Precipitation is a major component in the hydrological cycle and characterized by a high temporal and spatial variability. This is especially true in areas with a pronounced topography, such as the Tropical Andes in South America. In this region, which is particularly exposed to climate change, water is of high socio-economic importance [1
]. Several recent studies have addressed the challenges related to water resources management in the context of climate change and glacier recession in the Tropical Andes (e.g., [1
]). Robust knowledge about highly resolved spatio-temporal precipitation processes and variability is crucial to understand existing and future water resources problems. However, high resolution precipitation studies are limited, and the lack of a dense precipitation measurement network is a major barrier to improve our understanding [7
]. In addition, the limited number of available in situ observations does not account for the complex topography and the resulting high spatio-temporal precipitation variability in the Tropical Andes. Furthermore, uncertainties due to the data quality are frequent, especially from manned in situ stations [9
Available precipitation data provided by the national weather services in Ecuador and Peru, as well as available data portals such as the one by Schwarb et al. [10
] usually have a daily temporal resolution. However, investigating precipitation characteristics such as diurnally driven patterns, event durations, and intensities or precipitation types requires a temporal resolution higher than daily.
Weather and rainfall radar instruments are a promising approach to generate and analyze high resolution precipitation data and improve the understanding of precipitation characteristics and thus hydro-climatic implications. In the Tropical Andes, and more specifically in southern Ecuador, diurnal patterns and precipitation characteristics on the eastern slopes of the Andes were studied by Bendix et al. [11
]. Rainfall behavior was found to be mostly of a stratiform character, but the influences of local convection were detected for afternoon events, as well as nocturnal events [12
]. Another study in this area by Muñoz et al. [13
] assessed the error of estimated rainfall intensities by means of rain gauges of different resolutions along an altitudinal gradient. As a reference, optical disdrometer data were used. Here, the percent absolute bias was largest for low rainfall intensities (≤2 mm h
) and short time scales (≤10 min). In addition, no relation between rainfall depth and elevation was found. Recently, Orellana-Alvear et al. [14
] analyzed different rainfall types based on the mean volume diameter (Dm) of raindrops at different locations in southern Ecuador. The authors found that rainfall was more convective at lower altitudes with shorter duration events, whereas very light rain (drizzle) was more common at the higher range. Furthermore, Rollenbeck and Bendix [15
] used the information from an X-band local area weather radar (LAWR) for unveiling different weather situations that generate distinguished patterns of rainfall distribution in the high mountains of southern Ecuador.
The lack of high resolution precipitation data and monitoring in the Tropical Andes has been recognized, and efforts have been initiated to set up a radar weather network in the Tropical Andes (RadarNet-Sur), including the installation of new weather radars in southern Ecuador and recently in Piura, in northern Peru [16
]. This extended weather radar network covers the complex transition zone from the coastal plains of southern Ecuador and Peru, across the Andean Cordillera to the eastern foot of the mountains and will improve the knowledge on regional rainfall extremes and the flow dynamics in the region. Several studies have been recently carried out using data from the RadarNet-Sur network. Orellana-Alvear et al. [14
] derived several Z-R relationships for further radar quantitative precipitation estimation (QPE) using optical disdrometers. Here, a strong spatial variability of the Z-R relations was found within the radar extent. Furthermore, rainfall events and rainfall classes were derived from disdrometer data. Guallpa et al. [17
] analyzed the impact of different spatial (100–500 m) and temporal (1–60 min) resolutions of radar data on the cumulative QPE. It was found that rain event features were properly captured at 1 min temporal resolution, which highlighted the strong variability of the rainfall in the region. Recently, Orellana-Alvear et al. [18
] applied a machine learning model to optimize the QPE of the highest radar in the network. Although the results were promising (i.e., a correlation coefficient up to 0.83), the complex topography of the Andes remains an important factor that limits the accuracy of rainfall estimation in the area.
In the Andes of Peru, the lack of weather radars has thus far impeded improvement in the understanding of small scale rainfall processes, which limits further studies in the field of hydro-climatology. To our knowledge, the only study thus far based on radar data was conducted by Endries et al. [8
], who studied the temporal characteristics and vertical structure of precipitation in Cusco, Peru (and in La Paz, Bolivia) using a Micro-Rain Radar (MRR) and local in situ measurements and calculated backward trajectories of low level air masses for selected events. Further studies that analyzed precipitation dynamics along the western slopes of the Peruvian Andes utilized information from daily observations and a numerical weather model from the Lima region [19
] and conducted a comparison of in situ measurement based gridded precipitation datasets, remote sensing products, and WRF numerical weather model outputs in the Cordillera Blanca, to investigate the diurnal precipitation cycle at high spatial (3 km) and temporal (hourly) resolution [20
Nearly all of the analyses on rainfall dynamics described above fell short of explaining the underlying processes of rainfall types. This is mainly due to missing vertical information on the development of rainfall events at high temporal resolution. With the recently installed vertically pointing Micro-Rain Radars (MRR) in Huaraz, Peru, and Cuenca, Ecuador, a systematic analysis of rainfall types and their dynamics is possible for the first time. The continuous two year MRR datasets with a temporal resolution of 1 min allow for a detailed examination of event based precipitation duration and intensities, which is hampered using global satellite products or common rain gauges on a daily basis only. Thus, the main aims of our study are: (i) to identify the temporal precipitation characteristics and climatology at the two sites of the Andean Cordillera; (ii) to detect temporal highly resolved precipitation events and to relate these events to different precipitation types using a classification based on a fuzzy rule system (FRS). The paper is structured as follows: Section 2
gives an overview of the study areas, their general climatological features, and the related environmental circulation patterns, followed by the data and methods used for the classification of precipitation characteristics. In Section 4.1
, the precipitation events are examined in the context of their temporal characteristics, such as occurrence and event durations. In Section 4.2
, we show typical case studies for different precipitation types and present the results of the precipitation classification, followed by a discussion and conclusions.
