Clouds modulate the net radiative flux interacting with both shortwave and longwave radiation, but the uncertainties regarding their effect in polar regions are especially high, because ground observations are lacking and evaluation through satellites is made difficult by the high surface reflectance. In this work, the radiative regimes and sky conditions for five different stations, two in the Arctic (Ny-Ålesund, 78.92°N, 11.93°E, Barrow, 71.32°N, 156.61° W) and four in Antarctica (Neumayer, 70.68°S, 8.27°W; Syowa, 69.01°S, 39.58°E; South Pole, 90°S, 0°E ; DomeC, 75.01°S, 123.33°E) will be presented, considering the decade between 2010 and 2020. Measurements of broadband shortwave and longwave radiation components (both downwelling and upwelling) are collected within the frame of the Baseline Surface Radiation Network (BSRN) (Driemel et al. 2018). Observations, together with identification of the clear sky and overcast conditions will be compared with ERA5 reanalysis (Herschbach et al., 2023). Furthermore, the identified conditions based on estimated cloud fraction will serve as labels for a machine learning classification task, leveraging algorithms such as Random Forest and Long Short-Term Memory (LSTM) networks (i.e. Zeng et al., 2021; Sedlar et al., 2021). These models incorporate features including global and diffuse shortwave radiation, downward longwave radiation, solar zenith angle, surface air temperature, relative humidity, and the ratio of water vapor pressure to surface temperature. The Random Forest model will also compute feature importance, identifying the most influential variables in predicting sky conditions and providing insights into the relationships between these meteorological factors.BibliographyDriemel et al. (2018): Baseline Surface Radiation Network (BSRN): structure and data description (1992–2017). Riihimaki et al. (2019): Radiative Flux Analysis (RADFLUXANAL) Value-Added Product.Hersbach, H. et al. (2023): ERA5 hourly data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS) Zeng, Z. et al. (2021): Estimation and Long-term Trend Analysis of Surface Solar Radiation in Antarctica: A Case Study of Zhongshan Station. Adv. Atmos. Sci. 38, 1497–1509. Sedlar, J. et al. (2021): Development of a Random-Forest Cloud-Regime Classification Model Based on Surface Radiation and Cloud Products. J. Appl. Meteor. Climatol., 60, 477–491.

Regular meteorological observations in Padua started in 1725 and have continued unbroken up to the present, making the series one of the longest in the world. Daily mean temperatures and precipitation amounts have recently been homogenized for the entire 1725–2024 period, making it possible to add new measurements without further work. Starting from the temperature series, the trends of cold and warm spells are investigated in this paper. The ongoing warming that started in the 1970s is extensively analyzed on the basis of the variability of the mean values and a magnitude index that captures both the duration and intensity of a spell and by investigating the frequency of extreme events by means of Intensity–Duration–Frequency curves. The periods with the greatest deviation from the climatological average are analyzed in detail: February 1740 and April 1755, the months with the largest negative and positive temperature anomalies, respectively, in the 300-year-long series. Moreover, the analysis of snow occurrences extracted from the original logs, together with the pressure observations from the long series of London and Uppsala, made it possible to evaluate the most typical synoptic situations leading to snow events in Padua for the whole period.

This study aims to recover, interpret and analyse the early meteorological observations made in Venice by Bernardino Zendrini from 1738 to 1743. Zendrini used a cistern barometer, an Amontons-type air thermometer and an additional mercury thermometer, i.e., a de l’Isle one. By comparing and interpreting the existing details, the instruments have been re-imagined, interpreted and discussed and their unknown scale and calibration points have been calculated. The barometer readings needed standard corrections, which were not known at that time. The scale of the air thermometers was in inches of mercury. Zendrini used a reversed scale, with boiling water set to 0, but neglected the second calibration point and the length of the tube. In addition, he gave the thermoscopic readings without the corresponding pressures. The methodology for the calibration, validation and transformation of the readings into modern units, i.e., hPa and °C, is carefully discussed. This paper provides and analyses new data, and improves our knowledge about the history of science, meteorological measurements, instruments and observations in the first half of the 18th century.

