Maik Dobbermann

Bio: Maik Dobbermann is an academic researcher from University of Marburg. The author has contributed to research in topics: Clutter & User requirements document. The author has an hindex of 4, co-authored 5 publications receiving 77 citations.

RadarNet-Sur First Weather Radar Network in Tropical High Mountains

Abstract: Weather radar networks are indispensable tools for forecasting and disaster prevention in industrialized countries However, they are far less common in the countries of South America, which frequently suffer from an underdeveloped network of meteorological stations To address this problem in southern Ecuador, this article presents a novel radar network using cost-effective, single-polarization, X-band technology: the RadarNet-Sur The RadarNet-Sur network is based on three scanning X-band weather radar units that cover approximately 87,000 km2 of southern Ecuador Several instruments, including five optical disdrometers and two vertically aligned K-band Doppler radar profilers, are used to properly (inter) calibrate the radars Radar signal processing is a major issue in the high mountains of Ecuador because cost-effective radar technologies typically lack Doppler capabilities Thus, special procedures were developed for clutter detection and beam blockage correction by integrating ground-based

An Integrated Receiving and Processing Unit for MSG, NOAA and Terra/Aqua Data

Abstract: The cooperation of VCS Engineering and the Laboratory for Climatology and Remote Sensing (LCRS) at Marburg University (Germany) resulted in the integration of different satellite receiving and processing systems based on improvements of the existing hard and software design at Marburg Satellite Station (MSS). In a joint-venture between VCS and LCRS the algorithms for Level 1b / 2a products for MSG SEVIRI, NOAA AVHRR (VCS) and Terra/Aqua MODIS developed at LCRS are integrated into the VCS software package 2met! (Linux/Windows). Even though higher level operational products of the LCRS (implemented in Fortran and Java) are not part of 2met! yet, its modular architecture allows for the easy integration of further components.

Towards an integrated biodiversity and ecological research data management and archiving platform: the German federation for the curation of biological data (GFBio)

Abstract: Biodiversity research brings together the many facets of biological environmental research. Its data management is characterized by integration and is particularly challenging due to the large volume and tremendous heterogeneity of the data. At the same time, it is particularly important: A lot of the data is not reproducible. Once it is gone, potential knowledge that could have been gained from it is irrevocably lost. In this paper, we describe challenges to biodiversity data management along the data life cycle and sketch the solution that is currently being developed within the GFBio project, a collaborative effort of nineteen German research institutions ranging from museums and archives to biodiversity researchers and computer scientists.

Improving the Spatial Prediction of Soil Organic Carbon Stocks in a Complex Tropical Mountain Landscape by Methodological Specifications in Machine Learning Approaches.

Abstract: Tropical forests are significant carbon sinks and their soils' carbon storage potential is immense. However, little is known about the soil organic carbon (SOC) stocks of tropical mountain areas whose complex soil-landscape and difficult accessibility pose a challenge to spatial analysis. The choice of methodology for spatial prediction is of high importance to improve the expected poor model results in case of low predictor-response correlations. Four aspects were considered to improve model performance in predicting SOC stocks of the organic layer of a tropical mountain forest landscape: Different spatial predictor settings, predictor selection strategies, various machine learning algorithms and model tuning. Five machine learning algorithms: random forests, artificial neural networks, multivariate adaptive regression splines, boosted regression trees and support vector machines were trained and tuned to predict SOC stocks from predictors derived from a digital elevation model and satellite image. Topographical predictors were calculated with a GIS search radius of 45 to 615 m. Finally, three predictor selection strategies were applied to the total set of 236 predictors. All machine learning algorithms-including the model tuning and predictor selection-were compared via five repetitions of a tenfold cross-validation. The boosted regression tree algorithm resulted in the overall best model. SOC stocks ranged between 0.2 to 17.7 kg m-2, displaying a huge variability with diffuse insolation and curvatures of different scale guiding the spatial pattern. Predictor selection and model tuning improved the models' predictive performance in all five machine learning algorithms. The rather low number of selected predictors favours forward compared to backward selection procedures. Choosing predictors due to their indiviual performance was vanquished by the two procedures which accounted for predictor interaction.

