Ensemble and Probabilistic Estimation and Forecasting of Precipitation for Hydrologic Prediction II Posters
Presiding: D Seo, NOAA/NWS/Office of Hydrologic Development/Hydrology Laboratory; A Bradley, IIHR-Hydroscience and Engineering, University of Iowa
H51D-01 0830h
A Proposed Hydrologic Basins Project for HEPEX and THORPEX: a Joint Hydrological Ensemble Research Activity
The main objective of HEPEX is to bring the international hydrological community together with the meteorological community to demonstrate how to produce reliable hydrological ensemble forecasts that can be used with confidence to make decisions that have important consequences for the economy and for public health and safety. Key science issues to meet this objective involve development of reliable ensemble forecasts of future weather and climate events, estimation of ensemble initial conditions, accounting for uncertainty in hydrologic processes and post-processing to produce reliable hydrologic ensemble forecasts. The THORPEX program, organized through WMO, is directed towards accelerating improvements in 1-14 day weather forecasts for the benefit of society and the economy. One key part of the THORPEX plan is the establishment of a THORPEX Interactive Grand Global Ensemble (TIGGE). This will provide a framework for international collaboration on the development of ensemble prediction for NWP, create a multi-model ensemble database as a resource for THORPEX researchers, and constitute a facility to test the idea of a "future global interactive multi-model ensemble forecast system, which would generate numerical probabilistic products, available to all WMO Members. One way to P.I. oriented research to link the THORPEX and HEPEX activities might be to develop an international hydrological basins data set that would contain historical hydrometerological observations and forecasts and the would be updated periodically with new observations and forecasts, including ensemble forecasts from TIGGE. An initial source of hydrological basins data is a data set produced by the MOPEX (Model Parameter Estimation Experiment) that established to aid in the development of techniques for the a priori estimation of parameters used in land surface parameterization schemes of atmospheric and hydrological models. This presentation will suggest some of the research opportunities that this proposed hydrological basins data set would support.
H51D-02 0830h
Ensemble Streamflow Prediction in Korea: Past and Future 5 Years
The Ensemble Streamflow Prediction (ESP) approach was first introduced in 2000 by the Hydrology Research Group (HRG) at Seoul National University as an alternative probabilistic forecasting technique for improving the 'Water Supply Outlook' That is issued every month by the Ministry of Construction and Transportation in Korea. That study motivated the Korea Water Resources Corporation (KOWACO) to establish their seasonal probabilistic forecasting system for the 5 major river basins using the ESP approach. In cooperation with the HRG, the KOWACO developed monthly optimal multi-reservoir operating systems for the Geum river basin in 2004, which coupled the ESP forecasts with an optimization model using sampling stochastic dynamic programming. The user interfaces for both ESP and SSDP have also been designed for the developed computer systems to become more practical. More projects for developing ESP systems to the other 3 major river basins (i.e. the Nakdong, Han and Seomjin river basins) was also completed by the HRG and KOWACO at the end of December 2004. Therefore, the ESP system has become the most important mid- and long-term streamflow forecast technique in Korea. In addition to the practical aspects, resent research experience on ESP has raised some concerns into ways of improving the accuracy of ESP in Korea. Jeong and Kim (2002) performed an error analysis on its resulting probabilistic forecasts and found that the modeling error is dominant in the dry season, while the meteorological error is dominant in the flood season. To address the first issue, Kim et al. (2004) tested various combinations and/or combining techniques and showed that the ESP probabilistic accuracy could be improved considerably during the dry season when the hydrologic models were combined and/or corrected. In addition, an attempt was also made to improve the ESP accuracy for the flood season using climate forecast information. This ongoing project handles three types of climate forecast information: (1) the Monthly Industrial Meteorology Information Magazine (MIMIM) of the Korea Meteorological Administration (2) the Global Data Assimilation Prediction System (GDAPS), and (3) the US National Centers for Environmental Prediction (NCEP). Each of these forecasts is issued in a unique format: (1) MIMIM is a most-probable-event forecast, (2) GDAPS is a single series of deterministic forecasts, and (3) NCEP is an ensemble of deterministic forecasts. Other minor issues include how long the initial conditions influences the ESP accuracy, and how many ESP scenarios are needed to obtain the best accuracy. This presentation also addresses some future research that is needed for ESP in Korea.
H51D-03 0830h
An Integrated Multiscale Approach to River Flood Prediction Using a Land-Surface Hydrology Model
This work outlines and demonstrates a comprehensive hydrometeorological flood forecasting system that is interdisciplinary in nature, multiscale in its approach, and state-of-the-art in its use of forecasting techniques. This integrated approach links both meteorological and hydrological tools in order to realize a more accurate prediction of major flood events three to six days in advance. One focus is on a new non-linear method to improve global multi-model superensemble precipitation forecasts with an emphasis on successful prediction of intense rain areas. On average, the skill from such a technique is higher and the bias lower than any of the individual member models, and the overall character of the precipitation distribution is maintained through the 5-day forecast period. In addition, global and nested regional spectral models are integrated in hindcast mode. Output from such models as well as from the superensemble is used as forcing input to a physically-based, spatially-distributed hydrology model in order to predict streamflow response during selected flood events. In this terrestrial hydrology prediction system, a dynamical water routing scheme and other physical parameterizations are used in conjunction with a 3-D hydrologic model that keeps track of surface water and energy budgets, including surface-subsurface interactions, groundwater and hydraulic river routing. Experiments are run for the Limpopo River basin in southeastern Africa, where massive flooding occurred in both years 2000 and 2001. An important intermediate step in this process is the downscaling of the precipitation forecasts from the course resolution atmospheric models to the much finer resolution (1 to 10 km) required by the hydrology model. For this purpose, we examined the space-time scaling behavior of simulated precipitation fields from the NWP models at different resolutions and devised a simple physically-based multifractal downscaling algorithm that relies on the scaling consistency of multiple resolution forecasts. The physical constraints are imposed at multiple scales based on indices of storm dynamics and, where available, the climatology of storm structure inferred from of high-resolution merged multisensor precipitation data (raingauge, radar, and satellite data). The approach is demonstrated for real-time forecasting over the Oklahoma Mesonet and adjacent land areas during the summer and fall of 2003.