Articles | Volume 16, issue 10
https://doi.org/10.5194/gmd-16-2777-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-2777-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
24 May 2023
Development and technical paper |
| 24 May 2023
| 24 May 2023
A generalized spatial autoregressive neural network method for three-dimensional spatial interpolation
Junda Zhan
School of Earth Sciences, Zhejiang University, Hangzhou 310027, China
Sensen Wu
School of Earth Sciences, Zhejiang University, Hangzhou 310027, China
Zhejiang Provincial Key Laboratory of Geographic Information Science,
Hangzhou 310028, China
Jin Qi
School of Earth Sciences, Zhejiang University, Hangzhou 310027, China
Zhejiang Provincial Key Laboratory of Geographic Information Science,
Hangzhou 310028, China
Jindi Zeng
School of Earth Sciences, Zhejiang University, Hangzhou 310027, China
Mengjiao Qin
School of Earth Sciences, Zhejiang University, Hangzhou 310027, China
Zhejiang Provincial Key Laboratory of Geographic Information Science,
Hangzhou 310028, China
Yuanyuan Wang
Ocean Academy, Zhejiang University, Zhoushan 316022, China
Zhejiang Provincial Key Laboratory of Geographic Information Science,
Hangzhou 310028, China
Zhenhong Du
CORRESPONDING AUTHOR
School of Earth Sciences, Zhejiang University, Hangzhou 310027, China
Zhejiang Provincial Key Laboratory of Geographic Information Science,
Hangzhou 310028, China
Related authors
No articles found.
Ziyu Yin, Jiale Ding, Yi Liu, Ruoxu Wang, Yige Wang, Yijun Chen, Jin Qi, Sensen Wu, and Zhenhong Du
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-62, https://doi.org/10.5194/gmd-2024-62, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
In geography, understanding how relationships between different factors change over time and space is crucial. This study implements two neural network-based spatiotemporal regression models as well as an open-sourced Python package named GNNWR, to accurately capture the varying relationships between factors. This makes it a valuable tool for researchers in various fields, such as environmental science, urban planning, and public health.
Enjiang Yue, Mengjiao Qin, Linshu Hu, Sensen Wu, and Zhenhong Du
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-46, https://doi.org/10.5194/gmd-2024-46, 2024
Revised manuscript under review for GMD
Short summary
Short summary
Laboratory earthquakes is important means to understand natural earthquakes while previous work focused on transient prediction, lacking future prediction capability. We propose a method and evaluate on data from lab experiments with different slip behaviors. It outperforms state-of-the-art methods in modeling slip moments, intervals and predictions beyond trained horizons especially for challenging slip scenarios, which is crucial for quasi-periodic geophysical process like seismicity.
Related subject area
Earth and space science informatics
Random forests with spatial proxies for environmental modelling: opportunities and pitfalls
An improved global pressure and zenith wet delay model with optimized vertical correction considering the spatiotemporal variability in multiple height-scale factors
kNNDM CV: k-fold nearest-neighbour distance matching cross-validation for map accuracy estimation
GNNWR: An Open-Source Package of Spatiotemporal Intelligent Regression Methods for Modeling Spatial and Temporal Non-Stationarity
Accelerating Lagrangian transport simulations on graphics processing units: performance optimizations of Massive-Parallel Trajectory Calculations (MPTRAC) v2.6
The effect of lossy compression of numerical weather prediction data on data analysis: a case study using enstools-compression 2023.11
Focal-TSMP: deep learning for vegetation health prediction and agricultural drought assessment from a regional climate simulation
Tomofast-x 2.0: an open-source parallel code for inversion of potential field data with topography using wavelet compression
Functional analysis of variance (ANOVA) for carbon flux estimates from remote sensing data
The 4D reconstruction of dynamic geological evolution processes for renowned geological features
Machine learning for numerical weather and climate modelling: a review
Overcoming barriers to enable convergence research by integrating ecological and climate sciences: the NCAR–NEON system Version 1
Ensemble of optimised machine learning algorithms for predicting surface soil moisture content at a global scale
Hazard assessment modeling and software development of earthquake-triggered landslides in the Sichuan–Yunnan area, China
The Common Community Physics Package (CCPP) Framework v6
Causal deep learning models for studying the Earth system
A methodological framework for improving the performance of data-driven models: a case study for daily runoff prediction in the Maumee domain, USA
SHAFTS (v2022.3): a deep-learning-based Python package for simultaneous extraction of building height and footprint from sentinel imagery
Bayesian atmospheric correction over land: Sentinel-2/MSI and Landsat 8/OLI
Twenty-five years of the IPCC Data Distribution Centre at the DKRZ and the Reference Data Archive for CMIP data
Effectiveness and computational efficiency of absorbing boundary conditions for full-waveform inversion
LAND-SUITE V1.0: a suite of tools for statistically based landslide susceptibility zonation
Towards physics-inspired data-driven weather forecasting: integrating data assimilation with a deep spatial-transformer-based U-NET in a case study with ERA5
Fast infrared radiative transfer calculations using graphics processing units: JURASSIC-GPU v2.0
CSDMS: a community platform for numerical modeling of Earth surface processes
A new methodological framework for geophysical sensor combinations associated with machine learning algorithms to understand soil attributes
Model calibration using ESEm v1.1.0 – an open, scalable Earth system emulator
Turbidity maximum zone index: a novel model for remote extraction of the turbidity maximum zone in different estuaries
dh2loop 1.0: an open-source Python library for automated processing and classification of geological logs
Copula-based synthetic data augmentation for machine-learning emulators
Automated geological map deconstruction for 3D model construction using map2loop 1.0 and map2model 1.0
A spatially explicit approach to simulate urban heat mitigation with InVEST (v3.8.0)
S-SOM v1.0: a structural self-organizing map algorithm for weather typing
Using Shapley additive explanations to interpret extreme gradient boosting predictions of grassland degradation in Xilingol, China
Current status on the need for improved accessibility to climate models code
ClimateNet: an expert-labeled open dataset and deep learning architecture for enabling high-precision analyses of extreme weather
A spatiotemporal weighted regression model (STWR v1.0) for analyzing local nonstationarity in space and time
A new end-to-end workflow for the Community Earth System Model (version 2.0) for the Coupled Model Intercomparison Project Phase 6 (CMIP6)
HyLands 1.0: a hybrid landscape evolution model to simulate the impact of landslides and landslide-derived sediment on landscape evolution
Comparative analysis of atmospheric radiative transfer models using the Atmospheric Look-up table Generator (ALG) toolbox (version 2.0)
Fast domain-aware neural network emulation of a planetary boundary layer parameterization in a numerical weather forecast model
VISIR-1.b: ocean surface gravity waves and currents for energy-efficient navigation
Topological data analysis and machine learning for recognizing atmospheric river patterns in large climate datasets
Global hydro-climatic biomes identified via multitask learning
A run control framework to streamline profiling, porting, and tuning simulation runs and provenance tracking of geoscientific applications
An improved logistic regression model based on a spatially weighted technique (ILRBSWT v1.0) and its application to mineral prospectivity mapping
High-performance software framework for the calculation of satellite-to-satellite data matchups (MMS version 1.2)
A data model of the Climate and Forecast metadata conventions (CF-1.6) with a software implementation (cf-python v2.1)
Reverse engineering model structures for soil and ecosystem respiration: the potential of gene expression programming
A high-fidelity multiresolution digital elevation model for Earth systems
Carles Milà, Marvin Ludwig, Edzer Pebesma, Cathryn Tonne, and Hanna Meyer
Geosci. Model Dev., 17, 6007–6033, https://doi.org/10.5194/gmd-17-6007-2024, https://doi.org/10.5194/gmd-17-6007-2024, 2024
Short summary
Short summary
Spatial proxies, such as coordinates and distances, are often used as predictors in random forest models for predictive mapping. In a simulation and two case studies, we investigated the conditions under which their use is appropriate. We found that spatial proxies are not always beneficial and should not be used as a default approach without careful consideration. We also provide insights into the reasons behind their suitability, how to detect them, and potential alternatives.
