Dataset associated with "Forecasting Excessive Rainfall with Random Forests and a Deterministic Convection-Allowing Model"

Hill, Aaron

Dataset associated with "Forecasting Excessive Rainfall with Random Forests and a Deterministic Convection-Allowing Model"

dc.contributor.author	Hill, Aaron
dc.coverage.temporal	2017-01-01 -- 2018-12-31	en_US
dc.date.accessioned	2021-09-03T14:52:36Z
dc.date.available	2021-09-03T14:52:36Z
dc.date.issued	2021
dc.description	This dataset contains random forest (RF)-based forecasts for the various configurations described in our manuscript. The CONUS-wide forecasts are valid from 1 January 2017 to 31 December 2018 (2 years).	en_US
dc.description	Department of Atmospheric Science
dc.description.abstract	Approximately seven years of daily initializations from the convection-allowing National Severe Storms Laboratory Weather Research and Forecasting Model are used as inputs to train random forest (RF) machine learning models to probabilistically predict instances of excessive rainfall. Unlike other hazards, excessive rainfall does not have an accepted definition, so multiple definitions of excessive rainfall and flash flooding—including flash flood reports and 24-h average recurrence intervals (ARIs)—are used to explore RF configuration forecast sensitivities. RF forecasts are analogous to operational Weather Prediction Center (WPC) day-1 Excessive Rainfall Outlooks (EROs) and their resolution, reliability, and skill are strongly influenced by rainfall definitions and how inputs are assembled for training. Models trained with 1-yr ARI exceedances defined by the Stage-IV (ST4) precipitation analysis perform poorly in the northern Great Plains and Southwest United States, in part due to a high bias in the number of training events in these regions. Increasing the ARI threshold to 2 years or removing ST4 data from training, optimizing forecast skill geographically, and spatially averaging meteorological inputs for training generally results in improved CONUS-wide RF forecast skill. Both EROs and RF forecasts have seasonal skill—–poor forecasts in the late fall and winter and skillful forecasts in the summer and early fall. However, the EROs are consistently and significantly better than their RF counterparts, regardless of RF configuration, particularly in the summer months. The results suggest careful consideration should be made when developing ML-based probabilistic precipitation forecasts with convection-allowing model inputs, and further development is necessary to consider these forecast products for operational implementation.
dc.description.sponsorship	NA18OAR4590378.	en_US
dc.format.medium	netCDF
dc.format.medium	TXT
dc.identifier.uri	https://hdl.handle.net/10217/233672
dc.identifier.uri	http://dx.doi.org/10.25675/10217/233672
dc.language	English	en_US
dc.language.iso	eng	en_US
dc.publisher	Colorado State University. Libraries	en_US
dc.relation.ispartof	Research Data
dc.relation.isreferencedby	Hill, A. J. and R. S. Schumacher, 2021: Forecasting excessive rainfall with random forests and a deterministic convection-allowing model. Weather and Forecasting, 36, 1693-1711, https://doi.org/10.1175/WAF-D-21-0026.1	en_US
dc.rights.license	The material is open access and distributed under the terms and conditions of the Creative Commons Public Domain "No rights reserved" (https://creativecommons.org/share-your-work/public-domain/cc0/).
dc.rights.uri	https://creativecommons.org/share-your-work/public-domain/cc0/
dc.subject	Rainfall	en_US
dc.subject	Numerical weather prediction/forecasting	en_US
dc.subject	Operational forecasting	en_US
dc.subject	Machine learning	en_US
dc.title	Dataset associated with "Forecasting Excessive Rainfall with Random Forests and a Deterministic Convection-Allowing Model"	en_US
dc.type	Dataset	en_US