Articles | Volume 13, issue 7
https://doi.org/10.5194/gmd-13-3179-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-13-3179-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
14 Jul 2020
Development and technical paper |
| 14 Jul 2020
| 14 Jul 2020
Evaluation of three new surface irrigation parameterizations in the WRF-ARW v3.8.1 model: the Po Valley (Italy) case study
Arianna Valmassoi
CORRESPONDING AUTHOR
School of Architecture, Planning and Environmental Policy, University College Dublin (UCD), Richview, Dublin, Ireland
National Center for Atmospheric Research, Boulder, Colorado, USA
Jimy Dudhia
National Center for Atmospheric Research, Boulder, Colorado, USA
Silvana Di Sabatino
Department of Physics and Astronomy, University of Bologna, Bologna, Italy
Francesco Pilla
School of Architecture, Planning and Environmental Policy, University College Dublin (UCD), Richview, Dublin, Ireland
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Arianna Valmassoi and Jan D. Keller
Adv. Sci. Res., 18, 41–49, https://doi.org/10.5194/asr-18-41-2021, https://doi.org/10.5194/asr-18-41-2021, 2021
Francesco Barbano, Erika Brattich, Carlo Cintolesi, Abdul Ghafoor Nizamani, Silvana Di Sabatino, Massimo Milelli, Esther E. M. Peerlings, Sjoerd Polder, Gert-Jan Steeneveld, and Antonio Parodi
Atmos. Meas. Tech., 17, 3255–3278, https://doi.org/10.5194/amt-17-3255-2024, https://doi.org/10.5194/amt-17-3255-2024, 2024
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The characterization of the urban microclimate starts with atmospheric monitoring using a dense array of sensors to capture the spatial variations induced by the different morphology, land cover, and presence of vegetation. To provide a new sensor for this scope, this paper evaluates the outdoor performance of a commercial mobile sensor. The results mark the sensor's ability to capture the same atmospheric variability as the reference, making it a valid solution for atmospheric monitoring.
Antonio Giordani, Michael Kunz, Kristopher M. Bedka, Heinz Jürgen Punge, Tiziana Paccagnella, Valentina Pavan, Ines M. L. Cerenzia, and Silvana Di Sabatino
EGUsphere, https://doi.org/10.5194/egusphere-2023-2639, https://doi.org/10.5194/egusphere-2023-2639, 2023
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To improve the challenging representation of hazardous hailstorms, a proxy for hail frequency based on satellite cloud top detections, convective parameters from high-resolution reanalysis, and crowd-sourced reports, is presented. Hail likelihood peaks in central summer at 15 UTC over northern Italy and shows improved agreement with observations compared to a previous estimate. Separating ambient signatures based on hail severity, enhanced appropriateness for large hail occurrence is found.
Ditsuhi Iskandaryan, Silvana Di Sabatino, Francisco Ramos, and Sergio Trilles
AGILE GIScience Ser., 3, 6, https://doi.org/10.5194/agile-giss-3-6-2022, https://doi.org/10.5194/agile-giss-3-6-2022, 2022
Laura Tositti, Erika Brattich, Claudio Cassardo, Pietro Morozzi, Alessandro Bracci, Angela Marinoni, Silvana Di Sabatino, Federico Porcù, and Alessandro Zappi
Atmos. Chem. Phys., 22, 4047–4073, https://doi.org/10.5194/acp-22-4047-2022, https://doi.org/10.5194/acp-22-4047-2022, 2022
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We present a thorough investigation of an anomalous transport of mineral dust over a region renowned for excess airborne particulate matter, the Italian Po Valley, which occurred in late March 2021. Both the origin of this dust outbreak, which was localized in central Asia (i.e., the so-called Aralkum Desert), and the upstream synoptic conditions, investigated here in extreme detail using multiple integrated observations including in situ measurements and remote sensing, were atypical.
Arianna Valmassoi and Jan D. Keller
Adv. Sci. Res., 18, 41–49, https://doi.org/10.5194/asr-18-41-2021, https://doi.org/10.5194/asr-18-41-2021, 2021
Laddaporn Ruangpan, Zoran Vojinovic, Silvana Di Sabatino, Laura Sandra Leo, Vittoria Capobianco, Amy M. P. Oen, Michael E. McClain, and Elena Lopez-Gunn
Nat. Hazards Earth Syst. Sci., 20, 243–270, https://doi.org/10.5194/nhess-20-243-2020, https://doi.org/10.5194/nhess-20-243-2020, 2020
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This article aims to provide a critical review of the literature and indicate some directions for future research based on the current knowledge gaps in the area of nature-based solutions (NBSs) for hydro-meteorological risk reduction. The final full analysis was performed on 146 closely related articles. A review showed that many advancements related to NBSs have been made to date, but there are still many challenges that will play an important role in extending knowledge in the coming years.
P. A. Jiménez and J. Dudhia
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmdd-7-9063-2014, https://doi.org/10.5194/gmdd-7-9063-2014, 2014
Revised manuscript not accepted
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In spite of the substantial observational evidence supporting a higher drag over shallow waters than over the open ocean, regional and global models widely use a single formulation valid for the open ocean. Results of this work indicate that adding the extra drag is necessary to reconcile model results with long term observations of the wind profile within the first 100 m of the atmosphere, being the first modeling evidence supporting the reported added drag over shallow waters.