Chunhua Jiang, Xiang Gao, Huizhong Zhu, Shuaimin Wang, Sixuan Liu, Shaoni Chen, and Guangsheng Liu
Geosci. Model Dev., 17, 5939–5959, https://doi.org/10.5194/gmd-17-5939-2024, https://doi.org/10.5194/gmd-17-5939-2024, 2024
Short summary
Short summary
With ERA5 hourly data, we show spatiotemporal characteristics of pressure and zenith wet delay (ZWD) and propose an empirical global pressure and ZWD grid model with a broader operating space which can provide accurate pressure, ZWD, zenith hydrostatic delay, and zenith tropospheric delay estimates for any selected time and location over globe. IGPZWD will be of great significance for the tropospheric augmentation in real-time GNSS positioning and atmospheric water vapor remote sensing.
Jan Linnenbrink, Carles Milà, Marvin Ludwig, and Hanna Meyer
Geosci. Model Dev., 17, 5897–5912, https://doi.org/10.5194/gmd-17-5897-2024, https://doi.org/10.5194/gmd-17-5897-2024, 2024
Short summary
Short summary
Estimation of map accuracy based on cross-validation (CV) in spatial modelling is pervasive but controversial. Here, we build upon our previous work and propose a novel, prediction-oriented k-fold CV strategy for map accuracy estimation in which the distribution of geographical distances between prediction and training points is taken into account when constructing the CV folds. Our method produces more reliable estimates than other CV methods and can be used for large datasets.
Ziyu Yin, Jiale Ding, Yi Liu, Ruoxu Wang, Yige Wang, Yijun Chen, Jin Qi, Sensen Wu, and Zhenhong Du
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-62, https://doi.org/10.5194/gmd-2024-62, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
In geography, understanding how relationships between different factors change over time and space is crucial. This study implements two neural network-based spatiotemporal regression models as well as an open-sourced Python package named GNNWR, to accurately capture the varying relationships between factors. This makes it a valuable tool for researchers in various fields, such as environmental science, urban planning, and public health.
Lars Hoffmann, Kaveh Haghighi Mood, Andreas Herten, Markus Hrywniak, Jiri Kraus, Jan Clemens, and Mingzhao Liu
Geosci. Model Dev., 17, 4077–4094, https://doi.org/10.5194/gmd-17-4077-2024, https://doi.org/10.5194/gmd-17-4077-2024, 2024
Short summary
Short summary
Lagrangian particle dispersion models are key for studying atmospheric transport but can be computationally intensive. To speed up simulations, the MPTRAC model was ported to graphics processing units (GPUs). Performance optimization of data structures and memory alignment resulted in runtime improvements of up to 75 % on NVIDIA A100 GPUs for ERA5-based simulations with 100 million particles. These optimizations make the MPTRAC model well suited for future high-performance computing systems.
Oriol Tintó Prims, Robert Redl, Marc Rautenhaus, Tobias Selz, Takumi Matsunobu, Kameswar Rao Modali, and George Craig
EGUsphere, https://doi.org/10.5194/egusphere-2024-753, https://doi.org/10.5194/egusphere-2024-753, 2024
Short summary
Short summary
Advanced compression techniques can drastically reduce the size of meteorological datasets (by 5x to 150x) without compromising the data's scientific value. We developed a user-friendly tool called 'enstools-compression' that makes this compression simple for Earth scientists. This tool works seamlessly with common weather and climate data formats. Our work shows that lossy compression can significantly improve how researchers store and analyze large meteorological datasets.
Mohamad Hakam Shams Eddin and Juergen Gall
Geosci. Model Dev., 17, 2987–3023, https://doi.org/10.5194/gmd-17-2987-2024, https://doi.org/10.5194/gmd-17-2987-2024, 2024
Short summary
Short summary
In this study, we use deep learning and a climate simulation to predict the vegetation health as it would be observed from satellites. We found that the developed model can help to identify regions with a high risk of agricultural drought. The main applications of this study are to estimate vegetation products for periods where no satellite data are available and to forecast the future vegetation response to climate change based on climate scenarios.
Vitaliy Ogarko, Kim Frankcombe, Taige Liu, Jeremie Giraud, Roland Martin, and Mark Jessell
Geosci. Model Dev., 17, 2325–2345, https://doi.org/10.5194/gmd-17-2325-2024, https://doi.org/10.5194/gmd-17-2325-2024, 2024
Short summary
Short summary
We present a major release of the Tomofast-x open-source gravity and magnetic inversion code that is enhancing its performance and applicability for both industrial and academic studies. We focus on real-world mineral exploration scenarios, while offering flexibility for applications at regional scale or for crustal studies. The optimisation work described in this paper is fundamental to allowing more complete descriptions of the controls on magnetisation, including remanence.
Jonathan Hobbs, Matthias Katzfuss, Hai Nguyen, Vineet Yadav, and Junjie Liu
Geosci. Model Dev., 17, 1133–1151, https://doi.org/10.5194/gmd-17-1133-2024, https://doi.org/10.5194/gmd-17-1133-2024, 2024
Short summary
Short summary
The cycling of carbon among the land, oceans, and atmosphere is a closely monitored process in the global climate system. These exchanges between the atmosphere and the surface can be quantified using a combination of atmospheric carbon dioxide observations and computer models. This study presents a statistical method for investigating the similarities and differences in the estimated surface–atmosphere carbon exchange when different computer model assumptions are invoked.
Jiateng Guo, Zhibin Liu, Xulei Wang, Lixin Wu, Shanjun Liu, and Yunqiang Li
Geosci. Model Dev., 17, 847–864, https://doi.org/10.5194/gmd-17-847-2024, https://doi.org/10.5194/gmd-17-847-2024, 2024
Short summary
Short summary
This study proposes a 3D and temporally dynamic (4D) geological modeling method. Several simulation and actual cases show that the 4D spatial and temporal evolution of regional geological formations can be modeled easily using this method with smooth boundaries. The 4D modeling system can dynamically present the regional geological evolution process under the timeline, which will be helpful to the research and teaching on the formation of typical and complex geological features.
