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229 results for “Lamb”

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zenodo44/100

COQTEL dataset: Corrosion Quantification Through Extended use of Lamb waves

Corrosion is a major threat in the aeronautic industry, both in terms of safety and cost. Ultrasonic Lamb Waves (LW) appear to be very efficient for corrosion monitoring and can be made cost effective and versatile when emitted and received by a sparse array of piezoelectric elements (PZT). A LW solution relying on a sparse PZT array and allowing to monitor corrosion pit growth on stainless 316L grade steel plate is here used to collect data during a controlled corrosion experiment. Experimentally, the corrosion pit size is electrochemically controlled by both the imposed electrical potential and the injection of a corrosive NaCl solution through a capillary located at the desired pit location. In parallel, the corrosion pit growth is monitored in-situ every 10 seconds by sending and measuring LW using a sparse array of 4 PZTs bonded to the back of the steel plate enduring corrosion. Two independent experiments were achieved in order to assess the repeatability of the proposed approach. If embedded in aeronautical structure, such an approach could be a versatile and cost-effective alternative to actual non-destructive maintenance procedures that are time and manpower consuming. This dataset can thus ease the development of associated SHM algorithms and methodologies and help filling the gap actually existing between research and industry in that domain. This dataset has been used for the article "In-situ monitoring of µm-sized electrochemically generated corrosion pits using Lamb Waves managed by a sparse array of piezoelectric transducers" published in open access in the "Ultrasonics" peer reviewed journal by the same authors as the dataset.

opencc-by-4.0Nov 2024View details →

zenodo44/100

Himawari 8 band 8 derived product for Lamb wave analysis

Data from geostationary satellite Himawari 8 are processed for the analysis of Lamb waves that were generated by the eruption of Hunga Tonga-Hunga Haʻapai in Tonga on 15 January 2022. Himawari 8/9 gridded data are distributed by the Center for Environmental Remote Sensing (CEReS), Chiba University, Japan. The second time derivatives of band 8 thermal infrared images are stored. The used band was changed from version 1. The data format is NetCDF. The file name represents the date of the middle image used to generate each file (changed from version 2).

opencc-by-4.0Jan 2022View details →

zenodo44/100

Temperature-dependent Lamb wave signals in highly anisotropic CFRP

The dataset contains signals of propagating Lamb waves in highly anisotropic carbon fibre reinforced polymer (CFRP). The reinforcement is unidirectional along 0 deg. A detailed description of the material and its parameters is given in [1]. The arrangement of piezoelectric actuator A and sensors S1-S7 is shown in figure "plate_angular_pzt_arrangement_50x50.png". Sensors are placed at propagation angles from 0 deg to 90 deg with a step of 15 deg. It should be noted that two piezoelectric transducers bonded to both sides of the plate were used as the actuator. It allowed for exciting Lamb waves with dominant A0 and S0 modes, respectively. Hence, there are two respective zip files with data. The following parameters were used during measurements: <ul> <li>Temperatures: T=[50,40,30,20,10,0,-10,-20,-30,-40,-50];</li> <li>Number of cycles in Hann windowed signals: no_of_cycles=[2,2.5,3];</li> <li>Carrier frequencies of excitation signals [kHz]: frequencies=[20:10:250];</li> <li>Number of averages: 50;</li> <li>Sampling frequency: 10 MHz.</li> </ul> The following equipment was used in the experiment: <ul> <li>Environmental chamber by Angelantoni Test Technologies, model MyDiscovery 600 C;</li> <li>National Instruments waveform generator PXIe-5413;</li> <li>Krohn-Hite voltage amplifier model 7500;</li> <li>Cedrat Technologies LWDS amplifier (used as a charge amplifier);</li> <li>National Instruments oscilloscope PXIe-5105.</li> </ul> Files in CSV format contain environmental chamber data (temperature programme, actual temperature and humidity over time, etc.). This can be read and plotted in Matlab by running the script “Read_plot_environmental_chamber.m”. There is another file “Read_plot_environmental_chamber_plus_DS18B20_RH20_KROHN_A0.m” in which temperature was registered also by DS18B20 digital sensor. It loops over all measurements so that it can be used also for reading signals from “niscope_avg_waveform.mat” in respective subfolders. In particular, sensor signals are stored in the “niscope_avg_waveform” variable, a matrix of dimensions 8192x7.

opencc-by-4.0Aug 2022View details →

zenodo44/100

Herbarium specimen image of Nephroma lepidophyllum (Räsänen) Räsänen ex Gyeln. f. hypomelaenum Räsänen ex I.M. Lamb, part of the collection of Finnish Museum of Natural History LUOMUS, University of Helsinki

Part of a training dataset of scanned herbarium specimens. The data paper and a summary landing page will be published on Zenodo as it gets published. Content of this deposition: - A JSON-LD datafile listing the label data associated with this herbarium specimen. The Darwin and Dublin Core data standards are used for most values. - A JPEG image file of the scanned herbarium sheet. - A lossless TIFF image from which the JPEG image has been derived.

opencc-zeroNov 2018View details →

zenodo44/100

Southern Hemisphere Lamb Weather Types from historical GCM experiments and various reanalyses

