Pub Date : 2024-03-19DOI: 10.5194/gmd-17-2247-2024
Romain Pilon, Daniella I. V. Domeisen
Abstract. Persistent and organized convective cloud systems that arise in convergence zones can lead to the formation of synoptic cloud bands extending from the tropics to the extratropics. These cloud bands are responsible for heavy precipitation and are often a combination of tropical intrusions of extratropical Rossby waves and processes originating from the tropics. Detecting these cloud bands presents a valuable opportunity to enhance our understanding of the variability of these systems and the underlying processes that govern their behavior and that connect the tropics and the extratropics. This paper presents a new atmospheric cloud band detection method based on outgoing longwave radiation using computer vision techniques, which offers enhanced capabilities to identify long cloud bands across diverse gridded datasets and variables. The method is specifically designed to detect extended tropical–extratropical convective cloud bands, ensuring accurate identification and analysis of these dynamic atmospheric features in convergence zones. The code allows for easy configuration and adaptation of the algorithm to meet specific research needs. The method handles cloud band merging and splitting, which allows for an understanding of the life cycle of cloud bands and their climatology. This algorithm lays the groundwork for improving our understanding of the large-scale processes that are involved in the formation and life cycle of cloud bands and the connections between tropical and extratropical regions as well as evaluating the differences in cloud band types between different ocean basins.
{"title":"cloudbandPy 1.0: an automated algorithm for the detection of tropical–extratropical cloud bands","authors":"Romain Pilon, Daniella I. V. Domeisen","doi":"10.5194/gmd-17-2247-2024","DOIUrl":"https://doi.org/10.5194/gmd-17-2247-2024","url":null,"abstract":"Abstract. Persistent and organized convective cloud systems that arise in convergence zones can lead to the formation of synoptic cloud bands extending from the tropics to the extratropics. These cloud bands are responsible for heavy precipitation and are often a combination of tropical intrusions of extratropical Rossby waves and processes originating from the tropics. Detecting these cloud bands presents a valuable opportunity to enhance our understanding of the variability of these systems and the underlying processes that govern their behavior and that connect the tropics and the extratropics. This paper presents a new atmospheric cloud band detection method based on outgoing longwave radiation using computer vision techniques, which offers enhanced capabilities to identify long cloud bands across diverse gridded datasets and variables. The method is specifically designed to detect extended tropical–extratropical convective cloud bands, ensuring accurate identification and analysis of these dynamic atmospheric features in convergence zones. The code allows for easy configuration and adaptation of the algorithm to meet specific research needs. The method handles cloud band merging and splitting, which allows for an understanding of the life cycle of cloud bands and their climatology. This algorithm lays the groundwork for improving our understanding of the large-scale processes that are involved in the formation and life cycle of cloud bands and the connections between tropical and extratropical regions as well as evaluating the differences in cloud band types between different ocean basins.\u0000","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140230418","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 : 2024-03-19DOI: 10.5194/gmd-17-2221-2024
Ivan Hernandez, Leidy M. Castro-Rosero, Manuel Espino, Jose M. Alsina Torrent
Abstract. The transport mechanisms of floating marine debris in coastal zones remain poorly understood due to complex geometries and the influence of coastal processes, posing difficulties in incorporating them into Lagrangian numerical models. The numerical model LOCATE overcomes these challenges by coupling Eulerian hydrodynamic data at varying resolutions within nested grids using Parcels, a Lagrangian particle solver, to accurately simulate the motion of plastic particles where a high spatial coverage and resolution are required to resolve coastal processes. Nested grids performed better than a coarse-resolution grid when analysing the model's dispersion skill by comparing drifter data and simulated trajectories. A sensitivity analysis of different beaching conditions comparing spatiotemporal beaching patterns demonstrated notable differences in the land–water boundary detection between nested hydrodynamic grids and high-resolution shoreline data. The latter formed the basis for a beaching module that parameterised beaching by calculating the particle distance to the shore during the simulation. A realistic debris discharge scenario comparison around the Barcelona coastline using the distance-based beaching module in conjunction with nested grids or a coarse-resolution grid revealed very high levels of particle beaching (>91.5%) in each case, demonstrating the importance of appropriately parameterising beaching at coastal scales. In this scenario, high variability in particle residence times and beaching patterns was observed between simulations. These differences derived from how each option resolved the shoreline, with particle residence times being much higher in areas of intricate shoreline configurations when using nested grids, thus resolving complex structures that were undetectable using the coarse-resolution grid. LOCATE can effectively integrate high-resolution hydrodynamic data within nested grids to model the dispersion and deposition patterns of particles at coastal scales using high-resolution shoreline data for shoreline detection uniformity.
