Pub Date : 2023-09-22DOI: 10.5194/gmd-16-5401-2023
Johannes Röhrs, Yvonne Gusdal, Edel S. U. Rikardsen, Marina Durán Moro, Jostein Brændshøi, Nils Melsom Kristensen, Sindre Fritzner, Keguang Wang, Ann Kristin Sperrevik, Martina Idžanović, Thomas Lavergne, Jens Boldingh Debernard, Kai H. Christensen
Abstract. An operational ocean and sea ice forecast model, Barents-2.5, is implemented for short-term forecasting at the coast off northern Norway, the Barents Sea, and the waters around Svalbard. Primary forecast parameters are sea ice concentration (SIC), sea surface temperature (SST), and ocean currents. The model also provides input data for drift modeling of pollutants, icebergs, and search-and-rescue applications in the Arctic domain. Barents-2.5 has recently been upgraded to include an ensemble prediction system with 24 daily realizations of the model state. SIC, SST, and in situ hydrography are constrained through the ensemble Kalman filter (EnKF) data assimilation scheme executed in daily forecast cycles with a lead time up to 66 h. Here, we present the model setup and validation in terms of SIC, SST, in situ hydrography, and ocean and ice velocities. In addition to the model's forecast capabilities for SIC and SST, the performance of the ensemble in representing the model's uncertainty and the performance of the EnKF in constraining the model state are discussed.
{"title":"Barents-2.5km v2.0: an operational data-assimilative coupled ocean and sea ice ensemble prediction model for the Barents Sea and Svalbard","authors":"Johannes Röhrs, Yvonne Gusdal, Edel S. U. Rikardsen, Marina Durán Moro, Jostein Brændshøi, Nils Melsom Kristensen, Sindre Fritzner, Keguang Wang, Ann Kristin Sperrevik, Martina Idžanović, Thomas Lavergne, Jens Boldingh Debernard, Kai H. Christensen","doi":"10.5194/gmd-16-5401-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-5401-2023","url":null,"abstract":"Abstract. An operational ocean and sea ice forecast model, Barents-2.5, is implemented for short-term forecasting at the coast off northern Norway, the Barents Sea, and the waters around Svalbard. Primary forecast parameters are sea ice concentration (SIC), sea surface temperature (SST), and ocean currents. The model also provides input data for drift modeling of pollutants, icebergs, and search-and-rescue applications in the Arctic domain. Barents-2.5 has recently been upgraded to include an ensemble prediction system with 24 daily realizations of the model state. SIC, SST, and in situ hydrography are constrained through the ensemble Kalman filter (EnKF) data assimilation scheme executed in daily forecast cycles with a lead time up to 66 h. Here, we present the model setup and validation in terms of SIC, SST, in situ hydrography, and ocean and ice velocities. In addition to the model's forecast capabilities for SIC and SST, the performance of the ensemble in representing the model's uncertainty and the performance of the EnKF in constraining the model state are discussed.","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136062035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-22DOI: 10.5194/gmd-16-5449-2023
Guta Wakbulcho Abeshu, Fuqiang Tian, Thomas Wild, Mengqi Zhao, Sean Turner, A. F. M. Kamal Chowdhury, Chris R. Vernon, Hongchang Hu, Yuan Zhuang, Mohamad Hejazi, Hong-Yi Li
Abstract. This study enhances an existing global hydrological model (GHM), Xanthos, by adding a new water management module that distinguishes between the operational characteristics of irrigation, hydropower, and flood control reservoirs. We remapped reservoirs in the Global Reservoir and Dam (GRanD) database to the 0.5∘ spatial resolution in Xanthos so that a single lumped reservoir exists per grid cell, which yielded 3790 large reservoirs. We implemented unique operation rules for each reservoir type, based on their primary purposes. In particular, hydropower reservoirs have been treated as flood control reservoirs in previous GHM studies, while here, we determined the operation rules for hydropower reservoirs via optimization that maximizes long-term hydropower production. We conducted global simulations using the enhanced Xanthos and validated monthly streamflow for 91 large river basins, where high-quality observed streamflow data were available. A total of 1878 (296 hydropower, 486 irrigation, and 1096 flood control and others) out of the 3790 reservoirs are located in the 91 basins and are part of our reported results. The Kling–Gupta efficiency (KGE) value (after adding the new water management) is ≥ 0.5 and ≥ 0.0 in 39 and 81 basins, respectively. After adding the new water management module, model performance improved for 75 out of 91 basins and worsened for only 7. To measure the relative difference between explicitly representing hydropower reservoirs and representing hydropower reservoirs as flood control reservoirs (as is commonly done in other GHMs), we use the normalized root mean square error (NRMSE) and the coefficient of determination (R2). Out of the 296 hydropower reservoirs, the NRMSE is > 0.25 (i.e., considering 0.25 to represent a moderate difference) for over 44 % of the 296 reservoirs when comparing both the simulated reservoir releases and storage time series between the two simulations. We suggest that correctly representing hydropower reservoirs in GHMs could have important implications for our understanding and management of freshwater resource challenges at regional-to-global scales. This enhanced global water management modeling framework will allow the analysis of future global reservoir development and management from a coupled human–earth system perspective.