This dataset comprises six-hourly Lamb Weather Type (LWT) time series covering the period 1979-2005 for a) historical experiments run with 61 distinct GCMs from CMIP5 and 6 (specified in "get_historical_metadata.py" published at https://doi.org/10.5281/zenodo.4555367) and b) 3 distinct reanalyses (ERA-Interim, JRA-55 and ERA5, the latter extended to 2020). The LWT time series are provided on a 2.5º regular latitude-longitude grid covering the southern hemisphere between 30ºS and 70ºS. The full LWT approach covering 27 classes is applied and the corresponding results for the Northern Hemisphere were stored in a companion dataset at https://doi.org/10.5281/zenodo.4452080. The format of the files is netCDF-4, compressed with the netCDF Kitchen Sink command "ncks -4 -L 1". The Python code used to generate this dataset is available from https://doi.org/10.5281/zenodo.4555367   Note The LWT_SH.zip file contains all relevant data. Please ignore the separate netCDF files outside this zip file. These are old files that could not be deleted during the update from version 1 to 2 due to technical issues with Zenodo.   Historial Version 2 is a major dataset update featuring the following improvements: 1. The attributes from the netCDF source files "psl...nc" obtained from ESGF were copied into the files available here. These attributes are indicated with the prefix "udata...." (for "underlying data"). 2. All non-standard calenders from the underlying netCDF files from ESGF were converted into standard using the "xarray.Dataset.convert_calendar" function. The original calendar information was stored as additional netCDF attribute. 3. The "patch" method from Python's xesmf module was used to regrid the original psl data from the native GCM grid available from ESGF to the regular lat-lon 2.5° grid common to all applied GCMs and reanalyses. contact: Swen Brands, brandssf@ifca.unican.es   Principal Research Articles, Software and Complementary Datasets Associated with this Dataset Brands, S. (2022). A circulation-based performance atlas of the CMIP5 and 6 models for regional climate studies in the Northern Hemisphere mid-to- high latitudes. Geoscientific Model Development, 15 (4), 1375–1411. doi: https://doi.org/10.5194/gmd-15-1375-2022 Brands, S. (2022). A circulation-based performance atlas of the CMIP5 and 6 mod- els for regional climate studies in the northern hemisphere [data set]. Zenodo. doi: https://doi.org/10.5281/zenodo.4452080 Brands, S. (2022). Common error patterns in the regional atmospheric circulation simulated by the CMIP multi-model ensemble. Geophysical Research Letters, 49 (23), e2022GL101446. doi: https://doi.org/10.1029/2022GL101446 Brands, Swen, Tatebe, Hiroaki, Danek, Christopher, Fernández, Jesús, Swart, Neil C., Volodin, Evgeny, Kim, YoungHo, Collier, Mark, Bi, Dave, & Tongwen, Wu. (2022). Python code to calculate Lamb circulation types derived from historical CMIP simulations and reanalysis data. In Geoscientific Model Development: Vols. gmd-2020-418 (Version 4). Zenodo. https://doi.org/10.5281/zenodo.6390256 Brands, S., Fernández-Granja, J. A., Bedia, J., Casanueva, A., & Fernández, J. (2023). Auxiliary online material to Brands et al. (2023): A global climate model performance atlas for the Southern Hemisphere extratrop- ics based on regional atmospheric circulation patterns. figshare. doi: https://doi.org/10.6084/m9.figshare.22193443.v1 Brands, S., Fernández-Granja, J. A., Bedia, J., Casanueva, A., & Fernández, J. (2023b). Southern Hemisphere Lamb Weather Types from historical GCM experiments and various reanalyses (1.0) [data set]. Zenodo. doi: https://doi.org/10.5281/zenodo.7612988 Brands, S., Tatebe, H., Danek, C., Fernández, J., Swart, N., Volodin, E., . . . Tong- wen, W. (2023). GCM metadata archive get historical metadata.py (v1.1). Zenodo. doi: https://doi.org/10.5281/zenodo.7715383 Fernández-Granja, J. A., Brands, S., Bedia, J., Casanueva, A., & Fernández, J. (2023). Exploring the limits of the Jenkinson–Collison weather types clas- sification scheme: a global assessment based on various reanalyses. Climate Dynamics. doi: 10.1007/s00382-022-06658-7   References of the source GCMs and Early References of the Lamb Weather Typing Method Bentsen, M., Bethke, I., Debernard, J. B., Iversen, T., Kirkevåg, A., Seland, Ø., . . . Kristjánsson, J. E. (2013). The Norwegian Earth System Model, NorESM1-M – part 1: Description and basic evaluation of the physical climate. Geoscientific Model Development, 6 (3), 687–720. doi: 10.5194/gmd-6-687-2013 Bi, D., Dix, M., Marsland, S., O’Farrell, S., Sullivan, A., Bodman, R., . . . Heerde- gen, A. (2020). Configuration and spin-up of ACCESS-CM2, the new gener- ation Australian Community Climate and Earth System Simulator Coupled Model. Journal of Southern Hemisphere Earth Systems Science, 70 (1), 225- 251. doi: doi:10.1071/ES19040 Bi, D., Dix, M., Marsland, S. J., O’Farrell, S., Rashid, H., Uotila, P., . . . Puri, K. (2013). The ACCESS coupled model: description, control climate and evaluation. Australian Meteorological and Oceanographic Journal , 63 , 41-64. doi: 0.22499/2.6301.004 Boucher, O., Servonnat, J., Albright, A. L., Aumont, O., Balkanski, Y., Bastrikov, V., . . . Vuichard, N. (2020). Presentation and evaluation of the IPSL-CM6A- LR climate model. Journal of Advances in Modeling Earth Systems, 12 (7), e2019MS002010. doi: 10.1029/2019MS002010 Cao, J., Wang, B., Yang, Y.-M., Ma, L., Li, J., Sun, B., . . . Wu, L. (2018). The NUIST Earth System Model (NESM) version 3: description and prelimi- nary evaluation. Geoscientific Model Development, 11 (7), 2975–2993. doi: 10.5194/gmd-11-2975-2018 Cherchi, A., Fogli, P. G., Lovato, T., Peano, D., Iovino, D., Gualdi, S., . . . Navarra, A. (2019). Global mean climate and main patterns of variability in the CMCC- CM2 coupled model. Journal of Advances in Modeling Earth Systems, 11 (1), 185-209. doi: 10.1029/2018MS001369 Chylek, P., Li, J., Dubey, M. K., Wang, M., & Lesins, G. (2011). Observed and model simulated 20th century arctic temperature variability: Canadian Earth System Model CanESM2. Atmospheric Chemistry and Physics Discussions, 11 , 22893–22907. doi: 10.5194/acpd-11-22893-2011 Collins, W. J., Bellouin, N., Doutriaux-Boucher, M., Gedney, N., Halloran, P., Hinton, T., . . . Woodward, S. (2011). Development and evaluation of an Earth-System model – HadGEM2. Geoscientific Model Development, 4 (4),1051–1075.doi: 10.5194/gmd-4-1051-2011 Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., . . . Vitart, F. (2011). The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc., 137 (656, Part a), 553-597. doi: 10.1002/qj.828 Döscher, R., Acosta, M., Alessandri, A., Anthoni, P., Arneth, A., Arsouze, T., . . . Zhang, Q. (2021). The EC-Earth3 Earth System Model for the Coupled Model Intercomparison Project 6. Geoscientific Model Development Discussions, 2021 , 1–90. doi: 10.5194/gmd-2020-446 Dufresne, J.-L., Foujols, M.-A., Denvil, S., Caubel, A., Marti, O., Aumont, O., . . . Vuichard, N. (2013). Climate change projections using the IPSL-CM5 Earth System Model: from CMIP3 to CMIP5. Clim. Dyn., 40 (9-10), 2123-2165. doi: 10.1007/s00382-012-1636-1 Dunne, J. P., Horowitz, L. W., Adcroft, A. J., Ginoux, P., Held, I. M., John, J. G., . . . Zhao, M. (2020). The GFDL Earth System Model version 4.1 (GFDL- ESM 4.1): Overall coupled model description and simulation characteristics. Journal of Advances in Modeling Earth Systems, 12 (11), e2019MS002015. doi: https://doi.org/10.1029/2019MS002015 Dunne, J. P., John, J. G., Adcroft, A. J., Griffies, S. M., Hallberg, R. W., Shevli- akova, E., . . . Zadeh, N. (2012). GFDL’s ESM2 Global Coupled Climate- Carbon Earth System Models. Part I: Physical formulation and baseline simulation characteristics.Journal of Climate, 25 (19), 6646–6665. doi: https://doi.org/10.1175/JCLI-D-11-00560.1 Griffies, S., Winton, M., Donner, L., Horowitz, L., Downes, S., Farneti, R., . . . Zadeh, N. (2011). The GFDL-CM3 coupled climate model: Characteristics of the ocean and sea ice simulations. Journal of Climate, 24 , 3520-3544. doi: 10.1175/2011JCLI3964.1 Hajima, T., Watanabe, M., Yamamoto, A., Tatebe, H., Noguchi, M. A., Abe, M., . . . Kawamiya, M. (2020). Development of the MIROC-ES2L Earth system model and the evaluation of biogeochemical processes and feedbacks. Geoscientific Model Development, 13 (5), 2197–2244. doi: 10.5194/gmd-13-2197-2020 Hazeleger, W., Wang, X., Severijns, C., Briceag, S., Bintanja, R., Sterl, A., . . . van der Wiel, K. (2011). Ec-earth v2.2: Description and validation of a new seamless earth system prediction model.Climate Dynamics, 39 , 1-19. doi: 10.1007/s00382-011-1228-5 Held, I. M., Guo, H., Adcroft, A., Dunne, J. P., Horowitz, L. W., Krasting, J., . . . Zadeh, N. (2019). Structure and performance of GFDL’s CM4.0 climate model. Journal of Advances in Modeling Earth Systems, 11 (11), 3691-3727. doi: 10.1029/2019MS001829 Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., . . . Thépaut, J.