Exploiting SeaDataCloud Temperature and Salinity time series data collections and comparing with Copernicus - a novel approach with SOURCE tool

2020 ◽  
Author(s):  
Paolo Oliveri ◽  
SImona Simoncelli ◽  
Pierluigi DI Pietro ◽  
Sara Durante
Keyword(s):  
Time Series ◽  
Best Practices ◽  
Data Management ◽  
Time Series Data ◽  
Management Plan ◽  
Global Ocean ◽  
Series Data ◽  
Data Repository ◽  
Log File ◽  
Data Collections

<p>One of the main challenges for the present and future in ocean observations is to find best practices for data management: infrastructures like Copernicus and SeaDataCloud already take responsibility for assembly, archive, update and publish data. Here we present the strengths and weaknesses in a SeaDataCloud Temperature and Salinity time series data collections, in particular a tool able to recognize the different devices and platforms and to merge them with processed Copernicus platforms.</p><p>While Copernicus has the main target to quickly acquire and publish data, SeaDataNet aims to publish data with the best quality available. This two data repository should be considered together, since the originator can ingest the data in both the infrastructures or only in one, or partially in both. This results sometimes in data partially available in Copernicus or SeaDataCloud, with great impact for the researcher who wants to access as much data as possible. The data reprocessing should not be loaded on researchers' shoulders, since only skilled users in all data management plan know how merge the data.</p><p>The SeaDataCloud time series data collections is a Global Ocean soon-to-be-published dataset that will represent a reference for ocean researchers, released in binary, user friendly Ocean Data View format. The database management plan was originally for profiles, but had been adapted for time series, resolving several issues like the uniqueness of the identifiers (ID).</p><p>Here we present an extension of the SOURCE (Sea Observations Utility for Reprocessing. Calibration and Evaluation) Python package, able to enhance the data quality with redundant sophisticated methods and simplify their usage. </p><p>SOURCE increases quality control (Q/C) performances on observations using statistical quality check procedures that follows the ocean best practices guidelines, exploiting the following  issues:</p><ol><li>Find and aggregate all broken time series using likeness in ID parameter strings;</li> <li>Find and organize in a dictionary all different metadata variables;</li> <li>Correct time series time to match simpler measure units;</li> <li>Filter devices that are outside of a selected horizontal rectangle;</li> <li>Give some information on original Q/C scheme by SeaDataCloud infrastructure;</li> <li>Give information tables on platforms and on the merged ID string duplicates together with an errors log file (missing time, depth, data, wrong Q/C variables, etc.).</li> </ol><p>In particular, the duplicates table and the log file may be helpful to SeaDataCloud partners in order to update the data collection and make it finally available for the users.</p><p>The reconstructed SeaDataCloud time series data, divided by parameter and stored in a more flexible dataset, give the possibility to ingest it in the main part of the software, allowing to compare it with Copernicus time series, find the same platform using horizontal and vertical surroundings (without looking to ID) find and cleanup  duplicated data, merge the two databases to extend the data coverage.</p><p>This allow researchers to have the most wide and the best quality possible data for the final users release and to to use these data to calibrate and validate models, in order to reach an idea of a whole area sea conditions.</p>

scholarly journals The CH-IRP data set: a decade of fortnightly data of δ<sup>2</sup>H and δ<sup>18</sup>O in streamflow and precipitation in Switzerland

2020 ◽  
Author(s):  
Maria Staudinger ◽  
Stefan Seeger ◽  
Barbara Herbstritt ◽  
Michael Stoelzle ◽  
Jan Seibert ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Isotope Composition ◽  
Series Data ◽  
Data Repository ◽  
Summary Statistics ◽  
Individual Study ◽  
Data Set ◽  
Flow Pathways

Abstract. The stable isotopes of oxygen and hydrogen, 2H and 18O, provide information on water flow pathways and hydrologic catchment functioning. Here a data set of time series data on precipitation and streamflow isotope composition in Swiss medium-sized catchments, CH-IRP, is presented that is unique in terms of its long-term multi-catchment coverage along an alpine to pre-alpine gradient. The data set comprises fortnightly time series of both δ2H and δ18O as well as Deuterium excess from streamflow for 23 sites in Switzerland, together with summary statistics of the sampling at each station. Furthermore, time series of δ18O and δ2H in precipitation are provided for each catchment derived from interpolated datasets from the NISOT, GNIP and ANIP networks. For each station we compiled relevant metadata describing both the sampling conditions as well as catchment characteristics and climate infomation. Lab standards and errors are provided, and potentially problematic measurements are indicated to help the user decide on the applicability for individual study purposes. For the future, it is planned that the measurements will be continued at 14 stations as a long-term isotopic measurement network and the CH-IRP data set will, thus, be continuously be extended. The data set can be downloaded from data repository zenodo https://doi.org/10.5281/zenodo.3659679 (Staudinger et al., 2020).