{"title":"LOCATE v1.0: numerical modelling of floating marine debris dispersion in coastal regions using Parcels v2.4.2","authors":"Ivan Hernandez, Leidy M. Castro-Rosero, Manuel Espino, Jose M. Alsina Torrent","doi":"10.5194/gmd-17-2221-2024","DOIUrl":"https://doi.org/10.5194/gmd-17-2221-2024","url":null,"abstract":"Abstract. The transport mechanisms of floating marine debris in coastal zones remain poorly understood due to complex geometries and the influence of coastal processes, posing difficulties in incorporating them into Lagrangian numerical models. The numerical model LOCATE overcomes these challenges by coupling Eulerian hydrodynamic data at varying resolutions within nested grids using Parcels, a Lagrangian particle solver, to accurately simulate the motion of plastic particles where a high spatial coverage and resolution are required to resolve coastal processes. Nested grids performed better than a coarse-resolution grid when analysing the model's dispersion skill by comparing drifter data and simulated trajectories. A sensitivity analysis of different beaching conditions comparing spatiotemporal beaching patterns demonstrated notable differences in the land–water boundary detection between nested hydrodynamic grids and high-resolution shoreline data. The latter formed the basis for a beaching module that parameterised beaching by calculating the particle distance to the shore during the simulation. A realistic debris discharge scenario comparison around the Barcelona coastline using the distance-based beaching module in conjunction with nested grids or a coarse-resolution grid revealed very high levels of particle beaching (>91.5%) in each case, demonstrating the importance of appropriately parameterising beaching at coastal scales. In this scenario, high variability in particle residence times and beaching patterns was observed between simulations. These differences derived from how each option resolved the shoreline, with particle residence times being much higher in areas of intricate shoreline configurations when using nested grids, thus resolving complex structures that were undetectable using the coarse-resolution grid. LOCATE can effectively integrate high-resolution hydrodynamic data within nested grids to model the dispersion and deposition patterns of particles at coastal scales using high-resolution shoreline data for shoreline detection uniformity.\u0000","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140229396","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 : 2024-03-19DOI: 10.5194/gmd-17-2197-2024
Stefan J. Miller, Paul A. Makar, Colin J. Lee
Abstract. We describe a new Fortran computer program to solve the system of equations for the NH4+–Na+–Ca2+–K+–Mg2+–SO42-–NO3-–Cl−–H2O system, based on the algorithms of ISORROPIA II. Specifically, the code solves the system of equations describing the “forward” (gas + aerosol input) metastable state but with algorithm improvements and corrections. These algorithm changes allow the code to deliver more accurate solution results in formal evaluations of accuracy of the roots of the systems of equations, while reducing processing time in practical applications by about 50 %. The improved solution performance results from several implementation improvements relative to the original ISORROPIA algorithms. These improvements include (i) the use of the “interpolate, truncate and project” (ITP) root-finding approach rather than bisection, (ii) the allowance of search interval endpoints as valid roots at the onset of a search, (iii) the use of a more accurate method to solve polynomial subsystems of equations, (iv) the elimination of negative concentrations during iterative solutions, (v) corrections for mass conservation enforcement, and (vi) several code structure improvements. The new code may be run in either a “vectorization” mode wherein a global convergence criterion is used across multiple tests within the same chemical subspace or a “by case-by-case” mode wherein individual test cases are solved with the same convergence criteria. The latter approach was found to be more efficient on the compiler tested here, but users of the code are recommended to test both options on their own systems. The new code has been constructed to explicitly conserve the input mass for all species considered in the solver and is provided as open-source Fortran shareware.
