Articles | Volume 17, issue 4
https://doi.org/10.5194/gmd-17-1789-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-1789-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
29 Feb 2024
Model description paper |
| 29 Feb 2024
| 29 Feb 2024
Accounting for uncertainties in forecasting tropical-cyclone-induced compound flooding
Kees Nederhoff
CORRESPONDING AUTHOR
Deltares USA, 8601 Georgia Ave, Silver Spring, MD 20910, USA
Department of Coastal and Urban Risk & Resilience, CURR, UNESCO-IHE Institute for Water Education, P.O. BOX 3015, 2601 DA Delft, the Netherlands
Department of Hydraulic Engineering, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, the Netherlands
Maarten van Ormondt
Deltares USA, 8601 Georgia Ave, Silver Spring, MD 20910, USA
Jay Veeramony
Naval Research Lab, Stennis Space Center, MS 39529, USA
Ap van Dongeren
Department of Coastal and Urban Risk & Resilience, CURR, UNESCO-IHE Institute for Water Education, P.O. BOX 3015, 2601 DA Delft, the Netherlands
Marine and Coastal Management, Deltares, Boussinesqweg 1, Delft, 2629 HV, the Netherlands
José Antonio Álvarez Antolínez
Department of Hydraulic Engineering, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, the Netherlands
Tim Leijnse
Marine and Coastal Management, Deltares, Boussinesqweg 1, Delft, 2629 HV, the Netherlands
Institute for Environmental Studies (IVM), Vrije Universiteit Amsterdam, De Boelelaan 1111, 1081 HV Amsterdam, the Netherlands
Dano Roelvink
Department of Coastal and Urban Risk & Resilience, CURR, UNESCO-IHE Institute for Water Education, P.O. BOX 3015, 2601 DA Delft, the Netherlands
Marine and Coastal Management, Deltares, Boussinesqweg 1, Delft, 2629 HV, the Netherlands
Related authors
Tim Willem Bart Leijnse, Alessio Giardino, Kees Nederhoff, and Sofia Caires
Nat. Hazards Earth Syst. Sci., 22, 1863–1891, https://doi.org/10.5194/nhess-22-1863-2022, https://doi.org/10.5194/nhess-22-1863-2022, 2022
Short summary
Short summary
Deriving reliable estimates of design conditions resulting from tropical cyclones is a challenge of high relevance to coastal engineering. Here, having few historical observations is overcome by using the Tropical Cyclone Wind Statistical Estimation Tool (TCWiSE) to create thousands of synthetic realizations, representative of 1000 years of tropical cyclone activity for the Bay of Bengal. The use of synthetic tracks is shown to provide more reliable wind speed, storm surge and wave estimates.
Kees Nederhoff, Li Erikson, Anita Engelstad, Peter Bieniek, and Jeremy Kasper
The Cryosphere, 16, 1609–1629, https://doi.org/10.5194/tc-16-1609-2022, https://doi.org/10.5194/tc-16-1609-2022, 2022
Short summary
Short summary
Diminishing sea ice is impacting waves across the Arctic region. Recent work shows the effect of the sea ice on offshore waves; however, effects within the nearshore are less known. This study characterizes the wave climate in the central Beaufort Sea coast of Alaska. We show that the reduction of sea ice correlates strongly with increases in the average and extreme waves. However, found trends deviate from offshore, since part of the increase in energy is dissipated before reaching the shore.
Kees Nederhoff, Jasper Hoek, Tim Leijnse, Maarten van Ormondt, Sofia Caires, and Alessio Giardino
Nat. Hazards Earth Syst. Sci., 21, 861–878, https://doi.org/10.5194/nhess-21-861-2021, https://doi.org/10.5194/nhess-21-861-2021, 2021
Short summary
Short summary
The design of coastal protection affected by tropical cyclones is often based solely on the analysis of historical tropical cyclones (TCs). The simulation of numerous synthetic TC tracks based on historical data can overcome this limitation. In this paper, a new method for the generation of synthetic TC tracks is proposed, called the Tropical Cyclone Wind Statistical Estimation Tool (TCWiSE). TCWiSE can simulate thousands of tracks and wind fields in any oceanic basin based on any data source.
Kees Nederhoff, Alessio Giardino, Maarten van Ormondt, and Deepak Vatvani
Nat. Hazards Earth Syst. Sci., 19, 2359–2370, https://doi.org/10.5194/nhess-19-2359-2019, https://doi.org/10.5194/nhess-19-2359-2019, 2019
Short summary
Short summary
Tropical cyclone wind models are often used in engineering applications. However, these models lack the required accuracy when the size of the tropical cyclone is not known. In this paper, new relationships are derived to describe parameters affecting the size. These relationships are formulated using observed data and make it possible to estimate tropical cyclone size and to use this information in tropical cyclone wind models to obtain more reliable estimates of the tropical cyclone winds.
Robert McCall, Curt Storlazzi, Floortje Roelvink, Stuart Pearson, Roel de Goede, and José Antolínez
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-28, https://doi.org/10.5194/nhess-2024-28, 2024
Preprint under review for NHESS
Short summary
Short summary
Accurate predictions of wave-driven flooding are essential to manage risk on low-lying, reef-lined coasts. Models to provide this information are, however, computationally expensive. We present and validate a modelling system that simulates flood drivers on diverse and complex reef-lined coasts as competently as a full-physics model, but at a fraction of the computational cost to run. This development paves the way for application in large-scale early warning systems and flood risk assessments.
Panagiotis Athanasiou, Ap van Dongeren, Maarten Pronk, Alessio Giardino, Michalis Vousdoukas, and Roshanka Ranasinghe
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-313, https://doi.org/10.5194/essd-2023-313, 2023
Revised manuscript accepted for ESSD
Short summary
Short summary
The shape of the coast, the intensity of waves, the height of the water levels, the presence of people or critical infrastructure and the land use are important information to assess the vulnerability of the coast to coastal hazards. Here we provide 80 indicators of this kind, at consistent locations along the global ice-free coastline using open-access global datasets. These can be valuable for quick assessments of the vulnerability of the coast and at data-poor locations.
Dirk Eilander, Anaïs Couasnon, Tim Leijnse, Hiroaki Ikeuchi, Dai Yamazaki, Sanne Muis, Job Dullaart, Arjen Haag, Hessel C. Winsemius, and Philip J. Ward
Nat. Hazards Earth Syst. Sci., 23, 823–846, https://doi.org/10.5194/nhess-23-823-2023, https://doi.org/10.5194/nhess-23-823-2023, 2023
Short summary
Short summary
In coastal deltas, flooding can occur from interactions between coastal, riverine, and pluvial drivers, so-called compound flooding. Global models however ignore these interactions. We present a framework for automated and reproducible compound flood modeling anywhere globally and validate it for two historical events in Mozambique with good results. The analysis reveals differences in compound flood dynamics between both events related to the magnitude of and time lag between drivers.
D. Hulskemper, K. Anders, J. A. Á. Antolínez, M. Kuschnerus, B. Höfle, and R. Lindenbergh
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-2-W2-2022, 53–60, https://doi.org/10.5194/isprs-archives-XLVIII-2-W2-2022-53-2022, https://doi.org/10.5194/isprs-archives-XLVIII-2-W2-2022-53-2022, 2022
Panagiotis Athanasiou, Ap van Dongeren, Alessio Giardino, Michalis Vousdoukas, Jose A. A. Antolinez, and Roshanka Ranasinghe
Nat. Hazards Earth Syst. Sci., 22, 3897–3915, https://doi.org/10.5194/nhess-22-3897-2022, https://doi.org/10.5194/nhess-22-3897-2022, 2022
Short summary
Short summary
Sandy dunes protect the hinterland from coastal flooding during storms. Thus, models that can efficiently predict dune erosion are critical for coastal zone management and early warning systems. Here we develop such a model for the Dutch coast based on machine learning techniques, allowing for dune erosion estimations in a matter of seconds relative to available computationally expensive models. Validation of the model against benchmark data and observations shows good agreement.
Mariana C. A. Clare, Tim W. B. Leijnse, Robert T. McCall, Ferdinand L. M. Diermanse, Colin J. Cotter, and Matthew D. Piggott
Nat. Hazards Earth Syst. Sci., 22, 2491–2515, https://doi.org/10.5194/nhess-22-2491-2022, https://doi.org/10.5194/nhess-22-2491-2022, 2022
Short summary
Short summary
Assessing uncertainty is computationally expensive because it requires multiple runs of expensive models. We take the novel approach of assessing uncertainty from coastal flooding using a multilevel multifidelity (MLMF) method which combines the efficiency of less accurate models with the accuracy of more expensive models at different resolutions. This significantly reduces the computational cost but maintains accuracy, making previously unfeasible real-world studies possible.
Tim Willem Bart Leijnse, Alessio Giardino, Kees Nederhoff, and Sofia Caires
Nat. Hazards Earth Syst. Sci., 22, 1863–1891, https://doi.org/10.5194/nhess-22-1863-2022, https://doi.org/10.5194/nhess-22-1863-2022, 2022
Short summary
Short summary
Deriving reliable estimates of design conditions resulting from tropical cyclones is a challenge of high relevance to coastal engineering. Here, having few historical observations is overcome by using the Tropical Cyclone Wind Statistical Estimation Tool (TCWiSE) to create thousands of synthetic realizations, representative of 1000 years of tropical cyclone activity for the Bay of Bengal. The use of synthetic tracks is shown to provide more reliable wind speed, storm surge and wave estimates.
