Articles | Volume 14, issue 7
https://doi.org/10.5194/gmd-14-4357-2021
© Author(s) 2021. 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-14-4357-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
BARRA v1.0: kilometre-scale downscaling of an Australian regional atmospheric reanalysis over four midlatitude domains
Chun-Hsu Su
CORRESPONDING AUTHOR
Bureau of Meteorology, Docklands, Victoria 3008, Australia
Nathan Eizenberg
Department of Earth Sciences, The University of Melbourne, Parkville, Victoria 3010, Australia
Dörte Jakob
Bureau of Meteorology, Docklands, Victoria 3008, Australia
Paul Fox-Hughes
Bureau of Meteorology, Hobart, Tasmania 7000, Australia
Peter Steinle
Bureau of Meteorology, Docklands, Victoria 3008, Australia
Christopher J. White
Department of Civil and Environmental Engineering, University of Strathclyde, Glasgow, Scotland, UK
School of Engineering, University of Tasmania, Hobart, Australia
Charmaine Franklin
Bureau of Meteorology, Docklands, Victoria 3008, Australia
Related authors
Emma Howard, Chun-Hsu Su, Christian Stassen, Rajashree Naha, Harvey Ye, Acacia Pepler, Samuel S. Bell, Andrew J. Dowdy, Simon O. Tucker, and Charmaine Franklin
Geosci. Model Dev., 17, 731–757, https://doi.org/10.5194/gmd-17-731-2024, https://doi.org/10.5194/gmd-17-731-2024, 2024
Short summary
Short summary
The BARPA-R modelling configuration has been developed to produce high-resolution climate hazard projections within the Australian region. When using boundary driving data from quasi-observed historical conditions, BARPA-R shows good performance with errors generally on par with reanalysis products. BARPA-R also captures trends, known modes of climate variability, large-scale weather processes, and multivariate relationships.
Suwash Chandra Acharya, Rory Nathan, Quan J. Wang, Chun-Hsu Su, and Nathan Eizenberg
Hydrol. Earth Syst. Sci., 24, 2951–2962, https://doi.org/10.5194/hess-24-2951-2020, https://doi.org/10.5194/hess-24-2951-2020, 2020
Short summary
Short summary
BARRA is a high-resolution reanalysis dataset over the Oceania region. This study evaluates the performance of sub-daily BARRA precipitation at point and spatial scales over Australia. We find that the dataset reproduces some of the sub-daily characteristics of precipitation well, although it exhibits some spatial displacement errors, and it performs better in temperate than in tropical regions. The product is well suited to complement other estimates derived from remote sensing and rain gauges.
Suwash Chandra Acharya, Rory Nathan, Quan J. Wang, Chun-Hsu Su, and Nathan Eizenberg
Hydrol. Earth Syst. Sci., 23, 3387–3403, https://doi.org/10.5194/hess-23-3387-2019, https://doi.org/10.5194/hess-23-3387-2019, 2019
Short summary
Short summary
BARRA is a novel regional reanalysis for Australia. Our research demonstrates that it is able to characterize a rich spatial variation in daily precipitation behaviour. In addition, its ability to represent large rainfalls is valuable for the analysis of extremes. It is a useful complement to existing precipitation datasets for Australia, especially in sparsely gauged regions.
Chun-Hsu Su, Nathan Eizenberg, Peter Steinle, Dörte Jakob, Paul Fox-Hughes, Christopher J. White, Susan Rennie, Charmaine Franklin, Imtiaz Dharssi, and Hongyan Zhu
Geosci. Model Dev., 12, 2049–2068, https://doi.org/10.5194/gmd-12-2049-2019, https://doi.org/10.5194/gmd-12-2049-2019, 2019
Short summary
Short summary
The Bureau of Meteorology Atmospheric Regional Reanalysis for Australia (BARRA) is the first regional reanalysis for Australia, NZ, and SE Asia. It offers realistic depictions of near-surface meteorology at a scale required for emergency services, defence, and other major sectors such as energy and agriculture. It uses a consistent method of analysing the atmosphere, with a higher-resolution model over 1990 to 2018, and can provide greater understanding of past weather, including extreme events.
Conrad Wasko, Seth Westra, Rory Nathan, Acacia Pepler, Timothy H. Raupach, Andrew Dowdy, Fiona Johnson, Michelle Ho, Kathleen L. McInnes, Doerte Jakob, Jason Evans, Gabriele Villarini, and Hayley J. Fowler
Hydrol. Earth Syst. Sci., 28, 1251–1285, https://doi.org/10.5194/hess-28-1251-2024, https://doi.org/10.5194/hess-28-1251-2024, 2024
Short summary
Short summary
In response to flood risk, design flood estimation is a cornerstone of infrastructure design and emergency response planning, but design flood estimation guidance under climate change is still in its infancy. We perform the first published systematic review of the impact of climate change on design flood estimation and conduct a meta-analysis to provide quantitative estimates of possible future changes in extreme rainfall.
Emma Howard, Chun-Hsu Su, Christian Stassen, Rajashree Naha, Harvey Ye, Acacia Pepler, Samuel S. Bell, Andrew J. Dowdy, Simon O. Tucker, and Charmaine Franklin
Geosci. Model Dev., 17, 731–757, https://doi.org/10.5194/gmd-17-731-2024, https://doi.org/10.5194/gmd-17-731-2024, 2024
Short summary
Short summary
The BARPA-R modelling configuration has been developed to produce high-resolution climate hazard projections within the Australian region. When using boundary driving data from quasi-observed historical conditions, BARPA-R shows good performance with errors generally on par with reanalysis products. BARPA-R also captures trends, known modes of climate variability, large-scale weather processes, and multivariate relationships.
Ivana Čavlina Tomašević, Paul Fox-Hughes, Kevin Cheung, Višnjica Vučetić, Jon Marsden-Smedley, Paul Beggs, and Maja Telišman Prtenjak
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2023-210, https://doi.org/10.5194/nhess-2023-210, 2024
Manuscript not accepted for further review
Short summary
Short summary
We have analyzed a severe wildfire event in Tasmania, Australia that also developed thunderstorm clouds. The drivers of this compound hazard were highly complex, which included climatic factors (above normal heavy rain seasons followed by heatwave), weather systems (fronts and high winds) to heighten fire severity and unstable atmosphere to develop thunderstorm clouds, all in coincidence. Such event has demonstrated the difficulty to assess wildfire risk in a warming climate.
Mike Bush, Ian Boutle, John Edwards, Anke Finnenkoetter, Charmaine Franklin, Kirsty Hanley, Aravindakshan Jayakumar, Huw Lewis, Adrian Lock, Marion Mittermaier, Saji Mohandas, Rachel North, Aurore Porson, Belinda Roux, Stuart Webster, and Mark Weeks
Geosci. Model Dev., 16, 1713–1734, https://doi.org/10.5194/gmd-16-1713-2023, https://doi.org/10.5194/gmd-16-1713-2023, 2023
Short summary
Short summary
Building on the baseline of RAL1, the RAL2 science configuration is used for regional modelling around the UM partnership and in operations at the Met Office. RAL2 has been tested in different parts of the world including Australia, India and the UK. RAL2 increases medium and low cloud amounts in the mid-latitudes compared to RAL1, leading to improved cloud forecasts and a reduced diurnal cycle of screen temperature. There is also a reduction in the frequency of heavier precipitation rates.
Ivana Čavlina Tomašević, Kevin K. W. Cheung, Višnjica Vučetić, Paul Fox-Hughes, Kristian Horvath, Maja Telišman Prtenjak, Paul J. Beggs, Barbara Malečić, and Velimir Milić
Nat. Hazards Earth Syst. Sci., 22, 3143–3165, https://doi.org/10.5194/nhess-22-3143-2022, https://doi.org/10.5194/nhess-22-3143-2022, 2022
Short summary
Short summary
One of the most severe and impactful urban wildfire events in Croatian history has been reconstructed and analyzed. The study identified some important meteorological influences related to the event: the synoptic conditions of the Azores anticyclone, cold front, and upper-level shortwave trough all led to the highest fire weather index in 2017. A low-level jet, locally known as bura wind that can be explained by hydraulic jump theory, was the dynamic trigger of the event.
Enrico Tubaldi, Christopher J. White, Edoardo Patelli, Stergios Aristoteles Mitoulis, Gustavo de Almeida, Jim Brown, Michael Cranston, Martin Hardman, Eftychia Koursari, Rob Lamb, Hazel McDonald, Richard Mathews, Richard Newell, Alonso Pizarro, Marta Roca, and Daniele Zonta
Nat. Hazards Earth Syst. Sci., 22, 795–812, https://doi.org/10.5194/nhess-22-795-2022, https://doi.org/10.5194/nhess-22-795-2022, 2022
Short summary
Short summary
Bridges are critical infrastructure components of transport networks. A large number of these critical assets cross or are adjacent to waterways and are therefore exposed to the potentially devastating impact of floods. This paper discusses a series of issues and areas where improvements in research and practice are required in the context of risk assessment and management of bridges exposed to flood hazard, with the ultimate goal of guiding future efforts in improving bridge flood resilience.
Suwash Chandra Acharya, Rory Nathan, Quan J. Wang, Chun-Hsu Su, and Nathan Eizenberg
Hydrol. Earth Syst. Sci., 24, 2951–2962, https://doi.org/10.5194/hess-24-2951-2020, https://doi.org/10.5194/hess-24-2951-2020, 2020
Short summary
Short summary
BARRA is a high-resolution reanalysis dataset over the Oceania region. This study evaluates the performance of sub-daily BARRA precipitation at point and spatial scales over Australia. We find that the dataset reproduces some of the sub-daily characteristics of precipitation well, although it exhibits some spatial displacement errors, and it performs better in temperate than in tropical regions. The product is well suited to complement other estimates derived from remote sensing and rain gauges.
Mercy N. Ndalila, Grant J. Williamson, Paul Fox-Hughes, Jason Sharples, and David M. J. S. Bowman
Nat. Hazards Earth Syst. Sci., 20, 1497–1511, https://doi.org/10.5194/nhess-20-1497-2020, https://doi.org/10.5194/nhess-20-1497-2020, 2020
Short summary
Short summary
We analyse the evolution of a pyrocumulonimbus (pyroCb), or fire-induced thunderstorm, during the Forcett–Dunalley fire on 4 January 2013 and relate it to the prevailing fire weather and fire severity patterns. We show that the pyroCb reached an altitude of 15 km, was associated with elevated fire weather, and formed over a severely burned area. Additionally, we show that eastern Tasmania is prone to elevated fire weather which has implications for fire weather forecasting and fire management.
