Real-time extraction of the Madden-Julian oscillation using empirical mode decomposition and statistical forecasting with a VARMA model

A simple guide to the new technique of empirical mode decomposition (EMD) in a meteorological-climate forecasting context is presented. A single application of EMD to a time series essentially acts as a local high-pass filter. Hence, successive applications can be used to produce a bandpass filter that is highly efficient at extracting a broadband signal such as the Madden-Julian Oscillation (MJO). The basic EMD method is adapted to minimize end effects, such that it is suitable for use in real time. The EMD process is then used to efficiently extract the MJO signal from gridded time series of outgoing longwave radiation (OLR) data. A range of statistical models from the general class of vector autoregressive moving average (VARMA) models was then tested for their suitability in forecasting the MJO signal, as isolated by the EMD. A VARMA (5, 1) model was selected and its parameters determined by a maximum likelihood method using 17 yr of OLR data from 1980 to 1996. Forecasts were then made on the remaining independent data from 1998 to 2004. These were made in real time, as only data up to the date the forecast was made were used. The median skill of forecasts was accurate (defined as an anomaly correlation above 0.6) at lead times up to 25 days

The Climate-system Historical Forecast Project: providing open access to seasonal forecast ensembles from centers around the globe

Fil: Tompkins, Adrian M.. The Abdus Salam; ItaliaFil: Ortiz de Zarate, Maria Ines. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina. Centre National de la Recherche Scientifique; FranciaFil: Saurral, Ramiro Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina. Centre National de la Recherche Scientifique; FranciaFil: Vera, Carolina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina. Centre National de la Recherche Scientifique; FranciaFil: Saulo, Andrea Celeste. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Ministerio de Defensa. Secretaria de Planeamiento. Servicio Meteorológico Nacional; ArgentinaFil: Merryfield, William J.. Canadian Centre for Climate Modelling and Analysis; CanadáFil: Sigmond, Michael. Canadian Centre for Climate Modelling and Analysis; CanadáFil: Lee, Woo Sung. Canadian Centre for Climate Modelling and Analysis; CanadáFil: Baehr, Johanna. Universitat Hamburg; AlemaniaFil: Braun, Alain. Météo-France; FranciaFil: Amy Butler. National Ocean And Atmospheric Administration; Estados UnidosFil: Déqué, Michel. Météo-France; FranciaFil: Doblas Reyes, Francisco J.. Institució Catalana de Recerca i Estudis Avancats; España. Barcelona Supercomputing Center - Centro Nacional de Supercomputacion; EspañaFil: Gordon, Margaret. Met Office; Reino UnidoFil: Scaife, Adam A.. University of Exeter; Reino UnidoFil: Yukiko Imada. Japan Meteorological Agency. Meteorological Research Institute. Climate Research Department; JapónFil: Masayoshi Ishii. Japan Meteorological Agency. Meteorological Research Institute. Climate Research Department; JapónFil: Tomoaki Ose. Japan Meteorological Agency. Meteorological Research Institute. Climate Research Department; JapónFil: Kirtman, Ben. University of Miami; Estados UnidosFil: Kumar, Arun. National Ocean And Atmospheric Administration; Estados UnidosFil: Müller, Wolfgang A.. Max-Planck-Institut für Meteorologie; AlemaniaFil: Pirani, Anna. Université Paris-Saclay; FranciaFil: Stockdale, Tim. European Centre for Medium-Range Weather; Reino UnidoFil: Rixen, Michel. World Meteorological Organization. World Climate Research Programme; SuizaFil: Yasuda, Tamaki. Japan Meteorological Agency. Climate Prediction Division; Japó

