dataesr/scientific-paragraphs-categorization huggingface dataset:
Limited visibility can lead to overestimation of ice coverage. Local newspapers were used to gather early ice records for Lake Greifen from 1901 to 1948. Between 1949 and 1980, data were gathered from von Eugen and Orn  40. From 1963 onwards, data have also been obtained from the lake security service's observations distributed around the lake, in addition to webcams, and information from boats used for daily passenger traffic over the lake. Webcams observe the lake from the eastside opposite of Maur in the northwest and south directions. Boats, on the other hand, collect data from Maur to Uster (middle of lake). Observations are done mostly in the morning. The uncertainty of the data before 1949 is larger than data more recently. Lake Hallwil's ice phenology records began in 1901 and data were collected from the newspaper Luzerner Zeitung (1901-1954), the boat designer Urs Merz (1955-2010), and a webcam combined with social media afterwards.
Web cameras provided observations for ice coverage information from high locations due east in the morning. Urs Merz made many of the observations for most of the time series before he passed away. Taking into consideration the diverse information sources from the observers and other villagers along the lake, the data is likely reliable. Lake Murten's ice record began in 1901. Data were first obtained from the local newspaper Murtenbieter for the first 70 years (to 1970), after which it was based on observations of fishermen until 2006. After 2006, ice data have been obtained from webcam images and information from a boat service across the lake. The lake is observed in the northwest direction over the lake. Additionally, for Lake Murten, newspapers and fishermen reported information of freeze and thaw events, although the fishermen did not systematically record freeze-thaw events after 1971.
Lake Pfäffikon's ice record began in 1922. The earliest data were collected from newspapers. From 1942 to 1999, ice phenology information was supplemented with visual observations from Anton Hiestand (1942-1999)  and von Eugen & Örn  40 (data for 1948-1980). Observations were usually performed in the morning at multiple locations while observers were looking over the lake. Anton Hiestand and von Eugen & Örn 40 also provided two time series of systematic registration for freeze-thaw events which were also reported in newspapers. In addition, webcam images (since 2003) and social media have provided additional ice records. The ice record in Lake Sarnen began in 1901. Data were mostly obtained from the newspaper Obwaldner Volksfreund. In addition, the lake police and the fisher family Hofer have been providing scattered ice information based on their observations since 1971, and webcam images have been used since 2007.
Observations were made due southwest and north while observers took notes of ice phenology information including ice depth and ice coverage. In the period between 1947 and 1954, more information was provided from personal diaries and systematic registration. Only from 1947 to 1954 freezing events were registered systematically by observers at site, while for other periods the information on ice cover was only written down in case of freezing events of interest. Freeze and thaw events in Lake Sempach have been recorded since 1891. Data from 1891 to 1900 were recorded by Naturforschenden Gesellschaft Luzern, who started the scientific record. Ice phenology dates that occurred between 1901 and 1955 were noted in the Luzerner Zeitung newspaper. From 1955 onward, they are recorded in the fishermen family Hofer's diary.
The family would stand at the northwestern part of the lake in Oberkirch and look over the lake in the southeastern direction to obtain information. In addition, the degree to which the lake is frozen is noted. Lower Lake Constance (Untersee)'s ice phenology record started in 1887 by the Iceclub Steckborn to manage outdoor ice-skating activities. The ice club also kept the record of most freeze and thaw events, in addition to ice depth measurements. After 1955 up to 2012, ice phenology records were obtained from newspapers such as Newspaper Bote vom Untersee and Rhein to complete the data set. Iceclub Steckborn continues to perform observations from 1955 until present, but with less systematic registration in diaries. Webcams have been incorporated into the study since 2006. Webcams were set up along the southern lake border in Steckborn, Switzerland and directed due north to obtain images of the lake in the morning.
