Articles | Volume 17, issue 7
https://doi.org/10.5194/gmd-17-2569-2024
© Author(s) 2024. 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-17-2569-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
09 Apr 2024
Model evaluation paper |
| 09 Apr 2024
| 09 Apr 2024
A grid model for vertical correction of precipitable water vapor over the Chinese mainland and surrounding areas using random forest
Junyu Li
CORRESPONDING AUTHOR
College of Geomatics and Geoinformation, Guilin University of Technology, Guilin, China
Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin, China
Yuxin Wang
College of Geomatics and Geoinformation, Guilin University of Technology, Guilin, China
Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin, China
Lilong Liu
College of Geomatics and Geoinformation, Guilin University of Technology, Guilin, China
Yibin Yao
School of Geodesy and Geomatics, Wuhan University, Wuhan, China
Liangke Huang
College of Geomatics and Geoinformation, Guilin University of Technology, Guilin, China
Feijuan Li
College of Geomatics and Geoinformation, Guilin University of Technology, Guilin, China
Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin, China
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The existing zenith tropospheric delay (ZTD) models have limitations such as using a single fitting function, neglecting daily cycle variations, and relying on only one resolution grid data point for modeling. This model considers the daily cycle variation and latitude factor of ZTD, using the sliding window algorithm based on ERA5 atmospheric reanalysis data. The ZTD data from 545 radiosonde stations and MERRA-2 atmospheric reanalysis data are used to validate the accuracy of the GGZTD-P model.
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We developed a new global atmospheric weighted mean temperature (Tm) model considering time-varying lapse rate. Firstly, a global multidimensional Tm lapse rate model (NGGTm-H model) was developed using the sliding window algorithm. Secondly, the daily variation characteristics of Tm and its relationships with geographical situation were investigated. Finally, a hybrid-grid global Tm model considering time-varying lapse rate (NGGTm model) was developed.
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Geosci. Model Dev., 16, 7223–7235, https://doi.org/10.5194/gmd-16-7223-2023, https://doi.org/10.5194/gmd-16-7223-2023, 2023
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The existing zenith tropospheric delay (ZTD) models have limitations such as using a single fitting function, neglecting daily cycle variations, and relying on only one resolution grid data point for modeling. This model considers the daily cycle variation and latitude factor of ZTD, using the sliding window algorithm based on ERA5 atmospheric reanalysis data. The ZTD data from 545 radiosonde stations and MERRA-2 atmospheric reanalysis data are used to validate the accuracy of the GGZTD-P model.
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Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-5-W1-2023, 45–52, https://doi.org/10.5194/isprs-archives-XLVIII-5-W1-2023-45-2023, https://doi.org/10.5194/isprs-archives-XLVIII-5-W1-2023-45-2023, 2023
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C. Li, H. Peng, L. K. Huang, L. L. Liu, and S. F. Xie
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W10, 1147–1153, https://doi.org/10.5194/isprs-archives-XLII-3-W10-1147-2020, https://doi.org/10.5194/isprs-archives-XLII-3-W10-1147-2020, 2020
Z. X. Mo, L. K. Huang, H. Peng, L. L. Liu, and C. L. Kang
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W10, 1155–1160, https://doi.org/10.5194/isprs-archives-XLII-3-W10-1155-2020, https://doi.org/10.5194/isprs-archives-XLII-3-W10-1155-2020, 2020
Q. T. Wan, L. L. Liu, L. K. Huang, W. Zhou, Y. Z. Yang, and Z. X. Chen
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W10, 1169–1174, https://doi.org/10.5194/isprs-archives-XLII-3-W10-1169-2020, https://doi.org/10.5194/isprs-archives-XLII-3-W10-1169-2020, 2020
S. Wang, L. L. Liu, L. K. Huang, Y. Z. Yang, and H. Peng
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W10, 1175–1182, https://doi.org/10.5194/isprs-archives-XLII-3-W10-1175-2020, https://doi.org/10.5194/isprs-archives-XLII-3-W10-1175-2020, 2020
K. Y. Yang, L. L. Liu, and L. K. Huang
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W10, 1189–1195, https://doi.org/10.5194/isprs-archives-XLII-3-W10-1189-2020, https://doi.org/10.5194/isprs-archives-XLII-3-W10-1189-2020, 2020
J. M. Su, L. L. Liu, Q. T. Wan, Y. Z. Yang, and F. F. Li
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W10, 1289–1294, https://doi.org/10.5194/isprs-archives-XLII-3-W10-1289-2020, https://doi.org/10.5194/isprs-archives-XLII-3-W10-1289-2020, 2020
F. F. Li, L. L. Liu, L. K. Huang, W. Zhou, and S. Wang
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W10, 249–254, https://doi.org/10.5194/isprs-archives-XLII-3-W10-249-2020, https://doi.org/10.5194/isprs-archives-XLII-3-W10-249-2020, 2020
L. L. Liu, H. C. Liu, and C. F. Zhu
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W10, 261–264, https://doi.org/10.5194/isprs-archives-XLII-3-W10-261-2020, https://doi.org/10.5194/isprs-archives-XLII-3-W10-261-2020, 2020
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Yafang Guo, Chayan Roychoudhury, Mohammad Amin Mirrezaei, Rajesh Kumar, Armin Sorooshian, and Avelino F. Arellano
Geosci. Model Dev., 17, 4331–4353, https://doi.org/10.5194/gmd-17-4331-2024, https://doi.org/10.5194/gmd-17-4331-2024, 2024
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This research focuses on surface ozone (O3) pollution in Arizona, a historically air-quality-challenged arid and semi-arid region in the US. The unique characteristics of this kind of region, e.g., intense heat, minimal moisture, and persistent desert shrubs, play a vital role in comprehending O3 exceedances. Using the WRF-Chem model, we analyzed O3 levels in the pre-monsoon month, revealing the model's skill in capturing diurnal and MDA8 O3 levels.