J. A. Ruiz-Arias, J. Dudhia, and C. A. Gueymard
Geosci. Model Dev., 7, 1159–1174, https://doi.org/10.5194/gmd-7-1159-2014, https://doi.org/10.5194/gmd-7-1159-2014, 2014
J. A. Ruiz-Arias, J. Dudhia, C. A. Gueymard, and D. Pozo-Vázquez
Atmos. Chem. Phys., 13, 675–692, https://doi.org/10.5194/acp-13-675-2013, https://doi.org/10.5194/acp-13-675-2013, 2013
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Implementation of a Simple Actuator Disk for Large-Eddy Simulation in the Weather Research and Forecasting Model (WRF-SADLES v1.2) for wind turbine wake simulation
WRF-PDAF v1.0: implementation and application of an online localized ensemble data assimilation framework
Implementation and evaluation of diabatic advection in the Lagrangian transport model MPTRAC 2.6
An improved and extended parameterization of the CO2 15 µm cooling in the middle and upper atmosphere (CO2_cool_fort-1.0)
Development of a multiphase chemical mechanism to improve secondary organic aerosol formation in CAABA/MECCA (version 4.7.0)
Application of regional meteorology and air quality models based on the microprocessor without interlocked piped stages (MIPS) and LoongArch CPU platforms
Investigating ground-level ozone pollution in semi-arid and arid regions of Arizona using WRF-Chem v4.4 modeling
An objective identification technique for potential vorticity structures associated with African easterly waves
Importance of microphysical settings for climate forcing by stratospheric SO2 injections as modeled by SOCOL-AERv2
Assessment of surface ozone products from downscaled CAMS reanalysis and CAMS daily forecast using urban air quality monitoring stations in Iran
Open boundary conditions for atmospheric large-eddy simulations and their implementation in DALES4.4
Efficient and stable coupling of the SuperdropNet deep-learning-based cloud microphysics (v0.1.0) with the ICON climate and weather model (v2.6.5)
Three-dimensional variational assimilation with a multivariate background error covariance for the Model for Prediction Across Scales – Atmosphere with the Joint Effort for Data assimilation Integration (JEDI-MPAS 2.0.0-beta)
FUME 2.0 – Flexible Universal processor for Modeling Emissions
DEUCE v1.0: a neural network for probabilistic precipitation nowcasting with aleatoric and epistemic uncertainties
Evaluation of multi-season convection-permitting atmosphere – mixed-layer ocean simulations of the Maritime Continent
Investigating the impact of coupling HARMONIE-WINS50 (cy43) meteorology to LOTOS-EUROS (v2.2.002) on a simulation of NO2 concentrations over the Netherlands
Balloon drift estimation and improved position estimates for radiosondes
Emission ensemble approach to improve the development of multi-scale emission inventories
What is the relative impact of nudging and online coupling on meteorological variables, pollutant concentrations and aerosol optical properties?
Diagnosing drivers of PM2.5 simulation biases in China from meteorology, chemical composition, and emission sources using an efficient machine learning method
Validation and analysis of the Polair3D v1.11 chemical transport model over Quebec
Assimilation of GNSS tropospheric gradients into the Weather Research and Forecasting (WRF) model version 4.4.1
Identifying atmospheric rivers and their poleward latent heat transport with generalizable neural networks: ARCNNv1
Assessing acetone for the GISS ModelE2.1 Earth system model
Bergen metrics: composite error metrics for assessing performance of climate models using EURO-CORDEX simulations
A dynamic approach to three-dimensional radiative transfer in subkilometer-scale numerical weather prediction models: the dynamic TenStream solver v1.0
Evaluation and development of surface layer scheme representation of temperature inversions over boreal forests in Arctic wintertime conditions
Modelling wind farm effects in HARMONIE–AROME (cycle 43.2.2) – Part 1: Implementation and evaluation
Analytical and adaptable initial conditions for dry and moist baroclinic waves in the global hydrostatic model OpenIFS (CY43R3)
Challenges of constructing and selecting the “perfect” boundary conditions for the large-eddy simulation model PALM
A machine learning approach for evaluating Southern Ocean cloud radiative biases in a global atmosphere model
Decision Support System version 1.0 (DSS v1.0) for air quality management in Delhi, India
How non-equilibrium aerosol chemistry impacts particle acidity: the GMXe AERosol CHEMistry (GMXe–AERCHEM, v1.0) sub-submodel of MESSy
A grid model for vertical correction of precipitable water vapor over the Chinese mainland and surrounding areas using random forest
MEXPLORER 1.0.0 – a mechanism explorer for analysis and visualization of chemical reaction pathways based on graph theory
Advances and prospects of deep learning for medium-range extreme weather forecasting
An overview of the Western United States Dynamically Downscaled Dataset (WUS-D3)
cloudbandPy 1.0: an automated algorithm for the detection of tropical–extratropical cloud bands
PyRTlib: an educational Python-based library for non-scattering atmospheric microwave radiative transfer computations
Deep learning applied to CO2 power plant emissions quantification using simulated satellite images
Sensitivity of the WRF-Chem v4.4 simulations of ozone and formaldehyde and their precursors to multiple bottom-up emission inventories over East Asia during the KORUS-AQ 2016 field campaign
Optimising urban measurement networks for CO2 flux estimation: a high-resolution observing system simulation experiment using GRAMM/GRAL
Assessment of climate biases in OpenIFS version 43r3 across model horizontal resolutions and time steps
High-resolution multi-scaling of outdoor human thermal comfort and its intra-urban variability based on machine learning
Effects of vertical grid spacing on the climate simulated in the ICON-Sapphire global storm-resolving model
Development of the tangent linear and adjoint models of the global online chemical transport model MPAS-CO2 v7.3
Impacts of updated reaction kinetics on the global GEOS-Chem simulation of atmospheric chemistry
Spatial spin-up of precipitation in limited-area convection-permitting simulations over North America using the CRCM6/GEM5.0 model
Sensitivity of atmospheric rivers to aerosol treatment in regional climate simulations: insights from the AIRA identification algorithm
Hai Bui, Mostafa Bakhoday-Paskyabi, and Mohammadreza Mohammadpour-Penchah
Geosci. Model Dev., 17, 4447–4465, https://doi.org/10.5194/gmd-17-4447-2024, https://doi.org/10.5194/gmd-17-4447-2024, 2024
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We developed a new wind turbine wake model, the Simple Actuator Disc for Large Eddy Simulation (SADLES), integrated with the widely used Weather Research and Forecasting (WRF) model. WRF-SADLES accurately simulates wind turbine wakes at resolutions of a few dozen meters, aligning well with idealized simulations and observational measurements. This makes WRF-SADLES a promising tool for wind energy research, offering a balance between accuracy, computational efficiency, and ease of implementation.
