Is there a way to get accurate/valid data on temperature and rainfall for specific Ethiopian districts using a flat form/link?

Dear doctor

This is the introduction and the abstract of a notable study conducted by Camuffo et al. (2022)

"Extended datasets of past weather conditions are extremely valuable for the assessment of climate change and related consequences. The growing need for high-resolution, high-quality and long-term continuous records has required an enormous effort to recover and reconstruct early precipitation series. Documentary evidence and early instrumental records constitute an important source of data for historical climatology.

Almost all long instrumental series are affected by gaps, due to different reasons, such as instruments malfunctions, poor health or even death of the observer, political changes, wars, and so on. This leads to exclude periods with gaps from data analysis. Another approach is to try to reconstruct missing values, or validate uncertain records. This is particularly important especially in the early instrumental periods, when the data are scarce, but their recovery, correction and reconstruction are crucial for climate studies.

The problem with precipitation is twofold, since both frequency and amount have to be reconstructed. A number of techniques have been developed over the decades aimed at estimating missing values in precipitation time series, and their performance evaluated. Most of these techniques are hardly applicable to early series, in particular to reach daily resolution, due to a number of difficulties that will be discussed in the next sections.

In fact, when dealing with early series, the task is more challenging, as generally data have to be carefully interpreted and validated, and gaps are quite large (i.e. years). Moreover, most of the methods described in literature requires contemporary datasets from other stations, a condition that is quite rare for early series. Therefore, the choice of the gap-filling method depends strongly on the nature and amount of data and/or metadata available. This paper considers the following methods:

1.Relationship between monthly amounts and frequencies. Historically, this was the first attempt and it was based on the monthly relation between the total precipitation amount and the number of rainy days. In the presence of gaps, when the frequency was known from narrative sources, the missing amount was substituted with the matched value based on different criteria, e.g. similarities, return periods and so forth (Toaldo 1770; Crestani 1926, 1933). This method was applied at monthly and daily resolution.

2.Transformation of narrative sources into numerical values through analysis, classification, and calibration. The transformation from the narrative format to numerical proxy values is a challenging task, not only because of the difficulties in recovering and interpreting the historical sources, but for the very nature and quality of the proxy. The use of indices or categories in historical climatology follows a long tradition: different scales of indices for series of temperature and precipitation have been developed, and a number of protocols tailored for the typical climate of each specific climatic area have been established (Brazdil et al. 2019; Pfister et al. 2018; Dominguez-Castro et al. 2015a; Fernández-Fernández et al. 2015; Santos et al. 2015; Camuffo et al. 2013; Diodato 2007; Gimmi et al. 2006; Alcoforado et al. 2000; Rodrigo et al. 1994; Ge et al. 2005; Harvey-Fishenden and Macdonald 2021). The reverse approach, i.e. the transformation of reconstructed values into index form to make them comparable to weather descriptions, has also been studied (Bronnimann 2020). The indexation method has been applied to both normal and extreme events (e.g. droughts, storms, floods). This latter application is favoured by the natural tendency to document unusual weather/hydrological phenomena (Brázdil et al. 2012; Domínguez-Castro et al. 2012, 2015b; Barrera et al. 2006; Santorelli et al. 2003; Brunetti et al. 2002). The used scale is generally monthly, seasonal or yearly, and there are very few cases of daily resolution. Dominguez-Castro et al. (2015a) devised a 4-category rainfall index at daily scale, using also sub-daily information. Ge et al. (2005) converted qualitative description into quantitative values using precipitation events in which both were documented, but then the precipitation series was reconstructed at monthly level. In this paper, for the first time, the content analysis has not been limited to the indexation, and the reconstruction of daily amounts has been attempted.

3.Using records from one or more stations in the same climatic area. Several long series have been reconstructed, or had their gaps filled using data from one or more neighbouring contemporary stations, e.g. in the Alpine Region (Auer et al. 2007), in England and Wales (Craddock 1976; Lough et al. 1984; Simpson and Jones 2012); Iberian Peninsula (Prohom et al. 2016); Portugal (Alcoforado et al. 2000); Poland (Przybylak 2010); Switzerland (Pfister et al. 2019, 2020); Australia (Shubham et al. 2019; Gergis and Ashcroft 2013). In case of long gaps, the missing values in the target location have been calculated by interpolating a number of contemporary observations from surrounding stations using different methods (Kanda et al. 2018; Woldesenbet et al. 2017; Young 1992; Teegavarapu and Chandramouli 2005; Eischeid et al. 2000; Creutin and Obled 1982; Gentilucci et al. 2018; Ruane et al. 2015; Hasan and Croke 2013; Kim and Ryu 2016). The first problem is that more than one simultaneous record must be available in nearby sites with similar climate, and this is very unlikely in the early period, when only a few stations operated. It must be considered, however, that precipitation has high time and space variability, and a great density of stations is needed to assess statistically significant precipitation patterns. In addition, the quality and homogeneity of the datasets is a crucial element (Lanza and Cauteruccio 2022). The second key item is the choice of the interpolation method. When only one neighbouring station is available, a high correlation between the two datasets is required to give good predictions (Caldera et al. 2016).

Abstract

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."

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