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Improving Short-Term Urban Water Demand Forecasts with Reforecast Analog Ensembles
- Source :
- Journal of Water Resources Planning and Management. 142
- Publication Year :
- 2016
- Publisher :
- American Society of Civil Engineers (ASCE), 2016.
-
Abstract
- Urban water demand forecasting is key to municipal water supply management. Short-term urban water demands are influenced by weather conditions. Thus, short-term urban water demand forecasting could be improved by using accurate weather forecasting information. This study explores the potential of using an analog approach with a newly developed retrospective weather forecast (reforecast) of a numerical weather prediction (NWP) for improving short-term urban water demand forecasting. The analog method derives an analog ensemble forecast resampled from observed data (analogs) based on the reforecast of a NWP: the Global Ensemble Forecast System (GEFS). The probabilistic and ensemble mean forecasts generated from analogs of weekly total rainfall (WeekRain), number of rainy days in one week (RainDays), number of consecutive rainy days in one week (CosRainDays), number of hot days in one week (HotDays), and daily mean temperature of the first seven lead days (T) from the reforecast were evaluated using...
- Subjects :
- 010504 meteorology & atmospheric sciences
Meteorology
business.industry
0208 environmental biotechnology
Geography, Planning and Development
Ensemble average
Weather forecasting
Water supply
02 engineering and technology
Management, Monitoring, Policy and Law
computer.software_genre
Numerical weather prediction
01 natural sciences
020801 environmental engineering
Term (time)
Climatology
Environmental science
Urban water demand
Mean radiant temperature
business
Urban water
computer
0105 earth and related environmental sciences
Water Science and Technology
Civil and Structural Engineering
Subjects
Details
- ISSN :
- 19435452 and 07339496
- Volume :
- 142
- Database :
- OpenAIRE
- Journal :
- Journal of Water Resources Planning and Management
- Accession number :
- edsair.doi...........c6eb1db538ae80c015c187bb8ed3824c