Christoph Fischer, Andreas H. Fink, Elmar Schömer, Marc Rautenhaus, and Michael Riemer
Geosci. Model Dev., 17, 4213–4228, https://doi.org/10.5194/gmd-17-4213-2024, https://doi.org/10.5194/gmd-17-4213-2024, 2024
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Sandro Vattioni, Andrea Stenke, Beiping Luo, Gabriel Chiodo, Timofei Sukhodolov, Elia Wunderlin, and Thomas Peter
Geosci. Model Dev., 17, 4181–4197, https://doi.org/10.5194/gmd-17-4181-2024, https://doi.org/10.5194/gmd-17-4181-2024, 2024
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Najmeh Kaffashzadeh and Abbas-Ali Aliakbari Bidokhti
Geosci. Model Dev., 17, 4155–4179, https://doi.org/10.5194/gmd-17-4155-2024, https://doi.org/10.5194/gmd-17-4155-2024, 2024
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Franciscus Liqui Lung, Christian Jakob, A. Pier Siebesma, and Fredrik Jansson
Geosci. Model Dev., 17, 4053–4076, https://doi.org/10.5194/gmd-17-4053-2024, https://doi.org/10.5194/gmd-17-4053-2024, 2024
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Traditionally, high-resolution atmospheric models employ periodic boundary conditions, which limit simulations to domains without horizontal variations. In this research open boundary conditions are developed to replace the periodic boundary conditions. The implementation is tested in a controlled setup, and the results show minimal disturbances. Using these boundary conditions, high-resolution models can be forced by a coarser model to study atmospheric phenomena in realistic background states.
Caroline Arnold, Shivani Sharma, Tobias Weigel, and David S. Greenberg
Geosci. Model Dev., 17, 4017–4029, https://doi.org/10.5194/gmd-17-4017-2024, https://doi.org/10.5194/gmd-17-4017-2024, 2024
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In atmospheric models, rain formation is simplified to be computationally efficient. We trained a machine learning model, SuperdropNet, to emulate warm-rain formation based on super-droplet simulations. Here, we couple SuperdropNet with an atmospheric model in a warm-bubble experiment and find that the coupled simulation runs stable and produces reasonable results, making SuperdropNet a viable ML proxy for droplet simulations. We also present a comprehensive benchmark for coupling architectures.
Byoung-Joo Jung, Benjamin Ménétrier, Chris Snyder, Zhiquan Liu, Jonathan J. Guerrette, Junmei Ban, Ivette Hernández Baños, Yonggang G. Yu, and William C. Skamarock
Geosci. Model Dev., 17, 3879–3895, https://doi.org/10.5194/gmd-17-3879-2024, https://doi.org/10.5194/gmd-17-3879-2024, 2024
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We describe the multivariate static background error covariance (B) for the JEDI-MPAS 3D-Var data assimilation system. With tuned B parameters, the multivariate B gives physically balanced analysis increment fields in the single-observation test framework. In the month-long cycling experiment with a global 60 km mesh, 3D-Var with static B performs stably. Due to its simple workflow and minimal computational requirements, JEDI-MPAS 3D-Var can be useful for the research community.
Michal Belda, Nina Benešová, Jaroslav Resler, Peter Huszár, Ondřej Vlček, Pavel Krč, Jan Karlický, Pavel Juruš, and Kryštof Eben
Geosci. Model Dev., 17, 3867–3878, https://doi.org/10.5194/gmd-17-3867-2024, https://doi.org/10.5194/gmd-17-3867-2024, 2024
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For modeling atmospheric chemistry, it is necessary to provide data on emissions of pollutants. These can come from various sources and in various forms, and preprocessing of the data to be ingestible by chemistry models can be quite challenging. We developed the FUME processor to use a database layer that internally transforms all input data into a rigid structure, facilitating further processing to allow for emission processing from the continental to the street scale.
Bent Harnist, Seppo Pulkkinen, and Terhi Mäkinen
Geosci. Model Dev., 17, 3839–3866, https://doi.org/10.5194/gmd-17-3839-2024, https://doi.org/10.5194/gmd-17-3839-2024, 2024
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Probabilistic precipitation nowcasting (local forecasting for 0–6 h) is crucial for reducing damage from events like flash floods. For this goal, we propose the DEUCE neural-network-based model which uses data and model uncertainties to generate an ensemble of potential precipitation development scenarios for the next hour. Trained and evaluated with Finnish precipitation composites, DEUCE was found to produce more skillful and reliable nowcasts than established models.
Emma Howard, Steven Woolnough, Nicholas Klingaman, Daniel Shipley, Claudio Sanchez, Simon C. Peatman, Cathryn E. Birch, and Adrian J. Matthews
Geosci. Model Dev., 17, 3815–3837, https://doi.org/10.5194/gmd-17-3815-2024, https://doi.org/10.5194/gmd-17-3815-2024, 2024
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This paper describes a coupled atmosphere–mixed-layer ocean simulation setup that will be used to study weather processes in Southeast Asia. The set-up has been used to compare high-resolution simulations, which are able to partially resolve storms, to coarser simulations, which cannot. We compare the model performance at representing variability of rainfall and sea surface temperatures across length scales between the coarse and fine models.
Andrés Yarce Botero, Michiel van Weele, Arjo Segers, Pier Siebesma, and Henk Eskes
Geosci. Model Dev., 17, 3765–3781, https://doi.org/10.5194/gmd-17-3765-2024, https://doi.org/10.5194/gmd-17-3765-2024, 2024
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HARMONIE WINS50 reanalysis data with 0.025° × 0.025° resolution from 2019 to 2021 were coupled with the LOTOS-EUROS Chemical Transport Model. HARMONIE and ECMWF meteorology configurations against Cabauw observations (52.0° N, 4.9° W) were evaluated as simulated NO2 concentrations with ground-level sensors. Differences in crucial meteorological input parameters (boundary layer height, vertical diffusion coefficient) between the hydrostatic and non-hydrostatic models were analysed.
