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Complete and accurate data are necessary for analyzing and understanding trends in time-series datasets; however, many of the available time-series datasets have gaps that affect the analysis, especially in the earth sciences. As most available data have missing values, researchers use various interpolation methods or ad hoc approaches to data imputation. Since the analysis based on inaccurate data can lead to inaccurate conclusions, more accurate data imputation methods can provide accurate analysis. We present a spatial-temporal data imputation method using Empirical Mode Decomposition (EMD) based on spatial correlations. We call this method EMD-spatial data imputation or EMD-SDI. Though this method is applicable to other time-series data sets, here we demonstrate the method using temperature data. The EMD algorithm decomposes data into periodic components called intrinsic mode functions (IMF) and exactly reconstructs the original signal by summing these IMFs. EMD-SDI initially decomposes the data from the target station and other stations in the region into IMFs. EMD-SDI evaluates each IMF from the target station in turn and selects the IMF from other stations in the region with periodic behavior most correlated to target IMF. EMD-SDI then replaces a section of missing data in the target station IMF with the section from the most closely correlated IMF from the regional stations. We found that EMD-SDI selects the IMFs used for reconstruction from different stations throughout the region, not necessarily the station closest in the geographic sense. EMD-SDI accurately filled data gaps from 3 months to 5 years in length in our tests and favorably compares to a simple temporal method. EMD-SDI leverages regional correlation and the fact that different stations can be subject to different periodic behaviors. In addition to data imputation, the EMD-SDI method provides IMFs that can be used to better understand regional correlations and processes.

The purpose of this Coastal and Hydraulics Engineering Technical Note (CHETN) is to document and demonstrate the Rapid Operational Access and Maneuver Support (ROAMS) v2.0 computational scripting/application program interface (API) platform. ROAMS provides improved knowledge of potential lines of communication and vessel routes through hydrodynamic modeling and path optimization under a variety of environmental conditions and input-information qualities/sources. Primary focus of this document is given to the implementation of penalty barrierbased path optimization to provide guidance for subsequent work. The platform additionally provides object-oriented, script-based interaction with principal U.S. Army Corps of Engineers (USACE) hydrodynamic models. BACKGROUND: Military undertakings in waterborne environments can be broadly classified into two types of activities: logistics and operational. Logistics activities are concerned with the establishment of lines of communication (LOC) to efficiently move equipment, personnel, and provisions from an offshore intermediate staging base (ISB) to a Sea Port of Debarkation (SPOD). The SPOD may be but is not limited to a world-class port, an unimproved beach at the coastline, or an upstream site in an estuary. Locations are typically chosen through a combination of expert judgment, analyses of nautical charts, and scenario planning to avoid known environmental austere obstacles such as shoals, reefs, and wreckage. The qualitative nature of the military logistics planning process causes direct comparison of LOC to be challenging; furthermore, qualitative methods do not guarantee the selection of an optimal site that maximizes total throughput and uptime percentage. The military has some logistics tools that facilitate planning of this type such as the Analysis of Mobility Platform (AMP) (Mckinzie and Barnes 2004). Other systems to conduct environmental measurements, such as Joint-Logistics-Over-The-Shore (JLOTS) Environmental Monitoring System (JEMS), typically lack the capability to translate those measurements into applicable decisions (U.S. Transportation Command 2016). Military operational activities constitute any other type of actions that do not principally involve military logistics (Defense Technical Information Center 2011). These activities often require routes to be revised during the operation as environmental and mission conditions evolve. Operational activities are more likely to encounter obstacles including enemy combatants, manmade impediments such as mines, scuttled vessels, or environmental obstructions like reefs and shoals. An initial route for such operations is selected much like the military logistics case. Subsequent adjustments to routes may be done on an ad hoc basis at the discretion of the commanding officer using the improved information about the mission state.

Logistical and combat operations in riverine, estuarine, and coastal environments remain a key military focus due to limited maneuverability, imperfect knowledge, and rapidly changing constraints. Vessel operation in water environments can be enhanced by routing algorithms that integrate mission parameters with environmental data and vessel specifications. These algorithms must update predetermined routes in a timely manner as parameters and specifications change. The US Army Engineer Research and Development Center Coastal and Hydraulics Laboratory is developing the capability for military planners to rapidly optimize vessel routes in water environments by extending capabilities of the Rapid Operational Access and Maneuver Support (ROAMS) modeling platform. The ROAMS platform allows users to rapidly generate models of a water environment in limited-information conditions, utilizing the Adaptive Hydraulics and STeadystate spectral WAVE computational engines for the base two-dimensional hydrodynamics and waves, respectively. Routing capabilities are built on path search and penalty-barrier optimization to automatically produce routes that account for temporally changing environmental variables and vessel maneuverability. This work outlines the components of the ROAMS routing package and presents a case study using ROAMS in a northeastern American metropolitan area. Benefits and limitations of the ROAMS routing platform are discussed and future improvements are suggested.