2. Study Area
The main study area is located in the Tropical Andes as illustrated in Figure 1
with the locations of the MRR in Cuenca, Ecuador, and Huaraz, Peru. The general climate at both sites is determined by the South American monsoon system [21
], among others, driven by the seasonal cycle of the large scale circulation patterns and the inter-hemispherical migration of the Inter-Tropical Convergence Zone (ITCZ). During the austral summer season (December–February, DJF), a high pressure system, i.e., the Bolivian High (BH), is developed in the upper troposphere near 20
S and 60
]. In the lower troposphere, the ITCZ is located in the Southern Hemisphere, resulting in an easterly moisture transport from the Amazon Basin to the Andes Mountains, which act as a barrier. The low tropospheric easterly trade winds impinging this terrain cause an orographic enhancement of precipitation formation, which generates a climate divide between the eastern and western escarpment [25
]. In contrast, the western slopes are under the influence of the South Pacific High Pressure System (SPH), which induces stable and, thus, drier conditions [27
]. In the austral winter months (June–August, JJA), the ITCZ, as well as the SPH are shifted northward, and the central Andes experience their main dry season. The SPH causes large scale subsidence, which inhibits deep convection. Additionally, a strong westerly flow in the upper atmosphere blocks the transport of moisture from the Amazon. These conditions contrast with the northern Andes, which experience rainy periods due to prevailing easterlies [28
Cuenca is situated in the eastern fringe of the western Andean Cordillera in southern Ecuador in an inter-Andean depression downstream of the tributaries of the main rivers that cross the city. This inter-Andean depression constitutes the low part of an altitudinal gradient that ranges up to 4100 m a.s.l. The climate is characterized by a bimodal rainfall pattern with two wet seasons (MAM and SON). Cuenca has a mean annual precipitation of 969 mm [14
] and a mean temperature of 14
The city of Huaraz is located in the inter-Andean valley of the Rio Santa between the Cordillera Blanca, with some of the highest mountains in Peru (up to 6700 m) and the Cordillera Negra in the Province of Ancash. The climate in this area is characterized by a wet (DJF) and a dry season (JJA) as well, and the average temperatures in Huaraz are around 15
C all year round [30
]. The precipitation amounts in this region show a pronounced gradient from the mountain ranges of the Cordillera Blanca with up to 1000 mm per year [20
] to the coastal desert areas along the Pacific Ocean. In Huaraz, the annual precipitation sum varies between 600 and 1000 mm (based on the PISCO V2.1 dataset [31
Our study shows that at both sites, the majority of rainfall events are of short duration and that temporally high resolution in situ data are necessary to characterize the properties of precipitation properly. This is also an important point to keep in mind when space borne remote sensing precipitation products (e.g., GPM IMERG [46
]) are used to obtain information about precipitation in areas that lack sufficient ground observations, such as the Tropical Andes. Even though the spatial and temporal resolutions of such remote sensing products range down to 0.1
and 30 min respectively, it is likely that they will not fully capture the precipitation dynamics in complex terrain areas with a high spatio-temporal precipitation variability. With the two year MRR datasets from Cuenca and Huaraz, we were able to analyze the occurrence and duration of precipitation events, as well as the temporal evolution of precipitation with respect to rain rates, radar reflectivities, fall velocities, and drop size distributions. The number and durations of the events in Cuenca are consistent with the study from Orellana-Alvear et al. [14
], who analyzed these parameters using disdrometer data from March 2015 to August 2016. Our results also show that both sites have more events with higher precipitation intensity during the daytime. This was an interesting finding, since studies by Bendix et al. [11
] and Endries et al. [8
] observed a bimodal precipitation pattern in Ecuador and in Cusco, Peru. This feature was more pronounced in Huaraz than in Cuenca, which could be related to the regional topography of the two sites. Huaraz is located on the western fringes of the Andes, which have a larger east-west extension than in Cuenca, and therefore, the distance to the Amazon Basin is greater. Thus, deep convective activities are predominantly thermally induced and maintained by diabatic heating and local convergences due to the complex terrain, which enhances the formation of convective cells within an event. Nighttime events, on the other hand, are more frequent and have longer durations in Cuenca than in Huaraz. Cuenca is located closer to the Amazon Basin, where the mesoscale convective systems (MCS) frequently develop and move westwards with the trade winds, leading to nocturnal rainfall [12
]. These MCS are likely to cause the occasional nighttime precipitation in Huaraz as well. Furthermore, the results from the study by Endries et al. [8
] in Cusco, Peru, show a large number of overnight events with relatively high precipitation rates, which the authors relate to MCS in the Amazon Basin as well. Thus, at Cusco and La Paz, nighttime precipitation plays a major role, which contrasts with our findings, since higher rain rates were not observed after 22:00 LT at both sites in the current study. This was confirmed by the TBR data from Cuenca, where 70% of the measured rainfall was recorded during the daytime (9:00–21:00 LT) during the study period. Therefore, it remains an open question why nighttime precipitation contributes less at our study sites. This illustrates the very heterogeneous precipitation scheme in the Tropical Andes and the necessity for further studies.
