resolver.tudelft.nl/uuid:f204e188-13b9-49d8-a6dc-4fb7c20562fc (last access: 23 September 2022), 1973.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on pressure levels from 1959 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.bd0915c6, 2018a.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on single levels from 1959 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47, 2018b.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., J. Hogan, R., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Hu, J., Fu, Y., Zhang, P., Min, Q., Gao, Z., Wu, S., and Li, R.: Satellite Retrieval of Microwave Land Surface Emissivity under Clear and Cloudy Skies in China Using Observations from AMSR-E and MODIS, Remote Sens., 13, 3980, https://doi.org/10.3390/rs13193980, 2021.
Hu, X., Ge, J., Li, W., Du, J., Li, Q., and Mu, Q.: Vertical structure of tropical deep convective systems at different life stages from CloudSat observations, J. Geophys. Res., 126, e2021JD035115, https://doi.org/10.1029/2021JD035115, 2021.
Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S. A., and Collins, W. D.: Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models, J. Geophys. Res., 113, D13103, https://doi.org/10.1029/2008JD009944, 2008.
Jiménez, A., P., Dudhia, J., González-Rouco, J. F., Navarro, J., Montávez, P. J., and García-Bustamante, E.: A Revised Scheme for the WRF Surface Layer Formulation, Mon. Weather Rev., 140, 898–918, https://doi.org/10.1175/MWR-D-11-00056.1, 2012.
Kanji, A. J., Ladino, L. A., Wex, H., Boose, Y., Burkert-Hohn, M., Cziczo, D. J., and Krämer, M.:Overview of Ice Nucleating Particles, Meteor. Monographs, 58, 1.1–1.33, https://doi.org/10.1175/AMSMONOGRAPHS-D-16-0006.1, 2017.
Keat, W. J., Stein, T. H. M., Phaduli, E., Landman, S., Becker, E., Bopape, M.-J. M., Hanley, K. E., Lean, H. W., and Webster, S.: Convective initiation and stormlife cycles in convection-permitting simulations of the Met Office Unified Model over South Africa, Q. J. Roy. Meteor. Soc., 145, 1323–1336, https://doi.org/10.1002/qj.3487, 2019.
Korolev, A., McFarquhar, G., Field, P. R., Franklin, C., Lawson, P., Wang, Z., Williams, E., Abel, S. J., Axisa, D., Borrmann, S., Crosier, J., Fugal, J., Krämer, M., Lohmann, U., Schlenczek, O., Schnaiter, M., and Wendisch, M.: Mixed-Phase Clouds: Progress and Challenges, Meteor. Monographs, 58, 5.1–5.50, https://doi.org/10.1175/AMSMONOGRAPHS-D-17-0001.1, 2017.
Kostka, P. M., Weissmann, M., Buras, R., Mayer, B., and Stiller, O.: Observation operator for visible and near-infrared satellite reflectances, J. Atmos. Ocean. Tech., 31, 1216–1233, https://doi.org/10.1175/JTECH-D-13-00116.1, 2014.
Kubar, L. T. and Hartmann, D. L.: Vertical structure of tropical oceanic convective clouds and its relation to precipitation, Geophys. Res. Lett., 35, L03804, https://doi.org/10.1029/2007GL032811, 2008.
Kurzrock, F., Nguyen, H., Sauer, J., Chane Ming, F., Cros, S., Smith Jr., W. L., Minnis, P., Palikonda, R., Jones, T. A., Lallemand, C., Linguet, L., and Lajoie, G.: Evaluation of WRF-DART (ARW v3.9.1.1 and DART Manhattan release) multiphase cloud water path assimilation for short-term solar irradiance forecasting in a tropical environment, Geosci. Model Dev., 12, 3939–3954, https://doi.org/10.5194/gmd-12-3939-2019, 2019.
