Kathrin Lisa Kapper | Universidad Nacional Autónoma de México (original) (raw)

Papers by Kathrin Lisa Kapper

In this work we present new determinations of geomagnetic field intensity and direction from iron... more In this work we present new determinations of geomagnetic field intensity and direction from iron smelting kilns discovered at the metallurgical site of Korsimoro in Burkina Faso, West Africa. A large number of kilns were found at this site, which extends over an area up to 50 km2. Based on archeological investigations, the kilns are related to different types of smelting techniques that probably correspond to four distinct time periods. Radiocarbon ages were obtained from charcoal and confine the studied kilns to ages ranging from 700 to 1800 AD. Rock magnetic investigations on representative samples show that the main ferromagnetic mineral is magnetite. One kiln also shows a significant contribution of hematite and a high coercivity-low unblocking temperature magnetic phase (HCSLT). In general, directional results indicate a good agreement with results from neighboring kiln structures. Comparison of directions with models and European secular variation curves, as well as archeomag...

In this work we focus on the determination of the geomagnetic field intensity from iron smelting ... more In this work we focus on the determination of the geomagnetic field intensity from iron smelting kilns that were discovered at the metallurgical site of Korsimoro in Burkina Faso. A large number of kilns are found at this site, which extends over an area up to 50 km 2 . Based on archeological investigations, the kilns can be related to four different types of smelting techniques, which belong to four distinct time periods. Radiocarbon ages were obtained from charcoal and confine the studied kilns to ages ranging from 700 to 1800 AD. Rock-magnetic investigations on representative samples show that the main ferromagnetic mineral is magnetite. One kiln also shows a significant contribution of hematite and a high coercivity-low unblocking temperature

Scientific Reports, 2020

An amendment to this paper has been published and can be accessed via a link at the top of the pa... more An amendment to this paper has been published and can be accessed via a link at the top of the paper.

EGU General Assembly Conference Abstracts, Apr 1, 2016

Studia Geophysica et Geodaetica, 2017

The Cretaceous Normal Superchron is a period of great interest to investigate global scale variat... more The Cretaceous Normal Superchron is a period of great interest to investigate global scale variations of the geomagnetic field. Long periods of single polarity are still a matter of debate: up to now there are two contradicting theories, which try to relate geomagnetic field intensity and reversal rate. We aim to shed light on the geomagnetic field strength during the Cretaceous Normal Superchron because data are still scarce and of dissimilar quality. To obtain reliable, absolute paleointensity determinations we investigate volcanic rocks from the Western Cordillera of Colombia. Several age determinations allow relating the samples to an age of about 92.5 Ma. To characterize the samples, we investigate rock magnetic properties and determine the characteristic remanent magnetization behavior. To determine paleointensities, we use a multimethod approach: first, we apply the classic Thellier-Coe protocol, and then, the relatively new multispecimen method. Rock magnetic measurements indicate magnetite as the main ferrimagnetic mineral, a stable magnetization revealed by reversible and nearly reversible thermomagnetic curves, and grain sizes that are either in the pseudosingle domain range or a mixture of single and multidomain grains. Alternating field and thermal demagnetization are rather complex, although we observe a few vector diagrams with a single, essentially uni-vectorial component with a small viscous overprint. Paleointensity determination with the Thellier-Coe protocol was unsuccessful, while with the multispecimen protocol we obtained four successful determinations out of 20. The failure of the Thellier-Coe protocol can be attributed to multidomain grains, which were observed during demagnetization and in rock magnetic experiments, and to the inhomogeneity of the volcanic rocks. Our multispecimen paleointensity determinations support low field strength at around 90 Ma during the Cretaceous Normal Superchron.