Other points of the southern border of Lower Lake Constance such as Ermatingen are also observation sites. Social media is an additional means of collecting data. It is believed that the ice club observations are very precise. Lej da San Murezzan is located in an alpine valley and not on the Swiss plateau. The ice dataset for Lej da San Murezzan was started in 1831. Both the ice-on and ice-off data were collected based on visual observations. The methodology for collecting data in years prior to 2016 was unavailable. However, after 2016, observations were conducted on an hourly basis with assistance of two webcams at the north shore (St Moritz) that provide images toward the south and east directions for most of the lake. Additional information was obtained from water temperature measurements performed at the center of the lake.
During the era of the current Shinto priest, Mr. Kiyoshi Miyasaka, observations were conducted by the Chief Priest (Guji in Japanese) and some parishioners (Ujiko in Japanese) who live in Takashima (later Kowata) village at 6:30 AM every day from the start of January to the start of February. After 1897, people from Yatsurugi Shrine and Suwa Meteorological Observatory were trained to observe ice data of the lake. The ice-on date is recorded when the lake is completely covered with ice. For Lake Suwa, the date of the appearance of a sinusoidal ice ridge also called the "omiwatari" in Japanese is further noted. The data collection procedures may have slightly changed over the time-series because the principal reason for the observations was to forecast the agricultural harvest in the coming summer based on the direction of the omiwatari, so the ice-on dates did not need scientific accuracy.
After 1950, ice data have been attained via visual observations by Anna Ponomareva, Matrena Alsaeva, and Ekaterina Fomina (1950-1990); in addition to Andry Zhdanov, Rita Zhdanova and Elena Salva (1991 -present). Observers would conduct observations during the afternoon at the pier of boats 30 m away from the shore in Listvyanka while facing south and looking from east to west. Percentages of open water and ice coverage are noted during the observation progress. The ice-on date is recorded when lake Baikal is completely covered with ice. When more than one percent of the lake is open water, the date is recorded as the ice-off date. Freeze/thaw events as well as a description of freezing and thawing processes are additionally documented at various locations across the lake.
Data records All data is open access and can be found on Figshare  41. The datasets were split into three main files that include ice-on and ice-off dates (PhenologyData. csv), the physical characteristics of each lake including the geospatial coordinates (LakeCharacteristics. csv), and corresponding metadata for each lake, including the number of missing years and definitions of ice-on and ice-off (Definitions. csv). All datasets can be connected by the unique lake name (column name = lake) that is present in each. For those interested in examining patterns in ice phenology among lakes, the PhenologyData. csv would be all that is required. We also recommend downloading additional ice phenology data for hundreds of lakes and rivers around the Northern Hemisphere from the Lake and River Ice Phenology (LIAG) dataset stored at the National Snow and Ice Data Centre (NSIDC  42  ) or within Canada specifically from the Canadian Ice Database housed at the Polar Data Catalogue  43.
Some of the lakes provided in this study are a subset of those found within LIAG, although unfortunately detailed data descriptors had not been previously collected. For others interested in using the data in combination with remotely sensed data or downscaled climate models, we recommend joining the LakeCharacteristics. csv to the PhenologyData. csv and the Definitions. csv by the "lake" column. Potential resources that could be relevant for testing changing patterns in lake ice include the HydroLAKES database for geomorphic data  44  , Moderate Resolution Imaging Spectroradiometer (MODIS) for spectral bands to be used in remote sensing, or Climatic Research Unit of East Anglia for monthly climate values  45. We also recommend WorldClim for historical and future climate patterns under multiple general circulation models and four shared socio-economic pathways  46.
Although we could not precisely quantify uncertainty in ice-on and ice-off dates for these 100+ year long datasets, the uncertainty for many of the lakes may be in the order of magnitude of one day to several days. General factors contributing to uncertainty include: variability in personal judgement between lake ice observers, low visibility, or a lake freezing over the weekend or holidays. For example, in Wilson Lake, different observers sometimes report dates that are one day apart for the same year. Low visibility, including fog, incomplete views of lakes, and nightfall, such that a lake freezes overnight and has melted by the morning, can contribute additional uncertainty in ice-on and ice-off dates.
technical Validation We conducted quality assurance and quality control on the database to ensure each observation was accurate. The data were reviewed by multiple members of the team using similar methods to check for discrepancies. There were three components that required specific attention during review including: 1) lake names, 2) lake physical characteristics, 3) dates for ice-on or ice-off. Lake names were reviewed for consistency among observation records. In many instances, lakes had inconsistent names due to differences in locations surveyed, anglicization of lake names, or local synonyms. For instance, Lake Baikal was occasionally reported as Listvyanka, the town where the observations of the lake was reported. In another instance, Cobbossee Lake is often used for Cobbosseecontee Lake, presumably a local colloquialization of the name.
