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From rain to data: A review of the creation of monthly and daily station‐based gridded precipitation datasets

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Abstract Monthly and daily gridded precipitation datasets are one of the most demanded products in climatology and hydrology. These datasets describe the high spatial and temporal variability of precipitation as a continuous surface and for defined periods. However, due to the complex characteristics of precipitation, it is difficult to obtain accurate estimations. Thus, the creation of a gridded dataset from observations requires the comprehensive and precise application of quality control, reconstruction, and gridding procedures. Yet, despite multiple advances, most of the gridded datasets created and published since the mid‐1990s to the present use a wide variety of techniques, methods, and outputs, which can completely change the final representativity of the data. It is, therefore, critical to provide general guidelines for the development of future and more robust gridded datasets based on the data characteristics, geographical factors, and advanced statistical techniques. We identified gaps and challenges for near‐future perspectives and provide guidelines for implementing improved approaches based on the performance of 48 products. Finally, we concluded that, despite better spatial and temporal resolutions, data access, and data processing capabilities, observational coverage remains a challenge. Moreover, scientists should adopt tailored strategies to improve the representativity and uncertainty of the estimates. This article is categorized under: Science of Water > Hydrological Processes Science of Water > Water Extremes Science of Water > Methods
Three quality control workflow levels that depend on the comprehensiveness of the approach
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Percentage of the datasets (48 in total) that used the various techniques at each of the four stages: Quality control (QC), reconstruction (REC), gridding (GRI), and uncertainty (UNC). See Table 1 for a definition of the acronyms. The gray shaded boxes represent the absence of methods. KRG, LRG, and ADW also include combinations with other methods. LRG includes PRISM, and ADW includes Spheremap
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Altitudinal relationships in the SPREAD dataset (Serrano‐Notivoli, de Luis, et al., 2017) between the grid points and the nearest 10 observatories. The labels show the elevation ranges (ER), which are represented by the colors. In each of them, the outgoing (incoming) arrows represent the proportion of grid points in that (other) ER using the nearest stations from the same or different ER
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A profile graph of precipitation in Spain based on the SPREAD (Serrano‐Notivoli, Begueria et al., 2017). The annual precipitation (blue line) is related to the type of climate (background colors), is more regular in oceanic environments and shifts to irregular toward Mediterranean and arid areas. The uncertainty of the precipitation estimates (red line) has the inverse behavior
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The decision‐making regarding the spatial resolution of the grid: (a) an empirical depiction of the pixel sizes used in the existing datasets (based on Table 1 references), (b) a theoretical decision of pixel size based on the spatial and temporal resolution of the observations
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Three weighting schemes for the estimation of precipitation at new locations (red dots) from the nearest stations with observations (blue solid dots). The weight of each observation (width of the arrows) varies depending on the geographical distance (a), the correlation (b), or (c) a fixed radius that is set to limit the use of the farthest neighbors
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Science of Water > Methods
Science of Water > Water Extremes
Science of Water > Hydrological Processes

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