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Accurate weather observations are the keystone to many quantitative applications, such as precipitation monitoring and nowcasting, hydrological modelling and forecasting, climate studies, as well as understanding precipitation-driven natural hazards (i.e. floods, landslides, debris flow). Weather radars have been an increasingly popular tool since the 1940s to provide high spatial and temporal resolution precipitation data at the mesoscale, bridging the gap between synoptic and point scale observations. Yet, many institutions still struggle to tap the potential of the large archives of reflectivity, as there is still much to understand about factors that contribute to measurement errors, one of which is calibration. Calibration represents a substantial source of uncertainty in quantitative precipitation estimation (QPE). A miscalibration of a few dBZ can easily deteriorate the accuracy of precipitation estimates by an order of magnitude. Instances where rain cells carrying torrential rains are misidentified by the radar as moderate rain could mean the difference between a timely warning and a devastating flood.
Since 2012, the Philippine Atmospheric, Geophysical, and Astronomical Services Administration (PAGASA) has been expanding the country’s ground radar network. We had a first look into the dataset from one of the longest running radars (the Subic radar) after devastating week-long torrential rains and thunderstorms in August 2012 caused by the annual southwestmonsoon and enhanced by the north-passing Typhoon Haikui. The analysis of the rainfall spatial distribution revealed the added value of radar-based QPE in comparison to interpolated rain gauge observations. However, when compared with local gauge measurements, severe miscalibration of the Subic radar was found. As a consequence, the radar-based QPE would have underestimated the rainfall amount by up to 60% if they had not been adjusted by rain gauge observations—a technique that is not only affected by other uncertainties, but which is also not feasible in other regions of the country with very sparse rain gauge coverage.
Relative calibration techniques, or the assessment of bias from the reflectivity of two radars, has been steadily gaining popularity. Previous studies have demonstrated that reflectivity observations from the Tropical Rainfall Measuring Mission (TRMM) and its successor, the Global Precipitation Measurement (GPM), are accurate enough to serve as a calibration reference for ground radars over low-to-mid-latitudes (± 35 deg for TRMM; ± 65 deg for GPM). Comparing spaceborne radars (SR) and ground radars (GR) requires cautious consideration of differences in measurement geometry and instrument specifications, as well as temporal coincidence. For this purpose, we implement a 3-D volume matching method developed by Schwaller and Morris (2011) and extended by Warren et al. (2018) to 5 years worth of observations from the Subic radar. In this method, only the volumetric intersections of the SR and GR beams are considered.
Calibration bias affects reflectivity observations homogeneously across the entire radar domain. Yet, other sources of systematic measurement errors are highly heterogeneous in space, and can either enhance or balance the bias introduced by miscalibration. In order to account for such heterogeneous errors, and thus isolate the calibration bias, we assign a quality index to each matching SR–GR volume, and thus compute the GR calibration bias as a qualityweighted average of reflectivity differences in any sample of matching SR–GR volumes. We exemplify the idea of quality-weighted averaging by using beam blockage fraction (BBF) as a quality variable. Quality-weighted averaging is able to increase the consistency of SR and GR observations by decreasing the standard deviation of the SR–GR differences, and thus increasing the precision of the bias estimates.
To extend this framework further, the SR–GR quality-weighted bias estimation is applied to the neighboring Tagaytay radar, but this time focusing on path-integrated attenuation (PIA) as the source of uncertainty. Tagaytay is a C-band radar operating at a lower wavelength and is therefore more affected by attenuation. Applying the same method used for the Subic radar, a time series of calibration bias is also established for the Tagaytay radar.
Tagaytay radar sits at a higher altitude than the Subic radar and is surrounded by a gentler terrain, so beam blockage is negligible, especially in the overlapping region. Conversely, Subic radar is largely affected by beam blockage in the overlapping region, but being an SBand radar, attenuation is considered negligible. This coincidentally independent uncertainty contributions of each radar in the region of overlap provides an ideal environment to experiment with the different scenarios of quality filtering when comparing reflectivities from the two ground radars. The standard deviation of the GR–GR differences already decreases if we consider either BBF or PIA to compute the quality index and thus the weights. However, combining them multiplicatively resulted in the largest decrease in standard deviation, suggesting that taking both factors into account increases the consistency between the matched samples.
The overlap between the two radars and the instances of the SR passing over the two radars at the same time allows for verification of the SR–GR quality-weighted bias estimation method. In this regard, the consistency between the two ground radars is analyzed before and after bias correction is applied. For cases when all three radars are coincident during a significant rainfall event, the correction of GR reflectivities with calibration bias estimates from SR overpasses dramatically improves the consistency between the two ground radars which have shown incoherent observations before correction. We also show that for cases where adequate SR coverage is unavailable, interpolating the calibration biases using a moving average can be used to correct the GR observations for any point in time to some extent. By using the interpolated biases to correct GR observations, we demonstrate that bias correction reduces the absolute value of the mean difference in most cases, and therefore improves the consistency between the two ground radars.