We reviewed each of the lake names and created consistent labelling with each lake name being all lower case and multiple words separated with an underscore. For each lake name, we did not include any mention of the word "Lake" (except in the case of "Haystack Bay, Lake of Bays"). Accented or non-ASCII characters were replaced with non-accented, similar alphabetic equivalents. We included within our database a column that represents identically the italicized names of the lakes reported within this manuscript (LakeCharacteristics. csv). We obtained geospatial coordinates from the Lake Ice Analysis Group (LIAG), part of the National Snow and Ice Data Center (NSIDC). There were twelve lakes that were not matched in the NSIDC database and were instead collected by contacting the original data providers.
We reviewed these coordinates to ensure each occurred in the identified lake. In a few instances, we adjusted the coordinates of lakes that were slightly outside of the water to be located near the center of the lake. These discrepancies would occur because latitude and longitude from NSIDC were only reported to two decimal places, which often lacks the precision to accurately identify a point within the lake. Some observations had different geospatial coordinates but were located within the same lake. For consistency, we use the same latitude and longitude for all observations within the same lake. We collated characteristics and morphology of the lakes including elevation, surface area, mean lake depth, maximum lake depth, and shoreline length. We also provide columns for the data source that was used to obtain these data.
For instance, in many lakes, we were able to acquire this information from the LIAG database, but in other instances, we used very specific resources such as the Norwegian Water Resources and Energy Directorate for the Norwegian Lakes. We were not able to obtain lake characteristics and morphology for all variables and all lakes. The dates of ice-on and ice-off were reviewed for consistencies among lakes and anomalies among years. The first verification step was to confirm the correct year, as the 2019-2020 ice season, for example, would alternately be reported as 2019 or 2020; the latter of these, sensibly, was used in cases where only ice-out is recorded. All dates were revised to follow the format of year-month-day with year being in a four-digit format (i. e. YYYY-MM-DD). When no data were available, a blank was reported.
Most of the database provides information for the longest continuous ice presence, but in some cases there were intermittent freeze-thaw events. We provide a column specifying the freeze event that occurred when multiple freeze thaws would occur. Each additional freeze-thaw event would be labelled sequentially (i. e. , froze_1, froze_2,… froze_i). This formatting allows for recording of a near infinite number of freeze-thaw events. However, lakes rarely had more than three ice on-off dates. For a given season, ice-on has to occur before an ice-off event. We reviewed observations where ice-off dates were reported to occur before ice-on dates. There is typically a linear relationship between ice-on and ice-off dates. Therefore, to check for anomalies in dates, we conducted regressions between ice-on and ice-off day-of-year.
Dates that departed considerably from the linear pattern were reviewed to ensure accuracy. In some instances, the original observer or data collector from the lake was contacted to verify the date. We also checked the difference of the minimum and maximum date relative to the next closest value within the timeseries to determine if there were transcription errors. If the data provider included a "duration" column we added it to our dataset, but we found that these values were not consistent with the difference between ice-off date and ice-on date. Two of the most common reasons why the duration column had inconsistent estimates was because 1) some providers only subtracted ice-on date from ice-off date, while others added 1 to make it an inclusive duration, and 2) in years with multiple freeze-thaw events, some providers took the difference from the first ice-on and last ice-off, but others summed only the days when the lake was ice covered (i.
