Articles | Volume 15, issue 19
https://doi.org/10.5194/gmd-15-7353-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-7353-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
05 Oct 2022
Development and technical paper |
| 05 Oct 2022
| 05 Oct 2022
Repeatable high-resolution statistical downscaling through deep learning
Dánnell Quesada-Chacón
CORRESPONDING AUTHOR
Institute of Hydrology and Meteorology, Technische Universität Dresden, Dresden, Germany
Klemens Barfus
Institute of Hydrology and Meteorology, Technische Universität Dresden, Dresden, Germany
Christian Bernhofer
Institute of Hydrology and Meteorology, Technische Universität Dresden, Dresden, Germany
Related authors
Katja Frieler, Stefan Lange, Jacob Schewe, Matthias Mengel, Simon Treu, Christian Otto, Jan Volkholz, Christopher P. O. Reyer, Stefanie Heinicke, Colin Jones, Julia L. Blanchard, Cheryl S. Harrison, Colleen M. Petrik, Tyler D. Eddy, Kelly Ortega-Cisneros, Camilla Novaglio, Ryan Heneghan, Derek P. Tittensor, Olivier Maury, Matthias Büchner, Thomas Vogt, Dánnell Quesada Chacón, Kerry Emanuel, Chia-Ying Lee, Suzana J. Camargo, Jonas Jägermeyr, Sam Rabin, Jochen Klar, Iliusi D. Vega del Valle, Lisa Novak, Inga J. Sauer, Gitta Lasslop, Sarah Chadburn, Eleanor Burke, Angela Gallego-Sala, Noah Smith, Jinfeng Chang, Stijn Hantson, Chantelle Burton, Anne Gädeke, Fang Li, Simon N. Gosling, Hannes Müller Schmied, Fred Hattermann, Thomas Hickler, Rafael Marcé, Don Pierson, Wim Thiery, Daniel Mercado-Bettín, Robert Ladwig, Ana Isabel Ayala-Zamora, Matthew Forrest, Michel Bechtold, Robert Reinecke, Inge de Graaf, Jed O. Kaplan, Alexander Koch, and Matthieu Lengaigne
EGUsphere, https://doi.org/10.5194/egusphere-2025-2103, https://doi.org/10.5194/egusphere-2025-2103, 2025
Short summary
Short summary
This paper describes the experiments and data sets necessary to run historic and future impact projections, and the underlying assumptions of future climate change as defined by the 3rd round of the ISIMIP Project (Inter-sectoral Impactmodel Intercomparison Project, isimip.org). ISIMIP provides a framework for cross-sectorally consistent climate impact simulations to contribute to a comprehensive and consistent picture of the world under different climate-change scenarios.
Katja Frieler, Stefan Lange, Jacob Schewe, Matthias Mengel, Simon Treu, Christian Otto, Jan Volkholz, Christopher P. O. Reyer, Stefanie Heinicke, Colin Jones, Julia L. Blanchard, Cheryl S. Harrison, Colleen M. Petrik, Tyler D. Eddy, Kelly Ortega-Cisneros, Camilla Novaglio, Ryan Heneghan, Derek P. Tittensor, Olivier Maury, Matthias Büchner, Thomas Vogt, Dánnell Quesada Chacón, Kerry Emanuel, Chia-Ying Lee, Suzana J. Camargo, Jonas Jägermeyr, Sam Rabin, Jochen Klar, Iliusi D. Vega del Valle, Lisa Novak, Inga J. Sauer, Gitta Lasslop, Sarah Chadburn, Eleanor Burke, Angela Gallego-Sala, Noah Smith, Jinfeng Chang, Stijn Hantson, Chantelle Burton, Anne Gädeke, Fang Li, Simon N. Gosling, Hannes Müller Schmied, Fred Hattermann, Thomas Hickler, Rafael Marcé, Don Pierson, Wim Thiery, Daniel Mercado-Bettín, Robert Ladwig, Ana Isabel Ayala-Zamora, Matthew Forrest, Michel Bechtold, Robert Reinecke, Inge de Graaf, Jed O. Kaplan, Alexander Koch, and Matthieu Lengaigne
EGUsphere, https://doi.org/10.5194/egusphere-2025-2103, https://doi.org/10.5194/egusphere-2025-2103, 2025
Short summary
Short summary
This paper describes the experiments and data sets necessary to run historic and future impact projections, and the underlying assumptions of future climate change as defined by the 3rd round of the ISIMIP Project (Inter-sectoral Impactmodel Intercomparison Project, isimip.org). ISIMIP provides a framework for cross-sectorally consistent climate impact simulations to contribute to a comprehensive and consistent picture of the world under different climate-change scenarios.
Stefanie Fischer, Ronald Queck, Christian Bernhofer, and Matthias Mauder
EGUsphere, https://doi.org/10.5194/egusphere-2025-2118, https://doi.org/10.5194/egusphere-2025-2118, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
Accurate estimates of interception are important to assess the water availability in ecosystems. We analyzed rainfall interception for a forest site from plot to stand scale. During interception, eddy-covariance measurements of evaporation were systematically underestimated accounting for 24% of precipitation, while modelled interception evaporation accounted for 45%. As a consequence, we developed a hybrid correction approach to fit the evaporation data to both the energy and the water balance.
Ivan Vorobevskii, Jeongha Park, Dongkyun Kim, Klemens Barfus, and Rico Kronenberg
Hydrol. Earth Syst. Sci., 28, 391–416, https://doi.org/10.5194/hess-28-391-2024, https://doi.org/10.5194/hess-28-391-2024, 2024
Short summary
Short summary
High-resolution precipitation data are often a “must” as input for hydrological and hydraulic models (i.e. urban drainage modelling). However, station or climate projection data usually do not provide the required (e.g. sub-hourly) resolution. In the work, we present two new statistical models of different types to disaggregate precipitation from a daily to a 10 min scale. Both models were validated using radar data and then applied to climate models for 10 stations in Germany and South Korea.
Alberto Caldas-Alvarez, Markus Augenstein, Georgy Ayzel, Klemens Barfus, Ribu Cherian, Lisa Dillenardt, Felix Fauer, Hendrik Feldmann, Maik Heistermann, Alexia Karwat, Frank Kaspar, Heidi Kreibich, Etor Emanuel Lucio-Eceiza, Edmund P. Meredith, Susanna Mohr, Deborah Niermann, Stephan Pfahl, Florian Ruff, Henning W. Rust, Lukas Schoppa, Thomas Schwitalla, Stella Steidl, Annegret H. Thieken, Jordis S. Tradowsky, Volker Wulfmeyer, and Johannes Quaas
Nat. Hazards Earth Syst. Sci., 22, 3701–3724, https://doi.org/10.5194/nhess-22-3701-2022, https://doi.org/10.5194/nhess-22-3701-2022, 2022
Short summary
Short summary
In a warming climate, extreme precipitation events are becoming more frequent. To advance our knowledge on such phenomena, we present a multidisciplinary analysis of a selected case study that took place on 29 June 2017 in the Berlin metropolitan area. Our analysis provides evidence of the extremeness of the case from the atmospheric and the impacts perspectives as well as new insights on the physical mechanisms of the event at the meteorological and climate scales.
Ivan Vorobevskii, Thi Thanh Luong, Rico Kronenberg, Thomas Grünwald, and Christian Bernhofer
Hydrol. Earth Syst. Sci., 26, 3177–3239, https://doi.org/10.5194/hess-26-3177-2022, https://doi.org/10.5194/hess-26-3177-2022, 2022
Short summary
Short summary
In the study we analysed the uncertainties of the meteorological data and model parameterization for evaporation modelling. We have taken a physically based lumped BROOK90 model and applied it in three different frameworks using global, regional and local datasets. Validating the simulations with eddy-covariance data from five stations in Germany, we found that the accuracy model parameterization plays a bigger role than the quality of the meteorological forcing.
Judith Marie Pöschmann, Dongkyun Kim, Rico Kronenberg, and Christian Bernhofer
Nat. Hazards Earth Syst. Sci., 21, 1195–1207, https://doi.org/10.5194/nhess-21-1195-2021, https://doi.org/10.5194/nhess-21-1195-2021, 2021
Short summary
Short summary
We examined maximum rainfall values for different durations from 16 years of radar-based rainfall records for whole Germany. Unlike existing observations based on rain gauge data no clear linear relationship could be identified. However, by classifying all time series, we could identify three similar groups determined by the temporal structure of rainfall extremes observed in the study period. The study highlights the importance of using long data records and a dense measurement network.
Uta Moderow, Stefanie Fischer, Thomas Grünwald, Ronald Queck, and Christian Bernhofer
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2020-202, https://doi.org/10.5194/hess-2020-202, 2020
Preprint withdrawn
Short summary
Short summary
We analyzed three different estimates of evapotranspiration (ET) for four different sites along an elevation gradient of a low mountain range over 11 years. We found similar dependencies on meteorological variables for all three different ET estimates. Based on our analyses we recommend using a distinct ET estimate. Analysis further suggests that water temporally stored on plant surfaces should receive more attention. Our results contribute to determining reliable ET estimates.
Ivan Vorobevskii, Rico Kronenberg, and Christian Bernhofer
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2020-27, https://doi.org/10.5194/hess-2020-27, 2020
Preprint withdrawn
Short summary
Short summary
The paper aims to reveal features of behavior of compound precipitation-discharge extremes in small catchments of Saxony (Germany) using statistical methods with the following advances and innovations: binding meteorological and hydrological drivers of compound extremes, application of multivariate distribution to get return periods of daily-scale extremes, focus on small catchments, test interpolation methods for daily specific discharge, estimation of uncertainty of return period calculation.