scholarly journals Extending HydroShare to enable hydrologic time series data as social media

2015 ◽  
Vol 18 (2) ◽  
pp. 198-209 ◽  
Author(s):  
Jeffrey M. Sadler ◽  
Daniel P. Ames ◽  
Shaun J. Livingston
Keyword(s):  
Social Media ◽  
Time Series ◽  
Web Services ◽  
Data Management ◽  
Data Storage ◽  
Time Series Data ◽  
Series Data ◽  
Design Development ◽  
Scientific Discussion ◽  
Hydrologic Time Series

The Consortium of Universities for the Advancement of Hydrologic Science Inc. (CUAHSI) hydrologic information system (HIS) is a widely used service oriented system for time series data management. While this system is intended to empower the hydrologic sciences community with better data storage and distribution, it lacks support for the kind of ‘Web 2.0’ collaboration and social-networking capabilities being used in other fields. This paper presents the design, development, and testing of a software extension of CUAHSI's newest product, HydroShare. The extension integrates the existing CUAHSI HIS into HydroShare's social hydrology architecture. With this extension, HydroShare provides integrated HIS time series with efficient archiving, discovery, and retrieval of the data, extensive creator and science metadata, scientific discussion and collaboration around the data and other basic social media features. HydroShare provides functionality for online social interaction and collaboration while the existing HIS provides the distributed data management and web services framework. The extension is expected to enable scientists to access and share both national- and laboratory-scale hydrologic time series datasets in a standards-based web services architecture combined with social media functionality developed specifically for the hydrologic sciences.

A hybrid CNN-LSTM based model for the prediction of sea surface temperature using time-series satellite data.

2020 ◽  
Author(s):  
Pavan Kumar Jonnakuti ◽  
Udaya Bhaskar Tata Venkata Sai
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Satellite Data ◽  
Time Series Data ◽  
Sea Surface ◽  
Machine Learning Techniques ◽  
Global Ocean ◽  
Series Data

&lt;p&gt;Sea surface temperature (SST) is a key variable of the global ocean, which affects air-sea interaction processes. Forecasts based on statistics and machine learning techniques did not succeed in considering the spatial and temporal relationships of the time series data. Therefore, to achieve precision in SST prediction we propose a deep learning-based model, by which we can produce a more realistic and accurate account of SST &amp;#8216;behavior&amp;#8217; as it focuses both on space and time. Our hybrid CNN-LSTM model uses multiple processing layers to learn hierarchical representations by implementing 3D and 2D convolution neural networks as a method to better understand the spatial features and additionally we use LSTM to examine the temporal sequence of relations in SST time-series satellite data. Widespread studies, based on the historical satellite datasets spanning from 1980 - present time, in Indian Ocean region shows that our proposed deep learning-based CNN-LSTM model is extremely capable for short and mid-term daily SST prediction accurately exclusive based on the error estimates (obtained from LSTM) of the forecasted data sets.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Keywords: Deep Learning, Sea Surface Temperature, CNN, LSTM, Prediction.&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;

scholarly journals The CH-IRP data set: a decade of fortnightly data on <i>δ</i><sup>2</sup>H and <i>δ</i><sup>18</sup>O in streamflow and precipitation in Switzerland

2020 ◽  
Vol 12 (4) ◽  
pp. 3057-3066
Author(s):  
Maria Staudinger ◽  
Stefan Seeger ◽  
Barbara Herbstritt ◽  
Michael Stoelzle ◽  
Jan Seibert ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Isotope Composition ◽  
Series Data ◽  
Data Repository ◽  
Data Sets ◽  
Individual Study ◽  
Data Set ◽  
Flow Pathways

Abstract. The stable isotopes of oxygen and hydrogen, 18O and 2H, provide information on water flow pathways and hydrologic catchment functioning. Here a data set of time series data on precipitation and streamflow isotope composition in medium-sized Swiss catchments, CH-IRP, is presented that is unique in terms of its long-term multi-catchment coverage along an alpine to pre-alpine gradient. The data set comprises fortnightly time series of both δ2H and δ18O as well as deuterium excess from streamflow for 23 sites in Switzerland, together with summary statistics of the sampling at each station. Furthermore, time series of δ18O and δ2H in precipitation are provided for each catchment derived from interpolated data sets from the ISOT, GNIP and ANIP networks. For each station we compiled relevant metadata describing both the sampling conditions and catchment characteristics and climate information. Lab standards and errors are provided, and potentially problematic measurements are indicated to help the user decide on the applicability for individual study purposes. For the future, the measurements are planned to be continued at 14 stations as a long-term isotopic measurement network, and the CH-IRP data set will, thus, continuously be extended. The data set can be downloaded from data repository Zenodo at https://doi.org/10.5281/zenodo.4057967 (Staudinger et al., 2020).

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