AbstractMethods for generating ensemble mean precipitation forecasts from convection-allowing model (CAM) ensembles based on a simple average of all members at each grid-point can have limited utility because of amplitude reduction and over-prediction of light precipitation areas caused by averaging complex spatial fields with strong gradients and high amplitude features. To combat these issues with the simple ensemble mean, a method known as probability matching is commonly used to replace the ensemble mean amounts with amounts sampled from the distribution of ensemble member forecasts, which results in a field that has a bias approximately equal to the average bias of the ensemble members. Thus, the probability matched mean (PM-mean, hereafter) is viewed as a better representation of the ensemble members compared to the mean, and previous studies find that it is more skillful than any of the individual members.Herein, using nearly a year of data from a CAM-based ensemble running in real-time at the National Severe Storms Laboratory, evidence is provided that the superior performance of the PM-mean is at least partially an artifact of the spatial redistribution of precipitation amounts that occur when the PM-mean is computed over a large domain. Specifically, the PM-mean enlarges big areas of heavy precipitation and shrinks or even eliminates smaller ones. An alternative approach for the PM-mean is developed that restricts the grid-points used to those within a specified radius-of-influence. The new approach has an improved spatial representation of precipitation and is found to perform more skillfully than the PM-mean at large scales when using neighborhood-based verification metrics.
Weather and Forecasting – American Meteorological Society
Published: Jun 29, 2017
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 12 million articles from more than
10,000 peer-reviewed journals.
All for just $49/month
Read as many articles as you need. Full articles with original layout, charts and figures. Read online, from anywhere.
Keep up with your field with Personalized Recommendations and Follow Journals to get automatic updates.
It’s easy to organize your research with our built-in tools.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera