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Calibrating hourly precipitation forecasts with daily observations


Cattoen, C and Robertson, DE and Bennett, JC and Wang, QJ and Carey-Smith, TK, Calibrating hourly precipitation forecasts with daily observations, Journal of Hydrometeorology, 21, (7) pp. 1655-1673. ISSN 1525-755X (2020) [Refereed Article]


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Copyright 2020 American Meteorological Society. Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0)

DOI: doi:10.1175/JHM-D-19-0246.1


Calibrated high-temporal-resolution precipitation forecasts are desirable for a range of applications, for example, flood prediction in fast-rising rivers. However, high-temporal-resolution precipitation observations may not be available to support the establishment of calibration methods, particularly in regions with low population density or in developing countries. We present a new method to produce calibrated hourly precipitation ensemble forecasts from daily observations. Precipitation forecasts are taken from a high-resolution convective-scale numerical weather prediction (NWP) model run at the hourly time step. We conduct three experiments to develop the new calibration method: (i) calibrate daily precipitation totals and disaggregate daily forecasts to hourly; (ii) generate pseudohourly observations from daily precipitation observations, and use these to calibrate hourly precipitation forecasts; and (iii) combine aspects of (i) and (ii). In all experiments, we use the existing Bayesian joint probability model to calibrate the forecasts and the well-known Schaake shuffle technique to instill realistic spatial and temporal correlations in the ensembles. As hourly observations are not available, we use hourly patterns from the NWP as the template for the Schaake shuffle. The daily member matching method (DMM), method (iii), produces the best-performing ensemble precipitation forecasts over a range of metrics for forecast accuracy, bias, and reliability. The DMM method performs very similarly to the ideal case where hourly observations are available to calibrate forecasts. Overall, valuable spatial and temporal information from the forecast can be extracted for calibration with daily data, with a slight trade-off between forecast bias and reliability.

Item Details

Item Type:Refereed Article
Keywords:numerical weather prediction, calibration, ensemble rainfall forecasting
Research Division:Earth Sciences
Research Group:Hydrology
Research Field:Surface water hydrology
Objective Division:Environmental Policy, Climate Change and Natural Hazards
Objective Group:Understanding climate change
Objective Field:Effects of climate change on Australia (excl. social impacts)
UTAS Author:Bennett, JC (Mr James Bennett)
ID Code:142664
Year Published:2020
Web of Science® Times Cited:9
Deposited By:Oceans and Cryosphere
Deposited On:2021-02-04
Last Modified:2021-10-28
Downloads:6 View Download Statistics

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