Laboratory for
Climatology and Remote Sensing

Abstract:

Global evidence indicates that orography can exert a strong influence on rainfall, thereby giving rise to natural hazards such as flooding in mountain areas. Alongside orographic influences, urban areas can also act to intensify storms, but their impact on heavy rainfall in high elevations has not yet been investigated in detail. Here, we quantify the contribution of a high-altitude city to both rainfall intensification and its daytime and nighttime patterns, focusing on the city of Cuenca in the Ecuadorian Andes. Modeling several observed storms with and without the city suggests that the urban area can enhance downstream precipitation by over 20% over a pre-existing hotspot of orographically induced precipitation. This urban-induced rainfall intensification seems to exceed that observed for cities of comparable size outside mountainous regions. This may be explained by Cuenca’s valley setting and its high humidity compared to its surroundings, suggesting that high-altitude cities with similar morphologies could amplify combined orographic-dynamic and urban-thermodynamic effects.

Abstract:

We propose simple analytical equations for the modeling of clean and dusty flat glacier ice surfaces, which can be used to inversely derive the parameters of microstructure of flat bare glacier ice and snow using both ground – based and spaceborne observations of the hyperspectral solar reflectance. The retrievals are based on the asymptotic radiative transfer equations valid for the case of weak light absorption in the semi-infinite turbid medium. The light reflection at the air - ice and ice - air interfaces is fully accounted for. To demonstrate the validity of the approach, the derived equations are exemplarily applied to both ground – based and EnMAP satellite measurements over the Hardangerjøkulen glacier (Norway). A number of important parameters controlling spectral signatures of the snow and glacier ice surfaces have been derived. The ground-based measurements confirm that the theoretical formulation presented in this work can be used to represent the solar light spectral reflectivity of glaciers. The application to satellite hyperspectral imagery shows that this novel technique allows for the determination of the glacier ice albedo (spectral, broadband) based on spaceborne glacier ice reflectance measurement. Additionally, the results demonstrate that not spectrally neutral soot but rather deposited atmospheric dust which enhances the absorption towards UV is responsible for the light absorption by snow for the case studied. Spatial distribution maps of ice grain diameter and dust concentration are derived over the glacier. These findings show that the analytical theory presented in this work can support further research on the characterization and monitoring of glaciers based on current and upcoming hyperspectral remote sensing satellite missions.

Abstract:

Im Klimawandel wird erwartet, dass Wetterextreme weltweit zunehmen. Wie die Ahrflut im Juli 2021 gezeigt hat, ist auch Deutschland von katastrophalen Schäden durch Starkniederschläge betroffen, auch wenn die Attribuierung zum Klimawandel von einzelnen Extremereignissen schwierig ist. In den tropischen Anden führen solche Starkregenereignisse regelmäßig zu katastrophalen Überschwemmungen und Erdrutschen. Dies gilt auch für Ecuador, wo Klimamodellstudien einen Anstieg von Gesamtniederschlagsmenge und -intensität aufgrund des zukünftigen Klimawandels erwarten lassen.

Abstract:

Accurate forecasts of radiation fog are an objective of significant relevance due to its impact on traffic, aviation, and transportation. This study will explore the adaptation and enhancement of a previously developed Machine Learning-based nowcasting framework for radiation fog events. The objective is to explore the expansion potential to a spatial scale and model accuracy improvements through application at three distinct weather station locations that experience radiation fog. Further, the effectiveness of Numerical Weather Prediction (NWP) data as additional predictor variable source on model performance will be assessed. This will be performed through integration of datasets from German Weather Service (DWD) stations, Meteosat Second Generation (MSG) channel properties and regional reanalysis variables from COSMO NWP model. Distinct model variants based on different dataset combinations (Station, MSG+COSMO, Station+MSG+COSMO, Visibility-Only) will be evaluated. Using eXtreme Gradient Boosting (XGBoost) algorithm, the framework forecasts absolute visibility with 60-minute lead time. A persistence model serves as benchmark. Performance will be assessed using scoring metrics (Accuracy, Correlation, Percentage bias, Mean Absolute Error) across the full visibility range and three visibility threshold bounds (2 km, 1.1 km, 0.4 km). Temporal accuracy of fog formation and dissipation will be determined through evaluation of fog formation and dissipation time shifts. XGBoost models mostly outperform PM, with tendencies of Station+MSG+COSMO variant performing best and MSG+COSMO variant worst. Prediction difficulties arise in the 0.4 km threshold segment due to measurement resolution limitations and value imbalance of visibility data. The model variants reliably predict fog event transitions, with the majority forecasted with deviations < 30 minutes and only few events overseen. A consistent tendency towards delayed prediction is observed. Variability in model performances across station locations suggests that small-scale environmental characteristics contribute to different model robustness at distinct sites. The results indicate strong potential for further spatial framework extension. COSMO variables partially contribute to improved model performance. The framework marks a solid foundation for future exploitation.

