Web application technologies are growing rapidly with continuous innovation and improvements. This paper focuses on the popular Spring Boot [1] java-based framework for building web and enterprise applications and how it provides the flexibility for service-oriented architecture (SOA). One challenge with any Spring-based applications is its level of complexity with configurations. Spring Boot makes it easy to create and deploy stand-alone, production-grade Spring applications with very little Spring configuration. Example, if we consider Spring Model-View-Controller (MVC) framework [2], we need to configure dispatcher servlet, web jars, a view resolver, and component scan among other things. To solve this, Spring Boot provides several Auto Configuration options to setup the application with any needed dependencies. Another challenge is to identify the framework dependencies and associated library versions required to develop a web application. Spring Boot offers simpler dependency management by using a comprehensive, but flexible, framework and the associated libraries in one single dependency, which provides all the Spring related technology that you need for starter projects as compared to CRUD web applications. This framework provides a range of additional features that are common across many projects such as embedded server, security, metrics, health checks, and externalized configuration. Web applications are generally packaged as war and deployed to a web server, but Spring Boot application can be packaged either as war or jar file, which allows to run the application without the need to install and/or configure on the application server. In this paper, we discuss how Atmospheric Radiation Measurement (ARM) Data Center (ADC) at Oak Ridge National Laboratory, is using Spring Boot to create a SOA based REST [4] service API, that bridges the gap between frontend user interfaces and backend database. Using this REST service API, ARM scientists are now able to submit reports via a user form or a command line interface, which captures the same data quality or other important information about ARM data.

The primary objective of this study was to analyze the spatial variability of temperature and relative humidity across Kansas (KS) and Oklahoma (OK) for sparse and dense networks by comparing data from (1) the Surface Meteorological Observing System (SMOS) installations at the Atmospheric Radiation Measurement (ARM; Peppler et al. 2008) Program’s Southern Great Plains site and (2) the Oklahoma Mesonet (OKM; McPherson et al. 2007). Given the wealth of observations available from these networks, this study provided the unique opportunity to determine, within a quantifiable statistical limit, an optimal distance between stations deployed for observation of the climatological values of temperature and relative humidity. Average distances between a given station and its closest neighboring station for the ARM SMOS (~ 70 km) and the OKM (~ 30 km; Brotzge and Richardson 2003) networks provided an excellent framework for comparisons of sparse and dense observations (Figure 1). This study further lays groundwork for a future investigation to determine the necessary spacing between observations for initialization of gridded numerical models.

We present an overview of key aspects of the Atmospheric Radiation Measurement (ARM) Program Climate Research Facility (ACRF) data quality assurance program. Processes described include instrument deployment and calibration; instrument and facility maintenance; data collection and processing infrastructure; data stream inspection and assessment; problem reporting, review and resolution; data archival, display and distribution; data stream reprocessing; engineering and operations management; and the roles of value-added data processing and targeted field campaigns in specifying data quality and characterizing field measurements. The paper also includes a discussion of recent directions in ACRF data quality assurance. A comprehensive, end-to-end data quality assurance program is essential for producing a high-quality data set from measurements made by automated weather and climate networks. The processes developed during the ARM Program offer a possible framework for use by other instrumentation- and geographically-diverse data collection networks and highlight the myriad aspects that go into producing research-quality data.

The Atmospheric Radiation Measurement (ARM) Data Quality Office (DQO) has made a number of improvements and additions over the past year to its main tool for inspecting and assessing ARM data quality—the Data Quality Health and Status (DQ HandS) system (http://dq.arm.gov/). Among the improvements and additions, some of which are shown below, are the inclusion of ARM Mobile Facility (AMF) data; a new plot browser to facilitate the viewing of DQ HandS diagnostic plots; an improved method for writing and databasing weekly data quality assessment reports; a new automated daily alert, an improved method for searching ARM report databases (see Doty and Wagener’s abstract in this proceeding); addition of more instrument and value-added products output, and creation of a development version of DQ HandS that allows the present system to become a true production tool.