-N. (2020). The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146 (730), 1999-2049. doi:https://doi.org/10.1002/qj.3803 Jones, P. D., Hulme, M., & Briffa, K. R. (1993). A comparison of Lamb circulation types with an objective classification scheme. International Journal of Clima- tology, 13 (6), 655-663. doi: https://doi.org/10.1002/joc.3370130606 Kelley, M., Schmidt, G. A., Nazarenko, L. S., Bauer, S. E., Ruedy, R., Russell, G. L., . . . Yao, M.-S. (2020). GISS-E2.1: Configurations and climatology. Journal of Advances in Modeling Earth Systems, 12 (8), e2019MS002025. doi: 10.1029/2019MS002025 Kobayashi, S., Ota, Y., Harada, Y., Ebita, A., Moriya, M., Onoda, H., . . . Taka- hashi, K. (2015). The JRA-55 Reanalysis: General specifications and basic characteristics. Journal of the Meteorological Society of Japan. Ser. II , 93 (1), 5-48. doi: 10.2151/jmsj.2015-001 Lamb, H. (1972). British Isles weather types and a register of daily sequence of cir- culation patterns, 1861-1971. Geophysical Memoir , 116 , 85pp. (HMSO) Lee, J., Kim, J., Sun, M.-A., Kim, B.-H., Moon, H., Sung, H. M., . . . Byun, Y.- H. (2019). Evaluation of the Korea Meteorological Administration Ad- vanced Community Earth-System model (K-ACE). Asia-Pacific Journal of Atmospheric Sciences, 56 , 381–395. doi: https://doi.org/10.1007/ Lee, W.-L., Wang, Y.-C., Shiu, C.-J., Tsai, I., Tu, C.-Y., Lan, Y.-Y., . . . Hsu, H.-H. (2020). Taiwan Earth System Model version 1: description and evaluation of mean state. Geoscientific Model Development, 13 (9), 3887–3904. doi: 10.5194/gmd-13-3887-2020 Li, L., Lin, P., Yu, Y.-Q., Zhou, T., Liu, L., Liu, J., . . . Qiao, F.-L. (2013). The Flexible Global Ocean-Atmosphere-Land System Model, Grid-point ver- sion 2: FGOALS-g2. Advances in Atmospheric Sciences, 30 , 543-560.doi: 10.1007/s00376-012-2140-6 Li, L., Yu, Y., Tang, Y., Lin, P., Xie, J., Song, M., . . . Wei, J. (2020). The Flex- ible Global Ocean-Atmosphere-Land System Model Grid-point version 3 (FGOALS-g3): Description and evaluation. Journal of Advances in Model- ing Earth Systems, 12 (9), e2019MS002012. doi: https://doi.org/10.1029/ 2019MS002012 Martin, T. H. D. T. G. M., Bellouin, N., Collins, W. J., Culverwell, I. D., Halloran, P. R., Hardiman, S. C., . . . Wiltshire, A. (2011). The HadGEM2 family of Met Office Unified Model climate configurations. Geoscientific Model Development, 4 (3), 723–757. doi: 10.5194/gmd-4-723-2011 Mauritsen, T., Bader, J., Becker, T., Behrens, J., Bittner, M., Brokopf, R., . . . Roeckner, E. (2019). Developments in the MPI-M Earth System Model version 1.2 (MPI-ESM1.2) and its response to increasing CO2. Journal of Advances in Modeling Earth Systems, 11 (4), 998-1038. doi: 10.1029/2018MS001400 Pak, G., Noh, Y., Lee, M.-I., Yeh, S.-W., Kim, D., Kim, S.-Y., . . . Kim, Y. H. (2021). Korea Institute of Ocean Science and Technology Earth System Model and its simulation characteristics. Ocean Science Journal , 56 , 18-45. doi: 10.1007/s12601-021-00001-7 Park, S., Shin, J., Kim, S., Oh, E., & Kim, Y. (2019). Global climate simulated by the Seoul National University Atmosphere Model version 0 with a unified convection scheme (SAM0-UNICON). Journal of Climate, 32 (10), 2917-2949. doi: 10.1175/JCLI-D-18-0796.1 Roberts, M., Baker, A., Blockley, E., Calvert, D., Coward, A., Hewitt, H., . . . Vi- dale, P. (2019). Description of the resolution hierarchy of the global coupled HadGEM3-GC3.1 model as used in CMIP6 HighResMIP experiments. Geosci- entific Model Development Discussions, 1-47. doi: 10.5194/gmd-2019-148 Schmidt, G. A., Kelley, M., Nazarenko, L., Ruedy, R., Russell, G. L., Aleinov, I., . . . Zhang, J. (2014). Configuration and assessment of the GISS ModelE2 contributions to the CMIP5 archive. Journal of Advances in Modeling Earth Systems, 6 (1), 141-184. doi: 10.1002/2013MS000265 Scoccimarro, E., Gualdi, S., Bellucci, A., Sanna, A., Giuseppe Fogli, P., Manzini, E., . . . Navarra, A. (2011). Effects of tropical cyclones on ocean heat transport in a high-resolution coupled general circulation model. Journal of Climate, 24 (16), 4368-4384. doi: 10.1175/2011JCLI4104.1 Seland, Ø., Bentsen, M., Seland Graff, L., Olivié, D., Toniazzo, T., Gjermundsen, A., . . . Schulz, M. (2020). The Norwegian Earth System Model, NorESM2 – evaluation of the CMIP6 DECK and historical simulations. Geoscientific Model Development, 2020 , 1–68. doi: 10.5194/gmd-2019-378 Semmler, T., Danilov, S., Gierz, P., Goessling, H. F., Hegewald, J., Hinrichs, C., . . . Jung, T. (2020). Simulations for CMIP6 with the AWI Climate Model AWI-CM-1-1. Journal of Advances in Modeling Earth Systems, 12 (9), e2019MS002009. doi: 10.1029/2019MS002009 Swapna, P., Koll, R., Aparna, K., Kulkarni, K., Ag, P., Ashok, K., . . . Goswami, B. N. (2015). The IITM Earth System Model: Transformation of a seasonal prediction model to a long term climate model. Bulletin of the American Meteorological Society, 96 , 1351–1367. doi: 10.1175/BAMS-D-13-00276.1 Séférian, R., Nabat, P., Michou, M., Saint-Martin, D., Voldoire, A., Colin, J., . . . Madec, G. (2019). Evaluation of CNRM Earth System Model, CNRM-ESM2-1: Role of Earth system processes in present-day and future climate. Journal of Advances in Modeling Earth Systems, 11 (12), 4182-4227. doi: 10.1029/2019MS001791 Tamura, T., Ohshima, K. I., & Nihashi, S. (2008). Mapping of sea ice production for antarctic coastal polynyas. Geophysical Research Letters, 35 (7). doi: https:// doi.org/10.1029/2007GL032903 Tatebe, H., Ogura, T., Nitta, T., Komuro, Y., Ogochi, K., Takemura, T., . . . Ki- moto, M. (2019). Description and basic evaluation of simulated mean state, internal variability, and climate sensitivity in MIROC6. Geoscientific Model Development, 12 (7), 2727–2765. doi: 10.5194/gmd-12-2727-2019 Tegen, I., Neubauer, D., Ferrachat, S., Siegenthaler-Le Drian, C., Bey, I., Schutgens, N., . . . Lohmann, U. (2019). The global aerosol–climate model ECHAM6.3– HAM2.3 – part 1: Aerosol evaluation. Geoscientific Model Development, 12 (4), 1643–1677. doi: 10.5194/gmd-12-1643-2019 Voldoire, A., Saint-Martin, D., Sénési, S., Decharme, B., Alias, A., Chevallier, M., . . . Waldman, R. (2019). Evaluation of CMIP6 DECK experiments with CNRM-CM6-1. Journal of Advances in Modeling Earth Systems, 11 (7), 2177- 2213. doi: 10.1029/2019MS001683 Voldoire, A., Sanchez-Gomez, E., Salas y Melia, D., Decharme, B., Cassou, C., Sen- esi, S., . . . Chauvin, F. (2013). The CNRM-CM5.1 global climate model: description and basic evaluation. Clim. Dyn., 40 (9-10), 2091-2121. doi: 10.1007/s00382-011-1259-y Volodin, E., Diansky, N., & Gusev, A. (2010). Simulating present-day climate with the INMCM4.0 coupled model of the atmospheric and oceanic general circulations. Izvestiya, Atmospheric and Oceanic Physics, 46 , 414-431. doi: https://doi.org/10.1134/S000143381004002X Volodin, E., Mortikov, E., Kostrykin, S., Galin, V., Lykossov, V., Gritsun, A., . . . Iakovlev, N. (2017). Simulation of the present-day climate with the climate model INMCM5. Climate Dynamics, 49 , 3715–3734. doi: https://doi.org/10.1007/s00382-017-3539-7 Watanabe, M., Suzuki, T., O’ishi, R., Komuro, Y., Watanabe, S., Emori, S., . . . Kimoto, M. (2010). Improved climate simulation by MIROC5: Mean states, variability, and climate sensitivity. Journal of Climate, 23 , 6312-6335. doi: 10.1175/2010JCLI3679.1 Watanabe, S., Hajima, T., Sudo, K., Nagashima, T., Takemura, T., Okajima, H., . . . Kawamiya, M. (2011). MIROC-ESM 2010: model description and basic results of CMIP5-20c3m experiments. Geoscientific Model Development, 4 , 845-872. doi: 10.5194/gmd-4-845-2011 Wu, T., Lu, Y., Fang, Y., Xin, X., Li, L., Li, W., . . . Liu, X. (2019). The Beijing Climate Center Climate System Model (BCC-CSM): the main progress from CMIP5 to CMIP6. Geoscientific Model Development, 12 (4), 1573–1600. doi: 10.5194/gmd-12-1573-2019 Wu, T., Song, L., Li, W., Wang, Z., Zhang, H., Xin, X., . . . Zhou, M. (2014). An overview of BCC Climate System Model development and application for climate change studies. Acta Meteorologica Sinica, 28 , 34–56. doi: 10.1007/s13351-014-3041-7 Yukimoto, S., Kawai, H., Koshiro, T., Oshima, N., Yoshida, K., Urakawa, S., . . . Ishii, M. (2019). The Meteorological Research Institute Earth System Model version 2.0, MRI-ESM2.0: Description and basic evaluation of the physical component. Journal of the Meteorological Society of Japan. Ser. II , 97 (5), 931-965. doi: 10.2151/jmsj.2019-051 Yukimoto, S., Yoshimura, H., Hosaka, M., Sakami, T., Tsujino, H., Hirabara, M., . . . Kitoh, A. (2011). Meteorological Research Institute-Earth System Model version 1 (MRI-ESM1) — model description —.Technical Reports of the Meteorological Research Institute, 64 , 1-96. Ziehn, T., Chamberlain, M. A., Law, R. M., Lenton, A., Bodman, R. W., Dix, M., . . . Srbinovsky, J. (2020). The Australian Earth System Model: ACCESS- ESM1.5. Journal of Southern Hemisphere Earth Systems Science, 70 , 193-214. doi: https://doi.org/10.1071/ES19035