Sergey Chalov, Pavel Terskii, Thomas Pluntke, Ludmila Efimova, Vasiliy Efimov, Vladimir Belyaev, Anna Terskaia, Michal Habel, Daniel Karthe, and Christian Bernhofer
Proc. IAHS, 381, 7–11, https://doi.org/10.5194/piahs-381-7-2019, https://doi.org/10.5194/piahs-381-7-2019, 2019
Solomon Hailu Gebrechorkos, Stephan Hülsmann, and Christian Bernhofer
Hydrol. Earth Syst. Sci., 22, 4547–4564, https://doi.org/10.5194/hess-22-4547-2018, https://doi.org/10.5194/hess-22-4547-2018, 2018
Short summary
Short summary
In Africa field-based meteorological data are scarce; therefore global data sources based on remote sensing and climate models are often used as alternatives. To assess their suitability for a large and topographically complex area in East Africa, we evaluated multiple climate data products with available ground station data at multiple timescales over 21 regions. The comprehensive evaluation resulted in identification of preferential data sources to be used for climate and hydrological studies.
Chunjing Qiu, Dan Zhu, Philippe Ciais, Bertrand Guenet, Gerhard Krinner, Shushi Peng, Mika Aurela, Christian Bernhofer, Christian Brümmer, Syndonia Bret-Harte, Housen Chu, Jiquan Chen, Ankur R. Desai, Jiří Dušek, Eugénie S. Euskirchen, Krzysztof Fortuniak, Lawrence B. Flanagan, Thomas Friborg, Mateusz Grygoruk, Sébastien Gogo, Thomas Grünwald, Birger U. Hansen, David Holl, Elyn Humphreys, Miriam Hurkuck, Gerard Kiely, Janina Klatt, Lars Kutzbach, Chloé Largeron, Fatima Laggoun-Défarge, Magnus Lund, Peter M. Lafleur, Xuefei Li, Ivan Mammarella, Lutz Merbold, Mats B. Nilsson, Janusz Olejnik, Mikaell Ottosson-Löfvenius, Walter Oechel, Frans-Jan W. Parmentier, Matthias Peichl, Norbert Pirk, Olli Peltola, Włodzimierz Pawlak, Daniel Rasse, Janne Rinne, Gaius Shaver, Hans Peter Schmid, Matteo Sottocornola, Rainer Steinbrecher, Torsten Sachs, Marek Urbaniak, Donatella Zona, and Klaudia Ziemblinska
Geosci. Model Dev., 11, 497–519, https://doi.org/10.5194/gmd-11-497-2018, https://doi.org/10.5194/gmd-11-497-2018, 2018
Short summary
Short summary
Northern peatlands store large amount of soil carbon and are vulnerable to climate change. We implemented peatland hydrological and carbon accumulation processes into the ORCHIDEE land surface model. The model was evaluated against EC measurements from 30 northern peatland sites. The model generally well reproduced the spatial gradient and temporal variations in GPP and NEE at these sites. Water table depth was not well predicted but had only small influence on simulated NEE.
X. Wu, N. Vuichard, P. Ciais, N. Viovy, N. de Noblet-Ducoudré, X. Wang, V. Magliulo, M. Wattenbach, L. Vitale, P. Di Tommasi, E. J. Moors, W. Jans, J. Elbers, E. Ceschia, T. Tallec, C. Bernhofer, T. Grünwald, C. Moureaux, T. Manise, A. Ligne, P. Cellier, B. Loubet, E. Larmanou, and D. Ripoche
Geosci. Model Dev., 9, 857–873, https://doi.org/10.5194/gmd-9-857-2016, https://doi.org/10.5194/gmd-9-857-2016, 2016
Short summary
Short summary
The response of crops to changing climate and atmospheric CO2 could have large effects on food production, terrestrial carbon, water, energy fluxes and the climate feedbacks. We developed a new process-oriented terrestrial biogeochemical model named ORCHIDEE-CROP (v0), which integrates a generic crop phenology and harvest module into the land surface model ORCHIDEE. Our model has good ability to capture the spatial gradients of crop phenology, carbon and energy-related variables across Europe.
F. Richter, C. Döring, M. Jansen, O. Panferov, U. Spank, and C. Bernhofer
Hydrol. Earth Syst. Sci., 19, 3457–3474, https://doi.org/10.5194/hess-19-3457-2015, https://doi.org/10.5194/hess-19-3457-2015, 2015
Short summary
Short summary
Predicting hydrological effects of land use change, e.g. enhanced cultivation of short rotation coppices, requires an adequate parameterisation. Measurements and modelling results show that leaf area index, stomatal resistance and in particular start and length of growing season are most sensitive to soil hydrological quantities, like ground water recharge (GWR). Only simulations over 30 years, reflecting long-term climate variability, show even zero GWR, especially in succeeding dry years.
M. Renner, K. Brust, K. Schwärzel, M. Volk, and C. Bernhofer
Hydrol. Earth Syst. Sci., 18, 389–405, https://doi.org/10.5194/hess-18-389-2014, https://doi.org/10.5194/hess-18-389-2014, 2014
D. Lisniak, J. Franke, and C. Bernhofer
Hydrol. Earth Syst. Sci., 17, 2487–2500, https://doi.org/10.5194/hess-17-2487-2013, https://doi.org/10.5194/hess-17-2487-2013, 2013
Related subject area
Atmospheric sciences
Interpolating turbulent heat fluxes missing from a prairie observation on the Tibetan Plateau using artificial intelligence models
Carbon dioxide plume dispersion simulated at the hectometer scale using DALES: model formulation and observational evaluation
Low-level jets in the North and Baltic seas: mesoscale model sensitivity and climatology using WRF V4.2.1
SynRad v1.0: a radar forward operator to simulate synthetic weather radar observations from volcanic ash clouds
Chempath 1.0: an open-source pathway analysis program for photochemical models
PALACE v1.0: Paranal Airglow Line And Continuum Emission model
Atmospheric moisture tracking with WAM2layers v3
A new set of indicators for model evaluation complementing FAIRMODE's modelling quality objective (MQO)
Impact of multiple radar wind profiler data assimilation on convective-scale short-term rainfall forecasts: OSSE studies over the Beijing–Tianjin–Hebei region
New submodel for emissions from Explosive Volcanic ERuptions (EVER v1.1) within the Modular Earth Submodel System (MESSy, version 2.55.1)
Quantifying the oscillatory evolution of simulated boundary-layer cloud fields using Gaussian process regression
Numerical investigations on the modelling of ultrafine particles in SSH-aerosol-v1.3a: size resolution and redistribution
The third Met Office Unified Model–JULES Regional Atmosphere and Land Configuration, RAL3
The sensitivity of aerosol data assimilation to vertical profiles: case study of dust storm assimilation with LOTOS-EUROS v2.2
Knowledge-inspired fusion strategies for the inference of PM2.5 values with a neural network
Tuning the ICON-A 2.6.4 climate model with machine-learning-based emulators and history matching
A novel method for quantifying the contribution of regional transport to PM2.5 in Beijing (2013–2020): combining machine learning with concentration-weighted trajectory analysis
Quantification of CO2 hotspot emissions from OCO-3 SAM CO2 satellite images using deep learning methods
Diagnosis of winter precipitation types using the spectral bin model (version 1DSBM-19M): comparison of five methods using ICE-POP 2018 field experiment data
Improving winter condition simulations in SURFEX-TEB v9.0 with a multi-layer snow model and ice
UA-ICON with the NWP physics package (version ua-icon-2.1): mean state and variability of the middle atmosphere
Integrated Methane Inversion (IMI) 2.0: an improved research and stakeholder tool for monitoring total methane emissions with high resolution worldwide using TROPOMI satellite observations
HTAP3 Fires: towards a multi-model, multi-pollutant study of fire impacts
Using a data-driven statistical model to better evaluate surface turbulent heat fluxes in weather and climate numerical models: a demonstration study
Pochva: a new hydro-thermal process model in soil, snow, and vegetation for application in atmosphere numerical models
ClimKern v1.2: a new Python package and kernel repository for calculating radiative feedbacks
Accounting for effects of coagulation and model uncertainties in particle number concentration estimates based on measurements from sampling lines – a Bayesian inversion approach with SLIC v1.0
Top-down CO emission estimates using TROPOMI CO data in the TM5-4DVAR (r1258) inverse modeling suit
The Multi-Compartment Hg Modeling and Analysis Project (MCHgMAP): mercury modeling to support international environmental policy
Similarity-based analysis of atmospheric organic compounds for machine learning applications
Porting the Meso-NH atmospheric model on different GPU architectures for the next generation of supercomputers (version MESONH-v55-OpenACC)
Estimation of aerosol and cloud radiative heating rate in the tropical stratosphere using a radiative kernel method
Development of a High-Resolution Coupled SHiELD-MOM6 Model. Part I – Model Overview, Coupling Technique, and Validation in a Regional Setup
Evaluation of dust emission and land surface schemes in predicting a mega Asian dust storm over South Korea using WRF-Chem
Sensitivity studies of a four-dimensional local ensemble transform Kalman filter coupled with WRF-Chem version 3.9.1 for improving particulate matter simulation accuracy
A Bayesian method for predicting background radiation at environmental monitoring stations in local-scale networks
Inclusion of the ECMWF ecRad radiation scheme (v1.5.0) in the MAR (v3.14), regional evaluation for Belgium, and assessment of surface shortwave spectral fluxes at Uccle
Development of a fast radiative transfer model for ground-based microwave radiometers (ARMS-gb v1.0): validation and comparison to RTTOV-gb
Indian Institute of Tropical Meteorology (IITM) High-Resolution Global Forecast Model version 1: an attempt to resolve monsoon prediction deadlock
Cell-tracking-based framework for assessing nowcasting model skill in reproducing growth and decay of convective rainfall
NeuralMie (v1.0): an aerosol optics emulator
A REtrieval Method for optical and physical Aerosol Properties in the stratosphere (REMAPv1)
Simulation performance of planetary boundary layer schemes in WRF v4.3.1 for near-surface wind over the western Sichuan Basin: a single-site assessment
FootNet v1.0: development of a machine learning emulator of atmospheric transport
Updates and evaluation of NOAA's online-coupled air quality model version 7 (AQMv7) within the Unified Forecast System
Quantifying the analysis uncertainty for nowcasting application
Improving the ensemble square root filter (EnSRF) in the Community Inversion Framework: a case study with ICON-ART 2024.01
The MESSy DWARF (based on MESSy v2.55.2)
Generalized local fractions – a method for the calculation of sensitivities to emissions from multiple sources for chemically active species, illustrated using the EMEP MSC-W model (rv5.5)
SanDyPALM v1.0: Static and Dynamic Drivers for the PALM-4U Model to Facilitate Realistic Urban Microclimate Simulations
Quanzhe Hou, Zhiqiu Gao, Zexia Duan, and Minghui Yu
Geosci. Model Dev., 18, 4625–4641, https://doi.org/10.5194/gmd-18-4625-2025, https://doi.org/10.5194/gmd-18-4625-2025, 2025
Short summary
Short summary
This study evaluates various machine learning and statistical methods for interpolating turbulent heat flux data over the Tibetan Plateau. The Transformer model showed the best performance, leading to the development of the Transformer_CNN model, which combines global and local attention mechanisms. Results show that Transformer_CNN outperforms the other models and was successfully applied to interpolate heat flux data from 2007 to 2016.