Changliang Shao and Lars Nerger
Geosci. Model Dev., 17, 4433–4445, https://doi.org/10.5194/gmd-17-4433-2024, https://doi.org/10.5194/gmd-17-4433-2024, 2024
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This paper introduces and evaluates WRF-PDAF, a fully online-coupled ensemble data assimilation (DA) system. A key advantage of the WRF-PDAF configuration is its ability to concurrently integrate all ensemble states, eliminating the need for time-consuming distribution and collection of ensembles during the coupling communication. The extra time required for DA amounts to only 20.6 % per cycle. Twin experiment results underscore the effectiveness of the WRF-PDAF system.
Jan Clemens, Lars Hoffmann, Bärbel Vogel, Sabine Grießbach, and Nicole Thomas
Geosci. Model Dev., 17, 4467–4493, https://doi.org/10.5194/gmd-17-4467-2024, https://doi.org/10.5194/gmd-17-4467-2024, 2024
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Lagrangian transport models simulate the transport of air masses in the atmosphere. For example, one model (CLaMS) is well suited to calculating transport as it uses a special coordinate system and special vertical wind. However, it only runs inefficiently on modern supercomputers. Hence, we have implemented the benefits of CLaMS into a new model (MPTRAC), which is already highly efficient on modern supercomputers. Finally, in extensive tests, we showed that CLaMS and MPTRAC agree very well.
Manuel López-Puertas, Federico Fabiano, Victor Fomichev, Bernd Funke, and Daniel R. Marsh
Geosci. Model Dev., 17, 4401–4432, https://doi.org/10.5194/gmd-17-4401-2024, https://doi.org/10.5194/gmd-17-4401-2024, 2024
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The radiative infrared cooling of CO2 in the middle atmosphere is crucial for computing its thermal structure. It requires one however to include non-local thermodynamic equilibrium processes which are computationally very expensive, which cannot be afforded by climate models. In this work, we present an updated, efficient, accurate and very fast (~50 µs) parameterization of that cooling able to cope with CO2 abundances from half the pre-industrial values to 10 times the current abundance.
Felix Wieser, Rolf Sander, Changmin Cho, Hendrik Fuchs, Thorsten Hohaus, Anna Novelli, Ralf Tillmann, and Domenico Taraborrelli
Geosci. Model Dev., 17, 4311–4330, https://doi.org/10.5194/gmd-17-4311-2024, https://doi.org/10.5194/gmd-17-4311-2024, 2024
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The chemistry scheme of the atmospheric box model CAABA/MECCA is expanded to achieve an improved aerosol formation from emitted organic compounds. In addition to newly added reactions, temperature-dependent partitioning of all new species between the gas and aqueous phases is estimated and included in the pre-existing scheme. Sensitivity runs show an overestimation of key compounds from isoprene, which can be explained by a lack of aqueous-phase degradation reactions and box model limitations.
Zehua Bai, Qizhong Wu, Kai Cao, Yiming Sun, and Huaqiong Cheng
Geosci. Model Dev., 17, 4383–4399, https://doi.org/10.5194/gmd-17-4383-2024, https://doi.org/10.5194/gmd-17-4383-2024, 2024
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There is relatively limited research on the application of scientific computing on RISC CPU platforms. The MIPS architecture CPUs, a type of RISC CPUs, have distinct advantages in energy efficiency and scalability. The air quality modeling system can run stably on the MIPS and LoongArch platforms, and the experiment results verify the stability of scientific computing on the platforms. The work provides a technical foundation for the scientific application based on MIPS and LoongArch.
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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This study presents a method for identifying and tracking 3-D potential vorticity structures within African easterly waves (AEWs). Each identified structure is characterized by descriptors, including its 3-D position and orientation, which have been validated through composite comparisons. A trough-centric perspective on the descriptors reveals the evolution and distinct characteristics of AEWs. These descriptors serve as valuable statistical inputs for the study of AEW-related phenomena.
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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We investigate the sensitivity of aerosol size distributions in the presence of strong SO2 injections for climate interventions or after volcanic eruptions to the call sequence and frequency of the routines for nucleation and condensation in sectional aerosol models with operator splitting. Using the aerosol–chemistry–climate model SOCOL-AERv2, we show that the radiative and chemical outputs are sensitive to these settings at high H2SO4 supersaturations and how to obtain reliable results.