Understanding long-term cloud cover variability is essential for assessing past climate dynamics and human influences on atmospheric conditions. In Padua, instrumental weather records (temperature, precipitation, pressure) and descriptive sky observations date back to 1725, but quantitative cloud cover data, expressed as tenths of the sky covered by clouds, began in 1872 at the Astronomical Observatory. From 1920 to 1989, observations continued under the authority of the Meteorological Observatory of the Water Magistrate, and from 1951 to 1990, additional records by the Italian Air Force expressed in eighths of sky are available. These visual datasets—based on multiple daily observations—are complemented by satellite records (from 1983) and reanalysis such as ERA5 (from 1940) and NOAA 20CRv3 (from 1872 to 2015). The aim of this study is to reconstruct a homogenized, long-term total cloud cover (TCC) time series for Padua from 1872 to 2024, integrating all available observational sources. By comparing overlapping periods across different subseries and nearby ground-based stations, the analysis not only investigates consistency and potential discontinuities across datasets but also quantifies the reliability and limitations of historical visual observations. This work provides one of the few centennial-scale reconstructions of cloud cover in Europe, offering a valuable contribution to historical climatology and climate change studies.

The long instrumental series of precipitation in Pisa, the earliest one in Italy, has been reconstructed after the careful recovery and critical analysis of its history, data, and metadata. Precipitation amounts have been recovered from May 1707 to December 2024, but there are gaps due to lost data. The recovered dataset includes 47.4% of the total daily, 65.0% of monthly, and 77.4% of yearly values. Original observation registers and metadata are scarce or even missing, so a thorough investigation of contemporary sources has been performed to recover as much information as possible concerning observers, instruments, locations, exposures, measuring protocols, and ancient local units. The main features of the precipitation regime in Pisa have been investigated, and the variability in the amount and frequency at different time scales, as well as extreme events, have been analysed. Pisa is characterized by intense precipitation in autumn due to the penetration of Atlantic perturbations, and the most extreme daily events occur mainly in the transition period between the end of summer and the onset of autumn. A small decreasing trend has been found in the anomaly of the yearly amount in the 1867–2024 unbroken period, with the most remarkable month anomalies in summer. The time series of the Standard Precipitation Index indicates that the period around 1945 was particularly dry, and also indicates a slight increase in arid conditions over time, mainly in spring. The most extreme yearly amounts were found in the 18th century, and the series of the daily 90th and 95th percentiles show a small decreasing trend in the 1884–2004 period. The comparison with other contemporary Italian series made it possible to identify the peculiarity of the precipitation regime in Pisa, adding an important piece to the historical research on the climate of the Italian peninsula from a long-term perspective.

The fog in Padua, Italy, is the result of a complex interplay between local climate, pollution and synoptic-scale meteorological conditions. The modern definition of “fog” was adopted by the World Meteorological Organization, founded in 1950. Prior to that, no precise visibility threshold had been established, and early meteorological observers recorded its occurrence based on subjective criteria. The meteorological observations made in Padua since the mid-18th century include sky conditions and fog, but the distinction between fog and mist was undefined, making it difficult to compare records from different observers. Caution is therefore needed when analyzing fog occurrence to distinguish the climate signal from observational artifacts. For instance, at the Specola Observatory in Padua from 1773 to 1913, the fog that appeared only on the horizon—but not at the zenith—was often disregarded, since vertical visibility was crucial for astronomical observations. Starting from 1920, other manned stations began providing systematic fog records, which have continued to the present. The aim of this study is to reconstruct the occurrence of fog in Padua since the late 18th century. As available datasets —observational and instrumental—partly overlap in time, and data from nearby locations are available, it is possible to assess their mutual consistency, to evaluate the reliability of historical visual observations, and to investigate fog variability and trend over time.

The study aims to recover, interpret, and analyze the daily meteorological observations made in Venice by Tommaso Temanza from 1751 to 1769. These records are relevant because they provide direct information about the climate of the Little Ice Age. Temanza used a barometer, an air thermometer of Amontons’ type, and an additional mercury thermometer, i.e., Réaumur’s thermometer. These early instruments are presented and discussed in this study. The barometer readings needed standard corrections, which were unknown at that time. The scale of the air thermometer was arbitrary, and temperatures were measured in inches of mercury. For the Amontons thermometer, Temanza missed the calibration points and used a particular scale with the zero-point in the middle of the range. He gave two contradictory explanations for this choice, both of which are discussed in this paper. In the 18th century, the use of a singular value to represent the average temperature, called “Temperate”, was promoted by Michieli du Crest in Geneva and Toaldo in Padua. This work reconstructs the unknown scale, using contemporary observations by Giovanni Poleni and Giuseppe Toaldo in Padua (30 km west of Venice) and snowfall reported in the weather notes to determine the temperature point at 0 °C. A discussion is made about the calibration, validation, and conversion of readings from the original to modern units of pressure and temperature, i.e., hPa and °C, respectively. The recovered record of Venice is presented in comparison with Padua, Bologna, and Milan. The paper provides and analyzes the new dataset, and improves knowledge about the climate, history of science, instruments, and observations made in the mid-18th century.

