Data repository for: A multivariate approach to generate synthetic short-to-medium range hydro-meteorological forecasts across locations, variables, and lead times
| Authors: | |
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| Owners: | Zachary Paul Brodeur |
| Type: | Resource |
| Storage: | The size of this resource is 57.8 MB |
| Created: | Dec 14, 2020 at 1:22 a.m. |
| Last updated: | May 13, 2021 at 11:15 p.m. (Metadata update) |
| Published date: | May 13, 2021 at 11:15 p.m. |
| DOI: | 10.4211/hs.4382404b935f4fde99c7ff4ada264867 |
| Citation: | See how to cite this resource |
| Sharing Status: | Published |
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| Views: | 1576 |
| Downloads: | 74 |
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Abstract
The use of hydro-meteorological forecasts in water resources management holds great promise as a soft pathway to improve system performance. Methods for generating synthetic forecasts of hydro-meteorological variables are crucial for robust validation of forecast use, as numerical weather prediction hindcasts are only available for a relatively short period (10-40 years) that is insufficient for assessing risk related to forecast-informed decision-making during extreme events. We develop a generalized error model for synthetic forecast generation that is applicable to a range of forecasted variables used in water resources management. The approach samples from the distribution of forecast errors over the available hindcast period and adds them to long records of observed data to generate synthetic forecasts. The approach utilizes the Skew Generalized Error Distribution (SGED) to model marginal distributions of forecast errors that can exhibit heteroskedastic, auto-correlated, and non-Gaussian behavior. An empirical copula is used to capture covariance between variables, forecast lead times, and across space. We demonstrate the method for medium-range forecasts across Northern California in two case studies for 1) streamflow and 2) temperature and precipitation, which are based on hindcasts from the NOAA/NWS Hydrologic Ensemble Forecast System (HEFS) and the NCEP GEFS/R V2 climate model, respectively. The case studies highlight the flexibility of the model and its ability to emulate space-time structures in forecasts at scales critical for water resources management. The proposed method is generalizable to other locations and computationally efficient, enabling fast generation of long synthetic forecast ensembles that are appropriate for risk analysis.
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| Start Date: | 10/01/1948 |
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| End Date: | 09/30/2015 |
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| This resource is referenced by | Brodeur, Z., & Steinschneider, S. (2021). A multivariate approach to generate synthetic short-to-medium range hydro-meteorological forecasts across locations, variables, and lead times. Submitted Water Resources Research, December 2020. |
| 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 – December 2015, accessed 1 August 2020, https://www.esrl.noaa.gov/psd/forecasts/reforecast2/download.html. |
| The content of this resource is derived from | NOAA-CIRES-DOE, 2020: 20th Century Reanalysis Version 3. Subset used: October 1948 – December 2015, accessed 1 August 2020, https://psl.noaa.gov/data/gridded/data.20thC_ReanV3.html |
Credits
Funding Agencies
This resource was created using funding from the following sources:
| Agency Name | Award Title | Award Number |
|---|---|---|
| National Science Foundation | EnvS-1803563 |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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