<div class="page" title="Page 1"> <div class="layou... more <div class="page" title="Page 1"> <div class="layoutArea"> <div class="column"> <p>Monitoring of local snow avalanche releases are indispensable for many use cases. Existing lidar and radar technologies for monitoring local avalanche activity are costly and require closed source commercial software. These systems are often inflexible for exploring new use cases and too expensive for large scale applications, e.g., 100-1000 slopes. Therefore, developing reliable and inexpensive measurement and monitoring techniques with cutting- edge lidar and radar technology are highly required. Today, the automotive industry is a leading technology driver for lidar and radar sensors, because the largest challenge for achieving the next level of vehicle automation is to improve the reliability of its perception system. Automotive lidar sensors record high-resolution point clouds with very high acquisition frequencies of 10-20Hz and a range of up to 400m. High costs of mechanically spinning lidars (5-20kEUR) are still a limiting factor, but prices have already dropped significantly during the last decade and are expected to drop by another order of magnitude in the upcoming years. Modern automotive radar sensors operate at 24GHz and 77GHz, have a range of up to 300m, and provide raw data formats that allow the development of algorithms for detecting changes in the backscatter caused by avalanches. To exploit the potential of these newly emerging, cost- effective technologies for geoscientific applications, a stand-alone, modular sensor system called MOLISENS (MObile LIdar SENsor System) was developed in a cooperation between Virtual Vehicle Research Center and University of Graz. MOLISENS allows the modular incorporation of cutting-edge radar and lidar sensors. The open-source python package ‘pointcloudset’ was developed for handling, analyzing, and visualizing large datasets that consist of multiple point clouds recorded over time. This python package is designed to enable the development of new point cloud algorithms, and it is planned to extend the functionality to radar cluster data. Based on MOLISENS and pointcloudset, a strategy for their operational use in local avalanche monitoring is being developed.</p> </div> </div> </div>

Frontiers in remote sensing, Apr 21, 2023

<p>Snow avalanches pose a significant danger to the population and infrastructure i... more <p>Snow avalanches pose a significant danger to the population and infrastructure in the Austrian Alps. Although rigorous prevention and mitigation mechanisms are in place in Austria, accidents cannot be prevented, and victims are mourned every year. A comprehensive mapping of avalanches would be desirable to support the work of local avalanche commissions to improve future avalanche predictions. In recent years, mapping of avalanches from satellite images has been proven to be a promising and fast approach to monitor the avalanche activity. The Copernicus Sentinel-1 mission provides weather independent synthetic aperture radar data, free of charge since 2014, that has been shown to be suitable for avalanche mapping in a test region in Norway. Several recent approaches of avalanche detection make use of deep learning-based algorithms to improve the detection rate compared to conventional segmentation algorithms.</p> <p>          Building upon the success of these deep learning-based approaches, we are setting up a modular data pipeline to map previous avalanche cycles in Sentinel-1 imagery in the Austrian Alps. As segmentation algorithm we make use of a common U-Net approach as a baseline and compare it to mapping results from an additional algorithm that has originally been applied to an autonomous driving problem. As a first test case, the extensive labelled training dataset of around 25 000 avalanche outlines from Switzerland will be used to train the U-Net; further test cases will include the training dataset of around 3 000 avalanches in Norway and around 800 avalanches in Greenland. To obtain training data of avalanches in Austria we tested an approach by manually mapping avalanches from Sentinel-2 satellite imagery and aerial photos.</p> <p>          In a new approach, we will introduce high-resolution weather data, e.g., weather station data, to the learning-based algorithm to improve the detection performance. The avalanches detected with the algorithm will be quantitatively evaluated against held-out test sets and ground-truth data where available. Detection results in Austria will additionally be validated with in situ measurements from the MOLISENS lidar system and the RIEGL VZ-6000 laser scanner. Moreover, we will assess the possibilities of learning-based approaches in the context of avalanche forecasting.</p>

Scientific Reports, 2020

The geomagnetic field variations on the continent of Africa are still largely undeciphered for th... more The geomagnetic field variations on the continent of Africa are still largely undeciphered for the past two millennia. In spite of archaeological artefacts being reliable recorders of the ancient geomagnetic field strength, only few data have been reported for this continent so far. Here we use the Thellier-Coe and calibrated pseudo-Thellier methods to recover archaeointensity data from Burkina Faso and Ivory Coast (West Africa) from well-dated archaeological artefacts. By combining our 18 new data with previously published data from West Africa, we construct a reference curve for West Africa for the past 2000 years. To obtain a reliable curve of the archaeointensity variation, we evaluate a penalized smoothing spline fit and a stochastic modelling method, both combined with a bootstrap approach. Both intensity curves agree well, supporting the confidence in our proposed intensity variation during this time span, and small differences arise from the different methodologies of treati...