Catherine O. de Burgh-Day and Tennessee Leeuwenburg
Geosci. Model Dev., 16, 6433–6477, https://doi.org/10.5194/gmd-16-6433-2023, https://doi.org/10.5194/gmd-16-6433-2023, 2023
Short summary
Short summary
Machine learning (ML) is an increasingly popular tool in the field of weather and climate modelling. While ML has been used in this space for a long time, it is only recently that ML approaches have become competitive with more traditional methods. In this review, we have summarized the use of ML in weather and climate modelling over time; provided an overview of key ML concepts, methodologies, and terms; and suggested promising avenues for further research.
Danica L. Lombardozzi, William R. Wieder, Negin Sobhani, Gordon B. Bonan, David Durden, Dawn Lenz, Michael SanClements, Samantha Weintraub-Leff, Edward Ayres, Christopher R. Florian, Kyla Dahlin, Sanjiv Kumar, Abigail L. S. Swann, Claire M. Zarakas, Charles Vardeman, and Valerio Pascucci
Geosci. Model Dev., 16, 5979–6000, https://doi.org/10.5194/gmd-16-5979-2023, https://doi.org/10.5194/gmd-16-5979-2023, 2023
Short summary
Short summary
We present a novel cyberinfrastructure system that uses National Ecological Observatory Network measurements to run Community Terrestrial System Model point simulations in a containerized system. The simple interface and tutorials expand access to data and models used in Earth system research by removing technical barriers and facilitating research, educational opportunities, and community engagement. The NCAR–NEON system enables convergence of climate and ecological sciences.
Qianqian Han, Yijian Zeng, Lijie Zhang, Calimanut-Ionut Cira, Egor Prikaziuk, Ting Duan, Chao Wang, Brigitta Szabó, Salvatore Manfreda, Ruodan Zhuang, and Bob Su
Geosci. Model Dev., 16, 5825–5845, https://doi.org/10.5194/gmd-16-5825-2023, https://doi.org/10.5194/gmd-16-5825-2023, 2023
Short summary
Short summary
Using machine learning, we estimated global surface soil moisture (SSM) to aid in understanding water, energy, and carbon exchange. Ensemble models outperformed individual algorithms in predicting SSM under different climates. The best-performing ensemble included K-neighbours Regressor, Random Forest Regressor, and Extreme Gradient Boosting. This is important for hydrological and climatological applications such as water cycle monitoring, irrigation management, and crop yield prediction.
Xiaoyi Shao, Siyuan Ma, and Chong Xu
Geosci. Model Dev., 16, 5113–5129, https://doi.org/10.5194/gmd-16-5113-2023, https://doi.org/10.5194/gmd-16-5113-2023, 2023
Short summary
Short summary
Scientific understandings of the distribution of coseismic landslides, followed by emergency and medium- and long-term risk assessment, can reduce landslide risk. The aim of this study is to propose an improved three-stage spatial prediction strategy and develop corresponding hazard assessment software called Mat.LShazard V1.0, which provides a new application tool for coseismic landslide disaster prevention and mitigation in different stages.
Dominikus Heinzeller, Ligia Bernardet, Grant Firl, Man Zhang, Xia Sun, and Michael Ek
Geosci. Model Dev., 16, 2235–2259, https://doi.org/10.5194/gmd-16-2235-2023, https://doi.org/10.5194/gmd-16-2235-2023, 2023
Short summary
Short summary
The Common Community Physics Package is a collection of physical atmospheric parameterizations for use in Earth system models and a framework that couples the physics to a host model’s dynamical core. A primary goal for this effort is to facilitate research and development of physical parameterizations and physics–dynamics coupling methods while offering capabilities for numerical weather prediction operations, for example in the upcoming implementation of the Global Forecast System (GFS) v17.
Tobias Tesch, Stefan Kollet, and Jochen Garcke
Geosci. Model Dev., 16, 2149–2166, https://doi.org/10.5194/gmd-16-2149-2023, https://doi.org/10.5194/gmd-16-2149-2023, 2023
Short summary
Short summary
A recent statistical approach for studying relations in the Earth system is to train deep learning (DL) models to predict Earth system variables given one or several others and use interpretable DL to analyze the relations learned by the models. Here, we propose to combine the approach with a theorem from causality research to ensure that the deep learning model learns causal rather than spurious relations. As an example, we apply the method to study soil-moisture–precipitation coupling.
Yao Hu, Chirantan Ghosh, and Siamak Malakpour-Estalaki
Geosci. Model Dev., 16, 1925–1936, https://doi.org/10.5194/gmd-16-1925-2023, https://doi.org/10.5194/gmd-16-1925-2023, 2023
Short summary
Short summary
Data-driven models (DDMs) gain popularity in earth and environmental systems, thanks in large part to advancements in data collection techniques and artificial intelligence (AI). The performance of these models is determined by the underlying machine learning (ML) algorithms. In this study, we develop a framework to improve the model performance by optimizing ML algorithms and demonstrate the effectiveness of the framework using a DDM to predict edge-of-field runoff in the Maumee domain, USA.
Ruidong Li, Ting Sun, Fuqiang Tian, and Guang-Heng Ni
Geosci. Model Dev., 16, 751–778, https://doi.org/10.5194/gmd-16-751-2023, https://doi.org/10.5194/gmd-16-751-2023, 2023
Short summary
Short summary
We developed SHAFTS (Simultaneous building Height And FootprinT extraction from Sentinel imagery), a multi-task deep-learning-based Python package, to estimate average building height and footprint from Sentinel imagery. Evaluation in 46 cities worldwide shows that SHAFTS achieves significant improvement over existing machine-learning-based methods.
Feng Yin, Philip E. Lewis, and Jose L. Gómez-Dans
Geosci. Model Dev., 15, 7933–7976, https://doi.org/10.5194/gmd-15-7933-2022, https://doi.org/10.5194/gmd-15-7933-2022, 2022
Short summary
Short summary
The proposed SIAC atmospheric correction method provides consistent surface reflectance estimations from medium spatial-resolution satellites (Sentinel 2 and Landsat 8) with per-pixel uncertainty information. The outputs from SIAC have been validated against a wide range of ground measurements, and it shows that SIAC can provide accurate estimations of both surface reflectance and atmospheric parameters, with meaningful uncertainty information.
Martina Stockhause and Michael Lautenschlager
Geosci. Model Dev., 15, 6047–6058, https://doi.org/10.5194/gmd-15-6047-2022, https://doi.org/10.5194/gmd-15-6047-2022, 2022
Short summary
Short summary
The Data Distribution Centre (DDC) of the Intergovernmental Panel on Climate Change (IPCC) celebrates its 25th anniversary in 2022. DDC Partner DKRZ has supported the IPCC Assessments and preserved the quality-assured, citable climate model data underpinning the Assessment Reports over these years over the long term. With the introduction of the IPCC FAIR Guidelines into the current AR6, the value of DDC services has been recognized. However, DDC sustainability remains unresolved.