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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This paper assesses the capability of two state-of-the-art global datasets in simulating surface ozone over Iran using a new methodology. It is found that the global model data need to be downscaled for regulatory purposes or policy applications at local scales. The method can be useful not only for the evaluation but also for the prediction of other chemical species, such as aerosols.
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.
Junyu Li, Yuxin Wang, Lilong Liu, Yibin Yao, Liangke Huang, and Feijuan Li
Geosci. Model Dev., 17, 2569–2581, https://doi.org/10.5194/gmd-17-2569-2024, https://doi.org/10.5194/gmd-17-2569-2024, 2024
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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.
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.
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.
Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Grégoire Broquet, Gerrit Kuhlmann, and Marc Bocquet
Geosci. Model Dev., 17, 1995–2014, https://doi.org/10.5194/gmd-17-1995-2024, https://doi.org/10.5194/gmd-17-1995-2024, 2024
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Our research presents an innovative approach to estimating power plant CO2 emissions from satellite images of the corresponding plumes such as those from the forthcoming CO2M satellite constellation. The exploitation of these images is challenging due to noise and meteorological uncertainties. To overcome these obstacles, we use a deep learning neural network trained on simulated CO2 images. Our method outperforms alternatives, providing a positive perspective for the analysis of CO2M images.
Kyoung-Min Kim, Si-Wan Kim, Seunghwan Seo, Donald R. Blake, Seogju Cho, James H. Crawford, Louisa K. Emmons, Alan Fried, Jay R. Herman, Jinkyu Hong, Jinsang Jung, Gabriele G. Pfister, Andrew J. Weinheimer, Jung-Hun Woo, and Qiang Zhang
Geosci. Model Dev., 17, 1931–1955, https://doi.org/10.5194/gmd-17-1931-2024, https://doi.org/10.5194/gmd-17-1931-2024, 2024
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Three emission inventories were evaluated for East Asia using data acquired during a field campaign in 2016. The inventories successfully reproduced the daily variations of ozone and nitrogen dioxide. However, the spatial distributions of model ozone did not fully agree with the observations. Additionally, all simulations underestimated carbon monoxide and volatile organic compound (VOC) levels. Increasing VOC emissions over South Korea resulted in improved ozone simulations.
Sanam Noreen Vardag and Robert Maiwald
Geosci. Model Dev., 17, 1885–1902, https://doi.org/10.5194/gmd-17-1885-2024, https://doi.org/10.5194/gmd-17-1885-2024, 2024
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We use the atmospheric transport model GRAMM/GRAL in a Bayesian inversion to estimate urban CO2 emissions on a neighbourhood scale. We analyse the effect of varying number, precision and location of CO2 sensors for CO2 flux estimation. We further test the inclusion of co-emitted species and correlation in the inversion. The study showcases the general usefulness of GRAMM/GRAL in measurement network design.
Abhishek Savita, Joakim Kjellsson, Robin Pilch Kedzierski, Mojib Latif, Tabea Rahm, Sebastian Wahl, and Wonsun Park
Geosci. Model Dev., 17, 1813–1829, https://doi.org/10.5194/gmd-17-1813-2024, https://doi.org/10.5194/gmd-17-1813-2024, 2024
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The OpenIFS model is used to examine the impact of horizontal resolutions (HR) and model time steps. We find that the surface wind biases over the oceans, in particular the Southern Ocean, are sensitive to the model time step and HR, with the HR having the smallest biases. When using a coarse-resolution model with a shorter time step, a similar improvement is also found. Climate biases can be reduced in the OpenIFS model at a cheaper cost by reducing the time step rather than increasing the HR.
Ferdinand Briegel, Jonas Wehrle, Dirk Schindler, and Andreas Christen
Geosci. Model Dev., 17, 1667–1688, https://doi.org/10.5194/gmd-17-1667-2024, https://doi.org/10.5194/gmd-17-1667-2024, 2024
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We present a new approach to model heat stress in cities using artificial intelligence (AI). We show that the AI model is fast in terms of prediction but accurate when evaluated with measurements. The fast-predictive AI model enables several new potential applications, including heat stress prediction and warning; downscaling of potential future climates; evaluation of adaptation effectiveness; and, more fundamentally, development of guidelines to support urban planning and policymaking.
Hauke Schmidt, Sebastian Rast, Jiawei Bao, Amrit Cassim, Shih-Wei Fang, Diego Jimenez-de la Cuesta, Paul Keil, Lukas Kluft, Clarissa Kroll, Theresa Lang, Ulrike Niemeier, Andrea Schneidereit, Andrew I. L. Williams, and Bjorn Stevens
Geosci. Model Dev., 17, 1563–1584, https://doi.org/10.5194/gmd-17-1563-2024, https://doi.org/10.5194/gmd-17-1563-2024, 2024
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A recent development in numerical simulations of the global atmosphere is the increase in horizontal resolution to grid spacings of a few kilometers. However, the vertical grid spacing of these models has not been reduced at the same rate as the horizontal grid spacing. Here, we assess the effects of much finer vertical grid spacings, in particular the impacts on cloud quantities and the atmospheric energy balance.
