Articles | Volume 13, issue 4
Geosci. Model Dev., 13, 2109–2124, 2020
https://doi.org/10.5194/gmd-13-2109-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Geosci. Model Dev., 13, 2109–2124, 2020
https://doi.org/10.5194/gmd-13-2109-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model experiment description paper 28 Apr 2020
Model experiment description paper | 28 Apr 2020
Configuration and intercomparison of deep learning neural models for statistical downscaling
Jorge Baño-Medina et al.
Related authors
Joaquín Bedia, Jorge Baño-Medina, Mikel N. Legasa, Maialen Iturbide, Rodrigo Manzanas, Sixto Herrera, Ana Casanueva, Daniel San-Martín, Antonio S. Cofiño, and José Manuel Gutiérrez
Geosci. Model Dev., 13, 1711–1735, https://doi.org/10.5194/gmd-13-1711-2020, https://doi.org/10.5194/gmd-13-1711-2020, 2020
Short summary
Short summary
We introduce downscaleR, an open-source tool for statistical downscaling (SD) of climate information, implementing the most popular approaches and state-of-the-art techniques. It makes possible the development of end-to-end downscaling applications, from data retrieval to model building, validation, and prediction, bringing to climate scientists and practitioners a unique comprehensive framework for the development of complex and fully reproducible SD experiments.
Maialen Iturbide, José M. Gutiérrez, Lincoln M. Alves, Joaquín Bedia, Ruth Cerezo-Mota, Ezequiel Cimadevilla, Antonio S. Cofiño, Alejandro Di Luca, Sergio Henrique Faria, Irina V. Gorodetskaya, Mathias Hauser, Sixto Herrera, Kevin Hennessy, Helene T. Hewitt, Richard G. Jones, Svitlana Krakovska, Rodrigo Manzanas, Daniel Martínez-Castro, Gemma T. Narisma, Intan S. Nurhati, Izidine Pinto, Sonia I. Seneviratne, Bart van den Hurk, and Carolina S. Vera
Earth Syst. Sci. Data, 12, 2959–2970, https://doi.org/10.5194/essd-12-2959-2020, https://doi.org/10.5194/essd-12-2959-2020, 2020
Short summary
Short summary
We present an update of the IPCC WGI reference regions used in AR5 for the synthesis of climate change information. This revision was guided by the basic principles of climatic consistency and model representativeness (in particular for the new CMIP6 simulations). We also present a new dataset of monthly CMIP5 and CMIP6 spatially aggregated information using the new reference regions and describe a worked example of how to use this dataset to inform regional climate change studies.
Joaquín Bedia, Jorge Baño-Medina, Mikel N. Legasa, Maialen Iturbide, Rodrigo Manzanas, Sixto Herrera, Ana Casanueva, Daniel San-Martín, Antonio S. Cofiño, and José Manuel Gutiérrez
Geosci. Model Dev., 13, 1711–1735, https://doi.org/10.5194/gmd-13-1711-2020, https://doi.org/10.5194/gmd-13-1711-2020, 2020
Short summary
Short summary
We introduce downscaleR, an open-source tool for statistical downscaling (SD) of climate information, implementing the most popular approaches and state-of-the-art techniques. It makes possible the development of end-to-end downscaling applications, from data retrieval to model building, validation, and prediction, bringing to climate scientists and practitioners a unique comprehensive framework for the development of complex and fully reproducible SD experiments.
Related subject area
Atmospheric Sciences
Implementation of a synthetic inflow turbulence generator in idealised WRF v3.6.1 large eddy simulations under neutral atmospheric conditions
Numerical study of the effects of initial conditions and emissions on PM2.5 concentration simulations with CAMx v6.1: a Xi'an case study
A multi-year short-range hindcast experiment with CESM1 for evaluating climate model moist processes from diurnal to interannual timescales
Ground-based lidar processing and simulator framework for comparing models and observations (ALCF 1.0)
Development of an Ozone Monitoring Instrument (OMI) aerosol index (AI) data assimilation scheme for aerosol modeling over bright surfaces – a step toward direct radiance assimilation in the UV spectrum
IntelliO3-ts v1.0: a neural network approach to predict near-surface ozone concentrations in Germany
ISBA-MEB (SURFEX v8.1): model snow evaluation for local-scale forest sites
Evaluating and improving the treatment of gases in radiation schemes: the Correlated K-Distribution Model Intercomparison Project (CKDMIP)
GenChem v1.0 – a chemical pre-processing and testing system for atmospheric modelling
Incoming data quality control in high-resolution urban climate simulations: a Hong Kong–Shenzhen area urban climate simulation as a case study using the WRF/Noah LSM/SLUCM model (Version 3.7.1)
Configuration and evaluation of a global unstructured mesh atmospheric model (GRIST-A20.9) based on the variable-resolution approach
Description of the uEMEP_v5 downscaling approach for the EMEP MSC-W chemistry transport model
Development of a three-dimensional variational assimilation system for lidar profile data based on a size-resolved aerosol model in WRF–Chem model v3.9.1 and its application in PM2.5 forecasts across China
Using wavelet transform and dynamic time warping to identify the limitations of the CNN model as an air quality forecasting system
In-cloud scavenging scheme for sectional aerosol modules – implementation in the framework of the Sectional Aerosol module for Large Scale Applications version 2.0 (SALSA2.0) global aerosol module
Detection of atmospheric rivers with inline uncertainty quantification: TECA-BARD v1.0.1
TITAM (v1.0): the Time-Independent Tracking Algorithm for Medicanes
Effects of horizontal resolution and air–sea coupling on simulated moisture source for East Asian precipitation in MetUM GA6/GC2
On the tuning of atmospheric inverse methods: comparisons with the European Tracer Experiment (ETEX) and Chernobyl datasets using the atmospheric transport model FLEXPART
Sensitivity of aerosol optical properties to the aerosol size distribution over central Europe and the Mediterranean Basin using the WRF-Chem v.3.9.1.1 coupled model
PMIF v1.0: assessing the potential of satellite observations to constrain CO2 emissions from large cities and point sources over the globe using synthetic data
Multilayer cloud conditions in trade wind shallow cumulus – confronting two ICON model derivatives with airborne observations
A new parameterization of ice heterogeneous nucleation coupled to aerosol chemistry in WRF-Chem model version 3.5.1: evaluation through ISDAC measurements
Prioritising the sources of pollution in European cities: do air quality modelling applications provide consistent responses?