Arseniy Karagodin-Doyennel, Fredrik Jansson, Bart J. H. van Stratum, Hugo Denier van der Gon, Jordi Vilà-Guerau de Arellano, and Sander Houweling
Geosci. Model Dev., 18, 4571–4599, https://doi.org/10.5194/gmd-18-4571-2025, https://doi.org/10.5194/gmd-18-4571-2025, 2025
Short summary
Short summary
We introduce a new simulation platform based on the Dutch Atmospheric Large-Eddy Simulation (DALES) to simulate carbon dioxide (CO2) emissions and their dispersion in turbulent environments at a hectometer resolution. This model incorporates both anthropogenic emission inventories and online ecosystem fluxes. Simulation results for the main urban area in the Netherlands demonstrate the strong potential of DALES to improve CO2 emission modeling and to support mitigation strategies.
Bjarke T. E. Olsen, Andrea N. Hahmann, Nicolas G. Alonso-de-Linaje, Mark Žagar, and Martin Dörenkämper
Geosci. Model Dev., 18, 4499–4533, https://doi.org/10.5194/gmd-18-4499-2025, https://doi.org/10.5194/gmd-18-4499-2025, 2025
Short summary
Short summary
Low-level jets (LLJs) are strong winds in the lower atmosphere that are important for wind energy as turbines get taller. This study compares a weather model (WRF) with real data across five North and Baltic Sea sites. Adjusting the model improved accuracy over the widely used ERA5. In key offshore regions, LLJs occur 10–15 % of the time and significantly boost wind power, especially in spring and summer, contributing up to 30 % of total capacity in some areas.
Vishnu Nair, Anujah Mohanathan, Michael Herzog, David G. Macfarlane, and Duncan A. Robertson
Geosci. Model Dev., 18, 4417–4432, https://doi.org/10.5194/gmd-18-4417-2025, https://doi.org/10.5194/gmd-18-4417-2025, 2025
Short summary
Short summary
A numerical model that simulates the measurement processes behind the ground-based radars used to detect volcanic ash clouds is introduced. Using weather radars to detect volcanic clouds is not ideal, as fine ash particles are smaller than raindrops and remain undetected. We evaluate the performance of weather radars to study ash clouds and to identify optimal frequencies that balance the trade-off between a higher return signal and the higher path attenuation that comes at these higher frequencies.
Daniel Garduno Ruiz, Colin Goldblatt, and Anne-Sofie Ahm
Geosci. Model Dev., 18, 4433–4454, https://doi.org/10.5194/gmd-18-4433-2025, https://doi.org/10.5194/gmd-18-4433-2025, 2025
Short summary
Short summary
Photochemical models describe how the composition of the atmosphere changes due to chemical reactions, transport, and other processes. These models are useful for studying the composition of the Earth's and other planets' atmospheres. Understanding the results of these models can be difficult. Here, we build on previous work to develop open-source code that can identify the reaction chains (pathways) that produce the results of these models, facilitating the understanding of these results.
Stefan Noll, Carsten Schmidt, Patrick Hannawald, Wolfgang Kausch, and Stefan Kimeswenger
Geosci. Model Dev., 18, 4353–4398, https://doi.org/10.5194/gmd-18-4353-2025, https://doi.org/10.5194/gmd-18-4353-2025, 2025
Short summary
Short summary
Non-thermal emission from chemical reactions in the Earth's middle und upper atmosphere strongly contributes to the brightness of the night sky below about 2.3 µm. The new Paranal Airglow Line And Continuum Emission model calculates the emission spectrum and its variability with an unprecedented accuracy. Relying on a large spectroscopic data set from astronomical spectrographs and theoretical molecular/atomic data, this model is valuable for airglow research and astronomical observatories.
Peter Kalverla, Imme Benedict, Chris Weijenborg, and Ruud J. van der Ent
Geosci. Model Dev., 18, 4335–4352, https://doi.org/10.5194/gmd-18-4335-2025, https://doi.org/10.5194/gmd-18-4335-2025, 2025
Short summary
Short summary
We introduce a new version of WAM2layers (Water Accounting Model – 2 layers), a computer program that tracks how the weather brings water from one place to another. It uses data from weather and climate models, whose resolution is steadily increasing. Processing the latest data had become a challenge, and the updates presented here ensure that WAM2layers runs smoothly again. We also made it easier to use the program and to understand its source code. This makes it more transparent, reliable, and easier to maintain.
Alexander de Meij, Cornelis Cuvelier, Philippe Thunis, and Enrico Pisoni
Geosci. Model Dev., 18, 4231–4245, https://doi.org/10.5194/gmd-18-4231-2025, https://doi.org/10.5194/gmd-18-4231-2025, 2025
Short summary
Short summary
We assess relevance and utility indicators by evaluating nine Copernicus Atmospheric Monitoring Service models in calculated air pollutant values. For NO2, the results highlight difficulties at traffic stations. For PM2.5 and PM10 the bias and winter–summer gradients reveal issues. O3 evaluation shows that seasonal gradients are useful. Overall, the indicators reveal model limitations, yet there is a need to reconsider the strictness of some indicators for certain pollutants.
Juan Zhao, Jianping Guo, and Xiaohui Zheng
Geosci. Model Dev., 18, 4075–4101, https://doi.org/10.5194/gmd-18-4075-2025, https://doi.org/10.5194/gmd-18-4075-2025, 2025
Short summary
Short summary
A series of observing system simulation experiments are conducted to assess the impact of multiple radar wind profiler (RWP) networks on convective-scale numerical weather prediction. Results from three southwest-type heavy rainfall cases in the Beijing–Tianjin–Hebei region suggest the added forecast skill of ridge and foothill networks associated with the Taihang Mountains over the existing RWP network. This research provides valuable guidance for designing optimal RWP networks in the region.
Matthias Kohl, Christoph Brühl, Jennifer Schallock, Holger Tost, Patrick Jöckel, Adrian Jost, Steffen Beirle, Michael Höpfner, and Andrea Pozzer
Geosci. Model Dev., 18, 3985–4007, https://doi.org/10.5194/gmd-18-3985-2025, https://doi.org/10.5194/gmd-18-3985-2025, 2025
Short summary
Short summary
SO2 from explosive volcanic eruptions reaching the stratosphere can oxidize and form sulfur aerosols, potentially persisting for several years. We developed a new submodel, Explosive Volcanic ERuptions (EVER), that seamlessly includes stratospheric volcanic SO2 emissions in global numerical simulations based on a novel standard historical model setup, successfully evaluated with satellite observations. Sensitivity studies on the Nabro eruption in 2011 evaluate different emission methods.
Gunho Loren Oh and Philip H. Austin
Geosci. Model Dev., 18, 3921–3940, https://doi.org/10.5194/gmd-18-3921-2025, https://doi.org/10.5194/gmd-18-3921-2025, 2025
Short summary
Short summary
It is difficult to study the behaviour of a cloud field due to internal fluctuations and observational noise. We perform a high-resolution simulation of the boundary-layer cloud field and introduce statistical and numerical techniques, including machine-learning models, to study the evolution of the cloud field, which shows a periodic behaviour. We aim to use the numerical techniques to identify the underlying behaviour within noisy observations.
Oscar Jacquot and Karine Sartelet
Geosci. Model Dev., 18, 3965–3984, https://doi.org/10.5194/gmd-18-3965-2025, https://doi.org/10.5194/gmd-18-3965-2025, 2025
Short summary
Short summary
Modelling the size distribution and the number concentration is important to represent ultrafine particles. A new analytic formulation is presented to compute coagulation partition coefficients, allowing us to lower the numerical diffusion associated with the resolution of aerosol dynamics. The significance of this effect is assessed in a 0D box model and over greater Paris with a chemistry transport model, using different size resolutions of the particle distribution.