Mike Bush, Tom Allen, Caroline Bain, Ian Boutle, John Edwards, Anke Finnenkoetter, Charmaine Franklin, Kirsty Hanley, Humphrey Lean, Adrian Lock, James Manners, Marion Mittermaier, Cyril Morcrette, Rachel North, Jon Petch, Chris Short, Simon Vosper, David Walters, Stuart Webster, Mark Weeks, Jonathan Wilkinson, Nigel Wood, and Mohamed Zerroukat
Geosci. Model Dev., 13, 1999–2029, https://doi.org/10.5194/gmd-13-1999-2020, https://doi.org/10.5194/gmd-13-1999-2020, 2020
Short summary
Short summary
In this paper we define the first Regional Atmosphere and Land (RAL) science configuration for kilometre-scale modelling using the Unified Model (UM) as the basis for the atmosphere and the Joint UK Land Environment Simulator (JULES) for the land. RAL1 defines the science configuration of the dynamics and physics schemes of the atmosphere and land. This configuration will provide a model baseline for any future weather or climate model developments to be described against.
Suwash Chandra Acharya, Rory Nathan, Quan J. Wang, Chun-Hsu Su, and Nathan Eizenberg
Hydrol. Earth Syst. Sci., 23, 3387–3403, https://doi.org/10.5194/hess-23-3387-2019, https://doi.org/10.5194/hess-23-3387-2019, 2019
Short summary
Short summary
BARRA is a novel regional reanalysis for Australia. Our research demonstrates that it is able to characterize a rich spatial variation in daily precipitation behaviour. In addition, its ability to represent large rainfalls is valuable for the analysis of extremes. It is a useful complement to existing precipitation datasets for Australia, especially in sparsely gauged regions.
Chun-Hsu Su, Nathan Eizenberg, Peter Steinle, Dörte Jakob, Paul Fox-Hughes, Christopher J. White, Susan Rennie, Charmaine Franklin, Imtiaz Dharssi, and Hongyan Zhu
Geosci. Model Dev., 12, 2049–2068, https://doi.org/10.5194/gmd-12-2049-2019, https://doi.org/10.5194/gmd-12-2049-2019, 2019
Short summary
Short summary
The Bureau of Meteorology Atmospheric Regional Reanalysis for Australia (BARRA) is the first regional reanalysis for Australia, NZ, and SE Asia. It offers realistic depictions of near-surface meteorology at a scale required for emergency services, defence, and other major sectors such as energy and agriculture. It uses a consistent method of analysing the atmosphere, with a higher-resolution model over 1990 to 2018, and can provide greater understanding of past weather, including extreme events.
Simon F. B. Tett, Kuniko Yamazaki, Michael J. Mineter, Coralia Cartis, and Nathan Eizenberg
Geosci. Model Dev., 10, 3567–3589, https://doi.org/10.5194/gmd-10-3567-2017, https://doi.org/10.5194/gmd-10-3567-2017, 2017
Short summary
Short summary
The paper shows it is possible to automatically calibrate the parameters in the atmospheric component of two climate models. The resulting atmosphere–ocean models are often, but not always, stable and realistic. The computational cost to do this is feasible. The implications are that it is possible to generate multiple configurations of a single model with different parameter values but which all look similar to the standard model and that the techniques could be used to calibrate other models.
C. J. White, S. W. Franks, and D. McEvoy
Proc. IAHS, 370, 229–234, https://doi.org/10.5194/piahs-370-229-2015, https://doi.org/10.5194/piahs-370-229-2015, 2015
M. Newby, S. W. Franks, and C. J. White
Proc. IAHS, 370, 3–7, https://doi.org/10.5194/piahs-370-3-2015, https://doi.org/10.5194/piahs-370-3-2015, 2015
S. W. Franks, C. J. White, and M. Gensen
Proc. IAHS, 369, 31–36, https://doi.org/10.5194/piahs-369-31-2015, https://doi.org/10.5194/piahs-369-31-2015, 2015
Related subject area
Atmospheric sciences
Can TROPOMI NO2 satellite data be used to track the drop in and resurgence of NOx emissions in Germany between 2019–2021 using the multi-source plume method (MSPM)?
A spatiotemporally separated framework for reconstructing the sources of atmospheric radionuclide releases
A parameterization scheme for the floating wind farm in a coupled atmosphere–wave model (COAWST v3.7)
RoadSurf 1.1: open-source road weather model library
Calibrating and validating the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) urban cooling model: case studies in France and the United States
The ddeq Python library for point source quantification from remote sensing images (version 1.0)
Incorporating Oxygen Isotopes of Oxidized Reactive Nitrogen in the Regional Atmospheric Chemistry Mechanism, version 2 (ICOIN-RACM2)
A general comprehensive evaluation method for cross-scale precipitation forecasts
Implementation of a Simple Actuator Disk for Large-Eddy Simulation in the Weather Research and Forecasting Model (WRF-SADLES v1.2) for wind turbine wake simulation
WRF-PDAF v1.0: implementation and application of an online localized ensemble data assimilation framework
Implementation and evaluation of diabatic advection in the Lagrangian transport model MPTRAC 2.6
An improved and extended parameterization of the CO2 15 µm cooling in the middle and upper atmosphere (CO2_cool_fort-1.0)
Development of a multiphase chemical mechanism to improve secondary organic aerosol formation in CAABA/MECCA (version 4.7.0)
Application of regional meteorology and air quality models based on the microprocessor without interlocked piped stages (MIPS) and LoongArch CPU platforms
Investigating ground-level ozone pollution in semi-arid and arid regions of Arizona using WRF-Chem v4.4 modeling
An objective identification technique for potential vorticity structures associated with African easterly waves
Importance of microphysical settings for climate forcing by stratospheric SO2 injections as modeled by SOCOL-AERv2
Assessment of surface ozone products from downscaled CAMS reanalysis and CAMS daily forecast using urban air quality monitoring stations in Iran
Open boundary conditions for atmospheric large-eddy simulations and their implementation in DALES4.4
Efficient and stable coupling of the SuperdropNet deep-learning-based cloud microphysics (v0.1.0) with the ICON climate and weather model (v2.6.5)
Three-dimensional variational assimilation with a multivariate background error covariance for the Model for Prediction Across Scales – Atmosphere with the Joint Effort for Data assimilation Integration (JEDI-MPAS 2.0.0-beta)
FUME 2.0 – Flexible Universal processor for Modeling Emissions
DEUCE v1.0: a neural network for probabilistic precipitation nowcasting with aleatoric and epistemic uncertainties
Evaluation of multi-season convection-permitting atmosphere – mixed-layer ocean simulations of the Maritime Continent
Investigating the impact of coupling HARMONIE-WINS50 (cy43) meteorology to LOTOS-EUROS (v2.2.002) on a simulation of NO2 concentrations over the Netherlands
Balloon drift estimation and improved position estimates for radiosondes
Emission ensemble approach to improve the development of multi-scale emission inventories
What is the relative impact of nudging and online coupling on meteorological variables, pollutant concentrations and aerosol optical properties?
Diagnosing drivers of PM2.5 simulation biases in China from meteorology, chemical composition, and emission sources using an efficient machine learning method
Validation and analysis of the Polair3D v1.11 chemical transport model over Quebec
Assimilation of GNSS tropospheric gradients into the Weather Research and Forecasting (WRF) model version 4.4.1
Identifying atmospheric rivers and their poleward latent heat transport with generalizable neural networks: ARCNNv1
Assessing acetone for the GISS ModelE2.1 Earth system model
Bergen metrics: composite error metrics for assessing performance of climate models using EURO-CORDEX simulations
A dynamic approach to three-dimensional radiative transfer in subkilometer-scale numerical weather prediction models: the dynamic TenStream solver v1.0
Evaluation and development of surface layer scheme representation of temperature inversions over boreal forests in Arctic wintertime conditions
Modelling wind farm effects in HARMONIE–AROME (cycle 43.2.2) – Part 1: Implementation and evaluation
Analytical and adaptable initial conditions for dry and moist baroclinic waves in the global hydrostatic model OpenIFS (CY43R3)
Challenges of constructing and selecting the “perfect” boundary conditions for the large-eddy simulation model PALM
A machine learning approach for evaluating Southern Ocean cloud radiative biases in a global atmosphere model
Decision Support System version 1.0 (DSS v1.0) for air quality management in Delhi, India
How non-equilibrium aerosol chemistry impacts particle acidity: the GMXe AERosol CHEMistry (GMXe–AERCHEM, v1.0) sub-submodel of MESSy
A grid model for vertical correction of precipitable water vapor over the Chinese mainland and surrounding areas using random forest
MEXPLORER 1.0.0 – a mechanism explorer for analysis and visualization of chemical reaction pathways based on graph theory
Evaluating CHASER V4.0 global formaldehyde (HCHO) simulations using satellite, aircraft, and ground-based remote sensing observations
Advances and prospects of deep learning for medium-range extreme weather forecasting
An overview of the Western United States Dynamically Downscaled Dataset (WUS-D3)
cloudbandPy 1.0: an automated algorithm for the detection of tropical–extratropical cloud bands
PyRTlib: an educational Python-based library for non-scattering atmospheric microwave radiative transfer computations
TAMS: A Tracking, Classifying, and Variable-Assigning Algorithm for Mesoscale Convective Systems in Simulated and Satellite-Derived Datasets
Enrico Dammers, Janot Tokaya, Christian Mielke, Kevin Hausmann, Debora Griffin, Chris McLinden, Henk Eskes, and Renske Timmermans
Geosci. Model Dev., 17, 4983–5007, https://doi.org/10.5194/gmd-17-4983-2024, https://doi.org/10.5194/gmd-17-4983-2024, 2024
Short summary
Short summary
Nitrogen dioxide (NOx) is produced by sources such as industry and traffic and is directly linked to negative impacts on health and the environment. The current construction of emission inventories to keep track of NOx emissions is slow and time-consuming. Satellite measurements provide a way to quickly and independently estimate emissions. In this study, we apply a consistent methodology to derive NOx emissions over Germany and illustrate the value of having such a method for fast projections.
Yuhan Xu, Sheng Fang, Xinwen Dong, and Shuhan Zhuang
Geosci. Model Dev., 17, 4961–4982, https://doi.org/10.5194/gmd-17-4961-2024, https://doi.org/10.5194/gmd-17-4961-2024, 2024
Short summary
Short summary
Recent atmospheric radionuclide leakages from unknown sources have posed a new challenge in nuclear emergency assessment. Reconstruction via environmental observations is the only feasible way to identify sources, but simultaneous reconstruction of the source location and release rate yields high uncertainties. We propose a spatiotemporally separated reconstruction strategy that avoids these uncertainties and outperforms state-of-the-art methods with respect to accuracy and uncertainty ranges.
