Rain gauge simulator and first tests with a new mobile climate alert system in Brazil

Abstract

Background: Recent national developments in alert systems are the main motivation of this work. The aim is to provide an account on the development and first tests of a new Meteorological Alert System-MAS for mobile devices to deliver alert signals. The fundamentals encompass a summary description of the Brazilian government towards the installation and maintenance of a national wide climate sensor network where the new Meteorological Alert System can be integrated. The main challenges in installing and maintaining such a network in face of its continental scope are presented. Methods:The method describes the emulation of rain precipitation, which requires (a) the development of a data model for rain gauges (called DCP, or Data Collection Platforms) and (b) a data interface with the existing network. After testing several rain simulation models, the DCP system is converted into a signal server to provide parametric regulated data. The emulator facilitates the creation of pluviometric surrogate data and therefore the test of extreme situations. The MAS system is completed by the development of a front-end mobile application where the alerts are received by end users. We discuss classes and metrics used to evaluate the emulator performance and its integration to the alert system. We describe the DCP data structures, the rain simulator functions, and its interface with the MAS. Results: Rain gauge emulated data sets for several parametric conditions and test performance results of the mobile application integrated to the rain emulator are discussed. We present and discuss an interface to easily access the entire rain gauge network using mobile devices. Conclusions: Alert acquisition by the end user is a complex sequence of commands and integrated hardware involving a considerable amount of numerical work in weather forecasting. Consequently, modeling the information flow, and performing tests of a mobile application, justifies our initiative as a set-up stage prior to massive dissemination of an alert system fed by real data.