New strategies for vertical transport in chemistry transport models: application to the case of the Mount Etna eruption on 18 March 2012 with CHIMERE v2017r4
Sensitivity of spatial aerosol particle distributions to the boundary conditions in the PALM model system 6.0
Multi-layer coupling between SURFEX-TEB-v9.0 and Meso-NH-v5.3 for modelling the urban climate of high-rise cities
Description and evaluation of a detailed gas-phase chemistry scheme in the TM5-MP global chemistry transport model (r112)
Modeling lightning observations from space-based platforms (CloudScat.jl 1.0)
Flex_extract v7.1.2 – a software package to retrieve and prepare ECMWF data for use in FLEXPART
Land surface model influence on the simulated climatologies of temperature and precipitation extremes in the WRF v3.9 model over North America
Silicone v1.0.0: an open-source Python package for inferring missing emissions data for climate change research
Collisional growth in a particle-based cloud microphysical model: insights from column model simulations using LCM1D (v1.0)
The making of the New European Wind Atlas – Part 1: Model sensitivity
The Making of the New European Wind Atlas – Part 2: Production and evaluation
The Kinetic Energy Budget of the Atmosphere (KEBA) model 1.0: a simple yet physical approach for estimating regional wind energy resource potentials that includes the kinetic energy removal effect by wind turbines
Dynamic Anthropogenic activitieS impacting Heat emissions (DASH v1.0): development and evaluation
Role of atmospheric horizontal resolution in simulating tropical and subtropical South American precipitation in HadGEM3-GC31
Image-processing-based atmospheric river tracking method version 1 (IPART-1)
Simulating the forest fire plume dispersion, chemistry, and aerosol formation using SAM-ASP version 1.0
Development of an atmospheric chemistry model coupled to the PALM model system 6.0: Implementation and first applications
On the model uncertainties in Bayesian source reconstruction using the emission inverse modelling system FREARtool v1.0 and the Lagrangian transport and dispersion model Flexpart v9.0.2
A mass- and energy-conserving framework for using machine learning to speed computations: a photochemistry example
Evaluation of the interactive stratospheric ozone (O3v2 module) for the E3SM version 2 Earth System Model
The urban dispersion model EPISODE v10.0 – Part 1: An Eulerian and sub-grid-scale air quality model and its application in Nordic winter conditions
Can machine learning improve the model representation of turbulent kinetic energy dissipation rate in the boundary layer for complex terrain?
PAMTRA 1.0: the Passive and Active Microwave radiative TRAnsfer tool for simulating radiometer and radar measurements of the cloudy atmosphere
Predicting the morphology of ice particles in deep convection using the super-droplet method: development and evaluation of SCALE-SDM 0.2.5-2.2.0, -2.2.1, and -2.2.2
An exploratory performance assessment of the CHIMERE model (version 2017r4) for the northwestern Iberian Peninsula and the summer season
Development of the global atmospheric chemistry general circulation model BCC-GEOS-Chem v1.0: model description and evaluation
Jian Zhong, Xiaoming Cai, and Zheng-Tong Xie
Geosci. Model Dev., 14, 323–336, https://doi.org/10.5194/gmd-14-323-2021, https://doi.org/10.5194/gmd-14-323-2021, 2021
Short summary
Short summary
A synthetic inflow turbulence generator was implemented in the idealised Weather Research and Forecasting large eddy simulation. The inflow case yielded a mean velocity profile and second-moment profiles that agreed well with those generated using periodic boundary conditions, after a short adjustment distance. This implementation can be extended to a multi-scale seamless nesting simulation from a meso-scale domain with a kilometre-scale resolution to LES domains with metre-scale resolutions.
Han Xiao, Qizhong Wu, Xiaochun Yang, Lanning Wang, and Huaqiong Cheng
Geosci. Model Dev., 14, 223–238, https://doi.org/10.5194/gmd-14-223-2021, https://doi.org/10.5194/gmd-14-223-2021, 2021
Short summary
Short summary
Few studies have investigated the effects of initial conditions on the simulation or prediction of PM2.5 concentrations. Here, sensitivity experiments are used to explore the effects of three initial mechanisms (clean, restart, and continuous) and emissions in Xi’an in December 2016. According to this work, if the restart mechanism cannot be used due to computing resource and storage space limitations when forecasting PM2.5 concentrations, a spin-up time of at least 27 h is needed.
Hsi-Yen Ma, Chen Zhou, Yunyan Zhang, Stephen A. Klein, Mark D. Zelinka, Xue Zheng, Shaocheng Xie, Wei-Ting Chen, and Chien-Ming Wu
Geosci. Model Dev., 14, 73–90, https://doi.org/10.5194/gmd-14-73-2021, https://doi.org/10.5194/gmd-14-73-2021, 2021
Short summary
Short summary
We propose an experimental design of a suite of multi-year, short-term hindcasts and compare them with corresponding observations or measurements for periods based on different weather and climate phenomena. This atypical way of evaluating model performance is particularly useful and beneficial, as these hindcasts can give scientists a robust picture of modeled precipitation, and cloud and radiation processes from their diurnal variation to year-to-year variability.
Peter Kuma, Adrian J. McDonald, Olaf Morgenstern, Richard Querel, Israel Silber, and Connor J. Flynn
Geosci. Model Dev., 14, 43–72, https://doi.org/10.5194/gmd-14-43-2021, https://doi.org/10.5194/gmd-14-43-2021, 2021
Jianglong Zhang, Robert J. D. Spurr, Jeffrey S. Reid, Peng Xian, Peter R. Colarco, James R. Campbell, Edward J. Hyer, and Nancy L. Baker
Geosci. Model Dev., 14, 27–42, https://doi.org/10.5194/gmd-14-27-2021, https://doi.org/10.5194/gmd-14-27-2021, 2021
Short summary
Short summary
A first-of-its-kind scheme has been developed for assimilating Ozone Monitoring Instrument (OMI) aerosol index (AI) measurements into the Naval Aerosol Analysis and Predictive System. Improvements in model simulations demonstrate the utility of OMI AI data assimilation for improving the accuracy of aerosol model analysis over cloudy regions and bright surfaces. This study can be considered one of the first attempts at direct radiance assimilation in the UV spectrum for aerosol analyses.
Felix Kleinert, Lukas H. Leufen, and Martin G. Schultz
Geosci. Model Dev., 14, 1–25, https://doi.org/10.5194/gmd-14-1-2021, https://doi.org/10.5194/gmd-14-1-2021, 2021
Short summary
Short summary
With IntelliO3-ts v1.0, we present an artificial neural network as a new forecasting model for daily aggregated near-surface ozone concentrations with a lead time of up to 4 d. We used measurement and reanalysis data from more than 300 German monitoring stations to train, fine tune, and test the model. We show that the model outperforms standard reference models like persistence models and demonstrate that IntelliO3-ts outperforms climatological reference models for the first 2 d.
Adrien Napoly, Aaron Boone, and Théo Welfringer
Geosci. Model Dev., 13, 6523–6545, https://doi.org/10.5194/gmd-13-6523-2020, https://doi.org/10.5194/gmd-13-6523-2020, 2020
Short summary
Short summary
Accurate modeling of snow impact on surface energy and mass fluxes is required from land surface models. This new version of the SURFEX model improves the representation of the snowpack. In particular, it prevents its ablation from occurring too early in the season, which also leads to better soil temperatures and energy fluxes toward the atmosphere. This was made possible with a more explicit and distinct representation of each layer that constitutes the surface (soil, snow, and vegetation).
Robin J. Hogan and Marco Matricardi
Geosci. Model Dev., 13, 6501–6521, https://doi.org/10.5194/gmd-13-6501-2020, https://doi.org/10.5194/gmd-13-6501-2020, 2020
Short summary
Short summary
A key component of computer models used to predict weather and climate is the radiation scheme, which calculates how solar and infrared radiation heats and cools the atmosphere and surface, including the important role of greenhouse gases. This paper describes the experimental protocol and large datasets for a new project, CKDMIP, to evaluate and improve the accuracy of the treatment of atmospheric gases in the radiation schemes used worldwide, as well as their computational speed.