Ecosystem services, biodiversity and environmental change in a tropical mountain ecosystem of South Ecuador

Abstract: An interdisciplinary research unit consisting of 30 teams in the natural, economic and social sciences analyzed biodiversity and ecosystem services of a mountain rainforest ecosystem in the hotspot of the tropical Andes, with special reference to past, current and future environmental changes. The group assessed ecosystem services using data from ecological field and scenario-driven model experiments, and with the help of comparative field surveys of the natural forest and its anthropogenic replacement system for agriculture. The book offers insights into the impacts of environmental change on various service categories mentioned in the Millennium Ecosystem Assessment (2005): cultural, regulating, supporting and provisioning ecosystem services. Examples focus on biodiversity of plants and animals including trophic networks, and abiotic/biotic parameters such as soils, regional climate, water, nutrient and sediment cycles. The types of threats considered include land use and climate changes, as well as atmospheric fertilization. In terms of regulating and provisioning services, the emphasis is primarily on water regulation and supply as well as climate regulation and carbon sequestration. With regard to provisioning services, the synthesis of the book provides science-based recommendations for a sustainable land use portfolio including several options such as forestry, pasture management and the practices of indigenous peoples. In closing, the authors show how they integrated the local society by pursuing capacity building in compliance with the CBD-ABS (Convention on Biological Diversity - Access and Benefit Sharing), in the form of education and knowledge transfer for application.

A feasibility study of daytime fog and low stratus detection with TERRA/AQUA-MODIS over land

Abstract: A scheme for the detection of fog and low stratus over land during daytime based on data of the MODIS (Moderate Resolution Imaging Spectroradiometer) instrument is presented. The method is based on an initial threshold test procedure in the MODIS solar bands 1–7 (0.62–2.155 μm). Fog and low stratus detection generally relies on the definition of minimum and maximum fog and low stratus properties, which are converted to spectral thresholds by means of radiative transfer calculations (RTC). Extended sensitivity studies reveal that thresholds mainly depend on the solar zenith angle and, hence, illumination-dependent threshold functions are developed. Areas covered by snow, ice and mid-/high-level clouds as well as bright/hazy land surfaces are omitted from the initial classification result by means of a subsequent cloud-top height test based on MODIS IR band 31 (at 12 μm) and a NIR/VIS ratio test. The validation of the final fog and low stratus mask generally shows a satisfactory performance of the scheme. Validation problems occur due to the late overpass time of the TERRA platform and the time lag between SYNOP and satellite observations. Apparent misclassifications are mainly found at the edge of the fog layers, probably due to over-or underestimation of fog and low stratus cover in the transition zone from fog to haze.

Precipitation process and rainfall intensity differentiation using Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager data

Abstract: [1] A new day and night technique for precipitation process separation and rainfall intensity differentiation using the Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager is proposed. It relies on the conceptual design that convective clouds with higher rainfall intensities are characterized by a larger vertical extension and a higher cloud top. For advective-stratiform precipitation areas, it is assumed that areas with a higher cloud water path (CWP) and more ice particles in the upper parts are characterized by higher rainfall intensities. First, the rain area is separated into areas of convective and advective-stratiform precipitation processes. Next, both areas are divided into subareas of differing rainfall intensities. The classification of the convective area relies on information about the cloud top height gained from water vapor-IR differences and the IR cloud top temperature. The subdivision of the advective-stratiform area is based on information about the CWP and the particle phase in the upper parts. Suitable combinations of temperature differences (ΔT3.9–10.8, ΔT3.9–7.3, ΔT8.7–10.8, ΔT10.8–12.1) are incorporated to infer information about the CWP during nighttime, while a visible and a near-IR channel are considered during the daytime. ΔT8.7–10.8 and ΔT10.8–12.1 are particularly included to supply information about the cloud phase. Intensity differentiation is realized by using pixel-based confidences for each subarea calculated as a function of the respective value combinations of the previously mentioned variables. For the calculation of the confidences, the value combinations are compared with ground-based radar data. The proposed technique is validated against ground-based radar data and shows an encouraging performance (Heidke skill score 0.07–0.2 for 15-min intervals).