Daiane Iglesia Dolci, Felipe A. G. Silva, Pedro S. Peixoto, and Ernani V. Volpe
Geosci. Model Dev., 15, 5857–5881, https://doi.org/10.5194/gmd-15-5857-2022, https://doi.org/10.5194/gmd-15-5857-2022, 2022
Short summary
Short summary
We investigate and compare the theoretical and computational characteristics of several absorbing boundary conditions (ABCs) for the full-waveform inversion (FWI) problem. The different ABCs are implemented in an optimized computational framework called Devito. The computational efficiency and memory requirements of the ABC methods are evaluated in the forward and adjoint wave propagators, from simple to realistic velocity models.
Mauro Rossi, Txomin Bornaetxea, and Paola Reichenbach
Geosci. Model Dev., 15, 5651–5666, https://doi.org/10.5194/gmd-15-5651-2022, https://doi.org/10.5194/gmd-15-5651-2022, 2022
Short summary
Short summary
LAND-SUITE is a software package designed to support landslide susceptibility zonation. The software integrates, extends, and completes LAND-SE (Rossi et al., 2010; Rossi and Reichenbach, 2016). The software is implemented in R, a free software environment for statistical computing and graphics, and gives expert users the possibility to perform easier, more flexible, and more informed statistically based landslide susceptibility applications and zonations.
Ashesh Chattopadhyay, Mustafa Mustafa, Pedram Hassanzadeh, Eviatar Bach, and Karthik Kashinath
Geosci. Model Dev., 15, 2221–2237, https://doi.org/10.5194/gmd-15-2221-2022, https://doi.org/10.5194/gmd-15-2221-2022, 2022
Short summary
Short summary
There is growing interest in data-driven weather forecasting, i.e., to predict the weather by using a deep neural network that learns from the evolution of past atmospheric patterns. Here, we propose three components to add to the current data-driven weather forecast models to improve their performance. These components involve a feature that incorporates physics into the neural network, a method to add data assimilation, and an algorithm to use several different time intervals in the forecast.
Paul F. Baumeister and Lars Hoffmann
Geosci. Model Dev., 15, 1855–1874, https://doi.org/10.5194/gmd-15-1855-2022, https://doi.org/10.5194/gmd-15-1855-2022, 2022
Short summary
Short summary
The efficiency of the numerical simulation of radiative transport is shown on modern server-class graphics cards (GPUs). The low-cost prefactor on GPUs compared to general-purpose processors (CPUs) enables future large retrieval campaigns for multi-channel data from infrared sounders aboard low-orbit satellites. The validated research software JURASSIC is available in the public domain.
Gregory E. Tucker, Eric W. H. Hutton, Mark D. Piper, Benjamin Campforts, Tian Gan, Katherine R. Barnhart, Albert J. Kettner, Irina Overeem, Scott D. Peckham, Lynn McCready, and Jaia Syvitski
Geosci. Model Dev., 15, 1413–1439, https://doi.org/10.5194/gmd-15-1413-2022, https://doi.org/10.5194/gmd-15-1413-2022, 2022
Short summary
Short summary
Scientists use computer simulation models to understand how Earth surface processes work, including floods, landslides, soil erosion, river channel migration, ocean sedimentation, and coastal change. Research benefits when the software for simulation modeling is open, shared, and coordinated. The Community Surface Dynamics Modeling System (CSDMS) is a US-based facility that supports research by providing community support, computing tools and guidelines, and educational resources.
Danilo César de Mello, Gustavo Vieira Veloso, Marcos Guedes de Lana, Fellipe Alcantara de Oliveira Mello, Raul Roberto Poppiel, Diego Ribeiro Oquendo Cabrero, Luis Augusto Di Loreto Di Raimo, Carlos Ernesto Gonçalves Reynaud Schaefer, Elpídio Inácio Fernandes Filho, Emilson Pereira Leite, and José Alexandre Melo Demattê
Geosci. Model Dev., 15, 1219–1246, https://doi.org/10.5194/gmd-15-1219-2022, https://doi.org/10.5194/gmd-15-1219-2022, 2022
Short summary
Short summary
We used soil parent material, terrain attributes, and geophysical data from the soil surface to test and compare different and unprecedented geophysical sensor combination, as well as different machine learning algorithms to model and predict several soil attributes. Also, we analyzed the importance of pedoenvironmental variables. The soil attributes were modeled throughout different machine learning algorithms and related to different geophysical sensor combinations.
Duncan Watson-Parris, Andrew Williams, Lucia Deaconu, and Philip Stier
Geosci. Model Dev., 14, 7659–7672, https://doi.org/10.5194/gmd-14-7659-2021, https://doi.org/10.5194/gmd-14-7659-2021, 2021
Short summary
Short summary
The Earth System Emulator (ESEm) provides a fast and flexible framework for emulating a wide variety of Earth science datasets and tools for constraining (or tuning) models of any complexity. Three distinct use cases are presented that demonstrate the utility of ESEm and provide some insight into the use of machine learning for emulation in these different settings. The open-source Python package is freely available so that it might become a valuable tool for the community.
Chongyang Wang, Li Wang, Danni Wang, Dan Li, Chenghu Zhou, Hao Jiang, Qiong Zheng, Shuisen Chen, Kai Jia, Yangxiaoyue Liu, Ji Yang, Xia Zhou, and Yong Li
Geosci. Model Dev., 14, 6833–6846, https://doi.org/10.5194/gmd-14-6833-2021, https://doi.org/10.5194/gmd-14-6833-2021, 2021
Short summary
Short summary
The turbidity maximum zone (TMZ) is a special phenomenon in estuaries worldwide. However, the extraction methods and criteria used to describe the TMZ vary significantly both spatially and temporally. This study proposes an new index, the turbidity maximum zone index, based on the corresponding relationship of total suspended solid concentration and Chl a concentration, which could better extract TMZs in different estuaries and on different dates.
Ranee Joshi, Kavitha Madaiah, Mark Jessell, Mark Lindsay, and Guillaume Pirot
Geosci. Model Dev., 14, 6711–6740, https://doi.org/10.5194/gmd-14-6711-2021, https://doi.org/10.5194/gmd-14-6711-2021, 2021
Short summary
Short summary
We have developed a software that allows the user to extract and standardize drill hole information from legacy datasets and/or different drilling campaigns. It also provides functionality to upscale the lithological information. These functionalities were possible by developing thesauri to identify and group geological terminologies together.
David Meyer, Thomas Nagler, and Robin J. Hogan
Geosci. Model Dev., 14, 5205–5215, https://doi.org/10.5194/gmd-14-5205-2021, https://doi.org/10.5194/gmd-14-5205-2021, 2021
Short summary
Short summary
A major limitation in training machine-learning emulators is often caused by the lack of data. This paper presents a cheap way to increase the size of training datasets using statistical techniques and thereby improve the performance of machine-learning emulators.
