Estimating Mean Surface Backscatter from GPM Surface Backscatter Observations
The radar on the Global Precipitation Measurement (GPM) mission observes precipitation at 13.6 GHz (Ku-band) and 35.6 GHz (Ka-band) and also receives echoes from the earth’s surface. Statistics of surface measurements for non-raining conditions are saved in a database for later use in estimating the...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Eng |
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| Online Access: | https://www.mdpi.com/2673-4117/2/4/31 |
| _version_ | 1797504970472292352 |
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| author | Stephen L. Durden |
| author_facet | Stephen L. Durden |
| author_sort | Stephen L. Durden |
| collection | DOAJ |
| description | The radar on the Global Precipitation Measurement (GPM) mission observes precipitation at 13.6 GHz (Ku-band) and 35.6 GHz (Ka-band) and also receives echoes from the earth’s surface. Statistics of surface measurements for non-raining conditions are saved in a database for later use in estimating the precipitation path-integrated attenuation. Previous work by Meneghini and Jones (2011) showed that while averaging over larger latitude/longitude bins increase the number of samples, it can also increase sample variance due to spatial inhomogeneity in the data. As a result, Meneghini and Kim (2017) proposed a new, adaptive method of database construction, in which the number of measurements averaged depends on the spatial homogeneity. The purpose of this work is to re-visit previous, single-frequency results using dual-frequency data and optimal interpolation (kriging). Results include that (1) temporal inhomogeneity can create similar results as spatial, (2) Ka-band behavior is similar to Ku-band, (3) the Ku-/Ka-band difference has less spatial inhomogeneity than either band by itself, and (4) kriging and the adaptive method can reduce the sample variance. The author concludes that finer spatial and temporal resolution is necessary in constructing the database for single frequencies but less so for the Ku-/Ka-band difference. The adaptive approach reduces sample standard deviation with a relatively modest computational increase. |
| first_indexed | 2024-03-10T04:11:59Z |
| format | Article |
| id | doaj.art-473eb3e946334288befa4d32ba48245d |
| institution | Directory Open Access Journal |
| issn | 2673-4117 |
| language | English |
| last_indexed | 2024-03-10T04:11:59Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Eng |
| spelling | doaj.art-473eb3e946334288befa4d32ba48245d2023-11-23T08:09:41ZengMDPI AGEng2673-41172021-11-012449250010.3390/eng2040031Estimating Mean Surface Backscatter from GPM Surface Backscatter ObservationsStephen L. Durden0Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USAThe radar on the Global Precipitation Measurement (GPM) mission observes precipitation at 13.6 GHz (Ku-band) and 35.6 GHz (Ka-band) and also receives echoes from the earth’s surface. Statistics of surface measurements for non-raining conditions are saved in a database for later use in estimating the precipitation path-integrated attenuation. Previous work by Meneghini and Jones (2011) showed that while averaging over larger latitude/longitude bins increase the number of samples, it can also increase sample variance due to spatial inhomogeneity in the data. As a result, Meneghini and Kim (2017) proposed a new, adaptive method of database construction, in which the number of measurements averaged depends on the spatial homogeneity. The purpose of this work is to re-visit previous, single-frequency results using dual-frequency data and optimal interpolation (kriging). Results include that (1) temporal inhomogeneity can create similar results as spatial, (2) Ka-band behavior is similar to Ku-band, (3) the Ku-/Ka-band difference has less spatial inhomogeneity than either band by itself, and (4) kriging and the adaptive method can reduce the sample variance. The author concludes that finer spatial and temporal resolution is necessary in constructing the database for single frequencies but less so for the Ku-/Ka-band difference. The adaptive approach reduces sample standard deviation with a relatively modest computational increase.https://www.mdpi.com/2673-4117/2/4/31radarbackscatteraveragingstatisticskriging |
| spellingShingle | Stephen L. Durden Estimating Mean Surface Backscatter from GPM Surface Backscatter Observations Eng radar backscatter averaging statistics kriging |
| title | Estimating Mean Surface Backscatter from GPM Surface Backscatter Observations |
| title_full | Estimating Mean Surface Backscatter from GPM Surface Backscatter Observations |
| title_fullStr | Estimating Mean Surface Backscatter from GPM Surface Backscatter Observations |
| title_full_unstemmed | Estimating Mean Surface Backscatter from GPM Surface Backscatter Observations |
| title_short | Estimating Mean Surface Backscatter from GPM Surface Backscatter Observations |
| title_sort | estimating mean surface backscatter from gpm surface backscatter observations |
| topic | radar backscatter averaging statistics kriging |
| url | https://www.mdpi.com/2673-4117/2/4/31 |
| work_keys_str_mv | AT stephenldurden estimatingmeansurfacebackscatterfromgpmsurfacebackscatterobservations |