<p><span>Since its start in 2014, the Copernicus Sent... more <p><span>Since its start in 2014, the Copernicus Sentinel-1 programme has provided free of charge, weather independent, and high-resolution satellite Earth observations and has set major scientific advances in the detection of snow avalanches from satellite imagery in motion. Recently, operational avalanche detection from Sentinel-1 synthetic Aperture radar (SAR) images were successfully introduced for some test regions in Norway.</span> <span>However, current state of the art avalanche detection algorithms based on machine learning do not include weather history. We propose a novel way to encode weather data and include it into an automatic avalanche detection pipeline for the Austrian Alps.</span> <span>The approach consists of four steps. At first the raw data in netCDF format is downloaded, which consists of several meteorological parameters over several time steps. In the second step the weather data is downscaled onto the pixel locations of the SAR image. Then the data is aggregated over time, which produces a two-dimensional grid of one value per SAR pixel at the time when the SAR data was recorded. This aggregation function can range from simple averages to full snowpack models. In the final step, the grid is then converted to an image with greyscale values corresponding to the aggregated values. The resulting image is then ready to be fed into the machine learning pipeline.</span> <span>We will include this encoded weather history data to increase the avalanche detection performance, and investigate contributing factors with model interpretability tools and explainable artificial intelligence.</span></p>

<p>Each year, snow avalanches cause many casualties and tremendous damage t... more <p>Each year, snow avalanches cause many casualties and tremendous damage to infrastructure. Prevention and mitigation mechanisms for avalanches are established for specific regions only. However, the full extent of the overall avalanche activity is usually barely known as avalanches occur in remote areas making in-situ observations scarce. To overcome these challenges, an automated avalanche detection approach using the Copernicus Sentinel-1 synthetic aperture radar (SAR) data has recently been introduced for some test regions in Norway. This automated detection approach from SAR images is faster and gives more comprehensive results than field-based detection provided by avalanche experts. The Sentinel-1 programme has provided - and continues to provide - free of charge, weather-independent, and high-resolution satellite Earth observations since its start in 2014. Recent advances in avalanche detection use deep learning algorithms to improve the detection rates. Consequently, the performance potential and the availability of reliable training data make learning-based approaches an appealing option for avalanche detection.  </p><p>         In the framework of the exploratory project SnowAV_AT, we intend to build the basis for a state-of-the-art automated avalanche detection system for the Austrian Alps, including a "best practice" data processing pipeline and a learning-based approach applied to Sentinel-1 SAR images. As a first step towards this goal, we have compiled several labelled training datasets of previously detected avalanches that can be used for learning. Concretely, these datasets contain 19000 avalanches that occurred during a large event in Switzerland in January 2018, around 6000 avalanches that occurred in Switzerland in January 2019, and around 800 avalanches that occurred in Greenland in April 2016. The avalanche detection performance of our learning-based approach will be quantitatively evaluated against held-out test sets. Furthermore, we will provide qualitative evaluations using SAR images of the Austrian Alps to gauge how well our approach generalizes to unseen data that is potentially differently distributed than the training data. In addition, selected ground truth data from Switzerland, Greenland and Austria will allow us to validate the accuracy of the detection approach. As a particular novelty of our work, we will try to leverage high-resolution weather data and combine it with SAR images to improve the detection performance. Moreover, we will assess the possibilities of learning-based approaches in the context of the arguably more challenging avalanche forecasting problem.</p>

Scientific reports, Mar 28, 2017

We present absolute geomagnetic intensities from iron smelting furnaces discovered at the metallu... more We present absolute geomagnetic intensities from iron smelting furnaces discovered at the metallurgical site of Korsimoro, Burkina Faso. Up to now, archaeologists recognized four different types of furnaces based on different construction methods, which were related to four subsequent time periods. Additionally, radiocarbon ages obtained from charcoal confine the studied furnaces to ages ranging from 700-1700 AD, in good agreement with the archaeologically determined time periods for each type of furnace. Archaeointensity results reveal three main groups of Arai diagrams. The first two groups contain specimens with either linear Arai diagrams, or slightly curved diagrams or two phases of magnetization. The third group encompasses specimens with strong zigzag or curvature in their Arai diagrams. Specimens of the first two groups were accepted after applying selection criteria to guarantee the high quality of the results. Our data compared to palaeosecular variation curves show a simi...

Earth and Planetary Science Letters, 2015

The spatial resolution of numerical weather prediction and climate models is generally determined... more The spatial resolution of numerical weather prediction and climate models is generally determined by their grid spacing (∆x) or spectral truncation and the numerical implementation of dynamical core and model parametrisations. For example features of the scale 2∆x and 3∆x are smoothed to avoid numerical instabilities (e.g., aliasing effects) and parameterisations in connection to advection, pressure gradient force, and subgrid-scale diffusion can only be well represented at dimensions of at least four times the grid spacing. Some parametrisations, however, generate energy at the grid-spacing scale. These multiple effects on the effective resolution of models are investigated in this study for three high resolution regional climate models (RCMs) in dependence of their grid spacing.