Shaokun Deng, Shengmu Yang, Shengli Chen, Daoyi Chen, Xuefeng Yang, and Shanshan Cui
Geosci. Model Dev., 17, 4891–4909, https://doi.org/10.5194/gmd-17-4891-2024, https://doi.org/10.5194/gmd-17-4891-2024, 2024
Short summary
Short summary
Global offshore wind power development is moving from offshore to deeper waters, where floating offshore wind turbines have an advantage over bottom-fixed turbines. However, current wind farm parameterization schemes in mesoscale models are not applicable to floating turbines. We propose a floating wind farm parameterization scheme that accounts for the attenuation of the significant wave height by floating turbines. The results indicate that it has a significant effect on the power output.
Virve Eveliina Karsisto
Geosci. Model Dev., 17, 4837–4853, https://doi.org/10.5194/gmd-17-4837-2024, https://doi.org/10.5194/gmd-17-4837-2024, 2024
Short summary
Short summary
RoadSurf is an open-source library that contains functions from the Finnish Meteorological Institute’s road weather model. The evaluation of the library shows that it is well suited for making road surface temperature forecasts. The evaluation was done by making forecasts for about 400 road weather stations in Finland with the library. Accurate forecasts help road authorities perform salting and plowing operations at the right time and keep roads safe for drivers.
Perrine Hamel, Martí Bosch, Léa Tardieu, Aude Lemonsu, Cécile de Munck, Chris Nootenboom, Vincent Viguié, Eric Lonsdorf, James A. Douglass, and Richard P. Sharp
Geosci. Model Dev., 17, 4755–4771, https://doi.org/10.5194/gmd-17-4755-2024, https://doi.org/10.5194/gmd-17-4755-2024, 2024
Short summary
Short summary
The InVEST Urban Cooling model estimates the cooling effect of vegetation in cities. We further developed an algorithm to facilitate model calibration and evaluation. Applying the algorithm to case studies in France and in the United States, we found that nighttime air temperature estimates compare well with reference datasets. Estimated change in temperature from a land cover scenario compares well with an alternative model estimate, supporting the use of the model for urban planning decisions.
Gerrit Kuhlmann, Erik Koene, Sandro Meier, Diego Santaren, Grégoire Broquet, Frédéric Chevallier, Janne Hakkarainen, Janne Nurmela, Laia Amorós, Johanna Tamminen, and Dominik Brunner
Geosci. Model Dev., 17, 4773–4789, https://doi.org/10.5194/gmd-17-4773-2024, https://doi.org/10.5194/gmd-17-4773-2024, 2024
Short summary
Short summary
We present a Python software library for data-driven emission quantification (ddeq). It can be used to determine the emissions of hot spots (cities, power plants and industry) from remote sensing images using different methods. ddeq can be extended for new datasets and methods, providing a powerful community tool for users and developers. The application of the methods is shown using Jupyter notebooks included in the library.
Wendell W. Walters, Masayuki Takeuchi, Nga L. Ng, and Meredith G. Hastings
Geosci. Model Dev., 17, 4673–4687, https://doi.org/10.5194/gmd-17-4673-2024, https://doi.org/10.5194/gmd-17-4673-2024, 2024
Short summary
Short summary
The study introduces a novel chemical mechanism for explicitly tracking oxygen isotope transfer in oxidized reactive nitrogen and odd oxygen using the Regional Atmospheric Chemistry Mechanism, version 2. This model enhances our ability to simulate and compare oxygen isotope compositions of reactive nitrogen, revealing insights into oxidation chemistry. The approach shows promise for improving atmospheric chemistry models and tropospheric oxidation capacity predictions.
Bing Zhang, Mingjian Zeng, Anning Huang, Zhengkun Qin, Couhua Liu, Wenru Shi, Xin Li, Kefeng Zhu, Chunlei Gu, and Jialing Zhou
Geosci. Model Dev., 17, 4579–4601, https://doi.org/10.5194/gmd-17-4579-2024, https://doi.org/10.5194/gmd-17-4579-2024, 2024
Short summary
Short summary
By directly analyzing the proximity of precipitation forecasts and observations, a precipitation accuracy score (PAS) method was constructed. This method does not utilize a traditional contingency-table-based classification verification; however, it can replace the threat score (TS), equitable threat score (ETS), and other skill score methods, and it can be used to calculate the accuracy of numerical models or quantitative precipitation forecasts.
Hai Bui, Mostafa Bakhoday-Paskyabi, and Mohammadreza Mohammadpour-Penchah
Geosci. Model Dev., 17, 4447–4465, https://doi.org/10.5194/gmd-17-4447-2024, https://doi.org/10.5194/gmd-17-4447-2024, 2024
Short summary
Short summary
We developed a new wind turbine wake model, the Simple Actuator Disc for Large Eddy Simulation (SADLES), integrated with the widely used Weather Research and Forecasting (WRF) model. WRF-SADLES accurately simulates wind turbine wakes at resolutions of a few dozen meters, aligning well with idealized simulations and observational measurements. This makes WRF-SADLES a promising tool for wind energy research, offering a balance between accuracy, computational efficiency, and ease of implementation.
Changliang Shao and Lars Nerger
Geosci. Model Dev., 17, 4433–4445, https://doi.org/10.5194/gmd-17-4433-2024, https://doi.org/10.5194/gmd-17-4433-2024, 2024
Short summary
Short summary
This paper introduces and evaluates WRF-PDAF, a fully online-coupled ensemble data assimilation (DA) system. A key advantage of the WRF-PDAF configuration is its ability to concurrently integrate all ensemble states, eliminating the need for time-consuming distribution and collection of ensembles during the coupling communication. The extra time required for DA amounts to only 20.6 % per cycle. Twin experiment results underscore the effectiveness of the WRF-PDAF system.
Jan Clemens, Lars Hoffmann, Bärbel Vogel, Sabine Grießbach, and Nicole Thomas
Geosci. Model Dev., 17, 4467–4493, https://doi.org/10.5194/gmd-17-4467-2024, https://doi.org/10.5194/gmd-17-4467-2024, 2024
Short summary
Short summary
Lagrangian transport models simulate the transport of air masses in the atmosphere. For example, one model (CLaMS) is well suited to calculating transport as it uses a special coordinate system and special vertical wind. However, it only runs inefficiently on modern supercomputers. Hence, we have implemented the benefits of CLaMS into a new model (MPTRAC), which is already highly efficient on modern supercomputers. Finally, in extensive tests, we showed that CLaMS and MPTRAC agree very well.
Manuel López-Puertas, Federico Fabiano, Victor Fomichev, Bernd Funke, and Daniel R. Marsh
Geosci. Model Dev., 17, 4401–4432, https://doi.org/10.5194/gmd-17-4401-2024, https://doi.org/10.5194/gmd-17-4401-2024, 2024
Short summary
Short summary
The radiative infrared cooling of CO2 in the middle atmosphere is crucial for computing its thermal structure. It requires one however to include non-local thermodynamic equilibrium processes which are computationally very expensive, which cannot be afforded by climate models. In this work, we present an updated, efficient, accurate and very fast (~50 µs) parameterization of that cooling able to cope with CO2 abundances from half the pre-industrial values to 10 times the current abundance.
Felix Wieser, Rolf Sander, Changmin Cho, Hendrik Fuchs, Thorsten Hohaus, Anna Novelli, Ralf Tillmann, and Domenico Taraborrelli
Geosci. Model Dev., 17, 4311–4330, https://doi.org/10.5194/gmd-17-4311-2024, https://doi.org/10.5194/gmd-17-4311-2024, 2024
Short summary
Short summary
The chemistry scheme of the atmospheric box model CAABA/MECCA is expanded to achieve an improved aerosol formation from emitted organic compounds. In addition to newly added reactions, temperature-dependent partitioning of all new species between the gas and aqueous phases is estimated and included in the pre-existing scheme. Sensitivity runs show an overestimation of key compounds from isoprene, which can be explained by a lack of aqueous-phase degradation reactions and box model limitations.
Zehua Bai, Qizhong Wu, Kai Cao, Yiming Sun, and Huaqiong Cheng
Geosci. Model Dev., 17, 4383–4399, https://doi.org/10.5194/gmd-17-4383-2024, https://doi.org/10.5194/gmd-17-4383-2024, 2024
Short summary
Short summary
There is relatively limited research on the application of scientific computing on RISC CPU platforms. The MIPS architecture CPUs, a type of RISC CPUs, have distinct advantages in energy efficiency and scalability. The air quality modeling system can run stably on the MIPS and LoongArch platforms, and the experiment results verify the stability of scientific computing on the platforms. The work provides a technical foundation for the scientific application based on MIPS and LoongArch.
Yafang Guo, Chayan Roychoudhury, Mohammad Amin Mirrezaei, Rajesh Kumar, Armin Sorooshian, and Avelino F. Arellano
Geosci. Model Dev., 17, 4331–4353, https://doi.org/10.5194/gmd-17-4331-2024, https://doi.org/10.5194/gmd-17-4331-2024, 2024
Short summary
Short summary
This research focuses on surface ozone (O3) pollution in Arizona, a historically air-quality-challenged arid and semi-arid region in the US. The unique characteristics of this kind of region, e.g., intense heat, minimal moisture, and persistent desert shrubs, play a vital role in comprehending O3 exceedances. Using the WRF-Chem model, we analyzed O3 levels in the pre-monsoon month, revealing the model's skill in capturing diurnal and MDA8 O3 levels.
Christoph Fischer, Andreas H. Fink, Elmar Schömer, Marc Rautenhaus, and Michael Riemer
Geosci. Model Dev., 17, 4213–4228, https://doi.org/10.5194/gmd-17-4213-2024, https://doi.org/10.5194/gmd-17-4213-2024, 2024
Short summary
Short summary
This study presents a method for identifying and tracking 3-D potential vorticity structures within African easterly waves (AEWs). Each identified structure is characterized by descriptors, including its 3-D position and orientation, which have been validated through composite comparisons. A trough-centric perspective on the descriptors reveals the evolution and distinct characteristics of AEWs. These descriptors serve as valuable statistical inputs for the study of AEW-related phenomena.
Sandro Vattioni, Andrea Stenke, Beiping Luo, Gabriel Chiodo, Timofei Sukhodolov, Elia Wunderlin, and Thomas Peter
Geosci. Model Dev., 17, 4181–4197, https://doi.org/10.5194/gmd-17-4181-2024, https://doi.org/10.5194/gmd-17-4181-2024, 2024
Short summary
Short summary
We investigate the sensitivity of aerosol size distributions in the presence of strong SO2 injections for climate interventions or after volcanic eruptions to the call sequence and frequency of the routines for nucleation and condensation in sectional aerosol models with operator splitting. Using the aerosol–chemistry–climate model SOCOL-AERv2, we show that the radiative and chemical outputs are sensitive to these settings at high H2SO4 supersaturations and how to obtain reliable results.
