Abstract
Abstract. Environment and Climate Change Canada has initiated the production of a 1980–2018, 10 km, North American precipitation and surface reanalysis. ERA-Interim is used to initialize the Global Deterministic Reforecast System (GDRS) at a 39 km resolution. Its output is then dynamically downscaled to 10 km by the Regional Deterministic Reforecast System (RDRS). Coupled with the RDRS, the Canadian Land Data Assimilation System (CaLDAS) and Precipitation Analysis (CaPA) are used to produce surface and precipitation analyses. All systems used are close to operational model versions and configurations. In this study, a 7-year sample of the reanalysis (2011–2017) is evaluated. Verification results show that the skill of the RDRS is stable over time and equivalent to that of the current operational system. The impact of the coupling between RDRS and CaLDAS is explored using an early version of the reanalysis system which was run at 15 km resolution for the period 2010–2014, with and without the use of CaLDAS. Significant improvements are observed with CaLDAS in the lower troposphere and surface layer, especially for the 850 hPa dew point and absolute temperatures in summer. Precipitation is further improved through an offline precipitation analysis which allows the assimilation of additional observations of 24 h precipitation totals. The final dataset should be of particular interest for hydrological applications focusing on transboundary and northern watersheds, where existing products often show discontinuities at the border and assimilate very few – if any – precipitation observations.
Highlights
Atmospheric reanalysis datasets are invaluable tools allowing for better understanding and analysis of global and regional water cycles by integrating data assimilation techniques with state-of-the-art numerical models of the atmosphere and Earth’s surface observations of the water and energy cycle
Two strategies were considered in order to assess the precipitation reanalysis skill and bias at both 15 and 10 km: (1) seasonal accumulation maps were obtained for the CaPa24h reanalysis at both resolutions and visually compared to Stage IV MPE (Lin and Mitchell, 2005) and Global Precipitation Climatology Project (GPCP) (Adler et al, 2003, 2018) for each season of the period 2010–2014, and (2) verification scores routinely used for evaluating the operational Canadian Precipitation Analysis (CaPA) precipitation analysis were computed over the same period for both reanalysis products and for the background field used for each product
This paper presents and evaluates a methodology for minimizing the computational cost and the data processing burden associated with the production of an atmospheric reanalysis and still obtain consistent, high-quality and highresolution atmospheric, surface and precipitation fields
Summary
Atmospheric reanalysis datasets are invaluable tools allowing for better understanding and analysis of global and regional water cycles by integrating data assimilation techniques with state-of-the-art numerical models of the atmosphere and Earth’s surface observations of the water and energy cycle. The main objectives of this paper are (1) to present a strategy for the production, at relatively low computing cost, of high-resolution surface and precipitation regional reanalyses which includes two-way coupling between the land data assimilation system and the NWP model, as well as assimilation of precipitation observations, and (2) to evaluate a North American reanalysis based on the Global Environmental Multiscale (GEM) model (Côté et al, 1998a, b; Girard et al, 2014), obtained using the proposed methodology and currently under production at the Canadian Centre for Meteorological and Environmental Prediction (CCMEP) of Environment and Climate Change Canada (ECCC). These errors can be caused by limitations of the land-surface scheme and by an imbalance between the atmospheric forcing obtained from a reanalysis product and the surface feedback predicted by the landsurface model when a different horizontal resolution or different model is used
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