Üwe S. N. Best, Mick van der Wegen, Jasper Dijkstra, Johan Reyns, Bram C. van Prooijen, and Dano Roelvink
Earth Syst. Sci. Data, 14, 2445–2462, https://doi.org/10.5194/essd-14-2445-2022, https://doi.org/10.5194/essd-14-2445-2022, 2022
Short summary
Short summary
The combination of seawalls and vegetation may be the key to Guyana's survival against rising water levels; however knowledge about the system behaviour and use of vegetation is inadequate. This paper comprises the first dataset since the 1970s along the Guyana coastline. Instruments were deployed to capture data on the water levels, waves and sediment locally. Data revealed the ways in which sediment is transported and deposited, as well as the wave damping of the mangrove–mudflat system.
Kees Nederhoff, Li Erikson, Anita Engelstad, Peter Bieniek, and Jeremy Kasper
The Cryosphere, 16, 1609–1629, https://doi.org/10.5194/tc-16-1609-2022, https://doi.org/10.5194/tc-16-1609-2022, 2022
Short summary
Short summary
Diminishing sea ice is impacting waves across the Arctic region. Recent work shows the effect of the sea ice on offshore waves; however, effects within the nearshore are less known. This study characterizes the wave climate in the central Beaufort Sea coast of Alaska. We show that the reduction of sea ice correlates strongly with increases in the average and extreme waves. However, found trends deviate from offshore, since part of the increase in energy is dissipated before reaching the shore.
Kees Nederhoff, Jasper Hoek, Tim Leijnse, Maarten van Ormondt, Sofia Caires, and Alessio Giardino
Nat. Hazards Earth Syst. Sci., 21, 861–878, https://doi.org/10.5194/nhess-21-861-2021, https://doi.org/10.5194/nhess-21-861-2021, 2021
Short summary
Short summary
The design of coastal protection affected by tropical cyclones is often based solely on the analysis of historical tropical cyclones (TCs). The simulation of numerous synthetic TC tracks based on historical data can overcome this limitation. In this paper, a new method for the generation of synthetic TC tracks is proposed, called the Tropical Cyclone Wind Statistical Estimation Tool (TCWiSE). TCWiSE can simulate thousands of tracks and wind fields in any oceanic basin based on any data source.
Bram C. van Prooijen, Marion F. S. Tissier, Floris P. de Wit, Stuart G. Pearson, Laura B. Brakenhoff, Marcel C. G. van Maarseveen, Maarten van der Vegt, Jan-Willem Mol, Frank Kok, Harriette Holzhauer, Jebbe J. van der Werf, Tommer Vermaas, Matthijs Gawehn, Bart Grasmeijer, Edwin P. L. Elias, Pieter Koen Tonnon, Giorgio Santinelli, José A. A. Antolínez, Paul Lodewijk M. de Vet, Ad J. H. M. Reniers, Zheng Bing Wang, Cornelis den Heijer, Carola van Gelder-Maas, Rinse J. A. Wilmink, Cor A. Schipper, and Harry de Looff
Earth Syst. Sci. Data, 12, 2775–2786, https://doi.org/10.5194/essd-12-2775-2020, https://doi.org/10.5194/essd-12-2775-2020, 2020
Short summary
Short summary
To protect the Dutch coastal zone, sand is nourished and disposed at strategic locations. Simple questions like where, how, how much and when to nourish the sand are not straightforward to answer. This is especially the case around the Wadden Sea islands where sediment transport pathways are complicated. Therefore, a large-scale field campaign has been carried out on the seaward side of Ameland Inlet. Sediment transport, hydrodynamics, morphology and fauna in the bed were measured.
Matteo U. Parodi, Alessio Giardino, Ap van Dongeren, Stuart G. Pearson, Jeremy D. Bricker, and Ad J. H. M. Reniers
Nat. Hazards Earth Syst. Sci., 20, 2397–2414, https://doi.org/10.5194/nhess-20-2397-2020, https://doi.org/10.5194/nhess-20-2397-2020, 2020
Short summary
Short summary
We investigate sources of uncertainty in coastal flood risk assessment in São Tomé and Príncipe, a small island developing state. We find that, for the present-day scenario, uncertainty from depth damage functions and digital elevation models can be more significant than that related to the estimation of significant wave height or storm surge level. For future scenarios (year 2100), sea level rise prediction becomes the input with the strongest impact on coastal flood damage estimate.
Vo Quoc Thanh, Dano Roelvink, Mick van der Wegen, Johan Reyns, Herman Kernkamp, Giap Van Vinh, and Vo Thi Phuong Linh
Hydrol. Earth Syst. Sci., 24, 189–212, https://doi.org/10.5194/hess-24-189-2020, https://doi.org/10.5194/hess-24-189-2020, 2020
Short summary
Short summary
The Vietnamese Mekong Delta (VMD) is a rice bowl of not only Vietnam, but also the world; agriculture is the main source of livelihood in the delta. The VMD is facing threats related to water management and hydraulic structures. Dykes are built to protect agricultural crops in the floodplains and may influence water regimes downstream in the VMD. If the VMD floodplains are completely protected by dykes, yearly mean water levels could increase by 3 cm (at Can Tho) and 1.5 cm (at My Thuan).
Kees Nederhoff, Alessio Giardino, Maarten van Ormondt, and Deepak Vatvani
Nat. Hazards Earth Syst. Sci., 19, 2359–2370, https://doi.org/10.5194/nhess-19-2359-2019, https://doi.org/10.5194/nhess-19-2359-2019, 2019
Short summary
Short summary
Tropical cyclone wind models are often used in engineering applications. However, these models lack the required accuracy when the size of the tropical cyclone is not known. In this paper, new relationships are derived to describe parameters affecting the size. These relationships are formulated using observed data and make it possible to estimate tropical cyclone size and to use this information in tropical cyclone wind models to obtain more reliable estimates of the tropical cyclone winds.
Panagiotis Athanasiou, Ap van Dongeren, Alessio Giardino, Michalis Vousdoukas, Sandra Gaytan-Aguilar, and Roshanka Ranasinghe
Earth Syst. Sci. Data, 11, 1515–1529, https://doi.org/10.5194/essd-11-1515-2019, https://doi.org/10.5194/essd-11-1515-2019, 2019
Short summary
Short summary
This dataset provides the spatial distribution of nearshore slopes at a resolution of 1 km along the global coastline. The calculation was based on available global topo-bathymetric datasets and ocean wave reanalysis. The calculated slopes show skill in capturing the spatial variability of the nearshore slopes when compared against local observations. The importance of this variability is presented with a global coastal retreat assessment for an arbitrary sea level rise scenario.
L. Sembiring, M. van Ormondt, A. van Dongeren, and D. Roelvink
Nat. Hazards Earth Syst. Sci., 15, 1231–1242, https://doi.org/10.5194/nhess-15-1231-2015, https://doi.org/10.5194/nhess-15-1231-2015, 2015
A. Y. A. Omer, Y. S. A. Ali, J. A. Roelvink, A. Dastgheib, P. Paron, and A. Crosato
Earth Surf. Dynam., 3, 223–238, https://doi.org/10.5194/esurf-3-223-2015, https://doi.org/10.5194/esurf-3-223-2015, 2015
Related subject area
Numerical methods
Assessing effects of climate and technology uncertainties in large natural resource allocation problems
VISIR-2: ship weather routing in Python
Incremental analysis update (IAU) in the Model for Prediction Across Scales coupled with the Joint Effort for Data assimilation Integration (MPAS–JEDI 2.0.0)
Decision-making strategies implemented in SolFinder 1.0 to identify eco-efficient aircraft trajectories: application study in AirTraf 3.0
Developing meshing workflows in Gmsh v4.11 for the geologic uncertainty assessment of high-temperature aquifer thermal energy storage
Development and preliminary validation of a land surface image assimilation system based on the Common Land Model
NorSand4AI: a comprehensive triaxial test simulation database for NorSand constitutive model materials
ParticleDA.jl v.1.0: a distributed particle-filtering data assimilation package
HETerogeneous vectorized or Parallel (HETPv1.0): an updated inorganic heterogeneous chemistry solver for the metastable-state NH4+–Na+–Ca2+–K+–Mg2+–SO42−–NO3−–Cl−–H2O system based on ISORROPIA II
Three-dimensional geological modelling of igneous intrusions in LoopStructural v1.5.10
Estimating volcanic ash emissions using retrieved satellite ash columns and inverse ash transport modeling using VolcanicAshInversion v1.2.1, within the operational eEMEP (emergency European Monitoring and Evaluation Programme) volcanic plume forecasting system (version rv4_17)
An automatic mesh generator for coupled 1D–2D hydrodynamic models
Numerical coupling of aerosol emissions, dry removal, and turbulent mixing in the E3SM Atmosphere Model version 1 (EAMv1) – Part 1: Dust budget analyses and the impacts of a revised coupling scheme
Numerical coupling of aerosol emissions, dry removal, and turbulent mixing in the E3SM Atmosphere Model version 1 (EAMv1) – Part 2: A semi-discrete error analysis framework for assessing coupling schemes
jsmetrics v0.2.0: a Python package for metrics and algorithms used to identify or characterise atmospheric jet streams
P3D-BRNS v1.0.0: a three-dimensional, multiphase, multicomponent, pore-scale reactive transport modelling package for simulating biogeochemical processes in subsurface environments
MinVoellmy v1: a lightweight model for simulating rapid mass movements based on a modified Voellmy rheology
Scalable Feature Extraction and Tracking (SCAFET): a general framework for feature extraction from large climate data sets
Sweep interpolation: a cost-effective semi-Lagrangian scheme in the Global Environmental Multiscale model
CHONK 1.0: landscape evolution framework: cellular automata meets graph theory
Perspectives of physics-based machine learning strategies for geoscientific applications governed by partial differential equations