Najmeh Kaffashzadeh and Abbas-Ali Aliakbari Bidokhti
Geosci. Model Dev., 17, 4155–4179, https://doi.org/10.5194/gmd-17-4155-2024, https://doi.org/10.5194/gmd-17-4155-2024, 2024
Short summary
Short summary
This paper assesses the capability of two state-of-the-art global datasets in simulating surface ozone over Iran using a new methodology. It is found that the global model data need to be downscaled for regulatory purposes or policy applications at local scales. The method can be useful not only for the evaluation but also for the prediction of other chemical species, such as aerosols.
Franciscus Liqui Lung, Christian Jakob, A. Pier Siebesma, and Fredrik Jansson
Geosci. Model Dev., 17, 4053–4076, https://doi.org/10.5194/gmd-17-4053-2024, https://doi.org/10.5194/gmd-17-4053-2024, 2024
Short summary
Short summary
Traditionally, high-resolution atmospheric models employ periodic boundary conditions, which limit simulations to domains without horizontal variations. In this research open boundary conditions are developed to replace the periodic boundary conditions. The implementation is tested in a controlled setup, and the results show minimal disturbances. Using these boundary conditions, high-resolution models can be forced by a coarser model to study atmospheric phenomena in realistic background states.
Caroline Arnold, Shivani Sharma, Tobias Weigel, and David S. Greenberg
Geosci. Model Dev., 17, 4017–4029, https://doi.org/10.5194/gmd-17-4017-2024, https://doi.org/10.5194/gmd-17-4017-2024, 2024
Short summary
Short summary
In atmospheric models, rain formation is simplified to be computationally efficient. We trained a machine learning model, SuperdropNet, to emulate warm-rain formation based on super-droplet simulations. Here, we couple SuperdropNet with an atmospheric model in a warm-bubble experiment and find that the coupled simulation runs stable and produces reasonable results, making SuperdropNet a viable ML proxy for droplet simulations. We also present a comprehensive benchmark for coupling architectures.
Byoung-Joo Jung, Benjamin Ménétrier, Chris Snyder, Zhiquan Liu, Jonathan J. Guerrette, Junmei Ban, Ivette Hernández Baños, Yonggang G. Yu, and William C. Skamarock
Geosci. Model Dev., 17, 3879–3895, https://doi.org/10.5194/gmd-17-3879-2024, https://doi.org/10.5194/gmd-17-3879-2024, 2024
Short summary
Short summary
We describe the multivariate static background error covariance (B) for the JEDI-MPAS 3D-Var data assimilation system. With tuned B parameters, the multivariate B gives physically balanced analysis increment fields in the single-observation test framework. In the month-long cycling experiment with a global 60 km mesh, 3D-Var with static B performs stably. Due to its simple workflow and minimal computational requirements, JEDI-MPAS 3D-Var can be useful for the research community.
Michal Belda, Nina Benešová, Jaroslav Resler, Peter Huszár, Ondřej Vlček, Pavel Krč, Jan Karlický, Pavel Juruš, and Kryštof Eben
Geosci. Model Dev., 17, 3867–3878, https://doi.org/10.5194/gmd-17-3867-2024, https://doi.org/10.5194/gmd-17-3867-2024, 2024
Short summary
Short summary
For modeling atmospheric chemistry, it is necessary to provide data on emissions of pollutants. These can come from various sources and in various forms, and preprocessing of the data to be ingestible by chemistry models can be quite challenging. We developed the FUME processor to use a database layer that internally transforms all input data into a rigid structure, facilitating further processing to allow for emission processing from the continental to the street scale.
Bent Harnist, Seppo Pulkkinen, and Terhi Mäkinen
Geosci. Model Dev., 17, 3839–3866, https://doi.org/10.5194/gmd-17-3839-2024, https://doi.org/10.5194/gmd-17-3839-2024, 2024
Short summary
Short summary
Probabilistic precipitation nowcasting (local forecasting for 0–6 h) is crucial for reducing damage from events like flash floods. For this goal, we propose the DEUCE neural-network-based model which uses data and model uncertainties to generate an ensemble of potential precipitation development scenarios for the next hour. Trained and evaluated with Finnish precipitation composites, DEUCE was found to produce more skillful and reliable nowcasts than established models.
Emma Howard, Steven Woolnough, Nicholas Klingaman, Daniel Shipley, Claudio Sanchez, Simon C. Peatman, Cathryn E. Birch, and Adrian J. Matthews
Geosci. Model Dev., 17, 3815–3837, https://doi.org/10.5194/gmd-17-3815-2024, https://doi.org/10.5194/gmd-17-3815-2024, 2024
Short summary
Short summary
This paper describes a coupled atmosphere–mixed-layer ocean simulation setup that will be used to study weather processes in Southeast Asia. The set-up has been used to compare high-resolution simulations, which are able to partially resolve storms, to coarser simulations, which cannot. We compare the model performance at representing variability of rainfall and sea surface temperatures across length scales between the coarse and fine models.
Andrés Yarce Botero, Michiel van Weele, Arjo Segers, Pier Siebesma, and Henk Eskes
Geosci. Model Dev., 17, 3765–3781, https://doi.org/10.5194/gmd-17-3765-2024, https://doi.org/10.5194/gmd-17-3765-2024, 2024
Short summary
Short summary
HARMONIE WINS50 reanalysis data with 0.025° × 0.025° resolution from 2019 to 2021 were coupled with the LOTOS-EUROS Chemical Transport Model. HARMONIE and ECMWF meteorology configurations against Cabauw observations (52.0° N, 4.9° W) were evaluated as simulated NO2 concentrations with ground-level sensors. Differences in crucial meteorological input parameters (boundary layer height, vertical diffusion coefficient) between the hydrostatic and non-hydrostatic models were analysed.
Ulrich Voggenberger, Leopold Haimberger, Federico Ambrogi, and Paul Poli
Geosci. Model Dev., 17, 3783–3799, https://doi.org/10.5194/gmd-17-3783-2024, https://doi.org/10.5194/gmd-17-3783-2024, 2024
Short summary
Short summary
This paper presents a method for calculating balloon drift from historical radiosonde ascent data. The drift can reach distances of several hundred kilometres and is often neglected. Verification shows the beneficial impact of the more accurate balloon position on model assimilation. The method is not limited to radiosondes but would also work for dropsondes, ozonesondes, or any other in situ sonde carried by the wind in the pre-GNSS era, provided the necessary information is available.
Philippe Thunis, Jeroen Kuenen, Enrico Pisoni, Bertrand Bessagnet, Manjola Banja, Lech Gawuc, Karol Szymankiewicz, Diego Guizardi, Monica Crippa, Susana Lopez-Aparicio, Marc Guevara, Alexander De Meij, Sabine Schindlbacher, and Alain Clappier
Geosci. Model Dev., 17, 3631–3643, https://doi.org/10.5194/gmd-17-3631-2024, https://doi.org/10.5194/gmd-17-3631-2024, 2024
Short summary
Short summary
An ensemble emission inventory is created with the aim of monitoring the status and progress made with the development of EU-wide inventories. This emission ensemble serves as a common benchmark for the screening and allows for the comparison of more than two inventories at a time. Because the emission “truth” is unknown, the approach does not tell which inventory is the closest to reality, but it identifies inconsistencies that require special attention.
Laurent Menut, Bertrand Bessagnet, Arineh Cholakian, Guillaume Siour, Sylvain Mailler, and Romain Pennel
Geosci. Model Dev., 17, 3645–3665, https://doi.org/10.5194/gmd-17-3645-2024, https://doi.org/10.5194/gmd-17-3645-2024, 2024
Short summary
Short summary
This study is about the modelling of the atmospheric composition in Europe during the summer of 2022, when massive wildfires were observed. It is a sensitivity study dedicated to the relative impacts of two modelling processes that are able to modify the meteorology used for the calculation of the atmospheric chemistry and transport of pollutants.
Shuai Wang, Mengyuan Zhang, Yueqi Gao, Peng Wang, Qingyan Fu, and Hongliang Zhang
Geosci. Model Dev., 17, 3617–3629, https://doi.org/10.5194/gmd-17-3617-2024, https://doi.org/10.5194/gmd-17-3617-2024, 2024
Short summary
Short summary
Numerical models are widely used in air pollution modeling but suffer from significant biases. The machine learning model designed in this study shows high efficiency in identifying such biases. Meteorology (relative humidity and cloud cover), chemical composition (secondary organic components and dust aerosols), and emission sources (residential activities) are diagnosed as the main drivers of bias in modeling PM2.5, a typical air pollutant. The results will help to improve numerical models.
Shoma Yamanouchi, Shayamilla Mahagammulla Gamage, Sara Torbatian, Jad Zalzal, Laura Minet, Audrey Smargiassi, Ying Liu, Ling Liu, Forood Azargoshasbi, Jinwoong Kim, Youngseob Kim, Daniel Yazgi, and Marianne Hatzopoulou
Geosci. Model Dev., 17, 3579–3597, https://doi.org/10.5194/gmd-17-3579-2024, https://doi.org/10.5194/gmd-17-3579-2024, 2024
Short summary
Short summary
Air pollution is a major health hazard, and chemical transport models (CTMs) are valuable tools that aid in our understanding of the risks of air pollution at both local and regional scales. In this study, the Polair3D CTM of the Polyphemus air quality modeling platform was set up over Quebec, Canada, to assess the model’s capability in predicting key air pollutant species over the region, at seasonal temporal scales and at regional spatial scales.
Rohith Thundathil, Florian Zus, Galina Dick, and Jens Wickert
Geosci. Model Dev., 17, 3599–3616, https://doi.org/10.5194/gmd-17-3599-2024, https://doi.org/10.5194/gmd-17-3599-2024, 2024
Short summary
Short summary
Global Navigation Satellite Systems (GNSS) provides moisture observations through its densely distributed ground station network. In this research, we assimilate a new type of observation called tropospheric gradient observations, which has never been incorporated into a weather model. We develop a forward operator for gradient-based observations and conduct an assimilation impact study. The study shows significant improvements in the model's humidity fields.
