ResumoNeste trabalho elabora-se uma climatologia de eventos de chuvas intensas (ECIs) no Município do Rio de Janeiro (MRJ) a partir dos dados observados pelo Sistema Alerta Rio no período 1997-2016. Um critério objetivo de identificação de ECIs é avaliado, confrontando-se dados de chuva com informações sobre os transtornos deflagrados por tais eventos sobre a cidade. O critério estabelece que um ECI se caracteriza por um total pluviométrico diário igual ou superior a seu percentil 95, com ocorrência no mesmo dia de pelo menos um registro de chuva em 15 minutos também igual ou superior a seu percentil 95. Ademais a climatologia da precipitação no MRJ é revisitada. Os resultados indicam que 33% do total pluviométrico anual médio no MRJ (1192 mm) ocorre durante 6,6 ECIs. Os máximos (mínimos) de ECIs são observados no Sumaré e Mendanha (na Saúde e Tijuca), numa média 30 dias por ano. Em média os ECIs ocorrem com maior frequência no verão (43,7%), seguido do outono (30,9%), na primavera (18,2%) e inverno (7,2%). Nota-se em geral uma tendência de aumento no número de ECIs durante o período analisado. AbstractIn this work a climatology of Heavy Rainfall Events (HREs) is elaborated for the Municipality of Rio de Janeiro (MRJ), based on Alerta Rio´s rain gauge network for the period 1997-2016. An objective criterion for the identification of HREs is assessed by comparing rainfall data with data disaster caused by such events over the city. The criterion establishes that an HRE is characterized by a daily rainfall equal or above its 95th percentile, occurring on the same day at least one rainfall record in 15 minutes, also equal or above its 95 th percentile. In addition, the precipitation climatology in the MRJ is revisited. The results show that 33% of annual mean precipitation in MRJ (1192 mm) falls during 6.6 HREs. The maximum (minimum) of HREs are observed in Sumaré and Mendanha (in Tijuca and Saúde), on average of 30 days per year. In general, HREs occur more frequently in the summer (43.7%), followed by autumn (30.9%), spring (18.2%) and winter (7.2%). In general, there is a tendency for an increase in the number of HREs during the analyzed period.
Comparisons of Eta 5 km mesoscale model forecasts against observations in Cunha, Curucutu, Itanhaém, Paraibuna, Picinguaba, Santa Virgínia e São José dos Campos, located in Serra do Mar (SP) region, is carried out for 2008. The 2 m temperature, station level pressure, winds at 10 m and precipitation are evaluated for the 24 h, 48 h and 72 h forecasts. The results show that the atmospheric pressure was systematically underestimated (overestimated) in Paraibuna and Picinguaba (Cunha and Curucutu), due to differences between the model's altitude and the real station altitude, although its diurnal cycle is well predicted, with two maximum (0 and 12 Z) and two minimum (6 and 18 Z), as observed. For atmospheric pressure, model's performance is better at the 48 h forecast. The temperature's diurnal cycle is very well predicted. The temporal correlation between forecasts and observations are very high, varying from 73 to 91%. In some locations it was observed that temperature was overestimated (near 3ºC in Curucutu and Santa Virgínia and 2ºC in Itanhaém), and that it was a systematic error. The temperature forecasted 24 h in advance is superior than the other forecasts. In general the model shows a tendency of underestimate (overestimate) the frequence of occurrence of calm (strong) winds. The wind direction is the most difficult variable to forecast due probably to the differences between model's topography and the real topography. Although the model shows the characteristic turning of the wind during the day caused by the daily warming. The total monthly precipitation is well predicted, although in Itanhaém, Paraibuna and mainly in Picinguaba the values are overestimated. The frequence of occurrence of rainy events (total daily precipitation < 0,3 mm) is overestimated by the model, although it is the best predicted category, with higher ETS, BIAS and Hit. The analyses shows that one of the model's source of error is related to its topography.
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