Lawson, W. G. and Hansen, J. A.: Implications of Stochastic and Deterministic Filters as Ensemble-Based Data Assimilation Methods in Varying Regimes of Error Growth, Mon. Weather Rev., 132, 1966–1981, https://doi.org/10.1175/1520-0493(2004)132<1966:IOSADF>2.0.CO;2, 2004.
Lei, J., Bikel, P., and Snyder, C.: Comparison of Ensemble Kalman Filters under Non-Gaussianity, Mon. Weather Rev., 138, 1293–1306, https://doi.org/10.1175/2009MWR3133.1, 2010.
Lei, L., Anderson, J. L., and Romine, G. S.: Empirical Localization Functions for Ensemble Kalman Filter Data Assimilation in Regions with and without Precipitation, Mon. Weather Rev., 143, 3664–3679, https://doi.org/10.1175/MWR-D-14-00415.1, 2015.
Li, J., Geer, J. A., Okamoto, K., Otkin. A. J., Liu, Z., Han, W., and Wang, P.: Satellite All-sky Infrared Radiance Assimilation: Recent Progress and Future Perspectives, Adv. Atmos. Sci., 39, 9–21, https://doi.org/10.1007/s00376-021-1088-9, 2022.
Ma, Z., Maddy, E. S., Zhang, B., Zhu, T., and Boukabara, S. A.: Impact Assessment of Himawari-8 AHI Data Assimilation in NCEP GDAS/GFS with GSI, J. Atmos. Ocean. Tech., 34, 797–815, https://doi.org/10.1175/JTECH-D-16-0136.1, 2017.
Matricardi, M.: The generation of RTTOV regression coefficients for IASI and AIRS using a new profile training set and a new line-by-line database, ECMWF, Technical Memorandum, 564, 47 pp., https://doi.org/10.21957/59u3oc9es, 2008.
Mayer, B. and Kylling, A.: Technical note: The libRadtran software package for radiative transfer calculations - description and examples of use, Atmos. Chem. Phys., 5, 1855–1877, https://doi.org/10.5194/acp-5-1855-2005, 2005.
McCarty, W., Jedlovec, G., and Timothy L. M.: Impact of the assimilation of Atmospheric Infrared Sounder radiance measurements on short-term weather forecasts, J. Geophys. Res., 144, D18122, https://doi.org/10.1029/2008JD011626, 2009.
Migliorini, S. and Candy, B.: All-sky satellite data assimilation of microwave temperature sounding channels at the Met Office, Q. J. Roy. Meteor. Soc., 145, 867–883, https://doi.org/10.1002/qj.3470, 2019.
Mittermaier, M., Roberts, N., and Thompson, S. A.: A long-term assessment of precipitation forecast skill using the Fractions Skill Score, Meteorol. Appl., 20, 176–186, https://doi.org/10.1002/met.296, 2013.
Mülmenstädt, J., Sourdeval, O., Delanoë, J., and Quaas, J.: Frequency of occurrence of rain from liquid-, mixed-, and ice-phase clouds derived from A-Train satellite retrievals, Geophys. Res. Lett., 42, 6502–6509, https://doi.org/10.1002/2015GL064604, 2015.
Nakajima T. and King, M. D.: Determination of the optical thickness and effective particle radius of clouds from reflected solar radiation measurements. Part I: Theory, J. Atmos. Sci., 47, 1878–1893, https://doi.org/10.1175/1520-0469(1990)047<1878:DOTOTA>2.0.CO;2, 1990.
National Centers for Environmental Prediction, National Weather Service, NOAA, and U.S. Department of Commerce: NCEP FNL Operational Model Global Tropospheric Analyses, continuing from July 1999, Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory [data set], https://doi.org/10.5065/D6M043C6, 2000.
Pinheiro, F. R., van Leeuwen, P. J., and Geppert, G.: Efficient nonlinear data assimilation using synchronization in a particle filter, Q. J. Roy. Meteor. Soc., 145, 2510–2523, https://doi.org/10.1002/qj.3576, 2019.
