Comment on essd-2022-129
Peter E. LandHelen S. FindlayJamie D. ShutlerJean-François PiolléRichard SimsHannah GreenVassilis KitidisA. A. PolukhinI. I. Pipko
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Abstract. In recent years, large datasets of in situ marine carbonate system parameters (partial pressure of CO2 (pCO2), total alkalinity, dissolved inorganic carbon and pH) have been collated, quality controlled and made publicly available. These carbonate system datasets have highly variable data density in both space and time, especially in the case of pCO2, which is routinely measured at high frequency using underway measuring systems. This variation in data density can create biases when the data are used, for example for algorithm assessment, favouring datasets or regions with high data density. A common way to overcome data density issues is to bin the data into cells of equal latitude and longitude extent. This leads to bins with spatial areas that are latitude and projection dependent (e. g. become smaller and more elongated as the poles are approached). Additionally, as bin boundaries are defined without reference to the spatial distribution of the data or to geographical features, data clusters may be divided sub-optimally (e. g. a bin covering a region with a strong gradient). To overcome these problems and to provide a tool for matching surface in situ data with satellite, model and climatological data, which often have very different spatiotemporal scales both from the in situ data and from each other, a methodology has been created to group in situ data into ‘regions of interest’: spatiotemporal cylinders consisting of circles on the Earth’s surface extending over a period of time. These regions of interest are optimally adjusted to contain as many in situ measurements as possible. All surface in situ measurements of the same parameter contained in a region of interest are collated, including estimated uncertainties and regional summary statistics. The same grouping is applied to each of the non-in situ datasets in turn, producing a dataset of coincident matchups that are consistent in space and time. About 35 million in situ data points were matched with data from five satellite sources and five model and re-analysis datasets to produce a global matchup dataset of carbonate system data, consisting of ~286,000 regions of interest spanning 54 years from 1957 to 2020. Each region of interest is 100 km in diameter and 10 days in duration. An example application, the reparameterisation of a global total alkalinity algorithm, is shown. This matchup dataset can be updated as and when in situ and other datasets are updated, and similar datasets at finer spatiotemporal scale can be constructed, for example to enable regional studies. The matchup dataset provides users with a large multiparameter carbonate system dataset containing data from different sources, in one consistent, collated and standardised format suitable for model-data intercomparisons and model evaluations. The OceanSODA-MDB data can be downloaded from https://doi.org/10.12770/0dc16d62-05f6-4bbe-9dc4-6d47825a5931 (Land and Piollé, 2022).Keywords:
Longitude
Alkalinity
Summary The information change analysis in Mercator projection is made when the linear scale μ of projection and the scale of area p independent of the geodetic latitude B and longitude L are changed. Mercator projection was analysed in two options, namely, when the values of linear scale for a central meridian are equal to μ0=0,9996 and μ0=0,9998. The calculations have been made using the model of confidence intervals for changes of scale values and μ and P from μe and P e that are equal to the unit on the points displaced by one degree of latitude and longitude. The results of analysis have shown that the quantity of information practically is the same for both options of Mercator projection at given conditions.
Mercator projection
Longitude
Geographic coordinate system
Meridian (astronomy)
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Longitude
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Longitude
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Geographic coordinate system
Grid cell
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AbstractAbstractThe problem of computing marginal scales of latitude and longitude on a rectangular map on the Transverse Mercator projection, where the sheet boundaries are projection co-ordinate lines, may be solved in various ways. A simple method is to compute the latitudes and longitudes of the four corners of the sheet, and then, assuming a constant scale, to interpolate the parallels and meridians between these corner values. Although it is probably sufficiently accurate for practical purposes, this method is not precise. It is not difficult to adapt the fundamental formulce of the projection to give a direct solution of the problem.
Mercator projection
Longitude
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