opencc-by-4.0Feb 2023View details →

zenodo44/100

Northern Hemisphere Lamb Weather Types from historical GCM experiments and various reanalyses

This dataset contains 6-hourly instantaneous discrete Lamb circulation type time series (Lamb 1972) on a 2.5 degrees longitude-latitude grid covering the northern hemisphere extratropics between 30ºN and 70ºN for the period 1979-2005 or longer. These "Lamb catalogues" were calculated upon SLP data from the historical experiments run with 61 distinct GCMs participating in the Coupled Model Intercomparison Project phases 5 and 6, and also from three distinct reanalyses (ERA5 extended to 2020 from version 5 onwards, ERA-Interim and JRA-55). For 13 out of the aforementioned 61 GCMs, 72 additional runs are provided to explore the role of internal model variability. For more information, please refer to the following article: Brands, S.: A circulation-based performance atlas of the CMIP5 and 6 models for regional climate studies in the Northern Hemisphere mid-to-high latitudes, Geosci. Model Dev., 15, 1375–1411, https://doi.org/10.5194/gmd-15-1375-2022, 2022. or contact: brandssf@ifca.unican.es Reference: Lamb, H.: British Isles Weather types and a register of daily sequence of circulation patterns, 1861-1971, Geophysical Memoir, 116, 85pp., HMSO, 1972. CAUTION: When unpacked, this dataset occupies 110 GB of your local disk space. Update information: Version 2 of this archive includes the model_source_attributes.txt file containing the "source" attributes stored in the netCDF files obtained from ESGF. This attribute provides details about the individual component models within the coupled model configurations used in CMIP5 and 6. Version 3 of this archive includes 10 new GCMs, two additional runs for CNRM-CM6-1 and an updated version of model_source_attributes.txt Version 3.1 includes README.txt, which explains the content of the files located in the tar.gz file. Version 4 further includes Lamb Weather Type catalogues for the ERA5 reanalysis and 4 additional GCMs. All files have been compressed individually.  The <model_source_attributes.txt> file is depreciated and no longer updated. It is replaced by the Python function <get_historical_metadata.py> available from https://doi.org/10.5281/zenodo.4555367. This function contains an exhaustive metadata archive of the 60 GCMs considered here. Version 4.1 The LWT catalogue for CMCC-CM2-HR4 is included for consistency with the respective Southern Hemisphere dataset published at https://doi.org/10.5281/zenodo.7612987 Version 5 is a major dataset update featuring the following improvements: 1. The attributes from the netCDF source files "psl...nc" obtained from ESGF were copied into the files available here. These attributes are indicated with the prefix "udata...." (for "underlying data"). 2. All non-standard calenders from the underlying netCDF files from ESGF were converted into standard using the "xarray.Dataset.convert_calendar" function. The original calendar information was stored as additional netCDF attribute. 3. The "patch" method from Python's xesmf module was used to regrid the original psl data from the native GCM grid available from ESGF to the regular lat-lon 2.5° grid common to all applied GCMs and reanalyses. contact: Swen Brands, brandssf@ifca.unican.es   Principal Research Articles, Software and Complementary Datasets Associated with this Dataset Brands, S. (2022). A circulation-based performance atlas of the CMIP5 and 6 models for regional climate studies in the Northern Hemisphere mid-to- high latitudes. Geoscientific Model Development, 15 (4), 1375–1411. doi: https://doi.org/10.5194/gmd-15-1375-2022 Brands, S. (2022). A circulation-based performance atlas of the CMIP5 and 6 mod- els for regional climate studies in the northern hemisphere [data set]. Zenodo. doi: https://doi.org/10.5281/zenodo.4452080 Brands, S. (2022). Common error patterns in the regional atmospheric circulation simulated by the CMIP multi-model ensemble. Geophysical Research Letters, 49 (23), e2022GL101446. doi: https://doi.org/10.1029/2022GL101446 Brands, Swen, Tatebe, Hiroaki, Danek, Christopher, Fernández, Jesús, Swart, Neil C., Volodin, Evgeny, Kim, YoungHo, Collier, Mark, Bi, Dave, & Tongwen, Wu. (2022). Python code to calculate Lamb circulation types derived from historical CMIP simulations and reanalysis data. In Geoscientific Model Development: Vols. gmd-2020-418 (Version 4). Zenodo. https://doi.org/10.5281/zenodo.6390256 Brands, S., Fernández-Granja, J. A., Bedia, J., Casanueva, A., & Fernández, J. (2023). Auxiliary online material to Brands et al. (2023): A global climate model performance atlas for the Southern Hemisphere extratrop- ics based on regional atmospheric circulation patterns. figshare. doi: https://doi.org/10.6084/m9.figshare.22193443.v1 Brands, S., Fernández-Granja, J. A., Bedia, J., Casanueva, A., & Fernández, J. (2023b). Southern Hemisphere Lamb Weather Types from historical GCM experiments and various reanalyses (1.0) [data set]. Zenodo. doi: https://doi.org/10.5281/zenodo.7612988 Brands, S., Tatebe, H., Danek, C., Fernández, J., Swart, N., Volodin, E., . . . Tong- wen, W. (2023). GCM metadata archive get historical metadata.py (v1.1). Zenodo. doi: https://doi.org/10.5281/zenodo.7715383 Fernández-Granja, J. A., Brands, S., Bedia, J., Casanueva, A., & Fernández, J. (2023). Exploring the limits of the Jenkinson–Collison weather types clas- sification scheme: a global assessment based on various reanalyses. Climate Dynamics. doi: 10.1007/s00382-022-06658-7   References of the source GCMs and Early References of the Lamb Weather Typing Method Bentsen, M., Bethke, I., Debernard, J. B., Iversen, T., Kirkevåg, A., Seland, Ø., . . . Kristjánsson, J. E. (2013). The Norwegian Earth System Model, NorESM1-M – part 1: Description and basic evaluation of the physical climate. Geoscientific Model Development, 6 (3), 687–720. doi: 10.5194/gmd-6-687-2013 Bi, D., Dix, M., Marsland, S., O’Farrell, S., Sullivan, A., Bodman, R., . . . Heerde- gen, A. (2020). Configuration and spin-up of ACCESS-CM2, the new gener- ation Australian Community Climate and Earth System Simulator Coupled Model. Journal of Southern Hemisphere Earth Systems Science, 70 (1), 225- 251. doi: doi:10.1071/ES19040 Bi, D., Dix, M., Marsland, S. J., O’Farrell, S., Rashid, H., Uotila, P., . . . Puri, K. (2013). The ACCESS coupled model: description, control climate and evaluation. Australian Meteorological and Oceanographic Journal , 63 , 41-64. doi: 0.22499/2.6301.004 Boucher, O., Servonnat, J., Albright, A. L., Aumont, O., Balkanski, Y., Bastrikov, V., . . . Vuichard, N. (2020). Presentation and evaluation of the IPSL-CM6A- LR climate model. Journal of Advances in Modeling Earth Systems, 12 (7), e2019MS002010. doi: 10.1029/2019MS002010 Cao, J., Wang, B., Yang, Y.