David Simpson, Robert Bergström, Alan Briolat, Hannah Imhof, John Johansson, Michael Priestley, and Alvaro Valdebenito
Geosci. Model Dev., 13, 6447–6465, https://doi.org/10.5194/gmd-13-6447-2020, https://doi.org/10.5194/gmd-13-6447-2020, 2020
Short summary
Short summary
This paper outlines the structure and usage of the GenChem system, which includes a chemical pre-processor (GenChem.py) and a simple box model (boxChem). GenChem provides scripts and input files for converting chemical equations into differential form for use in atmospheric chemical transport models (CTMs) and/or the boxChem system. Although GenChem is primarily intended for users of the EMEP MSC-W CTM and related systems, boxChem can be run as a stand-alone chemical solver.
Zhiqiang Li, Bingcheng Wan, Yulun Zhou, and Hokit Wong
Geosci. Model Dev., 13, 6349–6360, https://doi.org/10.5194/gmd-13-6349-2020, https://doi.org/10.5194/gmd-13-6349-2020, 2020
Short summary
Short summary
Our results provide evidence of the effects of incoming land surface data quality on the accuracy of high-resolution urban climate simulations and emphasize the importance of the incoming data quality control.
Yihui Zhou, Yi Zhang, Jian Li, Rucong Yu, and Zhuang Liu
Geosci. Model Dev., 13, 6325–6348, https://doi.org/10.5194/gmd-13-6325-2020, https://doi.org/10.5194/gmd-13-6325-2020, 2020
Short summary
Short summary
This paper explores the configuration of a global atmospheric model (global-to-regional integrated forecast system-atmosphere; GRIST-A) with various multiresolution grids. The model performance is evaluated from dry dynamics to simple physics and full physics. The model is able to resolve the fine-scale structures in the grid-refinement region, and the adverse impact due to the mesh transition and the coarse-resolution area can be controlled well.
Bruce Rolstad Denby, Michael Gauss, Peter Wind, Qing Mu, Eivind Grøtting Wærsted, Hilde Fagerli, Alvaro Valdebenito, and Heiko Klein
Geosci. Model Dev., 13, 6303–6323, https://doi.org/10.5194/gmd-13-6303-2020, https://doi.org/10.5194/gmd-13-6303-2020, 2020
Short summary
Short summary
Air pollution is both a local and a global problem. Since measurements cannot be made everywhere, mathematical models are used to calculate air quality over cities or countries. Modelling over countries limits the level of detail of the models. For countries, the level of detail is only a few kilometres, so air quality at kerb sides is not properly represented. The uEMEP model is used together with the regional air quality model EMEP MSC-W to model details down to kerb side for all of Norway.
Yanfei Liang, Zengliang Zang, Dong Liu, Peng Yan, Yiwen Hu, Yan Zhou, and Wei You
Geosci. Model Dev., 13, 6285–6301, https://doi.org/10.5194/gmd-13-6285-2020, https://doi.org/10.5194/gmd-13-6285-2020, 2020
Ebrahim Eslami, Yunsoo Choi, Yannic Lops, Alqamah Sayeed, and Ahmed Khan Salman
Geosci. Model Dev., 13, 6237–6251, https://doi.org/10.5194/gmd-13-6237-2020, https://doi.org/10.5194/gmd-13-6237-2020, 2020
Short summary
Short summary
As using deep learning algorithms has become a popular data analytic technique, atmospheric scientists should have a balanced perception of their strengths and limitations so that they can provide a powerful analysis of complex data with well-established procedures. This study addresses significant limitations of an advanced deep learning algorithm, the convolutional neural network.
Eemeli Holopainen, Harri Kokkola, Anton Laakso, and Thomas Kühn
Geosci. Model Dev., 13, 6215–6235, https://doi.org/10.5194/gmd-13-6215-2020, https://doi.org/10.5194/gmd-13-6215-2020, 2020
Short summary
Short summary
This paper introduces an in-cloud wet deposition scheme for liquid and ice phase clouds for global aerosol–climate models. With the default setup, our wet deposition scheme behaves spuriously and better representation can be achieved with this scheme when black carbon is mixed with soluble compounds at emission time. This work is done as many of the global models fail to reproduce the transport of black carbon to the Arctic, which may be due to the poor representation of wet removal in models.
Travis A. O'Brien, Mark D. Risser, Burlen Loring, Abdelrahman A. Elbashandy, Harinarayan Krishnan, Jeffrey Johnson, Christina M. Patricola, John P. O'Brien, Ankur Mahesh, Prabhat, Sarahí Arriaga Ramirez, Alan M. Rhoades, Alexander Charn, Héctor Inda Díaz, and William D. Collins
Geosci. Model Dev., 13, 6131–6148, https://doi.org/10.5194/gmd-13-6131-2020, https://doi.org/10.5194/gmd-13-6131-2020, 2020
Short summary
Short summary
Researchers utilize various algorithms to identify extreme weather features in climate data, and we seek to answer this question: given a
plausibleweather event detector, how does uncertainty in the detector impact scientific results? We generate a suite of statistical models that emulate expert identification of weather features. We find that the connection between El Niño and atmospheric rivers – a specific extreme weather type – depends systematically on the design of the detector.
Enrique Pravia-Sarabia, Juan José Gómez-Navarro, Pedro Jiménez-Guerrero, and Juan Pedro Montávez
Geosci. Model Dev., 13, 6051–6075, https://doi.org/10.5194/gmd-13-6051-2020, https://doi.org/10.5194/gmd-13-6051-2020, 2020
Short summary
Short summary
This work shows TITAM, a time-independent tracking algorithm specifically suited for Mediterranean tropical-like cyclones, often referred to as medicanes. The methodology developed has the capacity to track multiple simultaneous cyclones, the ability to track a medicane in the presence of intense extratropical lows, and the potential to separate the medicane from other similar structures by handling the intermittent loss of structure and managing the tilting of the axis.
Liang Guo, Ruud J. van der Ent, Nicholas P. Klingaman, Marie-Estelle Demory, Pier Luigi Vidale, Andrew G. Turner, Claudia C. Stephan, and Amulya Chevuturi
Geosci. Model Dev., 13, 6011–6028, https://doi.org/10.5194/gmd-13-6011-2020, https://doi.org/10.5194/gmd-13-6011-2020, 2020
Short summary
Short summary
Precipitation over East Asia simulated in the Met Office Unified Model is compared with observations. Moisture sources of EA precipitation are traced using a moisture tracking model. Biases in moisture sources are linked to biases in precipitation. Using the tracking model, changes in moisture sources can be attributed to changes in SST, circulation and associated evaporation. This proves that the method used in this study is useful to identify the causes of biases in regional precipitation.
