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| Titel |
Developing the Integrated Multi-Satellite Retrievals for GPM (IMERG) |
| VerfasserIn |
G. J. Huffman, D. T. Bolvin, D. Braithwaite, K. Hsü, R. Joyce, C. Kidd, S. Sorooshian, P. Xie, S.-H. Yoo |
| Konferenz |
EGU General Assembly 2012
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 14 (2012) |
| Datensatznummer |
250064759
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| Zusammenfassung |
| The Integrated Multi-satellitE Retrievals for GPM (IMERG) will provide the Day-1
algorithm for computing combined precipitation estimates as part of GPM. The focus is
assembling the best time series of (nearly) global precipitation from the international
constellation of precipitation-relevant satellites and global surface precipitation gauge
analyses. It is planned that the time series will encompass both the TRMM and GPM eras,
and that the coverage will be extended to fully global as algorithms are developed that
provide skill in the difficult high-latitude environment. IMERG is being developed as a
unified U.S. algorithm that takes advantage of strengths in the three groups that are
contributing expertise:
1) the TRMM Multi-satellite Precipitation Analysis (TMPA), which addresses
inter-satellite calibration of precipitation estimates and monthly scale combination of satellite
and gauge analyses;
2) the CPC Morphing algorithm with Kalman Filtering (K-CMORPH), which provides
quality-weighted time interpolation of precipitation patterns following storm motion;
and
3) the Precipitation Estimation from Remotely Sensed Information using Artificial Neural
Networks using a Cloud Classification System (PERSIANN-CCS), which provides a
neural-network-based scheme for generating microwave-calibrated precipitation estimates
from geosynchronous infrared brightness temperatures.
In this talk we summarize the code-level integration on which IMERG is based, including
the important issues that drive the design and implementation, plans for testing and starting to
run the system, and current status. One concept being pioneered by the IMERG team is
that combination datasets should be computed multiple times at different latencies
to serve the needs of different groups of users. Although reprocessing all of the
latency “runs” complicates the reprocessing scenario, experience demonstrates
that it is essential for the users. Fortunately, the IMERG team has worked with the
Precipitation Processing System (PPS) to work out exactly such a reprocessing concept. |
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