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| H67: | Predicting Precipitation |
| Sponsor: |
Hydrology
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| CoSponsor: |
Atmospheric Sciences |
| Convener: |
Ana Nunes Scripps Institution of Oceanography, University of California, San Diego 9500 Gilman Drive MC 0224 La Jolla, CA, USA 858 822 1835 anunes@ucsd.edu Paul A. Kucera National Center for Atmospheric Research P.O. Box 3000 Boulder, CO, USA 303-497-2807 pkucera@ucar.edu Dong-Jun Seo NOAA/NWS/OHD Hydrology Laboratory & UCAR 1325 East-West Highway Silver Spring, MD, USA 301-713-0640 dongjun.seo@noaa.gov |
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1854 3314 3360 1840 6334 .
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| Description: | This special session seeks contributions from the research, operational and user communities that address a variety of issues related to predicting precipitation. The prediction of precipitation on daily to seasonal to centennial time scales remains a grand challenge for the hydrometeorological sciences. Progress in the prediction of precipitation in recent years has come from better model resolution and better access to data for model initialization along with some improvements in model physics. Most recently, new observational capabilities and insights on the predictability of precipitation are providing a basis for major improvements in the representation of the physics of precipitation processes in models. Although precipitation predictability is limited by the chaotic behavior of the atmosphere, there are distinct regions, seasons and surface conditions where the predictability of precipitation is enhanced. Understanding of the space-time characteristics of predictability and its causes can provide insights about ways to improve precipitation forecast systems. A particular focus of this session is the generation, post-processing, and application of ensemble/probabilistic Quantitative Precipitation Estimation (QPE) and Quantitative Precipitation Forecasts (QPF) for uncertainty-based hydrologic monitoring and prediction of soil moisture, flash and river floods, water resources and other water-related variables. The session builds on the past ensemble prediction sessions held at recent AGU meetings to provide a continuing forum for advancing and expediting interdisciplinary research and research-to-operations in ensemble hydrometeorological and hydrologic prediction. An important aspect of any precipitation prediction system is validation/verification. Typical QPE/QPF validation/verification is performed using a point to grid method (e.g., rain gauge observations to a radar rainfall grid or forecast grid point). Use of standard validation/verification measures (mean error, bias, mean absolute error, and root mean squared error, etc.) often indicate poorer performance for higher-resolution predictions because, among other things, the methods are unable to account for small-scale noise or discriminate types of errors such as displacement in time and/or space (location, intensity, and orientation errors, etc.) in the precipitation estimates/forecasts. This issue has motivated recent research and development of many new diagnostic verification techniques such as, but not limited to, scale decomposition, fuzzy neighborhood, and object-based methods for evaluating spatial precipitation estimates and forecasts. These predictability and validation/verification studies are complemented by opportunities to use both passive and active sensor data from advanced satellites (e.g. TRMM, CloudSat, A-train, and GPM) that enable the prediction community to detect and quantify the characteristics of precipitation and to better understand precipitation processes. Papers that outline the use of these data to help modelers to help enhance the predictability of precipitation including in-cloud processes are welcome. Contributions that deal with methods for generating probabilistically unbiased precipitation forcing via real-time or retrospective post-processing of numerical weather prediction (NWP) model output, model output statistics (MOS), precipitation observations from rain gauges, radars, and/or satellites are also welcome. Finally, the session welcomes all contributions related to the validation/verification of quantitative precipitation estimation/forecasts. The conveners are especially interested in studies that focus on the development of new spatial validation/ verification techniques, applications of these methods, and studies that use combined spatial and point to grid or grid-to-grid techniques. |
Union Sessions by Theme
There are no extra requirements to submit to these sessions.
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3. Climate & the Environment
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