Mark Jessell, Vitaliy Ogarko, Yohan de Rose, Mark Lindsay, Ranee Joshi, Agnieszka Piechocka, Lachlan Grose, Miguel de la Varga, Laurent Ailleres, and Guillaume Pirot
Geosci. Model Dev., 14, 5063–5092, https://doi.org/10.5194/gmd-14-5063-2021, https://doi.org/10.5194/gmd-14-5063-2021, 2021
Short summary
Short summary
We have developed software that allows the user to extract sufficient information from unmodified digital maps and associated datasets that we are able to use to automatically build 3D geological models. By automating the process we are able to remove human bias from the procedure, which makes the workflow reproducible.
Martí Bosch, Maxence Locatelli, Perrine Hamel, Roy P. Remme, Jérôme Chenal, and Stéphane Joost
Geosci. Model Dev., 14, 3521–3537, https://doi.org/10.5194/gmd-14-3521-2021, https://doi.org/10.5194/gmd-14-3521-2021, 2021
Short summary
Short summary
The article presents a novel approach to simulate urban heat mitigation from land use/land cover data based on three biophysical mechanisms: tree shade, evapotranspiration and albedo. An automated procedure is proposed to calibrate the model parameters to best fit temperature observations from monitoring stations. A case study in Lausanne, Switzerland, shows that the approach outperforms regressions based on satellite data and provides valuable insights into design heat mitigation policies.
Quang-Van Doan, Hiroyuki Kusaka, Takuto Sato, and Fei Chen
Geosci. Model Dev., 14, 2097–2111, https://doi.org/10.5194/gmd-14-2097-2021, https://doi.org/10.5194/gmd-14-2097-2021, 2021
Short summary
Short summary
This study proposes a novel structural self-organizing map (S-SOM) algorithm. The superiority of S-SOM is that it can better recognize the difference (or similarity) among spatial (or temporal) data used for training and thus improve the clustering quality compared to traditional SOM algorithms.
Batunacun, Ralf Wieland, Tobia Lakes, and Claas Nendel
Geosci. Model Dev., 14, 1493–1510, https://doi.org/10.5194/gmd-14-1493-2021, https://doi.org/10.5194/gmd-14-1493-2021, 2021
Short summary
Short summary
Extreme gradient boosting (XGBoost) can provide alternative insights that conventional land-use models are unable to generate. Shapley additive explanations (SHAP) can interpret the results of the purely data-driven approach. XGBoost achieved similar and robust simulation results. SHAP values were useful for analysing the complex relationship between the different drivers of grassland degradation.
Juan A. Añel, Michael García-Rodríguez, and Javier Rodeiro
Geosci. Model Dev., 14, 923–934, https://doi.org/10.5194/gmd-14-923-2021, https://doi.org/10.5194/gmd-14-923-2021, 2021
Short summary
Short summary
This work shows that it continues to be hard, if not impossible, to obtain some of the most used climate models worldwide. We reach this conclusion through a systematic study and encourage all development teams and research centres to make public the models they use to produce scientific results.
Prabhat, Karthik Kashinath, Mayur Mudigonda, Sol Kim, Lukas Kapp-Schwoerer, Andre Graubner, Ege Karaismailoglu, Leo von Kleist, Thorsten Kurth, Annette Greiner, Ankur Mahesh, Kevin Yang, Colby Lewis, Jiayi Chen, Andrew Lou, Sathyavat Chandran, Ben Toms, Will Chapman, Katherine Dagon, Christine A. Shields, Travis O'Brien, Michael Wehner, and William Collins
Geosci. Model Dev., 14, 107–124, https://doi.org/10.5194/gmd-14-107-2021, https://doi.org/10.5194/gmd-14-107-2021, 2021
Short summary
Short summary
Detecting extreme weather events is a crucial step in understanding how they change due to climate change. Deep learning (DL) is remarkable at pattern recognition; however, it works best only when labeled datasets are available. We create
ClimateNet– an expert-labeled curated dataset – to train a DL model for detecting weather events and predicting changes in extreme precipitation. This work paves the way for DL-based automated, high-fidelity, and highly precise analytics of climate data.
Xiang Que, Xiaogang Ma, Chao Ma, and Qiyu Chen
Geosci. Model Dev., 13, 6149–6164, https://doi.org/10.5194/gmd-13-6149-2020, https://doi.org/10.5194/gmd-13-6149-2020, 2020
Short summary
Short summary
This paper presents a spatiotemporal weighted regression (STWR) model for exploring nonstationary spatiotemporal processes in nature and socioeconomics. A value change rate is introduced in the temporal kernel, which presents significant model fitting and accuracy in both simulated and real-world data. STWR fully incorporates observed data in the past and outperforms geographic temporal weighted regression (GTWR) and geographic weighted regression (GWR) models in several experiments.
Sheri Mickelson, Alice Bertini, Gary Strand, Kevin Paul, Eric Nienhouse, John Dennis, and Mariana Vertenstein
Geosci. Model Dev., 13, 5567–5581, https://doi.org/10.5194/gmd-13-5567-2020, https://doi.org/10.5194/gmd-13-5567-2020, 2020
Short summary
Short summary
Every generation of MIP exercises introduces new layers of complexity and an exponential growth in the amount of data requested. CMIP6 required us to develop a new tool chain and forced us to change our methodologies. The new methods discussed in this paper provided us with an 18 times faster speedup over our existing methods. This allowed us to meet our deadlines and we were able to publish more than half a million data sets on the Earth System Grid Federation (ESGF) for the CMIP6 project.
Benjamin Campforts, Charles M. Shobe, Philippe Steer, Matthias Vanmaercke, Dimitri Lague, and Jean Braun
Geosci. Model Dev., 13, 3863–3886, https://doi.org/10.5194/gmd-13-3863-2020, https://doi.org/10.5194/gmd-13-3863-2020, 2020
Short summary
Short summary
Landslides shape the Earth’s surface and are a dominant source of terrestrial sediment. Rivers, then, act as conveyor belts evacuating landslide-produced sediment. Understanding the interaction among rivers and landslides is important to predict the Earth’s surface response to past and future environmental changes and for mitigating natural hazards. We develop HyLands, a new numerical model that provides a toolbox to explore how landslides and rivers interact over several timescales.
Jorge Vicent, Jochem Verrelst, Neus Sabater, Luis Alonso, Juan Pablo Rivera-Caicedo, Luca Martino, Jordi Muñoz-Marí, and José Moreno
Geosci. Model Dev., 13, 1945–1957, https://doi.org/10.5194/gmd-13-1945-2020, https://doi.org/10.5194/gmd-13-1945-2020, 2020
Short summary
Short summary
The modeling of light propagation through the atmosphere is key to process satellite images and to understand atmospheric processes. However, existing atmospheric models can be complex to use in practical applications. Here we aim at providing a new software tool to facilitate using advanced models and to generate large databases of simulated data. As a test case, we use this tool to analyze differences between several atmospheric models, showing the capabilities of this open-source tool.