Ulrich Voggenberger, Leopold Haimberger, Federico Ambrogi, and Paul Poli
Geosci. Model Dev., 17, 3783–3799, https://doi.org/10.5194/gmd-17-3783-2024, https://doi.org/10.5194/gmd-17-3783-2024, 2024
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This paper presents a method for calculating balloon drift from historical radiosonde ascent data. The drift can reach distances of several hundred kilometres and is often neglected. Verification shows the beneficial impact of the more accurate balloon position on model assimilation. The method is not limited to radiosondes but would also work for dropsondes, ozonesondes, or any other in situ sonde carried by the wind in the pre-GNSS era, provided the necessary information is available.
Philippe Thunis, Jeroen Kuenen, Enrico Pisoni, Bertrand Bessagnet, Manjola Banja, Lech Gawuc, Karol Szymankiewicz, Diego Guizardi, Monica Crippa, Susana Lopez-Aparicio, Marc Guevara, Alexander De Meij, Sabine Schindlbacher, and Alain Clappier
Geosci. Model Dev., 17, 3631–3643, https://doi.org/10.5194/gmd-17-3631-2024, https://doi.org/10.5194/gmd-17-3631-2024, 2024
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An ensemble emission inventory is created with the aim of monitoring the status and progress made with the development of EU-wide inventories. This emission ensemble serves as a common benchmark for the screening and allows for the comparison of more than two inventories at a time. Because the emission “truth” is unknown, the approach does not tell which inventory is the closest to reality, but it identifies inconsistencies that require special attention.
Laurent Menut, Bertrand Bessagnet, Arineh Cholakian, Guillaume Siour, Sylvain Mailler, and Romain Pennel
Geosci. Model Dev., 17, 3645–3665, https://doi.org/10.5194/gmd-17-3645-2024, https://doi.org/10.5194/gmd-17-3645-2024, 2024
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This study is about the modelling of the atmospheric composition in Europe during the summer of 2022, when massive wildfires were observed. It is a sensitivity study dedicated to the relative impacts of two modelling processes that are able to modify the meteorology used for the calculation of the atmospheric chemistry and transport of pollutants.
Shuai Wang, Mengyuan Zhang, Yueqi Gao, Peng Wang, Qingyan Fu, and Hongliang Zhang
Geosci. Model Dev., 17, 3617–3629, https://doi.org/10.5194/gmd-17-3617-2024, https://doi.org/10.5194/gmd-17-3617-2024, 2024
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Numerical models are widely used in air pollution modeling but suffer from significant biases. The machine learning model designed in this study shows high efficiency in identifying such biases. Meteorology (relative humidity and cloud cover), chemical composition (secondary organic components and dust aerosols), and emission sources (residential activities) are diagnosed as the main drivers of bias in modeling PM2.5, a typical air pollutant. The results will help to improve numerical models.
Shoma Yamanouchi, Shayamilla Mahagammulla Gamage, Sara Torbatian, Jad Zalzal, Laura Minet, Audrey Smargiassi, Ying Liu, Ling Liu, Forood Azargoshasbi, Jinwoong Kim, Youngseob Kim, Daniel Yazgi, and Marianne Hatzopoulou
Geosci. Model Dev., 17, 3579–3597, https://doi.org/10.5194/gmd-17-3579-2024, https://doi.org/10.5194/gmd-17-3579-2024, 2024
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Air pollution is a major health hazard, and chemical transport models (CTMs) are valuable tools that aid in our understanding of the risks of air pollution at both local and regional scales. In this study, the Polair3D CTM of the Polyphemus air quality modeling platform was set up over Quebec, Canada, to assess the model’s capability in predicting key air pollutant species over the region, at seasonal temporal scales and at regional spatial scales.
Rohith Thundathil, Florian Zus, Galina Dick, and Jens Wickert
Geosci. Model Dev., 17, 3599–3616, https://doi.org/10.5194/gmd-17-3599-2024, https://doi.org/10.5194/gmd-17-3599-2024, 2024
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Global Navigation Satellite Systems (GNSS) provides moisture observations through its densely distributed ground station network. In this research, we assimilate a new type of observation called tropospheric gradient observations, which has never been incorporated into a weather model. We develop a forward operator for gradient-based observations and conduct an assimilation impact study. The study shows significant improvements in the model's humidity fields.
Ankur Mahesh, Travis A. O'Brien, Burlen Loring, Abdelrahman Elbashandy, William Boos, and William D. Collins
Geosci. Model Dev., 17, 3533–3557, https://doi.org/10.5194/gmd-17-3533-2024, https://doi.org/10.5194/gmd-17-3533-2024, 2024
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Atmospheric rivers (ARs) are extreme weather events that can alleviate drought or cause billions of US dollars in flood damage. We train convolutional neural networks (CNNs) to detect ARs with an estimate of the uncertainty. We present a framework to generalize these CNNs to a variety of datasets of past, present, and future climate. Using a simplified simulation of the Earth's atmosphere, we validate the CNNs. We explore the role of ARs in maintaining energy balance in the Earth system.
Alexandra Rivera, Kostas Tsigaridis, Gregory Faluvegi, and Drew Shindell
Geosci. Model Dev., 17, 3487–3505, https://doi.org/10.5194/gmd-17-3487-2024, https://doi.org/10.5194/gmd-17-3487-2024, 2024
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This paper describes and evaluates an improvement to the representation of acetone in the GISS ModelE2.1 Earth system model. We simulate acetone's concentration and transport across the atmosphere as well as its dependence on chemistry, the ocean, and various global emissions. Comparisons of our model’s estimates to past modeling studies and field measurements have shown encouraging results. Ultimately, this paper contributes to a broader understanding of acetone's role in the atmosphere.