{"title":"HETerogeneous vectorized or Parallel (HETPv1.0): an updated inorganic heterogeneous chemistry solver for the metastable-state NH4+–Na+–Ca2+–K+–Mg2+–SO42−–NO3−–Cl−–H2O system based on ISORROPIA II","authors":"Stefan J. Miller, Paul A. Makar, Colin J. Lee","doi":"10.5194/gmd-17-2197-2024","DOIUrl":"https://doi.org/10.5194/gmd-17-2197-2024","url":null,"abstract":"Abstract. We describe a new Fortran computer program to solve the system of equations for the NH4+–Na+–Ca2+–K+–Mg2+–SO42-–NO3-–Cl−–H2O system, based on the algorithms of ISORROPIA II. Specifically, the code solves the system of equations describing the “forward” (gas + aerosol input) metastable state but with algorithm improvements and corrections. These algorithm changes allow the code to deliver more accurate solution results in formal evaluations of accuracy of the roots of the systems of equations, while reducing processing time in practical applications by about 50 %. The improved solution performance results from several implementation improvements relative to the original ISORROPIA algorithms. These improvements include (i) the use of the “interpolate, truncate and project” (ITP) root-finding approach rather than bisection, (ii) the allowance of search interval endpoints as valid roots at the onset of a search, (iii) the use of a more accurate method to solve polynomial subsystems of equations, (iv) the elimination of negative concentrations during iterative solutions, (v) corrections for mass conservation enforcement, and (vi) several code structure improvements. The new code may be run in either a “vectorization” mode wherein a global convergence criterion is used across multiple tests within the same chemical subspace or a “by case-by-case” mode wherein individual test cases are solved with the same convergence criteria. The latter approach was found to be more efficient on the compiler tested here, but users of the code are recommended to test both options on their own systems. The new code has been constructed to explicitly conserve the input mass for all species considered in the solver and is provided as open-source Fortran shareware.\u0000","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140229554","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 : 2024-03-15DOI: 10.5194/gmd-17-2165-2024
T. Gan, G. Tucker, E. Hutton, M. Piper, I. Overeem, A. Kettner, Benjamin Campforts, J. Moriarty, B. Undzis, Ethan Pierce, L. McCready
Abstract. Progress in better understanding and modeling Earth surface systems requires an ongoing integration of data and numerical models. Advances are currently hampered by technical barriers that inhibit finding, accessing, and executing modeling software with related datasets. We propose a design framework for Data Components, which are software packages that provide access to particular research datasets or types of data. Because they use a standard interface based on the Basic Model Interface (BMI), Data Components can function as plug-and-play components within modeling frameworks to facilitate seamless data–model integration. To illustrate the design and potential applications of Data Components and their advantages, we present several case studies in Earth surface processes analysis and modeling. The results demonstrate that the Data Component design provides a consistent and efficient way to access heterogeneous datasets from multiple sources and to seamlessly integrate them with various models. This design supports the creation of open data–model integration workflows that can be discovered, accessed, and reproduced through online data sharing platforms, which promotes data reuse and improves research transparency and reproducibility.
{"title":"CSDMS Data Components: data–model integration tools for Earth surface processes modeling","authors":"T. Gan, G. Tucker, E. Hutton, M. Piper, I. Overeem, A. Kettner, Benjamin Campforts, J. Moriarty, B. Undzis, Ethan Pierce, L. McCready","doi":"10.5194/gmd-17-2165-2024","DOIUrl":"https://doi.org/10.5194/gmd-17-2165-2024","url":null,"abstract":"Abstract. Progress in better understanding and modeling Earth surface systems requires an ongoing integration of data and numerical models. Advances are currently hampered by technical barriers that inhibit finding, accessing, and executing modeling software with related datasets. We propose a design framework for Data Components, which are software packages that provide access to particular research datasets or types of data. Because they use a standard interface based on the Basic Model Interface (BMI), Data Components can function as plug-and-play components within modeling frameworks to facilitate seamless data–model integration. To illustrate the design and potential applications of Data Components and their advantages, we present several case studies in Earth surface processes analysis and modeling. The results demonstrate that the Data Component design provides a consistent and efficient way to access heterogeneous datasets from multiple sources and to seamlessly integrate them with various models. This design supports the creation of open data–model integration workflows that can be discovered, accessed, and reproduced through online data sharing platforms, which promotes data reuse and improves research transparency and reproducibility.\u0000","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140240282","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 : 2024-03-15DOI: 10.5194/gmd-17-2141-2024
Pedro F. Arboleda-Obando, A. Ducharne, Z. Yin, P. Ciais
Abstract. Irrigation activities are important for sustaining food production and account for 70 % of total global water withdrawals. In addition, due to increased evapotranspiration (ET) and changes in the leaf area index (LAI), these activities have an impact on hydrology and climate. In this paper, we present a new irrigation scheme within the land surface model ORCHIDEE (ORganising Carbon and Hydrology in Dynamic EcosystEms)). It restrains actual irrigation according to available freshwater by including a simple environmental limit and using allocation rules that depend on local infrastructure. We perform a simple sensitivity analysis and parameter tuning to set the parameter values and match the observed irrigation amounts against reported values, assuming uniform parameter values over land. Our scheme matches irrigation withdrawals amounts at global scale, but we identify some areas in India, China, and the USA (some of the most intensively irrigated regions worldwide), where irrigation is underestimated. In all irrigated areas, the scheme reduces the negative bias of ET. It also exacerbates the positive bias of the leaf area index (LAI), except for the very intensively irrigated areas, where irrigation reduces a negative LAI bias. The increase in the ET decreases river discharge values, in some cases significantly, although this does not necessarily lead to a better representation of discharge dynamics. Irrigation, however, does not have a large impact on the simulated total water storage anomalies (TWSAs) and its trends. This may be partly explained by the absence of nonrenewable groundwater use, and its inclusion could increase irrigation estimates in arid and semiarid regions by increasing the supply. Correlation of irrigation biases with landscape descriptors suggests that the inclusion of irrigated rice and dam management could improve the irrigation estimates as well. Regardless of this complexity, our results show that the new irrigation scheme helps simulate acceptable land surface conditions and fluxes in irrigated areas, which is important to explore the joint evolution of climate, water resources, and irrigation activities.