Precipitation is one of the main meteorological variables in climate research and long records provide a unique, long-term knowledge of climatic variability and extreme events. Moreover, they are a prerequisite for climate modeling and reanalyses. Like all meteorological observations, in the early period, every observer used a personal measuring protocol. Instruments and their locations were not standardized and not always specified in the observer’s metadata. The situation began to change in 1873 with the foundation of the International Meteorological Committee, though the complete standardization of protocols, instruments, and exposure was reached in 1950 with the World Meteorological Organization. The aim of this paper is to present and discuss the methodology needed to recover and reconstruct early precipitation records and to provide high-quality dataset of precipitation usable for climate studies. The main issues that have to be addresses are described and critically analyzed based on the longest Italian precipitation series to which the methodology was successfully applied.

The Arctic is as a key place to perform environmental measurements given its combination of reduced human activity and increased sensitivity to climate change. The Svalbard archipelago constitutes an invaluable measurement location, due to its ease of access and the presence of the research centre of Ny-Ålesund. Sensors are usually not designed to sustain prolonged periods of time in demanding environments like the Arctic, therefore chances of failures, drift and errors are high. Maintenance and calibration of these sensors must be rigorous and frequent, to avoid poor quality data, or even their loss. Within the frame of EURAMET EMPIR project “MeteoMet 2”, calibration of the temperature sensors hosted by the Climate Change Tower (CCT), a unique research facility designed to monitor lower-atmosphere profiles of several meteorological quantities, has been performed. The calibration campaign pointed out sensors errors up to 1 °C and corrected the measurements, straightening the skewed temperature profiles. Absolute calibration uncertainties have been evaluated at ~0.2 °C, less than half those stated by the manufacturer, while an evaluation of relative uncertainties yielded values of just few 0.01°C. This experience stimulated the creation of an in-situ calibration facility, to the benefit of the whole scientific community based in Ny-Ålesund.

The study of long precipitation series constitutes an important issue in climate research and risk assessment. However, long datasets are affected by inhomogeneities that can lead to biased results. A frequent but sometimes underestimated problem is the definition of the climatological day. The choice of different starting times may lead to inhomogeneity within the same station and misalignment with other stations. In this work, the problem of temporal misalignment between precipitation datasets characterized by different starting times of the observation day is analyzed. The most widely used adjustment methods (1 day and uniform shift) and two new methods based on reanalysis (NOAA and ERA5) are evaluated in terms of temporal alignment, precipitation statistics, and percentile distributions. As test series, the hourly precipitation series of Padua and nearby stations in the period of 1993–2022 are selected. The results show that the reanalysis-based methods, in particular ERA5, outperform the others in temporal alignment, regardless of the station. But, for the periods in which reanalysis data are not available, 1-day and uniform shift methods can be considered viable alternatives. On the other hand, the reanalysis-based methods are not always the best option in terms of precipitation statistics, as they increase the precipitation frequency and reduce the mean value over wet days, NOAA much more than ERA5. The use of the series of a station near the target one, which is mandatory in case of missing data, can sometimes give comparable or even better results than any adjustment method. For the Padua series, the analysis is repeated at monthly and seasonal resolutions. In the tested series, the adjustment methods do not provide good results in summer and autumn, the two seasons mainly affected by heavy rains in Padua. Finally, the percentile distribution indicates that any adjustment method underestimates the percentile values, except ERA5, and that only the nearby station most correlated with Padua gives results comparable to ERA5.