Polkinghorne, R. and Vukicevic, T.: Data assimilation of cloud-affected radiances in a cloud-resolving model, Mon. Weather Rev., 139, 755–773, https://doi.org/10.1175/2010MWR3360.1, 2011.
Poterjoy, J.: A Localized Particle Filter for High-Dimensional Nonlinear Systems, Mon. Weather Rev., 144, 59–76, https://doi.org/10.1175/MWR-D-15-0163.1, 2016.
Prates, C., Migliorini, S., English, S., and Pavelinc, E.: Assimilation of satellite infrared sounding measurements in the presence of heterogeneous cloud fields, Q. J. Roy. Meteor. Soc., 140, 2062–2077, https://doi.org/10.1002/qj.2279, 2014.
Saunders, R., Hocking, J., Turner, E., Rayer, P., Rundle, D., Brunel, P., Vidot, J., Roquet, P., Matricardi, M., Geer, A., Bormann, N., and Lupu, C.: An update on the RTTOV fast radiative transfer model (currently at version 12), Geosci. Model Dev., 11, 2717–2737, https://doi.org/10.5194/gmd-11-2717-2018, 2018 (code available at: https://nwp-saf.eumetsat.int/site/software/rttov/rttov-v12/, last access: 5 March 2019).
Scheck, L.: A neural network based forward operator for visible satellite images and its adjoint, J. Quant. Spectrosc. Ra., 274, 107841, https://doi.org/10.1016/j.jqsrt.2021.107841, 2021.
Scheck, L., Frèrebeau, P., Buras-Schnell, R., and Mayer, B.: A fast radiative transfer method for the simulation of visible satellite imagery, J. Quant. Spectrosc. Ra., 175, 54–67, https://doi.org/10.1016/j.jqsrt.2016.02.008, 2016a.
Scheck, L., Hocking, J., and Saunders, R.: A comparison of MFASIS and RTTOV-DOM, Report of Visiting Scientist mission NWP_VS16_01 (Document ID, NWPSAF-MO-VS-054), EUMETSAT, https://nwpsaf.eu/vs_reports/nwpsaf-mo-vs-054.pdf (last access: 22 September 2022), 2016b.
Scheck, L., Weissmann, M., and Bernhard, M.: Efficient Methods to Account for Cloud-Top Inclination and Cloud Overlap in Synthetic Visible Satellite Images, J. Atmos. Ocean. Tech., 35, 665–685, https://doi.org/10.1175/JTECH-D-17-0057.1, 2018.
Scheck, L., Weissmann, M., and Bach, L.: Assimilating visible satellite images for convective-scale numerical weather prediction: A case-study, Q. J. Roy. Meteor. Soc., 146, 3165–3186, https://doi.org/10.1002/qj.3840, 2020.
Schröttle, J., Weissmann, M., Scheck, L., and Hutt, A.: Assimilating Visible and Infrared Radiances in Idealized Simulations of Deep Convection, Mon. Weather Rev., 148, 4357–4375, https://doi.org/10.1175/MWR-D-20-0002.1, 2020.
Shen, F., Xu, D., Min, J., Chu, Z., and Li, X.: Assimilation of radar radial velocity data with the WRF hybrid 4DEnVar system for the prediction of hurricane Ike (2008), Atmos. Res., 234, 104771, https://doi.org/10.1016/j.atmosres.2019.104771, 2020.
Shen, Z. and Tang, Y.: A modified ensemble Kalman particle filter for non-Gaussian systems with nonlinear measurement functions, J. Adv. Model. Earth Sy., 7, 50–66, https://doi.org/10.1002/2014MS000373, 2015.
Short, C. J. and Petch, J.: Reducing the spin-up of a regional NWP system without data assimilation, Q. J. Roy. Meteor. Soc., 148, 1623–1643, https://doi.org/10.1002/qj.4268, 2022.