Jiali Wang, Prasanna Balaprakash, and Rao Kotamarthi
Geosci. Model Dev., 12, 4261–4274, https://doi.org/10.5194/gmd-12-4261-2019, https://doi.org/10.5194/gmd-12-4261-2019, 2019
Short summary
Short summary
Parameterizations are frequently used in models representing physical phenomena and are often the computationally expensive portions of the code. Using model output from simulations performed using a weather model, we train deep neural networks to provide an accurate alternative to a physics-based parameterization. We demonstrate that a domain-aware deep neural network can successfully simulate the entire diurnal cycle of the boundary layer physics and the results are transferable.
Gianandrea Mannarini and Lorenzo Carelli
Geosci. Model Dev., 12, 3449–3480, https://doi.org/10.5194/gmd-12-3449-2019, https://doi.org/10.5194/gmd-12-3449-2019, 2019
Short summary
Short summary
The VISIR ship-routing model is updated in order to deal with ocean currents.
The optimal tracks we computed through VISIR in the Atlantic ocean show great seasonal and regional variability, following a variable influence of surface gravity waves and currents. We assess how these tracks contribute to voyage energy-efficiency gains through a standard indicator (EEOI) of the International Maritime Organization. Also, the new model features are validated against an exact analytical benchmark.
Grzegorz Muszynski, Karthik Kashinath, Vitaliy Kurlin, Michael Wehner, and Prabhat
Geosci. Model Dev., 12, 613–628, https://doi.org/10.5194/gmd-12-613-2019, https://doi.org/10.5194/gmd-12-613-2019, 2019
Short summary
Short summary
We present the automated method for recognizing atmospheric rivers in climate data, i.e., climate model output and reanalysis product. The method is based on topological data analysis and machine learning, both of which are powerful tools that the climate science community often does not use. An advantage of the proposed method is that it is free of selection of subjective threshold conditions on a physical variable. This method is also suitable for rapidly analyzing large amounts of data.
Christina Papagiannopoulou, Diego G. Miralles, Matthias Demuzere, Niko E. C. Verhoest, and Willem Waegeman
Geosci. Model Dev., 11, 4139–4153, https://doi.org/10.5194/gmd-11-4139-2018, https://doi.org/10.5194/gmd-11-4139-2018, 2018
Short summary
Short summary
Common global land cover and climate classifications are based on vegetation–climatic characteristics derived from observational data, ignoring the interaction between the local climate and biome. Here, we model the interplay between vegetation and local climate by discovering spatial relationships among different locations. The resulting global
hydro-climatic biomescorrespond to regions of coherent climate–vegetation interactions that agree well with traditional global land cover maps.
Wendy Sharples, Ilya Zhukov, Markus Geimer, Klaus Goergen, Sebastian Luehrs, Thomas Breuer, Bibi Naz, Ketan Kulkarni, Slavko Brdar, and Stefan Kollet
Geosci. Model Dev., 11, 2875–2895, https://doi.org/10.5194/gmd-11-2875-2018, https://doi.org/10.5194/gmd-11-2875-2018, 2018
Short summary
Short summary
Next-generation geoscientific models are based on complex model implementations and workflows. Next-generation HPC systems require new programming paradigms and code optimization. In order to meet the challenge of running complex simulations on new massively parallel HPC systems, we developed a run control framework that facilitates code portability, code profiling, and provenance tracking to reduce both the duration and the cost of code migration and development, while ensuring reproducibility.
Daojun Zhang, Na Ren, and Xianhui Hou
Geosci. Model Dev., 11, 2525–2539, https://doi.org/10.5194/gmd-11-2525-2018, https://doi.org/10.5194/gmd-11-2525-2018, 2018
Short summary
Short summary
Geographically weighted regression is a widely used method to deal with spatial heterogeneity, which is common in geostatistics. However, most existing software does not support logistic regression and cannot deal with missing data, which exist extensively in mineral prospectivity mapping. This work generalized logistic regression to spatial statistics based on a spatially weighted technique. The new model also supports an anisotropic local window, which is another innovative point.
Thomas Block, Sabine Embacher, Christopher J. Merchant, and Craig Donlon
Geosci. Model Dev., 11, 2419–2427, https://doi.org/10.5194/gmd-11-2419-2018, https://doi.org/10.5194/gmd-11-2419-2018, 2018
Short summary
Short summary
For calibration and validation purposes it is necessary to detect simultaneous data acquisitions from different spaceborne platforms. We present an algorithm and a software system which implements a general approach to resolve this problem. The multisensor matchup system (MMS) can detect simultaneous acquisitions in a large dataset (> 100 TB) and extract data for matching locations for further analysis. The MMS implements a flexible software infrastructure and allows for high parallelization.
David Hassell, Jonathan Gregory, Jon Blower, Bryan N. Lawrence, and Karl E. Taylor
Geosci. Model Dev., 10, 4619–4646, https://doi.org/10.5194/gmd-10-4619-2017, https://doi.org/10.5194/gmd-10-4619-2017, 2017
Short summary
Short summary
We present a formal data model for version 1.6 of the CF (Climate and Forecast) metadata conventions that provide a description of the physical meaning of geoscientific data and their spatial and temporal properties. We describe the CF conventions and how they lead to our CF data model, and compare it other data models for storing data and metadata. We present cf-python version 2.1: a software implementation of the CF data model capable of manipulating any CF-compliant dataset.
Iulia Ilie, Peter Dittrich, Nuno Carvalhais, Martin Jung, Andreas Heinemeyer, Mirco Migliavacca, James I. L. Morison, Sebastian Sippel, Jens-Arne Subke, Matthew Wilkinson, and Miguel D. Mahecha
Geosci. Model Dev., 10, 3519–3545, https://doi.org/10.5194/gmd-10-3519-2017, https://doi.org/10.5194/gmd-10-3519-2017, 2017
Short summary
Short summary
Accurate representation of land-atmosphere carbon fluxes is essential for future climate projections, although some of the responses of CO2 fluxes to climate often remain uncertain. The increase in available data allows for new approaches in their modelling. We automatically developed models for ecosystem and soil carbon respiration using a machine learning approach. When compared with established respiration models, we found that they are better in prediction as well as offering new insights.
Xinqiao Duan, Lin Li, Haihong Zhu, and Shen Ying
Geosci. Model Dev., 10, 239–253, https://doi.org/10.5194/gmd-10-239-2017, https://doi.org/10.5194/gmd-10-239-2017, 2017
Short summary
Short summary
This article proposes an optimized transformation for topographic datasets. The resulting topographic grid exhibits good surface approximation and quasi-uniform high-quality. Both features of the processed topography build a concrete base from which improved endogenous or exogenous parameters can be derived, and makes it suitable for Earth and environmental simulations.