Ondřej Tichý, Lukáš Ulrych, Václav Šmídl, Nikolaos Evangeliou, and Andreas Stohl
Geosci. Model Dev., 13, 5917–5934, https://doi.org/10.5194/gmd-13-5917-2020, https://doi.org/10.5194/gmd-13-5917-2020, 2020
Short summary
Short summary
We study the estimation of the temporal profile of an atmospheric release using formalization as a linear inverse problem. The problem is typically ill-posed, so all state-of-the-art methods need some form of regularization using additional information. We provide a sensitivity study on the prior source term and regularization parameters for the shape of the source term with a demonstration on the ETEX experimental release and the Cs-134 and Cs-137 dataset from the Chernobyl accident.
Laura Palacios-Peña, Jerome D. Fast, Enrique Pravia-Sarabia, and Pedro Jiménez-Guerrero
Geosci. Model Dev., 13, 5897–5915, https://doi.org/10.5194/gmd-13-5897-2020, https://doi.org/10.5194/gmd-13-5897-2020, 2020
Short summary
Short summary
The main objective of this work is to study the impact of the representation of aerosol size distribution on aerosol optical properties over central Europe and the Mediterranean Basin during a summertime aerosol episode using the WRF-Chem online model. Results reveal that the reduction in the standard deviation of the accumulation mode leads to the largest impacts on aerosol optical depth (AOD) representation due to a transfer of particles from the accumulation mode to the coarse mode.
Yilong Wang, Grégoire Broquet, François-Marie Bréon, Franck Lespinas, Michael Buchwitz, Maximilian Reuter, Yasjka Meijer, Armin Loescher, Greet Janssens-Maenhout, Bo Zheng, and Philippe Ciais
Geosci. Model Dev., 13, 5813–5831, https://doi.org/10.5194/gmd-13-5813-2020, https://doi.org/10.5194/gmd-13-5813-2020, 2020
Marek Jacob, Pavlos Kollias, Felix Ament, Vera Schemann, and Susanne Crewell
Geosci. Model Dev., 13, 5757–5777, https://doi.org/10.5194/gmd-13-5757-2020, https://doi.org/10.5194/gmd-13-5757-2020, 2020
Short summary
Short summary
We compare clouds in different cloud-resolving atmosphere simulations with airborne remote sensing observations. The focus is on warm shallow clouds in the Atlantic trade wind region. Those clouds are climatologically important but challenging for climate models. We use forward operators to apply instrument-specific thresholds for cloud detection to model outputs. In this comparison, the higher-resolution model better reproduces the layered cloud structure.
Setigui Aboubacar Keita, Eric Girard, Jean-Christophe Raut, Maud Leriche, Jean-Pierre Blanchet, Jacques Pelon, Tatsuo Onishi, and Ana Cirisan
Geosci. Model Dev., 13, 5737–5755, https://doi.org/10.5194/gmd-13-5737-2020, https://doi.org/10.5194/gmd-13-5737-2020, 2020
Bart Degraeuwe, Enrico Pisoni, and Philippe Thunis
Geosci. Model Dev., 13, 5725–5736, https://doi.org/10.5194/gmd-13-5725-2020, https://doi.org/10.5194/gmd-13-5725-2020, 2020
Short summary
Short summary
To make decisions on how to improve air quality, it is useful to identify the main sources of pollution for an area of interest. Often these sources of pollution are identified with complex models that, even if accurate, are time consuming and complex. In this work we use another approach, simplified models, to accomplish the same task. The results, computed with two different set of simplified models, show the main sources of pollution for selected cities, and the associated uncertainties.
Mathieu Lachatre, Sylvain Mailler, Laurent Menut, Solène Turquety, Pasquale Sellitto, Henda Guermazi, Giuseppe Salerno, Tommaso Caltabiano, and Elisa Carboni
Geosci. Model Dev., 13, 5707–5723, https://doi.org/10.5194/gmd-13-5707-2020, https://doi.org/10.5194/gmd-13-5707-2020, 2020
Short summary
Short summary
Excessive numerical diffusion is a major limitation in the representation of long-range transport in atmospheric models. In the present study, we focus on excessive diffusion in the vertical direction. We explore three possible ways of addressing this problem: increased vertical resolution, an advection scheme with anti-diffusive properties and more accurate representation of vertical wind. This study focused on a particular volcanic eruption event to improve atmospheric transport modeling.
Mona Kurppa, Pontus Roldin, Jani Strömberg, Anna Balling, Sasu Karttunen, Heino Kuuluvainen, Jarkko V. Niemi, Liisa Pirjola, Topi Rönkkö, Hilkka Timonen, Antti Hellsten, and Leena Järvi
Geosci. Model Dev., 13, 5663–5685, https://doi.org/10.5194/gmd-13-5663-2020, https://doi.org/10.5194/gmd-13-5663-2020, 2020
Short summary
Short summary
High-resolution modelling is needed to solve the aerosol concentrations in a complex urban area. Here, the performance of an aerosol module within the PALM model to simulate the detailed horizontal and vertical distribution of aerosol particles is studied. Further, sensitivity to the meteorological and aerosol boundary conditions is assessed using both model and observation data. The horizontal distribution is sensitive to the wind speed and stability, and the vertical to the wind direction.
Robert Schoetter, Yu Ting Kwok, Cécile de Munck, Kevin Ka Lun Lau, Wai Kin Wong, and Valéry Masson
Geosci. Model Dev., 13, 5609–5643, https://doi.org/10.5194/gmd-13-5609-2020, https://doi.org/10.5194/gmd-13-5609-2020, 2020
Short summary
Short summary
Cities change the local meteorological conditions, e.g. by increasing air temperature, which can negatively impact humans and infrastructure. The urban climate model TEB is able to calculate the meteorological conditions in low- and mid-rise cities since it interacts with the lowest level of an atmospheric model. Here, a multi-layer coupling of TEB is introduced to enable modelling the urban climate of cities with many skyscrapers; the new version is tested for the high-rise city of Hong Kong.
Stelios Myriokefalitakis, Nikos Daskalakis, Angelos Gkouvousis, Andreas Hilboll, Twan van Noije, Jason E. Williams, Philippe Le Sager, Vincent Huijnen, Sander Houweling, Tommi Bergman, Johann Rasmus Nüß, Mihalis Vrekoussis, Maria Kanakidou, and Maarten C. Krol
Geosci. Model Dev., 13, 5507–5548, https://doi.org/10.5194/gmd-13-5507-2020, https://doi.org/10.5194/gmd-13-5507-2020, 2020
Short summary
Short summary
This work documents and evaluates the detailed tropospheric gas-phase chemical mechanism MOGUNTIA in the three-dimensional chemistry transport model TM5-MP. The Rosenbrock solver, as generated by the KPP software, is implemented in the chemistry code, which can successfully replace the classical Euler backward integration method. The MOGUNTIA scheme satisfactorily simulates a large suite of oxygenated volatile organic compounds (VOCs) that are observed in the atmosphere at significant levels.
