WRF High Resolution Dynamical Downscaling of Precipitation for the Central Andes of Chile and Argentina

Abstract

This study evaluates the skill of the Weather Research and Forecasting (WRF) model to reproduce the variability of precipitation over the Central Andes of Chile and Argentina, a region characterized by complex topography. The simulation corresponds to a dynamical downscaling of ERA-Interim, in the period between 1996 and 2015, performed with two nested grids, at 9 and 3 km horizontal resolution. Precipitation data from 62 rain gauges from Chile and Argentina were used to evaluate the performance of WRF simulations carried out at the annual and warm-cold season analysis. The results of this study indicate that WRF at 9 and 3 km is able to reproduce the main characteristics of seasonal and interannual precipitation variability along the study area. On the windwards slopes of the Andes, however, WRF at 9 km presents a wet bias in relation to observation and WRF at 3 km. Additionally, WRF at 3 km achieves better performance of precipitation as elevation increases, most likely due to the better-resolved topography. To our knowledge, this is the first study that compares performance between nested domains on mountain areas that found a better match between the model and observations, as elevations increased.

Rainfall-Induced Landslide Early Warning System based on corrected mesoscale numerical models: an application for the Southern Andes

“…0.36 mm). Thus, the corrected results represent the precipitation fields in Andean areas with lower bias values than previous studies (Yáñez-Morroni et al, 2018;Schumacher et al, 2020). PP_M4a approach was found to reduce the bias efficiently for the study area.…”

“…For the first time in the Southern Andes, we showed how the WRF model can be integrated into RILEWS operating systems without the need to use ensembles, by use of bias correction processes. This opens the door to the implementation of precipitation-based prediction models without costly computer iterations by ensembles of models (Yáñez-Morroni et al, 2018;Schumacher et al, 2020). New studies of LEWS in the Southern Andes should be directed towards increasing the RIL database currently available.…”

“…Moreover, the mesoscale models demand intensive computing efforts increasing the difficultness coupling to RILEWS (Yáñez-Morroni et al, 2018;Schumacher et al, 2020;Yang et al, 2021). Nowadays, bias correction approaches contribute to reducing the time computing of mesoscale models, improving the estimation of precipitation using in-situ stations (Srivastava et al, 2015;Bannister et al, 2019;Heredia et al, 2018;Jeong & Lee, 2018;Osman et al, 2019;Worku et al, 2020).…”

Convection‐permitting modeling with regional climate models: Latest developments and next steps

“…In a CPRCM evaluation study over western Canada, Li, Li, et al (2019) explained that higher peaks and lower valleys over the Canadian Rockies introduced orographic precipitation differences between their CPRCM and coarse‐resolution observed gridded datasets, making a thorough evaluation difficult. Over the central Andes, an increase of CPRCM resolution led to smaller precipitation biases, with an improved performance as elevation increased (Schumacher et al, 2020) that was also associated with a better structure of cloud systems (Moya‐Álvarez et al, 2019). Performing a sensitivity study with a CPRCM using terrain height modifications, Rasmussen and Houze (2016) underlined the importance of the orographic control of the Andes on the initiation of convection and its upscale growth into MCSs in South America.…”

Rainfall-induced landslide early warning system based on corrected mesoscale numerical models: an application for the southern Andes

“…Currently, mesoscale models are restricted to the quality of their atmospheric forcings, requiring ensembles to be generated to obtain approximate solutions (Wayand et al, 2013). Moreover, the mesoscale models demand intensive computational efforts that increase the difficulty of coupling to RILEWSs (Yáñez-Morroni et al, 2018;Schumacher et al, 2020;Yang et al, 2021). Recently, mesoscale atmospheric models coupled to local weather stations have allowed the delimitation of areas susceptible to RILs by means of deterministic numerical models (Fustos et al, 2020a).…”