Further, we conducted a classification of convective and stratiform precipitation types using the information from the fall velocity profiles and an FRS, which provided reasonable results, although some misclassification occurred, e.g., nighttime deep convection in Huaraz. Furthermore, this approach showed occasional misclassification at the onset of convective events (cf. Figure 6
), which can be related to boundary effects. When precipitation starts, falling hydrometeors are already detected by the MRR in the higher range gates, which subsequently arrive at the ground with a certain time delay. Thus, low fall velocities in the lower MRR gates are prevalent in these cases, which leads to a high DOF for stratiform rain in the FRS. The precipitation not classified by the FRS (i.e., where the DOFs of the fuzzy rules were zero) occured predominately during daytime stratiform events, which had high fall velocities below the bright band, but low to moderate rainfall rates. This was often the case during the transition from convective to stratiform precipitation, as shown in Figure 6
In order to assess the robustness of the FRS approach, we additionally classified the precipitation following the concept of Bringi et al. [44
]. While the FRS classifier is based the microphysical properties such as the fall velocity, Bringi et al. [44
] use the standard deviation of the precipitation rates. This ensured a quasi-independence of the approaches. In both methods, a comparable result of the precipitation characteristics could be observed with generally > 92% stratiform precipitation (Table 3
and Table 4
). Even the partitioning of daytime/nighttime occurrences reflected a comparable behavior at both locations. With respect to precipitation characteristics, our results show that the majority of daytime rainfall consist of mixed convective/stratiform precipitation as presented in the case studies (cf. Figure 6
and Figure 8
). Afternoon precipitation events often have a convective onset with high rainfall intensities and then show a transition towards stratiform characteristics, which often have longer durations than the convective phase. This is also the reason for the high overall proportion of approximately 90% stratiform precipitation in our classification at both sites. Low intensity stratiform rainfall events without deep convection features occurred in the afternoon hours during the rainy season as well, but were less frequent. The nighttime events typically showed purely stratiform features.
The analysis of the DSD in the
domain indicates that the two precipitation types that we classified according to the FRS showedefined clusters and can thus be distinguished well. Furthermore, these results are consistent with the study by Bringi et al. [44
], who investigated drop size spectra in different climate zones. The DSD of stratiform precipitation at the sites in Cuenca and Huaraz are below the line that Bringi et al. [44
] used to characterize and separate stratiform and convective precipitation based on their average
values. Thus, our results confirm the stratiform classification of the FRS. Likewise, drop spectra of convective precipitation have a larger average
and a larger number of drops, although the differences between the two sites are more pronounced in this case. The reasons for these differences in the convective drop size spectra require further investigation.
Our study provides some first insights into the temporal and micro-physical precipitation characteristics in data scarce regions with complex topography, which holds especially for the site in Huaraz, Peru, where no detailed studies on precipitation had existed so far. The results from our study lead to a better understanding of precipitation dynamics in mountainous areas where ground observations are generally sparse. The added value of our study is the high temporal resolution. Many events are of a short duration and can therefore not be captured or resolved using remote sensing data products such as GPM IMERG.
A remaining challenge is the quantification of rainfall amounts from the MRR, which is often associated with high uncertainties. A comparison of rainfall totals between the MRR and a collocated TBR gauge in Cuenca showed that the MRR has a tendency to underestimate precipitation totals at that site, even for low precipitation sums. Incorrect MRR retrievals can occur in cases of strong vertical air motion, which is common during strong convection and leads to an aliasing of the Doppler spectra, as described in Tridon et al. [49
]. Updrafts/downdrafts during such events result in an over-/under-estimation of the MRR rain rates [33
]. Post-processing of the MRR data as done, e.g., by Maahn and Kollias [50
] could help to identify such erroneous MRR data retrievals and lead to an improvement of the MRR data quality. This would be a first step towards a better quantification of the MRR rain rates. On the other hand, the majority of precipitation was stratiform, where such effects as described above are unlikely to occur. A specific calibration of the MRR to a collocated rain gauge as suggested by Prat and Barros [51
] could therefore be conducted. However, due to the absence of any reliable nearby in situ measurement, such a procedure is not (yet) possible for the Huaraz MRR.