In this work we present new determinations of geomagnetic field intensity and direction from iron... more In this work we present new determinations of geomagnetic field intensity and direction from iron smelting kilns discovered at the metallurgical site of Korsimoro in Burkina Faso, West Africa. A large number of kilns were found at this site, which extends over an area up to 50 km2. Based on archeological investigations, the kilns are related to different types of smelting techniques that probably correspond to four distinct time periods. Radiocarbon ages were obtained from charcoal and confine the studied kilns to ages ranging from 700 to 1800 AD. Rock magnetic investigations on representative samples show that the main ferromagnetic mineral is magnetite. One kiln also shows a significant contribution of hematite and a high coercivity-low unblocking temperature magnetic phase (HCSLT). In general, directional results indicate a good agreement with results from neighboring kiln structures. Comparison of directions with models and European secular variation curves, as well as archeomag...

In this work we focus on the determination of the geomagnetic field intensity from iron smelting ... more In this work we focus on the determination of the geomagnetic field intensity from iron smelting kilns that were discovered at the metallurgical site of Korsimoro in Burkina Faso. A large number of kilns are found at this site, which extends over an area up to 50 km 2 . Based on archeological investigations, the kilns can be related to four different types of smelting techniques, which belong to four distinct time periods. Radiocarbon ages were obtained from charcoal and confine the studied kilns to ages ranging from 700 to 1800 AD. Rock-magnetic investigations on representative samples show that the main ferromagnetic mineral is magnetite. One kiln also shows a significant contribution of hematite and a high coercivity-low unblocking temperature

Scientific Reports, 2020

An amendment to this paper has been published and can be accessed via a link at the top of the pa... more An amendment to this paper has been published and can be accessed via a link at the top of the paper.

EGU General Assembly Conference Abstracts, Apr 1, 2016

Studia Geophysica et Geodaetica, 2017

The Cretaceous Normal Superchron is a period of great interest to investigate global scale variat... more The Cretaceous Normal Superchron is a period of great interest to investigate global scale variations of the geomagnetic field. Long periods of single polarity are still a matter of debate: up to now there are two contradicting theories, which try to relate geomagnetic field intensity and reversal rate. We aim to shed light on the geomagnetic field strength during the Cretaceous Normal Superchron because data are still scarce and of dissimilar quality. To obtain reliable, absolute paleointensity determinations we investigate volcanic rocks from the Western Cordillera of Colombia. Several age determinations allow relating the samples to an age of about 92.5 Ma. To characterize the samples, we investigate rock magnetic properties and determine the characteristic remanent magnetization behavior. To determine paleointensities, we use a multimethod approach: first, we apply the classic Thellier-Coe protocol, and then, the relatively new multispecimen method. Rock magnetic measurements indicate magnetite as the main ferrimagnetic mineral, a stable magnetization revealed by reversible and nearly reversible thermomagnetic curves, and grain sizes that are either in the pseudosingle domain range or a mixture of single and multidomain grains. Alternating field and thermal demagnetization are rather complex, although we observe a few vector diagrams with a single, essentially uni-vectorial component with a small viscous overprint. Paleointensity determination with the Thellier-Coe protocol was unsuccessful, while with the multispecimen protocol we obtained four successful determinations out of 20. The failure of the Thellier-Coe protocol can be attributed to multidomain grains, which were observed during demagnetization and in rock magnetic experiments, and to the inhomogeneity of the volcanic rocks. Our multispecimen paleointensity determinations support low field strength at around 90 Ma during the Cretaceous Normal Superchron.

