To Medium-Range Streamflow Forecasting

VOLUME 12 JOURNAL OF HYDROMETEOROLOGY OCTOBER 2011 REVIEW A Review of Quantitative Precipitation Forecasts and Their Use in Short- to Medium-Range Streamflow Forecasting LAN CUO Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China THOMAS C. PAGANO AND Q. J. WANG Land and Water Division, Commonwealth Scientific and Industrial Research Organisation, Highett, Victoria, Australia (Manuscript received 11 August 2010, in final form 28 February 2011) ABSTRACT Unknown future precipitation is the dominant source of uncertainty for many streamflow forecasts. Nu- merical weather prediction (NWP) models can be used to generate quantitative precipitation forecasts (QPF) to reduce this uncertainty. The usability and usefulness of NWP model outputs depend on the application time and space scales as well as forecast lead time. For streamflow nowcasting (very short lead times; e.g., 12 h), many applications are based on measured in situ or radar-based real-time precipitation and/or the extrapolation of recent precipitation patterns. QPF based on NWP model output may be more useful in extending forecast lead time, particularly in the range of a few days to a week, although low NWP model skill remains a major obstacle. Ensemble outputs from NWP models are used to articulate QPF uncertainty, improve forecast skill, and extend forecast lead times. Hydrologic prediction driven by these ensembles has been an active research field, although operational adoption has lagged behind. Conversely, relatively little study has been done on the hydrologic component (i.e., model, parameter, and initial condition) of uncertainty in the streamflow prediction system. Four domains of research are identified: selection and evaluation of NWP model–based QPF products, improved QPF products, appropriate hydrologic modeling, and integrated applications. 1. Introduction The work begins in the next section with a review of NWP models and their accuracy in predicting pre- a. Motivation and organization cipitation. This is followed by introductions to ensemble Recent advances in weather measurement and fore- QPF methods and streamflow forecasting techniques. casting have created opportunities to improve stream- Section 2 describes integrated systems that use NWP flow forecasts. The accuracy of weather forecasts has model outputs to force hydrologic models. It begins with steadily improved over the years, but it has been chal- deterministic forecasts, separating very short-range fore- lenging to integrate quantitative precipitation forecasts/ casts from short- to medium-range forecasts. Next, issues forecasting (QPF) into flood forecasting operations. of spatial scale and initial conditions are addressed, and This review investigates the current status of the appli- operational integrated systems are identified. The re- cation of QPF as a forcing for hydrologic models. The mainder of the work is on the characterization of un- objectives of the study are to identify current achieve- certainty and the role of ensembles. The study concludes ments and problems in the application of numerical with discussion and a set of recommendations. weather prediction (NWP) model outputs and to iden- b. NWP models tify frontiers for new research. NWP models use current weather conditions as input to atmospheric models to predict the evolution of weather Corresponding author address: Thomas Pagano, CSIRO, P.O. systems. These models represent the atmosphere as Box 56, Highett VIC 3190, Australia. a dynamic fluid and solve for its behavior through the E-mail: [email protected] use of mechanics and thermodynamics. DOI: 10.1175/2011JHM1347.1 713 Unauthenticated | Downloaded 10/02/21 02:30 AM UTC 714 JOURNAL OF HYDROMETEOROLOGY VOLUME 12 NWP models have improved since the 1940s because the intensity and shape of a storm may be correct but the of advantages in digital computing and improvements location of the storm is wrong. Ebert and McBride in measurement technology, including weather satellites (2000) found that displacement was the dominant source and extensive radiosonde and radar networks (Trenberth of QPF error. For extreme events (.100 mm day21), 1992). Progress has been made in the past 50 years in however, intensity was the dominant source of error. weather modeling and, as a result, forecast skill has im- Poor QPF skill has hindered hydrologic applications, proved (Buizza et al. 1999). particularly streamflow forecasting operations. Many Forecasting is difficult because the atmosphere is researchers have had to process and adjust NWP model a nonlinear, chaotic system (Lorenz 1969). A subtle output–based QPF to improve the reliability in the hy- change in the initial and boundary layer conditions of drologic prediction application (e.g., Damrath et al. a circulation system