Calibrating Hourly Precipitation Forecasts with Daily Observations
| Autor: | Quan J. Wang, Celine Cattoen, James C. Bennett, T. K. Carey-Smith, David E. Robertson |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Atmospheric Science
Matching (statistics) 010504 meteorology & atmospheric sciences Meteorology Bayesian probability 0207 environmental engineering 02 engineering and technology Numerical weather prediction 01 natural sciences Joint probability distribution Forecast bias Range (statistics) Calibration Environmental science Precipitation 020701 environmental engineering 0105 earth and related environmental sciences |
| Zdroj: | Journal of Hydrometeorology. 21:1655-1673 |
| ISSN: | 1525-7541 1525-755X |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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