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Duda, M. G., Wang, X.-Y., Wang, W., and Power, J. G.: A Description of the Advanced Research WRF Version 3 (No. NCAR/TN-475+STR), University Corporation for Atmospheric Research, https://doi.org/10.5065/D68S4MVH, 2008 (code available at: https://github.com/wrf-model/WRF/archive/refs/tags/v4.1.1.tar.gz, last access: 22 June 2019).
Stengel, M., Lindskog, M., Undén, P., and Gustafsson, N.: The impact of cloud-affected IR radiances on forecast accuracy of a limited-area NWP model, Q. J. Roy. Meteor. Soc., 139, 2081–2096, https://doi.org/10.1002/qj.2102, 2013.
Thompson, G., Field, P. R., Rasmussen, R. M., and Hall, W. D.: Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: implementation of a new snow parameterization, Mon. Weather Rev., 136, 5095–5115, https://doi.org/10.1175/1520-0493(2004)132<0519:EFOWPU>2.0.CO;2, 2008.
Tiedtke, M.: A comprehensive mass flux scheme for cumulus parameterization in large-scale models, Mon. Weather Rev., 117, 1779–1800, https://doi.org/10.1175/1520-0493(1989)117<1779:ACMFSF>2.0.CO;2, 1989.
Vidot, J. and Borbás, É.: Land surface VIS/NIR BRDF atlas for RTTOV-11: model and validation against SEVIRI land SAF albedo product, Q. J. Roy. Meteor. Soc., 140, 2186–2196, https://doi.org/10.1002/qj.2288, 2014.
Vidot, J., Brunel, P., Dumont, M., Carmagnola, C., and Hocking J.: The VIS/NIR Land and Snow BRDF Atlas for RTTOV: Comparison between MODIS MCD43C1 C5 and C6, Remote Sens., 10, 21, https://doi.org/10.3390/rs10010021, 2018.
Vukicevic, T., Greenwald, T., Zupanski, M., Zupanski, D., Vondar Harr, T., and Jones, A. S.: Mesoscale cloud state estimation from visible and infrared satellite radiance. Mon. Weather Rev., 132, 3066–3077, https://doi.org/10.1175/MWR2837.1, 2004.
Várnai T. and Marshak A.: Statistical Analysis of the Uncertainties in Cloud Optical Depth Retrievals Caused by Three-Dimensional Radiative Effects, J. Atmos. Sci., 58, 1540–1548, https://doi.org/10.1175/1520-0469(2001)058<1540:SAOTUI>2.0.CO;2, 2001.
White, A. T., Pour-Biazar, A., Doty, K., Dornblaser, B., and McNider, R. T.: Improving Cloud Simulation for Air Quality Studies through Assimilation of Geostationary Satellite Observations in Retrospective Meteorological Modeling, Mon. Weather Rev., 146, 29–48, https://doi.org/10.1175/MWR-D-17-0139.1, 2018.
Xu, D., Min, J., Shen, F., Ban, J., and Chen, P.: Assimilation of MWHS radiance data from the FY-3B satellite with the WRF Hybrid-3DVAR system for the forecasting of binary typhoons, J. Adv. Model. Earch Sy., 8, 1014–1028, https://doi.org/10.1002/2016MS000674, 2016.
Xu, K.-M. and Randall, A. D.: A Semiempirical Cloudiness Parameterization for Use in Climate Models, J. Atmos. Sci., 53, 3084–3102, https://doi.org/10.1175/1520-0469(1996)053<3084:ASCPFU>2.0.CO;2, 1996.
Xue, J. S.: Scientific issues and perspective of assimilation of meteorological satellite data, Acta Meteorol. Sin., 67, 903–911, https://doi.org/10.11676/qxxb2009.088, 2009 (in Chinese with English abstract).
Yan, Y. and Liu, Y.: Vertical Structures of Convective and Stratiform Clouds in Boreal Summer over the Tibetan Plateau and Its Neighboring Regions, Adv. Atmos. Sci., 36, 1089–1102, https://doi.org/10.1007/s00376-019-8229-4, 2019.