{"title":"Enhancing the representation of water management in global hydrological models","authors":"Guta Wakbulcho Abeshu, Fuqiang Tian, Thomas Wild, Mengqi Zhao, Sean Turner, A. F. M. Kamal Chowdhury, Chris R. Vernon, Hongchang Hu, Yuan Zhuang, Mohamad Hejazi, Hong-Yi Li","doi":"10.5194/gmd-16-5449-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-5449-2023","url":null,"abstract":"Abstract. This study enhances an existing global hydrological model (GHM), Xanthos, by adding a new water management module that distinguishes between the operational characteristics of irrigation, hydropower, and flood control reservoirs. We remapped reservoirs in the Global Reservoir and Dam (GRanD) database to the 0.5∘ spatial resolution in Xanthos so that a single lumped reservoir exists per grid cell, which yielded 3790 large reservoirs. We implemented unique operation rules for each reservoir type, based on their primary purposes. In particular, hydropower reservoirs have been treated as flood control reservoirs in previous GHM studies, while here, we determined the operation rules for hydropower reservoirs via optimization that maximizes long-term hydropower production. We conducted global simulations using the enhanced Xanthos and validated monthly streamflow for 91 large river basins, where high-quality observed streamflow data were available. A total of 1878 (296 hydropower, 486 irrigation, and 1096 flood control and others) out of the 3790 reservoirs are located in the 91 basins and are part of our reported results. The Kling–Gupta efficiency (KGE) value (after adding the new water management) is ≥ 0.5 and ≥ 0.0 in 39 and 81 basins, respectively. After adding the new water management module, model performance improved for 75 out of 91 basins and worsened for only 7. To measure the relative difference between explicitly representing hydropower reservoirs and representing hydropower reservoirs as flood control reservoirs (as is commonly done in other GHMs), we use the normalized root mean square error (NRMSE) and the coefficient of determination (R2). Out of the 296 hydropower reservoirs, the NRMSE is > 0.25 (i.e., considering 0.25 to represent a moderate difference) for over 44 % of the 296 reservoirs when comparing both the simulated reservoir releases and storage time series between the two simulations. We suggest that correctly representing hydropower reservoirs in GHMs could have important implications for our understanding and management of freshwater resource challenges at regional-to-global scales. This enhanced global water management modeling framework will allow the analysis of future global reservoir development and management from a coupled human–earth system perspective.","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136060155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-21DOI: 10.5194/gmd-16-5383-2023
Florian Zabel, Benjamin Poschlod
Abstract. Climate models provide the required input data for global or regional climate impact analysis in temporally aggregated form, often in daily resolution to save space on data servers. Today, many impact models work with daily data; however, sub-daily climate information is becoming increasingly important for more and more models from different sectors, such as the agricultural, water, and energy sectors. Therefore, the open-source Teddy tool (temporal disaggregation of daily climate model data) has been developed to disaggregate (temporally downscale) daily climate data to sub-daily hourly values. Here, we describe and validate the temporal disaggregation, which is based on the choice of daily climate analogues. In this study, we apply the Teddy tool to disaggregate bias-corrected climate model data from the Coupled Model Intercomparison Project Phase 6 (CMIP6). We choose to disaggregate temperature, precipitation, humidity, longwave radiation, shortwave radiation, surface pressure, and wind speed. As a reference, globally available bias-corrected hourly reanalysis WFDE5 (WATCH Forcing Data methodology applied to ERA5) data from 1980–2019 are used to take specific local and seasonal features of the empirical diurnal profiles into account. For a given location and day within the climate model data, the Teddy tool screens the reference data set to find the most similar meteorological day based on rank statistics. The diurnal profile of the reference data is then applied on the climate model. The physical dependency between variables is preserved, since the diurnal profile of all variables is taken from the same, most similar meteorological day of the historical reanalysis dataset. Mass and energy are strictly preserved by the Teddy tool to exactly reproduce the daily values from the climate models. For evaluation, we aggregate the hourly WFDE5 data to daily values and apply the Teddy tool for disaggregation. Thereby, we compare the original hourly data with the data disaggregated by Teddy. We perform a sensitivity analysis of different time window sizes used for finding the most similar meteorological day in the past. In addition, we perform a cross-validation and autocorrelation analysis for 30 globally distributed samples around the world that represent different climate zones. The validation shows that Teddy is able to reproduce historical diurnal courses with high correlations >0.9 for all variables, except for wind speed (>0.75) and precipitation (>0.5). We discuss the limitations of the method regarding the reproduction of precipitation extremes, interday connectivity, and disaggregation of end-of-century projections with strong warming. Depending on the use case, sub-daily data provided by the Teddy tool could make climate impact assessments more robust and reliable.
