ABSTRACT Defining a reference climate for precipitation is an important requirement in the development of climate change scenarios to support climate adaptation strategies. It is also important for many hydrological and water resource applications. This, however, remains a challenge in regions that are poorly covered by meteorological stations, such as northern Canada or mountainous regions. Reanalyses may represent an interesting option to define a reference climate in such regions. However, these need to be validated and corrected for bias before they can be used. In this paper, two data assimilation methods, Optimal Interpolation (OI) and Ensemble Optimal interpolation (EnOI), were used to combine four reanalysis datasets with observations in order to improve the representation of various precipitation indices across Canada. A total of 986 meteorological stations with minimally 20-year precipitation records over the 30-year reference period (1980–2009) were used. Annual values of ten Climate Precipitations Indices (CPIs) were estimated for each available dataset and were then combined (reanalysis plus observations) using OI and EnOI. A cross-validation strategy was finally applied to assess the relative performance of these datasets. Results suggest that combining reanalysis and observations through OI or EnOI improves CPI estimates at sites where no recorded precipitation is available. The EnOI dataset outperformed OI applied to each reanalysis independently. An evaluation of the gridded interpolated observational dataset from Natural Resources Canada showed it should be used with considerable caution for extreme CPIs because it can underestimate annual maximum 1-day precipitation, as well as overestimate the annual number of wet days.

中文翻译：

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使用最佳插值方法改进历史气候降水指数的表示

摘要 定义降水参考气候是制定气候变化情景以支持气候适应战略的重要要求。它对于许多水文和水资源应用也很重要。然而，这在气象站覆盖率低的地区仍然是一个挑战，例如加拿大北部或山区。重新分析可能是定义这些地区参考气候的一个有趣选择。但是，这些需要在使用之前进行验证和纠正偏差。在本文中，两种数据同化方法，最优插值 (OI) 和集合最优插值 (EnOI)，用于将四个再分析数据集与观测结果相结合，以改善加拿大各种降水指数的表示。总共使用了 986 个气象站，在 30 年参考期（1980-2009 年）内至少有 20 年的降水记录。为每个可用数据集估算了十个气候降水指数 (CPI) 的年值，然后使用 OI 和 EnOI 进行组合（再分析加观测）。最后应用交叉验证策略来评估这些数据集的相对性能。结果表明，通过 OI 或 EnOI 将再分析和观测相结合，可以改进没有可用降水记录的地点的 CPI 估计值。EnOI 数据集优于单独应用于每个再分析的 OI。