-M., Ma, L., Li, J., Sun, B., . . . Wu, L. (2018). The NUIST Earth System Model (NESM) version 3: description and prelimi- nary evaluation. Geoscientific Model Development, 11 (7), 2975–2993. doi: 10.5194/gmd-11-2975-2018 Cherchi, A., Fogli, P. G., Lovato, T., Peano, D., Iovino, D., Gualdi, S., . . . Navarra, A. (2019). Global mean climate and main patterns of variability in the CMCC- CM2 coupled model. Journal of Advances in Modeling Earth Systems, 11 (1), 185-209. doi: 10.1029/2018MS001369 Chylek, P., Li, J., Dubey, M. K., Wang, M., & Lesins, G. (2011). Observed and model simulated 20th century arctic temperature variability: Canadian Earth System Model CanESM2. Atmospheric Chemistry and Physics Discussions, 11 , 22893–22907. doi: 10.5194/acpd-11-22893-2011 Collins, W. J., Bellouin, N., Doutriaux-Boucher, M., Gedney, N., Halloran, P., Hinton, T., . . . Woodward, S. (2011). Development and evaluation of an Earth-System model – HadGEM2. Geoscientific Model Development, 4 (4),1051–1075.doi: 10.5194/gmd-4-1051-2011 Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., . . . Vitart, F. (2011). The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc., 137 (656, Part a), 553-597. doi: 10.1002/qj.828 Döscher, R., Acosta, M., Alessandri, A., Anthoni, P., Arneth, A., Arsouze, T., . . . Zhang, Q. (2021). The EC-Earth3 Earth System Model for the Coupled Model Intercomparison Project 6. Geoscientific Model Development Discussions, 2021 , 1–90. doi: 10.5194/gmd-2020-446 Dufresne, J.-L., Foujols, M.-A., Denvil, S., Caubel, A., Marti, O., Aumont, O., . . . Vuichard, N. (2013). Climate change projections using the IPSL-CM5 Earth System Model: from CMIP3 to CMIP5. Clim. Dyn., 40 (9-10), 2123-2165. doi: 10.1007/s00382-012-1636-1 Dunne, J. P., Horowitz, L. W., Adcroft, A. J., Ginoux, P., Held, I. M., John, J. G., . . . Zhao, M. (2020). The GFDL Earth System Model version 4.1 (GFDL- ESM 4.1): Overall coupled model description and simulation characteristics. Journal of Advances in Modeling Earth Systems, 12 (11), e2019MS002015. doi: https://doi.org/10.1029/2019MS002015 Dunne, J. P., John, J. G., Adcroft, A. J., Griffies, S. M., Hallberg, R. W., Shevli- akova, E., . . . Zadeh, N. (2012). GFDL’s ESM2 Global Coupled Climate- Carbon Earth System Models. Part I: Physical formulation and baseline simulation characteristics.Journal of Climate, 25 (19), 6646–6665. doi: https://doi.org/10.1175/JCLI-D-11-00560.1 Griffies, S., Winton, M., Donner, L., Horowitz, L., Downes, S., Farneti, R., . . . Zadeh, N. (2011). The GFDL-CM3 coupled climate model: Characteristics of the ocean and sea ice simulations. Journal of Climate, 24 , 3520-3544. doi: 10.1175/2011JCLI3964.1 Hajima, T., Watanabe, M., Yamamoto, A., Tatebe, H., Noguchi, M. A., Abe, M., . . . Kawamiya, M. (2020). Development of the MIROC-ES2L Earth system model and the evaluation of biogeochemical processes and feedbacks. Geoscientific Model Development, 13 (5), 2197–2244. doi: 10.5194/gmd-13-2197-2020 Hazeleger, W., Wang, X., Severijns, C., Briceag, S., Bintanja, R., Sterl, A., . . . van der Wiel, K. (2011). Ec-earth v2.2: Description and validation of a new seamless earth system prediction model.Climate Dynamics, 39 , 1-19. doi: 10.1007/s00382-011-1228-5 Held, I. M., Guo, H., Adcroft, A., Dunne, J. P., Horowitz, L. W., Krasting, J., . . . Zadeh, N. (2019). Structure and performance of GFDL’s CM4.0 climate model. Journal of Advances in Modeling Earth Systems, 11 (11), 3691-3727. doi: 10.1029/2019MS001829 Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., . . . Thépaut, J.-N. (2020). The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146 (730), 1999-2049. doi:https://doi.org/10.1002/qj.3803 Jones, P. D., Hulme, M., & Briffa, K. R. (1993). A comparison of Lamb circulation types with an objective classification scheme. International Journal of Clima- tology, 13 (6), 655-663. doi: https://doi.org/10.1002/joc.3370130606 Kelley, M., Schmidt, G. A., Nazarenko, L. S., Bauer, S. E., Ruedy, R., Russell, G. L., . . . Yao, M.-S. (2020). GISS-E2.1: Configurations and climatology. Journal of Advances in Modeling Earth Systems, 12 (8), e2019MS002025. doi: 10.1029/2019MS002025 Kobayashi, S., Ota, Y., Harada, Y., Ebita, A., Moriya, M., Onoda, H., . . . Taka- hashi, K. (2015). The JRA-55 Reanalysis: General specifications and basic characteristics. Journal of the Meteorological Society of Japan. Ser. II , 93 (1), 5-48. doi: 10.2151/jmsj.2015-001 Lamb, H. (1972). British Isles weather types and a register of daily sequence of cir- culation patterns, 1861-1971. Geophysical Memoir , 116 , 85pp. (HMSO) Lee, J., Kim, J., Sun, M.-A., Kim, B.-H., Moon, H., Sung, H. M., . . . Byun, Y.- H. (2019). Evaluation of the Korea Meteorological Administration Ad- vanced Community Earth-System model (K-ACE). Asia-Pacific Journal of Atmospheric Sciences, 56 , 381–395. doi: https://doi.org/10.1007/ Lee, W.-L., Wang, Y.-C., Shiu, C.-J., Tsai, I., Tu, C.-Y., Lan, Y.-Y., . . . Hsu, H.-H. (2020). Taiwan Earth System Model version 1: description and evaluation of mean state. Geoscientific Model Development, 13 (9), 3887–3904. doi: 10.5194/gmd-13-3887-2020 Li, L., Lin, P., Yu, Y.-Q., Zhou, T., Liu, L., Liu, J., . . . Qiao, F.-L. (2013). The Flexible Global Ocean-Atmosphere-Land System Model, Grid-point ver- sion 2: FGOALS-g2. Advances in Atmospheric Sciences, 30 , 543-560.doi: 10.1007/s00376-012-2140-6 Li, L., Yu, Y., Tang, Y., Lin, P., Xie, J., Song, M., . . . Wei, J. (2020). The Flex- ible Global Ocean-Atmosphere-Land System Model Grid-point version 3 (FGOALS-g3): Description and evaluation. Journal of Advances in Model- ing Earth Systems, 12 (9), e2019MS002012. doi: https://doi.org/10.1029/ 2019MS002012 Martin, T. H. D. T. G. M., Bellouin, N., Collins, W. J., Culverwell, I. D., Halloran, P. R., Hardiman, S. C., . . . Wiltshire, A. (2011). The HadGEM2 family of Met Office Unified Model climate configurations. Geoscientific Model Development, 4 (3), 723–757. doi: 10.5194/gmd-4-723-2011 Mauritsen, T., Bader, J., Becker, T., Behrens, J., Bittner, M., Brokopf, R., . . . Roeckner, E. (2019). Developments in the MPI-M Earth System Model version 1.2 (MPI-ESM1.2) and its response to increasing CO2. Journal of Advances in Modeling Earth Systems, 11 (4), 998-1038. doi: 10.1029/2018MS001400 Pak, G., Noh, Y., Lee, M.