摘要灌溉活动对维持粮食生产非常重要，占全球总取水量的 70%。此外，由于蒸散量（ET）的增加和叶面积指数（LAI）的变化，这些活动对水文和气候产生了影响。在本文中，我们在地表模型 ORCHIDEE（ORganising Carbon and Hydrology in Dynamic EcosystEms）中提出了一种新的灌溉方案。）它通过简单的环境限制和使用取决于当地基础设施的分配规则，根据可用淡水限制实际灌溉。我们进行了简单的敏感性分析和参数调整，以设定参数值，并将观测到的灌溉量与报告值进行匹配，同时假设土地上的参数值是统一的。我们的方案与全球范围内的灌溉取水量相匹配，但我们发现在印度、中国和美国（全球灌溉最密集的地区）的一些地区，灌溉量被低估了。在所有灌溉地区，该方案都减少了蒸散发的负偏差。它还加剧了叶面积指数（LAI）的正偏差，但灌溉非常密集的地区除外，在这些地区，灌溉减少了叶面积指数的负偏差。蒸散发的增加降低了河流的排泄值，在某些情况下降幅很大，但这并不一定能更好地反映排泄动态。然而，灌溉对模拟的总蓄水量异常值（TWSAs）及其变化趋势的影响并不大。部分原因可能是由于没有使用不可再生的地下水，而将其包括在内可能会通过增加供水量来提高干旱和半干旱地区的灌溉估算值。灌溉偏差与地貌描述因子的相关性表明，纳入灌溉水稻和水坝管理也可以改善灌溉估算。尽管存在这种复杂性，但我们的研究结果表明，新的灌溉方案有助于模拟灌溉区可接受的地表条件和通量，这对于探索气候、水资源和灌溉活动的共同演变非常重要。
{"title":"Validation of a new global irrigation scheme in the land surface model ORCHIDEE v2.2","authors":"Pedro F. Arboleda-Obando, A. Ducharne, Z. Yin, P. Ciais","doi":"10.5194/gmd-17-2141-2024","DOIUrl":"https://doi.org/10.5194/gmd-17-2141-2024","url":null,"abstract":"Abstract. Irrigation activities are important for sustaining food production and account for 70 % of total global water withdrawals. In addition, due to increased evapotranspiration (ET) and changes in the leaf area index (LAI), these activities have an impact on hydrology and climate. In this paper, we present a new irrigation scheme within the land surface model ORCHIDEE (ORganising Carbon and Hydrology in Dynamic EcosystEms)). It restrains actual irrigation according to available freshwater by including a simple environmental limit and using allocation rules that depend on local infrastructure. We perform a simple sensitivity analysis and parameter tuning to set the parameter values and match the observed irrigation amounts against reported values, assuming uniform parameter values over land. Our scheme matches irrigation withdrawals amounts at global scale, but we identify some areas in India, China, and the USA (some of the most intensively irrigated regions worldwide), where irrigation is underestimated. In all irrigated areas, the scheme reduces the negative bias of ET. It also exacerbates the positive bias of the leaf area index (LAI), except for the very intensively irrigated areas, where irrigation reduces a negative LAI bias. The increase in the ET decreases river discharge values, in some cases significantly, although this does not necessarily lead to a better representation of discharge dynamics. Irrigation, however, does not have a large impact on the simulated total water storage anomalies (TWSAs) and its trends. This may be partly explained by the absence of nonrenewable groundwater use, and its inclusion could increase irrigation estimates in arid and semiarid regions by increasing the supply. Correlation of irrigation biases with landscape descriptors suggests that the inclusion of irrigated rice and dam management could improve the irrigation estimates as well. Regardless of this complexity, our results show that the new irrigation scheme helps simulate acceptable land surface conditions and fluxes in irrigated areas, which is important to explore the joint evolution of climate, water resources, and irrigation activities.\u0000","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140238738","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 : 2024-03-15DOI: 10.5194/gmd-17-2187-2024
U. M. Durdag
Abstract. Geodetic observations are crucial for monitoring landslides, crustal movements, and volcanic activity. They are often integrated with data from interdisciplinary studies, including paleo-seismological, geological, and interferometric synthetic aperture radar observations, to analyze earthquake hazards. However, outliers in geodetic observations can significantly impact the accuracy of estimation results if not reliably identified. Therefore, assessing the outlier detection model's reliability is imperative to ensure accurate interpretations. Conventional and robust methods are based on the additive bias model, which may cause type-I and type-II errors. However, outliers can be regarded as additional unknown parameters in the Gauss–Markov model. It is based on modeling the outliers as unknown parameters, considering as many combinations as possible of outliers selected from the observation set. In addition, this method is expected to be more effective than conventional methods as it is based on the principle of minimal variance and eliminates the interdependence of decisions made in iterations. The primary purpose of this study is to seek an efficient outlier detection model in the geodetic networks. The efficiency of the proposed model was measured and compared with the robust and conventional methods by the mean success rate (MSR) indicator of different types and magnitudes of outliers. Thereby, this model enhances the MSR by almost 40 %–45 % compared to the Baarda and Danish (with the variance unknown case) method for multiple outliers. Besides, the proposed model is 20 %–30 % more successful than the others in the low-controllability observations of the leveling network.
