Search results for "Forecast skill"
showing 10 items of 11 documents
The UKC3 regional coupled environmental prediction system
2019
Abstract. This paper describes an updated configuration of the regional coupled research system, termed UKC3, developed and evaluated under the UK Environmental Prediction collaboration. This represents a further step towards a vision of simulating the numerous interactions and feedbacks between different physical and biogeochemical components of the environment across sky, sea and land using more integrated regional coupled prediction systems at km-scale resolution. The UKC3 coupled system incorporates models of the atmosphere (Met Office Unified Model), land surface with river routing (JULES), shelf-sea ocean (NEMO) and ocean surface waves (WAVEWATCH III), coupled together using OASIS3-MC…
Two-days ahead prediction of daily maximum concentrations of SO2, O3, PM10, NO2, CO in the urban area of Palermo, Italy
2007
Abstract Artificial neural networks are functional alternative techniques in modelling the intricate vehicular exhaust emission dispersion phenomenon. Pollutant predictions are notoriously complex when using either deterministic or stochastic models, which explains why this model was developed using a neural network. Neural networks have the ability to learn about non-linear relationships between the used variables. In this paper a recurrent neural network (Elman model) based forecaster for the prediction of daily maximum concentrations of SO2, O3, PM10, NO2, CO in the city of Palermo is proposed. The effectiveness of the presented forecaster was tested using a time series recorded between …
Performance of DEMETER calibration for rainfall forecasting purposes: Application to the July–August Sahelian rainfall
2008
International audience; This work assesses and compares the skill of direct and model-output-statistics (MOS) calibrated hindcasts of the July–August rainfall amounts for the dry period 1980–2000 over the Sahel issued from the Development of a European Multimodel Ensemble System for Seasonal to Interannual Prediction (DEMETER) experiment, with the aim to highlight among the simulated parameters, i.e., those potentially relevant for rainfall forecasts purposes. Three approaches were used: the DEMETER (1) direct rainfall, (2) MOS-calibrated rainfall, and (3) MOS-calibrated atmospheric dynamics and energy. Canonical correlation analyses (CCA) were employed in the two latter approaches to calib…
Extreme, wintertime Saharan dust intrusion in the Iberian Peninsula: Lidar monitoring and evaluation of dust forecast models during the February 2017…
2019
The research leading to these results has received funding from the H2020 program from the European Union (grant agreement no. 654109, 778349) and also from the Spanish Ministry of Industry, Economy and Competitiviness (MINECO, ref. CGL2013-45410-R, CGL2016-81092-R, CGL2017-85344-R, TEC2015-63832-P), the Spanish Ministry of Science, Innovation and Universities (ref. CGL2017-90884-REDT); the CommSensLab "Maria de Maeztu" Unity of Excellence (ref. MDM-2016-0600) financed by the Spanish Agencia Estatal de Investigación. Co-funding was also provided by the European Union through the European Regional Development Fund (ref. POCI-01-0145-FEDER-007690, ALT20-03-0145-FEDER-000004, ALT20-03-0145-FED…
A test of transferability for landslides susceptibility models under extreme climatic events: application to the Messina 2009 disaster
2014
A model building strategy is tested to assess the susceptibility for extreme climatic events driven shallow landslides. In fact, extreme climatic inputs such as storms typically are very local phenomena in the Mediterranean areas, so that with the exception of recently stricken areas, the landslide inventories which are required to train any stochastic model are actually unavailable. A solution is here proposed, consisting in training a susceptibility model in a source catchment, which was implemented by applying the binary logistic regression technique, and exporting its predicting function (selected predictors regressed coefficients) in a target catchment to predict its landslide distribu…
Water erosion susceptibility mapping by applying Stochastic Gradient Treeboost to the Imera Meridionale River Basin (Sicily, Italy)
2016
Abstract Soil erosion by water constitutes a serious problem affecting various countries. In the last few years, a number of studies have adopted statistical approaches for erosion susceptibility zonation. In this study, the Stochastic Gradient Treeboost (SGT) was tested as a multivariate statistical tool for exploring, analyzing and predicting the spatial occurrence of rill–interrill erosion and gully erosion. This technique implements the stochastic gradient boosting algorithm with a tree-based method. The study area is a 9.5 km 2 river catchment located in central-northern Sicily (Italy), where water erosion processes are prevalent, and affect the agricultural productivity of local commu…
An improvement of June-September rainfall forecasting in the Sahel based upon region April-May moist static energy content (1968-1997)
1999
This study provides statistical evidence that June–September Sahelian rainfall hindcasts currently based on oceanic thermal predictors apprehend more the negative trend than the interannual rainfall variations. Four physically meaningful predictors of June–September Sahel rainfall are first selected through the near-surface April–May information and several experimental hindcasts provided. We then discuss the skills achieved using regression techniques and cross-validated discriminant functions. In that context, 8/11 of the driest seasons and 8/10 of the wettest are correctly predicted. Finally using completely independent training and working periods we show that better and significant hin…
Factors selection in landslide susceptibility modelling on large scale following the gis matrix method: application to the river Beiro basin (Spain)
2012
A procedure to select the controlling factors connected to the slope instability has been defined. It allowed us to assess the landslide susceptibility in the Rio Beiro basin (about 10 km2) over the northeastern area of the city of Granada (Spain). Field and remote (Google EarthTM) recognition techniques allowed us to generate a landslide inventory consisting in 127 phenomena. To discriminate between stable and unstable conditions, a diagnostic area had been chosen as the one limited to the crown and the toe of the scarp of the landslide. 15 controlling or determining factors have been defined considering topographic, geologic, geomorphologic and pedologic available data. Univariate tests, …
Prediction of Dry-Season Precipitation in Tropical West Africa and Its Relation to Forcing from the Extratropics
2009
Abstract Precipitation during the boreal winter dry season in tropical West Africa is rare but occasionally results in significant impacts on the local population. The dynamics and predictability of this phenomenon have been studied very little. Here, a statistical evaluation of the climatology, dynamics, and predictions of dry-season wet events is presented for the region 7.5°–15°N, 10°W–10°E. The analysis is based upon Global Precipitation Climatology Project (GPCP) merged satellite–gauge pentad rainfall estimates and 5-day 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) precipitation forecasts, and covers the 23 dry seasons (November–February) during…
Forward logistic regression for earth-flow landslide susceptibility assessment in the Platani river basin (southern Sicily, Italy)
2013
Forward logistic regression has allowed us to derive an earth-flow susceptibility model for the Tumarrano river basin, which was defined by modeling the statistical relationships between an archive of 760 events and a set of 20 predictors. For each landslide in the inventory, a landslide identification point (LIP) was automatically produced as corresponding to the highest point along the boundary of the landslide polygons, and unstable conditions were assigned to cells at a distance up to 8 m. An equal number of stable cells (out of landslides) was then randomly extracted and appended to the LIPs to prepare the dataset for logistic regression. A model building strategy was applied to enlarg…