Cloud Optimized Raster Encoding (CORE): A Web-Native Streamable Format for Large Environmental Time Series

The Environmental Data Portal EnviDat aims to fuse data publication repository functionalities with next-generation web-based environmental geospatial information systems (web-EGIS) and Earth Observation (EO) data cube functionalities. User requirements related to mapping and visualization represent a major challenge for current environmental data portals. The new Cloud Optimized Raster Encoding (CORE) format enables an efficient storage and management of gridded data by applying video encoding algorithms. Inspired by the cloud optimized GeoTIFF (COG) format, the design of CORE is based on the same principles that enable efficient workflows on the cloud, addressing web-EGIS visualization challenges for large environmental time series in geosciences. CORE is a web-native streamable format that can compactly contain raster imagery as a data hypercube. It enables simultaneous exchange, preservation, and fast visualization of time series raster data in environmental repositories. The CORE format specifications are open source and can be used by other platforms to manage and visualize large environmental time series.

Accessing environmental time series data in R from Sensor Observation Services with ease

<p>Time series data of in-situ measurements is the key to many environmental studies. The first challenge in any analysis typically arises when the data needs to be imported into the analysis framework. Standardisation is one way to lower this burden. Unfortunately, relevant interoperability standards might be challenging for non-IT experts as long as they are not dealt with behind the scenes of a client application. One standard to provide access to environmental time series data is the Sensor Observation Service (SOS, ) specification published by the Open Geospatial Consortium (OGC). SOS instances are currently used in a broad range of applications such as hydrology, air quality monitoring, and ocean sciences. Data sets provided via an SOS interface can be found around the globe from Europe to New Zealand.</p><p>The R package sos4R (Nüst et al., 2011) is an extension package for the R environment for statistical computing and visualization (), which has been demonstrated a a powerful tools for conducting and communicating geospatial research (cf. Pebesma et al., 2012; ). sos4R comprises a client that can connect to an SOS server. The user can use it to query data from SOS instances using simple R function calls. It provides a convenience layer for R users to integrate observation data from data access servers compliant with the SOS standard without any knowledge about the underlying technical standards. To further improve the usability for non-SOS experts, a recent update to sos4R includes a set of wrapper functions, which remove complexity and technical language specific to OGC specifications. This update also features specific consideration of the OGC SOS 2.0 Hydrology Profile and thereby opens up a new scientific domain.</p><p>In our presentation we illustrate use cases and examples building upon sos4R easing the access of time series data in an R and Shiny () context. We demonstrate how the abstraction provided in the client library makes sensor observation data for accessible and further show how sos4R allows the seamless integration of distributed observations data, i.e., across organisational boundaries, into transparent and reproducible data analysis workflows.</p><p><strong>References</strong></p><p>Nüst D., Stasch C., Pebesma E. (2011) Connecting R to the Sensor Web. In: Geertman S., Reinhardt W., Toppen F. (eds) Advancing Geoinformation Science for a Changing World. Lecture Notes in Geoinformation and Cartography, Springer. </p><p>Pebesma, E., Nüst, D., & Bivand, R. (2012). The R software environment in reproducible geoscientific research. Eos, Transactions American Geophysical Union, 93(16), 163–163. </p>

Downtime Technique Using Artificial Intelligence: A Case Study for an Exposed Berthing Facility

Hydrodynamic modelling is significantly improved in the last decade, however, the coupling of these hydrodynamic models with methods to estimate berth downtime due to environmental conditions (wind, waves, currents) is less developed. The large number of environmental inputs (wind speed, wind direction, wave height, wave period, wave direction, current speed, current direction) and mooring outputs (vessel motions in six degrees of freedom, mooring lines, fender forces) to be considered in a downtime study requires major simplifications as the full wind-wave time series cannot be calculated. Downtime assessment is normally simplified by calculating a limited number of wind-wave combinations. A new approach is developed and presented in this paper by calculating the downtime for the long-term environmental time series using artificial neural networks. Neural networks are some of the most capable artificial intelligence tools for solving very complex problems like the present case. The approach has been applied for a bulk terminal project located in the Arabian Sea. The terminal has no shelter and is totally exposed to wind and swell waves. The wave climate for a 15 years period is established at the project site using spectral wave modelling software MIKE 21 SW. A large set of combined environmental conditions is selected for the dynamic mooring analysis of the vessel at berth. The time domain mooring analysis software MIKE 21 MA is used in this study. An inhouse Matlab code/program is developed using neural network to calculate the downtime for the long-term environmental time series based on the dynamic vessel response for the set of selected environmental combinations. This approach provides a more accurate downtime estimate which is important for the operability of such exposed facilities. The downtime tool is also tested for a different set of environmental combinations and mooring layouts in order to assess the sensitivity of these parameters on the downtime estimate. Up to the authors’ knowledge, this is the first published work applying artificial intelligence techniques for downtime studies.