{"title":"The Teddy tool v1.1: temporal disaggregation of daily climate model data for climate impact analysis","authors":"Florian Zabel, Benjamin Poschlod","doi":"10.5194/gmd-16-5383-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-5383-2023","url":null,"abstract":"Abstract. Climate models provide the required input data for global or regional climate impact analysis in temporally aggregated form, often in daily resolution to save space on data servers. Today, many impact models work with daily data; however, sub-daily climate information is becoming increasingly important for more and more models from different sectors, such as the agricultural, water, and energy sectors. Therefore, the open-source Teddy tool (temporal disaggregation of daily climate model data) has been developed to disaggregate (temporally downscale) daily climate data to sub-daily hourly values. Here, we describe and validate the temporal disaggregation, which is based on the choice of daily climate analogues. In this study, we apply the Teddy tool to disaggregate bias-corrected climate model data from the Coupled Model Intercomparison Project Phase 6 (CMIP6). We choose to disaggregate temperature, precipitation, humidity, longwave radiation, shortwave radiation, surface pressure, and wind speed. As a reference, globally available bias-corrected hourly reanalysis WFDE5 (WATCH Forcing Data methodology applied to ERA5) data from 1980–2019 are used to take specific local and seasonal features of the empirical diurnal profiles into account. For a given location and day within the climate model data, the Teddy tool screens the reference data set to find the most similar meteorological day based on rank statistics. The diurnal profile of the reference data is then applied on the climate model. The physical dependency between variables is preserved, since the diurnal profile of all variables is taken from the same, most similar meteorological day of the historical reanalysis dataset. Mass and energy are strictly preserved by the Teddy tool to exactly reproduce the daily values from the climate models. For evaluation, we aggregate the hourly WFDE5 data to daily values and apply the Teddy tool for disaggregation. Thereby, we compare the original hourly data with the data disaggregated by Teddy. We perform a sensitivity analysis of different time window sizes used for finding the most similar meteorological day in the past. In addition, we perform a cross-validation and autocorrelation analysis for 30 globally distributed samples around the world that represent different climate zones. The validation shows that Teddy is able to reproduce historical diurnal courses with high correlations >0.9 for all variables, except for wind speed (>0.75) and precipitation (>0.5). We discuss the limitations of the method regarding the reproduction of precipitation extremes, interday connectivity, and disaggregation of end-of-century projections with strong warming. Depending on the use case, sub-daily data provided by the Teddy tool could make climate impact assessments more robust and reliable.","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"399 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136236352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-19DOI: 10.5194/gmd-16-5339-2023
Tor Nordam, Ruben Kristiansen, Raymond Nepstad, Erik van Sebille, Andy M. Booth
Abstract. A common task in oceanography is to model the vertical movement of particles such as microplastics, nanoparticles, mineral particles, gas bubbles, oil droplets, fish eggs, plankton, or algae. In some cases, the distribution of the vertical rise or settling velocities of the particles in question can span a wide range, covering several orders of magnitude, often due to a broad particle size distribution or differences in density. This requires numerical methods that are able to adequately resolve a wide and possibly multi-modal velocity distribution. Lagrangian particle methods are commonly used for these applications. A strength of such methods is that each particle can have its own rise or settling speed, which makes it easy to achieve a good representation of a continuous distribution of speeds. An alternative approach is to use Eulerian methods, where the partial differential equations describing the transport problem are solved directly with numerical methods. In Eulerian methods, different rise or settling speeds must be represented as discrete classes, and in practice, only a limited number of classes can be included. Here, we consider three different examples of applications for a water column model: positively buoyant fish eggs, a mixture of positively and negatively buoyant microplastics, and positively buoyant oil droplets being entrained by waves. For each of the three cases, we formulate a model for the vertical transport based on the advection–diffusion equation with suitable boundary conditions and, in one case, a reaction term. We give a detailed description of an Eulerian and a Lagrangian implementation of these models, and we demonstrate that they give equivalent results for selected example cases. We also pay special attention to the convergence of the model results with an increasing number of classes in the Eulerian scheme and with the number of particles in the Lagrangian scheme. For the Lagrangian scheme, we see the 1/Np convergence, as expected for a Monte Carlo method, while for the Eulerian implementation, we see a second-order (1/Nk2) convergence with the number of classes.