<div class="page" title="Page 1"> <div class="layou... more <div class="page" title="Page 1"> <div class="layoutArea"> <div class="column"> <p>Monitoring of local snow avalanche releases are indispensable for many use cases. Existing lidar and radar technologies for monitoring local avalanche activity are costly and require closed source commercial software. These systems are often inflexible for exploring new use cases and too expensive for large scale applications, e.g., 100-1000 slopes. Therefore, developing reliable and inexpensive measurement and monitoring techniques with cutting- edge lidar and radar technology are highly required. Today, the automotive industry is a leading technology driver for lidar and radar sensors, because the largest challenge for achieving the next level of vehicle automation is to improve the reliability of its perception system. Automotive lidar sensors record high-resolution point clouds with very high acquisition frequencies of 10-20Hz and a range of up to 400m. High costs of mechanically spinning lidars (5-20kEUR) are still a limiting factor, but prices have already dropped significantly during the last decade and are expected to drop by another order of magnitude in the upcoming years. Modern automotive radar sensors operate at 24GHz and 77GHz, have a range of up to 300m, and provide raw data formats that allow the development of algorithms for detecting changes in the backscatter caused by avalanches. To exploit the potential of these newly emerging, cost- effective technologies for geoscientific applications, a stand-alone, modular sensor system called MOLISENS (MObile LIdar SENsor System) was developed in a cooperation between Virtual Vehicle Research Center and University of Graz. MOLISENS allows the modular incorporation of cutting-edge radar and lidar sensors. The open-source python package ‘pointcloudset’ was developed for handling, analyzing, and visualizing large datasets that consist of multiple point clouds recorded over time. This python package is designed to enable the development of new point cloud algorithms, and it is planned to extend the functionality to radar cluster data. Based on MOLISENS and pointcloudset, a strategy for their operational use in local avalanche monitoring is being developed.</p> </div> </div> </div>

Frontiers in remote sensing, Apr 21, 2023

<p>Snow avalanches pose a significant danger to the population and infrastructure i... more <p>Snow avalanches pose a significant danger to the population and infrastructure in the Austrian Alps. Although rigorous prevention and mitigation mechanisms are in place in Austria, accidents cannot be prevented, and victims are mourned every year. A comprehensive mapping of avalanches would be desirable to support the work of local avalanche commissions to improve future avalanche predictions. In recent years, mapping of avalanches from satellite images has been proven to be a promising and fast approach to monitor the avalanche activity. The Copernicus Sentinel-1 mission provides weather independent synthetic aperture radar data, free of charge since 2014, that has been shown to be suitable for avalanche mapping in a test region in Norway. Several recent approaches of avalanche detection make use of deep learning-based algorithms to improve the detection rate compared to conventional segmentation algorithms.</p> <p>          Building upon the success of these deep learning-based approaches, we are setting up a modular data pipeline to map previous avalanche cycles in Sentinel-1 imagery in the Austrian Alps. As segmentation algorithm we make use of a common U-Net approach as a baseline and compare it to mapping results from an additional algorithm that has originally been applied to an autonomous driving problem. As a first test case, the extensive labelled training dataset of around 25 000 avalanche outlines from Switzerland will be used to train the U-Net; further test cases will include the training dataset of around 3 000 avalanches in Norway and around 800 avalanches in Greenland. To obtain training data of avalanches in Austria we tested an approach by manually mapping avalanches from Sentinel-2 satellite imagery and aerial photos.</p> <p>          In a new approach, we will introduce high-resolution weather data, e.g., weather station data, to the learning-based algorithm to improve the detection performance. The avalanches detected with the algorithm will be quantitatively evaluated against held-out test sets and ground-truth data where available. Detection results in Austria will additionally be validated with in situ measurements from the MOLISENS lidar system and the RIEGL VZ-6000 laser scanner. Moreover, we will assess the possibilities of learning-based approaches in the context of avalanche forecasting.</p>

Scientific Reports, 2020

The geomagnetic field variations on the continent of Africa are still largely undeciphered for th... more The geomagnetic field variations on the continent of Africa are still largely undeciphered for the past two millennia. In spite of archaeological artefacts being reliable recorders of the ancient geomagnetic field strength, only few data have been reported for this continent so far. Here we use the Thellier-Coe and calibrated pseudo-Thellier methods to recover archaeointensity data from Burkina Faso and Ivory Coast (West Africa) from well-dated archaeological artefacts. By combining our 18 new data with previously published data from West Africa, we construct a reference curve for West Africa for the past 2000 years. To obtain a reliable curve of the archaeointensity variation, we evaluate a penalized smoothing spline fit and a stochastic modelling method, both combined with a bootstrap approach. Both intensity curves agree well, supporting the confidence in our proposed intensity variation during this time span, and small differences arise from the different methodologies of treati...