This thesis demonstrates that in general, taking into account systematic sources of uncertainty that are heterogeneous in space (e.g. BBF) and time (e.g. PIA) allows for a more consistent estimation of calibration bias, a homogeneous quantity. The bias still exhibits an unexpected variability in time, which hints that there are still other sources of errors that remain unexplored. Nevertheless, the increase in consistency between SR and GR as well as between the two ground radars, suggests that considering BBF and PIA in a weighted-averaging approach is a step in the right direction.
Despite the ample room for improvement, the approach that combines volume matching between radars (either SR–GR or GR–GR) and quality-weighted comparison is readily available for application or further scrutiny. As a step towards reproducibility and transparency in atmospheric science, the 3D matching procedure and the analysis workflows as well as sample data are made available in public repositories. Open-source software such as Python and wradlib are used for all radar data processing in this thesis. This approach towards open science provides both research institutions and weather services with a valuable tool that can be applied to radar calibration, from monitoring to a posteriori correction of archived data.
Spinning up large-scale coupled surface-subsurface numerical models can be a time and resource consuming task. If an uninformed initial condition is chosen, the spin-up can easily require 20 years of repeated simulations on high-performance computing machines. In this paper we compare the classical approach of starting from a fixed shallow depth to groundwater (here 3 m) with three more informed approaches for the definition of initial conditions in the spin up. In the first of these three approaches, we start from a known-steady state groundwater table, calculated with a 2-D groundwater model and the yearly net recharge, and combine it with an unsaturated zone that assumes hydrostatic conditions. In the second approach, we start from the same groundwater table combined with vertical profiles in the unsaturated zone with uniform vertical flow identical to the groundwater recharge. In the third approach we calculate a dynamic steady state from a simplified subsurface model combining a transient 2-D groundwater model with a limited number of 1-D transient unsaturated zone columns on top. Results for spinning-up a 3-D Parflow-CLM model using the different initial conditions show that large gains can be made by considering states in groundwater and the vadose zone that are consistent, i.e. where groundwater recharge and the vertical flux in the vadose zone agree. By this, the spin-up time was reduced from about 10 years to about 3 years of simulated time. In the light of seasonal fluctuations of net recharge, using the transient approach showed more stable results.
The Value of Empirical Data for Estimating the Parameters of a Sociohydrological Flood Risk Model
(2019)
In this paper, empirical data are used to estimate the parameters of a sociohydrological flood risk model. The proposed model, which describes the interactions between floods, settlement density, awareness, preparedness, and flood loss, is based on the literature. Data for the case study of Dresden, Germany, over a period of 200years, are used to estimate the model parameters through Bayesian inference. The credibility bounds of their estimates are small, even though the data are rather uncertain. A sensitivity analysis is performed to examine the value of the different data sources in estimating the model parameters. In general, the estimated parameters are less biased when using data at the end of the modeled period. Data about flood awareness are the most important to correctly estimate the parameters of this model and to correctly model the system dynamics. Using more data for other variables cannot compensate for the absence of awareness data. More generally, the absence of data mostly affects the estimation of the parameters that are directly related to the variable for which data are missing. This paper demonstrates that combining sociohydrological modeling and empirical data gives additional insights into the sociohydrological system, such as quantifying the forgetfulness of the society, which would otherwise not be easily achieved by sociohydrological models without data or by standard statistical analysis of empirical data.