{"title":"A comparison of Eulerian and Lagrangian methods for vertical particle transport in the water column","authors":"Tor Nordam, Ruben Kristiansen, Raymond Nepstad, Erik van Sebille, Andy M. Booth","doi":"10.5194/gmd-16-5339-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-5339-2023","url":null,"abstract":"Abstract. A common task in oceanography is to model the vertical movement of particles such as microplastics, nanoparticles, mineral particles, gas bubbles, oil droplets, fish eggs, plankton, or algae. In some cases, the distribution of the vertical rise or settling velocities of the particles in question can span a wide range, covering several orders of magnitude, often due to a broad particle size distribution or differences in density. This requires numerical methods that are able to adequately resolve a wide and possibly multi-modal velocity distribution. Lagrangian particle methods are commonly used for these applications. A strength of such methods is that each particle can have its own rise or settling speed, which makes it easy to achieve a good representation of a continuous distribution of speeds. An alternative approach is to use Eulerian methods, where the partial differential equations describing the transport problem are solved directly with numerical methods. In Eulerian methods, different rise or settling speeds must be represented as discrete classes, and in practice, only a limited number of classes can be included. Here, we consider three different examples of applications for a water column model: positively buoyant fish eggs, a mixture of positively and negatively buoyant microplastics, and positively buoyant oil droplets being entrained by waves. For each of the three cases, we formulate a model for the vertical transport based on the advection–diffusion equation with suitable boundary conditions and, in one case, a reaction term. We give a detailed description of an Eulerian and a Lagrangian implementation of these models, and we demonstrate that they give equivalent results for selected example cases. We also pay special attention to the convergence of the model results with an increasing number of classes in the Eulerian scheme and with the number of particles in the Lagrangian scheme. For the Lagrangian scheme, we see the 1/Np convergence, as expected for a Monte Carlo method, while for the Eulerian implementation, we see a second-order (1/Nk2) convergence with the number of classes.","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135014451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-19DOI: 10.5194/gmd-16-5365-2023
Young-Chan Noh, Yonghan Choi, Hyo-Jong Song, Kevin Raeder, Joo-Hong Kim, Youngchae Kwon
Abstract. To improve the initial condition (“analysis”) for numerical weather prediction, we attempt to assimilate observations from the Advanced Microwave Sounding Unit-A (AMSU-A) on board the low-Earth-orbiting satellites. The data assimilation system, used in this study, consists of the Data Assimilation Research Testbed (DART) and the Community Earth System Model as the global forecast model. Based on the ensemble Kalman filter scheme, DART supports the radiative transfer model that is used to simulate the satellite radiances from the model state. To make the AMSU-A data available to be assimilated in DART, preprocessing modules are developed, which consist of quality control, spatial thinning, and bias correction processes. In the quality control, two sub-processes are included, outlier test and channel selection, depending on the cloud condition and surface type. The bias correction process is divided into scan-bias correction and air-mass-bias correction. Like input data used in DART, the observation errors are also estimated for the AMSU-A channels. In the trial experiments, a positive analysis impact is obtained by assimilating the AMSU-A observations on top of the DART data assimilation system that already makes use of the conventional measurements. In particular, the analysis errors are significantly reduced in the whole troposphere and lower stratosphere over the Northern Hemisphere. Overall, this study demonstrates a positive impact on the analysis when the AMSU-A observations are assimilated in the DART assimilation system.