Mike Bush, David L. A. Flack, Huw W. Lewis, Sylvia I. Bohnenstengel, Chris J. Short, Charmaine Franklin, Adrian P. Lock, Martin Best, Paul Field, Anne McCabe, Kwinten Van Weverberg, Segolene Berthou, Ian Boutle, Jennifer K. Brooke, Seb Cole, Shaun Cooper, Gareth Dow, John Edwards, Anke Finnenkoetter, Kalli Furtado, Kate Halladay, Kirsty Hanley, Margaret A. Hendry, Adrian Hill, Aravindakshan Jayakumar, Richard W. Jones, Humphrey Lean, Joshua C. K. Lee, Andy Malcolm, Marion Mittermaier, Saji Mohandas, Stuart Moore, Cyril Morcrette, Rachel North, Aurore Porson, Susan Rennie, Nigel Roberts, Belinda Roux, Claudio Sanchez, Chun-Hsu Su, Simon Tucker, Simon Vosper, David Walters, James Warner, Stuart Webster, Mark Weeks, Jonathan Wilkinson, Michael Whitall, Keith D. Williams, and Hugh Zhang
Geosci. Model Dev., 18, 3819–3855, https://doi.org/10.5194/gmd-18-3819-2025, https://doi.org/10.5194/gmd-18-3819-2025, 2025
Short summary
Short summary
RAL configurations define settings for the Unified Model atmosphere and Joint UK Land Environment Simulator. The third version of the Regional Atmosphere and Land (RAL3) science configuration for kilometre- and sub-kilometre-scale modelling represents a major advance compared to previous versions (RAL2) by delivering a common science definition for applications in tropical and mid-latitude regions. RAL3 has more realistic precipitation distributions and an improved representation of clouds and visibility.
Mijie Pang, Jianbing Jin, Ting Yang, Xi Chen, Arjo Segers, Batjargal Buyantogtokh, Yixuan Gu, Jiandong Li, Hai Xiang Lin, Hong Liao, and Wei Han
Geosci. Model Dev., 18, 3781–3798, https://doi.org/10.5194/gmd-18-3781-2025, https://doi.org/10.5194/gmd-18-3781-2025, 2025
Short summary
Short summary
Aerosol data assimilation has gained popularity as it combines the advantages of modelling and observation. However, few studies have addressed the challenges in the prior vertical structure. Different observations are assimilated to examine the sensitivity of assimilation to vertical structure. Results show that assimilation can optimize the dust field in general. However, if the prior introduces an incorrect structure, the assimilation can significantly deteriorate the integrity of the aerosol profile.
Matthieu Dabrowski, José Mennesson, Jérôme Riedi, Chaabane Djeraba, and Pierre Nabat
Geosci. Model Dev., 18, 3707–3733, https://doi.org/10.5194/gmd-18-3707-2025, https://doi.org/10.5194/gmd-18-3707-2025, 2025
Short summary
Short summary
This work focuses on the prediction of aerosol concentration values at the ground level, which are a strong indicator of air quality, using artificial neural networks. A study of different variables and their efficiency as inputs for these models is also proposed and reveals that the best results are obtained when using all of them. Comparison between network architectures and information fusion methods allows for the extraction of knowledge on the most efficient methods in the context of this study.
Pauline Bonnet, Lorenzo Pastori, Mierk Schwabe, Marco Giorgetta, Fernando Iglesias-Suarez, and Veronika Eyring
Geosci. Model Dev., 18, 3681–3706, https://doi.org/10.5194/gmd-18-3681-2025, https://doi.org/10.5194/gmd-18-3681-2025, 2025
Short summary
Short summary
Tuning a climate model means adjusting uncertain parameters in the model to best match observations like the global radiation balance and cloud cover. This is usually done by running many simulations of the model with different settings, which can be time-consuming and relies heavily on expert knowledge. To make this process faster and more objective, we developed a machine learning emulator to create a large ensemble and apply a method called history matching to find the best settings.
Kang Hu, Hong Liao, Dantong Liu, Jianbing Jin, Lei Chen, Siyuan Li, Yangzhou Wu, Changhao Wu, Shitong Zhao, Xiaotong Jiang, Ping Tian, Kai Bi, Ye Wang, and Delong Zhao
Geosci. Model Dev., 18, 3623–3634, https://doi.org/10.5194/gmd-18-3623-2025, https://doi.org/10.5194/gmd-18-3623-2025, 2025
Short summary
Short summary
This study combines machine learning with concentration-weighted trajectory analysis to quantify regional transport PM2.5. From 2013–2020, local emissions dominated Beijing's pollution events. The Air Pollution Prevention and Control Action Plan reduced regional transport pollution, but the eastern region showed the smallest decrease. Beijing should prioritize local emission reduction while considering the east region's contributions in future strategies.
Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Grégoire Broquet, Gerrit Kuhlmann, and Marc Bocquet
Geosci. Model Dev., 18, 3607–3622, https://doi.org/10.5194/gmd-18-3607-2025, https://doi.org/10.5194/gmd-18-3607-2025, 2025
Short summary
Short summary
We developed a deep learning method to estimate CO2 emissions from power plants using satellite images. Trained and validated on simulated data, our model accurately predicts emissions despite challenges like cloud cover. When applied to real OCO3 satellite images, the results closely match reported emissions. This study shows that neural networks trained on simulations can effectively analyse real satellite data, offering a new way to monitor CO2 emissions from space.
Wonbae Bang, Jacob T. Carlin, Kwonil Kim, Alexander V. Ryzhkov, Guosheng Liu, and GyuWon Lee
Geosci. Model Dev., 18, 3559–3581, https://doi.org/10.5194/gmd-18-3559-2025, https://doi.org/10.5194/gmd-18-3559-2025, 2025
Short summary
Short summary
Microphysics model-based diagnosis, such as the spectral bin model (SBM), has recently been attempted to diagnose winter precipitation types. In this study, the accuracy of SBM-based precipitation type diagnosis is compared with other traditional methods. SBM has a relatively higher accuracy for dry-snow and wet-snow events, whereas it has lower accuracy for rain events. When the microphysics scheme in the SBM was optimized for the corresponding region, the accuracy for rain events improved.
Gabriel Colas, Valéry Masson, François Bouttier, Ludovic Bouilloud, Laura Pavan, and Virve Karsisto
Geosci. Model Dev., 18, 3453–3472, https://doi.org/10.5194/gmd-18-3453-2025, https://doi.org/10.5194/gmd-18-3453-2025, 2025
Short summary
Short summary
In winter, snow- and ice-covered artificial surfaces are important aspects of the urban climate. They may influence the magnitude of the urban heat island effect, but this is still unclear. In this study, we improved the representation of the snow and ice cover in the Town Energy Balance (TEB) urban climate model. Evaluations have shown that the results are promising for using TEB to study the climate of cold cities.
Markus Kunze, Christoph Zülicke, Tarique A. Siddiqui, Claudia C. Stephan, Yosuke Yamazaki, Claudia Stolle, Sebastian Borchert, and Hauke Schmidt
Geosci. Model Dev., 18, 3359–3385, https://doi.org/10.5194/gmd-18-3359-2025, https://doi.org/10.5194/gmd-18-3359-2025, 2025
Short summary
Short summary
We present the Icosahedral Nonhydrostatic (ICON) general circulation model with an upper-atmospheric extension with the physics package for numerical weather prediction (UA-ICON(NWP)). We optimized the parameters for the gravity wave parameterizations and achieved realistic modeling of the thermal and dynamic states of the mesopause regions. UA-ICON(NWP) now shows a realistic frequency of major sudden stratospheric warmings and well-represented solar tides in temperature.
Lucas A. Estrada, Daniel J. Varon, Melissa Sulprizio, Hannah Nesser, Zichong Chen, Nicholas Balasus, Sarah E. Hancock, Megan He, James D. East, Todd A. Mooring, Alexander Oort Alonso, Joannes D. Maasakkers, Ilse Aben, Sabour Baray, Kevin W. Bowman, John R. Worden, Felipe J. Cardoso-Saldaña, Emily Reidy, and Daniel J. Jacob
Geosci. Model Dev., 18, 3311–3330, https://doi.org/10.5194/gmd-18-3311-2025, https://doi.org/10.5194/gmd-18-3311-2025, 2025
Short summary
Short summary
Reducing emissions of methane, a powerful greenhouse gas, is a top policy concern for mitigating anthropogenic climate change. The Integrated Methane Inversion (IMI) is an advanced, cloud-based software that translates satellite observations into actionable emissions data. Here we present IMI version 2.0 with vastly expanded capabilities. These updates enable a wider range of scientific and stakeholder applications from individual basin to global scales with continuous emissions monitoring.
Cynthia H. Whaley, Tim Butler, Jose A. Adame, Rupal Ambulkar, Steve R. Arnold, Rebecca R. Buchholz, Benjamin Gaubert, Douglas S. Hamilton, Min Huang, Hayley Hung, Johannes W. Kaiser, Jacek W. Kaminski, Christoph Knote, Gerbrand Koren, Jean-Luc Kouassi, Meiyun Lin, Tianjia Liu, Jianmin Ma, Kasemsan Manomaiphiboon, Elisa Bergas Masso, Jessica L. McCarty, Mariano Mertens, Mark Parrington, Helene Peiro, Pallavi Saxena, Saurabh Sonwani, Vanisa Surapipith, Damaris Y. T. Tan, Wenfu Tang, Veerachai Tanpipat, Kostas Tsigaridis, Christine Wiedinmyer, Oliver Wild, Yuanyu Xie, and Paquita Zuidema
Geosci. Model Dev., 18, 3265–3309, https://doi.org/10.5194/gmd-18-3265-2025, https://doi.org/10.5194/gmd-18-3265-2025, 2025
Short summary
Short summary
The multi-model experiment design of the HTAP3 Fires project takes a multi-pollutant approach to improving our understanding of transboundary transport of wildland fire and agricultural burning emissions and their impacts. The experiments are designed with the goal of answering science policy questions related to fires. The options for the multi-model approach, including inputs, outputs, and model setup, are discussed, and the official recommendations for the project are presented.