Tao Zheng, Sha Feng, Jeffrey Steward, Xiaoxu Tian, David Baker, and Martin Baxter
Geosci. Model Dev., 17, 1543–1562, https://doi.org/10.5194/gmd-17-1543-2024, https://doi.org/10.5194/gmd-17-1543-2024, 2024
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The tangent linear and adjoint models have been successfully implemented in the MPAS-CO2 system, which has undergone rigorous accuracy testing. This development lays the groundwork for a global carbon flux data assimilation system, which offers the flexibility of high-resolution focus on specific areas, while maintaining a coarser resolution elsewhere. This approach significantly reduces computational costs and is thus perfectly suited for future CO2 geostationery and imager satellites.
Kelvin H. Bates, Mathew J. Evans, Barron H. Henderson, and Daniel J. Jacob
Geosci. Model Dev., 17, 1511–1524, https://doi.org/10.5194/gmd-17-1511-2024, https://doi.org/10.5194/gmd-17-1511-2024, 2024
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Accurate representation of rates and products of chemical reactions in atmospheric models is crucial for simulating concentrations of pollutants and climate forcers. We update the widely used GEOS-Chem atmospheric chemistry model with reaction parameters from recent compilations of experimental data and demonstrate the implications for key atmospheric chemical species. The updates decrease tropospheric CO mixing ratios and increase stratospheric nitrogen oxide mixing ratios, among other changes.
François Roberge, Alejandro Di Luca, René Laprise, Philippe Lucas-Picher, and Julie Thériault
Geosci. Model Dev., 17, 1497–1510, https://doi.org/10.5194/gmd-17-1497-2024, https://doi.org/10.5194/gmd-17-1497-2024, 2024
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Our study addresses a challenge in dynamical downscaling using regional climate models, focusing on the lack of small-scale features near the boundaries. We introduce a method to identify this “spatial spin-up” in precipitation simulations. Results show spin-up distances up to 300 km, varying by season and driving variable. Double nesting with comprehensive variables (e.g. microphysical variables) offers advantages. Findings will help optimize simulations for better climate projections.
Eloisa Raluy-López, Juan Pedro Montávez, and Pedro Jiménez-Guerrero
Geosci. Model Dev., 17, 1469–1495, https://doi.org/10.5194/gmd-17-1469-2024, https://doi.org/10.5194/gmd-17-1469-2024, 2024
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Atmospheric rivers (ARs) represent a significant source of water but are also related to extreme precipitation events. Here, we present a new regional-scale AR identification algorithm and apply it to three simulations that include aerosol interactions at different levels. The results show that aerosols modify the intensity and trajectory of ARs and redistribute the AR-related precipitation. Thus, the correct inclusion of aerosol effects is important in the simulation of AR behavior.
Cited articles
Aegerter,
C., Wang, J., Ge, C., Irmak, S., Oglesby, R., Wardlow, B., Yang, H., You, J., Shulski, M.,
Aegerter, C., Wang, J., Ge, C., Irmak, S., Oglesby, R., Wardlow, B., Yang, H., You, J., and
Shulski, M.: Mesoscale Modeling of the Meteorological Impacts of Irrigation during the 2012
Central Plains Drought, J. Appl. Meteorol. Climatol., 56, 1259–1283,
https://doi.org/10.1175/JAMC-D-16-0292.1, 2017. a, b, c, d, e, f
ARPAE: Bollettino agroclimatico mensile,
Luglio 2015, Tech. rep., Arpae Servizio idro-meteo-clima, 2015a. a
Bavi, A., Kashkuli, H. A., Boroomand, S., Naseri, A.,
and Albaji, M.: Evaporation losses from sprinkler irrigation systems under various operating
conditions, J. Appl. Sci., 9, 597–600, https://doi.org/10.3923/jas.2009.597.600, 2009. a
Bin Abdullah, K.: Use of water and
land for food security and environmental sustainability, in: Irrigation and Drainage, vol. 55,
219–222, John Wiley and Sons, Ltd., available
https://doi.org/10.1002/ird.254, 2006. a, b
Bonfils, C. and Lobell, D.: Empirical evidence
for a recent slowdown in irrigation-induced cooling, P. Natl. Acad. Sci. USA, 104,
13582–13587, https://doi.org/10.1073/pnas.0700144104, 2007. a
Boucher, O., Myhre, G., and
Myhre, A.: Direct human influence of irrigation on atmospheric water vapour and climate,
Clim. Dynam., 22, 597–603, https://doi.org/10.1007/s00382-004-0402-4, 2004. a, b, c, d
Cook, B. I., Puma, M. J., and Krakauer,
N. Y.: Irrigation induced surface cooling in the context of modern and increased greenhouse gas
forcing, Clim. Dynam., 37, 1587–1600, https://doi.org/10.1007/s00382-010-0932-x, 2010. a, b, c
Cook,
B. I., Shukla, S. P., Puma, M. J., and Nazarenko, L. S.: Irrigation as an historical climate
