5.5 Forecasting Rainfall When it Matters: Providing Context for Precipitation Forecasts

Tuesday, 12 January 2016: 2:30 PM
Room 255/257 ( New Orleans Ernest N. Morial Convention Center)
Richard Grumm, NOAA/NWSFO, State College, PA; and T. Alcott and J. W. Scheck

The key requirement for effective decision support is knowing what the potential impact of an event will be. Numerical guidance is often quite successful in identifying when it will rain, and how much rain will fall. The impact of rainfall is related to two additional factors: antecedent conditions and climatological significance. High impact and historic rainfall events often occur when these two components work together.

Key components of antecedent conditions include dynamic fields such as soil moisture and streamflow, and static fields such as topography and soil types. These conditions directly relate to the potential for both flash and river flooding, and certain combinations can favor a rapid response to heavy rainfall. Flash flood guidance is often used to determine how quickly the run-off may produce flooding given a specific amount of rainfall.

This paper will emphasize the second component related to whether an event is meteorologically and climatologically significant and will do so using a few cases from the winter, spring, summer and autumn of 2015. Numerical Weather Prediction model quantitative precipitation forecasts (QPF) can be placed in context of climatological data to assess whether an event is significant. At shorter ranges, this may be best accomplished by directly comparing high-resolution model QPF to rainfall return periods. In contrast, medium- and long-range QPF from operational ensembles is often low-resolution and uncalibrated, and forecasters may obtain better context by comparison to a model QPF climatology (i.e., an “M-Climate”).

The basic concept presented here is the value of using climatological data in the forecast process to identify if a meteorologically or climatologically significant rainfall event may occur. Combining this knowledge with knowledge of antecedent conditions may provide useful information related to decision making in anticipating potential high impact flood events.

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