Maurin Zouzoua, Sophie Bastin, Fabienne Lohou, Marie Lothon, Marjolaine Chiriaco, Mathilde Jome, Cécile Mallet, Laurent Barthes, and Guylaine Canut
Geosci. Model Dev., 18, 3211–3239, https://doi.org/10.5194/gmd-18-3211-2025, https://doi.org/10.5194/gmd-18-3211-2025, 2025
Short summary
Short summary
This study proposes using a statistical model to freeze errors due to differences in environmental forcing when evaluating the surface turbulent heat fluxes from numerical simulations with observations. The statistical model is first built with observations and then applied to the simulated environment to generate possibly observed fluxes. This novel method provides insight into differently evaluating the numerical formulation of turbulent heat fluxes with a long period of observational data.
Oxana Drofa
Geosci. Model Dev., 18, 3175–3209, https://doi.org/10.5194/gmd-18-3175-2025, https://doi.org/10.5194/gmd-18-3175-2025, 2025
Short summary
Short summary
This paper presents the result of many years of effort of the author, who developed an original mathematical numerical model of heat and moisture exchange processes in soil, vegetation, and snow. The author relied on her 30 years of research experience in atmospheric numerical modelling. The presented model is the fruit of the author's research on physical processes at the surface–atmosphere interface and their numerical approximation and aims at improving numerical weather forecasting and climate simulations.
Tyler P. Janoski, Ivan Mitevski, Ryan J. Kramer, Michael Previdi, and Lorenzo M. Polvani
Geosci. Model Dev., 18, 3065–3079, https://doi.org/10.5194/gmd-18-3065-2025, https://doi.org/10.5194/gmd-18-3065-2025, 2025
Short summary
Short summary
We developed ClimKern, a Python package and radiative kernel repository, to simplify calculating radiative feedbacks and make climate sensitivity studies more reproducible. Testing of ClimKern with sample climate model data reveals that radiative kernel choice may be more important than previously thought, especially in polar regions. Our work highlights the need for kernel sensitivity analyses to be included in future studies.
Matti Niskanen, Aku Seppänen, Henri Oikarinen, Miska Olin, Panu Karjalainen, Santtu Mikkonen, and Kari Lehtinen
Geosci. Model Dev., 18, 2983–3001, https://doi.org/10.5194/gmd-18-2983-2025, https://doi.org/10.5194/gmd-18-2983-2025, 2025
Short summary
Short summary
Particle size is a key factor determining the properties of aerosol particles which have a major influence on the climate and on human health. When measuring the particle sizes, however, sometimes the sampling lines that transfer the aerosol to the measurement device distort the size distribution, making the measurement unreliable. We propose a method to correct for the distortions and estimate the true particle sizes, improving measurement accuracy.
Johann Rasmus Nüß, Nikos Daskalakis, Fabian Günther Piwowarczyk, Angelos Gkouvousis, Oliver Schneising, Michael Buchwitz, Maria Kanakidou, Maarten C. Krol, and Mihalis Vrekoussis
Geosci. Model Dev., 18, 2861–2890, https://doi.org/10.5194/gmd-18-2861-2025, https://doi.org/10.5194/gmd-18-2861-2025, 2025
Short summary
Short summary
We estimate carbon monoxide emissions through inverse modeling, an approach where measurements of tracers in the atmosphere are fed to a model to calculate backwards in time (inverse) where the tracers came from. We introduce measurements from a new satellite instrument and show that, in most places globally, these on their own sufficiently constrain the emissions. This alleviates the need for additional datasets, which could shorten the delay for future carbon monoxide source estimates.
Ashu Dastoor, Hélène Angot, Johannes Bieser, Flora Brocza, Brock Edwards, Aryeh Feinberg, Xinbin Feng, Benjamin Geyman, Charikleia Gournia, Yipeng He, Ian M. Hedgecock, Ilia Ilyin, Jane Kirk, Che-Jen Lin, Igor Lehnherr, Robert Mason, David McLagan, Marilena Muntean, Peter Rafaj, Eric M. Roy, Andrei Ryjkov, Noelle E. Selin, Francesco De Simone, Anne L. Soerensen, Frits Steenhuisen, Oleg Travnikov, Shuxiao Wang, Xun Wang, Simon Wilson, Rosa Wu, Qingru Wu, Yanxu Zhang, Jun Zhou, Wei Zhu, and Scott Zolkos
Geosci. Model Dev., 18, 2747–2860, https://doi.org/10.5194/gmd-18-2747-2025, https://doi.org/10.5194/gmd-18-2747-2025, 2025
Short summary
Short summary
This paper introduces the Multi-Compartment Mercury (Hg) Modeling and Analysis Project (MCHgMAP) aimed at informing the effectiveness evaluations of two multilateral environmental agreements: the Minamata Convention on Mercury and the Convention on Long-Range Transboundary Air Pollution. The experimental design exploits a variety of models (atmospheric, land, oceanic ,and multimedia mass balance models) to assess the short- and long-term influences of anthropogenic Hg releases into the environment.
Hilda Sandström and Patrick Rinke
Geosci. Model Dev., 18, 2701–2724, https://doi.org/10.5194/gmd-18-2701-2025, https://doi.org/10.5194/gmd-18-2701-2025, 2025
Short summary
Short summary
Machine learning has the potential to aid the identification of organic molecules involved in aerosol formation. Yet, progress is stalled by a lack of curated atmospheric molecular datasets. Here, we compared atmospheric compounds with large molecular datasets used in machine learning and found minimal overlap with similarity algorithms. Our result underlines the need for collaborative efforts to curate atmospheric molecular data to facilitate machine learning models in atmospheric sciences.
Juan Escobar, Philippe Wautelet, Joris Pianezze, Florian Pantillon, Thibaut Dauhut, Christelle Barthe, and Jean-Pierre Chaboureau
Geosci. Model Dev., 18, 2679–2700, https://doi.org/10.5194/gmd-18-2679-2025, https://doi.org/10.5194/gmd-18-2679-2025, 2025
Short summary
Short summary
The Meso-NH weather research code is adapted for GPUs using OpenACC, leading to significant performance and energy efficiency improvements. Called MESONH-v55-OpenACC, it includes enhanced memory management, communication optimizations and a new solver. On the AMD MI250X Adastra platform, it achieved up to 6× speedup and 2.3× energy efficiency gain compared to CPUs. Storm simulations at 100 m resolution show positive results, positioning the code for future use on exascale supercomputers.
Jie Gao, Yi Huang, Jonathon S. Wright, Ke Li, Tao Geng, and Qiurun Yu
Geosci. Model Dev., 18, 2569–2586, https://doi.org/10.5194/gmd-18-2569-2025, https://doi.org/10.5194/gmd-18-2569-2025, 2025
Short summary
Short summary
The aerosol in the upper troposphere and stratosphere is highly variable, and its radiative effect is poorly understood. To estimate this effect, the radiative kernel is constructed and applied. The results show that the kernels can reproduce aerosol radiative effects and are expected to simulate stratospheric aerosol radiative effects. This approach reduces computational expense, is consistent with radiative model calculations, and can be applied to atmospheric models with speed requirements.
Joseph Mouallem, Kun Gao, Brandon G. Reichl, Lauren Chilutti, Lucas Harris, Rusty Benson, Niki Zadeh, Jing Chen, Jan-Huey Chen, and Cheng Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2025-1690, https://doi.org/10.5194/egusphere-2025-1690, 2025
Short summary
Short summary
We introduce a new high-resolution model that couple the atmosphere and ocean to better simulate extreme weather events. It combines GFDL’s advanced atmospheric and ocean models with a powerful coupling system that allows robust and efficient two-way interactions. Simulations show the model accurately captures hurricane behavior and its impact on the ocean. It also runs efficiently on supercomputers. This model is a key step toward improving extreme weather forecast.
Ji Won Yoon, Seungyeon Lee, Ebony Lee, and Seon Ki Park
Geosci. Model Dev., 18, 2303–2328, https://doi.org/10.5194/gmd-18-2303-2025, https://doi.org/10.5194/gmd-18-2303-2025, 2025
Short summary
Short summary
This study evaluates the Weather Research and Forecasting Model (WRF) coupled with Chemistry (WRF-Chem) to predict a mega Asian dust storm (ADS) over South Korea on 28–29 March 2021. We assessed combinations of five dust emission and four land surface schemes by analyzing meteorological and air quality variables. The best scheme combination reduced the root mean square error (RMSE) for particulate matter 10 (PM10) by up to 29.6 %, demonstrating the highest performance.
Jianyu Lin, Tie Dai, Lifang Sheng, Weihang Zhang, Shangfei Hai, and Yawen Kong
Geosci. Model Dev., 18, 2231–2248, https://doi.org/10.5194/gmd-18-2231-2025, https://doi.org/10.5194/gmd-18-2231-2025, 2025
Short summary
Short summary
The effectiveness of this assimilation system and its sensitivity to the ensemble member size and length of the assimilation window are investigated. This study advances our understanding of the selection of basic parameters in the four-dimensional local ensemble transform Kalman filter assimilation system and the performance of ensemble simulation in a particulate-matter-polluted environment.
Jens Peter Karolus Wenceslaus Frankemölle, Johan Camps, Pieter De Meutter, and Johan Meyers
Geosci. Model Dev., 18, 1989–2003, https://doi.org/10.5194/gmd-18-1989-2025, https://doi.org/10.5194/gmd-18-1989-2025, 2025
Short summary
Short summary
To detect anomalous radioactivity in the environment, it is paramount that we understand the natural background level. In this work, we propose a statistical model to describe the most likely background level and the associated uncertainty in a network of dose rate detectors. We train, verify, and validate the model using real environmental data. Using the model, we show that we can correctly predict the background level in a subset of the detector network during a known
anomalous event.