Calibration of absorbing boundary layers for geoacoustic wave modeling in pseudo-spectral time-domain methods
A revised parameterization for aerosol, cloud and precipitation pH for use in chemical forecasting systems (EQSAM4Clim-v12)
GeoINR 1.0: an implicit neural network approach to three-dimensional geological modelling
A comparison of Eulerian and Lagrangian methods for vertical particle transport in the water column
AutoQS v1: automatic parametrization of QuickSampling based on training images analysis
The Newton solver with step size control is faster than the Picard iteration in simulating ice flow (FEniCS-full-Stokes v1.1.0)
Implementation and application of ensemble optimal interpolation on an operational chemistry weather model for improving PM2.5 and visibility predictions
A dynamical core based on a discontinuous Galerkin method for higher-order finite-element sea ice modeling
GStatSim V1.0: a Python package for geostatistical interpolation and conditional simulation
Leveraging Google's Tensor Processing Units for tsunami-risk mitigation planning in the Pacific Northwest and beyond
Consistent Point Data Assimilation in Firedrake and Icepack
An improved subgrid channel model with upwind-form artificial diffusion for river hydrodynamics and floodplain inundation simulation
A model instability issue in the National Centers for Environmental Prediction Global Forecast System version 16 and potential solutions
A comparison of 3-D spherical shell thermal convection results at low to moderate Rayleigh number using ASPECT (version 2.2.0) and CitcomS (version 3.3.1)
LISFLOOD-FP 8.1: new GPU-accelerated solvers for faster fluvial/pluvial flood simulations
Fast approximate Barnes interpolation: illustrated by Python-Numba implementation fast-barnes-py v1.0
Strategies for conservative and non-conservative monotone remapping on the sphere
Modeling large‐scale landform evolution with a stream power law for glacial erosion (OpenLEM v37): benchmarking experiments against a more process-based description of ice flow (iSOSIA v3.4.3)
A mixed finite-element discretisation of the shallow-water equations
Multifidelity Monte Carlo estimation for efficient uncertainty quantification in climate-related modeling
Massively parallel modeling and inversion of electrical resistivity tomography data using PFLOTRAN
Parallelized domain decomposition for multi-dimensional Lagrangian random walk mass-transfer particle tracking schemes
The Intelligent Prospector v1.0: geoscientific model development and prediction by sequential data acquisition planning with application to mineral exploration
Predicting peak daily maximum 8 h ozone and linkages to emissions and meteorology in Southern California using machine learning methods (SoCAB-8HR V1.0)
Transfer learning for landslide susceptibility modeling using domain adaptation and case-based reasoning
ISMIP-HOM benchmark experiments using Underworld
spyro: a Firedrake-based wave propagation and full-waveform-inversion finite-element solver
Spatial filtering in a 6D hybrid-Vlasov scheme to alleviate adaptive mesh refinement artifacts: a case study with Vlasiator (versions 5.0, 5.1, and 5.2.1)
A Bayesian data assimilation framework for lake 3D hydrodynamic models with a physics-preserving particle filtering method using SPUX-MITgcm v1
Jevgenijs Steinbuks, Yongyang Cai, Jonas Jaegermeyr, and Thomas W. Hertel
Geosci. Model Dev., 17, 4791–4819, https://doi.org/10.5194/gmd-17-4791-2024, https://doi.org/10.5194/gmd-17-4791-2024, 2024
Short summary
Short summary
This paper applies a cutting-edge numerical method, SCEQ, to show how uncertain climate change and technological progress affect the future utilization of the world's scarce land resources. The paper's key insight is to illustrate how much global cropland will expand when future crop yields are unknown. The study finds the range of outcomes for land use change to be smaller when using this novel method compared to existing deterministic models.
Gianandrea Mannarini, Mario Leonardo Salinas, Lorenzo Carelli, Nicola Petacco, and Josip Orović
Geosci. Model Dev., 17, 4355–4382, https://doi.org/10.5194/gmd-17-4355-2024, https://doi.org/10.5194/gmd-17-4355-2024, 2024
Short summary
Short summary
Ship weather routing has the potential to reduce CO2 emissions, but it currently lacks open and verifiable research. The Python-refactored VISIR-2 model considers currents, waves, and wind to optimise routes. The model was validated, and its computational performance is quasi-linear. For a ferry sailing in the Mediterranean Sea, VISIR-2 yields the largest percentage emission savings for upwind navigation. Given the vessel performance curve, the model is generalisable across various vessel types.
Soyoung Ha, Jonathan J. Guerrette, Ivette Hernández Baños, William C. Skamarock, and Michael G. Duda
Geosci. Model Dev., 17, 4199–4211, https://doi.org/10.5194/gmd-17-4199-2024, https://doi.org/10.5194/gmd-17-4199-2024, 2024
Short summary
Short summary
To mitigate the imbalances in the initial conditions, this study introduces our recent implementation of the incremental analysis update (IAU) in the Model for Prediction Across Scales – Atmospheric (MPAS-A) component coupled with the Joint Effort for Data assimilation Integration (JEDI) through the cycling system. A month-long cycling run demonstrates the successful implementation of the IAU capability in the MPAS–JEDI cycling system.
Federica Castino, Feijia Yin, Volker Grewe, Hiroshi Yamashita, Sigrun Matthes, Simone Dietmüller, Sabine Baumann, Manuel Soler, Abolfazl Simorgh, Maximilian Mendiguchia Meuser, Florian Linke, and Benjamin Lührs
Geosci. Model Dev., 17, 4031–4052, https://doi.org/10.5194/gmd-17-4031-2024, https://doi.org/10.5194/gmd-17-4031-2024, 2024
Short summary
Short summary
We introduce SolFinder 1.0, a decision-making tool to select trade-offs between different objective functions for optimal aircraft trajectories, including fuel use, flight time, NOx emissions, contrail distance, and climate impact. The module is included in the AirTraf 3.0 submodel and uses weather conditions simulated by the EMAC atmospheric model. This paper focuses on the ability of SolFinder to identify eco-efficient trajectories, reducing a flight's climate impact at limited cost penalties.
Ali Dashti, Jens C. Grimmer, Christophe Geuzaine, Florian Bauer, and Thomas Kohl
Geosci. Model Dev., 17, 3467–3485, https://doi.org/10.5194/gmd-17-3467-2024, https://doi.org/10.5194/gmd-17-3467-2024, 2024
Short summary
Short summary
This study developed new meshing workflows to enable the automatic generation of meshes that follow geological models. The workflow allows for importing several geological models as input for Gmsh and later exporting the same number of high-quality meshes. This way, geological uncertainty is directly included in the numerical simulations. This study evaluates the impact of the geological uncertainty on thermohydraulic performance of two reservoirs for high-temperature heat storage applications.
Wangbin Shen, Zhaohui Lin, Zhengkun Qin, and Juan Li
Geosci. Model Dev., 17, 3447–3465, https://doi.org/10.5194/gmd-17-3447-2024, https://doi.org/10.5194/gmd-17-3447-2024, 2024
Short summary
Short summary
In this study, a land surface image assimilation system capable of optimizing the spatial structure of the background field is constructed by introducing the curvelet analysis method and taking the similarity of image structure as a weak constraint. The findings demonstrate that the assimilation of surface soil moisture observation images effectively and reasonably enhances the spatial structure of soil moisture analysis field.
Luan Carlos de Sena Monteiro Ozelim, Michéle Dal Toé Casagrande, and André Luís Brasil Cavalcante
Geosci. Model Dev., 17, 3175–3197, https://doi.org/10.5194/gmd-17-3175-2024, https://doi.org/10.5194/gmd-17-3175-2024, 2024
Short summary
Short summary
The paper addresses synthetic dataset challenges in nonlinear constitutive modeling of soils, providing two datasets: one with 2000 soil types, 40 test conditions each (160 000 triaxial tests), and a second with 2048 soil types, 42 test conditions each (172 032 triaxial tests). Each dataset is a 4000×10 matrix applicable for multivariate forecasting and geotechnical simulations. In addition, a new Python code is introduced, empowering researchers to leverage Python packages for NorSand analyses.
Daniel Giles, Matthew M. Graham, Mosè Giordano, Tuomas Koskela, Alexandros Beskos, and Serge Guillas
Geosci. Model Dev., 17, 2427–2445, https://doi.org/10.5194/gmd-17-2427-2024, https://doi.org/10.5194/gmd-17-2427-2024, 2024
Short summary
Short summary
Digital twins of physical and human systems informed by real-time data are becoming ubiquitous across a wide range of settings. Progress for researchers is currently limited by a lack of tools to run these models effectively and efficiently. A key challenge is the optimal use of high-performance computing environments. The work presented here focuses on a developed open-source software platform which aims to improve this usage, with an emphasis placed on flexibility, efficiency, and scalability.
Stefan J. Miller, Paul A. Makar, and Colin J. Lee
Geosci. Model Dev., 17, 2197–2219, https://doi.org/10.5194/gmd-17-2197-2024, https://doi.org/10.5194/gmd-17-2197-2024, 2024
Short summary
Short summary
This work outlines a new solver written in Fortran to calculate the partitioning of metastable aerosols at thermodynamic equilibrium based on the forward algorithms of ISORROPIA II. The new code includes numerical improvements that decrease the computational speed (compared to ISORROPIA II) while improving the accuracy of the partitioning solution.
