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Ground based rainfall information is hardly available in most high mountain areas of the world due to the remoteness and complex topography. Thus, proper understanding of spatio-temporal rainfall dynamics still remains a challenge in those areas. Satellite-based rainfall products may help if their rainfall assessment are of high quality. In this paper, microwave-based integrated multi-satellite retrieval for the Global Precipitation Measurement (GPM) (IMERG) (MW-based IMERG) was assessed along with the random-forest-based rainfall (RF-based rainfall) and infrared-only IMERG (IR-only IMERG) products against the quality-controlled rain radar network and meteorological stations of high temporal resolution over the Pacific coast and the Andes of Ecuador. The rain area delineation and rain estimation of each product were evaluated at a spatial resolution of 11 km2 and at the time of MW overpass from IMERG. The regionally calibrated RF-based rainfall at 2 km2 and 30 min was also investigated. The validation results indicate different essential aspects: (i) the best performance is provided by MW-based IMERG in the region at the time of MW overpass; (ii) RF-based rainfall shows better accuracy rather than the IR-only IMERG rainfall product. This confirms that applying multispectral IR data in retrieval can improve the estimation of rainfall compared with single-spectrum IR retrieval algorithms. (iii) All of the products are prone to low-intensity false alarms. (iv) The downscaling of higher-resolution products leads to lower product performance, despite regional calibration. The results show that more caution is needed when developing new algorithms for satellite-based, high-spatiotemporal-resolution rainfall products. The radar data validation shows better performance than meteorological stations because gauge data cannot correctly represent spatial rainfall in complex topography under convective rainfall environments. [ABSTRACT FROM AUTHOR]

Lack of rainfall information at high temporal resolution in areas with a complex topography as the Tropical Andes is one of the main obstacles to study its rainfall dynamics. Furthermore, rainfall types (e.g., stratiform, convective) are usually defined by using thresholds of some rainfall characteristics such as intensity and velocity. However, these thresholds highly depend on the local climate and the study area. In consequence, these thresholds are a constraining factor for the rainfall class definitions because they cannot be generalized. Thus, this study aims to analyze rainfall-event types by using a data-driven clustering approach based on the k-means algorithm that allows accounting for the similarities of rainfall characteristics of each rainfall type. It was carried out using three years of data retrieved from a vertically pointing Micro Rain Radar (MRR) and a laser disdrometer. The results show two main rainfall types (convective and stratiform) in the area which highly differ in their rainfall features. In addition, a mixed type was found as a subgroup of the stratiform type. The stratiform type was found more frequently throughout the year. Furthermore, rainfall events of short duration (less than 70 min) were prevalent in the study area. This study will contribute to analyze the rainfall formation processes and the vertical profile. [ABSTRACT FROM AUTHOR]

Discharge forecasting is a key component for early warning systems and extremely useful for decision makers. Forecasting models require accurate rainfall estimations of high spatial resolution and other geomorphological characteristics of the catchment, which are rarely available in remote mountain regions such as the Andean highlands. While radar data is available in some mountain areas, the absence of a well distributed rain gauge network makes it hard to obtain accurate rainfall maps. Thus, this study explored a Random Forest model and its ability to leverage native radar data (i.e., reflectivity) by providing a simplified but efficient discharge forecasting model for a representative mountain catchment in the southern Andes of Ecuador. This model was compared with another that used as input derived radar rainfall (i.e., rainfall depth), obtained after the transformation from reflectivity to rainfall rate by using a local Z-R relation and a rain gauge-based bias adjustment. In addition, the influence of a soil moisture proxy was evaluated. Radar and runoff data from April 2015 to June 2017 were used. Results showed that (i) model performance was similar by using either native or derived radar data as inputs (0.66 < NSE < 0.75; 0.72 < KGE < 0.78). Thus, exhaustive pre-processing for obtaining radar rainfall estimates can be avoided for discharge forecasting. (ii) Soil moisture representation as input of the model did not significantly improve model performance (i.e., NSE increased from 0.66 to 0.68). Finally, this native radar data-based model constitutes a promising alternative for discharge forecasting in remote mountain regions where ground monitoring is scarce and hardly available. [ABSTRACT FROM AUTHOR]

Despite many efforts of the radar community, quantitative precipitation estimation (QPE) from weather radar data remains a challenging topic. The high resolution of X-band radar imagery in space and time comes with an intricate correction process of reflectivity. The steep and high mountain topography of the Andes enhances its complexity. This study aims to optimize the rainfall derivation of the highest X-band radar in the world (4450 m a.s.l.) by using a random forest (RF) model and single Plan Position Indicator (PPI) scans. The performance of the RF model was evaluated in comparison with the traditional step-wise approach by using both, the Marshall-Palmer and a site-specific Z–R relationship. Since rain gauge networks are frequently unevenly distributed and hardly available at real time in mountain regions, bias adjustment was neglected. Results showed an improvement in the step-wise approach by using the site-specific (instead of the Marshall-Palmer) Z–R relationship. However, both models highly underestimate the rainfall rate (correlation coefficient < 0.69; slope up to 12). Contrary, the RF model greatly outperformed the step-wise approach in all testing locations and on different rainfall events (correlation coefficient up to 0.83; slope = 1.04). The results are promising and unveil a different approach to overcome the high attenuation issues inherent to X-band radars. [ABSTRACT FROM AUTHOR]