Jean-François Grailet, Robin J. Hogan, Nicolas Ghilain, David Bolsée, Xavier Fettweis, and Marilaure Grégoire
Geosci. Model Dev., 18, 1965–1988, https://doi.org/10.5194/gmd-18-1965-2025, https://doi.org/10.5194/gmd-18-1965-2025, 2025
Short summary
Short summary
The MAR (Modèle Régional Atmosphérique) is a regional climate model used for weather forecasting and studying the climate over various regions. This paper presents an update of MAR thanks to which it can precisely decompose solar radiation, in particular in the UV (ultraviolet) and photosynthesis ranges, both being critical to human health and ecosystems. As a first application of this new capability, this paper presents a method for predicting UV indices with MAR.
Yi-Ning Shi, Jun Yang, Wei Han, Lujie Han, Jiajia Mao, Wanlin Kan, and Fuzhong Weng
Geosci. Model Dev., 18, 1947–1964, https://doi.org/10.5194/gmd-18-1947-2025, https://doi.org/10.5194/gmd-18-1947-2025, 2025
Short summary
Short summary
Direct assimilation of observations from ground-based microwave radiometers (GMRs) holds significant potential for improving forecast accuracy. Radiative transfer models (RTMs) play a crucial role in direct data assimilation. In this study, we introduce a new RTM, the Advanced Radiative Transfer Modeling System – Ground-Based (ARMS-gb), designed to simulate brightness temperatures observed by GMRs along with their Jacobians. Several enhancements have been incorporated to achieve higher accuracy.
R. Phani Murali Krishna, Siddharth Kumar, A. Gopinathan Prajeesh, Peter Bechtold, Nils Wedi, Kumar Roy, Malay Ganai, B. Revanth Reddy, Snehlata Tirkey, Tanmoy Goswami, Radhika Kanase, Sahadat Sarkar, Medha Deshpande, and Parthasarathi Mukhopadhyay
Geosci. Model Dev., 18, 1879–1894, https://doi.org/10.5194/gmd-18-1879-2025, https://doi.org/10.5194/gmd-18-1879-2025, 2025
Short summary
Short summary
The High-Resolution Global Forecast Model (HGFM) is an advanced iteration of the operational Global Forecast System (GFS) model. HGFM can produce forecasts at a spatial scale of ~6 km in tropics. It demonstrates improved accuracy in short- to medium-range weather prediction over the Indian region, with notable success in predicting extreme events. Further, the model will be entrusted to operational forecasting agencies after validation and testing.
Jenna Ritvanen, Seppo Pulkkinen, Dmitri Moisseev, and Daniele Nerini
Geosci. Model Dev., 18, 1851–1878, https://doi.org/10.5194/gmd-18-1851-2025, https://doi.org/10.5194/gmd-18-1851-2025, 2025
Short summary
Short summary
Nowcasting models struggle with the rapid evolution of heavy rain, and common verification methods are unable to describe how accurately the models predict the growth and decay of heavy rain. We propose a framework to assess model performance. In the framework, convective cells are identified and tracked in the forecasts and observations, and the model skill is then evaluated by comparing differences between forecast and observed cells. We demonstrate the framework with four open-source models.
Andrew Geiss and Po-Lun Ma
Geosci. Model Dev., 18, 1809–1827, https://doi.org/10.5194/gmd-18-1809-2025, https://doi.org/10.5194/gmd-18-1809-2025, 2025
Short summary
Short summary
Particles in the Earth's atmosphere strongly impact the planet's energy budget, and atmosphere simulations require accurate representation of their interaction with light. This work introduces two approaches to represent light scattering by small particles. The first is a scattering simulator based on Mie theory implemented in Python. The second is a neural network emulator that is more accurate than existing methods and is fast enough to be used in climate and weather simulations.
Andrin Jörimann, Timofei Sukhodolov, Beiping Luo, Gabriel Chiodo, Graham Mann, and Thomas Peter
EGUsphere, https://doi.org/10.5194/egusphere-2025-145, https://doi.org/10.5194/egusphere-2025-145, 2025
Short summary
Short summary
Aerosol particles in the stratosphere affect our climate. Climate models therefore need an accurate description of their properties and evolution. Satellites measure how strongly aerosol particles extinguish light passing through the stratosphere. We describe a method to use such aerosol extinction data to retrieve the number and sizes of the aerosol particles and calculate their optical effects. The resulting data sets for models are validated against ground-based and balloon observations.
Qin Wang, Bo Zeng, Gong Chen, and Yaoting Li
Geosci. Model Dev., 18, 1769–1784, https://doi.org/10.5194/gmd-18-1769-2025, https://doi.org/10.5194/gmd-18-1769-2025, 2025
Short summary
Short summary
This study evaluates the performance of four planetary boundary layer (PBL) schemes in near-surface wind fields over the Sichuan Basin, China. Using 112 sensitivity experiments with the Weather Research and Forecasting (WRF) model and focusing on 28 wind events, it is found that wind direction was less sensitive to the PBL schemes. The quasi-normal scale elimination (QNSE) scheme captured temporal variations best, while the Mellor–Yamada–Janjić (MYJ) scheme had the least error in wind speed.
Tai-Long He, Nikhil Dadheech, Tammy M. Thompson, and Alexander J. Turner
Geosci. Model Dev., 18, 1661–1671, https://doi.org/10.5194/gmd-18-1661-2025, https://doi.org/10.5194/gmd-18-1661-2025, 2025
Short summary
Short summary
It is computationally expensive to infer greenhouse gas (GHG) emissions using atmospheric observations. This is partly due to the detailed model used to represent atmospheric transport. We demonstrate how a machine learning (ML) model can be used to simulate high-resolution atmospheric transport. This type of ML model will help estimate GHG emissions using dense observations, which are becoming increasingly common with the proliferation of urban monitoring networks and geostationary satellites.
Wei Li, Beiming Tang, Patrick C. Campbell, Youhua Tang, Barry Baker, Zachary Moon, Daniel Tong, Jianping Huang, Kai Wang, Ivanka Stajner, and Raffaele Montuoro
Geosci. Model Dev., 18, 1635–1660, https://doi.org/10.5194/gmd-18-1635-2025, https://doi.org/10.5194/gmd-18-1635-2025, 2025
Short summary
Short summary
The study describes the updates of NOAA's current UFS-AQMv7 air quality forecast model by incorporating the latest scientific and structural changes in CMAQv5.4. An evaluation during the summer of 2023 shows that the updated model overall improves the simulation of MDA8 O3 by reducing the bias by 8%–12% in the contiguous US. PM2.5 predictions have mixed results due to wildfire, highlighting the need for future refinements.
Yanwei Zhu, Aitor Atencia, Markus Dabernig, and Yong Wang
Geosci. Model Dev., 18, 1545–1559, https://doi.org/10.5194/gmd-18-1545-2025, https://doi.org/10.5194/gmd-18-1545-2025, 2025
Short summary
Short summary
Most works have delved into convective weather nowcasting, and only a few works have discussed the nowcasting uncertainty for variables at the surface level. Hence, we proposed a method to estimate uncertainty. Generating appropriate noises associated with the characteristic of the error in analysis can simulate the uncertainty of nowcasting. This method can contribute to the estimation of near–surface analysis uncertainty in both nowcasting applications and ensemble nowcasting development.
Joël Thanwerdas, Antoine Berchet, Lionel Constantin, Aki Tsuruta, Michael Steiner, Friedemann Reum, Stephan Henne, and Dominik Brunner
Geosci. Model Dev., 18, 1505–1544, https://doi.org/10.5194/gmd-18-1505-2025, https://doi.org/10.5194/gmd-18-1505-2025, 2025
Short summary
Short summary
The Community Inversion Framework (CIF) brings together methods for estimating greenhouse gas fluxes from atmospheric observations. The initial ensemble method implemented in CIF was found to be incomplete and could hardly be compared to other ensemble methods employed in the inversion community. In this paper, we present and evaluate a new implementation of the ensemble mode, building upon the initial developments.
Astrid Kerkweg, Timo Kirfel, Duong H. Do, Sabine Griessbach, Patrick Jöckel, and Domenico Taraborrelli
Geosci. Model Dev., 18, 1265–1286, https://doi.org/10.5194/gmd-18-1265-2025, https://doi.org/10.5194/gmd-18-1265-2025, 2025
Short summary
Short summary
Normally, the Modular Earth Submodel System (MESSy) is linked to complete dynamic models to create chemical climate models. However, the modular concept of MESSy and the newly developed DWARF component presented here make it possible to create simplified models that contain only one or a few process descriptions. This is very useful for technical optimisation, such as porting to GPUs, and can be used to create less complex models, such as a chemical box model.
Peter Wind and Willem van Caspel
EGUsphere, https://doi.org/10.5194/egusphere-2024-3571, https://doi.org/10.5194/egusphere-2024-3571, 2025
Short summary
Short summary
This paper presents a numerical method to assess the origin of air pollution. Combined with a numerical air pollution transport and chemistry model, it can follow the contributions from a large number of emission sources. The result is a series of maps that give the relative contributions from for example all European countries at each point.
Julian Vogel, Sebastian Stadler, Ganesh Chockalingam, Afshin Afshari, Johanna Henning, and Matthias Winkler
EGUsphere, https://doi.org/10.5194/egusphere-2025-144, https://doi.org/10.5194/egusphere-2025-144, 2025
Short summary
Short summary
This study presents a toolkit to simplify input data creation for the urban microclimate model PALM-4U. It introduces novel methods to automate the use of open data sources. Our analysis of four test cases created from different geographic data sources shows variations in temperature, humidity, and wind speed, influenced by data quality. Validation indicates that the automated methods yield results comparable to expert-driven approaches, facilitating user-friendly urban climate modeling.
