Overview of the 39 years of the Regional Surface and Precipitation Reanalysis System
Abstract
ECCC's recently completed version 2.1 of the Regional Surface and Precipitation Reanalysis covers 39 years, from 1980 to 2018, at the horizontal resolution of 10 km and provides several hourly surface variables, including precipitation, for North America. Temperature, humidity, precipitation and snow cover observations from multiple surface networks are assimilated. This dataset can serve various purposes, particularly 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. The primary purpose of the data is to serve as forcing files for running a more advanced and/or higher resolution, principally hydrological, but also other environmental models. This dataset has proven very helpful in multi-institutional projects requiring meteorological information in order to work towards improving hydrological processes and/or calibration. For instance, ECCC set up several contribution agreements with universities; cross-collaborations between universities are facilitated and afterwards, the results of the research grants are more directly and easily usable by ECCC in the next phases of the developments and improvements of the reanalysis datasets. Another important application of the reanalysis is trend and anomaly analysis, as well as, climate indices evaluations. In this presentation, SWE anomaly for Canada are illustrated. This anomaly is based on guidance from Regional Deterministic Prediction System to characterize current state and Regional Reanalysis Deterministic System to determine the past climate SWE values. Furthermore, details are given on evaluation of climate extreme precipitation indices for different Bukowski regions of North America. Precipitation indices of climate extremes are presented for 10-years versus 39-years from model background, leave-one-out analysis and final analysis in order to evaluate dependency on the length of the period considered and to quantify the improvements realised with analysed precipitation versus model background precipitation in term of bias, correlation and standard deviation.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2022
- Bibcode:
- 2022AGUFM.H45F..06D