Modelling monsoons: understanding and predicting current and future behaviour

The global monsoon system is so varied and complex that understanding and predicting its diverse behaviour remains a challenge that will occupy modellers for many years to come. Despite the difficult task ahead, an improved monsoon modelling capability has been realized through the inclusion of more detailed physics of the climate system and higher resolution in our numerical models. Perhaps the most crucial improvement to date has been the development of coupled ocean-atmosphere models. From subseasonal to interdecadal time scales, only through the inclusion of air-sea interaction can the proper phasing and teleconnections of convection be attained with respect to sea surface temperature variations. Even then, the response to slow variations in remote forcings (e.g., El Niño—Southern Oscillation) does not result in a robust solution, as there are a host of competing modes of variability that must be represented, including those that appear to be chaotic. Understanding the links between monsoons and land surface processes is not as mature as that explored regarding air-sea interactions. A land surface forcing signal appears to dominate the onset of wet season rainfall over the North American monsoon region, though the relative role of ocean versus land forcing remains a topic of investigation in all the monsoon systems. Also, improved forecasts have been made during periods in which additional sounding observations are available for data assimilation. Thus, there is untapped predictability that can only be attained through the development of a more comprehensive observing system for all monsoon regions. Additionally, improved parameterizations - for example, of convection, cloud, radiation, and boundary layer schemes as well as land surface processes - are essential to realize the full potential of monsoon predictability. A more comprehensive assessment is needed of the impact of black carbon aerosols, which may modulate that of other anthropogenic greenhouse gases. Dynamical considerations require ever increased horizontal resolution (probably to 0.5 degree or higher) in order to resolve many monsoon features including, but not limited to, the Mei-Yu/Baiu sudden onset and withdrawal, low-level jet orientation and variability, and orographic forced rainfall. Under anthropogenic climate change many competing factors complicate making robust projections of monsoon changes. Absent aerosol effects, increased land-sea temperature contrast suggests strengthened monsoon circulation due to climate change. However, increased aerosol emissions will reflect more solar radiation back to space, which may temper or even reduce the strength of monsoon circulations compared to the present day. Precipitation may behave independently from the circulation under warming conditions in which an increased atmospheric moisture loading, based purely on thermodynamic considerations, could result in increased monsoon rainfall under climate change. The challenge to improve model parameterizations and include more complex processes and feedbacks pushes computing resources to their limit, thus requiring continuous upgrades of computational infrastructure to ensure progress in understanding and predicting current and future behaviour of monsoons

Unfolding the procedure of characterizing recorded ultra low frequency, kHZ and MHz electromagetic anomalies prior to the L'Aquila earthquake as pre-seismic ones. Part I

Ultra low frequency, kHz and MHz electromagnetic anomalies were recorded prior to the L'Aquila catastrophic earthquake that occurred on April 6, 2009. The main aims of this contribution are: (i) To suggest a procedure for the designation of detected EM anomalies as seismogenic ones. We do not expect to be possible to provide a succinct and solid definition of a pre-seismic EM emission. Instead, we attempt, through a multidisciplinary analysis, to provide elements of a definition. (ii) To link the detected MHz and kHz EM anomalies with equivalent last stages of the L'Aquila earthquake preparation process. (iii) To put forward physically meaningful arguments to support a way of quantifying the time to global failure and the identification of distinguishing features beyond which the evolution towards global failure becomes irreversible. The whole effort is unfolded in two consecutive parts. We clarify we try to specify not only whether or not a single EM anomaly is pre-seismic in itself, but mainly whether a combination of kHz, MHz, and ULF EM anomalies can be characterized as pre-seismic one

The ECMWF Ensemble Prediction System: Looking Back (more than) 25 Years and Projecting Forward 25 Years

This paper has been written to mark 25 years of operational medium-range ensemble forecasting. The origins of the ECMWF Ensemble Prediction System are outlined, including the development of the precursor real-time Met Office monthly ensemble forecast system. In particular, the reasons for the development of singular vectors and stochastic physics - particular features of the ECMWF Ensemble Prediction System - are discussed. The author speculates about the development and use of ensemble prediction in the next 25 years.Comment: Submitted to Special Issue of the Quarterly Journal of the Royal Meteorological Society: 25 years of ensemble predictio