Research Article Prediction of Moderate and Heavy Rainfall in New Zealand Using Data Assimilation and Ensemble Yang Yang, Phillip Andrews, Trevor Carey-Smith, Michael Uddstrom, and Mike Revell National Institute of Water and Atmospheric Research (NIWA), Private Bag 14901, Wellington 6021, New Zealand Correspondence should be addressed to Yang Yang; y.yang@niwa.co.nz Received 7 December 2014; Accepted 5 February 2015 Academic Editor: Yuanfu Xie Copyright © 2015 Yang Yang et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Tis numerical weather prediction study investigates the efects of data assimilation and ensemble prediction on the forecast accuracy of moderate and heavy rainfall over New Zealand. In order to ascertain the optimal implementation of state-of-the-art 3Dvar and 4Dvar data assimilation techniques, 12 diferent experiments have been conducted for the period from 13 September to 18 October 2010 using the New Zealand limited area model. Verifcation has shown that an ensemble based on these experiments outperforms all of the individual members using a variety of metrics. In addition, the rainfall occurrence probability derived from the ensemble is a good predictor of heavy rainfall. Mountains signifcantly afect the performance of this ensemble which provides better forecasts of heavy rainfall over the South Island than over the North Island. Analysis suggests that underestimation of orographic lifing due to the relatively low resolution of the model (∼12 km) is a factor leading to this variability in heavy rainfall forecast skill. Tis study indicates that regional ensemble prediction with a suitably fne model resolution (≤5 km) would be a useful tool for forecasting heavy rainfall over New Zealand. 1. Introduction Te initial conditions of a numerical weather prediction (NWP) forecast are usually generated by data assimilation, a procedure statistically combining observations and a model forecast and utilising their respective error information to create an optimum estimate of the true atmospheric state compatible with the forecast model in use. Uncertainty and errors are unavoidable in the initial conditions of an NWP due to meteorological equipment errors, sampling errors, and data assimilation errors, and so forth. Ensemble forecasts have been used for some 20 years at major meteorological prediction centres to explore the impact of these uncertainties in the atmospheric initial conditions (and other boundary conditions) on NWP. Several methodologies have been used to establish the global ensemble systems (GES) including those based on the leading singular vectors of the operator [1, 2], bred vectors [3, 4], the Monte Carlo method [5], and the Monte Carlo based ensemble Kalman flter [6]. Te spatial resolution of currently operational GES is low (e.g., ∼32 km for the current operational ECMWF ensemble system). Many small scale processes in the atmosphere and the underlying surface and small scale mountains that signifcantly afect the evolution and development of severe weather are not resolved by GES. Regional ensemble systems (RES) were thus established. Te resolution of RES difers. Some have very high resolutions so that supercell storms and convections can be resolved (e.g., [7–9]). Te way to initialize a RES also difers. Some RESs are initialized from a GES. Some RESs randomly sample the climatological uncertainties of the initial state [10]. Others derive random perturbations from the background error statistics of an existing 3D/4Dvar system (e.g., [11–13]). Some RESs use diferent model physics and diferent global deterministic model outputs to generate members (e.g., [14, 15]). Some use diferent regional model outputs called multimodel ensemble [16]. For heavy rainfall forecasts, RES has shown higher forecasting skills than GES (e.g., [14, 17, 18]). New Zealand lies in the midlatitude southwest Pacifc, surrounded by ocean. Te two main islands are North Island and South Island. North Island (Figure 1(a)) has a “spine” of mountain ranges extending from the middle, with gentle rolling farmland on both sides. Te South Island is dominated by mountain ranges running its entire length. Te main Hindawi Publishing Corporation Advances in Meteorology Volume 2015, Article ID 460243, 14 pages http://dx.doi.org/10.1155/2015/460243