forcing, Clim. Dynam., 44, 1715–1730, https://doi.org/10.1007/s00382-014-2204-7, 2015. a, b
Daccache,
A., Ciurana, J. S., Rodriguez Diaz, J. A., and Knox, J. W.: Water and energy footprint of
irrigated agriculture in the Mediterranean region, Environ. Res. Lett., 9, 124014,
https://doi.org/10.1088/1748-9326/9/12/124014, 2014. a
de Vrese, P. and
Hagemann, S.: Uncertainties in modelling the climate impact of irrigation, Clim. Dynam., 51,
2023–2038, https://doi.org/10.1007/s00382-017-3996-z, 2018. a
Deangelis, A., Dominguez, F., Fan, Y., Robock, A., Kustu, M. D., and
Robinson, D.: Evidence of enhanced precipitation due to irrigation over the Great Plains of the
United States, J. Geophys. Res.-Atmos., 115, D15115, https://doi.org/10.1029/2010JD013892, 2010. a
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P.,
Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars,
A. C., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer,
A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg,
P., Köhler, M., Matricardi, M., Mcnally, A. P., Monge-Sanz, B. M., Morcrette, J. J., Park,
B. K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J. N., and Vitart, F.: The
ERA-Interim reanalysis: Configuration and performance of the data assimilation system,
Q. J. Roy. Meteor. Soc., 137, 553–597, https://doi.org/10.1002/qj.828, 2011. a, b
Della-Marta, P. M., Luterbacher, J., von Weissenfluh, H., Xoplaki, E.,
Brunet, M., and Wanner, H.: Summer heat waves over western Europe 1880–2003, their relationship
to large-scale forcings and predictability, Clim. Dynam., 29, 251–275,
https://doi.org/10.1007/s00382-007-0233-1, 2007. a
Douglas, E. M., Beltrán-Przekurat, A., Niyogi, D.,
Pielke, R. A., and Vörösmarty, C. J.: The impact of agricultural intensification and
irrigation on land-atmosphere interactions and Indian monsoon precipitation – A mesoscale
modeling perspective, Global Planet. Change, 67, 117–128,
https://doi.org/10.1016/j.gloplacha.2008.12.007, 2009. a
Ek, M. B.: Implementation of Noah land surface
model advances in the National Centers for Environmental Prediction operational mesoscale Eta
model, J. Geophys. Res., 108, 8851, https://doi.org/10.1029/2002JD003296, 2003. a
Eurostat: Agricultural products, Publications Office
of the European Union, Luxembourg, https://doi.org/10.2785/45595, 2013. a
Fader, M., Shi,
S., von Bloh, W., Bondeau, A., and Cramer, W.: Mediterranean irrigation under climate change: more
efficient irrigation needed to compensate for increases in irrigation water requirements,
Hydrol. Earth Syst. Sci., 20, 953–973, https://doi.org/10.5194/hess-20-953-2016, 2016. a, b, c
García-Herrera, R., Díaz, J., Trigo, R. M.,
Luterbacher, J., and Fischer, E. M.: A Review of the European Summer Heat Wave of 2003,
Crit. Rev. Environ. Sci. Technol., 40, 267–306, https://doi.org/10.1080/10643380802238137, 2010. a
Giorgi, F. and Lionello, P.: Climate change
projections for the Mediterranean region, Global Planet. Change, 63, 90–104,
https://doi.org/10.1016/j.gloplacha.2007.09.005, 2008. a
Guimberteau, M., Laval, K., Perrier, A., and Polcher,
J.: Global effect of irrigation and its impact on the onset of the Indian summer monsoon,
Clim. Dynam., 39, 1329–1348, https://doi.org/10.1007/s00382-011-1252-5, 2012. a, b, c
Harding, K. J., Twine, T. E.,
and Lu, Y.: Effects of dynamic crop growth on the simulated precipitation response to
irrigation, Earth Interact., 19, 1–31, https://doi.org/10.1175/EI-D-15-0030.1, 2015. a
Hong, S.-Y., Lim, K.-S.,
Kim, J.-H., Lim, J.-O. J., and Dudhia, J.: WRF Single-Moment 6-Class Microphysics Scheme (WSM6),
Weather, 2010, 5–6, 2005. a
Hong, S.-Y., Noh, Y., and Dudhia, J.:
A New Vertical Diffusion Package with an Explicit Treatment of Entrainment Processes,
Mon. Weather Rev., 134, 2318–2341, https://doi.org/10.1175/MWR3199.1, 2006. a
IPCC: Climate Change 2014 Impacts, Adaptation, and
Vulnerability Part A: Global and Sectoral Aspects, Working Group II Contribution to the Fifth
Assessment Report of the Intergovernmental Panel on Climate Change, , 2014. a
Jägermeyr, J., Gerten, D., Heinke, J., Schaphoff, S.,
Kummu, M., and Lucht, W.: Water savings potentials of irrigation systems: global simulation of
processes and linkages, Hydrol. Earth Syst. Sci., 19, 3073–3091,
https://doi.org/10.5194/hess-19-3073-2015, 2015. a
Kioutsioukis, I., de Meij, A., Jakobs, H., Katragkou, E.,
Vinuesa, J. F., and Kazantzidis, A.: High resolution WRF ensemble forecasting for irrigation:
Multi-variable evaluation, Atmos. Res., 167, 156–174, https://doi.org/10.1016/j.atmosres.2015.07.015,
2016. a
Krakauer, N. Y., Puma, M. J., Cook, B. I.,
Gentine, P., and Nazarenko, L.: Ocean–atmosphere interactions modulate irrigation's climate