could result in unpredictable out- 2000; Landman et al. 2001; Wood et al. 2004). Such ad- comes. NWP models have their deficiencies in de- justments are discussed further in section 2. scribing atmospheric physical and chemical processes. c. Ensemble QPF In addition, unavoidable random errors in atmospheric model parameters make it difficult for NWP models to NWP model output ensembles have been generated simulate atmospheric properties accurately (Buizza since the early 1990s (e.g., ECMWF ensembles started et al. 1999). NWP models perform worse in the Southern in 1993) and probabilistic weather forecasts have been Hemisphere than in the Northern (mostly because of a used to articulate forecast uncertainty. It is believed that previous lack of data in the Southern Hemisphere), al- NWP ensemble prediction systems exhibit greater though the difference has been narrowing. Simmons and forecast skill than any single NWP model control run or Hollingsworth (2002) attribute much of the improvement deterministic model run; ensembles increase forecast in the Southern Hemisphere to the increase in global accuracy and allow for skilful predictions at longer lead satellite data and effective data assimilation techniques. times (Buizza et al. 1999; Demeritt et al. 2007). Also, NWP models also have limited skill in the tropics NWP model–based probability forecasts issued on con- (Krishnamurti et al. 1999) because of 1) the limited data secutive days are usually more consistent than single- in this region for model initialization and 2) difficulties valued forecasts (Buizza 2008). in simulating cumulus convection (Koh and Ng 2009). In ensemble forecasting, one or more dimensions of QPF has proven to be one of the most difficult chal- forecast uncertainty are explored through the use of lenges in NWP modeling because of enormous vari- scenarios. There are many ways to categorize ensem- ability in space and time of the variables affecting the bles. One main distinction is the number of models used. precipitation production process (Golding 2000; Ebert Some ensembles combine deterministic forecasts from et al. 2003). QPF provides the total amount of expected multiple models (Hagedorn et al. 2005). Other ensem- liquid precipitation, and its skill is largely dependent on bles come from a single model with perturbed initial location, season, intensity, and storm type. conditions, boundary conditions, or parameters, among NWP models are generally good at predicting pre- others (Toth and Kalnay 1993). A collection of ensem- cipitation generated from synoptic frontal weather sys- bles from individual models has been called a ‘‘grand’’ tems (as opposed to convective systems; Olson et al. or ‘‘super’’ ensemble (Krishnamurti et al. 1999; Park 1995). The European Centre for Medium-Range et al. 2008). It is also possible to use forecasts (ensemble Weather Forecasts (ECMWF) NWP model performs or deterministic) from other lead times for the same better during winter than in summer (Buizza et al. 1999). target period to form what has been called ‘‘lagged en- Precipitation intensity can be a forecasting challenge; sembles’’ (Mittermaier 2007). Kobold and Susˇelj (2005) found that ECMWF under- Currently many forecasting centers are issuing regional estimated by 60% the 27–28 June 1997 precipitation and/or global ensemble weather forecasts. Table 1 lists events in a Slovenia catchment and ECMWF could not the centers that provide ensemble global NWP model describe precipitation variability properly. outputs, which are now incorporated in The Observ- Furthermore, although the model resolutions have ing System Research and Predictability Experiment increased, NWP models still struggle to accurately (THORPEX) Interactive Grand Global Ensemble forecast finescale weather systems such as local–regional (TIGGE) program (Bougeault et al. 2010). convective systems (e.g., thunderstorms) and orographic A few examples of research efforts in ensemble process, particularly at lead times beyond 12–48 h weather forecasting are Palmer et al. (1997), Sattler and (Golding 2000; Cerlini et al. 2005; Kaufmann et al. 2003; Feddersen (2005), Yuan et al. (2007), and Gebhardt Richard et al. 2003). In addition to subgrid-scale issues, et al. (2008). The ensemble members must represent the NWP models have challenges with displacement; that is, probability distribution of the state of atmosphere and Unauthenticated | Downloaded 10/02/21 02:30 AM UTC OCTOBER 2011 R E V I E W 715 TABLE 1. Centers that provide global ensemble forecast (Park et al. 2008 and THORPEX–TIGGE; http://tigge.ucar.edu/documentation.htm; see also http://dss.ucar.edu/datasets/ds330.2/docs/tiggedocumentation.pdf). Ensemble Horizontal Forecast length Forecasts Start Center Country/region members

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