Ankur Mahesh, Travis A. O'Brien, Burlen Loring, Abdelrahman Elbashandy, William Boos, and William D. Collins
Geosci. Model Dev., 17, 3533–3557, https://doi.org/10.5194/gmd-17-3533-2024, https://doi.org/10.5194/gmd-17-3533-2024, 2024
Short summary
Short summary
Atmospheric rivers (ARs) are extreme weather events that can alleviate drought or cause billions of US dollars in flood damage. We train convolutional neural networks (CNNs) to detect ARs with an estimate of the uncertainty. We present a framework to generalize these CNNs to a variety of datasets of past, present, and future climate. Using a simplified simulation of the Earth's atmosphere, we validate the CNNs. We explore the role of ARs in maintaining energy balance in the Earth system.
Alexandra Rivera, Kostas Tsigaridis, Gregory Faluvegi, and Drew Shindell
Geosci. Model Dev., 17, 3487–3505, https://doi.org/10.5194/gmd-17-3487-2024, https://doi.org/10.5194/gmd-17-3487-2024, 2024
Short summary
Short summary
This paper describes and evaluates an improvement to the representation of acetone in the GISS ModelE2.1 Earth system model. We simulate acetone's concentration and transport across the atmosphere as well as its dependence on chemistry, the ocean, and various global emissions. Comparisons of our model’s estimates to past modeling studies and field measurements have shown encouraging results. Ultimately, this paper contributes to a broader understanding of acetone's role in the atmosphere.
Alok K. Samantaray, Priscilla A. Mooney, and Carla A. Vivacqua
Geosci. Model Dev., 17, 3321–3339, https://doi.org/10.5194/gmd-17-3321-2024, https://doi.org/10.5194/gmd-17-3321-2024, 2024
Short summary
Short summary
Any interpretation of climate model data requires a comprehensive evaluation of the model performance. Numerous error metrics exist for this purpose, and each focuses on a specific aspect of the relationship between reference and model data. Thus, a comprehensive evaluation demands the use of multiple error metrics. However, this can lead to confusion. We propose a clustering technique to reduce the number of error metrics needed and a composite error metric to simplify the interpretation.
Richard Maier, Fabian Jakub, Claudia Emde, Mihail Manev, Aiko Voigt, and Bernhard Mayer
Geosci. Model Dev., 17, 3357–3383, https://doi.org/10.5194/gmd-17-3357-2024, https://doi.org/10.5194/gmd-17-3357-2024, 2024
Short summary
Short summary
Based on the TenStream solver, we present a new method to accelerate 3D radiative transfer towards the speed of currently used 1D solvers. Using a shallow-cumulus-cloud time series, we evaluate the performance of this new solver in terms of both speed and accuracy. Compared to a 3D benchmark simulation, we show that our new solver is able to determine much more accurate irradiances and heating rates than a 1D δ-Eddington solver, even when operated with a similar computational demand.
Julia Maillard, Jean-Christophe Raut, and François Ravetta
Geosci. Model Dev., 17, 3303–3320, https://doi.org/10.5194/gmd-17-3303-2024, https://doi.org/10.5194/gmd-17-3303-2024, 2024
Short summary
Short summary
Atmospheric models struggle to reproduce the strong temperature inversions in the vicinity of the surface over forested areas in the Arctic winter. In this paper, we develop modified simplified versions of surface layer schemes widely used by the community. Our modifications are used to correct the fact that original schemes place strong limits on the turbulent collapse, leading to a lower surface temperature gradient at low wind speeds. Modified versions show a better performance.
Jana Fischereit, Henrik Vedel, Xiaoli Guo Larsén, Natalie E. Theeuwes, Gregor Giebel, and Eigil Kaas
Geosci. Model Dev., 17, 2855–2875, https://doi.org/10.5194/gmd-17-2855-2024, https://doi.org/10.5194/gmd-17-2855-2024, 2024
Short summary
Short summary
Wind farms impact local wind and turbulence. To incorporate these effects in weather forecasting, the explicit wake parameterization (EWP) is added to the forecasting model HARMONIE–AROME. We evaluate EWP using flight data above and downstream of wind farms, comparing it with an alternative wind farm parameterization and another weather model. Results affirm the correct implementation of EWP, emphasizing the necessity of accounting for wind farm effects in accurate weather forecasting.
Clément Bouvier, Daan van den Broek, Madeleine Ekblom, and Victoria A. Sinclair
Geosci. Model Dev., 17, 2961–2986, https://doi.org/10.5194/gmd-17-2961-2024, https://doi.org/10.5194/gmd-17-2961-2024, 2024
Short summary
Short summary
An analytical initial background state has been developed for moist baroclinic wave simulation on an aquaplanet and implemented into OpenIFS. Seven parameters can be controlled, which are used to generate the background states and the development of baroclinic waves. The meteorological and numerical stability has been assessed. Resulting baroclinic waves have proven to be realistic and sensitive to the jet's width.
Jelena Radović, Michal Belda, Jaroslav Resler, Kryštof Eben, Martin Bureš, Jan Geletič, Pavel Krč, Hynek Řezníček, and Vladimír Fuka
Geosci. Model Dev., 17, 2901–2927, https://doi.org/10.5194/gmd-17-2901-2024, https://doi.org/10.5194/gmd-17-2901-2024, 2024
Short summary
Short summary
Boundary conditions are of crucial importance for numerical model (e.g., PALM) validation studies and have a large influence on the model results, especially when studying the atmosphere of real, complex, and densely built urban environments. Our experiments with different driving conditions for the large-eddy simulation model PALM show its strong dependency on boundary conditions, which is important for the proper separation of errors coming from the boundary conditions and the model itself.
Sonya L. Fiddes, Marc D. Mallet, Alain Protat, Matthew T. Woodhouse, Simon P. Alexander, and Kalli Furtado
Geosci. Model Dev., 17, 2641–2662, https://doi.org/10.5194/gmd-17-2641-2024, https://doi.org/10.5194/gmd-17-2641-2024, 2024
Short summary
Short summary
In this study we present an evaluation that considers complex, non-linear systems in a holistic manner. This study uses XGBoost, a machine learning algorithm, to predict the simulated Southern Ocean shortwave radiation bias in the ACCESS model using cloud property biases as predictors. We then used a novel feature importance analysis to quantify the role that each cloud bias plays in predicting the radiative bias, laying the foundation for advanced Earth system model evaluation and development.
Gaurav Govardhan, Sachin D. Ghude, Rajesh Kumar, Sumit Sharma, Preeti Gunwani, Chinmay Jena, Prafull Yadav, Shubhangi Ingle, Sreyashi Debnath, Pooja Pawar, Prodip Acharja, Rajmal Jat, Gayatry Kalita, Rupal Ambulkar, Santosh Kulkarni, Akshara Kaginalkar, Vijay K. Soni, Ravi S. Nanjundiah, and Madhavan Rajeevan
Geosci. Model Dev., 17, 2617–2640, https://doi.org/10.5194/gmd-17-2617-2024, https://doi.org/10.5194/gmd-17-2617-2024, 2024
Short summary
Short summary
A newly developed air quality forecasting framework, Decision Support System (DSS), for air quality management in Delhi, India, provides source attribution with numerous emission reduction scenarios besides forecasts. DSS shows that during post-monsoon and winter seasons, Delhi and its neighboring districts contribute to 30 %–40 % each to pollution in Delhi. On average, a 40 % reduction in the emissions in Delhi and the surrounding districts would result in a 24 % reduction in Delhi's pollution.
Simon Rosanka, Holger Tost, Rolf Sander, Patrick Jöckel, Astrid Kerkweg, and Domenico Taraborrelli
Geosci. Model Dev., 17, 2597–2615, https://doi.org/10.5194/gmd-17-2597-2024, https://doi.org/10.5194/gmd-17-2597-2024, 2024
Short summary
Short summary
The capabilities of the Modular Earth Submodel System (MESSy) are extended to account for non-equilibrium aqueous-phase chemistry in the representation of deliquescent aerosols. When applying the new development in a global simulation, we find that MESSy's bias in modelling routinely observed reduced inorganic aerosol mass concentrations, especially in the United States. Furthermore, the representation of fine-aerosol pH is particularly improved in the marine boundary layer.
Junyu Li, Yuxin Wang, Lilong Liu, Yibin Yao, Liangke Huang, and Feijuan Li
Geosci. Model Dev., 17, 2569–2581, https://doi.org/10.5194/gmd-17-2569-2024, https://doi.org/10.5194/gmd-17-2569-2024, 2024
Short summary
Short summary
In this study, we have developed a model (RF-PWV) to characterize precipitable water vapor (PWV) variation with altitude in the study area. RF-PWV can significantly reduce errors in vertical correction, enhance PWV fusion product accuracy, and provide insights into PWV vertical distribution, thereby contributing to climate research.
Rolf Sander
Geosci. Model Dev., 17, 2419–2425, https://doi.org/10.5194/gmd-17-2419-2024, https://doi.org/10.5194/gmd-17-2419-2024, 2024
Short summary
Short summary
The open-source software MEXPLORER 1.0.0 is presented here. The program can be used to analyze, reduce, and visualize complex chemical reaction mechanisms. The mathematics behind the tool is based on graph theory: chemical species are represented as vertices, and reactions as edges. MEXPLORER is a community model published under the GNU General Public License.
Hossain Mohammed Syedul Hoque, Kengo Sudo, Hitoshi Irie, Yanfeng He, and Md Firoz Khan
EGUsphere, https://doi.org/10.22541/essoar.169903618.82717612/v2, https://doi.org/10.22541/essoar.169903618.82717612/v2, 2024
Short summary
Short summary
Using multi-platform observations, we validated global formaldehyde (HCHO) simulations from a chemistry transport model. HCHO is a crucial intermediate of the chemical catalytic cycle that governs the ozone formation in the troposphere. The model was capable of replicating the observed spatiotemporal variability in HCHO. In a few cases, the model capability was limited. This is attributed to the uncertainties in the observations and the model parameters.
Leonardo Olivetti and Gabriele Messori
Geosci. Model Dev., 17, 2347–2358, https://doi.org/10.5194/gmd-17-2347-2024, https://doi.org/10.5194/gmd-17-2347-2024, 2024
Short summary
Short summary
In the last decades, weather forecasting up to 15 d into the future has been dominated by physics-based numerical models. Recently, deep learning models have challenged this paradigm. However, the latter models may struggle when forecasting weather extremes. In this article, we argue for deep learning models specifically designed to handle extreme events, and we propose a foundational framework to develop such models.
Stefan Rahimi, Lei Huang, Jesse Norris, Alex Hall, Naomi Goldenson, Will Krantz, Benjamin Bass, Chad Thackeray, Henry Lin, Di Chen, Eli Dennis, Ethan Collins, Zachary J. Lebo, Emily Slinskey, Sara Graves, Surabhi Biyani, Bowen Wang, Stephen Cropper, and the UCLA Center for Climate Science Team
Geosci. Model Dev., 17, 2265–2286, https://doi.org/10.5194/gmd-17-2265-2024, https://doi.org/10.5194/gmd-17-2265-2024, 2024
Short summary
Short summary
Here, we project future climate across the western United States through the end of the 21st century using a regional climate model, embedded within 16 latest-generation global climate models, to provide the community with a high-resolution physically based ensemble of climate data for use at local scales. Strengths and weaknesses of the data are frankly discussed as we overview the downscaled dataset.