Fernanda Alvarado-Neves, Laurent Ailleres, Lachlan Grose, Alexander R. Cruden, and Robin Armit
Geosci. Model Dev., 17, 1975–1993, https://doi.org/10.5194/gmd-17-1975-2024, https://doi.org/10.5194/gmd-17-1975-2024, 2024
Short summary
Short summary
Previous work has demonstrated that adding geological knowledge to modelling methods creates more accurate and reliable models. Following this reasoning, we added constraints from magma emplacement mechanisms into existing modelling frameworks to improve the 3D characterisation of igneous intrusions. We tested the method on synthetic and real-world case studies, and the results show that our method can reproduce intrusion morphologies with no manual processing and using realistic datasets.
André R. Brodtkorb, Anna Benedictow, Heiko Klein, Arve Kylling, Agnes Nyiri, Alvaro Valdebenito, Espen Sollum, and Nina Kristiansen
Geosci. Model Dev., 17, 1957–1974, https://doi.org/10.5194/gmd-17-1957-2024, https://doi.org/10.5194/gmd-17-1957-2024, 2024
Short summary
Short summary
It is vital to know the extent and concentration of volcanic ash in the atmosphere during a volcanic eruption. Whilst satellite imagery may give an estimate of the ash right now (assuming no cloud coverage), we also need to know where it will be in the coming hours. This paper presents a method for estimating parameters for a volcanic eruption based on satellite observations of ash in the atmosphere. The software package is open source and applicable to similar inversion scenarios.
Younghun Kang and Ethan J. Kubatko
Geosci. Model Dev., 17, 1603–1625, https://doi.org/10.5194/gmd-17-1603-2024, https://doi.org/10.5194/gmd-17-1603-2024, 2024
Short summary
Short summary
Models used to simulate the flow of coastal and riverine waters, including flooding, require a geometric representation (or mesh) of geographic features that exhibit a range of disparate spatial scales from large, open waters to small, narrow channels. Representing these features in an accurate way without excessive computational overhead presents a challenge. Here, we develop an automatic mesh generation tool to help address this challenge. Our results demonstrate the efficacy of our approach.
Hui Wan, Kai Zhang, Christopher J. Vogl, Carol S. Woodward, Richard C. Easter, Philip J. Rasch, Yan Feng, and Hailong Wang
Geosci. Model Dev., 17, 1387–1407, https://doi.org/10.5194/gmd-17-1387-2024, https://doi.org/10.5194/gmd-17-1387-2024, 2024
Short summary
Short summary
Sophisticated numerical models of the Earth's atmosphere include representations of many physical and chemical processes. In numerical simulations, these processes need to be calculated in a certain sequence. This study reveals the weaknesses of the sequence of calculations used for aerosol processes in a global atmosphere model. A revision of the sequence is proposed and its impacts on the simulated global aerosol climatology are evaluated.
Christopher J. Vogl, Hui Wan, Carol S. Woodward, and Quan M. Bui
Geosci. Model Dev., 17, 1409–1428, https://doi.org/10.5194/gmd-17-1409-2024, https://doi.org/10.5194/gmd-17-1409-2024, 2024
Short summary
Short summary
Generally speaking, accurate climate simulation requires an accurate evolution of the underlying mathematical equations on large computers. The equations are typically formulated and evolved in process groups. Process coupling refers to how the evolution of each group is combined with that of other groups to evolve the full set of equations for the whole atmosphere. This work presents a mathematical framework to evaluate methods without the need to first implement the methods.
Tom Keel, Chris Brierley, and Tamsin Edwards
Geosci. Model Dev., 17, 1229–1247, https://doi.org/10.5194/gmd-17-1229-2024, https://doi.org/10.5194/gmd-17-1229-2024, 2024
Short summary
Short summary
Jet streams are an important control on surface weather as their speed and shape can modify the properties of weather systems. Establishing trends in the operation of jet streams may provide some indication of the future of weather in a warming world. Despite this, it has not been easy to establish trends, as many methods have been used to characterise them in data. We introduce a tool containing various implementations of jet stream statistics and algorithms that works in a standardised manner.
Amir Golparvar, Matthias Kästner, and Martin Thullner
Geosci. Model Dev., 17, 881–898, https://doi.org/10.5194/gmd-17-881-2024, https://doi.org/10.5194/gmd-17-881-2024, 2024
Short summary
Short summary
Coupled reaction transport modelling is an established and beneficial method for studying natural and synthetic porous material, with applications ranging from industrial processes to natural decompositions in terrestrial environments. Up to now, a framework that explicitly considers the porous structure (e.g. from µ-CT images) for modelling the transport of reactive species is missing. We presented a model that overcomes this limitation and represents a novel numerical simulation toolbox.
Stefan Hergarten
Geosci. Model Dev., 17, 781–794, https://doi.org/10.5194/gmd-17-781-2024, https://doi.org/10.5194/gmd-17-781-2024, 2024
Short summary
Short summary
The Voellmy rheology has been widely used for simulating snow and rock avalanches. Recently, a modified version of this rheology was proposed, which turned out to be able to predict the observed long runout of large rock avalanches theoretically. The software MinVoellmy presented here is the first numerical implementation of the modified rheology. It consists of MATLAB and Python classes, where simplicity and parsimony were the design goals.
Arjun Babu Nellikkattil, Danielle Lemmon, Travis Allen O'Brien, June-Yi Lee, and Jung-Eun Chu
Geosci. Model Dev., 17, 301–320, https://doi.org/10.5194/gmd-17-301-2024, https://doi.org/10.5194/gmd-17-301-2024, 2024
Short summary
Short summary
This study introduces a new computational framework called Scalable Feature Extraction and Tracking (SCAFET), designed to extract and track features in climate data. SCAFET stands out by using innovative shape-based metrics to identify features without relying on preconceived assumptions about the climate model or mean state. This approach allows more accurate comparisons between different models and scenarios.
Mohammad Mortezazadeh, Jean-François Cossette, Ashu Dastoor, Jean de Grandpré, Irena Ivanova, and Abdessamad Qaddouri
Geosci. Model Dev., 17, 335–346, https://doi.org/10.5194/gmd-17-335-2024, https://doi.org/10.5194/gmd-17-335-2024, 2024
Short summary
Short summary
The interpolation process is the most computationally expensive step of the semi-Lagrangian (SL) approach. In this paper we implement a new interpolation scheme into the semi-Lagrangian approach which has the same computational cost as a third-order polynomial scheme but with the accuracy of a fourth-order interpolation scheme. This improvement is achieved by using two third-order backward and forward polynomial interpolation schemes in two consecutive time steps.
Boris Gailleton, Luca C. Malatesta, Guillaume Cordonnier, and Jean Braun
Geosci. Model Dev., 17, 71–90, https://doi.org/10.5194/gmd-17-71-2024, https://doi.org/10.5194/gmd-17-71-2024, 2024
Short summary
Short summary
This contribution presents a new method to numerically explore the evolution of mountain ranges and surrounding areas. The method helps in monitoring with details on the timing and travel path of material eroded from the mountain ranges. It is particularly well suited to studies juxtaposing different domains – lakes or multiple rock types, for example – and enables the combination of different processes.
Denise Degen, Daniel Caviedes Voullième, Susanne Buiter, Harrie-Jan Hendricks Franssen, Harry Vereecken, Ana González-Nicolás, and Florian Wellmann
Geosci. Model Dev., 16, 7375–7409, https://doi.org/10.5194/gmd-16-7375-2023, https://doi.org/10.5194/gmd-16-7375-2023, 2023
Short summary
Short summary
In geosciences, we often use simulations based on physical laws. These simulations can be computationally expensive, which is a problem if simulations must be performed many times (e.g., to add error bounds). We show how a novel machine learning method helps to reduce simulation time. In comparison to other approaches, which typically only look at the output of a simulation, the method considers physical laws in the simulation itself. The method provides reliable results faster than standard.
Carlos Spa, Otilio Rojas, and Josep de la Puente
Geosci. Model Dev., 16, 7237–7252, https://doi.org/10.5194/gmd-16-7237-2023, https://doi.org/10.5194/gmd-16-7237-2023, 2023
Short summary
Short summary
This paper develops a calibration methodology of all absorbing techniques typically used by Fourier pseudo-spectral time-domain (PSTD) methods for geoacoustic wave simulations. The main contributions of the paper are (a) an implementation and quantitative comparison of all absorbing techniques available for PSTD methods through a simple and robust numerical experiment, and (b) a validation of these absorbing techniques in several 3-D seismic scenarios with gradual heterogeneity complexities.
Swen Metzger, Samuel Rémy, Jason E. Williams, Vincent Huijnen, and Johannes Flemming
EGUsphere, https://doi.org/10.5194/egusphere-2023-2930, https://doi.org/10.5194/egusphere-2023-2930, 2023
Short summary
Short summary
EQSAM4Clim has recently been revised to provide an accurate and efficient method for calculating the acidity of atmospheric particles. It is based on an analytical concept that is sufficiently fast and free of numerical noise, which makes it attractive for air quality forecasting. Version 12 allows the calculation of aerosol composition based on the gas-liquid-solid and the reduced gas-liquid partitioning with the associated water uptake for both cases, including the acidity of the aerosols.
Michael Hillier, Florian Wellmann, Eric A. de Kemp, Boyan Brodaric, Ernst Schetselaar, and Karine Bédard
Geosci. Model Dev., 16, 6987–7012, https://doi.org/10.5194/gmd-16-6987-2023, https://doi.org/10.5194/gmd-16-6987-2023, 2023
Short summary
Short summary
Neural networks can be used effectively to model three-dimensional geological structures from point data, sampling geological interfaces, units, and structural orientations. Existing neural network approaches for this type of modelling are advanced by the efficient incorporation of unconformities, new knowledge inputs, and improved data fitting techniques. These advances permit the modelling of more complex geology in diverse geological settings, different-sized areas, and various data regimes.