Cited articles
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado,
G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I. J., Harp,
A., Irving, G., Isard, M., Jia, Y., Józefowicz, R., Kaiser, L., Kudlur,
M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D. G., Olah,
C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker,
P. A., Vanhoucke, V., Vasudevan, V., Viégas, F. B., Vinyals, O.,
Warden, P., Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X.: TensorFlow:
Large-Scale Machine Learning on Heterogeneous Systems, tensorflow.org [code],
https://www.tensorflow.org/ (last access: 12 December 2021), 2015. a
Alahmari, S. S., Goldgof, D. B., Mouton, P. R., and Hall, L. O.: Challenges
for the Repeatability of Deep Learning Models, IEEE Access, 8,
211860–211868, https://doi.org/10.1109/ACCESS.2020.3039833, 2020. a, b, c, d
Allaire, J. J., Ushey, K., Tang, Y., and Eddelbuettel, D.: Reticulate: R
Interface to Python, GitHub [code], https://github.com/rstudio/reticulate (last access: 12 December 2021),
2017. a
Association for Computing Machinery (ACM): Artifact Review and Badging
Version 2.0, ACM,
https://www.acm.org/publications/policies/artifact-review-badging,
2021. a
Bastian, O., Syrbe, R. U., Slavik, J., Moravec, J., Louda, J., Kochan, B.,
Kochan, N., Stutzriemer, S., and Berens, A.: Ecosystem services of
characteristic biotope types in the Ore Mountains (Germany/Czech Republic),
International Journal of Biodiversity Science, Ecosystem Services and
Management, 13, 51–71, https://doi.org/10.1080/21513732.2016.1248865, 2017. a
Bush, R., Dutton, A., Evans, M., Loft, R., and Schmidt, G. A.: Perspectives on
Data Reproducibility and Replicability in Paleoclimate and Climate Science,
Harvard Data Science Review, 2, https://doi.org/10.1162/99608f92.00cd8f85, 2020. a, b
Cannon, A. J.: Probabilistic multisite precipitation downscaling by an
expanded Bernoulli-Gamma density network, J. Hydrometeorol., 9,
1284–1300, https://doi.org/10.1175/2008JHM960.1, 2008. a
Cavazos, T. and Hewitson, B.: Performance of NCEP–NCAR reanalysis variables in
statistical downscaling of daily precipitation, Clim. Res., 28,
95–107, 2005. a
Chollet, F. et al.: Keras, GitHub [code], https://github.com/fchollet/keras (last access: 12 December 2021),
2015. a
CORDEX: CORDEX – ESGF data availability overview, [data set]
http://is-enes-data.github.io/CORDEX_status.html, last
access: 13 November 2021. a
Deutsch, C. V.: Correcting for negative weights in ordinary kriging,
Comput. Geosci., 22, 765–773, https://doi.org/10.1016/0098-3004(96)00005-2,
1996. a
Deutsch, C. V. and Journel, A. G.: GSLIB: Geostatistical Software Library and
User's Guide, second edn., Oxford University Press, ISBN 9780195100150, 1998. a
Flato, G., Marotzke, J., Abiodun, B., Braconnot, P., Chou, S., Collins, W.,
Cox, P., Driouech, F., Emori, S., Eyring, V., Forest, C., Gleckler, P.,
Guilyardi, É., Jakob, C., Kattsov, V., Reason, C., and Rummukainen, M.:
Evaluation of climate models, in: Climate Change 2013 – The Physical
Science Basis: Working Group I Contribution to the Fifth Assessment Report of
the Intergovernmental Panel on Climate Change, Cambridge
University Press, 741–866, https://doi.org/10.1017/CBO9781107415324.020, 2013. a
Goodman, S. N., Fanelli, D., and Ioannidis, J. P.: What does research
reproducibility mean?, Sci. Transl. Med., 8, 96–102,
https://doi.org/10.1126/SCITRANSLMED.AAF5027, 2016. a, b, c, d
Gutiérrez, J. M., Maraun, D., Widmann, M., Huth, R., Hertig, E.,
Benestad, R., Roessler, O., Wibig, J., Wilcke, R., Kotlarski, S., San
Martín, D., Herrera, S., Bedia, J., Casanueva, A., Manzanas, R.,
Iturbide, M., Vrac, M., Dubrovsky, M., Ribalaygua, J., Pórtoles, J.,
Räty, O., Räisänen, J., Hingray, B., Raynaud, D., Casado,
M. J., Ramos, P., Zerenner, T., Turco, M., Bosshard, T.,
Štěpánek, P., Bartholy, J., Pongracz, R., Keller, D. E.,
Fischer, A. M., Cardoso, R. M., Soares, P. M. M., Czernecki, B., and
Pagé, C.: An intercomparison of a large ensemble of statistical
downscaling methods over Europe: Results from the VALUE perfect predictor
cross-validation experiment, Int. J. Climatol., 39,
3750–3785, https://doi.org/10.1002/joc.5462, 2019. a, b, c
Hallett, J.: Climate change 2001: The scientific basis. Edited by J. T.
Houghton, Y. Ding, D. J. Griggs, N. Noguer, P. J. van der Linden, D. Xiaosu, K.
Maskell and C. A. Johnson. Contribution of Working Group I to the Third
Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge
University Press, Cambridge. 2001. 881 pp. ISBN 0521 01495 6., Q.
J. Roy. Meteor. Soc., 128, 1038–1039,
https://doi.org/10.1002/qj.200212858119, 2002. a
Harder, P., Jones, W., Lguensat, R., Bouabid, S., Fulton, J.,
Quesada-Chacón, D., Marcolongo, A., Stefanović, S., Rao, Y.,
Manshausen, P., and Watson-Parris, D.: NightVision: Generating Nighttime
Satellite Imagery from Infra-Red Observations, arXiv [preprint],
https://doi.org/10.48550/arXiv.2011.07017, 13 November 2020. a
He, X., Chaney, N. W., Schleiss, M., and Sheffield, J.: Spatial downscaling of
precipitation using adaptable random forests, Water Resour. Res., 52,
8217–8237, https://doi.org/10.1002/2016WR019034, 2016. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D.,
Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P.,
Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee,
D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M.,
Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E.,
Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti,
G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut,
J. N.: The ERA5 global reanalysis, Q. J. Roy.
Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a, b
Höhlein, K., Kern, M., Hewson, T., and Westermann, R.: A comparative
study of convolutional neural network models for wind field downscaling,
Meteorol. Appl., 27, e1961, https://doi.org/10.1002/met.1961, 2020. a
IPCC: Climate Change 2021: The Physical Science Basis. Contribution of Working
Group I to the Sixth Assessment Report of the Intergovernmental Panel on
Climate Change, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., Cambridge University Press,
Cambridge, United Kingdom and New York, NY, USA, 2391 pp.,
2021. a
Iturbide, M., Bedia, J., Herrera, S., Baño-Medina, J., Fernández,
J., Frías, M. D., Manzanas, R., San-Martín, D., Cimadevilla, E.,
Cofiño, A. S., and Gutiérrez, J. M.: The R-based climate4R open
framework for reproducible climate data access and post-processing,
Environ. Modell. Softw., 111, 42–54,
https://doi.org/10.1016/j.envsoft.2018.09.009, 2019. a
Jézéquel, F., Lamotte, J. L., and Saïd, I.: Estimation of
numerical reproducibility on CPU and GPU, Proceedings of the 2015 Federated
Conference on Computer Science and Information Systems, FedCSIS 2015, 13–16 September 2015, Łódź, Poland, 5,
675–680, https://doi.org/10.15439/2015F29, 2015. a, b, c
Joint Committee for Guides in Metrology: International Vocabulary of
Metrology – Basic and General Concepts and Associated Terms (VIM), 3rd
edn., Joint Committee for Guides in Metrology (JCGM), 1–127,
https://www.nist.gov/system/files/documents/pml/div688/grp40/International-Vocabulary-of-Metrology.pdf,
2006. a
Kingma, D. P. and Ba, J. L.: Adam: A method for stochastic optimization, 3rd
International Conference on Learning Representations, ICLR 2015 – Conference
Track Proceedings, San Diego, CA, USA, 7–9 May 2015, 1–15, https://doi.org/10.48550/ARXIV.1412.6980, 2015. a
Kronenberg, R. and Bernhofer, C.: A method to adapt radar-derived
precipitation fields for climatological applications, Meteorol.
