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Due to its geographic location and unique climatic circumstances, the Galápagos archipelago is renowned for its exceptional and high endemic biodiversity. Nevertheless, due to limited access to the permanent freshwater body in the archipelago, the freshwater budget is almost exclusively dependent on precipitation. However, with the lack of spatial and temporal distribution of rainfall information, it is not easy to understand the short- and long-term dynamics of rainfall in the Galápagos.The poster presents the new satellite-based rainfall retrieval algorithm, the Galápagos Rainfall Retrieval (GRR), which offers the potential for a high spatio-temporal resolution (2 km, 10 min) rainfall product in near real-time for the Galápagos archipelago.The algorithm is proposed to combine physical methods with machine learning in which sequences of Geostationary Earth Orbit infrared (GEO-IR) images are used to retrieve both cold season Garua drizzle and warm season convective rainfall.In the first step, a threshold technique and spectral differences are used to identify the cloudy regions and distinguish the low, middle, and high clouds. Next, the cloud-covered region will go under a different entity-based classification method (e.g., slope test/ML algorithm) for each cloud type to detect the low stratus/Garua drizzle and potentially convective core regions. The next test follows for all detected potentially convective core regions based on cloud formation over time and space to examine whether they are likely to be decaying. If the convective core is classified as decaying, it is labelled stratiform rain; otherwise, it is labelled as the active convective core.Finally, the rainfall assignment will be performed by training the random forest regression models. The convective and stratiform cells will be trained based on microwave-only IMERG-V06 rainfall data separately, meanwhile the cloudsat would be used to train the rainfall rate for the Garua detected regions. By the end, all of these steps are combined together as GRR product. The algorithm of GRR product will be developed for the time period 1/1/2022-1/1/2023 and then it would be applied to the entire available GOES-16 dataset.The validation will be conduct by: i) independent microwave-only IMERG-V06 rainfall data/cloudsat not used for model training ii) recently installed automated weather stations (AWS) network with high temporal resolution of 10 minutes covering a W-E and luff-lee transects over three islands (Isabela, S. Cruz, S. Cristóbal).

In the Intergovernmental Panel on Climate Change report (IPCC), “Climate Change 2022: Impacts, Adaptation and Vulnerability” it is stated that more frequent and intense extreme events due to climate change have a significant impact on the loss and damage of nature and people, which particularly holds for precipitation.In the Galápagos archipelago, the primary source of water supply is rainfall, hence rainfall plays an important role for biodiversity and people in this iconic but remote region. The main assumption for Galapagos is that water supply is dominated by the cool season’s light Garúa rainfall originating from the Pacific stratus, which will significantly decrease under global warming conditions. At the same time, rainfall in the warm season shows large variability, particularly during extreme ENSO (El Niño-Southern Oscillation) events. While in the current decade, a decrease of strong El Niño rainfall events was observed in the eastern tropical Pacific, most (but not all) climate model projections of the CMIP6 ensemble reveal stronger El Niño rainfall under future warming.To date, short and long-term rainfall dynamics in the Galápagos are not well understood, largely due to a lack of consistent spatially-time series of meteorological in-situ observations. The research project DARWIN ("Dynamics of precipitation in transition: The water source for the Galápagos Archipelago under climate change") has recently established 11 Automatic weather stations (AWS) covering a W-E and luff-lee transects over three islands (Isabela, S. Cruz, S. Cristóbal). The location of the stations is to consider different local and regional precipitation formation mechanisms. We seek to resolve influences of the Equatorial Counter Current and the Humboldt Current, as well as the topographic exposition towards the main airstream. Furthermore, the altitudinal gradients concerning vertical dynamics of the trade inversion are considered. One main goal of the DARWIN project is to produce area-wide rainfall information by satellite retrievals and WRF dynamical downscaling. While warm-season rainfall is mainly driven by intense convection events, cool-season Garúa is assumed to be more in the drizzle intensity range. The area-wide techniques must thus properly model the very different types of occurring rain intensities in the cool and warm seasons. Hence, the observations from the AWS used as test and training data must be as accurate as possible. Beyond standard meteorology, we focus on different advanced observation principles (light, optical, radar, gauge) and their intercomparison, and warrant high-resolution measurements (up to one minute) including a vertical profiling of rainfall. The AWS stations in the Garúa zone are additionally equipped by a harp-type fog collector. The poster will present the overall structure of the project and some first results of the AWS network, with a focus on temporally high-resolution rainfall dynamics during different weather situations and precipitation types along the transects.