The aim of this work is to investigate drought variability in Padua, northern Italy, over a nearly 300-year period, from 1725 to 2023. Two well-established and widely used indices are calculated, the standardized precipitation index (SPI) and the standardized precipitation evapotranspiration index (SPEI). They are compatible with a data series starting in the early instrumental period, as both can be estimated using only temperature and precipitation data. The Padua daily precipitation and temperature series from the early 18th century, which were recovered and homogenized with current observations, are used as datasets. The standard approach to estimate SPI and SPEI based on gamma and log-logistic probability distribution functions, respectively, is questioned, assessing the fitting performance of different distributions applied to monthly precipitation data. The best-performing distributions are identified for each index and accumulation period at annual and monthly scales, and their normality is evaluated. In general, they detect more extreme drought events than the standard functions. Moreover, the main statistical values of SPI are very similar, regardless of the approach type, as opposed to SPEI. The difference between SPI and SPEI time series calculated with the best-fit approach has increased since the mid-20th century, in particular in spring and summer, and can be related to ongoing global warming, which SPEI takes into account. The innovative trend analysis applied to SPEI12 indicates a general increasing trend in droughts, while for SPI12, it is significant only for severe events. Summer and fall are the most affected seasons. The critical drought intensity–duration–frequency curves provide an easily understandable relationship between the intensity, duration and frequency of the most severe droughts and allow for the calculation of return periods for the critical events of a certain duration. Moreover, the longest and most severe droughts over the 1725–2023 period are identified.

The Padua temperature series is one of the longest in the world, as daily observations started in 1725 and have continued almost unbroken to the present. Previous works recovered readings from the original logs, and digitalized and corrected observations from errors due to instruments, calibrations, sampling times and exposure. However, the series underwent some changes (location, elevation, observing protocols, and different averaging methods) that affected the homogeneity between sub-series. The aim of this work is to produce a homogenized temperature series for Padua, starting from the results of previous works, and connecting all the periods available. The homogenization of the observations has been carried out with respect to the modern era. A newly released paleo-reanalysis dataset, ModE-RA, is exploited to connect the most ancient data to the recent ones. In particular, the following has been carried out: the 1774–2023 daily mean temperature has been homogenized to the modern data; for the first time, the daily values of 1765–1773 have been merged and homogenized; and the daily observations of the 1725–1764 period have been connected and homogenized to the rest of the series. Snowfall observations, extracted from the same logs from which the temperatures were retrieved, help to verify the robustness of the homogenization procedure by looking at the temperature frequency distribution on snowy days, before and after the correction. The possibility of adding new measurements with no need to apply transformations or homogenization procedures makes it very easy to update the time series and make it immediately available for climate change analysis.

The study of precipitation amount at different time scales constitutes an important issue in climate research and risk assessment. When dealing with daily totals, a frequent but sometimes underestimated problem is at what time the observation day begins. The choice of different starting times may lead to incompatibility between stations and incorrect identification of extreme events. In this work, the problem of temporal misalignment between precipitation datasets characterized by different starting time of the observation day is analyzed. The most widely used adjustment methods (1day and uniform shift) and two methods based on reanalysis (NOAA and ERA5) are evaluated in terms of temporal alignment, precipitation statistics and percentile distributions. As test series, the precipitation amount collected from 9 a.m. local time (09 LT) on the previous day to 09 LT on the target day (9-9 datasets) of the Padua and nearby stations in the period 1993-2022 have been selected. Results show that the reanalysis-based methods, in particular ERA5, outperform the others in temporal alignment, regardless the station. But, for the periods in which reanalysis data are not available, “1day” method, which shifts the daily amount back one calendar day, and “unif” method, which distributes uniformly the daily total from a 2-day moving window surrounding the target date, can be considered valid alternatives. On the other side, concerning the precipitation statistics, the reanalysis-based methods are not the best option, as they increase the precipitation frequency and reduce the mean value over wet days, NOAA much more than ERA5. Nevertheless, the uniform method provides a larger deviation from the original daily series. The use of the series of a station nearby the target one, which is mandatory in case of missing data, gives similar or better results than applying any adjustment method to the 9-9 series. General conclusions can hardly be drawn as they depend on the method and station. For the Padua dataset, the analysis was repeated at monthly and seasonal resolution. In general, the adjustment series show the most relevant changes in the precipitation statistics in summer and less temporal alignment with the original series in summer and autumn, the two seasons mainly affected by heavy rains in Padua. Finally, the percentiles distribution, analyzed for all the methods and stations, indicates that any adjustment method underestimates the percentile values, except ERA5. Only Legnaro, the station most correlated with Padua, gives results like ERA5.