摘要。大地测量观测对于监测山体滑坡、地壳运动和火山活动至关重要。大地测量观测数据通常与古地震学、地质学和干涉合成孔径雷达观测等跨学科研究数据相结合，用于分析地震灾害。然而，如果不能可靠地识别大地测量观测数据中的异常值，就会严重影响估算结果的准确性。因此，评估异常值检测模型的可靠性对于确保准确解释至关重要。传统的稳健方法以加法偏差模型为基础，可能会造成 I 类和 II 类误差。然而，离群值可被视为高斯-马尔科夫模型中的额外未知参数。该方法将离群值作为未知参数建模，尽可能多地考虑从观测集中选取的离群值组合。此外，这种方法基于最小方差原则，消除了迭代中决策的相互依赖性，因此预计比传统方法更有效。本研究的主要目的是在大地测量网络中寻找一种高效的离群点检测模型。通过不同类型和大小的离群值的平均成功率（MSR）指标，对所提出模型的效率进行了测量，并与稳健方法和传统方法进行了比较。因此，与 Baarda 和 Danish（方差未知情况下）方法相比，该模型提高了近 40%-45% 的多重异常值平均成功率。此外，在平差网络的低可控性观测中，所提出的模型比其他模型的成功率高 20%-30%。
{"title":"Minimum-variance-based outlier detection method using forward-search model error in geodetic networks","authors":"U. M. Durdag","doi":"10.5194/gmd-17-2187-2024","DOIUrl":"https://doi.org/10.5194/gmd-17-2187-2024","url":null,"abstract":"Abstract. Geodetic observations are crucial for monitoring landslides, crustal movements, and volcanic activity. They are often integrated with data from interdisciplinary studies, including paleo-seismological, geological, and interferometric synthetic aperture radar observations, to analyze earthquake hazards. However, outliers in geodetic observations can significantly impact the accuracy of estimation results if not reliably identified. Therefore, assessing the outlier detection model's reliability is imperative to ensure accurate interpretations. Conventional and robust methods are based on the additive bias model, which may cause type-I and type-II errors. However, outliers can be regarded as additional unknown parameters in the Gauss–Markov model. It is based on modeling the outliers as unknown parameters, considering as many combinations as possible of outliers selected from the observation set. In addition, this method is expected to be more effective than conventional methods as it is based on the principle of minimal variance and eliminates the interdependence of decisions made in iterations. The primary purpose of this study is to seek an efficient outlier detection model in the geodetic networks. The efficiency of the proposed model was measured and compared with the robust and conventional methods by the mean success rate (MSR) indicator of different types and magnitudes of outliers. Thereby, this model enhances the MSR by almost 40 %–45 % compared to the Baarda and Danish (with the variance unknown case) method for multiple outliers. Besides, the proposed model is 20 %–30 % more successful than the others in the low-controllability observations of the leveling network.\u0000","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140240437","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 : 2024-03-13DOI: 10.5194/gmd-17-2117-2024
T. Extier, T. Caley, Didier Roche
Abstract. Stable water isotopes are used to infer changes in the hydrological cycle for different climate periods and various climatic archives. Following previous developments of δ18O in the coupled climate model of intermediate complexity, iLOVECLIM, we present here the implementation of the 1H2H16O and 1H217O water isotopes in the different components of this model and calculate the associated secondary markers deuterium excess (d-excess) and oxygen-17 excess (17O-excess) in the atmosphere and ocean. So far, the latter has only been modelled by the atmospheric model LMDZ4. Results of a 5000-year equilibrium simulation under preindustrial conditions are analysed and compared to observations and several isotope-enabled models for the atmosphere and ocean components. In the atmospheric component, the model correctly reproduces the first-order global distribution of the δ2H and d-excess as observed in the data (R=0.56 for δ2H and 0.36 for d-excess), even if local differences are observed. The model–data correlation is within the range of other water-isotope-enabled general circulation models. The main isotopic effects and the latitudinal gradient are properly modelled, similarly to previous water-isotope-enabled general circulation model simulations, despite a simplified atmospheric component in iLOVECLIM. One exception is observed in Antarctica where the model does not correctly estimate the water isotope composition, a consequence of the non-conservative behaviour of the advection scheme at a very low moisture content. The modelled 17O-excess presents a too-important dispersion of the values in comparison to the observations and is not correctly reproduced in the model, mainly because of the complex processes involved in the 17O-excess isotopic value. For the ocean, the model simulates an adequate isotopic ratio in comparison to the observations, except for local areas such as the surface of the Arabian Sea, a part of the Arctic and the western equatorial Indian Ocean. Data–model evaluation also presents a good match for the δ2H over the entire water column in the Atlantic Ocean, reflecting the influence of the different water masses.