Acknowledgements We thank all of the past data contributors to the ice phenology dataset without whom this compilation would not be possible, not exclusive to the Mohonk Mountain House and Mohonk Preserve including  Dan Smiley ,  Paul Huth ,  Daniel Smiley  Research Center staff, citizen scientists in the  Climate Trackers program , the  Lake Stewards of Maine ,  Tom Hoverstad ,  Paulette Janssen ,  Joseph Norton ,  Tom Skramstad , and  Larry Smisek. SS thanks the  Natural Sciences  and  Engineering Research Council Discovery Grant ,  Ontario Ministry of Innovation and   Science Early Researcher Award , and the  York University   Research Chair program  for funding to support this research. GAW received financial support from the  Swedish Research Council  (VR Grant No. 2020-03222  and  FORMAS  Grant No.
code availability We provide code in an open access repository that will assist in reviewing the database and examining patterns (https://github. com/afilazzola/IcePhenologyDatabase). Within the repository, we provide the Python code used for the creation of the dataset, quality assurance, and quality control. We included a set of visualization options for technical validation in Python. All Python code was conducted in Python Version 3. 8. 1 (http://www. python. org) using numpy  47  , pandas  48  , textract libraries. We also provide R code that was used as a second reviewer for technical validation. The code in the qaqc. r file was used to convert the dataset into "long" format where there is only one column each for ice-on and ice-off date. The same file may also be used by future users for a quick visualization of patterns within the lake ice database.
Study region: The studied region is the Greater Antilles (Cuba, Hispaniola, Jamaica and Puerto Rico) and the Lesser Antilles (Southern part of the Caribbean arc). Study focus: The performance of MSWEP, CHIRPS, PERSIANN-CDR, ERA-5 and GPM IMERG were evaluated to highlight their qualities and shortcomings and to guide researchers in the choice of these rainfall datasets to use for hydro-meteorological applications in this study area. Five quantitative (RMSE, KGE and his three components) and three qualitative (POD, FAR and CSI) statistical metrics are used to evaluate the amount and detection capacity of the rainfall datasets. Heavy rainfall percentiles are calculated to assess the ability of rainfall datasets to estimate rare and extreme rainfall. New hydrological insights for the region: MSWEP performs well for most statistical metrics and is recommended for most hydro-meteorological research.
CHIRPS and PERSIANN-CDR performs well in estimating the annual rainfall seasonality and are recommended for research on water resources management (irrigation, energy production, etc. ). CHIRPS also performs well in estimating heavy rainfall percentiles and is also recommended for statistical research of heavy rainfall events. ERA-5 and GPM IMERG have a good ability to capture wet and dry days and is recommended for determination of climatic research or atmospheric sciences applications. However, bias reduction methods for these rainfall gridded datasets are advised before applications due to their low KGE and high RMSE.
Introduction The Greater and Lesser Antilles region experienced 264 tropical cyclones between 1960 and 2013, accounting for 95 % of the total damage from all natural disasters in the region  (Burgess et al. , 2018). These tropical cyclones generate heavy rainfall events  (Khouakhi et al. , 2017)  that cause large floods, whose intensities and frequency will tend to increase due to climate change  (Peterson et al. , 2002). Given the vulnerability of Greater and Lesser Antilles territories to hydro-meteorological hazards, rainfall databases are needed on a daily or sub-daily timescale to calculate relevant indices (e. g. , the annual number of wet days above a certain threshold, 100-year rainfall maxima) to statistically characterize heavy rainfall events and to discuss their frequency and potential trends over time.
, 2019; Wannasin et al. , 2021). Currently, there are more than 10 precipitation products obtained by remote sensing available in the world, covering different periods and different spatiotemporal scales  (Centella-Artola et al. , 2020). These remotely sensed precipitation products were created mainly from (i) satellite data (infrared observations, passive microwave observations, soil moisture observations, etc. ), (ii) combinations of different data sources (satellite rainfall estimates  [SRE] , reanalysis rainfall products, observation data, etc. ) or (iii) using atmospheric physics models coupled with reference data to create reanalysis products such as ERA-5 reanalysis. Several papers have evaluated the performance of such rainfall gridded datasets (RGD) at different spatiotemporal scales and in different climate regions  (Baez-Villanueva et al.
