Proposed Cooperative Institute for Earth System Research and Data Science (DOC)

2021 ◽  
Vol 46 (2) ◽  
pp. 5-6
Keyword(s):  
Data Science ◽  
Earth System ◽  
System Research

scholarly journals Estudo da Temperatura da Superfície do Mar para os Oceanos Atlântico e Pacífico Utilizando a Técnica de Análises de Componente Principal e de Agrupamento (Study of Sea Surface Temperature for the Atlantic and Pacific Oceans Using the Technique of...)

2011 ◽  
Vol 4 (2) ◽  
pp. 264
Author(s):  
Madson Tavares Silva ◽  
Stephany C. F. Do Egito Costa ◽  
Manoel Francisco Gomes Filho ◽  
Daisy B. Lucena
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Research Laboratory ◽  
Principal Component ◽  
Component Analysis ◽  
Sea Surface ◽  
Earth System ◽  
System Research

Apresenta-se neste estudo a avaliação da metodologia de Análises Multivariadas: Análises em Componente Principal (ACP) e de Agrupamento (AA), aos dados de Temperatura da Superfície do Mar (TSM) para os Oceanos Atlântico (Norte (NATL), Tropical (TROP) e Sul (SATL)) e Pacifico (NIÑO1+2, NIÑO3.4, NIÑO3 e NIÑO4). Foram utilizados dados mensais de janeiro de 1950 a dezembro de 2010 de TSM obtidos na NOAA (National Oceanic and Atmospheric Administration/Earth System Research Laboratory). As regiões TROP e NIÑO4 apresentam as maiores TSM para os meses entre dezembro-julho. A região NATL apresenta no período de agosto-outubro seu maiores valores de TSM. A região NIÑo1+2 apresentou os menores valores de TSM. Os resultados da Análise em Componente Principal (ACP) identificaram maiores pesos na variação total explicada pelas duas primeiras componentes, que representam cerca de 100% da variância total dos dados de TSM. A Análise de Agrupamento (AA), pelo método Ward, permitiu o agrupamento das estações em três grupos homogêneos. Palavras - chave: Análises Multivariadas, Mudanças climáticas, Aquecimento Global.   Study of Sea Surface Temperature for the Atlantic and Pacific Oceans Using the Technique of Principal Component Analysis and Cluster   ABSTRACT Presented in this study was to evaluate the methodology of Multivariate Analysis: Principal Component Analysis (PCA) and cluster analysis (CA), the data of sea surface temperature (SST) for the Atlantic (North (NATL), Tropical (TROP) and South (Satler)) and Pacific (+2 NIÑO1, NIÑO3.4, and NIÑO3 NIÑO4). We used monthly data from January 1950 to December 2010 SST obtained from NOAA (National Oceanic and Atmospheric Administration / Earth System Research Laboratory). TROP and NIÑO4 regions have the highest SST for the months from December to July. NATL The region has in the period August-October SST your highest values +2 NIÑo1 The region had the lowest values of TSM. Results on Principal Component Analysis (PCA) identified higher weights in the total variation explained by the first two components, which represent about 100% of the total variance of SST. The Cluster Analysis (AA), the Ward method, allowed the grouping of stations into three homogeneous groups. Keywords: Multivariate Analysis, Climate Change, Global Warming.

scholarly journals Perspectives on Marine Data Science as a Blueprint for Emerging Data Science Disciplines

2021 ◽  
Vol 8 ◽  
Author(s):  
Maria-Theresia Verwega ◽  
Carola Trahms ◽  
Avan N. Antia ◽  
Thorsten Dickhaus ◽  
Enno Prigge ◽  
...  
Keyword(s):  
Data Science ◽  
Academic Career ◽  
Heterogeneous Data ◽  
Early Career ◽  
Earth System ◽  
Doctoral Research ◽  
Crucial Component ◽  
Interface Science ◽  
Marine Data ◽  
New Research

Earth System Sciences have been generating increasingly larger amounts of heterogeneous data in recent years. We identify the need to combine Earth System Sciences with Data Sciences, and give our perspective on how this could be accomplished within the sub-field of Marine Sciences. Marine data hold abundant information and insights that Data Science techniques can reveal. There is high demand and potential to combine skills and knowledge from Marine and Data Sciences to best take advantage of the vast amount of marine data. This can be accomplished by establishing Marine Data Science as a new research discipline. Marine Data Science is an interface science that applies Data Science tools to extract information, knowledge, and insights from the exponentially increasing body of marine data. Marine Data Scientists need to be trained Data Scientists with a broad basic understanding of Marine Sciences and expertise in knowledge transfer. Marine Data Science doctoral researchers need targeted training for these specific skills, a crucial component of which is co-supervision from both parental sciences. They also might face challenges of scientific recognition and lack of an established academic career path. In this paper, we, Marine and Data Scientists at different stages of their academic career, present perspectives to define Marine Data Science as a distinct discipline. We draw on experiences of a Doctoral Research School, MarDATA, dedicated to training a cohort of early career Marine Data Scientists. We characterize the methods of Marine Data Science as a toolbox including skills from their two parental sciences. All of these aim to analyze and interpret marine data, which build the foundation of Marine Data Science.