{"title":"Modelling water isotopologues (1H2H16O, 1H217O) in the coupled numerical climate model iLOVECLIM (version 1.1.5)","authors":"T. Extier, T. Caley, Didier Roche","doi":"10.5194/gmd-17-2117-2024","DOIUrl":"https://doi.org/10.5194/gmd-17-2117-2024","url":null,"abstract":"Abstract. Stable water isotopes are used to infer changes in the hydrological cycle for different climate periods and various climatic archives. Following previous developments of δ18O in the coupled climate model of intermediate complexity, iLOVECLIM, we present here the implementation of the 1H2H16O and 1H217O water isotopes in the different components of this model and calculate the associated secondary markers deuterium excess (d-excess) and oxygen-17 excess (17O-excess) in the atmosphere and ocean. So far, the latter has only been modelled by the atmospheric model LMDZ4. Results of a 5000-year equilibrium simulation under preindustrial conditions are analysed and compared to observations and several isotope-enabled models for the atmosphere and ocean components. In the atmospheric component, the model correctly reproduces the first-order global distribution of the δ2H and d-excess as observed in the data (R=0.56 for δ2H and 0.36 for d-excess), even if local differences are observed. The model–data correlation is within the range of other water-isotope-enabled general circulation models. The main isotopic effects and the latitudinal gradient are properly modelled, similarly to previous water-isotope-enabled general circulation model simulations, despite a simplified atmospheric component in iLOVECLIM. One exception is observed in Antarctica where the model does not correctly estimate the water isotope composition, a consequence of the non-conservative behaviour of the advection scheme at a very low moisture content. The modelled 17O-excess presents a too-important dispersion of the values in comparison to the observations and is not correctly reproduced in the model, mainly because of the complex processes involved in the 17O-excess isotopic value. For the ocean, the model simulates an adequate isotopic ratio in comparison to the observations, except for local areas such as the surface of the Arabian Sea, a part of the Arctic and the western equatorial Indian Ocean. Data–model evaluation also presents a good match for the δ2H over the entire water column in the Atlantic Ocean, reflecting the influence of the different water masses.\u0000","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140246521","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 : 2024-03-13DOI: 10.5194/gmd-17-2077-2024
Jérémy Bernard, E. Bocher, Matthieu Gousseff, François Leconte, Elisabeth Le Saux Wiederhold
Abstract. Geographical features may have a considerable effect on local climate. The local climate zone (LCZ) system proposed by Stewart and Oke (2012) is nowadays seen as a standard approach for classifying any zone according to a set of urban canopy parameters. While many methods already exist to map the LCZ, only few tools are openly and freely available. This paper presents the algorithm implemented in the GeoClimate software to identify the LCZ of any place in the world based on vector data. Six types of information are needed as input: the building footprint, road and rail networks, water, vegetation, and impervious surfaces. First, the territory is partitioned into reference spatial units (RSUs) using the road and rail network, as well as the boundaries of large vegetation and water patches. Then 14 urban canopy parameters are calculated for each RSU. Their values are used to classify each unit to a given LCZ type according to a set of rules. GeoClimate can automatically prepare the inputs and calculate the LCZ for two datasets, namely OpenStreetMap (OSM, available worldwide) and the BD TOPO® v2.2 (BDT, a French dataset produced by the national mapping agency). The LCZ are calculated for 22 French communes using these two datasets in order to evaluate the effect of the dataset on the results. About 55 % of all areas have obtained the same LCZ type, with large differences when differentiating this result by city (from 30 % to 82 %). The agreement is good for large patches of forest and water, as well as for compact mid-rise and open low-rise LCZ types. It is lower for open mid-rise and open high-rise, mainly due to the height underestimation of OSM buildings located in open areas. Through its simplicity of use, GeoClimate has great potential for new collaboration in the LCZ field. The software (and its source code) used to produce the LCZ data is freely available at https://doi.org/10.5281/zenodo.6372337 (Bocher et al., 2022); the scripts and data used for the purpose of this article can be freely accessed at https://doi.org/10.5281/zenodo.7687911 (Bernard et al., 2023) and are based on the R package available at https://doi.org/10.5281/zenodo.7646866 (Gousseff, 2023).