Alok K. Samantaray, Priscilla A. Mooney, and Carla A. Vivacqua
Geosci. Model Dev., 17, 3321–3339, https://doi.org/10.5194/gmd-17-3321-2024, https://doi.org/10.5194/gmd-17-3321-2024, 2024
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Any interpretation of climate model data requires a comprehensive evaluation of the model performance. Numerous error metrics exist for this purpose, and each focuses on a specific aspect of the relationship between reference and model data. Thus, a comprehensive evaluation demands the use of multiple error metrics. However, this can lead to confusion. We propose a clustering technique to reduce the number of error metrics needed and a composite error metric to simplify the interpretation.
Richard Maier, Fabian Jakub, Claudia Emde, Mihail Manev, Aiko Voigt, and Bernhard Mayer
Geosci. Model Dev., 17, 3357–3383, https://doi.org/10.5194/gmd-17-3357-2024, https://doi.org/10.5194/gmd-17-3357-2024, 2024
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Based on the TenStream solver, we present a new method to accelerate 3D radiative transfer towards the speed of currently used 1D solvers. Using a shallow-cumulus-cloud time series, we evaluate the performance of this new solver in terms of both speed and accuracy. Compared to a 3D benchmark simulation, we show that our new solver is able to determine much more accurate irradiances and heating rates than a 1D δ-Eddington solver, even when operated with a similar computational demand.
Julia Maillard, Jean-Christophe Raut, and François Ravetta
Geosci. Model Dev., 17, 3303–3320, https://doi.org/10.5194/gmd-17-3303-2024, https://doi.org/10.5194/gmd-17-3303-2024, 2024
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Atmospheric models struggle to reproduce the strong temperature inversions in the vicinity of the surface over forested areas in the Arctic winter. In this paper, we develop modified simplified versions of surface layer schemes widely used by the community. Our modifications are used to correct the fact that original schemes place strong limits on the turbulent collapse, leading to a lower surface temperature gradient at low wind speeds. Modified versions show a better performance.
Jana Fischereit, Henrik Vedel, Xiaoli Guo Larsén, Natalie E. Theeuwes, Gregor Giebel, and Eigil Kaas
Geosci. Model Dev., 17, 2855–2875, https://doi.org/10.5194/gmd-17-2855-2024, https://doi.org/10.5194/gmd-17-2855-2024, 2024
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Wind farms impact local wind and turbulence. To incorporate these effects in weather forecasting, the explicit wake parameterization (EWP) is added to the forecasting model HARMONIE–AROME. We evaluate EWP using flight data above and downstream of wind farms, comparing it with an alternative wind farm parameterization and another weather model. Results affirm the correct implementation of EWP, emphasizing the necessity of accounting for wind farm effects in accurate weather forecasting.
Clément Bouvier, Daan van den Broek, Madeleine Ekblom, and Victoria A. Sinclair
Geosci. Model Dev., 17, 2961–2986, https://doi.org/10.5194/gmd-17-2961-2024, https://doi.org/10.5194/gmd-17-2961-2024, 2024
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An analytical initial background state has been developed for moist baroclinic wave simulation on an aquaplanet and implemented into OpenIFS. Seven parameters can be controlled, which are used to generate the background states and the development of baroclinic waves. The meteorological and numerical stability has been assessed. Resulting baroclinic waves have proven to be realistic and sensitive to the jet's width.
Jelena Radović, Michal Belda, Jaroslav Resler, Kryštof Eben, Martin Bureš, Jan Geletič, Pavel Krč, Hynek Řezníček, and Vladimír Fuka
Geosci. Model Dev., 17, 2901–2927, https://doi.org/10.5194/gmd-17-2901-2024, https://doi.org/10.5194/gmd-17-2901-2024, 2024
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Boundary conditions are of crucial importance for numerical model (e.g., PALM) validation studies and have a large influence on the model results, especially when studying the atmosphere of real, complex, and densely built urban environments. Our experiments with different driving conditions for the large-eddy simulation model PALM show its strong dependency on boundary conditions, which is important for the proper separation of errors coming from the boundary conditions and the model itself.
Sonya L. Fiddes, Marc D. Mallet, Alain Protat, Matthew T. Woodhouse, Simon P. Alexander, and Kalli Furtado
Geosci. Model Dev., 17, 2641–2662, https://doi.org/10.5194/gmd-17-2641-2024, https://doi.org/10.5194/gmd-17-2641-2024, 2024
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In this study we present an evaluation that considers complex, non-linear systems in a holistic manner. This study uses XGBoost, a machine learning algorithm, to predict the simulated Southern Ocean shortwave radiation bias in the ACCESS model using cloud property biases as predictors. We then used a novel feature importance analysis to quantify the role that each cloud bias plays in predicting the radiative bias, laying the foundation for advanced Earth system model evaluation and development.
Gaurav Govardhan, Sachin D. Ghude, Rajesh Kumar, Sumit Sharma, Preeti Gunwani, Chinmay Jena, Prafull Yadav, Shubhangi Ingle, Sreyashi Debnath, Pooja Pawar, Prodip Acharja, Rajmal Jat, Gayatry Kalita, Rupal Ambulkar, Santosh Kulkarni, Akshara Kaginalkar, Vijay K. Soni, Ravi S. Nanjundiah, and Madhavan Rajeevan
Geosci. Model Dev., 17, 2617–2640, https://doi.org/10.5194/gmd-17-2617-2024, https://doi.org/10.5194/gmd-17-2617-2024, 2024
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A newly developed air quality forecasting framework, Decision Support System (DSS), for air quality management in Delhi, India, provides source attribution with numerous emission reduction scenarios besides forecasts. DSS shows that during post-monsoon and winter seasons, Delhi and its neighboring districts contribute to 30 %–40 % each to pollution in Delhi. On average, a 40 % reduction in the emissions in Delhi and the surrounding districts would result in a 24 % reduction in Delhi's pollution.
