Analysis of SisPI Performance to Represent the North Atlantic Subtropical Anticyclone

This research evaluates the performance of the Short-Range Forecast System (SisPI by its acronym in Spanish) to represent the North Atlantic subtropical anticyclone over the parent domain during the 2020 wet season. For this, an average for the 2010–2019 decade was calculated using data from the ERA...

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Bibliographic Details
Published in:ECAS 2022
Main Authors: Jaina María Paula Méndez, Maibys Sierra Lorenzo, Pedro Manuel González Jardines
Format: Text
Language:English
Published: Multidisciplinary Digital Publishing Institute 2022
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Online Access:https://doi.org/10.3390/ecas2022-12804
Description
Summary:This research evaluates the performance of the Short-Range Forecast System (SisPI by its acronym in Spanish) to represent the North Atlantic subtropical anticyclone over the parent domain during the 2020 wet season. For this, an average for the 2010–2019 decade was calculated using data from the ERA5 reanalysis at different levels of the troposphere for variables of geopotential height, relative humidity, temperature and wind to characterize the main systems that disturb the weather in the study area, to obtain the corresponding anomalies and to determine if the errors influence these anomalies or the SisPI configuration. For this, it was necessary to interpolate SisPI data to make them match the resolution of ERA5 reanalysis and to be able to perform the calculations and generate the maps, for which a Python code was designed. The results suggest that SisPI tends to locate the high geopotential areas further south of their real position, which modifies the synoptic flow forecasted. On the other hand, the northern and southern borders of the domain have the largest errors, mainly to the north, where, according to the decadal mean and the anomalies obtained in 2020, a baroclinic zone that creates additional noise tends to be generated. To the south, this baroclinic zone lies on segments of the ITCZ which may also be the reason for additional errors in the model.