Alejandro Luque, Francisco José Gordillo-Vázquez, Dongshuai Li, Alejandro Malagón-Romero, Francisco Javier Pérez-Invernón, Anthony Schmalzried, Sergio Soler, Olivier Chanrion, Matthias Heumesser, Torsten Neubert, Víctor Reglero, and Nikolai Østgaard
Geosci. Model Dev., 13, 5549–5566, https://doi.org/10.5194/gmd-13-5549-2020, https://doi.org/10.5194/gmd-13-5549-2020, 2020
Short summary
Short summary
Lightning flashes are often recorded from space-based platforms. Besides being valuable inputs for weather forecasting, these observations also enable research into fundamental questions regarding lightning physics. To exploit them, it is essential to understand how light propagates from a lightning flash to a space-based observation instrument. Here, we present an open-source software tool to model this process that extends on previous work and overcomes some of the existing limitations.
Anne Tipka, Leopold Haimberger, and Petra Seibert
Geosci. Model Dev., 13, 5277–5310, https://doi.org/10.5194/gmd-13-5277-2020, https://doi.org/10.5194/gmd-13-5277-2020, 2020
Short summary
Short summary
Flex_extract v7.1 is an open-source software to retrieve and prepare meteorological fields from the European Centre for Medium-Range Weather Forecasts (ECMWF) MARS archive to serve as input for the FLEXTRA–FLEXPART atmospheric transport modelling system. It can be used by public as well as member-state users and enables the retrieval of a variety of different data sets, including the new reanalysis ERA5. Instructions are given for installation along with typical usage scenarios.
Almudena García-García, Francisco José Cuesta-Valero, Hugo Beltrami, Fidel González-Rouco, Elena García-Bustamante, and Joel Finnis
Geosci. Model Dev., 13, 5345–5366, https://doi.org/10.5194/gmd-13-5345-2020, https://doi.org/10.5194/gmd-13-5345-2020, 2020
Robin D. Lamboll, Zebedee R. J. Nicholls, Jarmo S. Kikstra, Malte Meinshausen, and Joeri Rogelj
Geosci. Model Dev., 13, 5259–5275, https://doi.org/10.5194/gmd-13-5259-2020, https://doi.org/10.5194/gmd-13-5259-2020, 2020
Short summary
Short summary
Many models project how human activity can lead to more or less climate change, but most of these models do not project all climate-relevant emissions, potentially biasing climate projections. This paper outlines a Python package called Silicone, which can add missing emissions in a flexible yet high-throughput manner. It does this
infillingbased on more complete literature projections. It facilitates a more complete understanding of the climate impact of alternative emission pathways.
Simon Unterstrasser, Fabian Hoffmann, and Marion Lerch
Geosci. Model Dev., 13, 5119–5145, https://doi.org/10.5194/gmd-13-5119-2020, https://doi.org/10.5194/gmd-13-5119-2020, 2020
Short summary
Short summary
Particle-based cloud models use simulation particles for the representation of cloud particles like droplets or ice crystals. The collision and merging of cloud particles (i.e. collisional growth a.k.a. collection in the case of cloud droplets and aggregation in the case of ice crystals) was found to be a numerically challenging process in such models. The study presents verification exercises in a 1D column model, where sedimentation and collisional growth are the only active processes.
Andrea N. Hahmann, Tija Sīle, Björn Witha, Neil N. Davis, Martin Dörenkämper, Yasemin Ezber, Elena García-Bustamante, J. Fidel González-Rouco, Jorge Navarro, Bjarke T. Olsen, and Stefan Söderberg
Geosci. Model Dev., 13, 5053–5078, https://doi.org/10.5194/gmd-13-5053-2020, https://doi.org/10.5194/gmd-13-5053-2020, 2020
Short summary
Short summary
Wind energy resource assessment routinely uses numerical weather prediction model output. We describe the evaluation procedures used for picking the suitable blend of model setup and parameterizations for simulating European wind climatology with the WRF model. We assess the simulated winds against tall mast measurements using a suite of metrics, including the Earth Mover's Distance, which diagnoses the performance of each ensemble member using the full wind speed and direction distribution.
Martin Dörenkämper, Bjarke T. Olsen, Björn Witha, Andrea N. Hahmann, Neil N. Davis, Jordi Barcons, Yasemin Ezber, Elena García-Bustamante, J. Fidel González-Rouco, Jorge Navarro, Mariano Sastre-Marugán, Tija Sīle, Wilke Trei, Mark Žagar, Jake Badger, Julia Gottschall, Javier Sanz Rodrigo, and Jakob Mann
Geosci. Model Dev., 13, 5079–5102, https://doi.org/10.5194/gmd-13-5079-2020, https://doi.org/10.5194/gmd-13-5079-2020, 2020
Short summary
Short summary
This is the second of two papers that document the creation of the New European Wind Atlas (NEWA). The paper includes a detailed description of the technical and practical aspects that went into running the mesoscale simulations and the microscale downscaling for generating the climatology. A comprehensive evaluation of each component of the NEWA model chain is presented using observations from a large set of tall masts located all over Europe.
Axel Kleidon and Lee M. Miller
Geosci. Model Dev., 13, 4993–5005, https://doi.org/10.5194/gmd-13-4993-2020, https://doi.org/10.5194/gmd-13-4993-2020, 2020
Short summary
Short summary
When winds are used as renewable energy by more and more wind turbines, one needs to account for the effect of wind turbines on the atmospheric flow. The Kinetic Energy Budget of the Atmosphere (KEBA) model provides a simple, physics-based approach to account for this effect very well when compared to much more detailed numerical simulations with an atmospheric model. KEBA should be useful to derive lower, more realistic wind energy resource potentials of different regions.
Isabella Capel-Timms, Stefán Thor Smith, Ting Sun, and Sue Grimmond
Geosci. Model Dev., 13, 4891–4924, https://doi.org/10.5194/gmd-13-4891-2020, https://doi.org/10.5194/gmd-13-4891-2020, 2020
Short summary
Short summary
Thermal emissions or anthropogenic heat fluxes (QF) from human activities impact the local- and larger-scale urban climate. DASH considers both urban form and function in simulating QF by use of an agent-based structure that includes behavioural characteristics of city populations. This allows social practices to drive the calculation of QF as occupants move, varying by day type, demographic, location, activity, and socio-economic factors and in response to environmental conditions.
Paul-Arthur Monerie, Amulya Chevuturi, Peter Cook, Nicholas P. Klingaman, and Christopher E. Holloway
Geosci. Model Dev., 13, 4749–4771, https://doi.org/10.5194/gmd-13-4749-2020, https://doi.org/10.5194/gmd-13-4749-2020, 2020
Short summary
Short summary
In this study, we assess how increasing the horizontal resolution of HadGEM3-GC31 can allow simulating better tropical and subtropical South American precipitation. We compare simulations of HadGEM3-GC3.1, performed at three different horizontal resolutions. We show that increasing resolution allows decreasing precipitation biases over the Andes and northeast Brazil and improves the simulation of daily precipitation distribution.
Guangzhi Xu, Xiaohui Ma, Ping Chang, and Lin Wang
Geosci. Model Dev., 13, 4639–4662, https://doi.org/10.5194/gmd-13-4639-2020, https://doi.org/10.5194/gmd-13-4639-2020, 2020
Short summary
Short summary
We observed considerable limitations in existing atmospheric river (AR) detection methods and looked into other disciplines for inspirations of tackling the AR detection problem. A new method is derived from an image-processing technique and encodes the spatiotemporal-scale information of AR systems, which is a key physical ingredient of ARs that is more stable than the vapor flux intensities, making it more suitable for climate-scale studies when models often have different biases.
