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Data uploading time for PostgreSQL, MongoDB, SciDB, and LotDB.

Data uploading time for PostgreSQL, MongoDB, SciDB, and LotDB.

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Figure 4. MERRA-2 in Panoply: (a) gridded dataset in plot view, (b)...
Figure 5. Data uploading time for PostgreSQL, MongoDB, SciDB, and LotDB.
Figure 6. Data volume in different containers.
Spatiotemporal queries.
Data pre-processing & uploading time and data size in different containers.
Integrating memory-mapping and N-dimensional hash function for fast and efficient grid-based climate data query
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Database systems are pervasive components in the current big data era. However, efficiently managing and querying grid-based or array-based multidimensional climate data are still beyond the capabilities of most databases. The mismatch between the array data model and relational data model limited the performance to query multidimensional data in a...
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Context 1
... the pre-processing, PostgreSQL, SciDB, and MongoDB all require additional time for data uploading. The detailed time variation is shown in Figure 5. The vertical axis records the data uploading time to each database from CSV files, and the horizontal axis represents different uploading cases for different raw data size. ...

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... Data curah hujan disini mengambil produk PRECTOTCORR MERRA-2 oleh Kantor Pemodelan dan Asimilasi Global (GMAO) Goddard-NASA [19] yang dapat diakses melalui situs POWER-NASA (gambar 5). ...
MAPPING RAINFALL [ISOHYET] IN AREAS WITH A SHORTAGE OF RAIN STATIONS USING MANUAL STATION TECHNIQUE AND PRECTOTCORR MERRA-2 DATA ANALYSIS
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  • Aug 2023
Rainfall observation data from each rain station in an area is so important to measure the intensity of rainfall in that region. The number of rain stations that are not ideal will affect the process of acquiring rainfall data which is the primary data required for regional rainfall mapping. This study uses the manual station technique and analysis of satellite precipitation data as an alternative aid for mapping annual rainfall with the Isohyet method for areas constrained by a shortage of rain stations like the Nabire Regency area. The purpose of the research was to study the intensity of rainfall in the Nabire Regency area based on data PRECTOTCORR, bias-corrected precipitation by MERRA-2 for the last ten years of precipitation data from 2014-2023 taken from the POWER-NASA climate map and managed through ArcGIS software. The area of Nabire Regency with 12.075,00 km2 includes the category of place with high rainfall intensity where the average rainfall ranges from 2000 mm – 5000 mm per year. The results of rainfall mapping with IDW (Inverse Distance Weighting) interpolation and Kriging interpolation show that there are several areas with the highest rainfall, Napan, Siriwo, Kimi, Menou, and Dipa, while the lowest rainfall areas are in the coastal areas, Teluk Umar and the northern region of Wapoga.
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