Tor Nordam, Ruben Kristiansen, Raymond Nepstad, Erik van Sebille, and Andy M. Booth
Geosci. Model Dev., 16, 5339–5363, https://doi.org/10.5194/gmd-16-5339-2023, https://doi.org/10.5194/gmd-16-5339-2023, 2023
Short summary
Short summary
We describe and compare two common methods, Eulerian and Lagrangian models, used to simulate the vertical transport of material in the ocean. They both solve the same transport problems but use different approaches for representing the underlying equations on the computer. The main focus of our study is on the numerical accuracy of the two approaches. Our results should be useful for other researchers creating or using these types of transport models.
Mathieu Gravey and Grégoire Mariethoz
Geosci. Model Dev., 16, 5265–5279, https://doi.org/10.5194/gmd-16-5265-2023, https://doi.org/10.5194/gmd-16-5265-2023, 2023
Short summary
Short summary
Multiple‐point geostatistics are widely used to simulate complex spatial structures based on a training image. The use of these methods relies on the possibility of finding optimal training images and parametrization of the simulation algorithms. Here, we propose finding an optimal set of parameters using only the training image as input. The main advantage of our approach is to remove the risk of overfitting an objective function.
Niko Schmidt, Angelika Humbert, and Thomas Slawig
EGUsphere, https://doi.org/10.5194/egusphere-2023-1569, https://doi.org/10.5194/egusphere-2023-1569, 2023
Short summary
Short summary
Future sea-level rise is of big significance for coastal regions. The melting and acceleration of glaciers plays a major role in sea-level change. Computer simulation of glaciers costs a lot of computational resources. In this publication, we test a new way of simulating glaciers. This approach produces the same results but has the advantage that it needs much less computation time. As simulations can be obtained with fewer computation resources, higher resolution and physics becomes affordable.
Siting Li, Ping Wang, Hong Wang, Yue Peng, Zhaodong Liu, Wenjie Zhang, Hongli Liu, Yaqiang Wang, Huizheng Che, and Xiaoye Zhang
Geosci. Model Dev., 16, 4171–4191, https://doi.org/10.5194/gmd-16-4171-2023, https://doi.org/10.5194/gmd-16-4171-2023, 2023
Short summary
Short summary
Optimizing the initial state of atmospheric chemistry model input is one of the most essential methods to improve forecast accuracy. Considering the large computational load of the model, we introduce an ensemble optimal interpolation scheme (EnOI) for operational use and efficient updating of the initial fields of chemical components. The results suggest that EnOI provides a practical and cost-effective technique for improving the accuracy of chemical weather numerical forecasts.
Thomas Richter, Véronique Dansereau, Christian Lessig, and Piotr Minakowski
Geosci. Model Dev., 16, 3907–3926, https://doi.org/10.5194/gmd-16-3907-2023, https://doi.org/10.5194/gmd-16-3907-2023, 2023
Short summary
Short summary
Sea ice covers not only the pole regions but affects the weather and climate globally. For example, its white surface reflects more sunlight than land. The oceans around the poles are therefore kept cool, which affects the circulation in the oceans worldwide. Simulating the behavior and changes in sea ice on a computer is, however, very difficult. We propose a new computer simulation that better models how cracks in the ice change over time and show this by comparing to other simulations.
Emma J. MacKie, Michael Field, Lijing Wang, Zhen Yin, Nathan Schoedl, Matthew Hibbs, and Allan Zhang
Geosci. Model Dev., 16, 3765–3783, https://doi.org/10.5194/gmd-16-3765-2023, https://doi.org/10.5194/gmd-16-3765-2023, 2023
Short summary
Short summary
Earth scientists often have to fill in spatial gaps in measurements. This gap-filling or interpolation can be accomplished with geostatistical methods, where the statistical relationships between measurements are used to inform how these gaps should be filled. Despite the broad utility of these methods, there are few freely available geostatistical software applications. We present GStatSim, a Python package for performing different geostatistical interpolation methods.
Ian Madden, Simone Marras, and Jenny Suckale
Geosci. Model Dev., 16, 3479–3500, https://doi.org/10.5194/gmd-16-3479-2023, https://doi.org/10.5194/gmd-16-3479-2023, 2023
Short summary
Short summary
To aid risk managers who may wish to rapidly assess tsunami risk but may lack high-performance computing infrastructure, we provide an accessible software package able to rapidly model tsunami inundation over real topography by leveraging Google's Tensor Processing Unit, a high-performance hardware. Minimally trained users can take advantage of the rapid modeling abilities provided by this package via a web browser thanks to the ease of use of Google Cloud Platform.
Reuben W. Nixon-Hill, Daniel Shapero, Colin J. Cotter, and David A. Ham
EGUsphere, https://doi.org/10.48550/arXiv.2304.06058, https://doi.org/10.48550/arXiv.2304.06058, 2023
Short summary
Short summary
Scientists often use models to study complex processes, like the movement of ice sheets, and compare them to measurements for estimating hard-to-measure quantities. We highlight an approach that ensures accurate results from point data sources (such as height measurements) by evaluating the numerical solution at true point locations. This method improves accuracy, can aid communication between scientists, and is well suited for integration with specialised software that automates the processes.
Youtong Rong, Paul Bates, and Jeffrey Neal
Geosci. Model Dev., 16, 3291–3311, https://doi.org/10.5194/gmd-16-3291-2023, https://doi.org/10.5194/gmd-16-3291-2023, 2023
Short summary
Short summary
A novel subgrid channel (SGC) model is developed for river–floodplain modelling, allowing utilization of subgrid-scale bathymetric information while performing computations on relatively coarse grids. By including adaptive artificial diffusion, potential numerical instability, which the original SGC solver had, in low-friction regions such as urban areas is addressed. Evaluation of the new SGC model through structured tests confirmed that the accuracy and stability have improved.
Xiaqiong Zhou and Hann-Ming Henry Juang
Geosci. Model Dev., 16, 3263–3274, https://doi.org/10.5194/gmd-16-3263-2023, https://doi.org/10.5194/gmd-16-3263-2023, 2023
Short summary
Short summary
The National Centers for Environmental Prediction Global Forecast System version 16 experienced model instability failures in real-time runs resolved by increasing the minimum thickness depth parameter. Further investigation revealed that the issue was caused by the advection of geopotential heights at the model's layer interfaces. By replacing high-order boundary conditions with zero-gradient boundary conditions for interface-wind reconstruction, the instability was effectively addressed.
Grant T. Euen, Shangxin Liu, Rene Gassmöller, Timo Heister, and Scott D. King
Geosci. Model Dev., 16, 3221–3239, https://doi.org/10.5194/gmd-16-3221-2023, https://doi.org/10.5194/gmd-16-3221-2023, 2023
Short summary
Short summary
Due to the increasing availability of high-performance computing over the past few decades, numerical models have become an important tool for research. Here we test two geodynamic codes that produce such models: ASPECT, a newer code, and CitcomS, an older one. We show that they produce solutions that are extremely close. As methods and codes become more complex over time, showing reproducibility allows us to seamlessly link previously known information to modern methodologies.
Mohammad Kazem Sharifian, Georges Kesserwani, Alovya Ahmed Chowdhury, Jeffrey Neal, and Paul Bates
Geosci. Model Dev., 16, 2391–2413, https://doi.org/10.5194/gmd-16-2391-2023, https://doi.org/10.5194/gmd-16-2391-2023, 2023
Short summary
Short summary
This paper describes a new release of the LISFLOOD-FP model for fast and efficient flood simulations. It features a new non-uniform grid generator that uses multiwavelet analyses to sensibly coarsens the resolutions where the local topographic variations are smooth. Moreover, the model is parallelised on the graphical processing units (GPUs) to further boost computational efficiency. The performance of the model is assessed for five real-world case studies, noting its potential applications.
Bruno K. Zürcher
Geosci. Model Dev., 16, 1697–1711, https://doi.org/10.5194/gmd-16-1697-2023, https://doi.org/10.5194/gmd-16-1697-2023, 2023
Short summary
Short summary
We present a novel algorithm to efficiently compute Barnes interpolation, which is a method for transforming data values recorded at irregularly spaced points into a corresponding regular grid. In contrast to naive implementations with an algorithmic complexity that depends on the product of the number of sample points and the number of grid points, our approach reduces this dependency to their sum.
David H. Marsico and Paul A. Ullrich
Geosci. Model Dev., 16, 1537–1551, https://doi.org/10.5194/gmd-16-1537-2023, https://doi.org/10.5194/gmd-16-1537-2023, 2023
Short summary
Short summary
Climate models involve several different components, such as the atmosphere, ocean, and land models. Information needs to be exchanged, or remapped, between these models, and devising algorithms for performing this exchange is important for ensuring the accuracy of climate simulations. In this paper, we examine the efficacy of several traditional and novel approaches to remapping on the sphere and demonstrate where our approaches offer improvement.
Moritz Liebl, Jörg Robl, Stefan Hergarten, David Lundbek Egholm, and Kurt Stüwe
Geosci. Model Dev., 16, 1315–1343, https://doi.org/10.5194/gmd-16-1315-2023, https://doi.org/10.5194/gmd-16-1315-2023, 2023
Short summary
Short summary
In this study, we benchmark a topography-based model for glacier erosion (OpenLEM) with a well-established process-based model (iSOSIA). Our experiments show that large-scale erosion patterns and particularly the transformation of valley length geometry from fluvial to glacial conditions are very similar in both models. This finding enables the application of OpenLEM to study the influence of climate and tectonics on glaciated mountains with reasonable computational effort on standard PCs.
