Investigating the potential of NAO index to forecast droughts in Sicily
2007 annual AGU hydrology days was held at Colorado State University on March 19 - March 21, 2007. Includes bibliographical references. Drought monitoring and forecasting is essential for an effective drought preparedness and mitigation. The use of large-scale climatic patterns, such as El Nino Sout...
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ftmountainschol:oai:mountainscholar.org:10217/200689 2023-06-11T04:14:54+02:00 Investigating the potential of NAO index to forecast droughts in Sicily Hydrology days 2007 AGU hydrology days 2007 Cancelliere, A., author Di Mauro, G., author Bonaccorso, B., author Rossi, G., author Colorado State University, publisher 2020-02-04T20:45:35Z born digital proceedings (reports) application/pdf https://hdl.handle.net/10217/200689 https://doi.org/10.25675/10217/200689 English eng eng Colorado State University. Libraries Hydrology Days - Department of Civil and Environmental Engineering https://hdl.handle.net/10217/200689 http://dx.doi.org/10.25675/10217/200689 ©Hydrology Days. Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright. Text 2020 ftmountainschol https://doi.org/10.25675/10217/200689 2023-04-29T17:47:43Z 2007 annual AGU hydrology days was held at Colorado State University on March 19 - March 21, 2007. Includes bibliographical references. Drought monitoring and forecasting is essential for an effective drought preparedness and mitigation. The use of large-scale climatic patterns, such as El Nino Southern Oscillation (ENSO), North Atlantic Oscillation (NAO ) or European Blocking (EB), can potentially improve the forecasting of drought evolution in time and space, provided the influence of such indices on the climatic variability in a region is verified. In the present paper, a stochastic model for the seasonal forecasting of the Standardized Precipitation Index (SPI), developed in previous works, is extended in order to include information from NAO index. In particular SPI forecasts at a generic time horizon M are analytically determined, in terms of conditional expectation, as a function of a finite number of past observations of SPI and NAO, assuming a multivariate normal as the underlying distribution. In addition, an expression of the Mean Square Error (MSE) of prediction is also derived, which allows confidence intervals of prediction to be estimated. The forecasting performance of the model is verified by hindcasting observed SPI values computed on monthly areal average precipitation series observed in Sicily and validation is carried out by repeatedly applying a jackknife scheme. Preliminary results of the comparison between the model based only on the past observations of SPI values and the one that includes also the NAO index, seem to indicate a slight improvement of the latter model. Such results however cannot be considered conclusive and further analyses are needed in order to better assess the use of NAO as a predictor for droughts in Sicily. Text North Atlantic North Atlantic oscillation Mountain Scholar (Digital Collections of Colorado and Wyoming) |
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Mountain Scholar (Digital Collections of Colorado and Wyoming) |
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ftmountainschol |
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English |
description |
2007 annual AGU hydrology days was held at Colorado State University on March 19 - March 21, 2007. Includes bibliographical references. Drought monitoring and forecasting is essential for an effective drought preparedness and mitigation. The use of large-scale climatic patterns, such as El Nino Southern Oscillation (ENSO), North Atlantic Oscillation (NAO ) or European Blocking (EB), can potentially improve the forecasting of drought evolution in time and space, provided the influence of such indices on the climatic variability in a region is verified. In the present paper, a stochastic model for the seasonal forecasting of the Standardized Precipitation Index (SPI), developed in previous works, is extended in order to include information from NAO index. In particular SPI forecasts at a generic time horizon M are analytically determined, in terms of conditional expectation, as a function of a finite number of past observations of SPI and NAO, assuming a multivariate normal as the underlying distribution. In addition, an expression of the Mean Square Error (MSE) of prediction is also derived, which allows confidence intervals of prediction to be estimated. The forecasting performance of the model is verified by hindcasting observed SPI values computed on monthly areal average precipitation series observed in Sicily and validation is carried out by repeatedly applying a jackknife scheme. Preliminary results of the comparison between the model based only on the past observations of SPI values and the one that includes also the NAO index, seem to indicate a slight improvement of the latter model. Such results however cannot be considered conclusive and further analyses are needed in order to better assess the use of NAO as a predictor for droughts in Sicily. |
format |
Text |
author |
Cancelliere, A., author Di Mauro, G., author Bonaccorso, B., author Rossi, G., author Colorado State University, publisher |
spellingShingle |
Cancelliere, A., author Di Mauro, G., author Bonaccorso, B., author Rossi, G., author Colorado State University, publisher Investigating the potential of NAO index to forecast droughts in Sicily |
author_facet |
Cancelliere, A., author Di Mauro, G., author Bonaccorso, B., author Rossi, G., author Colorado State University, publisher |
author_sort |
Cancelliere, A., author |
title |
Investigating the potential of NAO index to forecast droughts in Sicily |
title_short |
Investigating the potential of NAO index to forecast droughts in Sicily |
title_full |
Investigating the potential of NAO index to forecast droughts in Sicily |
title_fullStr |
Investigating the potential of NAO index to forecast droughts in Sicily |
title_full_unstemmed |
Investigating the potential of NAO index to forecast droughts in Sicily |
title_sort |
investigating the potential of nao index to forecast droughts in sicily |
publisher |
Colorado State University. Libraries |
publishDate |
2020 |
url |
https://hdl.handle.net/10217/200689 https://doi.org/10.25675/10217/200689 |
genre |
North Atlantic North Atlantic oscillation |
genre_facet |
North Atlantic North Atlantic oscillation |
op_relation |
Hydrology Days - Department of Civil and Environmental Engineering https://hdl.handle.net/10217/200689 http://dx.doi.org/10.25675/10217/200689 |
op_rights |
©Hydrology Days. Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright. |
op_doi |
https://doi.org/10.25675/10217/200689 |
_version_ |
1768371275101634560 |