the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
12 Jan 2021
12 Jan 2021
A high-resolution gridded dataset of daily temperature and precipitation records (1980–2018) for Trentino – South Tyrol (north-eastern Italian Alps)
- 1Institute for Earth Observation, Eurac Research, Bolzano, 39100, Italy
- 2Institute for Alpine Environment, Eurac Research, Bolzano, 39100, Italy
- 3SSPT-MET-CLIM, ENEA, Rome, 00196, Italy
- 1Institute for Earth Observation, Eurac Research, Bolzano, 39100, Italy
- 2Institute for Alpine Environment, Eurac Research, Bolzano, 39100, Italy
- 3SSPT-MET-CLIM, ENEA, Rome, 00196, Italy
Abstract. A high-resolution gridded dataset of daily mean temperature and precipitation series spanning the period 1980ndash;2018 was built for Trentino ndash; South Tyrol, a mountainous region in north-eastern Italy, starting from an archive of observation series from more than 200 meteorological stations, covering the regional domain and surrounding countries. The original station data underwent a processing chain including quality and consistency checks, homogeneity tests, with the homogenization of the most relevant breaks in the series, and a filling procedure of daily gaps aiming at maximizing the data availability. Using the processed database, an anomaly-based interpolation scheme was applied to project the daily station observations of mean temperature and precipitation onto a regular grid of 250 m × 250 m resolution. The accuracy of the resulting dataset was evaluated by the leave-one-out station cross-validation. Averaged over all sites, interpolated daily temperature and precipitation show no bias, with a mean absolute error (MAE) of about 1.5 °C and 1.1 mm and a mean correlation of 0.97 and 0.91, respectively. The obtained daily fields were used to discuss the spatial representation of selected past events and the distribution of the main climatological features over the region, which shows the role of the mountainous terrain in defining the temperature and precipitation gradients. In addition, the suitability of the dataset to be combined with other high-resolution products was evaluated through a comparison of the gridded observations with snow-cover maps from remote sensing observations. The presented dataset provides an accurate insight on the spatio-temporal distribution of temperature and precipitation over the mountainous terrain of Trentino – South Tyrol and a valuable support for local and regional applications. The dataset is publicly available at https://www.pangaea.de/tok/2bea918566a8c18a728e098858856cd1fcc8dbe4, Crespi et al. (2020).
Alice Crespi et al.
Status: final response (author comments only)
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RC1: 'Comment on essd-2020-346', Anonymous Referee #1, 07 Feb 2021
This article describes the production of a high-resolution observational gridded dataset over Trentino - South Tyrol. The daily aggregated variables considered are temperature and precipitation.
The article is well structured and the presentation is clear and concise. The Introduction highlights the benefit of the study and includes a good review of the relevant literature on the topic. “Data and Methods” describes the study area and the observational database in a satisfactory manner. The interpolation scheme presented builds on a classical two-step approach. First, the climatologies are generated, then the authors use daily anomalies in their spatial analysis scheme, based on the underlying assumption of working with more Gaussian random fields. The “results and discussion” section includes the evaluation and presents a number of significant examples. As far as I can judge, there are no major flaws in the statistical analysis and the conclusions are well supported by the results. The accuracy and precision of the results are reasonable and comparable to state-of-the-art products in the Alps.
The presented method is not particularly original, because it has been applied before in the Alps, as the authors points out. The merit of this work is in the careful application of the method at such a high spatial resolution (250 m!) over complex terrain and with a pretty dense observational network. Furthermore, the final dataset is publicly available and this is a great merit of the authors. If the authors will regularly update this dataset, as they mention in their future plans (line 420), I can foresee a bright future for this dataset, which could be the basis for environmental applications and research in that part of the Alps.
In conclusion, the study is valuable. My advice to the editor is to publish it (almost) as it is. I just have a few comments.
Comments:
Figure 8, total precipitation climatology. The figure shows that the elevation has an effect on the spatial distribution of precipitation. However, it looks like the distance from the sea (or from the Po plain) also has an effect on this variable. Have the authors considered to include this variate (i.e. distance from the sea) in their study?
It is not clear if this dataset can be used to extract climatological trends of temperature and/or precipitation. Considering the construction of the observational dataset used, I think the answer may be positive. However, given the importance of such an application, I would recommend discussing this point explicitly in the text. Perhaps, the right place where to discuss this issue would be at the end of Sec 3.1 (right after the related discussion on Fig. 7), with a further reference in the conclusions.
- AC1: 'Reply to review of Anonymous Referee #1', Alice Crespi, 06 Apr 2021
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RC2: 'Comment on essd-2020-346', Anonymous Referee #2, 08 Feb 2021
The manuscript “A high-resolution gridded dataset of daily temperature and precipitation records (1980 – 2018) for Trentino – South Tyrol (northeastern Italian Alps)” submitted by Crespi et al., presents a novel and well prepared precipitation and temperature gridded dataset for the Region Trentino-South Tyrol in North-East Italy. The main strengths of the dataset in comparison to previous works are the spatial resolution, the temporal extension up to 2018 and the aim of the authors of keeping it up to date.
I did not find particular issues in the proposed methodology and the dataset is publicly available. I consider therefor the work of interest for the readers of the journal and useful for the scientific community. Some minor comments and suggestions are listed below:
- Some more information (e.g., resolution, accuracy, measurement error) about the quality of measured temperature and precipitation time series would be important to better appreciate the quality of the interpolated results. In fact, it seems that the interpolation error is in the same order of the measurement error, which is a nice attribute of the dataset.
- I did not get why the dataset was compared with snow-cover maps instead of (for example) other gridded products (e.g., Adler et al., 2015) or remote sensing products. P and T datasets for large parts of the region in fact were investigated in recent works such as:
Mei, Y., Anagnostou, E.N., Nikolopoulos, E.I., Borga, M., 2014. Error analysis of satellite precipitation products in mountainous basins. J. Hydrometeorol. 15, 1778–1793.
Duan, Z., Liu, J.Z., Tuo, Y., Chiogna, G., Disse, M., 2016. Evaluation of eight high spatial resolution gridded precipitation products in Adige Basin (Italy) at multiple temporal and spatial scales. Sci. Total Environ
Maybe the authors could better justify this choice and/or they may find these references useful for section 2.1.
- Line 71 please specify which local gradients you mean
- Lines 113-116 since the focus is on precipitation and temperature, and the area investigated is larger than the Adige basin itself, I think these lines could be removed.
- The correction of 48 precipitation time series gives a particular relevance of this step to the entire process, in my view. Some more information about how the correction factors were applied from monthly to daily time series and how large were the applied adjustments would be interesting.
- I suggest to improve figure 3 providing also information about the relative areal contribution of each elevation range. For example, a second x-axis with the cumulative area of the studied region.
- Sections 2.3.1 and 2.3.2 are a bit difficult to follow. I understand that providing too much mathematical details in the main text would make it probably too long, but in my view an appendix with a more rigorous description of the procedure would be beneficial.
- Figure 5, the color-code to interpret the heat map is missing.
- AC2: 'Reply to review of Anonymous Referee #2', Alice Crespi, 06 Apr 2021
Alice Crespi et al.
Data sets
High-resolution daily series (1980-2018) and monthly climatologies (1981-2010) of mean temperature and precipitation for Trentino - South Tyrol (north-eastern Italian Alps) Alice Crespi, Michael Matiu, Giacomo Bertoldi, Marcello Petitta, and Marc Zebisch https://www.pangaea.de/tok/2bea918566a8c18a728e098858856cd1fcc8dbe4
Alice Crespi et al.
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