Chantelle R. Lonsdale, Matthew J. Alvarado, Anna L. Hodshire, Emily Ramnarine, and Jeffrey R. Pierce
Geosci. Model Dev., 13, 4579–4593, https://doi.org/10.5194/gmd-13-4579-2020, https://doi.org/10.5194/gmd-13-4579-2020, 2020
Short summary
Short summary
The System for Atmospheric Modelling (SAM) has been coupled with the detailed gas/aerosol chemistry model, the Aerosol Simulation Program (ASP), to capture cross-plume concentration gradients as fire plumes evolve downwind. SAM-ASP v1.0 will lead to the development of parameterizations of near-source biomass burning chemistry that can be used to more accurately simulate biomass burning chemical and physical transformations of trace gases and aerosols within coarser chemical transport models.
Basit Khan, Sabine Banzhaf, Edward C. Chan, Renate Forkel, Farah Kanani-Sühring, Klaus Ketelsen, Mona Kurppa, Björn Maronga, Matthias Mauder, Siegfried Raasch, Emmanuele Russo, Martijn Schaap, and Matthias Sühring
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-286, https://doi.org/10.5194/gmd-2020-286, 2020
Revised manuscript accepted for GMD
Short summary
Short summary
This article describes the implementation of an online-coupled gas-phase chemistry model in the micro-scale PALM model system 6.0. The model reads emission input and perform transport, chemical transformation and dry deposition of chemical compounds while aerosol processes are described by the sectional aerosol model, SALSA. Several pre-compiled ready-to-use chemical mechanisms are included in the chemistry model code, however, user can also easily implement other mechanisms.
Pieter De Meutter, Ian Hoffman, and Kurt Ungar
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-162, https://doi.org/10.5194/gmd-2020-162, 2020
Revised manuscript accepted for GMD
Short summary
Short summary
Inverse atmospheric transport modelling is an important tool in several disciplines. However, the specification of atmospheric transport model error remains challenging. In this paper, we employ a state-of-the-art ensemble technique combined with a state-of-the-art Bayesian inference algorithm to infer point sources. Our research helps to fill the gap in our understanding of model error in the context of inverse atmospheric transport modelling.
Patrick Obin Sturm and Anthony S. Wexler
Geosci. Model Dev., 13, 4435–4442, https://doi.org/10.5194/gmd-13-4435-2020, https://doi.org/10.5194/gmd-13-4435-2020, 2020
Short summary
Short summary
Large air quality and climate models calculate different physical and chemical phenomena in separate operators within the overall model, some of which are computationally intensive. Machine learning tools can memorize the behavior of these operators and replace them, but the replacements must still obey physical laws, like conservation principles. This work derives a mathematical framework for machine learning replacements that conserves properties, such as mass or energy, to machine precision.
Qi Tang, Michael J. Prather, Juno Hsu, Daniel J. Ruiz, Philip J. Cameron-Smith, Shaocheng Xie, and Jean-Christophe Golaz
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-293, https://doi.org/10.5194/gmd-2020-293, 2020
Revised manuscript accepted for GMD
Paul D. Hamer, Sam-Erik Walker, Gabriela Sousa-Santos, Matthias Vogt, Dam Vo-Thanh, Susana Lopez-Aparicio, Philipp Schneider, Martin O. P. Ramacher, and Matthias Karl
Geosci. Model Dev., 13, 4323–4353, https://doi.org/10.5194/gmd-13-4323-2020, https://doi.org/10.5194/gmd-13-4323-2020, 2020
Short summary
Short summary
EPISODE is an air quality model designed to give information on air pollution in cities down to distances measured in metres from the roadside and other pollution sources. We demonstrate that EPISODE can adequately describe nitrogen dioxide air pollution in a case study in six Norwegian cities. From this, we conclude that EPISODE can be used to provide air quality information to public bodies and society in order to help in the understanding and management of air pollution in urban environments.
Nicola Bodini, Julie K. Lundquist, and Mike Optis
Geosci. Model Dev., 13, 4271–4285, https://doi.org/10.5194/gmd-13-4271-2020, https://doi.org/10.5194/gmd-13-4271-2020, 2020
Short summary
Short summary
While turbulence dissipation rate (ε) is an essential parameter for the prediction of wind speed, its current representation in weather prediction models is inaccurate, especially in complex terrain. In this study, we leverage the potential of machine-learning techniques to provide a more accurate representation of turbulence dissipation rate. Our results show a 30 % reduction in the average error compared to the current model representation of ε and a total elimination of its average bias.
Mario Mech, Maximilian Maahn, Stefan Kneifel, Davide Ori, Emiliano Orlandi, Pavlos Kollias, Vera Schemann, and Susanne Crewell
Geosci. Model Dev., 13, 4229–4251, https://doi.org/10.5194/gmd-13-4229-2020, https://doi.org/10.5194/gmd-13-4229-2020, 2020
Short summary
Short summary
The Passive and Active Microwave TRAnsfer tool (PAMTRA) is a public domain software package written in Python and Fortran for the simulation of microwave remote sensing observations. PAMTRA models the interaction of radiation with gases, clouds, precipitation, and the surface using either in situ observations or model output as input parameters. The wide range of applications is demonstrated for passive (radiometer) and active (radar) instruments on ground, airborne, and satellite platforms.
Shin-ichiro Shima, Yousuke Sato, Akihiro Hashimoto, and Ryohei Misumi
Geosci. Model Dev., 13, 4107–4157, https://doi.org/10.5194/gmd-13-4107-2020, https://doi.org/10.5194/gmd-13-4107-2020, 2020
Short summary
Short summary
Using the super-droplet method, we constructed a detailed numerical model of mixed-phase clouds based on kinetic description and subsequently demonstrated that a large-eddy simulation of a cumulonimbus which predicts ice particle morphology without assuming ice categories or mass–dimension relationships is possible. Our results strongly support the particle-based modeling methodology’s efficacy for simulating mixed-phase clouds.
Swen Brands, Guillermo Fernández-García, Marta García Vivanco, Marcos Tesouro Montecelo, Nuria Gallego Fernández, Anthony David Saunders Estévez, Pablo Enrique Carracedo García, Anabela Neto Venâncio, Pedro Melo Da Costa, Paula Costa Tomé, Cristina Otero, María Luz Macho, and Juan Taboada
Geosci. Model Dev., 13, 3947–3973, https://doi.org/10.5194/gmd-13-3947-2020, https://doi.org/10.5194/gmd-13-3947-2020, 2020
Short summary
Short summary
The capability of numerical models to predict air quality depends on many factors. Here, the role of the applied model resolution, emission configuration and model chemistry is assessed for the CHIMERE model and the northwestern Iberian Peninsula. Although heterogeneous results are obtained, the forecasts can be systematically improved by increasing the vertical resolution in the lower and middle troposphere. This finding might also apply to other regions with similar characteristics.