Appl., 22, 636–649, https://doi.org/10.1002/met.1498, 2015. a, b
Kurtzer, G. M., Sochat, V., and Bauer, M. W.: Singularity: Scientific
containers for mobility of compute, PLoS ONE, 12, 1–20,
https://doi.org/10.1371/journal.pone.0177459, 2017. a
Li, H., Xu, Z., Taylor, G., Studer, C., and Goldstein, T.: Visualizing the
loss landscape of neural nets, in: Advances in Neural Information Processing
Systems, edited by: Bengio, S., Wallach, H., Larochelle, H., Grauman, K.,
Cesa-Bianchi, N., and Garnett, R., vol. 31, Curran Associates, Inc.,
https://proceedings.neurips.cc/paper/2018/file/a41b3bb3e6b050b6c9067c67f663b915-Paper.pdf (last access: 13 November 2021),
2018. a
Manzanas, R., Frías, M. D., Cofiño, A. S., and Gutiérrez,
J. M.: Validation of 40 year multimodel seasonal precipitation forecasts:
The role of ENS on the global skill, J. Geophys. Res., 119,
1708–1719, https://doi.org/10.1002/2013JD020680, 2014. a
Maraun, D. and Widmann, M.: Statistical downscaling and bias correction for
climate research, Cambridge University Press, https://doi.org/10.1017/9781107588783, 2018. a, b, c
Maraun, D., Widmann, M., Gutiérrez, J. M., Kotlarski, S., Chandler,
R. E., Hertig, E., Wibig, J., Huth, R., and Wilcke, R. A. I.: Earth's Future
VALUE: A framework to validate downscaling approaches for climate change
studies, Earth's Future, 3, 1–14, https://doi.org/10.1002/2014EF000259, 2014. a, b, c
Mühr, B., Kubisch, S., Marx, A., and Wisotzky, C.: CEDIM Forensic
Disaster Analysis “Dürre & Hitzewelle Sommer 2018 (Deutschland)”,
2018, 1–19,
https://www.researchgate.net/publication/327156086_CEDIM_Forensic_Disaster_Analysis_Durre_Hitzewelle_Sommer_2018_Deutschland_Report_No_1,
2018. a
Pang, B., Yue, J., Zhao, G., and Xu, Z.: Statistical Downscaling of
Temperature with the Random Forest Model, Adv. Meteorol., 2017,
7265178, https://doi.org/10.1155/2017/7265178, 2017. a
Pastén-Zapata, E., Jones, J. M., Moggridge, H., and Widmann, M.:
Evaluation of the performance of Euro-CORDEX Regional Climate Models for
assessing hydrological climate change impacts in Great Britain: A comparison
of different spatial resolutions and quantile mapping bias correction
methods, J. Hydrol., 584, 124653,
https://doi.org/10.1016/j.jhydrol.2020.124653, 2020. a
Plesser, H. E.: Reproducibility vs. Replicability: A brief history of a
confused terminology, Front. Neuroinform., 11, 1–4,
https://doi.org/10.3389/fninf.2017.00076, 2018. a
Pour, S. H., Shahid, S., and Chung, E. S.: A Hybrid Model for Statistical
Downscaling of Daily Rainfall, Procedia Engineer., 154, 1424–1430,
https://doi.org/10.1016/j.proeng.2016.07.514, 2016. a
Quesada-Chacón, D.: Singularity container for “Repeatable high-resolution
statistical downscaling through deep learning”, Zenodo [code],
https://doi.org/10.5281/zenodo.5809705, 2021a. a, b
Quesada-Chacón, D.: Predictors and predictand for “Repeatable high-resolution
statistical downscaling through deep learning”, Zenodo [data set],
https://doi.org/10.5281/zenodo.5809553, 2021b. a
Quesada-Chacón, D.: dquesadacr/Rep_SDDL: Submission to GMD, Zenodo [code],
https://doi.org/10.5281/zenodo.5856118, 2022a. a, b
Quesada-Chacón, D.: Rendered description of the source code of “Repeatable
high-resolution statistical downscaling through deep learning”, GitHub [code],
https://github.com/dquesadacr/Rep_SDDL, last access: 11 July
2022b. a
Quesada-Chacón, D., Barfus, K., and Bernhofer, C.: Climate change
projections and extremes for Costa Rica using tailored predictors from CORDEX
model output through statistical downscaling with artificial neural
networks, Int. J. Climatol., 41, 211–232,
https://doi.org/10.1002/joc.6616, 2020. a
ReKIS: Regionales Klimainformationssystem Sachsen, Sachsen-Anhalt, Thüringen,
https://rekis.hydro.tu-dresden.de (last access: 11 July 2022),
2021. a
Riach, D.: TensorFlow Determinism (slides),
https://bit.ly/dl-determinism-slides-v3 (last access: 11 July
2022), 2021. a
Richter, D.: Ergebnisse methodischer Untersuchungen zur Korrektur des
systematischen Messfehlers des Hellmann-Niederschlagsmessers, Berichte des
Deutschen Wetterdienstes 194, Offenbach am Main, 93 pp., ISBN 978-3-88148-309-4, 1995. a
Ronneberger, O., Fischer, P., and Brox, T.: U-Net: Convolutional Networks for
Biomedical Image Segmentation, arXiv [preprint],
https://doi.org/10.48550/arXiv.1505.04597, 18 May 2015. a, b
Rougier, N. P., Hinsen, K., Alexandre, F., Arildsen, T., Barba, L. A.,
Benureau, F. C., Brown, C. T., DeBuy, P., Caglayan, O., Davison, A. P.,
Delsuc, M. A., Detorakis, G., Diem, A. K., Drix, D., Enel, P., Girard, B.,
Guest, O., Hall, M. G., Henriques, R. N., Hinaut, X., Jaron, K. S., Khamassi,
M., Klein, A., Manninen, T., Marchesi, P., McGlinn, D., Metzner, C., Petchey,
O., Plesser, H. E., Poisot, T., Ram, K., Ram, Y., Roesch, E., Rossant, C.,
Rostami, V., Shifman, A., Stachelek, J., Stimberg, M., Stollmeier, F., Vaggi,
F., Viejo, G., Vitay, J., Vostinar, A. E., Yurchak, R., and Zito, T.:
Sustainable computational science: The ReScience Initiative, PeerJ Computer
Science, 3, e142, https://doi.org/10.7717/peerj-cs.142, 2017. a, b
Serifi, A., Günther, T., and Ban, N.: Spatio-Temporal Downscaling of
Climate Data Using Convolutional and Error-Predicting Neural Networks,
Frontiers in Climate, 3, 1–15, https://doi.org/10.3389/fclim.2021.656479, 2021. a
Srivastava, R. K., Greff, K., and Schmidhuber, J.: Highway Networks, arXiv [preprint],
https://doi.org/10.48550/arXiv.1505.00387, 3 May 2015. a
Stoddart, C.: Is there a reproducibility crisis in science?, Nature,
3–5, https://doi.org/10.1038/d41586-019-00067-3, 2016. a
Taylor, K., Stouffer, R., and Meehl, G.: An Overview of CMIP5 and the
Experiment Design, B. Am. Meteorol. Soc., 93,
485–498, https://doi.org/10.1175/BAMS-D-11-00094.1, 2012. a
TOP500: AlphaCentauri – NEC HPC 22S8Ri-4, EPYC 7352 24C 2.3GHz, NVIDIA A100
SXM4 40 GB, Infiniband HDR200,
https://top500.org/system/179942/, last access: 11 July 2022,
2022. a
Tripathi, S., Srinivas, V. V., and Nanjundiah, R. S.: Downscaling of
precipitation for climate change scenarios: A support vector machine
approach, J. Hydrol., 330, 621–640,
https://doi.org/10.1016/j.jhydrol.2006.04.030, 2006. a
Vandal, T., Kodra, E., Ganguly, S., Michaelis, A., Nemani, R., and Ganguly,
A. R.: Generating high resolution climate change projections through single
image super-resolution: An abridged version, International Joint Conference
on Artificial Intelligence, Stockholm, 13–19 July 2018, 5389–5393, https://doi.org/10.24963/ijcai.2018/759,
2018. a
von Storch, H., Zorita, E., and Cubasch, U.: Downscaling of Global Climate
Change Estimates to Regional Scales: An Application to Iberian Rainfall in
Wintertime, J. Climate, 6, 1161–1171,
https://doi.org/10.1175/1520-0442(1993)006<1161:DOGCCE>2.0.CO;2, 1993.
a, b
Voosen, P.: Global temperatures in 2020 tied record highs, Science, 371,
334–335, https://doi.org/10.1126/science.371.6527.334, 2021. a
Wackernagel, H.: Multivariate geostatistics: an introduction with
applications, Springer, Berlin, https://doi.org/10.1007/978-3-662-05294-5, 2010. a
Wahl, S., Bollmeyer, C., Crewell, S., Figura, C., Friederichs, P., Hense, A.,
Keller, J. D., and Ohlwein, C.: A novel convective-scale regional reanalysis
COSMO-REA2: Improving the representation of precipitation, Meteorol.
Z., 26, 345–361, https://doi.org/10.1127/metz/2017/0824, 2017. a
Wilby, R. and Wigley, T.: Downscaling general circulation model output: a
review of methods and limitations, Prog. Phys. Geog., 21, 530–548, https://doi.org/10.1177/030913339702100403, 1997. a
WMO: 2021 one of the seven warmest years on record, WMO consolidated data
shows,
https://public.wmo.int/en/media/press-release/2021-one-of-seven-warmest-years-record-wmo-consolidated-data-shows,
last access: 11 July 2022. a
Xu, B., Wang, N., Chen, T., and Li, M.: Empirical Evaluation of Rectified
Activations in Convolutional Network, arXiv [preprint],
https://doi.org/10.48550/arXiv.1505.00853, 5 May 2015. a
Zhou, Z., Rahman Siddiquee, M. M., Tajbakhsh, N., and Liang, J.: Unet++: A
nested u-net architecture for medical image segmentation, Lecture Notes in
Computer Science (including subseries Lecture Notes in Artificial
Intelligence and Lecture Notes in Bioinformatics), vol. 11045, Springer, Cham,
https://doi.org/10.1007/978-3-030-00889-5_1, 2018. a, b
Short summary
We improved the performance of past perfect prognosis statistical downscaling methods while achieving full model repeatability with GPU-calculated deep learning models using the TensorFlow, climate4R, and VALUE frameworks. We employed the ERA5 reanalysis as predictors and ReKIS (eastern Ore Mountains, Germany, 1 km resolution) as precipitation predictand, while incorporating modern deep learning architectures. The achieved repeatability is key to accomplish further milestones with deep learning.
We improved the performance of past perfect prognosis statistical downscaling methods while...
