Quality control process of the daily rainfall series available in Catalonia from 1855 to the present

Llabrés‐Brustenga, Alba; Rius, Anna; Rodríguez‐Solà, Raül; Casas‐Castillo, M. Carmen; Redaño, Á.

doi:10.1007/s00704-019-02772-5

Cited by 27 publications

(18 citation statements)

References 15 publications

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“…First, several tests of homogeneity and data quality were applied at daily scale by Llabrés‐Brustenga et al . (2019) to distinguish the acceptable time series. Second, for very short data lags of a few days, unknown daily rain amounts have been substituted by those generated by a kriging interpolation process (Stein, 1999; Press et al ., 2007) taking into account available daily records close to the gauge without data and the topography of the area around these emplacements.…”

Section: Databasementioning

confidence: 99%

Autoregressive process of monthly rainfall amounts in Catalonia (NE Spain) and improvements on predictability of length and intensity of drought episodes

Lana

Rodríguez‐Solà

Martínez

et al. 2020

Intl Journal of Climatology

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Advantages offered by a pluviometric network in Catalonia (NE Spain) have permitted a detailed analysis of the two primary results derived from the autoregressive ARIMA process applied to monthly rainfall amounts. The first was the spatial distribution of the necessary number of previous monthly amounts needed to predict the next amount. The second was the spatial distribution of discrepancies between the real recorded amount and those derived from the autoregressive process. The heterogeneous spatial distribution of both parameters of the ARIMA process is in accordance with the complex spatial distribution of the monthly rainfall regime in Catalonia and fractal/multifractal analyses of several monthly rainfall time series. The statistical distribution of monthly discrepancies also permits a prediction of the probable evolution at monthly scale of drought episodes in terms of length and accumulated rainfall deficit. The results of the ARIMA algorithm are characterized by a few cases of rain gauges with remarkable differences (10-20 mmÁmonth-1) between real and autoregressive amounts, a predominant number of emplacements lowering 10 mmÁmonth-1 and a not negligible number of cases with discrepancies lower than 5 mmÁmonth-1. In terms of percentages with respect to average monthly amounts, most of discrepancies do not exceed 15%, and only in a very few cases they are within the 20-40% range. It is also worth mentioning that the discrepancies between real and predicted drought lengths use to be no longer than 1 month. Results corresponding to monthly series recorded at two Earth Sciences observatories are described with more detail to illustrate the advantages offered by the ARIMA autoregressive process in the prediction of beginning, continuity and end of drought episodes. K E Y W O R D S autoregressive process, drought episodes, monthly rainfall amounts, NE Spain 1 | INTRODUCTION Drought episodes in the Mediterranean region are quite usual, and sometimes characterized by long dry spells of several months depending on the threshold level assumed to define the dry spells (

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Section: Databasementioning

confidence: 99%

Autoregressive process of monthly rainfall amounts in Catalonia (NE Spain) and improvements on predictability of length and intensity of drought episodes

Lana

Rodríguez‐Solà

Martínez

et al. 2020

Intl Journal of Climatology

Self Cite

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“…Note that the time period 1969-2015 was used in this study because the length of continuous missing values are more than one year before 1969. Between 1969 and 2015, the continuous missing daily precipitation data (less than 0.01%) were filled by using the average values of the same days [40][41][42]. A total of 25 stations in and around the HJRB were used, 13 of which are within the HJRB and the rest are outside but close to the edges of the basin.…”

Section: Study Area and Datamentioning

confidence: 99%

Regional Frequency Analysis of Precipitation Extremes and Its Spatio-Temporal Patterns in the Hanjiang River Basin, China

Hao

Yuan

et al. 2019

Atmosphere

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Extreme events such as rainstorms and floods are likely to increase in frequency due to the influence of global warming, which is expected to put considerable pressure on water resources. This paper presents a regional frequency analysis of precipitation extremes and its spatio-temporal pattern characteristics based on well-known index-flood L-moments methods and the application of advanced statistical tests and spatial analysis techniques. The results indicate the following conclusions. First, during the period between 1969 and 2015, the annual precipitation extremes at Fengjie station show a decreasing trend, but the Wuhan station shows an increasing trend, and the other 24 stations have no significant trend at a 5% confidence level. Secondly, the Hanjiang River Basin can be categorized into three homogenous regions by hierarchical clustering analysis with the consideration of topography and mean precipitation in these areas. The GEV, GNO, GPA and P III distributions fit better for most of the basin and MARE values range from 3.19% to 6.41% demonstrating that the best-fit distributions for each homogenous region is adequate in predicting the quantiles estimates. Thirdly, quantile estimates are reliable enough when the return period is less than 100 years, however estimates for a higher return period (e.g., 1000 years) become unreliable. Further, the uncertainty of quantiles estimations is growing with the growing return periods and the estimates based on R95P series have a smaller uncertainty to describe the extreme precipitation in the Hanjiang river basin (HJRB). Furthermore, In the HJRB, most of the extreme precipitation events (more than 90%) occur during the rainy season between May and October, and more than 30% of these extreme events concentrate in July, which is mainly impacted by the sub-tropical monsoon climate. Finally, precipitation extremes are mainly concentrated in the areas of Du River, South River and Daba Mountain in region I and Tianmen, Wuhan and Zhongxiang stations in region III, located in the upstream of Danjiangkou Reservoir and Jianghan Plain respectively. There areas provide sufficient climate conditions (e.g., humidity and precipitation) responsible for the occurring floods and will increase the risk of natural hazards to these areas.

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“…The main differences are the height of the wind sensors (2, 6, and 10 m) or how the rainfall is measured and recorded. In any case, and thanks to the Quality Control Unit, the final data show a good homogeneity [39].…”

Section: Automatic Weather Station Networkmentioning

confidence: 74%

The Lightning Jump Algorithm for Nowcasting Convective Rainfall in Catalonia

Farnell

Rigo

2020

Atmosphere

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Previous studies in Catalonia (NE Iberian Peninsula) showed a direct relationship between the Lightning Jump (LJ) and severe weather, from the study of different events, occurring in the last few years in this region. This research goes a step beyond by studying the relationship between LJ and heavy rainfall, considering different criteria. It selects those episodes exceeding the 40 mm/h threshold, dividing them between those with or without LJ occurrence (3760 and 14,238 cases, respectively). The time and distance criteria (<150 km and <50 min, respectively) allow the detection of rainfall episodes with LJ, to establish an accurate relationship between the jump and the heavy rain occurrence. Then, lightning and radar data are analyzed, considering monthly and hourly distributions. Skill scores for the period 2013–2018 showed good results, especially in summer, with values of POD ≃ 90% and FAR ≃ 10%

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Quality control process of the daily rainfall series available in Catalonia from 1855 to the present

Cited by 27 publications

References 15 publications

Autoregressive process of monthly rainfall amounts in Catalonia (NE Spain) and improvements on predictability of length and intensity of drought episodes

Autoregressive process of monthly rainfall amounts in Catalonia (NE Spain) and improvements on predictability of length and intensity of drought episodes

Regional Frequency Analysis of Precipitation Extremes and Its Spatio-Temporal Patterns in the Hanjiang River Basin, China

The Lightning Jump Algorithm for Nowcasting Convective Rainfall in Catalonia

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