摘要为了改善数值天气预报的初始条件(“分析”)，我们尝试吸收来自低地球轨道卫星上的高级微波探测单元- a (AMSU-A)的观测结果。本研究使用的数据同化系统由数据同化研究试验台(data assimilation Research Testbed, DART)和社区地球系统模型(Community Earth system Model)作为全球预报模型组成。DART基于集合卡尔曼滤波方案，支持辐射传输模型，用于从模型状态模拟卫星辐射。为了使AMSU-A数据能够被DART同化，开发了包括质量控制、空间细化和偏差校正在内的预处理模块。在质量控制中，根据云层状况和地表类型，包括离群值检验和通道选择两个子过程。偏置校正过程分为扫描偏置校正和空气质量偏置校正。与DART中使用的输入数据一样，对AMSU-A信道的观测误差也进行了估计。在试验中，通过在DART数据同化系统上同化AMSU-A观测结果，获得了积极的分析影响，DART数据同化系统已经使用了常规测量。特别是在整个对流层和北半球平流层下层，分析误差明显减小。总的来说，本研究表明，当AMSU-A观测在DART同化系统中同化时，对分析有积极影响。
{"title":"Assimilation of the AMSU-A radiances using the CESM (v2.1.0) and the DART (v9.11.13)–RTTOV (v12.3)","authors":"Young-Chan Noh, Yonghan Choi, Hyo-Jong Song, Kevin Raeder, Joo-Hong Kim, Youngchae Kwon","doi":"10.5194/gmd-16-5365-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-5365-2023","url":null,"abstract":"Abstract. To improve the initial condition (“analysis”) for numerical weather prediction, we attempt to assimilate observations from the Advanced Microwave Sounding Unit-A (AMSU-A) on board the low-Earth-orbiting satellites. The data assimilation system, used in this study, consists of the Data Assimilation Research Testbed (DART) and the Community Earth System Model as the global forecast model. Based on the ensemble Kalman filter scheme, DART supports the radiative transfer model that is used to simulate the satellite radiances from the model state. To make the AMSU-A data available to be assimilated in DART, preprocessing modules are developed, which consist of quality control, spatial thinning, and bias correction processes. In the quality control, two sub-processes are included, outlier test and channel selection, depending on the cloud condition and surface type. The bias correction process is divided into scan-bias correction and air-mass-bias correction. Like input data used in DART, the observation errors are also estimated for the AMSU-A channels. In the trial experiments, a positive analysis impact is obtained by assimilating the AMSU-A observations on top of the DART data assimilation system that already makes use of the conventional measurements. In particular, the analysis errors are significantly reduced in the whole troposphere and lower stratosphere over the Northern Hemisphere. Overall, this study demonstrates a positive impact on the analysis when the AMSU-A observations are assimilated in the DART assimilation system.","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135014582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-19DOI: 10.5194/gmd-16-5323-2023
Stijn Van Leuven, Pieter De Meutter, Johan Camps, Piet Termonia, Andy Delcloo
Abstract. Wet deposition plays a crucial role in the removal of aerosols from the atmosphere. Yet, large uncertainties remain in its implementation in atmospheric transport models, specifically in the parameterisation schemes that are often used. Recently, a new wet deposition scheme was introduced in FLEXPART. The input parameters for its wet deposition scheme can be altered by the user and may be case-specific. In this paper, a new method is presented to optimise the wet scavenging rates in atmospheric transport models such as FLEXPART. The optimisation scheme is tested in a case study of aerosol-attached 137Cs following the Fukushima Daiichi nuclear power plant accident. From this, improved values for the wet scavenging input parameters in FLEXPART are suggested.
