Abstract
The performance of the Weather Research and Forecasting (WRF) model version 3.8.1 at convection-permitting scale is evaluated by means of several sensitivity simulations over southern Peru down to a grid resolution of 1 km, whereby the main focus is on the domain with 5 km horizontal resolution. Different configurations of microphysics, cumulus, longwave radiation and planetary boundary layer schemes are tested. For the year 2008, the simulated precipitation amounts and patterns are compared to gridded observational data sets and weather station data gathered from Peru, Bolivia and Brazil. The temporal correlation of simulated monthly precipitation sums against in-situ and gridded observational data show that the most challenging regions for WRF are the slopes along both sides of the Andes, i.e., elevations between 1000 and 3000 m above sea level. The pattern correlation analysis between simulated precipitation and station data suggests that all tested WRF setups perform rather poorly along the northeastern slopes of the Andes during the entire year. In the southwestern region of the domain the performance of all setups is better except for the driest period (May–September). The results of the pattern correlation to the gridded observational data sets show that all setups perform reasonably well except along both slopes during the dry season. The precipitation patterns reveal that the typical setup used over Europe is too dry throughout the entire year, and that the experiment with the combination of the single-moment 6-class microphysics scheme and the Grell–Freitas cumulus parameterization in the domains with resolutions larger than 5 km, suitable for East Africa, does not perfectly apply to other equatorial regions such as the Amazon basin in southeastern Peru. The experiment with the Stony–Brook University microphysics scheme and the Grell-Freitas cumulus parameterization tends to overestimate precipitation over the northeastern slopes of the Andes, but allows to enforce a positive feedback between the soil moisture, air temperature, relative humidity, mid-level cloud cover and finally, also precipitation. Hence, this setup is the one providing the most accurate results over the Peruvian Amazon, and particularly over the department of Madre de Dios, which is a region of interest because it is considered the biodiversity hotspot of Peru. The robustness of this particular parameterization option is backed up by similar results obtained during wet climate conditions observed in 2012.
Highlights
The performance of the Weather Research and Forecasting (WRF) model version 3.8.1 at convection-permitting scale is evaluated by means of several sensitivity simulations over southern Peru down to a grid resolution of 1 km, whereby the main focus is on the domain with 5 km horizontal resolution
All the gridded observation based data are previously bi-linearly interpolated to the respective WRF grid using the function of the Climate Data Operator (CDO; Schulzweida, 2019)
This study aims at determining the optimum setup for WRF to accurately simulate the observed precipitation patterns and amounts over the Amazon basin in southeastern Peru
Summary
Tropical regions are known for their high level of biodiversity (Lamoreux et al, 2006), and for their rather complex weather 25 and climate conditions. Blocked by the Andes, the flow is channeled towards the south 35 and establishes the South American low-level jet (Marengo et al, 2004) This low-level jet can trigger convective systems over southern South America (Salio et al, 2007). Over the western tropical coasts of Ecuador and Peru, the rain is mainly forced by El Niño–Southern Oscillation (ENSO), as the heat from the warm ocean waters is transferred to the atmosphere inducing convection and heavy precipitation. This is usually observed in February and March, when the maximum sea surface temperature anomalies are observed over the Pacific Ocean (Hu et al, 2019). The atmospheric part of ENSO, i.e., the Walker circulation over the Pacific ocean, modifies the location of the main ascent and descent regions (Grimm, 2003; Ropelewski and Bell, 2008; Sasaki et al, 2015), and alters the advection of moist and warm air towards the continent (Rutllant and Fuenzalida, 1991)
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