Name
Title | ||
|---|---|---|
Description and Validation of an Automated Objective Technique for Identification and Characterization of the Integrated Water Vapor Signature of Atmospheric Rivers |
Abstract | ||
|---|---|---|
An automated, objective tool for identifying and characterizing the integrated water vapor (IWV) signature of atmospheric rivers (ARs) based on satellite-observed or model-derived IWV fields has been developed, demonstrated, and validated. ARs are narrow plumes of intense water vapor transport that have been found to be an important contributor to major flooding events in the western U.S. and to seasonal water supply. Previous results demonstrated that the associated IWV signature is an effective proxy for the ARs themselves and that signature is used in this work to characterize the features. The technique employs basic objective criteria for the length ( 2000 km), width ( 1000 km), and IWV content ( 2 cm) of the plumes and standard image processing techniques including thresholding and skeletonization to identify the ARs. Extracted characteristics for the identified plumes include their position, width, core IWV content, orientation, lifetime, and propagation speed. The performance of the AR detection tool (ARDT) was validated over five cool seasons by comparing the AR IWV signature identified by the tool with visually identified events from an existing landfalling AR climatology. The ARDT performed extremely well with a critical success index of 92.4% and a 98.5% probability of detection. Differences were largely the result of subjective decisions in visual classification and tradeoffs in the tool sensitivity between missing actual ARs and inclusion of non-AR features. Future improvements include refined computations of the length and width of AR features and extension of the technique to apply directly to measurements of the water vapor transport. Overall, the ARDT appears well suited for the development of extended AR climatologies and- the comparison and verification of forecasts of ARs. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/TGRS.2012.2211024 | Geoscience and Remote Sensing, IEEE Transactions |
Keywords | Field | DocType |
Atmospheric river,automated detection,forecast verification,integrated water vapor,meteorology,passive microwave remote sensing,pattern recognition | Meteorology,Remote sensing,Image processing,Atmospheric model,Skeletonization,Forecast verification,Weather forecasting,Statistical power,Mathematics,Water vapor,Atmospheric river | Journal |
Volume | Issue | ISSN |
51 | 4 | 0196-2892 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gary A. Wick | 1 | 4 | 6.59 |
| Paul J. Neiman | 2 | 0 | 0.34 |
| F. Martin Ralph | 3 | 0 | 0.34 |