Meteorological observations over the last four decades are of paramount importance to investigating ongoing climate change. An important issue is the quality and reliability of the climatic series, which are fundamental prerequisites to drawing the correct conclusions. Homogeneity tests are used to detect discontinuities whose interpretation is facilitated by metadata availability. In this work, daily minimum and maximum temperature measurements collected in Padua, Italy, between 1980 and 2022 are examined. During this period, the weather station of Padua center underwent many changes in location or instruments; therefore, some tests have been used to identify and remove their effects and obtain homogeneous series. Some well-known absolute tests have been applied to investigate the shift in the mean value: Standard Normal Homogeneity test (SNH), Buishand U and range tests, Pettitt test, F-test, and STARS. Relative tests have been applied too, using several stations nearby Padua and two reanalysis datasets (ERA5 and MERIDA) as reference series to enhance the picture of the local situation and provide more robust conclusions. The applied tests identify change-points in the years in which a change in instrument or the location of the station has occurred, confirming that these changes have compromised the homogeneity of the series. The sub-series obtained, splitting the observations in correspondence with these change-points, have been homogenized with respect to a selected period. The corrected series of the minimum and maximum temperatures are more coherent with the modern warming trend. The transfer functions to be applied to future measurements of minimum temperature have been calculated, while the series of maximum temperature measurements can be directly extended.

A distinction is made between data rescue (i.e., copying, digitizing, and archiving) and data recovery that implies deciphering, interpreting, and transforming early instrumental readings and their metadata to obtain high-quality datasets in modern units. This requires a multidisciplinary approach that includes: palaeography and knowledge of Latin and other languages to read the handwritten logs and additional documents; history of science to interpret the original text, data, and metadata within the cultural frame of the 17th, 18th, and early 19th centuries; physics and technology to recognize bias of early instruments or calibrations, or to correct for observational bias; and astronomy to calculate and transform the original time in canonical hours that started from twilight. The liquid-in-glass thermometer was invented in 1641 and the earliest temperature records started in 1654. Since then, different types of thermometers have been invented, based on the thermal expansion of air or selected thermometric liquids with deviation from linearity. Reference points, thermometric scales, and calibration methodologies were not comparable, and not always adequately described. Thermometers had various locations and exposures, e.g., indoor, outdoor, on windows, gardens or roofs, facing different directions. Readings were made only one or a few times a day, not necessarily respecting a precise time schedule: this bias is analysed for the most popular combinations of reading times. The time was based on sundials and local Sun, but the hours were counted starting from twilight. In 1789–1790, Italy changed system and all cities counted hours from their lower culmination (i.e., local midnight), so that every city had its local time; in 1866, all the Italian cities followed the local time of Rome; in 1893, the whole of Italy adopted the present-day system, based on the Coordinated Universal Time and the time zones. In 1873, when the International Meteorological Committee (IMC) was founded, later transformed into the World Meteorological Organization (WMO), a standardization of instruments and observational protocols was established, and all data became fully comparable. In dealing with the early instrumental period, from 1654 to 1873, the comparison, correction, and homogenization of records is quite difficult, mainly because of the scarcity or even absence of metadata. This paper deals with this confused situation, discussing the main problems, but also the methodologies to recognize missing metadata, distinguish indoor from outdoor readings, correct and transform early datasets in unknown or arbitrary units into modern units, and, finally, in which cases it is possible to reach the quality level required by the WMO. The aim is to explain the methodology needed to recover early instrumental records, i.e., the operations that should be performed to decipher, interpret, correct, and transform the original raw data into a high-quality dataset of temperature, usable for climate studies.

The aim of this work is to reconstruct the 1812–1864 period of the Padua precipitation series at the daily level, using a local precipitation Log. Missing readings, cumulative amounts, and gaps often affect early precipitation series, as observers did not follow a precise protocol. Therefore, the daily amount and frequency reported in the register of observations are not homogeneous with other periods, neither comparable with other contemporary series, and need a correction. The correction methodology has been based on the daily weather notes written in the Log in parallel to the readings. Taking advantage of periods in which both weather observations and instrumental readings were regularly taken, the terms used to describe the precipitation type and intensity have been classified, analyzed statistically, calibrated, and transformed into numerical values. The weather notes enable the distribution of precipitation to be determined based on the cumulative amounts collected on consecutive rainy days into the likely precipitation that occurred on every single rainy day. In the case of missing readings, the presence of weather notes enables the missing amounts to be estimated using the relationships found previously. Finally, the recovery of additional contemporary documents made it possible to fill some gaps in this period. Using this approach, 52 years of the long Padua precipitation series have been corrected: precipitation collected for two or more rainy days has been distributed according to the actual rainy days; the rain amount fully recovered and most of the missing values reconstructed; the false extreme events corrected.