Simon Rosanka, Holger Tost, Rolf Sander, Patrick Jöckel, Astrid Kerkweg, and Domenico Taraborrelli
Geosci. Model Dev., 17, 2597–2615, https://doi.org/10.5194/gmd-17-2597-2024, https://doi.org/10.5194/gmd-17-2597-2024, 2024
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The capabilities of the Modular Earth Submodel System (MESSy) are extended to account for non-equilibrium aqueous-phase chemistry in the representation of deliquescent aerosols. When applying the new development in a global simulation, we find that MESSy's bias in modelling routinely observed reduced inorganic aerosol mass concentrations, especially in the United States. Furthermore, the representation of fine-aerosol pH is particularly improved in the marine boundary layer.
Rolf Sander
Geosci. Model Dev., 17, 2419–2425, https://doi.org/10.5194/gmd-17-2419-2024, https://doi.org/10.5194/gmd-17-2419-2024, 2024
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The open-source software MEXPLORER 1.0.0 is presented here. The program can be used to analyze, reduce, and visualize complex chemical reaction mechanisms. The mathematics behind the tool is based on graph theory: chemical species are represented as vertices, and reactions as edges. MEXPLORER is a community model published under the GNU General Public License.
Hossain Mohammed Syedul Hoque, Kengo Sudo, Hitoshi Irie, Yanfeng He, and Md Firoz Khan
EGUsphere, https://doi.org/10.22541/essoar.169903618.82717612/v2, https://doi.org/10.22541/essoar.169903618.82717612/v2, 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 of 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 capability was limited. This is attributed to the uncertainties in the observations and the model parameters.
Leonardo Olivetti and Gabriele Messori
Geosci. Model Dev., 17, 2347–2358, https://doi.org/10.5194/gmd-17-2347-2024, https://doi.org/10.5194/gmd-17-2347-2024, 2024
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In the last decades, weather forecasting up to 15 d into the future has been dominated by physics-based numerical models. Recently, deep learning models have challenged this paradigm. However, the latter models may struggle when forecasting weather extremes. In this article, we argue for deep learning models specifically designed to handle extreme events, and we propose a foundational framework to develop such models.
Stefan Rahimi, Lei Huang, Jesse Norris, Alex Hall, Naomi Goldenson, Will Krantz, Benjamin Bass, Chad Thackeray, Henry Lin, Di Chen, Eli Dennis, Ethan Collins, Zachary J. Lebo, Emily Slinskey, Sara Graves, Surabhi Biyani, Bowen Wang, Stephen Cropper, and the UCLA Center for Climate Science Team
Geosci. Model Dev., 17, 2265–2286, https://doi.org/10.5194/gmd-17-2265-2024, https://doi.org/10.5194/gmd-17-2265-2024, 2024
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Here, we project future climate across the western United States through the end of the 21st century using a regional climate model, embedded within 16 latest-generation global climate models, to provide the community with a high-resolution physically based ensemble of climate data for use at local scales. Strengths and weaknesses of the data are frankly discussed as we overview the downscaled dataset.
Romain Pilon and Daniela I. V. Domeisen
Geosci. Model Dev., 17, 2247–2264, https://doi.org/10.5194/gmd-17-2247-2024, https://doi.org/10.5194/gmd-17-2247-2024, 2024
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This paper introduces a new method for detecting atmospheric cloud bands to identify long convective cloud bands that extend from the tropics to the midlatitudes. The algorithm allows for easy use and enables researchers to study the life cycle and climatology of cloud bands and associated rainfall. This method provides insights into the large-scale processes involved in cloud band formation and their connections between different regions, as well as differences across ocean basins.
Salvatore Larosa, Domenico Cimini, Donatello Gallucci, Saverio Teodosio Nilo, and Filomena Romano
Geosci. Model Dev., 17, 2053–2076, https://doi.org/10.5194/gmd-17-2053-2024, https://doi.org/10.5194/gmd-17-2053-2024, 2024
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PyRTlib is an attractive educational tool because it provides a flexible and user-friendly way to broadly simulate how electromagnetic radiation travels through the atmosphere as it interacts with atmospheric constituents (such as gases, aerosols, and hydrometeors). PyRTlib is a so-called radiative transfer model; these are commonly used to simulate and understand remote sensing observations from ground-based, airborne, or satellite instruments.
Cited articles
Adeyemi, B. and Joerg, S.: Analysis of Water Vapor over Nigeria Using Radiosonde and Satellite Data, J. Appl. Meteorol. Climatol., 51, 1855–1866, https://doi.org/10.1175/jamc-d-11-0119.1, 2012.
Albergel, C., Dutra, E., Munier, S., Calvet, J.-C., Munoz-Sabater, J., de Rosnay, P., and Balsamo, G.: ERA-5 and ERA-Interim driven ISBA land surface model simulations: which one performs better?, Hydrol. Earth Syst. Sci., 22, 3515–3532, https://doi.org/10.5194/hess-22-3515-2018, 2018.
Alshawaf, F., Fersch, B., Hinz, S., Kunstmann, H., Mayer, M., and Meyer, F. J.: Water vapor mapping by fusing InSAR and GNSS remote sensing data and atmospheric simulations, Hydrol. Earth Syst. Sci., 19, 4747–4764, https://doi.org/10.5194/hess-19-4747-2015, 2015.
Bevis, M., Businger, S., Herring, T. A., Rocken, C., Anthes, R. A., and Ware, R. H.: GPS Meteorology – Remote-Sensing of Atmospheric Water-Vapor Using the Global Positioning System, J. Geophys. Res.-Atmos., 97, 15787–15801, https://doi.org/10.1029/92jd01517, 1992.
