Improving probabilistic ensemble forecasts of convection through the application of QPF-POP relationships

dc.contributor.advisor William A. Gallus
dc.contributor.author Schaffer, Christopher
dc.contributor.department Geological and Atmospheric Sciences
dc.date 2018-08-11T18:00:59.000
dc.date.accessioned 2020-06-30T02:37:57Z
dc.date.available 2020-06-30T02:37:57Z
dc.date.copyright Fri Jan 01 00:00:00 UTC 2010
dc.date.embargo 2013-06-05
dc.date.issued 2010-01-01
dc.description.abstract <p>Quantitative precipitation forecasts provide an accumulated precipitation amount for a given</p> <p>time period, and accurate forecasts depend on the correct prediction of areal coverage,</p> <p>timing, and intensity of precipitation. These forecasts are important to a variety of people for</p> <p>many different purposes, so expressing a likelihood of precipitation is also useful. Most</p> <p>simply, probabilities of precipitation are determined by considering the percentage of</p> <p>ensemble members forecasting precipitation greater than a specified threshold amount.</p> <p>Probabilities of precipitation can also be formed from quantitative precipitation forecasts</p> <p>through statistical post-processing. Past research has shown that there are many ways to</p> <p>post-process precipitation data, such as by binning the precipitation amounts, applying</p> <p>statistical calibration, and/or considering the percentage of an area receiving precipitation.</p> <p>The main goal of this study was to expand upon relationships between quantitative</p> <p>precipitation forecasts and probabilities of precipitation by developing new approaches that</p> <p>yield more accurate probabilities of precipitation than methods that are currently more</p> <p>commonly used. Ensemble forecasts from the 2007 and 2008 NOAA Hazardous Weather</p> <p>Testbed Spring Experiments were used to provide quantitative precipitation forecasts for</p> <p>various days. In the study, four main approaches were developed and tested extensively</p> <p>using Brier scores and other statistics. Brier scores for different approaches were compared</p> <p>to traditional methods of calculating probabilities of precipitation. It was shown at both 20</p> <p>km and 4 km grid spacings that new approaches were able to produce statistically significantly better forecasts than a traditional method that relies upon calibration of POP forecasts derived using equal-weighting of ensemble members. A deterministic approach developed during the study was also able to produce forecasts comparable to those of the calibrated traditional method.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/etd/11758/
dc.identifier.articleid 2777
dc.identifier.contextkey 2807975
dc.identifier.doi https://doi.org/10.31274/etd-180810-1289
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath etd/11758
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/25964
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/etd/11758/Schaffer_iastate_0097M_10994.pdf|||Fri Jan 14 18:57:32 UTC 2022
dc.subject.disciplines Earth Sciences
dc.subject.keywords ensemble
dc.subject.keywords POP
dc.subject.keywords probabilistic
dc.subject.keywords QPF
dc.title Improving probabilistic ensemble forecasts of convection through the application of QPF-POP relationships
dc.type article
dc.type.genre thesis
dspace.entity.type Publication
relation.isOrgUnitOfPublication 29272786-4c4a-4d63-98d6-e7b6d6730c45
thesis.degree.level thesis
thesis.degree.name Master of Science
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