The European Standard EN 15757: 2010 'Conservation of Cultural Property-Specifications for temperature and relative humidity to limit climate-induced mechanical damage in organic hygroscopic materials' is a guide specifying the allowed limits of variability of the indoor climate, in particular relative humidity (RH) to preserve cultural heritage objects and collections composed of climate-vulnerable materials. This paper is finalized to provide useful elements to improve the Standard at its next revision, based on focused research. The methodologies and the mathematical tools used are performed on 18 case studies representing different buildings, climates, and use, including heated and unheated buildings, museums, churches, concert halls, archives, and storage rooms. The first aim is to compare the method based on the centred moving average suggested by Annex A of EN15757 with an alternative method based on percentile interpolation to calculate the reference RH values, and in particular the safe band of RH variability, as well as the upper and lower risky bands. It has been found that the two methods provided the same results, but the latter is easier to manage. The second aim is to verify if the duration of the record necessary for the determination of the safe band is really 13 months of measurements as required by the Standard to account for the specific request of the centred moving average with a 30-day time window. This paper demonstrates that the same goal may be reached with a 12-month record, but extracting from the record itself the two periods required by the time window, i.e., the last 15 days of the year will be copied before the start of the record, and the same with the first 15 days after the end. The third aim is to test if the particular choice of the width of the time window is influential on the width of the safe band, and to assess the relationship between the width of the safe band and the width of the time window. The results show that the safe band logarithmically depends on the length of the time window, so it is crucial to respect the 30-day window established by the Standard.

After an overview of the problems concerning the early rain-gauges and their thresholds, a study is made to investigate the impact that the instrumental threshold of a rain-gauge has on the distribution of precipitation frequency and amount. Tests are performed using two historical datasets, i.e., the observations by Giovanni Poleni in Padua from 1725 to 1760, and Jacopo Bartolomeo Beccari in Bologna from 1723 to 1765, and two modern rain-gauge records, i.e., taken at the Botanical Garden, Padua, and at the Hydrographic, Bologna, from 1990 to 2019. The tests involve applying a filter to the datasets to simulate the action of an instrumental threshold. The result is that the threshold has an enormous impact on the frequency and a smaller one on the total amount. The study includes how the threshold affects the percentile distribution of precipitation amounts. The results provide indications to correct and interpret early records and to test their quality. Moreover, they are useful in the analysis of long time series composed by datasets derived from different instruments for climate studies.

The Col Margherita (MRG) Observatory is a high-altitude background station located in the Eastern Italian Alps. Its elevation and distance from major anthropogenic and natural sources make it ideal for monitoring baseline mercury levels and investigating its geochemical cycles. In this work, total gaseous mercury (TGM), ozone (O3) and meteorological variables were studied to investigate seasonal and diurnal variability of TGM measurements from March 2018 to May 2019. We found that the year-round mean TGM concentration was 1.8 times higher than the annual atmospheric mercury measurements previously reported during the GMOS project at the same measurement site. The seasonal variation of TGM was characterized by high values in spring and summer and lower values in winter. A systematic diel pattern of TGM was obtained, with low concentrations during the daytime and higher concentrations in the late evening. Spatial patterns and temporal changes in TGM, back-trajectories (BTs) and planetary boundary layer (PBL) analysis showed that total gaseous mercury levels were influenced by local meteorology, as well as regional and long-range transport of air masses. The lowest TGM concentrations in winter are associated with high wind speeds, low boundary layer height and cleaner air masses originating from the Western sector. Conversely, the highest TGM concentrations in spring and summer were attributed to polluted air masses passing over North-eastern and Continental Europe and were probably favoured by the higher PBL height. During the snow cover season, investigation of TGM peak events also demonstrated the potential influence of re-emission processes of previously reactive mercury deposition over snow surfaces.

The aim of this work is to analyse and compare different methodologies to fill gaps in early precipitation series, and to evaluate which time resolution is reachable, i.e. monthly or daily one. The following methods are applied and tested to fill the 1764–1767 gap in the precipitation series of Padua: (1) using a relationship between monthly amounts and frequencies; (2) transforming a daily log with visual observations into numerical values through analysis, classification, and calibration; (3) substituting the missing values with an instrumental record from a nearby, contemporary station in the same climatic area. To apply the second method, the descriptions reported in the Morgagni Logs are grouped in 37 classes and transformed into numerical values, using for calibration the observed amounts in the Poleni record over the 24-year common period. As a third method, the series of Temanza and Pollaroli in Venice is used to fill the gap, and the application of a factor scale based on the ratio Padua/Venice tempted. The results of these three methods are discussed and commented.