Two Different Integration Methods for Weather Radar-Based Quantitative Precipitation Estimation
We discuss two different integration methods for radar-based quantitative precipitation estimation (QPE): the echo intensity integral and the rain intensity integral. Theoretical analyses and simulations were used to test differences between these two methods. Cumulative rainfall calculated by the e...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2017-01-01
|
| Series: | Advances in Meteorology |
| Online Access: | http://dx.doi.org/10.1155/2017/1269748 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832545367865950208 |
|---|---|
| author | Jing Ren Yong Huang Li Guan Jie Zhou |
| author_facet | Jing Ren Yong Huang Li Guan Jie Zhou |
| author_sort | Jing Ren |
| collection | DOAJ |
| description | We discuss two different integration methods for radar-based quantitative precipitation estimation (QPE): the echo intensity integral and the rain intensity integral. Theoretical analyses and simulations were used to test differences between these two methods. Cumulative rainfall calculated by the echo intensity integral is usually greater than that from rain intensity integral. The difference of calculated precipitation using these two methods is generally smaller for stable precipitation systems and larger for unstable precipitation systems. If the echo intensity signal is sinusoidal, the discrepancy between the two methods is most significant. For stratiform and convective precipitation, the normalized error ranges from −0.138 to −0.15 and from −0.11 to −0.122, respectively. If the echo intensity signal is linear, the normalized error ranges from 0 to −0.13 and from 0 to −0.11, respectively. If the echo intensity signal is exponential, the normalized error ranges from 0 to −0.35 and from 0 to −0.30, respectively. When both the integration scheme and real radar data were used to estimate cumulative precipitation for one day, their spatial distributions were similar. |
| format | Article |
| id | doaj-art-53033d8441e54c76880de92c0282752b |
| institution | Kabale University |
| issn | 1687-9309 1687-9317 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Meteorology |
| spelling | doaj-art-53033d8441e54c76880de92c0282752b2025-02-03T07:26:04ZengWileyAdvances in Meteorology1687-93091687-93172017-01-01201710.1155/2017/12697481269748Two Different Integration Methods for Weather Radar-Based Quantitative Precipitation EstimationJing Ren0Yong Huang1Li Guan2Jie Zhou3Key Laboratory of Atmospheric Sciences and Satellite Remote Sensing of Anhui Province, Anhui Meteorology Institute, Hefei 230031, ChinaKey Laboratory of Atmospheric Sciences and Satellite Remote Sensing of Anhui Province, Anhui Meteorology Institute, Hefei 230031, ChinaNanjing University of Information Science & Technology, Nanjing 210044, ChinaKey Laboratory of Atmospheric Sciences and Satellite Remote Sensing of Anhui Province, Anhui Meteorology Institute, Hefei 230031, ChinaWe discuss two different integration methods for radar-based quantitative precipitation estimation (QPE): the echo intensity integral and the rain intensity integral. Theoretical analyses and simulations were used to test differences between these two methods. Cumulative rainfall calculated by the echo intensity integral is usually greater than that from rain intensity integral. The difference of calculated precipitation using these two methods is generally smaller for stable precipitation systems and larger for unstable precipitation systems. If the echo intensity signal is sinusoidal, the discrepancy between the two methods is most significant. For stratiform and convective precipitation, the normalized error ranges from −0.138 to −0.15 and from −0.11 to −0.122, respectively. If the echo intensity signal is linear, the normalized error ranges from 0 to −0.13 and from 0 to −0.11, respectively. If the echo intensity signal is exponential, the normalized error ranges from 0 to −0.35 and from 0 to −0.30, respectively. When both the integration scheme and real radar data were used to estimate cumulative precipitation for one day, their spatial distributions were similar.http://dx.doi.org/10.1155/2017/1269748 |
| spellingShingle | Jing Ren Yong Huang Li Guan Jie Zhou Two Different Integration Methods for Weather Radar-Based Quantitative Precipitation Estimation Advances in Meteorology |
| title | Two Different Integration Methods for Weather Radar-Based Quantitative Precipitation Estimation |
| title_full | Two Different Integration Methods for Weather Radar-Based Quantitative Precipitation Estimation |
| title_fullStr | Two Different Integration Methods for Weather Radar-Based Quantitative Precipitation Estimation |
| title_full_unstemmed | Two Different Integration Methods for Weather Radar-Based Quantitative Precipitation Estimation |
| title_short | Two Different Integration Methods for Weather Radar-Based Quantitative Precipitation Estimation |
| title_sort | two different integration methods for weather radar based quantitative precipitation estimation |
| url | http://dx.doi.org/10.1155/2017/1269748 |
| work_keys_str_mv | AT jingren twodifferentintegrationmethodsforweatherradarbasedquantitativeprecipitationestimation AT yonghuang twodifferentintegrationmethodsforweatherradarbasedquantitativeprecipitationestimation AT liguan twodifferentintegrationmethodsforweatherradarbasedquantitativeprecipitationestimation AT jiezhou twodifferentintegrationmethodsforweatherradarbasedquantitativeprecipitationestimation |