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Data Repository for 'Spatial bias in medium-range forecasts of heavy precipitation in the Sacramento River basin: Implications for water management '
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| Storage: | The size of this resource is 37.6 MB | |
| Created: | Jan 30, 2020 at 5:40 p.m. | |
| Last updated: | Jan 30, 2020 at 9:12 p.m. | |
| Citation: | See how to cite this resource |
| Sharing Status: | Public |
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| Views: | 1162 |
| Downloads: | 12 |
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| Comments: | 1 comment |
Abstract
Forecasts of heavy precipitation delivered by atmospheric rivers (ARs) are becoming increasingly important for both flood control and water supply management in reservoirs across California. This study examines the hypothesis that medium-range forecasts of heavy precipitation at the basin scale exhibit recurrent spatial biases that are driven by mesoscale and synoptic scale features of associated AR events. This hypothesis is tested for heavy precipitation events in the Sacramento River basin using 36 years of NCEP medium-range reforecasts from 1984 to 2019. For each event we cluster precipitation forecast error across western North America for lead times ranging from 1 to 15 days. Integrated vapor transport (IVT), 500 hPa geopotential heights, and landfall characteristics of ARs are composited across clusters and lead times to diagnose the causes of precipitation forecast biases. We investigate the temporal evolution of forecast error to characterize its persistence across lead times, and explore the accuracy of forecasted IVT anomalies across different domains of the North American west coast during heavy precipitation events in the Sacramento basin. Our results identify recurrent spatial patterns of precipitation forecast error consistent with errors of forecasted synoptic scale features, especially at long (5-15 day) leads. Moreover, we find evidence that forecasts of AR landfalls well outside of the latitudinal bounds of the Sacramento basin precede heavy precipitation events within the basin. These results suggest the potential for using medium-range forecasts of large-scale climate features across the Pacific-North American sector, rather than just local forecasts of basin-scale precipitation, when designing forecast-informed reservoir operations.
Subject Keywords
Coverage
Spatial
Content
Related Resources
| The content of this resource is derived from | NOAA/NCEP, 2013: NCEP Global Ensemble Forecasting System (GEFS, version 10, updated daily). NOAA’s 2nd-generation global ensemble reforecast dataset. Subset used: December 1984 – March 2019, accessed 22 August 2019, https://www.esrl.noaa.gov/psd/forecasts/reforecast2/download.html. |
| The content of this resource is derived from | NOAA/NCEP, 2002: NCEP-DOE AMIP-II (Reanalysis 2, updated daily). NOAA National Centers for Environmental Prediction, NOAA/OAR/ESRL PSD. Subset used: December 1984 – March 2019, accessed 20 August 2019, https://www.esrl.noaa.gov/psd/. |
| The content of this resource is derived from | 2013: GPCC First Guess Daily Product at 1.0°: Near real-time first guess daily land-surface precipitation from rain-gauges based on SYNOP data. Subset used: January 2009 – March 2019, accessed 20 August 2019, https://doi.org/10.5676/DWD_GPCC/FG_D_100. |
| The content of this resource is derived from | 2015: GPCC Full Data Daily Version 1.0 at 1.0°: Daily land-surface precipitation from rain-gauges built on GTS-based and historic data. Subset used: December 1984 – December 2016, accessed 20 August 2019, https://doi.org/10.5676/DWD_GPCC/FD_D_V1_100. |
| The content of this resource is derived from | Gershunov, A., Shulgina, T., Ralph, F. M., Lavers, D. A., and J. J. Rutz, 2017: Assessing the climate-scale variability of atmospheric rivers affecting western North America. Geophysical Research Letters, 44, 15, 7900–7908, https://doi.org/10.1002/2017GL074175. |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
Comments
Zachary Paul Brodeur 4 years, 9 months ago
Based on current space limitations, computer code for the project is provide, but no raw or derived data. These can be obtained from the corresponding author: Zach Brodeur - zpb4@cornell.edu
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