Laboratory for
Climatology and Remote Sensing

Abstract:

There are many uncertainties in radiation fog forecast. Continuous effort is being made to improve the forecast. A supplementary and increasingly popular approach to numerical weather forecast is the forecast with machine learning (ML) algorithms. While numerical weather forecast is based on mathematical models with partial differential equations, ML algorithms take a more heuristic approach. The latter strategy calls for three steps. Precise data preprocessing is the initial step. This implies that after preprocessing, the dataset must contain the forecast-relevant information in a way that the algorithm can learn from it. This is not a trivial step because it necessitates a thorough understanding of the fundamental principles underlying radiation fog. Even when the relevant information is contained in the data, it is not always evident, especially in severely unbalanced fog datasets. The best strategy to achieve a pleasing result may therefore not be to simply feed the algorithm all the data and variables that are available. So that the appropriate dynamics may be detected by the algorithm, the data and information should be adjusted accordingly. The second step is the data splitting into training, validation and test datasets. The ability to predict fog is driven by the temporally linked process that describes the ongoing change in atmospheric state but in order to guarantee constant independence between the training, validation and test dataset, the data splitting method must consider this temporally linked information between the individual datapoints. Otherwise, the algorithm’s forecast accuracy can be based on the temporally correlated information content of the individual data points. The third step is the interpretation of the model scores. When looking at the forecast score alone, it is a very abstract number that does not directly allow a statement about the forecast performance of the model. In order to evaluate the model performance, two baselines are of relevance: algorithm complexity and dataset complexity. A baseline for algorithm complexity justifies the chosen algorithm and also classifies the model performance. A baseline for dataset complexity also classifies the model performance and enables a better comparability of different datasets. Following these principles, our current objective is to improve the ML based fog forecast with XGBoost for a forecasting period up to four hours for the station in Linden-Leihgestern (Germany). The training and evaluation are based on the Expanding Window Approach (Vorndran et al. 2022) that considers the autocorrelation of a fog time series and maintains the temporal order during both training and evaluation. The evaluation is based on a score for each of the following categories: Overall performance, fog formation, and fog dissipation. The results are set in relation to different baselines to evaluate the performance and the dataset complexity. Building on this scheme, newly preprocessed data led to an improvement in the prediction of radiation fog for the station in Linden-Leihgestern. We will present the most recent findings from our research.

Abstract:

This study addresses transpiration in a tropical evergreen mountain forest in the Ecuadorian Andes from the leaf to the stand level, with emphasis on nocturnal plant-water relations. The stand level: Evapotranspiration (ET) measured over 12 months with the Eddy-Covariance (ECov) technique proved as the major share (79%) of water received from precipitation. Irrespective of the humid climate, the vegetation transpired day and night. On average, 15.3% of the total daily ET were due to nocturnal transpiration. Short spells of drought increased daily ET, mainly by enhanced nighttime transpiration. Following leaf transpiration rather than air temperature and atmospheric water vapor deficit, ET showed its maximum already in the morning hours. The tree level: Due to the humid climate, the total water consumption of trees was generally low. Nevertheless, xylem sap flux measurements separated the investigated tree species into a group showing relatively high and another one with low sap flux rates. The leaf level: Transpiration rates of Tapirira guianensis, a member of the high-flux-rate group, were more than twice those of Ocotea aciphylla, a representative of the group showing low sap flux rates. Representatives of the Tapirira group operated at a relatively high leaf water potential but with a considerable diurnal amplitude, while the leaves of the Ocotea group showed low water potential and small diurnal fluctuations. Overall, the Tapirira group performed anisohydrically and the Ocotea group isohydrically. Grouping of the tree species by their water relations complied with the extents of the diurnal stem circumference fluctuations. Nighttime transpiration and hydrological type: In contrast to the isohydrically performing trees of the Ocotea group, the anisohydric trees showed considerable water vapour pressure deficit (VPD)-dependent nocturnal transpiration. Therefore, we conclude that nighttime ET at the forest level is mainly sourced by the tree species with anisohydric performance.