James Kent, Thomas Melvin, and Golo Albert Wimmer
Geosci. Model Dev., 16, 1265–1276, https://doi.org/10.5194/gmd-16-1265-2023, https://doi.org/10.5194/gmd-16-1265-2023, 2023
Short summary
Short summary
This paper introduces the Met Office's new shallow water model. The shallow water model is a building block towards the Met Office's new atmospheric dynamical core. The shallow water model is tested on a number of standard spherical shallow water test cases, including flow over mountains and unstable jets. Results show that the model produces similar results to other shallow water models in the literature.
Anthony Gruber, Max Gunzburger, Lili Ju, Rihui Lan, and Zhu Wang
Geosci. Model Dev., 16, 1213–1229, https://doi.org/10.5194/gmd-16-1213-2023, https://doi.org/10.5194/gmd-16-1213-2023, 2023
Short summary
Short summary
This work applies a novel technical tool, multifidelity Monte Carlo (MFMC) estimation, to three climate-related benchmark experiments involving oceanic, atmospheric, and glacial modeling. By considering useful quantities such as maximum sea height and total (kinetic) energy, we show that MFMC leads to predictions which are more accurate and less costly than those obtained by standard methods. This suggests MFMC as a potential drop-in replacement for estimation in realistic climate models.
Piyoosh Jaysaval, Glenn E. Hammond, and Timothy C. Johnson
Geosci. Model Dev., 16, 961–976, https://doi.org/10.5194/gmd-16-961-2023, https://doi.org/10.5194/gmd-16-961-2023, 2023
Short summary
Short summary
We present a robust and highly scalable implementation of numerical forward modeling and inversion algorithms for geophysical electrical resistivity tomography data. The implementation is publicly available and developed within the framework of PFLOTRAN (http://www.pflotran.org), an open-source, state-of-the-art massively parallel subsurface flow and transport simulation code. The paper details all the theoretical and implementation aspects of the new capabilities along with test examples.
Lucas Schauer, Michael J. Schmidt, Nicholas B. Engdahl, Stephen D. Pankavich, David A. Benson, and Diogo Bolster
Geosci. Model Dev., 16, 833–849, https://doi.org/10.5194/gmd-16-833-2023, https://doi.org/10.5194/gmd-16-833-2023, 2023
Short summary
Short summary
We develop a multi-dimensional, parallelized domain decomposition strategy for mass-transfer particle tracking methods in two and three dimensions, investigate different procedures for decomposing the domain, and prescribe an optimal tiling based on physical problem parameters and the number of available CPU cores. For an optimally subdivided diffusion problem, the parallelized algorithm achieves nearly perfect linear speedup in comparison with the serial run-up to thousands of cores.
John Mern and Jef Caers
Geosci. Model Dev., 16, 289–313, https://doi.org/10.5194/gmd-16-289-2023, https://doi.org/10.5194/gmd-16-289-2023, 2023
Short summary
Short summary
In this work, we formulate the sequential geoscientific data acquisition problem as a problem that is similar to playing chess against nature, except the pieces are not fully observed. Solutions to these problems are given in AI and rarely used in geoscientific data planning. We illustrate our approach to a simple 2D problem of mineral exploration.
Ziqi Gao, Yifeng Wang, Petros Vasilakos, Cesunica E. Ivey, Khanh Do, and Armistead G. Russell
Geosci. Model Dev., 15, 9015–9029, https://doi.org/10.5194/gmd-15-9015-2022, https://doi.org/10.5194/gmd-15-9015-2022, 2022
Short summary
Short summary
While the national ambient air quality standard of ozone is based on the 3-year average of the fourth highest 8 h maximum (MDA8) ozone concentrations, these predicted extreme values using numerical methods are always biased low. We built four computational models (GAM, MARS, random forest and SVR) to predict the fourth highest MDA8 ozone in Southern California using precursor emissions, meteorology and climatological patterns. All models presented acceptable performance, with GAM being the best.
Zhihao Wang, Jason Goetz, and Alexander Brenning
Geosci. Model Dev., 15, 8765–8784, https://doi.org/10.5194/gmd-15-8765-2022, https://doi.org/10.5194/gmd-15-8765-2022, 2022
Short summary
Short summary
A lack of inventory data can be a limiting factor in developing landslide predictive models, which are crucial for supporting hazard policy and decision-making. We show how case-based reasoning and domain adaptation (transfer-learning techniques) can effectively retrieve similar landslide modeling situations for prediction in new data-scarce areas. Using cases in Italy, Austria, and Ecuador, our findings support the application of transfer learning for areas that require rapid model development.
Till Sachau, Haibin Yang, Justin Lang, Paul D. Bons, and Louis Moresi
Geosci. Model Dev., 15, 8749–8764, https://doi.org/10.5194/gmd-15-8749-2022, https://doi.org/10.5194/gmd-15-8749-2022, 2022
Short summary
Short summary
Knowledge of the internal structures of the major continental ice sheets is improving, thanks to new investigative techniques. These structures are an essential indication of the flow behavior and dynamics of ice transport, which in turn is important for understanding the actual impact of the vast amounts of water trapped in continental ice sheets on global sea-level rise. The software studied here is specifically designed to simulate such structures and their evolution.
Keith J. Roberts, Alexandre Olender, Lucas Franceschini, Robert C. Kirby, Rafael S. Gioria, and Bruno S. Carmo
Geosci. Model Dev., 15, 8639–8667, https://doi.org/10.5194/gmd-15-8639-2022, https://doi.org/10.5194/gmd-15-8639-2022, 2022
Short summary
Short summary
Finite-element methods (FEMs) permit the use of more flexible unstructured meshes but are rarely used in full waveform inversions (FWIs), an iterative process that reconstructs velocity models of earth’s subsurface, due to computational and memory storage costs. To reduce those costs, novel software is presented allowing the use of high-order mass-lumped FEMs on triangular meshes, together with a material-property mesh-adaptation performance-enhancing strategy, enabling its use in FWIs.
Konstantinos Papadakis, Yann Pfau-Kempf, Urs Ganse, Markus Battarbee, Markku Alho, Maxime Grandin, Maxime Dubart, Lucile Turc, Hongyang Zhou, Konstantinos Horaites, Ivan Zaitsev, Giulia Cozzani, Maarja Bussov, Evgeny Gordeev, Fasil Tesema, Harriet George, Jonas Suni, Vertti Tarvus, and Minna Palmroth
Geosci. Model Dev., 15, 7903–7912, https://doi.org/10.5194/gmd-15-7903-2022, https://doi.org/10.5194/gmd-15-7903-2022, 2022
Short summary
Short summary
Vlasiator is a plasma simulation code that simulates the entire near-Earth space at a global scale. As 6D simulations require enormous amounts of computational resources, Vlasiator uses adaptive mesh refinement (AMR) to lighten the computational burden. However, due to Vlasiator’s grid topology, AMR simulations suffer from grid aliasing artifacts that affect the global results. In this work, we present and evaluate the performance of a mechanism for alleviating those artifacts.
Artur Safin, Damien Bouffard, Firat Ozdemir, Cintia L. Ramón, James Runnalls, Fotis Georgatos, Camille Minaudo, and Jonas Šukys
Geosci. Model Dev., 15, 7715–7730, https://doi.org/10.5194/gmd-15-7715-2022, https://doi.org/10.5194/gmd-15-7715-2022, 2022
Short summary
Short summary
Reconciling the differences between numerical model predictions and observational data is always a challenge. In this paper, we investigate the viability of a novel approach to the calibration of a three-dimensional hydrodynamic model of Lake Geneva, where the target parameters are inferred in terms of distributions. We employ a filtering technique that generates physically consistent model trajectories and implement a neural network to enable bulk-to-skin temperature conversion.
Cited articles
Alfieri, L., Burek, P., Dutra, E., Krzeminski, B., Muraro, D., Thielen, J., and Pappenberger, F.: GloFAS – global ensemble streamflow forecasting and flood early warning, Hydrol. Earth Syst. Sci., 17, 1161–1175, https://doi.org/10.5194/hess-17-1161-2013, 2013.
Ayyad, M., Orton, P. M., El Safty, H., Chen, Z., and Hajj, M. R.: Ensemble forecast for storm tide and resurgence from Tropical Cyclone Isaias, Weather Clim. Extrem., 38, 100504, https://doi.org/10.1016/j.wace.2022.100504, 2022.
Bakker, T. M., Antolínez, J. A. A., Leijnse, T., Pearson, S. G., and Giardino, A.: Estimating tropical cyclone-induced wind, waves, and surge: A general methodology based on representative tracks, Coast. Eng., 176, 104154, https://doi.org/10.1016/j.coastaleng.2022.104154, 2022.
Brackins, J. T. and Kalyanapu, A. J.: Evaluation of parametric precipitation models in reproducing tropical cyclone rainfall patterns, J. Hydrol., 580, 124255, https://doi.org/10.1016/j.jhydrol.2019.124255, 2020.
Cangialosi, J. P., Blake, E., Demaria, M., Penny, A., Latto, A., Rappaport, E., and Tallapragada, V.: Recent progress in tropical cyclone intensity forecasting at the national hurricane center, Weather Forecast., 35, 1913–1922, https://doi.org/10.1175/WAF-D-20-0059.1, 2020.