{"title":"A generic algorithm to automatically classify urban fabric according to the local climate zone system: implementation in GeoClimate 0.0.1 and application to French cities","authors":"Jérémy Bernard, E. Bocher, Matthieu Gousseff, François Leconte, Elisabeth Le Saux Wiederhold","doi":"10.5194/gmd-17-2077-2024","DOIUrl":"https://doi.org/10.5194/gmd-17-2077-2024","url":null,"abstract":"Abstract. Geographical features may have a considerable effect on local climate. The local climate zone (LCZ) system proposed by Stewart and Oke (2012) is nowadays seen as a standard approach for classifying any zone according to a set of urban canopy parameters. While many methods already exist to map the LCZ, only few tools are openly and freely available. This paper presents the algorithm implemented in the GeoClimate software to identify the LCZ of any place in the world based on vector data. Six types of information are needed as input: the building footprint, road and rail networks, water, vegetation, and impervious surfaces. First, the territory is partitioned into reference spatial units (RSUs) using the road and rail network, as well as the boundaries of large vegetation and water patches. Then 14 urban canopy parameters are calculated for each RSU. Their values are used to classify each unit to a given LCZ type according to a set of rules. GeoClimate can automatically prepare the inputs and calculate the LCZ for two datasets, namely OpenStreetMap (OSM, available worldwide) and the BD TOPO® v2.2 (BDT, a French dataset produced by the national mapping agency). The LCZ are calculated for 22 French communes using these two datasets in order to evaluate the effect of the dataset on the results. About 55 % of all areas have obtained the same LCZ type, with large differences when differentiating this result by city (from 30 % to 82 %). The agreement is good for large patches of forest and water, as well as for compact mid-rise and open low-rise LCZ types. It is lower for open mid-rise and open high-rise, mainly due to the height underestimation of OSM buildings located in open areas. Through its simplicity of use, GeoClimate has great potential for new collaboration in the LCZ field. The software (and its source code) used to produce the LCZ data is freely available at https://doi.org/10.5281/zenodo.6372337 (Bocher et al., 2022); the scripts and data used for the purpose of this article can be freely accessed at https://doi.org/10.5281/zenodo.7687911 (Bernard et al., 2023) and are based on the R package available at https://doi.org/10.5281/zenodo.7646866 (Gousseff, 2023).\u0000","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140246432","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 : 2024-03-12DOI: 10.5194/gmd-17-2053-2024
S. Larosa, Domenico Cimini, D. Gallucci, S. Nilo, F. Romano
Abstract. This article introduces PyRTlib, a new standalone Python package for non-scattering line-by-line microwave radiative transfer simulations. PyRTlib is a flexible and user-friendly tool for computing down- and upwelling brightness temperatures and related quantities (e.g., atmospheric absorption, optical depth, opacity, mean radiating temperature) written in Python, a language commonly used nowadays for scientific software development, especially by students and early-career scientists. PyRTlib allows for simulating observations from ground-based, airborne, and satellite microwave sensors in clear-sky and in cloudy conditions (under non-scattering Rayleigh approximation). The intention for PyRTlib is not to be a competitor to state-of-the-art atmospheric radiative transfer codes that excel in speed and/or versatility (e.g., ARTS, Atmospheric Radiative Transfer Simulator; RTTOV, Radiative Transfer for TOVS (Television Infrared Observation Satellite (TIROS) Operational Vertical Sounder)). The intention is to provide an educational tool, completely written in Python, to readily simulate atmospheric microwave radiative transfer from a variety of input profiles, including predefined climatologies, global radiosonde archives, and model reanalysis. The paper presents quick examples for the built-in modules to access popular open data archives. The paper also presents examples for computing the simulated brightness temperature for different platforms (ground-based, airborne, and satellite), using various input profiles, showing how to easily modify other relevant parameters, such as the observing angle (zenith, nadir, slant), surface emissivity, and gas absorption model. PyRTlib can be easily embedded in other Python codes needing atmospheric microwave radiative transfer (e.g., surface emissivity models and retrievals). Despite its simplicity, PyRTlib can be readily used to produce present-day scientific results, as demonstrated by two examples showing (i) an absorption model comparison and validation with ground-based radiometric observations and (ii) uncertainty propagation of spectroscopic parameters through the radiative transfer calculations following a rigorous approach. To our knowledge, the uncertainty estimate is not provided by any other currently available microwave radiative transfer code, making PyRTlib unique for this aspect in the atmospheric microwave radiative transfer code scenario.