Romain Pilon and Daniela I. V. Domeisen
Geosci. Model Dev., 17, 2247–2264, https://doi.org/10.5194/gmd-17-2247-2024, https://doi.org/10.5194/gmd-17-2247-2024, 2024
Short summary
Short summary
This paper introduces a new method for detecting atmospheric cloud bands to identify long convective cloud bands that extend from the tropics to the midlatitudes. The algorithm allows for easy use and enables researchers to study the life cycle and climatology of cloud bands and associated rainfall. This method provides insights into the large-scale processes involved in cloud band formation and their connections between different regions, as well as differences across ocean basins.
Salvatore Larosa, Domenico Cimini, Donatello Gallucci, Saverio Teodosio Nilo, and Filomena Romano
Geosci. Model Dev., 17, 2053–2076, https://doi.org/10.5194/gmd-17-2053-2024, https://doi.org/10.5194/gmd-17-2053-2024, 2024
Short summary
Short summary
PyRTlib is an attractive educational tool because it provides a flexible and user-friendly way to broadly simulate how electromagnetic radiation travels through the atmosphere as it interacts with atmospheric constituents (such as gases, aerosols, and hydrometeors). PyRTlib is a so-called radiative transfer model; these are commonly used to simulate and understand remote sensing observations from ground-based, airborne, or satellite instruments.
Kelly M. Núñez Ocasio and Zachary L. Moon
EGUsphere, https://doi.org/10.5194/egusphere-2024-259, https://doi.org/10.5194/egusphere-2024-259, 2024
Short summary
Short summary
TAMS is an open-source mesoscale convective system tracking and classifying Python-based package that can be used to study observed and simulated systems. Each step of the algorithm is described in this paper with examples showing how to make use of visualization and post-processing tools within the package. A unique and valuable feature of this tracker is its support for unstructured grids in the identification stage and grid-independent tracking.
Cited articles
Acharya, S. C., Nathan, R., Wang, Q. J., Su, C.-H., and Eizenberg, N.: Ability of an Australian reanalysis dataset to characterise sub-daily precipitation, Hydrol. Earth Syst. Sci., 24, 2951–2962, https://doi.org/10.5194/hess-24-2951-2020, 2020.
Arakawa, A. and Lamb, V. R.:
Computational design of the basic dynamical processes of the UCLA general circulation model,
Methods in Computational Physics: Advances in Research and Applications,
17, 173–265, https://doi.org/10.1016/B978-0-12-460817-7.50009-4, 1977.
Argüeso, D., Evans, J. P., Fita, L., and Bormann, K. J.:
Temperature response to future urbanization and climate change,
Clim. Dynam.,
42, 2183–2199, https://doi.org/10.1007/s00382-013-1789-6, 2014.
Bermejo, R. and Staniforth, A.:
The conversion of semi-Lagrangian advection schemes to quasi-monotone schemes,
Mon. Weather. Rev.,
120, 2622–2632, https://doi.org/10.1175/1520-0493(1992)120<2622:TCOSLA>2.0.CO;2, 1992.
Best, M. J., Pryor, M., Clark, D. B., Rooney, G. G., Essery, R. L. H., Ménard, C. B., Edwards, J. M., Hendry, M. A., Porson, A., Gedney, N., Mercado, L. M., Sitch, S., Blyth, E., Boucher, O., Cox, P. M., Grimmond, C. S. B., and Harding, R. J.: The Joint UK Land Environment Simulator (JULES), model description – Part 1: Energy and water fluxes, Geosci. Model Dev., 4, 677–699, https://doi.org/10.5194/gmd-4-677-2011, 2011.
Borsche, M., Kaiser-Weiss, A. K., Undén, P., and Kaspar, F.: Methodologies to characterize uncertainties in regional reanalyses, Adv. Sci. Res., 12, 207–218, https://doi.org/10.5194/asr-12-207-2015, 2015.
Boutle, I. A., Abel, S. J., Hill, P. G., and Morcrette, C. J.:
Spatial variability of liquid cloud and rain: observations and microphysical effects,
Q. J. Roy. Meteor. Soc.,
140, 583–594, https://doi.org/10.1002/qj.2140, 2014a.
Boutle, I. A., Eyre, J. E. J., and Lock, A. P.:
Seamless stratocumulus simulation across the turbulent gray zone,
Mon. Weather Rev.,
142, 1655–1668, https://doi.org/10.1175/MWR-D-13-00229.1, 2014b.
Bromwich, D. H., Wilson, A. B., Bai, L., Moore, G. W. K., and Bauer, P.:
A comparison of the regional Arctic System Reanalysis and the global ERA-Interim Reanalysis for the Arctic,
Q. J. Roy. Meteor. Soc.,
142, 644–658, https://doi.org/10.1002/qj.2527, 2016.
Brousseau, P., Seity, Y., Ricard, D., and Léger, J.:
Improvement of the forecast of convective activity from the AROME-France system,
Q. J. Roy. Meteor. Soc.,
142, 2231–2243, https://doi.org/10.1002/qj.2822, 2016.
Bureau of Meteorology:
APS2 upgrade of the ACCESS-R numerical weather prediction system, NOC Operations Bulletin Number 107,
available at: http://www.bom.gov.au/australia/charts/bulletins/apob107-external.pdf (last access: 31 August 2020), 2016.
Bureau of Meteorology:
APS2 upgrade of the ACCESS-C numerical weather prediction system, NOC Operations Bulletin Number 114,
available at: http://www.bom.gov.au/australia/charts/bulletins/BNOC_Operations_Bulletin_114.pdf (last access: 31 August 2020), 2018.
Bureau of Meteorology:
Atmospheric high-resolution regional reanalysis for Australia,
available at: http://www.bom.gov.au/research/projects/reanalysis (last access: 1 May 2020), 2020.
Bush, M., Allen, T., Bain, C., Boutle, I., Edwards, J., Finnenkoetter, A., Franklin, C., Hanley, K., Lean, H., Lock, A., Manners, J., Mittermaier, M., Morcrette, C., North, R., Petch, J., Short, C., Vosper, S., Walters, D., Webster, S., Weeks, M., Wilkinson, J., Wood, N., and Zerroukat, M.: The first Met Office Unified Model–JULES Regional Atmosphere and Land configuration, RAL1, Geosci. Model Dev., 13, 1999–2029, https://doi.org/10.5194/gmd-13-1999-2020, 2020.
Calmet, I., Mestayer, P. G., van Eijk, A. M. J., and Herlédant, O.:
A coastal day summer breeze study, Part 2: High-resolution numerical simulation of sea-breeze local influences,
Bound.-Lay. Meteorol.,
167, 27–51, https://doi.org/10.1007/s10546-017-0319-1, 2018.
Cattoën, C., Robertson, D. E., Bennett, J. C., Wang, Q. J., and Carey-Smith, T. K.:
Calibrating Hourly Precipitation Forecasts with Daily Observations,
J. Hydrometeorol.,
21, 1655–1673, https://doi.org/10.1175/JHM-D-19-0246.1, 2020.
Champion, A. J. and Hodges, K.:
Importance of resolution and model configuration when downscaling extreme precipitation,
Tellus A,
66, 23993, https://doi.org/10.3402/tellusa.v66.23993, 2014.
Charney, J. G. and Phillips, N. A.:
Numerical integration of the quasi-geostrophic equations for barotropic and simple baroclinic flows,
J. Meteorol.,
10, 71–99, https://doi.org/10.1175/1520-0469(1953)010<0071:NIOTQG>2.0.CO;2, 1953.
Chubb, T., Manton, M., Siems, S., and Peace, A. D.:
Evaluation of the AWAP daily precipitation spatial analysis with an independent gauge network in the Snowy Mountains,
Journal of Southern Hemisphere Earth Systems Science,
66, 55–67, https://doi.org/10.22499/3.6601.006, 2016.
Clark, P., Roberts, N., Lean, H., Ballard, S. P., and Charlton-Perez, C.:
Convection-permitting models: a step-change in rainfall forecasting,
Meteorol. Appl.,
23, 165–181, https://doi.org/10.1002/met.1538, 2016.
Davies, T., Cullen, M. J. P., Malcolm, A. J., Mawson, M. H., Staniforth, A., White, A. A., and Wood, N.:
A new dynamical core for the Met Office's global and regional modelling of the atmosphere,
Q. J. Roy. Meteor. Soc.,
131, 1759–1782, https://doi.org/10.1256/qj.04.101, 2005.
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol. C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Holm, E. V., Isaksen, L., Kallberg, P., Kohler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J. J., Park, B. K., Peubey, C., de Rosnay, P., Tavolato, C., Thepaut, J. N., and Vitart, F.:
The Era-Interim reanalysis: Configuration and performance of the data assimilation system,
Q. J. Roy. Meteor. Soc.,
137, 553–597, https://doi.org/10.1002/qj.828, 2011.
Dharssi, I., Steinle, P., and Fernon, J.:
Improved numerical weather predictions by using optimised urban model parameter values and satellite derived tree heights,
in: MODSIM2015, 21st International Congress on Modelling and Simulation,
edited by: Weber, T., McPhee, M. J., and Anderssen, R. S.,
Modelling and Simulation Society of Australia and New Zealand, December 2015, 1161–1167, ISBN: 978-0-9872143-5-5,
available at: https://www.mssanz.org.au/modsim2015/M4/dharssi.pdf (last access: 31 August 2020), 2015.
Di Luca, A., de Elía, R., and Laprise, R.:
Challenges in the quest for added value of regional climate dynamical downscaling,
Current Climate Change Reports,
1, 10–21, https://doi.org/10.1007/s40641-015-0003-9, 2015.
Di Luca, A., Argüeso, D., Evans, J. P., de Elía, R., and Laprise, R.:
Quantifying the overall added value of dynamical downscaling and the contribution from different spatial scales,
J. Geophys. Res.-Atmos.,
121, 1575–1590, https://doi.org/10.1002/2015JD024009, 2016.
Dixon, M., Li, Z., Lean, H., Roberts, N., and Ballard, S.:
Impact of data assimilation on forecasting convection over the United Kingdom using a high resolution version of the Met Office Unified Model,
Mon. Weather Rev.,
137, 1562–1584, https://doi.org/10.1175/2008MWR2561.1, 2009.