Common evaluation/evolution of cloud-radiation processes from 25km S2S to 3km NWP

2020 ◽  
Author(s):  
Stan Benjamin ◽  
Joseph Joseph Olson ◽  
Shan Sun ◽  
Georg Georg Grell ◽  
Curtis Curtis Alexander
Keyword(s):  
Solar Radiation ◽  
Turbulent Mixing ◽  
Earth System ◽  
Subgrid Scale ◽  
System Research ◽  
Shallow Convection ◽  
Near Surface ◽  
Land System ◽  
Scale Models ◽  
Physical Parameterizations

<p>Subgrid-scale cloud representation and the closely related surface-energy balance continue to be a central challenge from subseasonal-to-seasonal models down to storm-scale models applied for forecast duration of only a few hours. Previously, NOAA/ESRL confirmed this issue from 3-km model (HRRR using WRF-ARW) for short-range forecasting including sub-grid-scale cloud representation up to a 25-km subseasonal model (FV3-GFS) testing a common suite of scale-aware physical parameterizations.  </p><p>In a major physics suite component -- modified representation of subgrid cloud water resulted in much improved agreement with radiation measurements as shown with 2018-2020 testing of the 3km HRRR model. Latest results will be shown using SURFRAD radiation and METAR ceiling observations, indicating much improved bias in downward solar radiation and in cloud location (via mean absolute error metric), as well as with 2m temperature and precipitation.</p><p>In addition, new evaluations with the same convection-allowing suite (“mesoscale” suite) of physical parameterizations revised further for subseasonal 30-day tests over summer and winter periods with the 25km NOAA FV3-GFS model. These results are compared with CERES-estimated cloud and downward solar radiation fields. The radiation results from this very preliminary subseasonal test with the ESRL-HRRR physics suite will be compared with previous subseasonal tests using the GFS physics suite and at different horizontal resolution.  This global application now confirms much better downward solar-radiation results over oceans for both January and June from a Nov-2019 version over a 2018 of the “mesoscale” suite.</p><p>Background: NOAA Earth System Research Laboratory, together with NCAR, has developed this parameterization suite (turbulent mixing, deep/shallow convection, 9-layer land/snow/vegetation/lake model) to improve PBL biases (temperature and moisture) including better representation of clouds and precipitation. This parameterization suite development has been accompanied by an effort for improved data assimilation of clouds, near-surface observations and radar for the atmosphere-land system.</p><p>Subgrid-scale cloud representation continues to be a central challenge from subseasonal-to-seasonal models down to storm-scale models applied for forecast duration of only a few hours.   Previously, NOAA/ESRL confirmed this issue from 3-km model (HRRR) for short-range forecasting including sub-grid-scale cloud representation up to a 60-km subseasonal model testing a common suite of scale-aware physical parameterizations.   Some progress has been made in 2018-2019 to substantially reduce cloud deficiency and excessive downward solar radiation at least over land areas.</p><p>Recent development and refinements to this common suite of physical parameterizations for scale-aware deep/shallow convection and boundary-layer mixing over this wide range of time and spatial scales will be reported in this presentation showing some progress. Evaluation of components of this suite is being evaluated for cloud/radiation (using SURFRAD, CERES, METAR ceiling) and near-surface (METAR, mesonet, aircraft, rawinsonde).</p><p>NOAA Earth System Research Laboratory, together with NCAR, has developed this parameterization suite (turbulent mixing, deep/shallow convection, 9-layer land/snow/vegetation model) to improve PBL biases (temperature and moisture) including better representation of clouds and precipitation. This parameterization suite development has been accompanied by an effort for improved data assimilation of clouds, near-surface observations and radar for the atmosphere-land system.  </p><p>The MYNN boundary-layer EDMF scheme (Olson, et al 2019), RUC land-surface model (Smirnova et al. 2016 MWR), Grell-Freitas scheme (2014, Atmos. Chem. Phys.), and aerosol-aware cloud microphysics (Thompson and Eidhammer 2015) have been applied and tested extensively for the NOAA hourly updated 3-km High-Resolution Rapid Refresh (HRRR) and 13-km Rapid Refresh model/assimilation systems over the United States and North America.   This mesoscale but also scale-aware suite is being tested,</p>

Polarization Lidar at Summit, Greenland, for the Detection of Cloud Phase and Particle Orientation