Xiao Lu, Lin Zhang, Tongwen Wu, Michael S. Long, Jun Wang, Daniel J. Jacob, Fang Zhang, Jie Zhang, Sebastian D. Eastham, Lu Hu, Lei Zhu, Xiong Liu, and Min Wei
Geosci. Model Dev., 13, 3817–3838, https://doi.org/10.5194/gmd-13-3817-2020, https://doi.org/10.5194/gmd-13-3817-2020, 2020
Short summary
Short summary
This study presents the development and evaluation of a new climate chemistry model, BCC-GEOS-Chem v1.0, which couples the GEOS-Chem chemical transport model as an atmospheric chemistry component in the Beijing Climate Center atmospheric general circulation model. A 3-year (2012–2014) simulation of BCC-GEOS-Chem v1.0 shows that the model captures well the spatiotemporal distributions of tropospheric ozone, other gaseous pollutants, and aerosols.
Cited articles
Ba, W., Du, P., Liu, T., Bao, A., Luo, M., Hassan, M., and Qin, C.: Simulating hydrological responses to climate change using dynamic and statistical downscaling methods: a case study in the Kaidu River Basin, Xinjiang, China, J. Arid Land, 10, 905–920, https://doi.org/10.1007/s40333-018-0068-0, 2018. a
Baño Medina, J., Manzanas, R., and Gutiérrez, J. M.:
SantanderMetGroup/DeepDownscaling: GMD paper accepted for publication
(Version v1.2), Zenodo, https://doi.org/10.5281/zenodo.3731351, 2020. a, b
Bedia, J., Baño-Medina, J., Legasa, M. N., Iturbide, M., Manzanas, R., Herrera, S., Casanueva, A., San-Martín, D., Cofiño, A. S., and Gutiérrez, J. M.: Statistical downscaling with the downscaleR package (v3.1.0): contribution to the VALUE intercomparison experiment, Geosci. Model Dev., 13, 1711–1735, https://doi.org/10.5194/gmd-13-1711-2020, 2020. a, b, c, d
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
Chapman, W. E., Subramanian, A. C., Monache, L. D., Xie, S. P., and Ralph,
F. M.: Improving Atmospheric River Forecasts With Machine
Learning, Geophys. Res. Lett., 46, 10627–10635, https://doi.org/10.1029/2019GL083662, 2019. a
Chen, S.-T., Yu, P.-S., and Tang, Y.-H.: Statistical downscaling of daily
precipitation using support vector machines and multivariate analysis,
J. Hydrol., 385, 13–22, https://doi.org/10.1016/j.jhydrol.2010.01.021,
2010. a
Chollet, F.: Keras, available at: https://keras.io (last access: 23 April 2020), 2015. a
Galmarini, S., Cannon, A. J., Ceglar, A., Christensen, O. B.,
de Noblet-Ducoudré, N., Dentener, F., Doblas-Reyes, F. J., Dosio, A.,
Gutierrez, J. M., Iturbide, M., Jury, M., Lange, S., Loukos, H., Maiorano,
A., Maraun, D., McGinnis, S., Nikulin, G., Riccio, A., Sanchez, E.,
Solazzo, E., Toreti, A., Vrac, M., and Zampieri, M.: Adjusting climate model
bias for agricultural impact assessment: How to cut the mustard, Climate
Services, 13, 65–69, https://doi.org/10.1016/j.cliser.2019.01.004, 2019. a
Gentine, P., Pritchard, M., Rasp, S., Reinaudi, G., and Yacalis, G.: Could
Machine Learning Break the Convection Parameterization Deadlock?,
Geophys. Res. Lett., 45, 5742–5751, https://doi.org/10.1029/2018GL078202,
2018. a
Gutiérrez, J. M., San-Martín, D., Brands, S., Manzanas, R., and Herrera,
S.: Reassessing Statistical Downscaling Techniques for Their Robust
Application under Climate Change Conditions, J. Climate, 26,
171–188, https://doi.org/10.1175/JCLI-D-11-00687.1, 2013. a
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., 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, 2018. a, b, c, d, e, f
Gutowski Jr., W. J., Giorgi, F., Timbal, B., Frigon, A., Jacob, D., Kang, H.-S., Raghavan, K., Lee, B., Lennard, C., Nikulin, G., O'Rourke, E., Rixen, M., Solman, S., Stephenson, T., and Tangang, F.: WCRP COordinated Regional Downscaling EXperiment (CORDEX): a diagnostic MIP for CMIP6, Geosci. Model Dev., 9, 4087–4095, https://doi.org/10.5194/gmd-9-4087-2016, 2016. 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
Hertig, E., Maraun, D., Bartholy, J., Pongracz, R., Vrac, M., Mares, I.,
Gutiérrez, J. M., Wibig, J., Casanueva, A., and Soares, P. M. M.:
Comparison of statistical downscaling methods with respect to extreme events
over Europe: Validation results from the perfect predictor experiment of
the COST Action VALUE, Int. J. Climatol., 39,
3846–3867, https://doi.org/10.1002/joc.5469, 2019. 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. Model. Softw., 111, 42–54, https://doi.org/10.1016/j.envsoft.2018.09.009,
2019. a, b, c
Kharin, V. V. and Zwiers, F. W.: On the ROC score of probability forecasts,
J. Climate, 16, 4145–4150,
https://doi.org/10.1175/1520-0442(2003)016<4145:OTRSOP>2.0.CO;2, 2003. a
Larraondo, P. R., Renzullo, L. J., Inza, I., and Lozano, J. A.: A data-driven
approach to precipitation parameterizations using convolutional
encoder-decoder neural networks, arXiv [physics], arXiv: 1903.10274, 2019. a
Liu, Y., Racah, E., Prabhat, Correa, J., Khosrowshahi, A., Lavers, D., Kunkel, K., Wehner, M., and Collins, W.: Application of Deep Convolutional Neural Networks for Detecting Extreme Weather in Climate Datasets, arXiv:1605.01156 [cs], 2016. a
Manzanas, R., Frías, M. D., Cofiño, A. S., and Gutiérrez, J. M.:
Validation of 40 year multimodel seasonal precipitation forecasts: The role
of ENSO on the global skill, J. Geophys. Res.-Atmos.,
119, 1708–1719, https://doi.org/10.1002/2013JD020680, 2014. a
Manzanas, R., Brands, S., San-Martín, D., Lucero, A., Limbo, C., and
Gutiérrez, J. M.: Statistical downscaling in the tropics can be sensitive
to reanalysis choice: A case study for precipitation in the Philippines,
J. Climate, 28, 4171–4184, https://doi.org/10.1175/JCLI-D-14-00331.1, 2015. a
Manzanas, R., Lucero, A., Weisheimer, A., and Gutiérrez, J. M.: Can bias
correction and statistical downscaling methods improve the skill of seasonal
precipitation forecasts?, Clim. Dynam., 50, 1161–1176,
https://doi.org/10.1007/s00382-017-3668-z, 2018. a
Maraun, D., Widmann, M., Gutiérrez, J. M., Kotlarski, S., Chandler, R. E.,
Hertig, E., Wibig, J., Huth, R., and Wilcke, R. A.: VALUE: A framework to
validate downscaling approaches for climate change studies, Earth's Future,
3, 1–14, https://doi.org/10.1002/2014EF000259, 2015. a, b
Maraun, D., Huth, R., Gutiérrez, J. M., Martín, D. S., Dubrovsky, M.,
Fischer, A., Hertig, E., Soares, P. M. M., Bartholy, J., Pongracz, R.,
