How complex climate networks complement eigen techniques for the statistical analysis of climatological data

Eigen techniques such as empirical orthogonal function (EOF) or coupled pattern (CP) / maximum covariance analysis have been frequently used for detecting patterns in multivariate climatological data sets. Recently, statistical methods originating from the theory of complex networks have been employed for the very same purpose of spatio-temporal analysis. This climate network (CN) analysis is usually based on the same set of similarity matrices as is used in classical EOF or CP analysis, e.g., the correlation matrix of a single climatological field or the cross-correlation matrix between two distinct climatological fields. In this study, formal relationships as well as conceptual differences between both eigen and network approaches are derived and illustrated using exemplary global precipitation, evaporation and surface air temperature data sets. These results allow to pinpoint that CN analysis can complement classical eigen techniques and provides additional information on the higher-order structure of statistical interrelationships in climatological data. Hence, CNs are a valuable supplement to the statistical toolbox of the climatologist, particularly for making sense out of very large data sets such as those generated by satellite observations and climate model intercomparison exercises.Comment: 18 pages, 11 figure

Poboljšanje uspješnosti prognoze oborine nad Indijom primjenom metode višemodelskog ansambla

In this paper a Multi-Model Ensemble (MM E) technique is experimented for improving day to day rainfall forecast over India in short to medium range time scale during summer monsoon of 2010. Four operational global Numerical Weather Prediction (NWP) models namely, ECMWF, JMA, NCEP GFS and UKMO available on real time basis at India Meteorological Department (IMD), New Delhi are used simultaneously with appropriate weights to obtain the MME Technique. In this technique, weights for each NWP model at each grid point is assigned on the basis of the correlation coefficient (CC) between model forecasts and observed daily rainfall time series of south west monsoon (JJAS) season. Apart from MM E, a simple ensemble mean (ENSM ) forecast are also generated and experimented. The rainfall prediction skill of the weighted MM E is examined against ENSM and member models. The inter-comparison reveals that the weighted MM E is able to provide more accurate forecast of rainfall over Indian monsoon region by taking the strength of each constituent member model. It has been further found that the rainfall prediction skill of MM E is higher as compared to ENSM and member models in the short range time scale. The rainfall prediction skill of weighted MM E technique improved significantly over India.U ovom radu primijenjena je metoda višemodelskog ansambla (MME) s ciljem poboljšanja kratkoročnih do srednjoročnih dnevnih prognoza količine oborine nad Indijom tijekom ljetnog monsuna 2010. godine. pri tome su istovremeno te s odgovarajućim težinama korištena četiri operativna globalna modela za numeričku prognozu vremena (NWP): ECMWF, JMA, NCEP, GFS i UKMO, a koji su na raspolaganju u realnom vremenu pri Indijskom meteorološkom odsjeku (IMD) u New Delhiju. Težine za svaki NWP model u svakoj točki mreže pridijeljene su na temelju koeficijenta korelacije (CC) između modelskih prognoza i mjerenog niza dnevne količine oborine za sezonu jugozapadnog monsuna (od lipnja do rujna). Pored MME, generirane su i ispitane jednostavne prognoze dobivene srednjakom ansambla (ENSM). Uspješnost prognoze količine oborine dobivene MME metodom procijenjena je usporedbom rezultata dobivenih tom metodom i onih na temelju ENSM te sa svakim pojedinačnim modelom. Međusobna usporedba pokazuje da metoda MME točnije prognozira količinu oborine u području indijskog monsuna ponderiranjem doprinosa svakog pojedinog modela u ansamblu. Nadalje, utvrđena je veća uspješnost kratkoročnih prognoza količine oborine pomoću metode mmE u odnosu na rezultate metode ENSM te u odnosu na prognoze pojedinačnih modela ansambla. Primjena ponderirane metode MME značajno poboljšava uspješnost prognoze količine oborine nad Indijom

Moisture source identification for precipitation associated with tropical cyclone development over the Indian Ocean: a Lagrangian approach

Financiado para publicación en acceso aberto: Universidade de Vigo/CISUGIn this study, we investigated the moisture sources for precipitation through a Lagrangian approach during the genesis, intensification, and dissipation phases of all tropical cyclones (TCs) that occurred over the two hemispheric sub-basins of the Indian Ocean (IO) from 1980 to 2018. In the North IO (NIO), TCs formed and reached their maximum intensity on both sides of the Indian Peninsula, to the east in the Bay of Bengal (BoB), and to the west in the Arabian Sea (AS). The oceanic areas where TCs occurred were their main moisture sources for precipitation associated with TCs. Additionally, for TCs over the BoB, continental sources from the Ganges River basin and the South China Sea also played a notable role; for TCs over the AS, the Somali Low-Level jet (along the African coast in a northerly direction) also acted as an essential moisture transport. In the South IO (SIO), the western, central, and eastern basins were identified as the preferred areas for the genesis and development of TCs. During TC activity, the central IO and the Wharton and Perth basins mostly supplied atmospheric moisture. The Mascarene High circulation was the main moisture transport mechanism for the precipitation of TCs formed in the SIO basin. In both basins, during their intensification process, TCs gained more moisture (even more intensely when reaching the hurricane category) than during the genesis or dissipation stages. Additionally, the modulation during monsoonal seasons of the moisture contribution to the TCs was more noticeable over the NIO basin than for the SIO. Overall, the moisture uptake for precipitation from the sources for TCs occurred slightly faster in the NIO basin than in the SIO basin.Xunta de Galicia | Ref. ED481B 2019/070Xunta de Galicia | Ref. ED481A-2020/193Xunta de Galicia | Ref. ED431C 2021/44Agencia Estatal de Investigación | Ref. RTI2018-095772-B-I00Agencia Estatal de Investigación | Ref. PID2021-122314OB-I0

The global monsoon system: research and forecast

The main objective of this workshop was to provide a forum for discussion between researchers and forecasters on the current status of monsoon forecasting and on priorities and opportunities for monsoon research. WMO hopes that through this series of quadrennial workshops, the following goals can be accomplished: (a) to update forecasters on the latest reseach findings and forecasting technology; (b) to update researchers on monsoon analysis and forecasting; (c) to identify basic and applied research priorities and opportunities; (d) to identify opportunities and priorities for acquiring observations; (e) to discuss the approach of a web-based training document in order to update forecasters on developments of direct relevance to monsoon forecasting