Conditioning Stochastic Rainfall Replicates on Remote Sensing Data
Temporally and spatially variable rainfall replicates are frequently required in hydrologic applications of ensemble forecasting and data assimilation. Ensemble methods can be expected to work better when the rainfall replicates more closely resemble observed storms. In particular, the replicates sh...
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| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers, IEEE Geoscience and Remote Sensing Society
2010
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| Online Access: | http://hdl.handle.net/1721.1/60256 https://orcid.org/0000-0002-2810-1722 https://orcid.org/0000-0002-8362-4761 |
| _version_ | 1826196073227485184 |
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| author | Entekhabi, Dara Wojcik, Rafal McLaughlin, Dennis Konings, Alexandra G. |
| author2 | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Entekhabi, Dara Wojcik, Rafal McLaughlin, Dennis Konings, Alexandra G. |
| author_sort | Entekhabi, Dara |
| collection | MIT |
| description | Temporally and spatially variable rainfall replicates are frequently required in hydrologic applications of ensemble forecasting and data assimilation. Ensemble methods can be expected to work better when the rainfall replicates more closely resemble observed storms. In particular, the replicates should capture the intermittency and variability that are dominant features of rainfall events. In this paper, we present a new probabilistic procedure for generating realistic rainfall replicates that are constrained by (or conditioned on) remote sensing measurements. The procedure uses remotely sensed cloud top temperatures to identify potentially rainy regions. The cloud top temperatures are obtained from visible/infrared instruments in geostationary orbit. A multipoint geostatistical algorithm generates areas of nonzero rain (rain clusters) within each cloudy region. This algorithm relies on statistics derived from ground-based weather radar [National Operational Weather Radar (NOWRAD)] data. A truncated multiplicative cascade generates rain rates within each rain cluster. A computational experiment based on summer 2004 data from the Central U.S. indicates that the rainfall replicates simulated by the procedure are visually and statistically similar to individual NOWRAD images and to a large ensemble of NOWRAD images collected throughout the summer simulation period. |
| first_indexed | 2024-09-23T10:20:07Z |
| format | Article |
| id | mit-1721.1/60256 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T10:20:07Z |
| publishDate | 2010 |
| publisher | Institute of Electrical and Electronics Engineers, IEEE Geoscience and Remote Sensing Society |
| record_format | dspace |
| spelling | mit-1721.1/602562022-09-26T17:18:04Z Conditioning Stochastic Rainfall Replicates on Remote Sensing Data Entekhabi, Dara Wojcik, Rafal McLaughlin, Dennis Konings, Alexandra G. Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Entekhabi, Dara Entekhabi, Dara Wojcik, Rafal McLaughlin, Dennis Konings, Alexandra G. Temporally and spatially variable rainfall replicates are frequently required in hydrologic applications of ensemble forecasting and data assimilation. Ensemble methods can be expected to work better when the rainfall replicates more closely resemble observed storms. In particular, the replicates should capture the intermittency and variability that are dominant features of rainfall events. In this paper, we present a new probabilistic procedure for generating realistic rainfall replicates that are constrained by (or conditioned on) remote sensing measurements. The procedure uses remotely sensed cloud top temperatures to identify potentially rainy regions. The cloud top temperatures are obtained from visible/infrared instruments in geostationary orbit. A multipoint geostatistical algorithm generates areas of nonzero rain (rain clusters) within each cloudy region. This algorithm relies on statistics derived from ground-based weather radar [National Operational Weather Radar (NOWRAD)] data. A truncated multiplicative cascade generates rain rates within each rain cluster. A computational experiment based on summer 2004 data from the Central U.S. indicates that the rainfall replicates simulated by the procedure are visually and statistically similar to individual NOWRAD images and to a large ensemble of NOWRAD images collected throughout the summer simulation period. 2010-12-09T17:22:35Z 2010-12-09T17:22:35Z 2009-05 Article http://purl.org/eprint/type/JournalArticle 0196-2892 http://hdl.handle.net/1721.1/60256 Wojcik, R. et al. “Conditioning Stochastic Rainfall Replicates on Remote Sensing Data.” Geoscience and Remote Sensing, IEEE Transactions on 47.8 (2009): 2436-2449. © 2009, IEEE https://orcid.org/0000-0002-2810-1722 https://orcid.org/0000-0002-8362-4761 en_US http://dx.doi.org/10.1109/tgrs.2009.2016413 IEEE transactions on geoscience and remote sensing Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf Institute of Electrical and Electronics Engineers, IEEE Geoscience and Remote Sensing Society IEEE |
| spellingShingle | Entekhabi, Dara Wojcik, Rafal McLaughlin, Dennis Konings, Alexandra G. Conditioning Stochastic Rainfall Replicates on Remote Sensing Data |
| title | Conditioning Stochastic Rainfall Replicates on Remote Sensing Data |
| title_full | Conditioning Stochastic Rainfall Replicates on Remote Sensing Data |
| title_fullStr | Conditioning Stochastic Rainfall Replicates on Remote Sensing Data |
| title_full_unstemmed | Conditioning Stochastic Rainfall Replicates on Remote Sensing Data |
| title_short | Conditioning Stochastic Rainfall Replicates on Remote Sensing Data |
| title_sort | conditioning stochastic rainfall replicates on remote sensing data |
| url | http://hdl.handle.net/1721.1/60256 https://orcid.org/0000-0002-2810-1722 https://orcid.org/0000-0002-8362-4761 |
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