{"title":"An optimisation method to improve modelling of wet deposition in atmospheric transport models: applied to FLEXPART v10.4","authors":"Stijn Van Leuven, Pieter De Meutter, Johan Camps, Piet Termonia, Andy Delcloo","doi":"10.5194/gmd-16-5323-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-5323-2023","url":null,"abstract":"Abstract. Wet deposition plays a crucial role in the removal of aerosols from the atmosphere. Yet, large uncertainties remain in its implementation in atmospheric transport models, specifically in the parameterisation schemes that are often used. Recently, a new wet deposition scheme was introduced in FLEXPART. The input parameters for its wet deposition scheme can be altered by the user and may be case-specific. In this paper, a new method is presented to optimise the wet scavenging rates in atmospheric transport models such as FLEXPART. The optimisation scheme is tested in a case study of aerosol-attached 137Cs following the Fukushima Daiichi nuclear power plant accident. From this, improved values for the wet scavenging input parameters in FLEXPART are suggested.","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"149 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135060717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-15DOI: 10.5194/gmd-16-5305-2023
Yannic Fischler, Thomas Kleiner, Christian Bischof, Jeremie Schmiedel, Roiy Sayag, Raban Emunds, Lennart Frederik Oestreich, Angelika Humbert
Abstract. The subglacial hydrological system affects (i) the motion of ice sheets through sliding, (ii) the location of lakes at the ice margin, and (iii) the ocean circulation by freshwater discharge directly at the grounding line or (iv) via rivers flowing over land. For modeling this hydrology system, a previously developed porous-media concept called the confined–unconfined aquifer system (CUAS) is used. To allow for realistic simulations at the ice sheet scale, we developed CUAS-MPI, an MPI-parallel C/C++ implementation of CUAS (MPI: Message Passing Interface), which employs the Portable, Extensible Toolkit for Scientific Computation (PETSc) infrastructure for handling grids and equation systems. We validate the accuracy of the numerical results by comparing them with a set of analytical solutions to the model equations, which involve two types of boundary conditions. We then investigate the scaling behavior of CUAS-MPI and show that CUAS-MPI scales up to 3840 MPI processes running a realistic Greenland setup on the Lichtenberg HPC system. Our measurements also show that CUAS-MPI reaches a throughput comparable to that of ice sheet simulations, e.g., the Ice-sheet and Sea-level System Model (ISSM). Lastly, we discuss opportunities for ice sheet modeling, explore future coupling possibilities of CUAS-MPI with other simulations, and consider throughput bottlenecks and limits of further scaling.
{"title":"A parallel implementation of the confined–unconfined aquifer system model for subglacial hydrology: design, verification, and performance analysis (CUAS-MPI v0.1.0)","authors":"Yannic Fischler, Thomas Kleiner, Christian Bischof, Jeremie Schmiedel, Roiy Sayag, Raban Emunds, Lennart Frederik Oestreich, Angelika Humbert","doi":"10.5194/gmd-16-5305-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-5305-2023","url":null,"abstract":"Abstract. The subglacial hydrological system affects (i) the motion of ice sheets through sliding, (ii) the location of lakes at the ice margin, and (iii) the ocean circulation by freshwater discharge directly at the grounding line or (iv) via rivers flowing over land. For modeling this hydrology system, a previously developed porous-media concept called the confined–unconfined aquifer system (CUAS) is used. To allow for realistic simulations at the ice sheet scale, we developed CUAS-MPI, an MPI-parallel C/C++ implementation of CUAS (MPI: Message Passing Interface), which employs the Portable, Extensible Toolkit for Scientific Computation (PETSc) infrastructure for handling grids and equation systems. We validate the accuracy of the numerical results by comparing them with a set of analytical solutions to the model equations, which involve two types of boundary conditions. We then investigate the scaling behavior of CUAS-MPI and show that CUAS-MPI scales up to 3840 MPI processes running a realistic Greenland setup on the Lichtenberg HPC system. Our measurements also show that CUAS-MPI reaches a throughput comparable to that of ice sheet simulations, e.g., the Ice-sheet and Sea-level System Model (ISSM). Lastly, we discuss opportunities for ice sheet modeling, explore future coupling possibilities of CUAS-MPI with other simulations, and consider throughput bottlenecks and limits of further scaling.","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"3 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135395398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-14DOI: 10.5194/gmd-16-5281-2023
Thibaud Sarica, Alice Maison, Yelva Roustan, Matthias Ketzel, Steen Solvang Jensen, Youngseob Kim, Christophe Chaillou, Karine Sartelet
Abstract. Populations in urban areas are exposed to high local concentrations of pollutants, such as nitrogen dioxide and particulate matter, because of unfavourable dispersion conditions and the proximity to traffic. To simulate these concentrations over cities, models like the street-network model MUNICH (Model of Urban Network of Intersecting Canyons and Highways) rely on parameterizations to represent the air flow and the concentrations of pollutants in streets. In the current version, MUNICH v2.0, concentrations are assumed to be homogeneous in each street segment. A new version of MUNICH, where the street volume is discretized, is developed to represent the street gradients and to better estimate peoples' exposure. Three vertical levels are defined in each street segment. A horizontal discretization is also introduced under specific conditions by considering two zones with a parameterization taken from the Operational Street Pollution Model (OSPM). Simulations are performed over two districts of Copenhagen, Denmark, and one district of greater Paris, France. Results show an improvement in the comparison to observations, with higher concentrations at the bottom of the street, closer to traffic, of pollutants emitted by traffic (NOx, black carbon, organic matter). These increases reach up to 60 % for NO2 and 30 % for PM10 in comparison to MUNICH v2.0. The aspect ratio (ratio between building height and street width) influences the extent of the increase of the first-level concentrations compared to the average of the street. The increase is higher for wide streets (low aspect ratio and often higher traffic) by up to 53 % for NOx and 18 % for PM10. Finally, a sensitivity analysis with regard to the influence of the street network highlights the importance of using the model MUNICH with a network rather than with a single street.