Done, J., Davis, C. A., and Weisman, M.:
The next generation of NWP: explicit forecasts of convection using the weather research and forecasting (WRF) model,
Atmos. Sci. Lett.,
5, 110–117, https://doi.org/10.1002/asl.72, 2004.
Donlon, C. J., Martin, M., Stark, J. D., Roberts-Jones, J., Fiedler, E., and Wimmer, W.:
The Operational Sea Surface Temperature and Sea Ice analysis (OSTIA) system,
Remote Sens. Environ.,
116, 140–158, https://doi.org/10.1016/j.rse.2010.10.017, 2012.
Ebert, E. E.:
Neighborhood verification: A strategy for rewarding close forecasts,
Weather Forecast.,
24, 1498–1510, https://doi.org/10.1175/2009WAF2222251.1, 2009.
Ebita, A., Kobayashi, S., Ota, Y., Moriya, M., Kumabe, R., Onogi, K., Harada, Y., Yasui, S., Miyaoka, K., Takahashi, K., Kamahori, H., Kobayashi, C., Endo, H., Soma, M., Oikawa, Y., and Ishimizu, T.:
The Japanese 55-year reanalysis JRA-55: An interim report,
SOLA,
7, 149–152, https://doi.org/10.2151/sola.2011-038, 2011.
Edwards, J. M. and Slingo, A.:
Studies with a flexible new radiation code. I: Choosing a configuration for a largescale model,
Q. J. Roy. Meteor. Soc.,
122, 689–719, https://doi.org/10.1002/qj.49712253107, 1996.
Fosser, G., Khodayar, S., and Berg, P:
Benefit of convection permitting climate model simulations in the representation of convective precipitation,
Clim. Dynam.,
44, 45–60, https://doi.org/10.1007/s00382-014-2242-1, 2015.
Frank, C. W., Pospichal, B., Wahl, S., Keller, J. D., Hence, A., and Crewell, S.:
The added value of high resolution regional reanalyses for wind power applications,
Renew. Energ.,
148, 1094–1109, https://doi.org/10.1016/j.renene.2019.09.138, 2020.
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., da Silva, A. M., Gu, W., Kim, G., Koster, R., Lucchesi, R., Merkova, D., Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert, S. D., Sienkiewicz, M., and Zhao, B.:
The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2),
J. Climate,
30, 5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017.
Gerard, L., Piriou, J., Brožková, R., Geleyn, J., and Banciu, D.:
Cloud and precipitation parameterization in a meso-gamma-scale operational weather prediction model,
Mon. Weather Rev.,
137, 3960–3977, https://doi.org/10.1175/2009MWR2750.1, 2009.
Glahn, H. R. and Lowry, D. A.:
The use of model output statistics (MOS) in objective weather forecasting,
J. Appl. Meteorol.,
11, 1203–1211, https://doi.org/10.1175/1520-0450(1972)011<1203:TUOMOS>2.0.CO;2, 1972.
Gregory, D. and Rowntree, P. R.:
A mass flux convection scheme with representation of cloud ensemble characteristics and stability-dependent closure,
Mon. Weather Rev.,
118, 1483–1506, https://doi.org/10.1175/1520-0493(1990)118<1483:AMFCSW>2.0.CO;2, 1990.
Gregow, H., Jylhä, K., Mäkelä, H. M., Aalto, J., Manninen, T., Karlsson, P., Kaiser-Weiss, A. K., Kaspar, F., Poli, P., Tan, D. G., Obregon, A., and Su, Z.:
Worldwide survey of awareness and needs concerning reanalyses and respondents views on climate services,
B. Am. Meteorol. Soc.,
97, 1461–1473, https://doi.org/10.1175/BAMS-D-14-00271.1, 2016.
Griffiths, D. J., Colquhoun, J. R., Batt, K. L., and Casinader, T. R.:
Severe thunderstorms in New South Wales: Climatology and means of assessing the impact of climate change,
Climatic Change,
25, 369–388, https://doi.org/10.1007/BF01098382, 1993.
Halliwell, C., Boutle, I., and Hanley, K.:
Subgrid turbulence scheme, Unified Model Documentation Paper 28,
Met Office, Exeter, UK, 2007.
Hanley, K. E., Plant, R. S., Stein, T. H. M., Hogan, R. J., Nicol, J. C., Lean, H. W., Halliwell, C. and Clark, P. A.:
Mixing-length controls on high-resolution simulations of convective storms,
Q. J. Roy. Meteor. Soc.,
141, 272-284, https://doi.org/10.1002/qj.2356, 2015.
Hartley, A., MacBean, N., Georgievski, G., and Bontemps, S.:
Uncertainty in plant functional type distributions and its impact on land surface models,
Remote Sens. Environ.,
203, 71–89, https://doi.org/10.1016/j.rse.2017.07.037, 2017.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., Chiara, G. D., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.:
The ERA5 global reanalysis,
Q. J. Roy. Meteor. Soc.,
146, 1999– 2049, https://doi.org/10.1002/qj.3803, 2020.
Jakob, D., Su, C.-H., Eizenberg, N., Kociuba, G., Steinle, P., Fox-Hughes, P., and Bettio, L.:
An atmospheric high-resolution regional reanalysis for Australia,
B. Aus. Meteorol. Oceanog. Soc.,
30, 16–23, 2017.
Jermey, P. M. and Renshaw, R. J.:
Precipitation representation over a two-year period in regional reanalysis,
Q. J. Roy. Meteor. Soc.,
142, 1300-1310, https://doi.org/10.1002/qj.2733, 2016.
Jones, D. A., Wang, W., and Fawcett, R.:
High-quality spatial climate data-sets for Australia,
Aust. Meteorol. Ocean.,
58, 233–248, 2009.
Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L., Iredell, M., Saha, S., White, G., Woollen, J., Zhu, Y., Chelliah, M., Ebisuzaki, W., Higgins, W., Janowiak, J., Mo, K. C., Ropelewski, C., Wang, J., Leetmaa,A., Reynolds, R., Jenne, R., and Joseph, D.:
The NCEP/NCAR 40-Year Reanalysis Project,
B. Am. Meteorol. Soc.,
77, 437–472, https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2, 1996.
Kendon, E. J., Ban, N., Roberts, N. M., Fowler, H. J., Roberts, M. J., Chan, S. C., Evans, J. P., Fosser, G., and Wilkinson, J. M.:
Do convection-permitting regional climate models improve projections of future precipitation change?,
B. Am. Meteorol. Soc.,
98, 79–93, https://doi.org/10.1175/BAMS-D-15-0004.1, 2017.
Kendon, E. J., Stratton, R. A., Tucker, S., Marsham, J. H., Berthou, S., Rowell, D. P., and Senior, C. A.:
Enhanced future changes in wet and dry extremes over Africa at convection-permitting scale,
Nat Commun.,
10, 1794, https://doi.org/10.1038/s41467-019-09776-9, 2019.
Kendon, E. J., Prein, A. F., and Senior, C. A.:
Challenges and outlook for convective-permitting climate modelling,
Philos. T. R. Soc. A,
379, 20190547, https://doi.org/10.1098/rsta.2019.0547, 2021.
King, A. D., Alexander, L. V., and Donat, M. G.:
The efficacy of using gridded data to examine extreme rainfall characteristics: a case study for Australia,
Int. J. Climatol.,
33, 2376–2387, https://doi.org/10.1002/joc.3588, 2012.
Kuleshov, Y., de Hoedt, G., Wright, W., and Brewster, A.:
Thunderstorm distribution and frequency in Australia,
Aust. Meteorol. Mag.,
51, 145–154, 2002.
Louis, J.-F.:
A parametric model of vertical eddy fluxes in the atmosphere,
Bound.-Lay. Meteorol.,
17, 187–202, 1979.
Lean, H. W., Clark, P. A., Dixon, M., Roberts, N. M., Fitch, A., Forbes, R., Halliwell, C.:
Characteristics of high-resolution versions of the Met Office Unified Model for forecasting convection over the United Kingdom,
Mon. Weather Rev.,
136, 3408–3424, https://doi.org/10.1175/2008MWR2332.1, 2008.
Leutwyler, D., Lüthi, D., Ban, N., Fuhrer, O., and Schär, C.:
Evaluation of the convection-resolving climate modeling approach on continental scales,
J. Geophys. Res.-Atmos.,
122, 5237–5258, https://doi.org/10.1002/2016JD026013, 2017.
Lock, A. P., Brown, A. R., Bush, M. R., Martin, G. M., and Smith, R. N. B.:
A new boundary layer mixing scheme. Part I: Scheme description and single-column model tests,
Mon. Weather Rev.,
128, 3187–3199, https://doi.org/10.1175/1520-0493(2000)128<3187:ANBLMS>2.0.CO;2, 2000.
Lock, A., Edwards, J., and Boutle, I.:
The parametrization of boundary layer processes,
Unified Model Documentation Paper 024, vn10.6, 2016.
Lopez, M. A., Hartmann, D. L., Blossey, P. N., Wood, R., Bretherton, C. S., and Kubar, T. L.:
A test of the simulation of tropical convective cloudiness by a cloud-resolving model,
J. Climate,
22, 2834–2849, https://doi.org/10.1175/2008JCLI2272.1, 2009.
Loveland, T. R., Reed, B. C., Brown, J. F., Ohlen, D. O., Zhu, Z., Yang, L., and Merchant, J. W.:
Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data,
Int. J. Remote Sens.,
21, 1303–1330, https://doi.org/10.1080/014311600210191, 2000.
Ma, Y. and Liu, H.:
Large-eddy simulations of atmospheric flows over complex terrain using the immersed-boundary method in the Weather Research and Forecasting model,
Bound.-Lay. Meteorol.,
165, 421–445, https://doi.org/10.1007/s10546-017-0283-9, 2017.
Mahmood, S., Davie, J., Jermey, P., Renshaw, R., George, J. P., Rajagopal, E. N., and Rani, S. I.:
Indian monsoon data assimilation and analysis regional reanalysis: Configuration and performance,
Atmos. Sci. Lett.,
19, e808, https://doi.org/10.1002/asl.808, 2018.
Mailhot, J., Bélair, S., Charron, M., Doyle, C., Joe, P., Abrahamowicz, M., Bernier, N. B., Denis, B., Erfani, A., Frenette, R., Giguére, A., Issac, G. A., McLennan, N., McTaggart-Cowan, R., Milbrandt, J., and Tong, L.:
Environment Canada's experimental numerical weather prediction systems for the Vancouver 2010 Winter Olympic and Paralympic Games,
B. Am. Meteorol. Soc.,
91, 1073–1086, https://doi.org/10.1175/2010BAMS2913.1, 2010.