Abstract:

Latent heat flux is a central element of land-atmosphere interactions under climate change. Knowledge is particularly poor in the biodiversity hotspot of the Andes, where heat flux measurements using eddy covariance stations are scarce and land surface models (LSMs) often oversimplify the complexity of the ecosystems. The main objective of this study is to perform latent heat flux simulations for the tropical South Eastern (SE) Ecuadorian Andes using a coupled LSM framework, and to test the performance with heat flux and soil moisture data collected from a tropical high-altitude pasture. Prior to testing, we applied multi-criteria model calibration of sensitive model parameters, focusing on improving simulated soil water conditions and radiation fluxes as a prerequisite for proper heat flux simulations. The most sensitive parameters to improve soil moisture and radiation flux simulations were soil porosity, saturated hydraulic conductivity, leaf area index, soil colour and NIR (Near Infrared) leaf optical properties. The best calibrated model run showed a very good performance for half-hourly latent heat flux simulations with an R2 of 0.8 and an RMSE of 34.0 W m−2, outperforming simulations with uncalibrated and uncoupled LSM simulations in comparable areas. The slight overall overestimation in the simulated latent heat flux can be related to (i) simulation uncertainties in the canopy heat budget, (ii) an imbalance in the observed flux data and (iii) slight overestimations in the simulated soil moisture. Although our study focuses on latent heat fluxes and their relation to simulated radiation fluxes and soil moisture, model outputs of sensible heat fluxes were also discussed. The systematic overestimation of sensible heat flux in the model seems to be mainly a result of overestimated canopy temperatures. The improved simulation for latent heat flux has a high translational potential to support land use strategies in the tropical Andes under climate change.

Abstract:

An understanding of sub-hourly precipitation variability in the Galapagos Islands is crucial for water resource management and effective biodiversity conservation. This study compares the diurnal cycle and event-scale precipitation characteristics (ESPC), such as mean and maximum intensity, duration and rainfall accumulation at different altitudes during El Niño-Southern Oscillation (ENSO) 2022-2024 on Santa Cruz Island. The La Niña phase was analyzed from April 2022 to January 2023 and the El Niño phase from June 2023 to April 2024. Precipitation data, recorded every 10 minutes, was collected from a recently established network of automatic weather stations, which were strategically positioned at three windward and two leeward sites. The results suggest that the diurnal cycle was influenced by altitude, with a maximum vari ability between morning and afternoon, regardless of ENSO phase. During La Niña, ESPC exhibited similarities at interme diate altitudes at both windward and leeward sides. However, rainfall events at the island’s summit were less intense and of longer duration. During El Niño, the highest intensities were observed along the coast and at intermediate altitudes of both windward and leeward locations. In contrast, at the top of the island, rainfall events were less intense and more prolonged. At all altitudes, more than half of the rainfall events corresponded to garúa events, and at the top of the island, almost all events were of this type. At this altitude, the contribution of garúa events to the total rainfall accumulation was 80% and 85% for La Niña and El Niño, respectively. This study provides a detailed analysis of how sub-hourly precipitation varies significantly at different altitudes on the windward and leeward sides as a function of ENSO phases, providing valuable baseline information for future studies in this unique and fragile ecosystem.

Abstract:

Understanding the partitioning of downward shortwave radiation into direct and diffuse components is essential for modeling ecosystem energy fluxes. Accurate partitioning functions are critical for land surface models (LSMs) coupled with climate models, yet these functions often depend on regional cloud and aerosol conditions. While data for developing semi-empirical partitioning functions are abundant in mid-latitudes, their performance in tropical regions, particularly in the high Andes, remains poorly understood due to scarce ground-based measurements. This study analyzed a unique dataset of shortwave radiation components from a tropical mountain rainforest (MRF) in southern Ecuador, developing and testing a locally adapted partitioning function using Random Forest Regression. The model achieved high accuracy in predicting the percentage of diffuse radiation (%Dif; R2=0.95, RMSE = 5.33, MAE = 3.74) and absolute diffuse radiation (R2=0.99, RMSE = 5.30, MAE = 14). When applied to simulate upward shortwave radiation, the model outperformed commonly used partitioning functions achieving the lowest RMSE (8.62) and MAE (5.82) while matching the highest R2 (0.97). These results underscore the importance of regionally adapted radiation partitioning functions for improving LSM performance, particularly in complex tropical environments. The adapted LSM will be further utilized for studies on heat fluxes and carbon sequestration.

Abstract:

In Analytical Methods in Radiative Transfer, a team of distinguished researchers delivers a comprehensive exploration of solutions to practical problems of modern atmospheric optics related to solar light interaction with the terrestrial atmosphere and the remote sensing of clouds, aerosols, and gases. The authors describe analytic methods in radiative transfer that help explain atmospheric phenomena. The book includes discussions on the interaction of solar light with the atmosphere. Readers will also benefit from thorough reviews of various analytical radiative transfer techniques, for various turbid media, including media with phase functions extended in the forward direction, and also semi-infinite, non-absorbing, weakly absorbing, and strongly absorbing light scattering media. Analytical Methods in Radiative Transfer also includes: A thorough introduction to exact solutions of the radiative transfer equation, including situations of single scattering, as well as isotropic and Rayleigh scattering A comprehensive exploration of approximate solutions for scalar radiative transfer, including single and multiple light scattering separation and the case of semi-infinite media such as snow In-depth examinations of the applications of analytical methods in atmospheric radiative transfer, including aerosol remote sensing, cloud remote sensing, and the remote sensing of trace gases Perfect for meteorologists, climatologists and graduate students studying physics, Analytical Methods in Radiative Transfer is also an indispensable resource for geophysicists seeking a practical exploration of modern atmospheric optics.