The Postmasburg Manganese Field (PMF), Northern Cape Province, South Africa, once represented one of the largest sources of manganese ore worldwide. Two belts of manganese ore deposits have been distinguished in the PMF, namely the Western Belt of ferruginous manganese ores and the Eastern Belt of siliceous manganese ores. Prevailing models of ore formation in these two belts invoke karstification of manganese-rich dolomites and residual accumulation of manganese wad which later underwent diagenetic and low-grade metamorphic processes. For the most part, the role of hydrothermal processes and metasomatic alteration towards ore formation has not been adequately discussed. Here we report an abundance of common and some rare Al-, Na-, K- and Ba-bearing minerals, particularly aegirine, albite, microcline, banalsite, serandite-pectolite, paragonite and natrolite in Mn ores of the PMF, indicative of hydrothermal influence. Enrichments in Na, K and/or Ba in the ores are generally on a percentage level for most samples analysed through bulk-rock techniques. The presence of As-rich tokyoite also suggests the presence of As and V in the hydrothermal fluid. The fluid was likely oxidized and alkaline in nature, akin to a mature basinal brine. Various replacement textures, particularly of Na- and K- rich minerals by Ba-bearing phases, suggest sequential deposition of gangue as well as ore-minerals from the hydrothermal fluid, with Ba phases being deposited at a later stage. The stratigraphic variability of the studied ores and their deviation from the strict classification of ferruginous and siliceous ores in the literature, suggests that a re-evaluation of genetic models is warranted. New Ar-Ar ages for K-feldspars suggest a late Neoproterozoic timing for hydrothermal activity. This corroborates previous geochronological evidence for regional hydrothermal activity that affected Mn ores at the PMF but also, possibly, the high-grade Mn ores of the Kalahari Manganese Field to the north. A revised, all-encompassing model for the development of the manganese deposits of the PMF is then proposed, whereby the source of metals is attributed to underlying carbonate rocks beyond the Reivilo Formation of the Campbellrand Subgroup. The main process by which metals are primarily accumulated is attributed to karstification of the dolomitic substrate. The overlying Asbestos Hills Subgroup banded iron formation (BIF) is suggested as a potential source of alkali metals, which also provides a mechanism for leaching of these BIFs to form high-grade residual iron ore deposits.
Movement ecology aims to provide common terminology and an integrative framework of movement research across all groups of organisms. Yet such work has focused on unitary organisms so far, and thus the important group of filamentous fungi has not been considered in this context. With the exception of spore dispersal, movement in filamentous fungi has not been integrated into the movement ecology field. At the same time, the field of fungal ecology has been advancing research on topics like informed growth, mycelial translocations, or fungal highways using its own terminology and frameworks, overlooking the theoretical developments within movement ecology. We provide a conceptual and terminological framework for interdisciplinary collaboration between these two disciplines, and show how both can benefit from closer links: We show how placing the knowledge from fungal biology and ecology into the framework of movement ecology can inspire both theoretical and empirical developments, eventually leading towards a better understanding of fungal ecology and community assembly. Conversely, by a greater focus on movement specificities of filamentous fungi, movement ecology stands to benefit from the challenge to evolve its concepts and terminology towards even greater universality. We show how our concept can be applied for other modular organisms (such as clonal plants and slime molds), and how this can lead towards comparative studies with the relationship between organismal movement and ecosystems in the focus.
A lasting legacy of the International Polar Year (IPY) 2007–2008 was the promotion of the Permafrost Young Researchers Network (PYRN), initially an IPY outreach and education activity by the International Permafrost Association (IPA). With the momentum of IPY, PYRN developed into a thriving network that still connects young permafrost scientists, engineers, and researchers from other disciplines. This research note summarises (1) PYRN’s development since 2005 and the IPY’s role, (2) the first 2015 PYRN census and survey results, and (3) PYRN’s future plans to improve international and interdisciplinary exchange between young researchers. The review concludes that PYRN is an established network within the polar research community that has continually developed since 2005. PYRN’s successful activities were largely fostered by IPY. With >200 of the 1200 registered members active and engaged, PYRN is capitalising on the availability of social media tools and rising to meet environmental challenges while maintaining its role as a successful network honouring the legacy of IPY.
During the earliest Triassic microbial mats flourished in the photic zones of marginal seas, generating widespread microbialites. It has been suggested that anoxic conditions in shallow marine environments, linked to the end-Permian mass extinction, limited mat-inhibiting metazoans allowing for this microbialite expansion. The presence of a diverse suite of proxies indicating oxygenated shallow sea-water conditions (metazoan fossils, biomarkers and redox proxies) from microbialite successions have, however, challenged the inference of anoxic conditions. Here, the distribution and faunal composition of Griesbachian microbialites from China, Iran, Turkey, Armenia, Slovenia and Hungary are investigated to determine the factors that allowed microbialite-forming microbial mats to flourish following the end-Permian crisis. The results presented here show that Neotethyan microbial buildups record a unique faunal association due to the presence of keratose sponges, while the Palaeotethyan buildups have a higher proportion of molluscs and the foraminifera Earlandia. The distribution of the faunal components within the microbial fabrics suggests that, except for the keratose sponges and some microconchids, most of the metazoans were transported into the microbial framework via wave currents. The presence of both microbialites and metazoan associations were limited to oxygenated settings, suggesting that a factor other than anoxia resulted in a relaxation of ecological constraints following the mass extinction event. It is inferred that the end-Permian mass extinction event decreased the diversity and abundance of metazoans to the point of significantly reducing competition, allowing photosynthesis-based microbial mats to flourish in shallow water settings and resulting in the formation of widespread microbialites.