Estimation of radar-rainfall error spatial correlation
The study presents a theoretical framework for estimating the radar-rainfall error spatial correlation (ESC) using data from relatively dense rain gauge networks. The error is defined as the difference between the radar estimate and the corresponding true areal rainfall. The method is analogous to t...
| Main Authors: | , , , , |
|---|---|
| Format: | Journal Article |
| Language: | English |
| Published: |
2012
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/95610 http://hdl.handle.net/10220/8337 |
| _version_ | 1811683612498591744 |
|---|---|
| author | Villarini, Gabriele. Smith, James A. Mandapaka, Pradeep V. Krajewski, Witold F. Ciach, Grzegorz J. |
| author_facet | Villarini, Gabriele. Smith, James A. Mandapaka, Pradeep V. Krajewski, Witold F. Ciach, Grzegorz J. |
| author_sort | Villarini, Gabriele. |
| collection | NTU |
| description | The study presents a theoretical framework for estimating the radar-rainfall error spatial correlation (ESC) using data from relatively dense rain gauge networks. The error is defined as the difference between the radar estimate and the corresponding true areal rainfall. The method is analogous to the error variance separation that corrects the error variance of a radar-rainfall product for gauge representativeness
errors. The study demonstrates the necessity to consider the area–point uncertainties while estimating the spatial correlation structure in the radar-rainfall errors. To validate the method, the authors conduct a Monte Carlo simulation experiment with synthetic fields with known error spatial correlation structure. These tests reveal that the proposed method, which accounts for the area–point distortions in the estimation of radar-rainfall ESC, performs very effectively. The authors then apply the method to estimate the ESC of the National Weather Service’s standard hourly radar-rainfall products, known as digital precipitation arrays (DPA). Data from the Oklahoma Micronet rain gauge network (with the grid step of about 5 km) are used as the ground reference for the DPAs. This application shows that the radar-rainfall errors are spatially correlated with a correlation distance of about 20 km. The results also demonstrate that the spatial correlations of radar–gauge differences are considerably underestimated, especially at small distances, as the area–point uncertainties are ignored. |
| first_indexed | 2024-10-01T04:15:30Z |
| format | Journal Article |
| id | ntu-10356/95610 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:15:30Z |
| publishDate | 2012 |
| record_format | dspace |
| spelling | ntu-10356/956102020-09-26T21:28:29Z Estimation of radar-rainfall error spatial correlation Villarini, Gabriele. Smith, James A. Mandapaka, Pradeep V. Krajewski, Witold F. Ciach, Grzegorz J. DRNTU::Engineering::Civil engineering::Water resources The study presents a theoretical framework for estimating the radar-rainfall error spatial correlation (ESC) using data from relatively dense rain gauge networks. The error is defined as the difference between the radar estimate and the corresponding true areal rainfall. The method is analogous to the error variance separation that corrects the error variance of a radar-rainfall product for gauge representativeness errors. The study demonstrates the necessity to consider the area–point uncertainties while estimating the spatial correlation structure in the radar-rainfall errors. To validate the method, the authors conduct a Monte Carlo simulation experiment with synthetic fields with known error spatial correlation structure. These tests reveal that the proposed method, which accounts for the area–point distortions in the estimation of radar-rainfall ESC, performs very effectively. The authors then apply the method to estimate the ESC of the National Weather Service’s standard hourly radar-rainfall products, known as digital precipitation arrays (DPA). Data from the Oklahoma Micronet rain gauge network (with the grid step of about 5 km) are used as the ground reference for the DPAs. This application shows that the radar-rainfall errors are spatially correlated with a correlation distance of about 20 km. The results also demonstrate that the spatial correlations of radar–gauge differences are considerably underestimated, especially at small distances, as the area–point uncertainties are ignored. Accepted version 2012-07-24T01:02:23Z 2019-12-06T19:18:13Z 2012-07-24T01:02:23Z 2019-12-06T19:18:13Z 2008 2008 Journal Article Mandapaka, P. V., Krajewski, W. F., Ciach, G. J., Villarini, G., & Smith, J. A. (2008). Estimation of Radar-rainfall Error Spatial Correlation. Advances in Water Resources, 32(7), 1020–1030. https://hdl.handle.net/10356/95610 http://hdl.handle.net/10220/8337 10.1016/j.advwatres.2008.08.014 en Advances in water resources © 2008 Elsevier. This is the author created version of a work that has been peer reviewed and accepted for publication by Advances in Water Resources, Elsevier. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: DOI [http://dx.doi.org/10.1016/j.advwatres.2008.08.014]. application/pdf |
| spellingShingle | DRNTU::Engineering::Civil engineering::Water resources Villarini, Gabriele. Smith, James A. Mandapaka, Pradeep V. Krajewski, Witold F. Ciach, Grzegorz J. Estimation of radar-rainfall error spatial correlation |
| title | Estimation of radar-rainfall error spatial correlation |
| title_full | Estimation of radar-rainfall error spatial correlation |
| title_fullStr | Estimation of radar-rainfall error spatial correlation |
| title_full_unstemmed | Estimation of radar-rainfall error spatial correlation |
| title_short | Estimation of radar-rainfall error spatial correlation |
| title_sort | estimation of radar rainfall error spatial correlation |
| topic | DRNTU::Engineering::Civil engineering::Water resources |
| url | https://hdl.handle.net/10356/95610 http://hdl.handle.net/10220/8337 |
| work_keys_str_mv | AT villarinigabriele estimationofradarrainfallerrorspatialcorrelation AT smithjamesa estimationofradarrainfallerrorspatialcorrelation AT mandapakapradeepv estimationofradarrainfallerrorspatialcorrelation AT krajewskiwitoldf estimationofradarrainfallerrorspatialcorrelation AT ciachgrzegorzj estimationofradarrainfallerrorspatialcorrelation |