impacts, Earth Syst. Dynam., 7, 863–876, https://doi.org/10.5194/esd-7-863-2016, 2016. a
Kueppers, L. M., Snyder, M. A., Sloan, L. C., Cayan, D.,
Jin, J., Kanamaru, H., Kanamitsu, M., Miller, N. L., Tyree, M., Du, H., and Weare, B.: Seasonal
temperature responses to land-use change in the western United States, Global Planet. Change,
60, 250–264, https://doi.org/10.1016/j.gloplacha.2007.03.005, 2008. a, b
Lawston,
P. M., Santanello, J. A., Zaitchik, B. F., and Rodell, M.: Impact of Irrigation Methods on Land
Surface Model Spinup and Initialization of WRF Forecasts, J. Hydrometeorol., 16, 1135–1154,
https://doi.org/10.1175/jhm-d-14-0203.1, 2015. a
Lee, E., Sacks,
W. J., Chase, T. N., and Foley, J. A.: Simulated impacts of irrigation on the atmospheric
circulation over Asia, J. Geophys. Res.-Atmos., 116, 1–13, https://doi.org/10.1029/2010JD014740, 2011. a
Lobell, D., Bala, G., Mirin, A., Phillips, T., Maxwell, R., and Rotman, D.: Regional differences
in the influence of irrigation on climate, J. Climate, 22, 2248–2255,
https://doi.org/10.1175/2008JCLI2703.1, 2009. a
Lobell, D. B., Bala, G.,
Bonfils, C., and Duffy, P. B.: Potential bias of model projected greenhouse warming in irrigated
regions, Geophys. Res. Lett., 33, L13709, https://doi.org/10.1029/2006GL026770, 2006. a, b
Lobell, D. B., Bonfils, C. J., Kueppers, L. M., and Snyder, M. A.: Irrigation cooling effect on
temperature and heat index extremes, Geophys. Res. Lett., 35, L09705,
https://doi.org/10.1029/2008GL034145, 2008a. a, b
Lobell, D. B., Burke, M. B., Tebaldi, C., Mastrandrea, M. D., Falcon,
W. P., and Naylor, R. L.: Prioritizing climate change adaptation needs for food security in
2030, Science, 319, 607–610, https://doi.org/10.1126/science.1152339, 2008b. a
Lv, G., Kang, Y., Li, L., and Wan, S.:
Effect of irrigation methods on root development and profile soil water uptake in winter wheat,
Irrig. Sci., 28, 387–398, https://doi.org/10.1007/s00271-009-0200-1, 2010. a
Mooney, P. A., Mulligan., F. J.,
and Fealy, R.: Evaluation of the sensitivity of the weather research and forecasting model to
parameterization schemes for regional climates of europe over the period 1990-95, J. Climate, 26,
1002–1017, https://doi.org/10.1175/JCLI-D-11-00676.1, 2013. a
Nair, S.,
Johnson, J., and Wang, C.: Efficiency of irrigation water use: A review from the perspectives of
multiple disciplines, Agron. J., 105, 351–363, https://doi.org/10.2134/agronj2012.0421, 2013. a
National
National Centers for Environmental Prediction/National Weather Service/NOAA/U.S. Department of Commerce: NCEP GFS 0.25
Degree Global Forecast Grids Historical Archive, Research Data Archive at the National Center
for Atmospheric Research, Computational and Information Systems Laboratory, Tech. rep.,
https://doi.org/10.5065/D65D8PWK, 2015. a
Oleson, K. W., Lawrence, D. M., Bonan, G. B., Flanner, M. G., Kluzek, E., J, P., Levis, S.,
Swenson, S. C., Thornton, E., Feddema, J., Heald, C. L., Lamarque, J.-F., Niu, G.-y., Qian, T.,
Running, S., Sakaguchi, K., Yang, L., Zeng, X., and Zeng, X.: Technical Description of version
4.0 of the Community Land Model (CLM), available at:
National Center for Atmospheric Research, Boulder, CO, 2013. a, b, c
Ozdogan, M., Rodell, M., Beaudoing, H. K., and Toll, D. L.: Simulating the Effects of Irrigation
over the United States in a Land Surface Model Based on Satellite-Derived Agricultural Data,
J. Hydrometeorol., 11, 171–184, https://doi.org/10.1175/2009jhm1116.1, 2009. a
Pielke, R. A. and Zeng, X.: Influence on severe storm
development of irrigated land, Natl. Weather Digest, 14, 16–17, 1989. a
Puma, M. J. and Cook, B. I.: Effects of irrigation on
global climate during the 20th century, J. Geophys. Res.-Atmos., 115, D16120,
https://doi.org/10.1029/2010JD014122, 2010. a, b
Qian, Y., Huang, M., Yang,
B., and Berg, L. K.: A Modeling Study of Irrigation Effects on Surface Fluxes and Land–Air–Cloud
Interactions in the Southern Great Plains, J. Hydrometeorol., 14, 700–721,
https://doi.org/10.1175/jhm-d-12-0134.1, 2013. a, b, c, d
Running, S., Mu, Q., and Zhao,
M.: MOD16A2 MODIS/Terra Net Evapotranspiration 8-Day L4 Global 500m SIN Grid V006 [Data set],
NASA EOSDIS Land Processes DAAC, https://doi.org/10.5067/MODIS/MOD16A2.006, 2017. a, b
Saeed, F., Hagemann, S., and
Jacob, D.: Impact of irrigation on the South Asian summer monsoon, Geophys. Res. Lett., 36,
L20711, https://doi.org/10.1029/2009GL040625, 2009. a
Saha, S., Moorthi, S., Wu, X., Wang, J., Nadiga, S., Tripp, P., Behringer, D., Hou, Y.-T., Chuang, H.-y., Iredell, M., Ek, M., Meng, J., Yang, R., Mendez, M. P., van den Dool, H., Zhang, Q., Wang, W., Chen, M., and Becker, E.: The NCEP Climate Forecast System Version 2. J. Climate, 27, 2185–2208, https://doi.org/10.1175/JCLI-D-12-00823.1, 2014. a