<p><span>Since its start in 2014, the Copernicus Sent... more <p><span>Since its start in 2014, the Copernicus Sentinel-1 programme has provided free of charge, weather independent, and high-resolution satellite Earth observations and has set major scientific advances in the detection of snow avalanches from satellite imagery in motion. Recently, operational avalanche detection from Sentinel-1 synthetic Aperture radar (SAR) images were successfully introduced for some test regions in Norway.</span> <span>However, current state of the art avalanche detection algorithms based on machine learning do not include weather history. We propose a novel way to encode weather data and include it into an automatic avalanche detection pipeline for the Austrian Alps.</span> <span>The approach consists of four steps. At first the raw data in netCDF format is downloaded, which consists of several meteorological parameters over several time steps. In the second step the weather data is downscaled onto the pixel locations of the SAR image. Then the data is aggregated over time, which produces a two-dimensional grid of one value per SAR pixel at the time when the SAR data was recorded. This aggregation function can range from simple averages to full snowpack models. In the final step, the grid is then converted to an image with greyscale values corresponding to the aggregated values. The resulting image is then ready to be fed into the machine learning pipeline.</span> <span>We will include this encoded weather history data to increase the avalanche detection performance, and investigate contributing factors with model interpretability tools and explainable artificial intelligence.</span></p>

<p>Each year, snow avalanches cause many casualties and tremendous damage t... more <p>Each year, snow avalanches cause many casualties and tremendous damage to infrastructure. Prevention and mitigation mechanisms for avalanches are established for specific regions only. However, the full extent of the overall avalanche activity is usually barely known as avalanches occur in remote areas making in-situ observations scarce. To overcome these challenges, an automated avalanche detection approach using the Copernicus Sentinel-1 synthetic aperture radar (SAR) data has recently been introduced for some test regions in Norway. This automated detection approach from SAR images is faster and gives more comprehensive results than field-based detection provided by avalanche experts. The Sentinel-1 programme has provided - and continues to provide - free of charge, weather-independent, and high-resolution satellite Earth observations since its start in 2014. Recent advances in avalanche detection use deep learning algorithms to improve the detection rates. Consequently, the performance potential and the availability of reliable training data make learning-based approaches an appealing option for avalanche detection.  </p><p>         In the framework of the exploratory project SnowAV_AT, we intend to build the basis for a state-of-the-art automated avalanche detection system for the Austrian Alps, including a "best practice" data processing pipeline and a learning-based approach applied to Sentinel-1 SAR images. As a first step towards this goal, we have compiled several labelled training datasets of previously detected avalanches that can be used for learning. Concretely, these datasets contain 19000 avalanches that occurred during a large event in Switzerland in January 2018, around 6000 avalanches that occurred in Switzerland in January 2019, and around 800 avalanches that occurred in Greenland in April 2016. The avalanche detection performance of our learning-based approach will be quantitatively evaluated against held-out test sets. Furthermore, we will provide qualitative evaluations using SAR images of the Austrian Alps to gauge how well our approach generalizes to unseen data that is potentially differently distributed than the training data. In addition, selected ground truth data from Switzerland, Greenland and Austria will allow us to validate the accuracy of the detection approach. As a particular novelty of our work, we will try to leverage high-resolution weather data and combine it with SAR images to improve the detection performance. Moreover, we will assess the possibilities of learning-based approaches in the context of the arguably more challenging avalanche forecasting problem.</p>

Scientific reports, Mar 28, 2017

We present absolute geomagnetic intensities from iron smelting furnaces discovered at the metallu... more We present absolute geomagnetic intensities from iron smelting furnaces discovered at the metallurgical site of Korsimoro, Burkina Faso. Up to now, archaeologists recognized four different types of furnaces based on different construction methods, which were related to four subsequent time periods. Additionally, radiocarbon ages obtained from charcoal confine the studied furnaces to ages ranging from 700-1700 AD, in good agreement with the archaeologically determined time periods for each type of furnace. Archaeointensity results reveal three main groups of Arai diagrams. The first two groups contain specimens with either linear Arai diagrams, or slightly curved diagrams or two phases of magnetization. The third group encompasses specimens with strong zigzag or curvature in their Arai diagrams. Specimens of the first two groups were accepted after applying selection criteria to guarantee the high quality of the results. Our data compared to palaeosecular variation curves show a simi...

Earth and Planetary Science Letters, 2015

The spatial resolution of numerical weather prediction and climate models is generally determined... more The spatial resolution of numerical weather prediction and climate models is generally determined by their grid spacing (∆x) or spectral truncation and the numerical implementation of dynamical core and model parametrisations. For example features of the scale 2∆x and 3∆x are smoothed to avoid numerical instabilities (e.g., aliasing effects) and parameterisations in connection to advection, pressure gradient force, and subgrid-scale diffusion can only be well represented at dimensions of at least four times the grid spacing. Some parametrisations, however, generate energy at the grid-spacing scale. These multiple effects on the effective resolution of models are investigated in this study for three high resolution regional climate models (RCMs) in dependence of their grid spacing.