STABISODB – A STABLE ISOTOPE DATABASE FOR EARTH SYSTEM RESEARCH

2020 ◽  
Author(s):  
Ethan Grossman ◽  
◽  
Michael M. Joachimski ◽  
Cristina Krause ◽  
Wolfgang Kiessling
2011 ◽  
Vol 4 (2) ◽  
pp. 264
Author(s):  
Madson Tavares Silva ◽  
Stephany C. F. Do Egito Costa ◽  
Manoel Francisco Gomes Filho ◽  
Daisy B. Lucena

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.


2020 ◽  
Author(s):  
Stan Benjamin ◽  
Joseph Joseph Olson ◽  
Shan Sun ◽  
Georg Georg Grell ◽  
Curtis Curtis Alexander

<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>


2013 ◽  
Vol 30 (8) ◽  
pp. 1635-1655 ◽  
Author(s):  
Ryan R. Neely ◽  
Matthew Hayman ◽  
Robert Stillwell ◽  
Jeffrey P. Thayer ◽  
R. Michael Hardesty ◽  
...  

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.


2020 ◽  
Vol 8 ◽  
Author(s):  
Jun She ◽  
H. E. Markus Meier ◽  
Miroslaw Darecki ◽  
Patrick Gorringe ◽  
Vibeke Huess ◽  
...  

2008 ◽  
Vol 25 (8) ◽  
pp. 1369-1382 ◽  
Author(s):  
Reginald J. Hill ◽  
W. Alan Brewer ◽  
Sara C. Tucker

Abstract The NOAA/Earth System Research Laboratory (ESRL) has two coherent Doppler lidar systems that have been deployed on board research vessels to obtain data during several experiments. The instruments measure the wind velocity relative to the motion of the lidar; therefore, correction for the motion of the platform is required. This article gives a thorough analysis of the correction for lidar velocity measurements. The analysis is general enough to be applied to Doppler velocity measurements from all monostatic ship- and aircraftborne lidars and radars, and generalization to bistatic systems is achievable. The correction is demonstrated using miniature master-oscillator power-amplifier (mini-MOPA) Doppler velocity data obtained during the Rain in Cumulus over the Ocean (RICO) experiment.


Science ◽  
2009 ◽  
Vol 325 (5938) ◽  
pp. 245-245 ◽  
Author(s):  
W. V. Reid ◽  
C. Brechignac ◽  
Y. Tseh Lee

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