{"title":"Modelling concentration heterogeneities in streets using the street-network model MUNICH","authors":"Thibaud Sarica, Alice Maison, Yelva Roustan, Matthias Ketzel, Steen Solvang Jensen, Youngseob Kim, Christophe Chaillou, Karine Sartelet","doi":"10.5194/gmd-16-5281-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-5281-2023","url":null,"abstract":"Abstract. Populations in urban areas are exposed to high local concentrations of pollutants, such as nitrogen dioxide and particulate matter, because of unfavourable dispersion conditions and the proximity to traffic. To simulate these concentrations over cities, models like the street-network model MUNICH (Model of Urban Network of Intersecting Canyons and Highways) rely on parameterizations to represent the air flow and the concentrations of pollutants in streets. In the current version, MUNICH v2.0, concentrations are assumed to be homogeneous in each street segment. A new version of MUNICH, where the street volume is discretized, is developed to represent the street gradients and to better estimate peoples' exposure. Three vertical levels are defined in each street segment. A horizontal discretization is also introduced under specific conditions by considering two zones with a parameterization taken from the Operational Street Pollution Model (OSPM). Simulations are performed over two districts of Copenhagen, Denmark, and one district of greater Paris, France. Results show an improvement in the comparison to observations, with higher concentrations at the bottom of the street, closer to traffic, of pollutants emitted by traffic (NOx, black carbon, organic matter). These increases reach up to 60 % for NO2 and 30 % for PM10 in comparison to MUNICH v2.0. The aspect ratio (ratio between building height and street width) influences the extent of the increase of the first-level concentrations compared to the average of the street. The increase is higher for wide streets (low aspect ratio and often higher traffic) by up to 53 % for NOx and 18 % for PM10. Finally, a sensitivity analysis with regard to the influence of the street network highlights the importance of using the model MUNICH with a network rather than with a single street.","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135552131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-14DOI: 10.5194/gmd-16-5265-2023
Mathieu Gravey, Grégoire Mariethoz
Abstract. Multiple-point geostatistics are widely used to simulate complex spatial structures based on a training image. The practical applicability of these methods relies on the possibility of finding optimal training images and parametrization of the simulation algorithms. While methods for automatically selecting training images are available, parametrization can be cumbersome. Here, we propose to find an optimal set of parameters using only the training image as input. The difference between this and previous work that used parametrization optimization is that it does not require the definition of an objective function. Our approach is based on the analysis of the errors that occur when filling artificially constructed patterns that have been borrowed from the training image. Its main advantage is to eliminate the risk of overfitting an objective function, which may result in variance underestimation or in verbatim copy of the training image. Since it is not based on optimization, our approach finds a set of acceptable parameters in a predictable manner by using the knowledge and understanding of how the simulation algorithms work. The technique is explored in the context of the recently developed QuickSampling algorithm, but it can be easily adapted to other pixel-based multiple-point statistics algorithms using pattern matching, such as direct sampling or single normal equation simulation (SNESIM).