Cited articles
Abd El-Hady, A. E.-N. M., Abdelaty, E. F., and Salama, A. E.: GIS-mapping of
soil available plant nutrients (potentiality, gradient, anisotropy), OJSS,
8, 315–329, https://doi.org/10.4236/ojss.2018.812023, 2018.
Adhikary, S. K., Muttil, N., and Yilmaz, A. G.: Cokriging for enhanced
spatial interpolation of rainfall in two Australian catchments, Hydrol.
Process., 31, 2143–2161, https://doi.org/10.1002/hyp.11163, 2017.
Allard, D., Senoussi, R., and Porcu, E.: Anisotropy models for spatial data,
Math. Geosci., 48, 305–328, https://doi.org/10.1007/s11004-015-9594-x, 2016.
Arowolo, A. O., Bhowmik, A. K., Qi, W., and Deng, X.: Comparison of spatial
interpolation techniques to generate high-resolution climate surfaces for
Nigeria, Int. J. Climatol, 37, 179–192, https://doi.org/10.1002/joc.4990,
2017.
Aumond, P., Can, A., Mallet, V., De Coensel, B., Ribeiro, C., Botteldooren,
D., and Lavandier, C.: Kriging-based spatial interpolation from measurements
for sound level mapping in urban areas, J. Acoust.
Soc. Am., 143, 2847–2857, https://doi.org/10.1121/1.5034799,
2018.
Babak, O. and Deutsch, C. V.: Statistical approach to inverse distance
interpolation, Stoch. Environ. Res. Risk. Assess., 23, 543–553,
https://doi.org/10.1007/s00477-008-0226-6, 2009.
Chen, D., Tian, Y., Yao, T., and Ou, T.: Satellite measurements reveal
strong anisotropy in spatial coherence of climate variations over the Tibet
Plateau, Sci. Rep., 6, 30304, https://doi.org/10.1038/srep30304, 2016.
Cheng, M., Wang, Y., Engel, B., Zhang, W., Peng, H., Chen, X., and Xia, H.:
Performance assessment of spatial interpolation of precipitation for
hydrological process simulation in the Three Gorges Basin, Water, 9, 838,
https://doi.org/10.3390/w9110838, 2017.
da Silva Júnior, J. C., Medeiros, V., Garrozi, C., Montenegro, A., and
Gonçalves, G. E.: Random forest techniques for spatial interpolation of
evapotranspiration data from Brazilian's Northeast, Comput.
Electron. Agr., 166, 105017,
https://doi.org/10.1016/j.compag.2019.105017, 2019.
Du, Z., Wang, Z., Wu, S., Zhang, F., and Liu, R.: Geographically neural
network weighted regression for the accurate estimation of spatial
non-stationarity, Int. J. Geogr. Inf. Sci.,
34, 1353–1377, https://doi.org/10.1080/13658816.2019.1707834, 2020.
Gao, B., Hu, M., Wang, J., Xu, C., Chen, Z., Fan, H., and Ding, H.: Spatial
interpolation of marine environment data using P-MSN, Int. J.
Geogr. Inf. Sci., 34, 577–603,
https://doi.org/10.1080/13658816.2019.1683183, 2020.
Greenberg, J. A., Rueda, C., Hestir, E. L., Santos, M. J., and Ustin, S. L.:
Least cost distance analysis for spatial interpolation, Comput.
Geosci., 37, 272–276, https://doi.org/10.1016/j.cageo.2010.05.012,
2011.
He, K., Zhang, X., Ren, S., and Sun, J.: Delving deep into rectifiers:
surpassing human-level performance on ImageNet classification, in: 2015 IEEE
International Conference on Computer Vision (ICCV), Santiago, Chile, 1026–1034,
https://doi.org/10.1109/ICCV.2015.123, 2015.
Hu, S., Cheng, Q., Wang, L., and Xu, D.: Modeling land price distribution
using multifractal IDW interpolation and fractal filtering method, Landscape
Urban Plan., 110, 25–35,
https://doi.org/10.1016/j.landurbplan.2012.09.008, 2013.
Ioffe, S. and Szegedy, C.: Batch normalization: accelerating deep network
training by reducing internal covariate shift, in: Proceedings of the 32nd
International Conference on International Conference on Machine Learning –
Volume 37, Lille, France, 448–456, https://doi.org/10.48550/arXiv.1502.03167, 2015.
Jian, Z. and Jin, H.: Ocean carbon cycle and tropical forcing of climate
evolution, Adv. Earth Sci., 23, 221–227,
https://doi.org/10.11867/j.issn.1001-8166.2008.03.0221, 2008.
Kanevski, M., Timonin, V., and Pozdnoukhov, A.: Automatic Decision-Oriented
Mapping of Pollution Data, in: Computational Science and Its Applications –
ICCSA 2008, edited by: Gervasi, O., Murgante, B., Laganà, A.,
Taniar, D., Mun, Y., and Gavrilova, M. L., Springer Berlin Heidelberg,
Berlin, Heidelberg, vol. 5072, 678–691, https://doi.org/10.1007/978-3-540-69839-5_50, 2008.
Krige, D. G.: A statistical approach to some basic mine valuation problems
on the Witwatersrand, Journal of the Chemical, Metallurgical and Mining
Society of South Africa, 53, 159–162, 1952.
Kumar, S., Lal, R., and Liu, D.: A geographically weighted regression
kriging approach for mapping soil organic carbon stock, Geoderma, 189–190,
627–634, https://doi.org/10.1016/j.geoderma.2012.05.022, 2012.
Li, C., Liu, Y., Wu, J., Wang, C., and Liu, X.: A contrastive study on the
spatial interpolation for precipitation data using back propagation learning
algorithm and support vector machine model – a case study of Gansu
Province, Grassland and Turf, 38, 12–19,
https://doi.org/10.13817/j.cnki.cyycp.2018.04.002, 2018.
Li, Z., Zhang, X., Zhu, R., Zhang, Z., and Weng, Z.: Integrating
data-to-data correlation into inverse distance weighting, Comput. Geosci., 24,
203–216, https://doi.org/10.1007/s10596-019-09913-9, 2020.
Liang, Q., Nittel, S., Whittier, J. C., and Bruin, S.: Real-time inverse
distance weighting interpolation for streaming sensor data, Trans.
GIS, 22, 1179–1204, https://doi.org/10.1111/tgis.12458, 2018.
Liu, Z., Wu, X., Xu, J., Li, H., Lu, S., Sun, C., and Cao, M.: China Argo
project: progress in China Argo ocean observations and data applications,
Acta Oceanol. Sin., 36, 1–11, https://doi.org/10.1007/s13131-017-1035-x,
2017.
Longley, P. A., Goodchild, M., Maguire, D. J., and Rhind, D. W. (Eds.):
Geographic information systems & science, 3rd Edn., Wiley, Hoboken, NJ,
539 pp., ISBN 978-0-470-72144-5, 2011.
Lu, G. Y. and Wong, D. W.: An adaptive inverse-distance weighting spatial
interpolation technique, Comput. Geosci., 34, 1044–1055,
https://doi.org/10.1016/j.cageo.2007.07.010, 2008.