Cashwell, E. D. and Everett, C. J.: A Practical Manual on the Monte Carlo Method for Random Walk Problems, Los Alamos Scientific Laboratory of the University of California, Los Alamos, NM, https://www.osti.gov/biblio/4314838 (last access: 6 January 2023), 1959.
Chavas, D., Lin, N., and Emanuel, K. A.: A Model for the Complete Radial Structure of the Tropical Cyclone Wind Field. Part I: Comparison with Observed Structure, J. Atmos. Sci., 72, 3647–3662, https://doi.org/10.1175/JAS-D-15-0014.1, 2015.
Chen, B. F., Kuo, Y. Te, and Huang, T. S.: A deep learning ensemble approach for predicting tropical cyclone rapid intensification, Atmos. Sci. Lett., 24, e1151, https://doi.org/10.1002/asl.1151, 2023.
Choi, C.-Y. and Nam, J.-C.: Cluster analysis of Tropical Cyclones making landfall on the Korean Peninsula, Adv. Atmos. Sci., 26, 202–210, https://doi.org/10.1007/s00376-009-0202-1, 2009.
Deltares: Wind Enhance Scheme for cyclone modelling – User Manual, 1–110, 2018.
Deltares: Beira Coastal Protection Preparation study: flood hazard modelling, document number 11205711-003-ZKS-0002, 2021.
DeMaria, M., Knaff, J., Knabb, R., Lauer, C., Sampson, C., and DeMaria, R. T.: A New Method for Estimating Tropical Cyclone Wind Speed Probabilities, Weather Forecast., 24, 1573–1591, https://doi.org/10.1175/2009WAF2222286.1, 2009.
DeMaria, M., Knaff, J. A., Brennan, M. J., Brown, D., Knabb, R. D., DeMaria, R. T., Schumacher, A., Lauer, C. A., Roberts, D. P., Sampson, C. R., Santos, P., Sharp, D., and Winters, K. A.: Improvements to the operational tropical cyclone wind speed probability model, Weather Forecast., 28, 586–602, https://doi.org/10.1175/WAF-D-12-00116.1, 2013.
de Vries, H.: Probability Forecasts for Water Levels at the Coast of The Netherlands, Mar. Geod., 32, 100–107, https://doi.org/10.1080/01490410902869185, 2009.
Done, J. M., Ge, M., Holland, G. J., Dima-West, I., Phibbs, S., Saville, G. R., and Wang, Y.: Modelling global tropical cyclone wind footprints, Nat. Hazards Earth Syst. Sci., 20, 567–580, https://doi.org/10.5194/nhess-20-567-2020, 2020.
Doyle, J., Hodur, R., Chen, S., Jin, Y., Msokaitis, J., Wang, S., Hendricks, E., Jin, J., and Smith, T.: Tropical Cyclone Prediction Using COAMPS-TC, Oceanography, 27, 104–115, https://doi.org/10.5670/oceanog.2014.72, 2014.
Easterling, D. R., Meehl, G. A., Parmesan, C., Changnon, S. A., Karl, T. R., and Mearns, L. O.: Climate extremes: observations, modeling, and impacts, Science, 289, 2068–2074, https://doi.org/10.1126/science.289.5487.2068, 2000.
Egbert, G. D. and Erofeeva, S. Y.: Efficient inverse modeling of barotropic ocean tides, J. Atmos. Ocean. Technol., 19, 183–204, https://doi.org/10.1175/1520-0426(2002)019<0183:EIMOBO>2.0.CO;2, 2002.
Eilander, D., Couasnon, A., Leijnse, T., Ikeuchi, H., Yamazaki, D., Muis, S., Dullaart, J., Haag, A., Winsemius, H. C., and Ward, P. J.: A globally applicable framework for compound flood hazard modeling, Nat. Hazards Earth Syst. Sci., 23, 823–846, https://doi.org/10.5194/nhess-23-823-2023, 2023.
Emerton, R., Cloke, H., Ficchi, A., Hawker, L., de Wit, S., Speight, L., Prudhomme, C., Rundell, P., West, R., Neal, J., Cuna, J., Harrigan, S., Titley, H., Magnusson, L., Pappenberger, F., Klingaman, N., and Stephens, E.: Emergency flood bulletins for Cyclones Idai and Kenneth: A critical evaluation of the use of global flood forecasts for international humanitarian preparedness and response, Int. J. Disaster Risk Reduct., 50, 101811, https://doi.org/10.1016/j.ijdrr.2020.101811, 2020.
Flowerdew, J., Horsburgh, K., Wilson, C., and Mylne, K.: Development and evaluation of an ensemble forecasting system for coastal storm surges, Q. J. Roy. Meteor. Soc., 136, 1444–1456, https://doi.org/10.1002/qj.648, 2010.
Fossell, K. R., Ahijevych, D., Morss, R. E., Snyder, C., and Davis, C.: The practical predictability of storm tide from tropical cyclones in the gulf of Mexico, Mon. Weather Rev., 145, 5103–5121, https://doi.org/10.1175/MWR-D-17-0051.1, 2017.
Fujita, T.: Pressure Distribution Within Typhoon, Geophysical Magazine, 23, 437–451, 1952.
Goerss, J. S.: Prediction of consensus tropical cyclone track forecast error, Mon. Weather Rev., 135, 1985–1993, https://doi.org/10.1175/MWR3390.1, 2007.
Gonzalez, T. and Taylor, A.: Development of the NWS' Probabilistic Tropical Storm Surge Model, Poster presented at 33rd Conference on Hurricanes and Tropical Meteorology, Ponte Verda, FL, April 2018, https://ams.confex.com/ams/33HURRICANE/webprogram/Handout/Paper340247/186_Gonzalez_P-Surge_AMS_Final-PDF.pdf (last access: 16 May 2023), 2018.
Harper, B. A., Kepert, J. D., and Ginger, J. D.: Guidelines for converting between various wind averaging periods in tropical cyclone conditions, WMO, October, https://library.wmo.int/viewer/48652?medianame=wmo-td_1555_en (last access: 26 December 2023), 2010.
Hasegawa, H., Kohno, N., and Itoh, M.: Development of Storm Surge Model in Japan Meteorological Agency, in: 14th International Workshop on Wave Hindcasting and Forecasting & 5th Coastal Hazard Symposium, Key West, Florida, November 2014, 1–6, http://www.waveworkshop.org/14thWaves/Papers/JCOMM_2015_J4.pdf (last access: 28 June 2023), 2015.
Holland, G. J.: An analytical model of the wind and pressure profiles in hurricanes, Mon. Weather Rev., 108, 1212–1218, https://doi.org/10.1175/1520-0493(1980)108<1212:AAMOTW>2.0.CO;2, 1980.
Holland, G. J., Belanger, J., and Fritz, A.: A Revised Model for Radial Profiles of Hurricane Winds, Am. Meteorol. Soc., 138, 4393–4401, https://doi.org/10.1175/2010MWR3317.1, 2010.
Hu, K., Chen, Q., and Fitzpatrick, P.: Assessment of a Parametric Hurricane Surface Wind Model for Tropical Cyclones in the Gulf of Mexico In: Advances in Hurricane Research – Modelling, Meteorology, Preparedness and Impacts, InTech, https://doi.org/10.5772/51288, 2012.
International Hydrographic Organization and Intergovernmental Oceanographic Commission: The IHO-IOC GEBCO Cook Book, in: IOC Manuals and Guides 63, IHO Publication B-11, Monaco, October 2019, 493 pp., 2003.
IPET: Performance evaluation of the New Orleans and Southeast Louisiana Hurricane Protection System draft final report of the Interagency Performance Evaluation Task Force volume VIII – engineering and operational risk and reliability analysis, https://usace.contentdm.oclc.org/digital/collection/p266001coll1/id/2844/ (last access: 11 August 2022), 2006.
Jelesnianski, C. P., Chen, J., and Shaffer, W. A.: SLOSH: Sea, Lake, and Overland Surges from Hurricanes, NOAA Tech. Rep., NWS 48, NOAA AOML Library, Miami, Florida, https://repository.library.noaa.gov/view/noaa/7235 (last access: 3 November 2022), 1992.
Joint Typhoon Warning Center (JTWC): Annual Tropical Cyclone Report: 2020 [PDF file], https://www.metoc.navy.mil/jtwc/products/atcr/2020atcr.pdf (last access: 26 December 2023), 2021.
Joint Typhoon Warning Center (JTWC): Typhoon Idai (2021) Best Track Data [data set], https://www.metoc.navy.mil/jtwc/jtwc.html (last access: 3 March 2023), 2022.
Kernkamp, H. W. J., Van Dam, A., Stelling, G. S., and de Goede, E. D.: Efficient scheme for the shallow water equations on unstructured grids with application to the Continental Shelf, Ocean Dynam., 61, 1175–1188, https://doi.org/10.1007/s10236-011-0423-6, 2011.
Lamers, A., Devi S, S., Sharma, M., Berg, R., Gálvez, J. M., Yu, Z., Kriat, T., Cardos, S., Grant, D., and Moron, L. A.: Forecasting Tropical Cyclone Rainfall and Flooding Hazards and Impacts, Tropical Cyclone Research and Review, 12, 100–112, https://doi.org/10.1016/j.tcrr.2023.06.005, 2023.
Lecacheux, S., Rohmer, J., Paris, F., Pedreros, R., Quetelard, H., and Bonnardot, F.: Toward the probabilistic forecasting of cyclone-induced marine flooding by overtopping at Reunion Island aided by a time-varying random-forest classification approach, Nat. Hazards, 105, 227–251, https://doi.org/10.1007/s11069-020-04307-y, 2021.
