Reproducing High Spatiotemporal Resolution Precipitable Water Distributions Using Numerical Prediction Data
Water vapor is an important greenhouse gas that affects regional climatic and weather processes. Atmospheric water vapor content is highly variable spatially and temporally, and continuous quantification over a wide area is problematic. However, existing methods for measuring precipitable water (PW)...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/14/7/1177 |
| _version_ | 1797590303450857472 |
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| author | Shin Akatsuka |
| author_facet | Shin Akatsuka |
| author_sort | Shin Akatsuka |
| collection | DOAJ |
| description | Water vapor is an important greenhouse gas that affects regional climatic and weather processes. Atmospheric water vapor content is highly variable spatially and temporally, and continuous quantification over a wide area is problematic. However, existing methods for measuring precipitable water (PW) have advantages and disadvantages in terms of spatiotemporal resolution. This study uses high temporal resolution numerical prediction data and high spatial resolution elevation to reproduce PW distributions with high spatiotemporal resolution. This study also focuses on the threshold for elevation correction, improving temporal resolution, and reproducing PW distributions in near real time. Results show that using the water vapor content in intervals between the ground surface and 1000-hPa isobaric surface as the threshold value for elevation correction and generating hourly numerical prediction data using the Akima spline interpolation method enabled the reproduction of hourly PW distributions for 75% of the global navigation satellite system observation stations in the target region throughout the year with a root mean square error of 3 mm or less. These results suggest that using the mean value of monthly correction coefficients for the past years enables the reproduction of PW distributions in near real time following the acquisition of numerical prediction data. |
| first_indexed | 2024-03-11T01:18:40Z |
| format | Article |
| id | doaj.art-77e44059fa324c19a184988e74787179 |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-03-11T01:18:40Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-77e44059fa324c19a184988e747871792023-11-18T18:16:36ZengMDPI AGAtmosphere2073-44332023-07-01147117710.3390/atmos14071177Reproducing High Spatiotemporal Resolution Precipitable Water Distributions Using Numerical Prediction DataShin Akatsuka0School of Systems Engineering, Kochi University of Technology, 185 Miyanokuchi, Tosayamada, Kami City 782-8502, JapanWater vapor is an important greenhouse gas that affects regional climatic and weather processes. Atmospheric water vapor content is highly variable spatially and temporally, and continuous quantification over a wide area is problematic. However, existing methods for measuring precipitable water (PW) have advantages and disadvantages in terms of spatiotemporal resolution. This study uses high temporal resolution numerical prediction data and high spatial resolution elevation to reproduce PW distributions with high spatiotemporal resolution. This study also focuses on the threshold for elevation correction, improving temporal resolution, and reproducing PW distributions in near real time. Results show that using the water vapor content in intervals between the ground surface and 1000-hPa isobaric surface as the threshold value for elevation correction and generating hourly numerical prediction data using the Akima spline interpolation method enabled the reproduction of hourly PW distributions for 75% of the global navigation satellite system observation stations in the target region throughout the year with a root mean square error of 3 mm or less. These results suggest that using the mean value of monthly correction coefficients for the past years enables the reproduction of PW distributions in near real time following the acquisition of numerical prediction data.https://www.mdpi.com/2073-4433/14/7/1177atmospheric water vapordigital elevation modelspline interpolation |
| spellingShingle | Shin Akatsuka Reproducing High Spatiotemporal Resolution Precipitable Water Distributions Using Numerical Prediction Data Atmosphere atmospheric water vapor digital elevation model spline interpolation |
| title | Reproducing High Spatiotemporal Resolution Precipitable Water Distributions Using Numerical Prediction Data |
| title_full | Reproducing High Spatiotemporal Resolution Precipitable Water Distributions Using Numerical Prediction Data |
| title_fullStr | Reproducing High Spatiotemporal Resolution Precipitable Water Distributions Using Numerical Prediction Data |
| title_full_unstemmed | Reproducing High Spatiotemporal Resolution Precipitable Water Distributions Using Numerical Prediction Data |
| title_short | Reproducing High Spatiotemporal Resolution Precipitable Water Distributions Using Numerical Prediction Data |
| title_sort | reproducing high spatiotemporal resolution precipitable water distributions using numerical prediction data |
| topic | atmospheric water vapor digital elevation model spline interpolation |
| url | https://www.mdpi.com/2073-4433/14/7/1177 |
| work_keys_str_mv | AT shinakatsuka reproducinghighspatiotemporalresolutionprecipitablewaterdistributionsusingnumericalpredictiondata |