-I., Yeh, S.-W., Kim, D., Kim, S.-Y., . . . Kim, Y. H. (2021). Korea Institute of Ocean Science and Technology Earth System Model and its simulation characteristics. Ocean Science Journal , 56 , 18-45. doi: 10.1007/s12601-021-00001-7 Park, S., Shin, J., Kim, S., Oh, E., & Kim, Y. (2019). Global climate simulated by the Seoul National University Atmosphere Model version 0 with a unified convection scheme (SAM0-UNICON). Journal of Climate, 32 (10), 2917-2949. doi: 10.1175/JCLI-D-18-0796.1 Roberts, M., Baker, A., Blockley, E., Calvert, D., Coward, A., Hewitt, H., . . . Vi- dale, P. (2019). Description of the resolution hierarchy of the global coupled HadGEM3-GC3.1 model as used in CMIP6 HighResMIP experiments. Geosci- entific Model Development Discussions, 1-47. doi: 10.5194/gmd-2019-148 Schmidt, G. A., Kelley, M., Nazarenko, L., Ruedy, R., Russell, G. L., Aleinov, I., . . . Zhang, J. (2014). Configuration and assessment of the GISS ModelE2 contributions to the CMIP5 archive. Journal of Advances in Modeling Earth Systems, 6 (1), 141-184. doi: 10.1002/2013MS000265 Scoccimarro, E., Gualdi, S., Bellucci, A., Sanna, A., Giuseppe Fogli, P., Manzini, E., . . . Navarra, A. (2011). Effects of tropical cyclones on ocean heat transport in a high-resolution coupled general circulation model. Journal of Climate, 24 (16), 4368-4384. doi: 10.1175/2011JCLI4104.1 Seland, Ø., Bentsen, M., Seland Graff, L., Olivié, D., Toniazzo, T., Gjermundsen, A., . . . Schulz, M. (2020). The Norwegian Earth System Model, NorESM2 – evaluation of the CMIP6 DECK and historical simulations. Geoscientific Model Development, 2020 , 1–68. doi: 10.5194/gmd-2019-378 Semmler, T., Danilov, S., Gierz, P., Goessling, H. F., Hegewald, J., Hinrichs, C., . . . Jung, T. (2020). Simulations for CMIP6 with the AWI Climate Model AWI-CM-1-1. Journal of Advances in Modeling Earth Systems, 12 (9), e2019MS002009. doi: 10.1029/2019MS002009 Swapna, P., Koll, R., Aparna, K., Kulkarni, K., Ag, P., Ashok, K., . . . Goswami, B. N. (2015). The IITM Earth System Model: Transformation of a seasonal prediction model to a long term climate model. Bulletin of the American Meteorological Society, 96 , 1351–1367. doi: 10.1175/BAMS-D-13-00276.1 Séférian, R., Nabat, P., Michou, M., Saint-Martin, D., Voldoire, A., Colin, J., . . . Madec, G. (2019). Evaluation of CNRM Earth System Model, CNRM-ESM2-1: Role of Earth system processes in present-day and future climate. Journal of Advances in Modeling Earth Systems, 11 (12), 4182-4227. doi: 10.1029/2019MS001791 Tamura, T., Ohshima, K. I., & Nihashi, S. (2008). Mapping of sea ice production for antarctic coastal polynyas. Geophysical Research Letters, 35 (7). doi: https:// doi.org/10.1029/2007GL032903 Tatebe, H., Ogura, T., Nitta, T., Komuro, Y., Ogochi, K., Takemura, T., . . . Ki- moto, M. (2019). Description and basic evaluation of simulated mean state, internal variability, and climate sensitivity in MIROC6. Geoscientific Model Development, 12 (7), 2727–2765. doi: 10.5194/gmd-12-2727-2019 Tegen, I., Neubauer, D., Ferrachat, S., Siegenthaler-Le Drian, C., Bey, I., Schutgens, N., . . . Lohmann, U. (2019). The global aerosol–climate model ECHAM6.3– HAM2.3 – part 1: Aerosol evaluation. Geoscientific Model Development, 12 (4), 1643–1677. doi: 10.5194/gmd-12-1643-2019 Voldoire, A., Saint-Martin, D., Sénési, S., Decharme, B., Alias, A., Chevallier, M., . . . Waldman, R. (2019). Evaluation of CMIP6 DECK experiments with CNRM-CM6-1. Journal of Advances in Modeling Earth Systems, 11 (7), 2177- 2213. doi: 10.1029/2019MS001683 Voldoire, A., Sanchez-Gomez, E., Salas y Melia, D., Decharme, B., Cassou, C., Sen- esi, S., . . . Chauvin, F. (2013). The CNRM-CM5.1 global climate model: description and basic evaluation. Clim. Dyn., 40 (9-10), 2091-2121. doi: 10.1007/s00382-011-1259-y Volodin, E., Diansky, N., & Gusev, A. (2010). Simulating present-day climate with the INMCM4.0 coupled model of the atmospheric and oceanic general circulations. Izvestiya, Atmospheric and Oceanic Physics, 46 , 414-431. doi: https://doi.org/10.1134/S000143381004002X Volodin, E., Mortikov, E., Kostrykin, S., Galin, V., Lykossov, V., Gritsun, A., . . . Iakovlev, N. (2017). Simulation of the present-day climate with the climate model INMCM5. Climate Dynamics, 49 , 3715–3734. doi: https://doi.org/10.1007/s00382-017-3539-7 Watanabe, M., Suzuki, T., O’ishi, R., Komuro, Y., Watanabe, S., Emori, S., . . . Kimoto, M. (2010). Improved climate simulation by MIROC5: Mean states, variability, and climate sensitivity. Journal of Climate, 23 , 6312-6335. doi: 10.1175/2010JCLI3679.1 Watanabe, S., Hajima, T., Sudo, K., Nagashima, T., Takemura, T., Okajima, H., . . . Kawamiya, M. (2011). MIROC-ESM 2010: model description and basic results of CMIP5-20c3m experiments. Geoscientific Model Development, 4 , 845-872. doi: 10.5194/gmd-4-845-2011 Wu, T., Lu, Y., Fang, Y., Xin, X., Li, L., Li, W., . . . Liu, X. (2019). The Beijing Climate Center Climate System Model (BCC-CSM): the main progress from CMIP5 to CMIP6. Geoscientific Model Development, 12 (4), 1573–1600. doi: 10.5194/gmd-12-1573-2019 Wu, T., Song, L., Li, W., Wang, Z., Zhang, H., Xin, X., . . . Zhou, M. (2014). An overview of BCC Climate System Model development and application for climate change studies. Acta Meteorologica Sinica, 28 , 34–56. doi: 10.1007/s13351-014-3041-7 Yukimoto, S., Kawai, H., Koshiro, T., Oshima, N., Yoshida, K., Urakawa, S., . . . Ishii, M. (2019). The Meteorological Research Institute Earth System Model version 2.0, MRI-ESM2.0: Description and basic evaluation of the physical component. Journal of the Meteorological Society of Japan. Ser. II , 97 (5), 931-965. doi: 10.2151/jmsj.2019-051 Yukimoto, S., Yoshimura, H., Hosaka, M., Sakami, T., Tsujino, H., Hirabara, M., . . . Kitoh, A. (2011). Meteorological Research Institute-Earth System Model version 1 (MRI-ESM1) — model description —.Technical Reports of the Meteorological Research Institute, 64 , 1-96. Ziehn, T., Chamberlain, M. A., Law, R. M., Lenton, A., Bodman, R. W., Dix, M., . . . Srbinovsky, J. (2020). The Australian Earth System Model: ACCESS- ESM1.5. Journal of Southern Hemisphere Earth Systems Science, 70 , 193-214. doi: https://doi.org/10.1071/ES19035

