Scale characterization and correction of diurnal cycle errors in MAPLE

The most widely used technique for nowcasting of quantitative precipitation in operational and research centers is the Lagrangian extrapolation of the latest radar observations. However, this technique has a limited forecast skill because of the assumptionmade on its formulation, such as the fact that the motion vectors do not change and, evenmore important for convective events, neglect any growth or decay in the precipitation field. In this work, the McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation (MAPLE) errors have been computed for 10 yr of radar composite data over the continental United States. The study of these errors shows systematic bias depending on the time of day. This effect is related to the solar cycle, whose heating energy results in an increase in the average rainfall in the afternoon. This external forcing interacts with the atmospheric system, creating local initiation and dissipation of convection depending on orography, land use, cloud coverage, etc. The signal of the diurnal cycle inMAPLEprecipitation forecast has been studied in different locations and spatial scales as a function of lead time in order to recognize where, when, and for which spatial scales the signal is significant. This information has been used in the development of a scaling correction scheme where the mean errors due to the diurnal cycle are adjusted. The results show that the developed methodology improves the forecast for the spatial scales and locations where the diurnal cycle signal is significant.Peer ReviewedPostprint (published version

Impact of Bayesian weighting in a probabilistic nowcasting from INCA and C-LAEF

Presentación realizada en la 3rd European Nowcasting Conference, celebrada en la sede central de AEMET en Madrid del 24 al 26 de abril de 2019

Conference Report: Third European Nowcasting Conference

The third European Nowcasting Conference took place in Madrid, Spain, from 24 to 26 April 2019. The conference was structured into four thematic sessions i) observations as basis for nowcasting, ii) seamless prediction, iii) nowcasting techniques, systems and products, iv) verification, societal impacts, applications and user aspects. This report summarizes the scientific contributions presented and the discussed scientific questions

Conference Report: Fourth European Nowcasting Conference

The fourth European Nowcasting Conference took place as an online event from 21 to 24 March 2022, organized by the EUMETNET (European National Meteorological and Hydrological Services Network) Nowcasting Program (E-NWC), and kindly supported by EUMETCAL (EUMETNET Education and Training Collaborative Network of the National Meteorological Services within Europe). More than 110 participants attended the conference. 46 conference’s presentations were given within the 0) opening session, a session on 1) observation as a basis for nowcasting, 2) seamless prediction with a special focus on Artificial Intelligence (AI), 3) nowcasting systems, products, and techniques and 4) verification, impacts on society, as well as applications and aspects of users. This report summarizes the scientific contributions presented and the discussed scientific questions

Aplicación web para la gestión de asignaturas

Este proyecto se ha llevado a cabo con la finalidad de implementar una aplicación web que permita una comunicación flexible entre alumnos y profesores dentro del contexto de una asignatura impartida en un curso determinado. Cuando hablamos de una aplicación Web nos estamos refiriendo a una aplicación cuyo código fuente se ejecuta en una máquina remota o `Servidor' y que muestra los resultados en la máquina cliente que la solicita mediante un navegador Web. Para poder hacer uso de estas aplicaciones se suele utilizar una conexión a Internet, o es su defecto, una intranet. En todo caso, es necesario que haya un servidor y múltiples clientes interconectados. El servidor debe ser capaz de interpretar el código fuente y los clientes deben de disponer de un navegador que interprete la información recibida (puede servir cualquiera de los navegadores que existen en el mercado, aunque es preferible que se encuentre actualizado para asegurar que los resultados son mostrados correctamente). La idea principal es que desde la aplicación, tanto alumnos como profesores puedan obtener información y gestionar ciertos aspectos de las asignaturas a las que pertenezcan. La aplicación dispondrá de dos vistas para diferenciar las funcionalidades del profesorado y del alumnado.Atencia Micó, A. (2012). Aplicación web para la gestión de asignaturas. http://hdl.handle.net/10251/17447.Archivo delegad

Combining 15 years of microwave SST and along-track SSH to estimate ocean surface currents

International audienceOcean surface current is one of the main oceanographic variables. To estimate and track these currents, we use satellite measurements of Sea Surface Height (SSH), but these data are sparse in space and time, as they are collected along altimeter tracks. However, Sea Surface Temperature (SST) observations are much more complete in both space and time, and so the covariance of SST and SSH can be exploited to use SST datasets to help fill in the missing information about ocean currents where SSH data are lacking. Here, we test a new data- driven methodology combining SST and SSH information to estimate the ocean surface currents in the Agulhas current

Scale characterization and correction of diurnal cycle errors in MAPLE

The most widely used technique for nowcasting of quantitative precipitation in operational and research centers is the Lagrangian extrapolation of the latest radar observations. However, this technique has a limited forecast skill because of the assumptionmade on its formulation, such as the fact that the motion vectors do not change and, evenmore important for convective events, neglect any growth or decay in the precipitation field. In this work, the McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation (MAPLE) errors have been computed for 10 yr of radar composite data over the continental United States. The study of these errors shows systematic bias depending on the time of day. This effect is related to the solar cycle, whose heating energy results in an increase in the average rainfall in the afternoon. This external forcing interacts with the atmospheric system, creating local initiation and dissipation of convection depending on orography, land use, cloud coverage, etc. The signal of the diurnal cycle inMAPLEprecipitation forecast has been studied in different locations and spatial scales as a function of lead time in order to recognize where, when, and for which spatial scales the signal is significant. This information has been used in the development of a scaling correction scheme where the mean errors due to the diurnal cycle are adjusted. The results show that the developed methodology improves the forecast for the spatial scales and locations where the diurnal cycle signal is significant.Peer Reviewe