Mesinger, F., DiMego, G., Kalnay, E., Mitchell, K., Shafran, P. C., Ebisuzaki, W., Jović, D., Woollen, J., Rogers, E., Berbery, E. H., Ek, M. B., Fan, Y., Grumbine, R., Higgins, W., Li, H., Lin, Y., Manikin, G., Parrish, D., and Shi, W.:
North American Regional Reanalysis,
B. Am. Meteorol. Soc.,
87, 343–360, https://doi.org/10.1175/BAMS-87-3-343, 2006.
Mueller, N., Lewis, A., Roberts, D., Ring, S., Melrose, R., Sixsmith, J., Lymburner, L., McIntyre, A., Tan, P., Curnow, S., and Ip, A.:
Water observations from space: Mapping surface water from 25 years of Landsat imagery across Australia,
Remote Sens. Environ.,
174, 341–352, https://doi.org/10.1016/j.rse.2015.11.003, 2016.
Oliver, H., Shin, M., Matthews, D., Sanders, O., Bartholomew, S., Clark, A., Fitzpatrick, B., van Haren, R., Hut, R., and Drost, N.:
Workflow automation for cycling systems,
Comput. Sci. Eng.,
21, 7–21, https://doi.org/10.1109/MCSE.2019.2906593, 2019.
Peel, M. C., Finlayson, B. L., and McMahon, T. A.: Updated world map of the Köppen–Geiger climate classification, Hydrol. Earth Syst. Sci., 11, 1633–1644, https://doi.org/10.5194/hess-11-1633-2007, 2007.
Prein, A. F., Langhans, W., Fosser, G., Ferrone, A., Ban, N., Goergen, K., Keller, M., Tölle, M., Gutjahr, O., Feser, F., Brisson, E., Kollet, S., Schmidli, J., Van Lipzig, N. P. M, and Leung, R.:
A review on regional convection-permitting climate modeling: Demonstrations, prospects, and challenges,
Rev. Geophys.,
53, 323–361, https://doi.org/10.1002/2014RG000475, 2015.
Puri, K., Dietachmayer, G., Steinle, P., Dix, M., Rikus, L., Logan,L., Naughton, M., Tingwell, C., Xiao, Y., Barras, V., Bermous, I., Bowen, R., Deschamps, L., Franklin, C., Fraser, J., Glowacki, T., Harris, B., Lee, J., Le, T., Roff, G., Sulaiman, A., Sims, H., Sun, X., Sun, Z., Zhu, H., Chattopadhyay, M. and Engel, C.:
Implementation of the initial ACCESS numerical weather prediction system,
Aust. Meteorol. Ocean.,
63, 265–284, 2013.
Rennie, S., Rikus, L., Eizenberg, N., Steinle, P., and Krysta, M.:
Impact of Doppler radar wind observations on Australian high-resolution numerical weather prediction,
Weather Forecast.,
35, 309–324, https://doi.org/10.1175/WAF-D-19-0100.1, 2020.
Roberts, N. M. and Lean, H. W.:
Scale-selective verification of rainfall accumulations from high-resolution forecasts of convective events,
Mon. Weather Rev.,
136, 78–97, https://doi.org/10.1175/2007MWR2123.1, 2007.
Roberts-Jones, J., Fiedler, E. K., and Martin, M. J.:
Daily, global, high-resolution SST and sea ice reanalysis for 1985–2007 Using the OSTIA system,
J. Climate,
25, 6215–6232, https://doi.org/10.1175/JCLI-D-11-00648.1, 2012.
Schär, C., Fuhrer, O., Arteaga, A., Ban, N., Charpilloz, C., Di Girolamo, S., Hentgen, L., Hoefler, T., Lapillonne, X., Leutwyler, D., Osterried, K., Panosetti, D., Rüdisühli, S., Schlemmer, L., Schulthess, T. C., Sprenger, M., Ubbiali, S., and Wernli, H.:
Kilometer-scale climate models: Prospects and challenges,
B. Am. Meteorol. Soc.,
101, E567–E587, https://doi.org/10.1175/BAMS-D-18-0167.1, 2020.
Seed, A., Duthie, E., and Chumchean, S.: Rainfields: The Australian Bureau of Meteorology system for quantitative precipitation estimation, Abstract P6B.8, Proceedings of the 33rd AMS Conference on Radar Meteorology, Cairns, Australia, 6–10 August 2007.
Simard, M., Pinto, N., Fisher, J. B., and Baccini, A.:
Mapping forest canopy height globally with spaceborne lidar,
J. Geophys. Res.-Biogeo.,
116, G04021, https://doi.org/10.1029/2011JG001708, 2011.
Sinclair, S. and Pegram, G.:
Combining radar and rain gauge rainfall estimates using conditional merging,
Atmos. Sci. Lett.,
6, 19–22, https://doi.org/10.1002/asl.85, 2005.
Smagorinsky, J.:
General circulation experiments with the primitive equations. I: The basic experiment,
Mon. Weather Rev.,
91, 99–164, 1963.
Smith, R. N. B.:
A scheme for predicting layer cloud and their water content in a general circulation model,
Q. J. Roy. Meteor. Soc.,
116, 435–460, https://doi.org/10.1002/qj.49711649210, 1990.
Steeneveld, G.-J.:
Current challenges in understanding and forecasting stable boundary layers over land and ice,
Front. Environ. Sci.,
2, 41, https://doi.org/10.3389/fenvs.2014.00041, 2014.
Stein, T. H. M., Hogan, R. J., Clark, P. A., Halliwell, C. E., Hanley, K. E., Lean, H. W., Nicol, J. C., and Plant, R. S.:
The DYMECS Project: A statistical approach for the evaluation of convective storms in high-resolution NWP models,
B. Am. Meteorol. Soc.,
96, 939–951, https://doi.org/10.1175/BAMS-D-13-00279.1, 2015.
Su, C.-H., Eizenberg, N., Steinle, P., Jakob, D., Fox-Hughes, P., White, C. J., Rennie, S., Franklin, C., Dharssi, I., and Zhu, H.: BARRA v1.0: the Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia, Geosci. Model Dev., 12, 2049–2068, https://doi.org/10.5194/gmd-12-2049-2019, 2019.
Vitolo, C., Napoli, C. D., Giuseppe, F. D., Cloke, H. L., and Pappenberger, F.:
Mapping combined wildfire and heat stress hazards to improve evidence-based decision making,
Environ. Internat.,
127, 21–34, https://doi.org/10.1016/j.envint.2019.03.008, 2019.
Wahl, S., Bollmeyer, C., Crewell, S., Figura, C., Friederichs, P., Hense, A., Keller, J. D., and Ohlwein, C.:
A novel convective-scale regional reanalysis COSMO-REA2: Improving the representation of precipitation,
Meteorol. Z.,
26, 345–361, https://doi.org/10.1127/metz/2017/0824, 2017.
Walsh, K. J. E, White, C. J., McInnes, K. L, Holmes, J., Schuster, S., Richter, H., Evans, J. P., Di Luca, A. and Warren, R. A.:
Natural hazards in Australia: storms, wind and hail,
Climatic Change,
139, 55–67, https://doi.org/10.1007/s10584-016-1737-7, 2016.
Walters, D., Boutle, I., Brooks, M., Melvin, T., Stratton, R., Vosper, S., Wells, H., Williams, K., Wood, N., Allen, T., Bushell, A., Copsey, D., Earnshaw, P., Edwards, J., Gross, M., Hardiman, S., Harris, C., Heming, J., Klingaman, N., Levine, R., Manners, J., Martin, G., Milton, S., Mittermaier, M., Morcrette, C., Riddick, T., Roberts, M., Sanchez, C., Selwood, P., Stirling, A., Smith, C., Suri, D., Tennant, W., Vidale, P. L., Wilkinson, J., Willett, M., Woolnough, S., and Xavier, P.: The Met Office Unified Model Global Atmosphere 6.0/6.1 and JULES Global Land 6.0/6.1 configurations, Geosci. Model Dev., 10, 1487–1520, https://doi.org/10.5194/gmd-10-1487-2017, 2017.
Walters, D., Baran, A. J., Boutle, I., Brooks, M., Earnshaw, P., Edwards, J., Furtado, K., Hill, P., Lock, A., Manners, J., Morcrette, C., Mulcahy, J., Sanchez, C., Smith, C., Stratton, R., Tennant, W., Tomassini, L., Van Weverberg, K., Vosper, S., Willett, M., Browse, J., Bushell, A., Carslaw, K., Dalvi, M., Essery, R., Gedney, N., Hardiman, S., Johnson, B., Johnson, C., Jones, A., Jones, C., Mann, G., Milton, S., Rumbold, H., Sellar, A., Ujiie, M., Whitall, M., Williams, K., and Zerroukat, M.: The Met Office Unified Model Global Atmosphere 7.0/7.1 and JULES Global Land 7.0 configurations, Geosci. Model Dev., 12, 1909–1963, https://doi.org/10.5194/gmd-12-1909-2019, 2019.
Wilkinson, J. M. and Jorge Bornemann, F.:
A lightning forecast for the London 2012 Olympics opening ceremony,
Weather,
69, 16–19, https://doi.org/10.1002/wea.2176, 2014.
Wilson, D. R. and Ballard, S. P.:
A microphysically based precipitation scheme for the UK Meteorological Office Unified Model,
Q. J. Roy. Meteor. Soc.,
125, 1607–1636, https://doi.org/10.1002/qj.49712555707, 1999.
Wood, N., Staniforth, A., White, A., Allen, T., Diamantakis, M., Gross, M., Melvin, T., Smith, C., Vosper, S., Zerroukat, M., and Thuburn, J.:
An inherently mass-conserving semi-implicit semi-Lagrangian discretization of the deep-atmosphere global nonhydrostatic equations,
Q. J. Roy. Meteor. Soc.,
140, 1505–1520, https://doi.org/10.1002/qj.2235, 2014.
Zerroukat, M. and Shipway, B. J.:
ZLF (Zero Lateral Flux): a simple mass conservation method for semi-Lagrangian-based limited-area models,
Q. J. Roy. Meteor. Soc,
143, 2578–2584, https://doi.org/10.1002/qj.3108, 2017.
Short summary
The Bureau of Meteorology Atmospheric Regional Reanalysis for Australia (BARRA) has produced a very high-resolution reconstruction of Australian historical weather from 1990 to 2018. This paper demonstrates the added weather and climate information to supplement coarse- or moderate-resolution regional and global reanalyses. The new climate data can allow greater understanding of past weather, including extreme events, at very local kilometre scales.
The Bureau of Meteorology Atmospheric Regional Reanalysis for Australia (BARRA) has produced a...