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Lamb et al. (2020): Global Whole Lithosphere Isostasy datasets

Model outputs from Lamb, S., Moore, J., Perez-Gussinye, M., Stern, T. (2020). Global whole lithosphere isostasy: implications for surface elevations, structure, strength and densities of the continental lithosphere, Geochem, Geophys, Geosyst., doi :10.1029/2020GC009150 Data sets supplied here are the outcome of modelling described in the text. Files are given in either ASCII or GMT grd format. Data Set S1 (ds01.grd). Gridded crustal model of Antarctica based on whole lithosphere isostasy described in this study, and used to construct Figure 7c. Data columns are: x distance, y distance, crustal thickness. In GMT grd format with bounds in km -R-3000/3000/-3000/3000 -I5.  Uses same projection as Bedmap 2 - see Fretwell et al. (2013) for details of projection. Suggested colour palette in GMT: seis -T0/60/2.5  -I Data Set S2 (ds02.xyz). Average elevation and crustal thickness of continental interiors calculated in this study, used to plot Figure 3c and described in text, using a standard lithospheric thickness of 100 km. Data columns are: Name, area, average elevation (m), average reduced elevation for 100 km standard lithosphere (m), average lithospheric thickness (km), average crustal thickness (km), 1 sigma uncertainty in elevation (m) or reduced elevation (m), 1 sigma uncertainty in lithospheric thickness (km), 1 sigma uncertainty in crustal thickness (km). ASCII file. Data Set S3 (ds03.grd). Gridded elevation anomalies (observed elevation – elevation calculated from whole lithosphere isostasy), as described in text and used to construct Figure 8. Data columns are: Longitude, Latitude, elevation anomaly (m). In GMT grd format with bounds in degrees -R-179/179/-60/80 -I1. Suggested colour palette in GMT: seis -T-2000/2000/200 -Z -I -M -D --COLOR_NAN=white Data Set S4 (ds04.grd). Gridded global crustal density perturbation model calculated to give zero elevation anomaly, based on elevation anomalies in Data Set 3, used to plot Fig. 9a. Data columns are: Longitude, Latitude, crustal density perturbation in kgm-3. In GMT grd format with bounds in degrees -R-179/179/-60/80 -I1. Suggested colour palette in GMT: seis -T-200/200/10 -Z -I -M -D --COLOR_NAN=white Data Set S5 (ds05.grd). Gridded global conductive lithosphere mantle density perturbation model calculated to give zero elevation anomaly, based on elevation anomalies in Data Set 3, used to plot Fig. 9b. Data columns are: Longitude, Latitude, mantle density perturbation in kgm-3. In GMT grd format with bounds in degrees -R-179/179/-60/80 -I1. Suggested clour palette in GMT: seis -T-50/50/5 -Z -I -M -D --COLOR_NAN=white Data Set S6 (ds06.grd). Gridded global crustal thickness perturbation model calculated to give zero elevation anomaly, based on elevation anomalies in Data Set 3, used to plot Fig. 9c. Data columns are: Longitude, Latitude, crustal thickness in km. In GMT grd format with bounds in degrees -R-179/179/-60/80 -I1. Suggested colour palette in GMT: seis -T-10/10/1 -Z -I -M -D --COLOR_NAN=white Data Set S7 (ds07.grd). Gridded global conductive lithosphere thickness perturbation model calculated to give zero elevation anomaly, based on elevation anomalies in Data Set 3, used to plot Fig. 9d. Data columns are: Longitude, Latitude,  thickness in km. In GMT grd format with bounds in degrees -R-179/179/-60/80 -I1. Suggested colour palette in GMT: seis -T-100/100/5 -Z -I -M -D --COLOR_NAN=white Data Set S8 (ds08.grd). Compilation of gridded ratios of elastic thickness to conductive lithospheric thickness in the continents used to construct Figure 10c and d. Data columns are: Longitude, Latitude, ratio of elastic thickness to conductive lithospheric thickness from sources cited below. In GMT grd format with bounds in degrees -R-179/179/-60/80 -I1. Suggested colour palette in GMT: rainbow -T0/1/0.05 -Z -I -D --COLOR_NAN=white Data references: Lowry, A.R. and Pérez-Gussinyé, M., 2011. The role of crustal quartz in controlling Cordilleran deformation. Nature, 471(7338), 353-357. Pérez‐Gussinyé, M., Lowry, A.R., Watts, A.B. and Velicogna, I., (2004). On the recovery of effective elastic thickness using spectral methods: examples from synthetic data and from the Fennoscandian Shield. Journal of Geophysical Research: Solid Earth, 109(B10). Pérez-Gussinyé, M. and Watts, A.B., (2005). The long-term strength of Europe and its implications for plate-forming processes. Nature, 436(7049), 381. Pérez‐Gussinyé, M., Lowry, A.R. and Watts, A.B., (2007). Effective elastic thickness of South America and its implications for intracontinental deformation. Geochemistry, Geophysics, Geosystems, 8(5). Pérez‐Gussinyé, M., Lowry, A.R., Phipps Morgan, J. and Tassara, A., (2008). Effective elastic thickness variations along the Andean margin and their relationship to subduction geometry. Geochemistry, Geophysics, Geosystems, 9(2). Pérez-Gussinyé, M., Metois, M., Fernández, M., Vergés, J., Fullea, J. and Lowry, A.R., (2009). Effective elastic thickness of Africa and its relationship to other proxies for lithospheric structure and surface tectonics. Earth and Planetary Science Letters, 287(1-2), 152-167. Swain, C.J. and Kirby, J.F., 2006. An effective elastic thickness map of Australia from wavelet transforms of gravity and topography using Forsyth's method. Geophysical Research Letters, 33(2).