, 2019). In the Caribbean region, several studies have used RGD as baseline data to study spatial and temporal rainfall variability  (Angeles et al. , 2010; Jones et al. , 2016; Jury, 2016) , flood mapping  (Bozza et al. , 2016)  and the dynamic mechanisms that determine the spatial and temporal patterns of the seasonal cycle  (Martinez et al. , 2019; Moron et al. , 2016) , but very few studies compared such databases with rainfall observations. in the Caribbean region, which including the Greater and Lesser Antilles. However, very few studies have evaluated their performance specifically in the Greater and Lesser Antilles. A first comparison between rainfall observations, reanalysis, satellite and coupled model datasets was performed by  Jury (2009). The first part of the study aimed to evaluate in a qualitative   (2020)  were done on a monthly timescale.
Nevertheless, using long (typically several decades) temporal series at the daily or sub-daily timestep is required for hydrological studies dedicated to heavy rainfall and flood statistics. The performance of these rainfall gridded datasets products varies from one timescale to another, and they tend to perform better on a monthly than a daily timescale  (Sultana and Nasrollahi, 2018; Tan et al. , 2015). In this context, this work aims to evaluate the general performance of five rainfall gridded datasets (RGD) available at the daily time step over the period 1980-2019 (MSWEP, CHIRPS, ERA-5, PERSIANN-CDR and GPM IMERG). These four RGD are chosen according to (i) their performance in the Greater and Lesser Antilles on a monthly timescale  (Centella-Artola et al. , 2020)  and (ii) their availability on a daily timescale over several decades.
Presentation of the reference rain gauges The set of 146 rainfall series that we will use in this study comes from several institutions: Météo-France, the Hydrometeorological Unit of Haiti (UHM), the Caribbean Institute for Meteorology and Hydrology (CIMH), the National Oceanic and Atmospheric Administration (NOAA), the Meteorological Service of Jamaica (MetService), the Meteorological Institute of the Republic of Cuba (InsMet) and the National Meteorological Office of the Dominican Republic (ONAMET). Fig. 2  presents the gaps in the data availability for each dataset.
Quality control of the reference rain gauges Rain gauge data are used as reference data to evaluate the performance of RGD in the Greater and Lesser Antilles. These reference data can have considerable measurement errors  (Sevruk et al. , 2009; Viney and Bates, 2004) , therefore, quality control is an essential step before evaluating the RGD. To identify and discard periods or rain gauges with significant measurement errors, we used a procedure with the following steps (this method is inspired by that of  Beck et al. , 2019) : a) Remove the rainy days with more than 1000 mm/d, b) Remove months with more than 20 days of missing data, c) Remove years with more than 200 days of missing data, d) Remove stations with less than 5 years of data, e) Test the uniformity of the data series from the annual sum of the stations by the method of cumulative residuals as described by  Bois (1987).
Independence of the rain gauges from the stations used for RGD development Of the five RGD used in our analysis, three RGD (MSWEP, CHIRPS and PERSIANN-CDR) have been used rainfall products derived from rain gauges such as GPCC  (Schneider et al. , 2014) , GSOD (https://www. ncei. noaa. gov/access/metadata/landing-page/bin/iso? id=gov. noaa. ncdc:C00516), GHCN  (Menne et al. , 2012)  and GPCP  (Adler et al. , 2003) , and two RGD (CHIRPS and MSWEP) have been used rain gauges directly. The rain gauges used by CHIRPS are form Mexico, Central America, South America and sub Saharan Africa  (Funk et al. , 2015)  and not from the Greater and the Lesser Antilles. However,  Beck et al. (2019)  have been used daily rainfall series form the Greater and the Lesser Antilles to calibrate MSWEP rainfall dataset.
We excluded the 24 rain gauges (Figure A. 2 in appendix) identified as positioned within a 1-km radius from rain gauges mentioned as used within the MSWEP V2. 8 dataset to ensure the independence of the rain gauges from the stations used for RGD development. Among the 103 rain gauges remaining, 27 are in  Guadeloupe, 22 in Martinique, 19 in Dominican Republic, 5 in Haiti, 2 in Cuba, 10 in Jamaica, 6  in Puerto Rico and 12 in small islands where CIMH provided rain gauge data.