Leijnse, T., van Ormondt, M., Nederhoff, K., and van Dongeren, A.: Modeling compound flooding in coastal systems using a computationally efficient reduced-physics solver: Including fluvial, pluvial, tidal, wind- and wave-driven processes, Coast. Eng., 163, 103796, https://doi.org/10.1016/j.coastaleng.2020.103796, 2021.
Liang, Q., Du, G., Hall, J. W., and Borthwick, A. G.: Flood Inundation Modeling with an Adaptive Quadtree Grid Shallow Water Equation Solver, J. Hydraul. Eng., 134, 1603–1610, https://doi.org/10.1061/(ASCE)0733-9429(2008)134:11(1603), 2008.
Lin, N., Emanuel, K., Oppenheimer, M., and Vanmarcke, E.: Physically based assessment of hurricane surge threat under climate change, Nat. Clim. Change, 2, 462–467, https://doi.org/10.1038/nclimate1389, 2012.
Lu, P., Lin, N., Emanuel, K., Chavas, D., and Smith, J.: Assessing hurricane rainfall mechanisms using a physics-based model: Hurricanes Isabel (2003) and Irene (2011), J. Atmos. Sci., 75, 2337–2358, https://doi.org/10.1175/JAS-D-17-0264.1, 2018.
Luettich, R. A., Westerink, J. J., and Scheffner, N. W.: ADCIRC: An Advanced Three-Dimensional Circulation Model for Shelves Coasts and Estuaries, Report 1: Theory and Methodology of ADCIRC-2DDI and ADCIRC-3DL, Dredging Research Program Technical Report DRP-92-6, https://erdc-library.erdc.dren.mil/jspui/handle/11681/4618 (last access: 3 November 2022), 1992.
Matheson, J. E. and Winkler, R. L.: Scoring Rules for Continuous Probability Distributions, Manage. Sci., 22, 1087–1096, https://doi.org/10.1287/mnsc.22.10.1087, 1976.
McAdie, C. J. and Lawrence, M. B.: Improvements in tropical cyclone track forecasting in the Atlantic Basin, 1970–98, B. Am. Meteorol. Soc., 81, 989–997, https://doi.org/10.1175/1520-0477(2000)081<0989:IITCTF>2.3.CO;2, 2000.
Mori, N. and Shimura, T.: Tropical cyclone-induced coastal sea level projection and the adaptation to a changing climate, Cambridge Prism. Coast. Futur., 1, e4, https://doi.org/10.1017/cft.2022.6, 2023.
Nakagawa, M.: Outline of the High Resolution Global Model at the Japan Meteorological Agency, RSMC Tokyo-Typhoon Cent. Tech. Rev. 1125–38, https://www.jma.go.jp/jma/jma-eng/jma-center/rsmc-hp-pub-eg/techrev/text11-1.pdf (last access: 28 June 2023), 2009.
NASA GPM: Global Preciptation Measurement (GPM) of Cyclone Idai, https://gpm.nasa.gov/tropical-storm-idai-measured-gpm (last access: 16 March 2023), 2019.
National Hurricane Center: About NHC Graphics, https://www.nhc.noaa.gov/aboutnhcgraphics.shtml#WATCHWARN, last access: 27 June 2023.
Nederhoff, K. and van Ormondt, M.: Tropical Cyclone Forecasting Framework: TC-FF (v1.0.0-beta), Zenodo [code and data set], https://doi.org/10.5281/zenodo.10433070, 2023.
Nederhoff, K., Giardino, A., van Ormondt, M., and Vatvani, D.: Estimates of tropical cyclone geometry parameters based on best-track data, Nat. Hazards Earth Syst. Sci., 19, 2359–2370, https://doi.org/10.5194/nhess-19-2359-2019, 2019.
Nederhoff, K., Hoek, J., Leijnse, T., van Ormondt, M., Caires, S., and Giardino, A.: Simulating synthetic tropical cyclone tracks for statistically reliable wind and pressure estimations, Nat. Hazards Earth Syst. Sci., 21, 861–878, https://doi.org/10.5194/nhess-21-861-2021, 2021.
Neumann, B., Vafeidis, A. T., Zimmermann, J., and Nicholls, R. J.: Future coastal population growth and exposure to sea-level rise and coastal flooding – A global assessment, PLoS One, 10, e0131375, https://doi.org/10.1371/journal.pone.0118571, 2015.
Nguyen, D. T. and Chen, S. T.: Real-time probabilistic flood forecasting using multiple machine learning methods, Water, 12, 787, https://doi.org/10.3390/w12030787, 2020.
Rappaport, E. N.: Fatalities in the united states from atlantic tropical cyclones: New data and interpretation, B. Am. Meteorol. Soc., 95, 341–346, https://doi.org/10.1175/BAMS-D-12-00074.1, 2014.
Resio, D. T. and Irish, J. L.: Tropical Cyclone Storm Surge Risk, Curr. Clim. Chang. Reports, 1, 74–84, https://doi.org/10.1007/s40641-015-0011-9, 2015.
Roberts, M. J., Camp, J., Seddon, J., Vidale, P. L., Hodges, K., Vannière, B., Mecking, J., Haarsma, R., Bellucci, A., Scoccimarro, E., Caron, L. P., Chauvin, F., Terray, L., Valcke, S., Moine, M. P., Putrasahan, D., Roberts, C. D., Senan, R., Zarzycki, C., Ullrich, P., Yamada, Y., Mizuta, R., Kodama, C., Fu, D., Zhang, Q., Danabasoglu, G., Rosenbloom, N., Wang, H., and Wu, L.: Projected Future Changes in Tropical Cyclones Using the CMIP6 HighResMIP Multimodel Ensemble, Geophys. Res. Lett., 47, e2020GL088662, https://doi.org/10.1029/2020GL088662, 2020.
Roy, C. and Kovordányi, R.: Tropical cyclone track forecasting techniques – A review, Atmos. Res., 104–105, 40–69, https://doi.org/10.1016/j.atmosres.2011.09.012, 2012.
Rye, C. J. and Boyd, J. A.: Downward Counterfactual Analysis in Insurance Tropical Cyclone Models: A Miami Case Study, in: Hurricane Risk in a Changing Climate, edited by: Collins, J. M. and Done, J. M., vol 2, Springer, Cham, 207–232, https://doi.org/10.1007/978-3-031-08568-0_9, 2022.
Schwerdt, R. W., Ho, F., and Watkins, R. R.: Meteorological criteria for standard project hurricane and probable maximum hurricane windfields, gulf and east coasts of the United States NOAA Technical Report NWS 23, 1979.
Suh, S. W., Lee, H. Y., Kim, H. J., and Fleming, J. G.: An efficient early warning system for typhoon storm surge based on time-varying advisories by coupled ADCIRC and SWAN, 617–646 pp., https://doi.org/10.1007/s10236-015-0820-3, 2015.
Taylor, A. and Glahn, B.: Probabilistic guidance for hurricane storm surge, Proc. 88th AMS Annu. Meet., New Orleans, Louisiana, USA, 20–24 January 2008, https://ams.confex.com/ams/88Annual/webprogram/Paper132793.html (last access: 16 May 2023), 2008.
Trenberth, K. E., Dai, A., Rasmussen, R. M., and Parsons, D. B.: The Changing Character of Precipitation, B. Am. Meteorol. Soc., 84, 1205–1218, https://doi.org/10.1175/BAMS-84-9-1205, 2003.
United States Department of Agriculture: National Engineering Handbook Chapter 7 Hydrologic Soil Groups, United States Department of Agriculture, https://directives.sc.egov.usda.gov/22526.wba (last access: 13 March 2023), 2009.
UN OCHA: Business Guide: Cyclone Idai, 1–3, https://www.unocha.org/publications/report/mozambique/mozambique-cyclone-idai-flash-update-no-1-15-march-2019 (last access: 21 March 2023), 2019.
van Ormondt, M., Nederhoff, K., and Van Dongeren, A.: Delft Dashboard: a quick setup tool for hydrodynamic models, J. Hydroinformatics, 22, 510–527, https://doi.org/10.2166/hydro.2020.092, 2020.
Wahl, T., Jain, S., Bender, J., Meyers, S. D., and Luther, M. E.: Increasing risk of compound flooding from storm surge and rainfall for major US cities, Nat. Clim. Chang., 5, 1093–1097, https://doi.org/10.1038/nclimate2736, 2015.
Wing, O. E. J., Bates, P. D., Sampson, C. C., Smith, A. M., Johnson, K. A., and Erickson, T. A.: Validation of a 30 m resolution flood hazard model of the conterminous United States, Water Resour. Res., 53, 7968–7986, https://doi.org/10.1002/2017WR020917, 2017.
Yamazaki, D., Ikeshima, D., Tawatari, R., Yamaguchi, T., O'Loughlin, F., Neal, J. C., Sampson, C. C., Kanae, S., and Bates, P. D.: A high-accuracy map of global terrain elevations, Geophys. Res. Lett., 44, 5844–5853, https://doi.org/10.1002/2017GL072874, 2017.
Short summary
Forecasting tropical cyclones and their flooding impact is challenging. Our research introduces the Tropical Cyclone Forecasting Framework (TC-FF), enhancing cyclone predictions despite uncertainties. TC-FF generates global wind and flood scenarios, valuable even in data-limited regions. Applied to cases like Cyclone Idai, it showcases potential in bettering disaster preparation, marking progress in handling cyclone threats.
Forecasting tropical cyclones and their flooding impact is challenging. Our research...