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/a:1010933404324, 2001.
Chen, B. Y., Jin, L. J., Wang, J. Y., Jin, W. P., and Wang, W.: Wide-Area Retrieval of Water Vapor Field Using an Improved Node Parameterization Tomography, IEEE Geosci. Remote Sens. Lett., 20, 1–5, https://doi.org/10.1109/lgrs.2023.3293824, 2023a.
Chen, B. Y., Tan, J. S., Wang, W., Dai, W. J., Ao, M. S., and Chen, C. H.: Tomographic Reconstruction of Water Vapor Density Fields From the Integration of GNSS Observations and Fengyun-4A Products, IEEE T. Geosci. Remote, 61, 1–12, https://doi.org/10.1109/tgrs.2023.3239392, 2023b.
Chen, Q. M., Song, S. L., Heise, S., Liou, Y. A., Zhu, W. Y., and Zhao, J. Y.: Assessment of ZTD derived from ECMWF/NCEP data with GPS ZTD over China, GPS Solut., 15, 415–425, https://doi.org/10.1007/s10291-010-0200-x, 2011.
Dessler, A. E. and Sherwood, S. C.: Atmospheric Science a Matter of Humidity, Science, 323, 1020–1021, https://doi.org/10.1126/science.1171264, 2009.
Ding, M. H.: A second generation of the neural network model for predicting weighted mean temperature, GPS Solut., 24, 61, https://doi.org/10.1007/s10291-020-0975-3, 2020.
Dogan, A. H. and Erdogan, B.: A new empirical troposphere model using ERA5's monthly averaged hourly dataset, J. Atmos. Sol.-Terr. Phys., 232, 105865, https://doi.org/10.1016/j.jastp.2022.105865, 2022.
Dousa, J. and Elias, M.: An improved model for calculating tropospheric wet delay, Geophys. Res. Lett., 41, 4389–4397, https://doi.org/10.1002/2014gl060271, 2014.
Du, Z., Yao, Y. B., and Zhao, Q. Z.: Novel Validation and Calibration Strategy for Total Precipitable Water Products of Fengyun-2 Geostationary Satellites, IEEE T. Geosci. Remote, 61, 12, https://doi.org/10.1109/tgrs.2023.3295091, 2023.
Emardson, T. R. and Johansson, J. M.: Spatial interpolation of the atmospheric water vapor content between sites in a ground-based GPS network, Geophys. Res. Lett., 25, 3347–3350, https://doi.org/10.1029/98gl02504, 1998.
European Centre for Medium-Range Weather Forecasts: Monthly averaged data on pressure levels, European Centre for Medium-Range Weather Forecasts [data set], https://cds.climate.copernicus.eu/, last access: 29 March 2024.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horanyi, A., Munoz-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., Hogan, R. J., Holm, E., Janiskova, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thepaut, J.-N.: The ERA5 global reanalysis, Q. J. R. Meteorol. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Huang, L., Mo, Z., Liu, L., and Xie, S.: An empirical model for the vertical correction of precipitable water vapor considering the time-varying lapse rate for Mainland China, Acta Geodaetica et Cartographica Sinica, 50, 1320–1330, https://doi.org/10.11947/j.AGCS.2021.20200530, 2021.
Huang, L., Wang, X., Xiong, S., Li, J., Liu, L., Mo, Z., Fu, B., and He, H.: High-precision GNSS PWV retrieval using dense GNSS sites and in-situ meteorological observations for the evaluation of MERRA-2 and ERA5 reanalysis products over China, Atmos. Res., 276, 106247, https://doi.org/10.1016/j.atmosres.2022.106247, 2022.
Huang, L., Liu, W., Mo, Z., Zhang, H., Li, J., Chen, F., Liu, L., and Jiang, W.: A new model for vertical adjustment of precipitable water vapor with consideration of the time-varying lapse rate, GPS Solut., 27, 170, https://doi.org/10.1007/s10291-023-01506-5, 2023.
Jacob, D.: The role of water vapour in the atmosphere. A short overview from a climate modeller's point of view, Phys. Chem. Earth Pt. A, 26, 523–527, https://doi.org/10.1016/s1464-1895(01)00094-1, 2001.
Keil, C., Röpnack, A., Craig, G. C., and Schumann, U.: Sensitivity of quantitative precipitation forecast to height dependent changes in humidity, Geophys. Res. Lett., 35, L09812, https://doi.org/10.1029/2008gl033657, 2008.
Kouba, J.: Implementation and testing of the gridded Vienna Mapping Function 1 (VMF1), J. Geodesy, 82, 193–205, https://doi.org/10.1007/s00190-007-0170-0, 2008.
Lam, R. R., Sanchez-Gonzalez, A., Willson, M., Wirnsberger, P., Fortunato, M., Pritzel, A., Ravuri, S. V., Ewalds, T., Alet, F., Eaton-Rosen, Z., Hu, W., Merose, A., Hoyer, S., Holland, G., Stott, J., Vinyals, O., Mohamed, S., and Battaglia, P. W.: GraphCast: Learning skillful medium-range global weather forecasting, arXiv [preprint], https://doi.org/10.48550/arXiv.2212.12794, 2022.
Leckner, B.: The spectral distribution of solar radiation at the earth's surface – elements of a model, Sol. Energy, 20, 143–150, https://doi.org/10.1016/0038-092X78)90187-1, 1978.
Li, H. B., Wang, X. M., Choy, S., Wu, S. Q., Jiang, C. H., Zhang, J. L., Qiu, C., Li, L., and Zhang, K. F.: A New Cumulative Anomaly-Based Model for the Detection of Heavy Precipitation Using GNSS-Derived Tropospheric Products, IEEE T. Geosci. Remote, 60, 1–18, https://doi.org/10.1109/tgrs.2021.3137014, 2022a.