{"title":"PyRTlib: an educational Python-based library for non-scattering atmospheric microwave radiative transfer computations","authors":"S. Larosa, Domenico Cimini, D. Gallucci, S. Nilo, F. Romano","doi":"10.5194/gmd-17-2053-2024","DOIUrl":"https://doi.org/10.5194/gmd-17-2053-2024","url":null,"abstract":"Abstract. This article introduces PyRTlib, a new standalone Python package for non-scattering line-by-line microwave radiative transfer simulations. PyRTlib is a flexible and user-friendly tool for computing down- and upwelling brightness temperatures and related quantities (e.g., atmospheric absorption, optical depth, opacity, mean radiating temperature) written in Python, a language commonly used nowadays for scientific software development, especially by students and early-career scientists. PyRTlib allows for simulating observations from ground-based, airborne, and satellite microwave sensors in clear-sky and in cloudy conditions (under non-scattering Rayleigh approximation). The intention for PyRTlib is not to be a competitor to state-of-the-art atmospheric radiative transfer codes that excel in speed and/or versatility (e.g., ARTS, Atmospheric Radiative Transfer Simulator; RTTOV, Radiative Transfer for TOVS (Television Infrared Observation Satellite (TIROS) Operational Vertical Sounder)). The intention is to provide an educational tool, completely written in Python, to readily simulate atmospheric microwave radiative transfer from a variety of input profiles, including predefined climatologies, global radiosonde archives, and model reanalysis. The paper presents quick examples for the built-in modules to access popular open data archives. The paper also presents examples for computing the simulated brightness temperature for different platforms (ground-based, airborne, and satellite), using various input profiles, showing how to easily modify other relevant parameters, such as the observing angle (zenith, nadir, slant), surface emissivity, and gas absorption model. PyRTlib can be easily embedded in other Python codes needing atmospheric microwave radiative transfer (e.g., surface emissivity models and retrievals). Despite its simplicity, PyRTlib can be readily used to produce present-day scientific results, as demonstrated by two examples showing (i) an absorption model comparison and validation with ground-based radiometric observations and (ii) uncertainty propagation of spectroscopic parameters through the radiative transfer calculations following a rigorous approach. To our knowledge, the uncertainty estimate is not provided by any other currently available microwave radiative transfer code, making PyRTlib unique for this aspect in the atmospheric microwave radiative transfer code scenario.\u0000","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140248637","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 : 2024-03-07DOI: 10.5194/gmd-17-2015-2024
M. Pehl, Felix Schreyer, Gunnar Luderer
Abstract. This paper presents an extension of industry modelling within the REMIND integrated assessment model to industry subsectors and a projection of future industry subsector activity and energy demand for different baseline scenarios for use with the REMIND model. The industry sector is the largest greenhouse-gas-emitting energy demand sector and is considered a mitigation bottleneck. At the same time, industry subsectors are heterogeneous and face distinct emission mitigation challenges. By extending the multi-region, general equilibrium integrated assessment model REMIND to an explicit representation of four industry subsectors (cement, chemicals, steel, and other industry production), along with subsector-specific carbon capture and sequestration (CCS), we are able to investigate industry emission mitigation strategies in the context of the entire energy–economy–climate system, covering mitigation options ranging from reduced demand for industrial goods, fuel switching, and electrification to endogenous energy efficiency increases and carbon capture. We also present the derivation of both activity and final energy demand trajectories for the industry subsectors for use with the REMIND model in baseline scenarios, based on short-term continuation of historic trends and long-term global convergence. The system allows for selective variation of specific subsector activity and final energy demand across scenarios and regions to create consistent scenarios for a wide range of socioeconomic drivers and scenario story lines, like the Shared Socioeconomic Pathways (SSPs).
{"title":"Modelling long-term industry energy demand and CO2 emissions in the system context using REMIND (version 3.1.0)","authors":"M. Pehl, Felix Schreyer, Gunnar Luderer","doi":"10.5194/gmd-17-2015-2024","DOIUrl":"https://doi.org/10.5194/gmd-17-2015-2024","url":null,"abstract":"Abstract. This paper presents an extension of industry modelling within the REMIND integrated assessment model to industry subsectors and a projection of future industry subsector activity and energy demand for different baseline scenarios for use with the REMIND model. The industry sector is the largest greenhouse-gas-emitting energy demand sector and is considered a mitigation bottleneck. At the same time, industry subsectors are heterogeneous and face distinct emission mitigation challenges. By extending the multi-region, general equilibrium integrated assessment model REMIND to an explicit representation of four industry subsectors (cement, chemicals, steel, and other industry production), along with subsector-specific carbon capture and sequestration (CCS), we are able to investigate industry emission mitigation strategies in the context of the entire energy–economy–climate system, covering mitigation options ranging from reduced demand for industrial goods, fuel switching, and electrification to endogenous energy efficiency increases and carbon capture. We also present the derivation of both activity and final energy demand trajectories for the industry subsectors for use with the REMIND model in baseline scenarios, based on short-term continuation of historic trends and long-term global convergence. The system allows for selective variation of specific subsector activity and final energy demand across scenarios and regions to create consistent scenarios for a wide range of socioeconomic drivers and scenario story lines, like the Shared Socioeconomic Pathways (SSPs).\u0000","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140259638","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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