Abstract:

Canopy habitats challenge researchers with their intrinsically difficult access. The current scarcity of climatic data from forest canopies limits our understanding of the conditions and environmental variability of these diverse and dynamic habitats. We present 307 days of climate records collected between 2019 and 2020 in the tropical rainforest canopy of the Yasuní National Park, Ecuador. We monitored climate with a 10-min temporal resolution in the middle crowns of eight canopy trees. The distance between canopy climate stations ranged from 700 m to 10 km. Apart from air temperature, relative humidity, leaf wetness, and photosynthetically active radiation (PAR), measured in each canopy climate station, global radiation, rainfall, and wind speed were measured in different subsets of them. We processed the eight data series to omit erroneous records resulting from sensor failures or lack of the solar-based power supply. In addition to the eight original data series, we present three derived data series, two aggregating canopy climate for valleys or for ridges (from four stations each), and one overall average (from the eight stations). This last derived data series contains 306 days, while the shortest of the original data series covers 22 days and the longest 296 days. In addition to the data, two open-source tools, developed in RStudio, are presented that facilitate data visualization (a dashboard) and data exploration (a filtering app) of the original and aggregated records.

Abstract:

Tropical forests are highly diverse at many spatial scales. In these forests, small-sized canopy organisms can form species-rich communities already within a few cm2. Understanding how species numbers increase when expanding the sampling along the tree and the forest is critical for evaluating the processes maintaining biodiversity. We therefore studied epiphytic bryophyte diversity in tree crowns and along trunks across spatial scales in a tropical lowland forest in Amazonian Ecuador, sampling bryophytes in 100-cm2 quadrats on 24 trees (15–22 quadrats each) using a spatially hierarchical design, analyzing alpha and beta diversity at different spatial grains and extents. At the smallest grain, tree crowns held more bryophyte species than trunks, but at the largest grain the trunks held most species (93 vs. 77), as beta diversity was higher among trunks than among crowns. However, except for trunks at the largest extent (all 24 trees), the highest beta diversity among quadrats was always found between crowns and trunks. Species turnover strongly dominated beta diversity at all spatial scales. This and the high species richness resulted in highly unpredictable species compositions, especially in trunk communities. These patterns suggest different controls of diversity in crowns than on trunks and an important role for chance processes in shaping these communities. The high beta diversity within trees, in combination with the large effort involved in climbing trees, implies that diversity sampling of small canopy organisms is most efficient using an intensive (many plots on few trees) rather than extensive (many trees across a large area) sampling.

Abstract:

There is a growing demand to train Earth Observation (EO) data users in how to access and use existing and upcoming data. A promising tool for data-related training is computational notebooks, which are interactive web applications that combine text, code and computational output. Here, we present the Learning Tool for Python (LTPy), which is a training course (based on Jupyter notebooks) on atmospheric composition data. LTPy consists of more than 70 notebooks and has taught over 1000 EO data users so far, whose feedback is overall positive. We adapted five guiding principles from different fields (mainly scientific computing and Jupyter notebook research) to make the Jupyter notebooks more educational and reusable. The Jupyter notebooks developed (i) follow the literate programming paradigm by a text/code ratio of 3, (ii) use instructional design elements to improve navigation and user experience, (iii) modularize functions to follow best practices for scientific computing, (iv) leverage the wider Jupyter ecosystem to make content accessible and (v) aim for being reproducible. We see two areas for future developments: first, to collect feedback and evaluate whether the instructional design elements proposed meet their objective; and second, to develop tools that automatize the implementation of best practices.

Abstract:

The current goal of our research is to improve the machine learning (ML) based fog forecast for a forecasting period up to four hours for the station in Linden-Leihgestern. The prediction of radiation fog is still subject to large uncertainties. In particular, the precise prediction of fog start and dissipation, i.e. the transitions, is very difficult. The high-frequency fluctuations of the variables in the formation and dissipation phases pose a particular challenge to ML models. These strong fluctuations make it difficult to extract the necessary information about the past, namely increasing or decreasing trend. However, the temporal evolution in the past is determining for the development of radiation fog. Thus, these dynamics must be prepared in such a way that they can be learned during model training. Therefore, different smoothing levels were tested using a Gaussian moving average filter. Furthermore, additional trend variables for model training were generated that carry information about the temporal evolution of previous data points. Training and evaluation have been carried out with the Expanding Window Approach (Vorndran et al. 2022) that has recently been accepted as a training and validation method for radiation fog prediction. Building on this scheme with the tree-based algorithm XGBoost, the newly preprocessed data led to an improvement in the prediction of radiation fog for the station in Linden-Leihgestern. The results from this research will be presented in the poster session. The study is funded by the DFG research project “FOG-ML FOrecasting radiation foG by combining station and satellite data using Machine Learning”.