Numerical Weather Prediction models (NWP) have been used extensively since the ’40-’50s. Despite the advances in the field, the representation and forecast of the magnitude and variability of tropical processes in models is still a challenge. One of the steps to improve the precipitation forecasts using limited-area models is to evaluate which set of physical schemes and model domain configurations represent in a better way the actual behavior observed in the tropics. We implemented, as a part of a regional risk management strategy, two different operational weather forecast strategies for a complex terrain region in the Andes mountain range in northern South America. Both strategies, together, generate a total of eleven different forecasts every day, using the Weather Research and Forecasting model (WRF) with initial and boundary conditions from the Global Forecast System (GFS). The first configuration, implemented over five years ago and referred to as SYNAPSIS, includes three nested domains (18, 6 and 2 km) and is carried out every day using the 12 UTC GFS run and three different microphysics parametrizations: Eta Ferrier scheme, Purdue Lin Scheme and Thompson Scheme. The forecast lead-time of the latter strategy is 120 hours, and it does not use data assimilation. Since December of 2017, we implemented a second configuration termed RDFS, with two nested domains (12 and 2.4 Km), which carried out four times a day using the 00, 06, 12 and 18 UTC GFS runs. This configuration has a 30-hours lead time with the Thompson microphysics scheme. In RDFS, two WRF forecast runs are performed for each start hour, one assimilating weather radar reflectivity and the other without assimilation as control run, for a total of eight forecast runs daily. In this study, we assess the rainfall and temperature forecasts for all the different configurations using precipitation derived from reflectivity from weather radar, and air temperature at 2m from a network of automatic weather stations. We use 6 hourly and monthly skill scores (RMSE, BIAS, and Correlation coefficient) to quantify the precipitation differences between the SYNAPSIS and the RDFS configurations. To evaluate the impact of data assimilation in the precipitation forecast, we aggregate the results in a region within the inner domain, and then we calculate the average precipitation forecast between 0 and 36 predicted hours for RDFS with and without data assimilation. The results suggest a strong relationship between the forecast start time and the improve of precipitation forecast accuracy using data assimilation. The diurnal cycle of precipitation in the study region has a minimum in the morning (12 UTC) and a maximum in the afternoon (00 UTC) and during the night (09 UTC). The correspondence between the forecast improvement using data assimilation and the diurnal cycle of precipitation is likely due to the amount of assimilated data. In order to quantify the precipitation differences between the diffe

Cities are the most sensitive and vulnerable places to climate variability and change and weather-related extreme events given high population density, with the aggravating factor that urban climate also suffers modifications due to the widespread replacement of the natural surface altering the local thermal conditions. The Aburrá Valley is a narrow valley located at the tropical Andes in northern South America with urban areas between 1300 and 2000 m.a.s.l, a population of approximate 3.9 million people, and a comfortable climate relative to standard indoor conditions. In this work, we examine observed weather patterns in the local scale and the urban canopy layer (UCL) using data from weather stations at sites with different surface features regarding vegetation/non-impervious fractions and urban structure (Sky View Factor SVF). UCL data is available from two data sources, the first one from a field campaign using all-in-one weather sensors at the valley´s bottom, and the second one from a low-cost sensor network with robust temperature and humidity data as part of a local citizen science project with measurements in a diverse altitude range. Results suggest that at the local scale there exist different climate mean conditions due to altitude, with significant weather variability depending on radiation levels and rainfall occurrence, but at the same time, the urban effects are evident since the lowest altitude stations do not necessarily register the highest temperatures depending on the local characteristics. UCL measurements show that, while the altitude defines a background state, there are notable differences between places mainly influenced by insolation changes due to vegetation around and above sensors. Currently, the local population does not perceive thermal stress as a risk factor because it is not difficult to find places with appropriate thermal conditions when thermal discomfort arises. However, this research is relevant considering the projected local surface temperature increase due to climate change and the inexistence of baseline studies assessing the thermal comfort in outdoors to support local adaptation actions. The results of this study are useful for urban planning and building design to improve thermal conditions, especially in open spaces.