{"title":"AutoQS v1: automatic parametrization of QuickSampling based on training images analysis","authors":"Mathieu Gravey, Grégoire Mariethoz","doi":"10.5194/gmd-16-5265-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-5265-2023","url":null,"abstract":"Abstract. Multiple-point geostatistics are widely used to simulate\u0000complex spatial structures based on a training image. The practical\u0000applicability of these methods relies on the possibility of finding optimal\u0000training images and parametrization of the simulation algorithms. While\u0000methods for automatically selecting training images are available,\u0000parametrization can be cumbersome. Here, we propose to find an optimal set\u0000of parameters using only the training image as input. The difference between\u0000this and previous work that used parametrization optimization is that it\u0000does not require the definition of an objective function. Our approach is\u0000based on the analysis of the errors that occur when filling artificially\u0000constructed patterns that have been borrowed from the training image. Its\u0000main advantage is to eliminate the risk of overfitting an objective\u0000function, which may result in variance underestimation or in verbatim copy\u0000of the training image. Since it is not based on optimization, our approach\u0000finds a set of acceptable parameters in a predictable manner by using the\u0000knowledge and understanding of how the simulation algorithms work. The\u0000technique is explored in the context of the recently developed QuickSampling\u0000algorithm, but it can be easily adapted to other pixel-based multiple-point\u0000statistics algorithms using pattern matching, such as direct sampling or\u0000single normal equation simulation (SNESIM).","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134913960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-13DOI: 10.5194/gmd-16-5237-2023
Daniel Yazgi, Tinja Olenius
Abstract. New-particle formation from condensable gases is a common atmospheric process that has significant but uncertain effects on aerosol particle number concentrations and aerosol–cloud–climate interactions. Assessing the formation rates of nanometer-sized particles from different vapors is an active field of research within atmospheric sciences, with new data being constantly produced by molecular modeling and experimental studies. Such data can be used in large-scale climate and air quality models through parameterizations or lookup tables. Molecular cluster dynamics modeling, ideally benchmarked against measurements when available for the given precursor vapors, provides a straightforward means to calculate high-resolution formation rate data over wide ranges of atmospheric conditions. Ideally, the incorporation of such data should be easy, efficient and flexible in the sense that same tools can be conveniently applied for different data sets in which the formation rate depends on different parameters. In this work, we present a tool to generate and interpolate lookup tables of formation rates for user-defined input parameters. The table generator primarily applies cluster dynamics modeling to calculate formation rates from an input quantum chemistry data set defined by the user, but the interpolator may also be used for tables generated by other models or data sources. The interpolation routine uses a multivariate interpolation algorithm, which is applicable to different numbers of independent parameters, and gives fast and accurate results with typical interpolation errors of up to a few percent. These routines facilitate the implementation and testing of different aerosol formation rate predictions in large-scale models, allowing the straightforward inclusion of new or updated data without the need to apply separate parameterizations or routines for different data sets that involve different chemical compounds or other parameters.
{"title":"J-GAIN v1.1: a flexible tool to incorporate aerosol formation rates obtained by molecular models into large-scale models","authors":"Daniel Yazgi, Tinja Olenius","doi":"10.5194/gmd-16-5237-2023","DOIUrl":"https://doi.org/10.5194/gmd-16-5237-2023","url":null,"abstract":"Abstract. New-particle formation from condensable gases is a common atmospheric process that has significant but uncertain effects on aerosol particle number concentrations and aerosol–cloud–climate interactions. Assessing the formation rates of nanometer-sized particles from different vapors is an active field of research within atmospheric sciences, with new data being constantly produced by molecular modeling and experimental studies. Such data can be used in large-scale climate and air quality models through parameterizations or lookup tables. Molecular cluster dynamics modeling, ideally benchmarked against measurements when available for the given precursor vapors, provides a straightforward means to calculate high-resolution formation rate data over wide ranges of atmospheric conditions. Ideally, the incorporation of such data should be easy, efficient and flexible in the sense that same tools can be conveniently applied for different data sets in which the formation rate depends on different parameters. In this work, we present a tool to generate and interpolate lookup tables of formation rates for user-defined input parameters. The table generator primarily applies cluster dynamics modeling to calculate formation rates from an input quantum chemistry data set defined by the user, but the interpolator may also be used for tables generated by other models or data sources. The interpolation routine uses a multivariate interpolation algorithm, which is applicable to different numbers of independent parameters, and gives fast and accurate results with typical interpolation errors of up to a few percent. These routines facilitate the implementation and testing of different aerosol formation rate predictions in large-scale models, allowing the straightforward inclusion of new or updated data without the need to apply separate parameterizations or routines for different data sets that involve different chemical compounds or other parameters.","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135689297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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