Li, H. B., Wang, X. M., Choy, S., Jiang, C. H., Wu, S. Q., Zhang, J. L., Qiu, C., Zhou, K., Li, L., Fu, E. R., and Zhang, K. F.: Detecting heavy rainfall using anomaly-based percentile thresholds of predictors derived from GNSS-PWV, Atmos. Res., 265, 105912, https://doi.org/10.1016/j.atmosres.2021.105912, 2022b.
Li, J., Zhang, Q., Liu, L., Yao, Y., Huang, L., Chen, F., Zhou, L., and Zhang, B.: A refined zenith tropospheric delay model for Mainland China based on the global pressure and temperature 3 (GPT3) model and random forest, GPS Solut., 27, 172, https://doi.org/10.1007/s10291-023-01513-6, 2023a.
Li, J., Wang, Y., Liu, L., Yao, Y., Huang, L., and Li, F.: A Grid Model for Vertical Correction of Precipitable Water Vapor over the Chinese Mainland and Surrounding Areas Using Random Forest, Zenodo [data set], https://doi.org/10.5281/zenodo.10124326, 2023b.
Lu, C. X., Zhang, Y. S., Zheng, Y. X., Wu, Z. L., and Wang, Q. Y.: Precipitable water vapor fusion of MODIS and ERA5 based on convolutional neural network, GPS Solut., 27, 15, https://doi.org/10.1007/s10291-022-01357-6, 2023.
Probst, P. and Boulesteix, A.-L.: To tune or not to tune the number of trees in random forest, J. Mach. Learn. Res., 18, 6673–6690, https://doi.org/10.48550/arXiv.1705.05654, 2017.
Raval, A. and Ramanathan, V.: Observational determination of the greenhouse effect, Nature, 342, 758–761, https://doi.org/10.1038/342758a0, 1989.
Ravuri, S., Lenc, K., Willson, M., Kangin, D., Lam, R., Mirowski, P., Fitzsimons, M., Athanassiadou, M., Kashem, S., Madge, S., Prudden, R., Mandhane, A., Clark, A., Brock, A., Simonyan, K., Hadsell, R., Robinson, N., Clancy, E., Arribas, A., and Mohamed, S.: Skilful precipitation nowcasting using deep generative models of radar, Nature, 597, 672–677, https://doi.org/10.1038/s41586-021-03854-z, 2021.
Reitan, C. H.: Surface Dew Point and Water Vapor Aloft, J. Appl. Meteorol., 2, 776–779, https://doi.org/10.1175/1520-0450(1963)002<0776:SDPAWV>2.0.CO;2, 1963.
Renju, R., Raju, C. S., Mathew, N., Antony, T., and Moorthy, K. K.: Microwave radiometer observations of interannual water vapor variability and vertical structure over a tropical station, J. Geophys. Res.-Atmos., 120, 4585–4599, https://doi.org/10.1002/2014jd022838, 2015.
Rocken, C., Anthes, R., Exner, M., Hunt, D., Sokolovskiy, S., Ware, R., Gorbunov, M., Schreiner, W., Feng, D., Herman, B., Kuo, Y. H., and Zou, X.: Analysis and validation of GPS/MET data in the neutral atmosphere, J. Geophys. Res.-Atmos., 102, 29849–29866, https://doi.org/10.1029/97jd02400, 1997.
Rodriguez, J. D., Perez, A., and Lozano, J. A.: Sensitivity Analysis of k-Fold Cross Validation in Prediction Error Estimation, IEEE Trans. Pattern Anal. Mach. Intell., 32, 569–575, https://doi.org/10.1109/tpami.2009.187, 2010.
Rose, B. E. J. and Rencurrel, M. C.: The Vertical Structure of Tropospheric Water Vapor: Comparing Radiative and Ocean-Driven Climate Changes, J. Climate, 29, 4251–4268, https://doi.org/10.1175/jcli-d-15-0482.1, 2016.
Ross, R. J. and Elliott, W. P.: Tropospheric water vapor climatology and trends over North America: 1973–93, J. Climate, 9, 3561–3574, https://doi.org/10.1175/1520-0442(1996)009<3561:Twvcat>2.0.Co;2, 1996.
Sagi, O. and Rokach, L.: Ensemble learning: A survey, Wiley Interdiscip. Rev.-Data Mining Knowl. Discov., 8, e1249, https://doi.org/10.1002/widm.1249, 2018.
Santos, M. S., Soares, J. P., Abreu, P. H., Araujo, H., and Santos, J.: Cross-Validation for Imbalanced Datasets: Avoiding Overoptimistic and Overfitting Approaches, IEEE Comput. Intell. Mag., 13, 59–76, https://doi.org/10.1109/mci.2018.2866730, 2018.
Senkal, O.: Solar radiation and precipitable water modeling for Turkey using artificial neural networks, Meteorol. Atmos. Phys., 127, 481–488, https://doi.org/10.1007/s00703-015-0372-6, 2015.
Sun, Z. Y., Zhang, B., and Yao, Y. B.: An ERA5-Based Model for Estimating Tropospheric Delay and Weighted Mean Temperature Over China With Improved Spatiotemporal Resolutions, Earth and Space Science, 6, 1926–1941, https://doi.org/10.1029/2019ea000701, 2019.
Sun, Z. Y., Zhang, B., and Yao, Y. B.: Improving the Estimation of Weighted Mean Temperature in China Using Machine Learning Methods, Remote Sens., 13, 1016, https://doi.org/10.3390/rs13051016, 2021.
Tomasi, C.: Precipitable water vapor in atmospheres characterized by temperature inversions, J. Appl. Meteorol., 16, 237–243, https://doi.org/10.1175/1520-0450(1977)016<0237:PWW1AC>2.0.C0;2, 1977.