Ma, X., Luan, S., Ding, C., Liu, H., and Wang, Y.: Spatial interpolation of
missing annual average daily traffic data using Copula-based model, IEEE
Intell. Transp. Sy., 11, 158–170,
https://doi.org/10.1109/MITS.2019.2919504, 2019.
Matheron, G.: Principles of geostatistics, Econ. Geol., 58, 1246–1266,
https://doi.org/10.2113/gsecongeo.58.8.1246, 1963.
Pan, S., Tian, S., Wang, X., Dai, L., Gao, C., and Tong, J.: Comparing
different spatial interpolation methods to predict the distribution of
fishes: a case study from Coilia nasus in the Changjiang River Estuary, Acta
Oceanol. Sin., 40, 1–14, https://doi.org/10.1007/s13131-021-1789-z,
2021.
Rigol, J. P., Jarvis, C. H., and Stuart, N.: Artificial neural networks as a
tool for spatial interpolation, Int. J. Geogr.
Inf. Sci., 15, 323–343,
https://doi.org/10.1080/13658810110038951, 2001.
Riser, S. C., Freeland, H. J., Roemmich, D., Wijffels, S., Troisi, A.,
Belbéoch, M., Gilbert, D., Xu, J., Pouliquen, S., Thresher, A., Le
Traon, P.-Y., Maze, G., Klein, B., Ravichandran, M., Grant, F., Poulain,
P.-M., Suga, T., Lim, B., Sterl, A., Sutton, P., Mork, K.-A.,
Vélez-Belchí, P. J., Ansorge, I., King, B., Turton, J., Baringer,
M., and Jayne, S. R.: Fifteen years of ocean observations with the global
Argo array, Nat. Clim. Change, 6, 145–153,
https://doi.org/10.1038/nclimate2872, 2016.
Samal, A. R., Sengupta, R. R., and Fifarek, R. H.: Modelling spatial
anisotropy of gold concentration data using GIS-based interpolated maps and
variogram analysis: Implications for structural control of mineralization, J.
Earth Syst. Sci., 120, 583–593, https://doi.org/10.1007/s12040-011-0091-4,
2011.
Sekulić, A., Kilibarda, M., Heuvelink, G. B. M., Nikolić, M., and
Bajat, B.: Random Forest Spatial Interpolation, Remote Sensing, 12, 1687,
https://doi.org/10.3390/rs12101687, 2020.
Shepard, D.: A two-dimensional interpolation function for irregularly-spaced
data, in: Proceedings of the 1968 23rd ACM national conference, the 1968
23rd ACM National Conference, New York, NY, US, 517–524,
https://doi.org/10.1145/800186.810616, 1968.
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and
Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from
overfitting, J. Mach. Learn. Res., 15, 1929–1958,
https://dl.acm.org/doi/10.5555/2627435.2670313, 2014.
Sun, M., Liu, S., and Liu, J.: IDW interpolation algorithm considering
spatial anisotropy, Computer Engineering and Design, 41, 983–987,
https://doi.org/10.16208/j.issn1000-7024.2020.04.014, 2020.
Szczepańska, A., Gościewski, D., and Gerus-Gościewska, M.: A
GRID-based spatial interpolation method as a tool supporting real estate
market analyses, IJGI, 9, 39, https://doi.org/10.3390/ijgi9010039, 2020.
Tamura, S. and Tateishi, M.: Capabilities of a four-layered feedforward
neural network: four layers versus three, IEEE T. Neural Networ., 8,
251–255, https://doi.org/10.1109/72.557662, 1997.
Tang, M., Wu, X., Agrawal, P., Pongpaichet, S., and Jain, R.: Integration of
diverse data sources for spatial PM2.5 data interpolation, IEEE T.
Multimedia, 19, 408–417, https://doi.org/10.1109/TMM.2016.2613639, 2017.
Tao, H., Liao, X., Zhao, D., Gong, X., and Cassidy, D. P.: Delineation of
soil contaminant plumes at a co-contaminated site using BP neural networks
and geostatistics, Geoderma, 354, 113878,
https://doi.org/10.1016/j.geoderma.2019.07.036, 2019.
Tobler, W. R.: A computer movie simulating urban growth in the Detroit
region, Econ. Geogr., 46, 234–240, https://doi.org/10.2307/143141,
1970.
Wang, D., Chen, Y., and Zhan, W.: A geometric model to simulate thermal
anisotropy over a sparse urban surface (GUTA-sparse), Remote Sens.
Environ., 209, 263–274, https://doi.org/10.1016/j.rse.2018.02.051, 2018.
Wang, Q.: The fast three-dimentional kriging interpolation method based on Delaunay, ME thesis, University of Electronic Science and Technology of China, China, 71 pp., 2015.
Watson, D. F. and Philip, G. M.: A refinement of inverse distance weighted
interpolation, Geoprocessing, 2, 315–327,
https://doi.org/10.1016/S0735-1097(97)00186-1, 1985.
Wu, S., Du, Z., Wang, Y., Lin, T., Zhang, F., and Liu, R.: Modeling
spatially anisotropic nonstationary processes in coastal environments based
on a directional geographically neural network weighted regression, Sci.
Total Environ., 709, 136097,
https://doi.org/10.1016/j.scitotenv.2019.136097, 2020.
Zeng, J., Zhang, F., Wu, S., Du, Z., and Liu, R.: Spatial interpolation
based on spatial auto-regressive neural network, Journal of Zhejiang
University (Science Edition), 47, 572–581,
https://doi.org/10.3785/j.issn.1008-9497.2020.05.009, 2020.
Zhan, J. and Wu, S.: GSARNN Code and Data, figshare [data set], https://doi.org/10.6084/m9.figshare.18739571.v1, 2023.
Zhang, X., Liu, G., Wang, H., and Li, X.: Application of a Hybrid
Interpolation Method Based on Support Vector Machine in the Precipitation
Spatial Interpolation of Basins, Water, 9, 760,
https://doi.org/10.3390/w9100760, 2017.
Zhang, Y., Hidalgo, J., and Parker, D.: Impact of variability and anisotropy
in the correlation decay distance for precipitation spatial interpolation in
China, Clim. Res., 74, 81–93, https://doi.org/10.3354/cr01486, 2017.
Zhang, Y., Feng, M., Zhang, W., Wang, H., and Wang, P.: A Gaussian process
regression-based sea surface temperature interpolation algorithm, J. Ocean.
Limnol., 39, 1211–1221, https://doi.org/10.1007/s00343-020-0062-1, 2021.
Short summary
We develop a generalized spatial autoregressive neural network model used for three-dimensional spatial interpolation. Taking the different changing trend of geographic elements along various directions into consideration, the model defines spatial distance in a generalized way and integrates it into the process of spatial interpolation with the theories of spatial autoregression and neural network. Compared with traditional methods, the model achieves better performance and is more adaptable.
We develop a generalized spatial autoregressive neural network model used for three-dimensional...