Widmann, M., Casado, M. J., Ramos, P., and Bedia, J.: The VALUE perfect
predictor experiment: Evaluation of temporal variability, Int. J. Climatol., 39, 3786–3818, https://doi.org/10.1002/joc.5222, 2019. a
Miao, Q., Pan, B., Wang, H., Hsu, K., and Sorooshian, S.: Improving Monsoon
Precipitation Prediction Using Combined Convolutional and Long
Short Term Memory Neural Network, Water, 11, 977,
https://doi.org/10.3390/w11050977, 2019. a
Misra, S., Sarkar, S., and Mitra, P.: Statistical downscaling of precipitation
using long short-term memory recurrent neural networks, Theor. Appl. Climatol., 134, 1179–1196, https://doi.org/10.1007/s00704-017-2307-2, 2018. a
Pan, B., Hsu, K., AghaKouchak, A., and Sorooshian, S.: Improving
Precipitation Estimation Using Convolutional Neural Network,
Water Resour. Res., 55, 2301–2321, https://doi.org/10.1029/2018WR024090,
2019. 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
Pradhan, R., Aygun, R. S., Maskey, M., Ramachandran, R., and Cecil, D. J.:
Tropical Cyclone Intensity Estimation Using a Deep Convolutional
Neural Network, IEEE T. Image Process., 27, 692–702,
https://doi.org/10.1109/TIP.2017.2766358, 2018. a
Rasp, S., Pritchard, M. S., and Gentine, P.: Deep learning to represent subgrid processes in climate models, P. Natl. Acad. Sci. USA, 115, 9684–9689, https://doi.org/10.1073/pnas.1810286115, 2018. a
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J.,
Carvalhais, N., and Prabhat: Deep learning and process understanding for
data-driven Earth system science, Nature, 566, 195–204,
https://doi.org/10.1038/s41586-019-0912-1, 2019. a, b, c
Riley, P.: Three pitfalls to avoid in machine learning, Nature, 572, 27–29,
https://doi.org/10.1038/d41586-019-02307-y, 2019. a
Rodrigues, E. R., Oliveira, I., Cunha, R. L. F., and Netto, M. A. S.:
DeepDownscale: a Deep Learning Strategy for High-Resolution
Weather Forecast, arXiv:1808.05264 [cs, stat], 2018. a
Sachindra, D. A. and Kanae, S.: Machine learning for downscaling: the use of
parallel multiple populations in genetic programming, Stoch. Env. Res. Risk A., 33, 1497–1533, https://doi.org/10.1007/s00477-019-01721-y, 2019. a
Sachindra, D. A., Ahmed, K., Rashid, M. M., Shahid, S., and Perera, B. J. C.:
Statistical downscaling of precipitation using machine learning techniques,
Atmos. Res., 212, 240–258, https://doi.org/10.1016/j.atmosres.2018.05.022, 2018. a, b
Sanderson, M., Arbuthnott, K., Kovats, S., Hajat, S., and Falloon, P.: The use of climate information to estimate future mortality from high ambient
temperature: A systematic literature review, PLOS one, 12, e0180369,
https://doi.org/10.1371/journal.pone.0180369, 2017. a
Scher, S. and Messori, G.: Weather and climate forecasting with neural networks: using general circulation models (GCMs) with different complexity as a study ground, Geosci. Model Dev., 12, 2797–2809, https://doi.org/10.5194/gmd-12-2797-2019, 2019. a
Schmidhuber, J.: Deep learning in neural networks: An overview, Neural
Networks, 61, 85–117, https://doi.org/10.1016/j.neunet.2014.09.003,
2015. a, b
Schoof, J. and Pryor, S.: Downscaling temperature and precipitation: a
comparison of regression-based methods and artificial neural networks,
Int. J. Climatol., 21, 773–790, https://doi.org/10.1002/joc.655,
2001. a
Teutschbein, C., Wetterhall, F., and Seibert, J.: Evaluation of different
downscaling techniques for hydrological climate-change impact studies at the
catchment scale, Clim. Dynam., 37, 2087–2105,
https://doi.org/10.1007/s00382-010-0979-8, 2011. 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., and Ganguly, A. R.: Intercomparison of Machine
Learning Methods for Statistical Downscaling: The Case of Daily
and Extreme Precipitation, arXiv:1702.04018 [stat],
2017a. a
Vandal, T., Kodra, E., Ganguly, S., Michaelis, A., Nemani, R., and Ganguly,
A. R.: DeepSD: Generating High Resolution Climate Change
Projections through Single Image Super-Resolution,
arXiv:1703.03126 [cs.CV], 2017b. a
Vandal, T., Kodra, E., and Ganguly, A. R.: Intercomparison of machine learning methods for statistical downscaling: the case of daily and extreme
precipitation, Theor. Appl. Climatol., 137, 557–570,
https://doi.org/10.1007/s00704-018-2613-3, 2019.
a, b
Wang, J., Nathan, R., Horne, A., Peel, M. C., Wei, Y., and Langford, J.:
Evaluating four downscaling methods for assessment of climate change impact
on ecological indicators, Environ. Model. Softw., 96, 68–82,
https://doi.org/10.1016/j.envsoft.2017.06.016, 2017. a
Wang, Z., Liu, K., Li, J., Zhu, Y., and Zhang, Y.: Various Frameworks and
Libraries of Machine Learning and Deep Learning: A Survey,
Arch. Computat. Methods Eng., Springer, https://doi.org/10.1007/s11831-018-09312-w, 2019. a
Widmann, M., Bedia, J., Gutiérrez, J. M., Bosshard, T., Hertig, E., Maraun,
D., Casado, M. J., Ramos, P., Cardoso, R. M., Soares, P. M. M., Ribalaygua,
J., Pagé, C., Fischer, A. M., Herrera, S., and Huth, R.: Validation of
spatial variability in downscaling results from the VALUE perfect predictor
experiment, Int. J. Climatol., 39, 3819–3845,
https://doi.org/10.1002/joc.6024, 2019. a
Wilby, R. L., Wigley, T. M. L., Conway, D., Jones, P. D., Hewitson, B. C.,
Main, J., and Wilks, D. S.: Statistical downscaling of general circulation
model output: A comparison of methods, Water Resour. Res., 34,
2995–3008, https://doi.org/10.1029/98WR02577, 1998. a, b
Yang, C., Wang, N., Wang, S., and Zhou, L.: Performance comparison of three
predictor selection methods for statistical downscaling of daily
precipitation, Theor. Appl. Climatol., 131, 43–54,
https://doi.org/10.1007/s00704-016-1956-x, 2016. a
Short summary
In this study we intercompare different deep learning topologies for statistical downscaling purposes. As compared to the top-ranked methods in the largest-to-date downscaling intercomparison study, our results better predict the local climate variability. Moreover, deep learning approaches can be suitably applied to large regions (e.g., continents), which can therefore foster the use of statistical downscaling in flagship initiatives such as CORDEX.
In this study we intercompare different deep learning topologies for statistical downscaling...