2013 ◽  
Vol 30 (8) ◽  
pp. 1635-1655 ◽  
Author(s):  
Ryan R. Neely ◽  
Matthew Hayman ◽  
Robert Stillwell ◽  
Jeffrey P. Thayer ◽  
R. Michael Hardesty ◽  
...  
Keyword(s):  
Full Range ◽  
Particle Orientation ◽  
Earth System ◽  
System Research ◽  
Cloud Properties ◽  
Atmospheric State ◽  
Initial Results

Abstract Accurate measurements of cloud properties are necessary to document the full range of cloud conditions and characteristics. The Cloud, Aerosol Polarization and Backscatter Lidar (CAPABL) has been developed to address this need by measuring depolarization, particle orientation, and the backscatter of clouds and aerosols. The lidar is located at Summit, Greenland (72.6°N, 38.5°W; 3200 m MSL), as part of the Integrated Characterization of Energy, Clouds, Atmospheric State, and Precipitation at Summit Project and NOAA's Earth System Research Laboratory's Global Monitoring Division's lidar network. Here, the instrument is described with particular emphasis placed upon the implementation of new polarization methods developed to measure particle orientation and improve the overall accuracy of lidar depolarization measurements. Initial results from the lidar are also shown to demonstrate the ability of the lidar to observe cloud properties.

NFDI4Earth – addressing the digital needs of Earth System Sciences

2021 ◽  
Author(s):  
Peter Braesicke ◽  
Jörg Seegert ◽  
Hannes Thiemann ◽  
Lars Bernard
Keyword(s):  
Cultural Change ◽  
Data Science ◽  
Support Service ◽  
Support Network ◽  
Inclusive Development ◽  
Earth System ◽  
Data Repositories ◽  
Self Evaluation ◽  
Multiple Challenges ◽  
Day To Day Operations

<p>NFDI4Earth addresses the digital needs of Earth System (ES) Sciences (ESS) in Germany. ES scientists cooperate in international and interdisciplinary networks with the overarching aim to understand the functioning of and interactions within the ES and address the multiple challenges of global change.</p> <p>NFDI4Earth is a community-driven process providing researchers with access to FAIR, coherent, and open ES data, innovative research data management (RDM) and data science methods. The NFDI4Earth work plan comprises four task areas (TA):</p> <p>TA1 2Participate will engage with the ESS community and secures that NFDI4Earth is driven by user requirements: Pilots, small agile projects proposed by the community leverage existing technologies and manifest the researchers’ RDM needs. The Incubator Lab identifies promising new tools and scouts for trends in ES Data Science. The EduHubs produce open, ready to use educational resources on implementing FAIR principles in the ESS. The Academy will connect young researchers and their data-driven research to NFDI4Earth.</p> <p>TA2 2Facilitate realizes the OneStop4All as the web-based entry point to FAIR, open and innovative RDM in ESS. It supports users on how to find, access, share, publish and work with ES data. Specific user requests beyond the scope of the OneStop4All will be routed to a distributed User Support Network. TA2 will also unlock the wealth of data that exists in governmental data repositories and will collaborate with all services on supporting long-term archiving.</p> <p>TA3 2Interoperate aims at interoperability and coherence of the heterogeneous, segmented range of ESS RDM services. The ecosystems of ESS (meta-)data and software repositories, data science services and collaboration platforms will be synthesised. Based on common standards, TA3 provides consistent methods for a self-evaluation of RDM offerings. TA3 works on NFDI cross-cutting topics, provides a Living Handbook and ensures co-operation with international RDM initiatives and standardisation bodies.</p> <p>TA4 2Coordinate facilitates the overall management of the NFDI4Earth consortium. TA4 acts as central support service and coordination of the technical implementations. It also offers virtual research environments. The NFDI4Earth Coordination Office will support the NFDI4Earth community in day-to-day operations and acts as the NFDI4Earth point of contact. It develops a commonly agreed model for a sustainable operation of NFDI4Earth.</p> <p>The NFDI4Earth governance aims for an open and inclusive development of the NFDI4Earth services. As one example, so-called interest groups can be initiated by the NFDI4Earth community to explore individual topics in greater depth and provide input and feedback to the NFDI4Earth developments. Moreover, as a community we will work on a commonly accepted NFDI4Earth FAIRness and Openness Commitment that is key to fostering a cultural change towards FAIR and Open RDM for all.</p>

STABISODB – A STABLE ISOTOPE DATABASE FOR EARTH SYSTEM RESEARCH

2020 ◽  
Author(s):  
Ethan Grossman ◽  
◽  
Michael M. Joachimski ◽  
Cristina Krause ◽  
Wolfgang Kiessling
Keyword(s):  
Stable Isotope ◽  
Earth System ◽  
System Research
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