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Ultrasonic guided-wave experiment data for manuscript entitled 'A homogenisation scheme for Lamb ultrasound wave dispersion in textilecomposites through multiscale wave and finite element modelling'

This data set contains the ultrasonic guided wave signals (signal amplitudes as a function of time for different sensors) that were generated and recorded using the transducers and controlling instrument in support of the manuscript entitled 'A homogenisation scheme for Lamb ultrasound wave dispersion in textile composites through multiscale wave and finite element modelling'. The controlling software was programmed in MATLAB and that the attached files are in accordance to the .mat file format. The file names follow the notation described below with an example: S1_10kHz_2cyc (illustrated with an example): S1 represents the number of sensors; 10kHz represents the exciting frequency; 2cyc represents the cycle number of input waveform. Details on the experiment setup are provided within an extra file ('Readme' file).

openmit-licenseFeb 2021View details →

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Experimental data in support of manuscript entitled 'A homogenisation scheme for Lamb ultrasound wave dispersion in textile composites through multiscale wave and finite element modelling'

openmit-licenseFeb 2021View details →

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Synthetic dataset of a full wavefield representing the propagation of Lamb waves and their interactions with delaminations

The dataset contains 475 simulated cases of full wavefield of Lamb waves propagation in a plate made of carbon fibre reinforced plastic (CFRP). The simulated 475 cases represent delaminations with different locations, shapes, and sizes. The following random factors were simulated in each case: <ul> <li>delamination geometrical size (ellipse minor and major axis randomly selected from the interval [10 mm, 40 mm],</li> <li>delamination angle (randomly selected from the interval [0◦ , 180◦]),</li> <li>coordinates of the centre of delamination (randomly selected from the interval [0 mm, 250 mm − δ] and [250 mm + δ, 500 mm], where δ = 10 mm).</li> </ul> The guided waves were excited at the centre of the plate by applying equivalent piezoelectric forces. The excitation was in the form of toneburst sine signal modulated by the Hann window. The carrier frequency is assumed 50 kHz, and the modulation frequency is 10 kHz. The total wave propagation time was set to 0.75 ms so that the guided wave can propagate to plate edges and back to the actuator twice. The number of time integration steps was 150000 which was selected for the stability of the central difference scheme. The material is a typical cross-ply CFRP laminate. The stacking sequence [0/90]4 was used in the model. The properties of a single-ply were as follows [GPa]: C11 = 52.55, C12 = 6.51, C22 = 51.83, C44 = 2.93, C55 = 2.92, C66 = 3.81. The assumed mass density was 1522.4 kg/m3. These properties were selected so that simulated numerically wave front patterns and wavelengths are similar to the wavefields measured by SLDV on CFRP specimens used later on for testing the developed methods for delamination identification. The shortest wavelength of propagating A0 Lamb wave mode was 21.2 mm for numerical simulations and 19.5 mm for experimental measurements. In each delamination case, 512 frames were generated to visualise the propagation of Lamb waves and their interactions and reflections from the delamination and edges. The numerically generated dataset resembles the velocity measurements acquired by the scanning laser Doppler vibrometer (SLDV) at the bottom surface of the plate of dimensions 500×500 mm. The uploaded dataset contains two ZIP files: <ol> <li> The first file contains 475 folders regarding all cases of different delaminations and their interaction with Lamb waves. In each case, there are 512 images in PNG format representing the propagation of guided waves.</li> <li> The second file contains: <ul> <li>475 images in PNG format representing the ground truth of the delaminations.</li> <li>CSV file contains all info regarding delaminations.</li> </ul> </li> </ol>

opencc-by-4.0Sep 2021View details →

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Composite geostationary weather satellite images (second time derivative of water vapor channel) for visualizing Lamb waves

Second time derivative of water vapor channel (6.2 micrometer) brightness temperature from geostationary weather satellites (Units: K s-2) Himawari-8 (original data obtained from NICT Science Cloud) GOES-16/17 (original data obtained from Amazon AWS) Meteosat-8/9/10/11 (original data obtained from EUMETSAT)   Time interval of the files: 5 minutes   Time interval of each satellite, dt for time derivative: Himaawri-8, GOES-16/17: 10 minutes, 10 minutes Meteosat-8/9/11: 15 minutes, 15 minutes Meteosat-10: 5 minutes, 10 minutes   Each file contains the latest images from those satellites at that time. The time stamp for each satellite represents the beginning of each full-disk scan.   Bias correction: Himawari-8: bias removal for each swath GOES-16/17, Meteosat-11: bias removal for each east-west line Meteosat-8/9/10: bias removal for each east-west line (note: satellite attitude was not stable)   Smoothing: Band-pass filter for each full-disk image separately: 2-40 degrees on lat-lon coordinate Stronger smoothing at latitudes higher than 60 degrees north/south   Down-sampling: Full-disk images were mapped to a 0.04-degree lat-lon coordinate. Then, composite images were produced at the 0.2-degree resolution.   Version 2: Improved interpolation algorithm Himawari-8: improved geolocation Meteosat-8/9: improved treatment of noise near the edge of full disk images

opencc-by-4.0May 2022View details →

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