Yang, C., Liu, Z., Bresch, J., Rizvi, S. R. H., Huang, X.-Y., and Min, J.: AMSR2 all-sky radiance assimilation and its impact on the analysis and forecast of Hurricane Sandy with a limited-area data assimilation system, Tellus A, 68, 30917, https://doi.org/10.3402/tellusa.v68.30917, 2016.
Yang, J., Zhang, Z., Wei, C., Lu, F., and Guo, Q.: Introducing the New Generation of Chinese Geostationary Weather Satellites, Fengyun-4, B. Am. Meteorol. Soc., 98, 1737–1658, https://doi.org/10.1175/BAMS-D-16-0065.1, 2017.
Zhang, A. and Fu. Y.: Life Cycle Effects on the Vertical Structure of Precipitation in East China Measured by Himawari-8 and GPM DPR, Mon. Weather Rev., 146, 2183–2199, https://doi.org/10.1175/MWR-D-18-0085.1, 2018.
Zhang, C., Wang, Y., and Hamilton, K.: Improved Representation of Boundary Layer Clouds over the Southeast Pacific in ARW-WRF Using a Modified Tiedtke Cumulus Parameterization Scheme, Mon. Weather Rev., 139, 3489–3513, https://doi.org/10.1175/MWR-D-10-05091.1, 2011.
Zhang, M., Zupanski, M., Kim, M.-J., and Knaff, J. A.: Assimilating AMSU-A Radiances in the TC Core Area with NOAA Operational HWRF (2011) and a Hybrid Data Assimilation System: Danielle (2010), Mon. Weather Rev., 141, 3889–2907, https://doi.org/10.1175/MWR-D-12-00340.1, 2013.
Zhang, P., Zhu, L., Tang, S., Gao, L., Chen, L., Zheng, W., Han, X., Chen, J., and Shao, J.: General Comparison of FY-4A/AGRI With Other GEO/LEO Instruments and Its Potential and Challenges in Non-meteorological Applications, Front. Earth Sci., 6, 224, https://doi.org/10.3389/feart.2018.00224, 2019.
Zhang, T., Sun, J., and Yang, L.: A Numerical Study of Effects of Radiation on Deep Convective Warm Based Cumulus Cloud Development with a 3-D Radiative Transfer Model, Atmosphere, 11, 1187, https://doi.org/10.3390/atmos11111187, 2020.
Zhou, Y. B., Liu, Y. B., and Liu, C.: A machine learning-based method to account for 3D Short-Wave radiative effects in 1D satellite observation operators, J. Quant. Spectrosc. Ra., 275, 107891, https://doi.org/10.1016/j.jqsrt.2021.107891, 2021.
Zhou, Y. B., Liu, Y. B., Huo, Z. Y., and Li, Y.: WRF-DART/RTTOV input and (processed) output files for GMD-2022-30, Zenodo [data set], https://doi.org/10.5281/zenodo.7028828, 2022.
Zhu, Y., Liu, E., Mahajan, R., Thomas, C., Groff, D., Van Delst, P., Collard, A., Treadon, R., and Derber, C. J.: All-Sky Microwave Radiance Assimilation in NCEP's GSI Analysis System, Mon. Weather Rev., 144, 4709–4735, https://doi.org/10.1175/MWR-D-15-0445.1, 2016.
Short summary
The study evaluates the performance of the Data Assimilation Research Testbed (DART), equipped with the recently added forward operator Radiative Transfer for TOVS (RTTOV), in assimilating FY-4A visible images into the Weather Research and Forecasting (WRF) model. The ability of the WRF-DART/RTTOV system to improve the forecasting skills for a tropical storm over East Asia and the Western Pacific is demonstrated in an Observing System Simulation Experiment framework.
The study evaluates the performance of the Data Assimilation Research Testbed (DART), equipped...
