Sensitivity of the Himalayan orography representation in simulation of winter precipitation using Regional Climate Model (RegCM) nested in a GCM

This document is the Accepted Manuscript version of the following article: Tiwari, P.R., Kar, S.C., Mohanty, U.C., Climate Dynamics (2017). The final publication is available at Springer via https://link.springer.com/article/10.1007%2Fs00382-017-3567-3. The Accepted Manuscript is under embargo. Embargo end date: 24 February 2018.The role of the Himalayan orography representationin a Regional Climate Model (RegCM4) nested inNCMRWF global spectral model is examined in simulatingthe winter circulation and associated precipitation over theNorthwest India (NWI; 23°–37.5°N and 69°–85°E) region.For this purpose, nine different set of orography representationsfor nine distinct precipitation years (three years eachfor wet, normal and dry) have been considered by increasing(decreasing) 5, 10, 15, and 20% from the mean height(CNTRL) of the Himalaya in RegCM4 model. Validationwith various observations revealed a good improvementin reproducing the precipitation intensity and distributionwith increased model height compared to the resultsobtained from CNTRL and reduced orography experiments.Further it has been found that, increase in heightby 10% (P10) increases seasonal precipitation about 20%,while decrease in height by 10% (M10) results around 28%reduction in seasonal precipitation as compared to CNTRLexperiment over NWI region. This improvement in precipitationsimulation comes due to better representation ofvertical pressure velocity and moisture transport as thesefactors play an important role in wintertime precipitationprocesses over NWI region. Furthermore, a comparison of model-simulated precipitation with observed precipitationat 17 station locations has been also carried out. Overall,the results suggest that when the orographic increment of10% (P10) is applied on RegCM4 model, it has better skillin simulating the precipitation over the NWI region andthis model is a useful tool for further regional downscalingstudies.Peer reviewe

Case studies of seasonal rainfall forecasts for Hong Kong and its vicinity using a regional climate model

Seasonal climate forecasts are one of the most promising tools for providing early warnings for natural hazards such as floods and droughts. Using two case studies, this paper documents the skill of a regional climate model in the seasonal forecasting of below normal rainfall in southern China during the rainy seasons of July-August-September 2003 and April-May-June 2004. The regional model is based on the Regional Spectral Model of the National Centers for Environmental Prediction of the United States. It is the first time that the model has been applied to a region dominated by the East Asian Monsoon. The article shows that the regional climate model, when being forced by reasonably good forecasts from a global model, can generate useful seasonal rainfall forecasts for the region, where it is dominated by the East Asia monsoon. The spatial details of the dry conditions obtained from the regional climate model forecast are also found to be comparable with the observed distribution. © Springer Science+Business Media, Inc. 2007.link_to_subscribed_fulltex

Computational models for musical sounds sources

As a result of the progress in information technologies, algorithms for sound generation and transformation are now ubiquitous in multimedia systems, even though their performance and quality is rarely satisfactory. For the specific needs of music production and multimedia art, sound models are needed which are versatile, responsive to user's expectations, and having high audio quality. Moreover, for human-machine interaction model flexibility is a major issue. We will review some of the most important computational models that axe being used in musical sound production, and we will see that models based on the physics of actual or virtual objects can meet most of the requirements, thus allowing the user to rely on high-level descriptions of the sounding entities

On the reprocessing and reanalysis of observations for climate

The long observational record is critical to our understanding of the Earth’s climate, but most observing systems were not developed with a climate objective in mind. As a result, tremendous efforts have gone into assessing and reprocessing the data records to improve their usefulness in climate studies. The purpose of this paper is to both review recent progress in reprocessing and reanalyzing observations, and summarize the challenges that must be overcome in order to improve our understanding of climate and variability. Reprocessing improves data quality through more scrutiny and improved retrieval techniques for individual observing systems, while reanalysis merges many disparate observations with models through data assimilation, yet both aim to provide a climatology of Earth processes. Many challenges remain, such as tracking the improvement of processing algorithms and limited spatial coverage. Reanalyses have fostered significant research, yet reliable global trends in many physical fields are not yet attainable, despite significant advances in data assimilation and numerical modeling. Oceanic reanalyses have made significant advances in recent years, but will only be discussed here in terms of progress toward integrated Earth system analyses. Climate data sets are generally adequate for process studies and large-scale climate variability. Communication of the strengths, limitations and uncertainties of reprocessed observations and reanalysis data, not only among the community of developers, but also with the extended research community, including the new generations of researchers and the decision makers is crucial for further advancement of the observational data records. It must be emphasized that careful investigation of the data and processing methods are required to use the observations appropriately

Prospective view on sound synthesis BCI control in light of two paradigms of cognitive neuroscience

International audienceDifferent trends and perspectives on sound synthesis control issues within a cognitive neuroscience framework are addressed in this article. Two approaches for sound synthesis based on the modelling of physical sources and on the modelling of perceptual effects involving the identification of invariant sound morphologies (linked to sound semiotics) are exposed. Depending on the chosen approach, we as- sume that the resulting synthesis models can fall under either one of the theoretical frameworks inspired by the representational-computational or enactive paradigms. In particular, a change of viewpoint on the epistemological position of the end user from a third to a first person, inherently involves different conceptualizations of the interaction between the listener and the sounding object. This differentiation also influences the design of the control strategy enabling an expert or an intuitive sound manipulation. Finally, as a perspective to this survey, Explicit and Implicit Brain Control Interfaces (BCI) are described with respect to the previous theoreti- cal frameworks, and a semiotic-based BCI aiming at increasing the intuitiveness of synthesis control processes is envisaged. These interfaces may open for new appli- cations adapted to either handicapped or healthy subjects