Preliminary Data Validation and Reconstruction of Temperature and Precipitation in Central Italy

Gentilucci, Matteo; Barbieri, Maurizio; Burt, Peter; D'Aprile, Fabrizio

doi:10.20944/preprints201806.0055.v1

Cited by 18 publications

(1 citation statement)

References 3 publications

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“…With regard to Italy, its long tradition in climate observations makes a large number of data series potentially available to the climatology community and final users (Brunetti et al ., ; Maugeri et al ., ; Acquaotta et al ., ). Several papers document the use of such data for climate analysis at the scale of Italian regions, like in Marche (Gentilucci et al ., ), Emilia‐Romagna (Tomozeiu et al ., ), Calabria (Federico et al ., ), Sardinia (Chessa et al ., ), Trentino (Eccel et al ., ), Piedmont (Acquaotta et al ., ), along with reports from various regional environmental agencies. At the national level, the use of the synoptic stations from the Italian Air Force National Meteorological Service (henceforth AM) is documented, inter alia, in Toreti and Desiato (), who used 49 AM stations to analyse seasonal temperatures from 1961 to 2006, and in Fioravanti et al .…”

Section: Introductionmentioning

confidence: 99%

A new homogenized daily data set for temperature variability assessment in Italy

Fioravanti

Piervitali

Desiato

2019

Intl Journal of Climatology

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We present a new data set of quality‐controlled and homogenized daily maximum (Tmax) and minimum (Tmin) temperature for Italy. The data set includes 144 Tmax and 139 Tmin long‐term series, covering the period 1961–2017. First, the paper provides a description of data sources and quality controls implemented for the detection of erroneous daily observations. Next, the temperature records used for this work are introduced. Following strict data continuity and completeness requirements, we identified more than 500 time series with at least 20 years of valid data (raw data set), which were spatially partitioned using a hierarchical clustering approach. For each cluster, the time series homogeneity was assessed using two different statistical automatic approaches: ACMANT and Climatol. The results of the homogenization process are illustrated only for the long‐term series subset. Both homogenization methods revealed the presence of non‐climatic discontinuities in most of the temperature series. Although Climatol detected a slightly lower number of breakpoints than ACMANT, the two methods are in good agreement with respect to the statistics which describe the number and timing of the breakpoints. Since no metadata are available, the plausibility of the homogenized time series was evaluated using different statistical measures: RMSE, Spearman correlation coefficient and trends estimation. Our results show that the homogenized data sets are more spatially coherent than the raw time series. In particular, the analysis of the annual temperature trends shows more realistic and reliable climatic patterns when the homogenized data sets are considered. For our data, the homogenization process only marginally changes the annual and seasonal warming trend values found for the area‐averaged anomaly raw series. The Tmax and Tmin homogenized data set will be regularly updated and is intended to be used for a variety of climate studies that require data at daily resolution, as the analysis of climate extremes.

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Section: Introductionmentioning

confidence: 99%

A new homogenized daily data set for temperature variability assessment in Italy

Fioravanti

Piervitali

Desiato

2019

Intl Journal of Climatology

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Rainy Day Prediction Model with Climate Covariates Using Artificial Neural Network MLP, Pilot Area: Central Italy

Gentilucci

Pambianchi

2022

Climate

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The reconstruction of daily precipitation data is a much-debated topic of great practical use, especially when weather stations have missing data. Missing data are particularly numerous if rain gauges are poorly maintained by their owner institutions and if they are located in inaccessible areas.In this context, an attempt was made to assess the possibility of reconstructing daily rainfall data from other climatic variables other than the rainfall itself, namely atmospheric pressure, relative humidity and prevailing wind direction.The pilot area for the study was identified in Central Italy, especially on the Adriatic side, and 119 weather stations were considered.The parameters of atmospheric pressure, humidity and prevailing wind direction were reconstructed at all weather stations on a daily basis by means of various models, in order to obtain almost continuous values rain gauge by rain gauge. The results obtained using neural networks to reconstruct daily precipitation revealed a lack of correlation for the prevailing wind direction, while correlation is significant for humidity and atmospheric pressure, although they explain only 10–20% of the total precipitation variance. At the same time, it was verified by binary logistic regression that it is certainly easier to understand when it will or will not rain without determining the amount. In this case, in fact, the model achieves an accuracy of about 80 percent in identifying rainy and non-rainy days from the aforementioned climatic parameters. In addition, the modelling was also verified on all rain gauges at the same time and this showed reliability comparable to an arithmetic average of the individual models, thus showing that the neural network model fails to prepare a model that performs better from learning even in the case of many thousands of data (over 400,000). This shows that the relationships between precipitation, relative humidity and atmospheric pressure are predominantly local in nature without being able to give rise to broader generalisations.

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Site Selection for a Network of Weather Stations Using AHP and Near Analysis in a GIS Environment in Amazonas, NW Peru

Briceño

López

et al. 2021

Climate

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Meteorological observations play a major role in land management; thus, it is vital to properly plan the monitoring network of weather stations (WS). This study, therefore, selected ‘highly suitable’ sites with the objective of replanning the WS network in Amazonas, NW Peru. A set of 11 selection criteria for WS sites were identified and mapped in a Geographic Information System, as well as their importance weights were determined using Analytic Hierarchy Process and experts. A map of the suitability of the territory for WS sites was constructed by weighted superimposition of the criteria maps. On this map, the suitability status of the 20 existing WS sites was then assessed and, if necessary, relocated. New ‘highly suitable’ sites were determined by the Near Analysis method using existing WS (some relocated). The territory suitability map for WS showed that 0.3% (108.55 km2) of Amazonas has ‘highly suitable’ characteristics to establish WS. This ‘highly suitable’ territory corresponds to 26,683 polygons (of ≥30 × 30 m each), from which 100 polygons were selected in 11 possible distributions of new WS networks in Amazonas, with different number and distance of new WS in each distribution. The implementation of this methodology will be a useful support tool for WS network planning.

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Preliminary Data Validation and Reconstruction of Temperature and Precipitation in Central Italy

Cited by 18 publications

References 3 publications

A new homogenized daily data set for temperature variability assessment in Italy

A new homogenized daily data set for temperature variability assessment in Italy

Rainy Day Prediction Model with Climate Covariates Using Artificial Neural Network MLP, Pilot Area: Central Italy

Site Selection for a Network of Weather Stations Using AHP and Near Analysis in a GIS Environment in Amazonas, NW Peru

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