Seneviratne, S. I. and
Stöckli, R.: Climate Variability and Extremes during the Past 100 Years, Springer
Netherlands, Dordrecht, https://doi.org/10.1007/978-1-4020-6766-2, 2008. a
Siebert, S. and
Döll, P.: Quantifying blue and green virtual water contents in global crop production as
well as potential production losses without irrigation, J. Hydrol., 384, 198–217,
https://doi.org/10.1016/j.jhydrol.2009.07.031, 2010. a
Siebert, S.,
Henrich, V., Frenken, K., and Burke, J.: Global Map of Irrigation Areas version 5., Food and
Agriculture Organization of the United Nations,
2013. a
Skamarock, W., Klemp, J., Dudhi, J., Gill, D., Barker, D., Duda, M., Huang,
X.-Y., Wang, W., and Powers, J.: A Description of the Advanced Research WRF Version 3, Technical
Report, p. 113,
https://doi.org/10.5065/D6DZ069T, 2008. a, b, c
Sorooshian, S., Li,
J., Hsu, K.-l., and Gao, X.: How significant is the impact of irrigation on the local
hydroclimate in California's Central Valley? Comparison of model results with ground and
remote-sensing data, J. Geophys. Res., 116, D06102, https://doi.org/10.1029/2010JD014775, 2011. a, b, c
Sorooshian, S.,
Aghakouchak, A., and Li, J.: Influence of irrigation on land hydrological processes over
California, J. Geophys. Res.-Atmos., 119, 13137–13152, https://doi.org/10.1002/2014JD022232, 2014. a
Sridhar, V.: Tracking the Influence of Irrigation on Land
Surface Fluxes and Boundary Layer Climatology, J. Cont. Water Res. Educ., 152, 79–93,
https://doi.org/10.1111/j.1936-704X.2013.03170.x, 2013. a
Stergiou, I.,
Tagaris, E., and Sotiropoulou, R.-E. P.: Sensitivity Assessment of WRF Parameterizations over
Europe, Proceedings, 1, 119, https://doi.org/10.3390/ecas2017-04138, 2017. a
Tewari, M., Chen, F., Wang, W., Dudhia, J., Lemone,
M. A., Mitchell, K., Ek, M., Gayno, G., Wegiel, J., and Cuenca, R. H.: Implementation and
verification of the Unified Noah Land surface model in the WRF model, 16th conference on
numerical weather prediction,
pp. 11–15, 2004. a
Thiery, W., Davin, E. L., Lawrence, D. M., Hirsch, A. L., Hauser, M., and Seneviratne, S. I.:
Present-day irrigation mitigates heat extremes, J. Geophys. Res.-Atmos., 122, 1403–1422,
https://doi.org/10.1002/2016JD025740, 2017. a, b, c, d
Thornthwaite, C. W.: An Approach toward a Rational
Classification of Climate, Geograph. Rev., 38, 55, https://doi.org/10.2307/210739, 1948. a
Tuinenburg, O. A. and
de Vries, J. P. R.: Irrigation Patterns Resemble ERA-Interim Reanalysis Soil Moisture Additions,
Geophys. Res. Lett., 44, 10341–10348, https://doi.org/10.1002/2017GL074884, 2017. a, b
Valmassoi, A.: Development of three new surface irrigation
parameterizations in the WRF-ARW model: evaluation for the Po Valley (Italy) case study,
Mendeley Data, v2, https://doi.org/10.17632/t3b6rtccj9, 2019. a
Van Alfen, N. K.: Water: advanced irrigation technologies,
2nd Edn., San Diego, CA, USA, Elsevier, Vol. 5, 378–406, 2014. a
Wada, Y., Wisser, D., Eisner,
S., Flörke, M., Gerten, D., Haddeland, I., Hanasaki, N., Masaki, Y., Portmann, F. T.,
Stacke, T., Tessler, Z., and Schewe, J.: Multimodel projections and uncertainties of irrigation
water demand under climate change, Geophys. Res. Lett., 40, 4626–4632,
https://doi.org/10.1002/grl.50686, 2013. a
Wei, J., Dirmeyer,
P. A., Wisser, D., Bosilovich, M. G., and Mocko, D. M.: Where Does the Irrigation Water Go? An
Estimate of the Contribution of Irrigation to Precipitation Using MERRA, J. Hydrometeorol., 14,
275–289, https://doi.org/10.1175/JHM-D-12-079.1, 2013. a, b, c
Zampieri, M., Ceglar, A., Dentener, F., Dosio, A., Naumann, G., Van
Den Berg, M., and Toreti, A.: When will current climate extremes affecting maize production
become the norm?, Earth's Future, 7, 113–122, https://doi.org/10.1029/2018EF000995, 2019. a
Zhang, C. and Wang, Y.: Projected future
changes of tropical cyclone activity over the Western North and South Pacific in a 20-km-Mesh
regional climate model, J. Climate, 30, 5923–5941, https://doi.org/10.1175/JCLI-D-16-0597.1, 2017. a
Short summary
Irrigation affects the atmosphere and models are required to understand its full impact. However, there is no agreed procedure to describe irrigation within regional models. The present study introduces three new methods to integrate this process into the models and validates it for the Po Valley in northern Italy. All the tests done show that the results are improved with the new irrigation techniques when compared against some measures (e.g., temperature, potential evapotranspiration).
Irrigation affects the atmosphere and models are required to understand its full impact....