Tyralis, H., Papacharalampous, G., and Langousis, A.: A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources, Water, 11, 910, https://doi.org/10.3390/w11050910, 2019.
University of Wyoming: Radiosonde profiles, University of Wyoming [data set], https://www.ncei.noaa.gov/pub/data/igra/, last access: 29 March 2024.
Wang, L. A., Zhou, X. D., Zhu, X. K., Dong, Z. D., and Guo, W. S.: Estimation of biomass in wheat using random forest regression algorithm and remote sensing data, Crop J., 4, 212–219, https://doi.org/10.1016/j.cj.2016.01.008, 2016.
Wang, X. M., Zhang, K. F., Wu, S. Q., Fan, S. J., and Cheng, Y. Y.: Water vapor-weighted mean temperature and its impact on the determination of precipitable water vapor and its linear trend, J. Geophys. Res.-Atmos., 121, 833–852, https://doi.org/10.1002/2015jd024181, 2016.
Wang, X. Z., Chen, F. Y., Ke, F. Y., and Xu, C.: An Empirical Grid Model for Precipitable Water Vapor, Remote Sens., 14, 6174, https://doi.org/10.3390/rs14236174, 2022.
Wang, Y. Z., Liu, H. L., Zhang, Y., Duan, M. Z., Tang, S. H., and Deng, X. B.: Validation of FY-4A AGRI layer precipitable water products using radiosonde data, Atmos. Res., 253, 105502, https://doi.org/10.1016/j.atmosres.2021.105502, 2021.
Yang, F., Guo, J., Meng, X., Shi, J., Zhang, D., and Zhao, Y.: An improved weighted mean temperature (Tm) model based on GPT2w with Tm lapse rate, GPS Solut., 24, 46, https://doi.org/10.1007/s10291-020-0953-9, 2020.
Yang, F., Sun, Y. L., Meng, X. L., Guo, J. M., and Gong, X.: Assessment of tomographic window and sampling rate effects on GNSS water vapor tomography, Satell. Navig., 4, 7, https://doi.org/10.1186/s43020-023-00096-4, 2023.
Zhai, P. M. and Eskridge, R. E.: Atmospheric water vapor over China, J. Climat, 10, 2643–2652, https://doi.org/10.1175/1520-0442(1997)010<2643:Awvoc>2.0.Co;2, 1997.
Zhang, B. and Yao, Y. B.: Precipitable water vapor fusion based on a generalized regression neural network, J. Geodesy, 95, 36, https://doi.org/10.1007/s00190-021-01482-z, 2021.
Zhang, B., Yao, Y. B., Xin, L. Y., and Xu, X. Y.: Precipitable water vapor fusion: an approach based on spherical cap harmonic analysis and Helmert variance component estimation, J. Geodesy, 93, 2605–2620, https://doi.org/10.1007/s00190-019-01322-1, 2019.
Zhang, H. X., Yuan, Y. B., Li, W., and Zhang, B. C.: A Real-Time Precipitable Water Vapor Monitoring System Using the National GNSS Network of China: Method and Preliminary Results, IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens., 12, 1587–1598, https://doi.org/10.1109/jstars.2019.2906950, 2019.
Zhang, K., Wan, M., Wu, S., Zhen, S., Dantong, Z., Sun, P., and Jiang, L.: New Model for Vertical Distribution and Variation of Atmospheric Water Vapor – A Case Study for China, Research Square [preprint], https://doi.org/10.21203/rs.3.rs-1497870/v1, 2022.
Zhang, Q., Ye, J. H., Zhang, S. C., and Han, F.: Precipitable Water Vapor Retrieval and Analysis by Multiple Data Sources: Ground-Based GNSS, Radio Occultation, Radiosonde, Microwave Satellite, and NWP Reanalysis Data, J. Sens., 2018, 3428303, https://doi.org/10.1155/2018/3428303, 2018.
Zhang, W. X., Lou, Y. D., Cao, Y. C., Liang, H., Shi, C., Huang, J. F., Liu, W. X., Zhang, Y., and Fan, B. B.: Corrections of Radiosonde-Based Precipitable Water Using Ground-Based GPS and Applications on Historical Radiosonde Data Over China, J. Geophys. Res.-Atmos., 124, 3208–3222, https://doi.org/10.1029/2018jd029662, 2019.
Zhang, Y. L., Cai, C. S., Chen, B. Y., and Dai, W. J.: Consistency Evaluation of Precipitable Water Vapor Derived From ERA5, ERA-Interim, GNSS, and Radiosondes Over China, Radio Sci., 54, 561–571, https://doi.org/10.1029/2018rs006789, 2019.
Zhao, Y. Y., Zhao, H. W., Li, J. Q., and Xiao, G. W.: Comprehensive Validation and Calibration of MODIS PWV over Mainland China, Atmosphere, 13, 1763, https://doi.org/10.3390/atmos13111763, 2022.
Zheng, Y. X., Lu, C. X., Wu, Z. L., Liao, J. C., Zhang, Y. S., and Wang, Q. Y.: Machine Learning-Based Model for Real-Time GNSS Precipitable Water Vapor Sensing, Geophys. Res. Lett., 49, e2021GL096408, https://doi.org/10.1029/2021gl096408, 2022.
Zhu, M. C., Yu, X. W., and Sun, W.: A coalescent grid model of weighted mean temperature for China region based on feedforward neural network algorithm, GPS Solut., 26, 70, https://doi.org/10.1007/s10291-022-01254-y, 2022.
Short summary
In this study, we have developed a model (RF-PWV) to characterize precipitable water vapor (PWV) variation with altitude in the study area. RF-PWV can significantly reduce errors in vertical correction, enhance PWV fusion product accuracy, and provide insights into PWV vertical distribution, thereby contributing to climate research.
In this study, we have developed a model (RF-PWV) to characterize precipitable water vapor (PWV)...
