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In this study, we investigate how immersive 3D geovisualization can be used in higher education. Based on MacEachren and Kraak's geovisualization cube, we examine the usage of immersive 3D geovisualization and its usefulness in a research-based learning module on flood risk, called GEOSimulator. Results of a survey among participating students reveal benefits, such as better orientation in the study area, higher interactivity with the data, improved discourse among students and enhanced motivation through immersive 3D geovisualization. This suggests that immersive 3D visualization can effectively be used in higher education and that 3D CAVE settings enhance interactive learning between students.
In 2009, a group of prominent Earth scientists introduced the "planetary boundaries" (PB) framework: they suggested nine global control variables, and defined corresponding "thresholds which, if crossed, could generate unacceptable environmental change". The concept builds on systems theory, and views Earth as a complex adaptive system in which anthropogenic disturbances may trigger non-linear, abrupt, and irreversible changes at the global scale, and "push the Earth system outside the stable environmental state of the Holocene". While the idea has been remarkably successful in both science and policy circles, it has also raised fundamental concerns, as the majority of suggested processes and their corresponding planetary boundaries do not operate at the global scale, and thus apparently lack the potential to trigger abrupt planetary changes.
This paper picks up the debate with specific regard to the planetary boundary on "global freshwater use". While the bio-physical impacts of excessive water consumption are typically confined to the river basin scale, the PB proponents argue that water-induced environmental disasters could build up to planetary-scale feedbacks and system failures. So far, however, no evidence has been presented to corroborate that hypothesis. Furthermore, no coherent approach has been presented to what extent a planetary threshold value could reflect the risk of regional environmental disaster. To be sure, the PB framework was revised in 2015, extending the planetary freshwater boundary with a set of basin-level boundaries inferred from environmental water flow assumptions. Yet, no new evidence was presented, either with respect to the ability of those basin-level boundaries to reflect the risk of regional regime shifts or with respect to a potential mechanism linking river basins to the planetary scale.
So while the idea of a planetary boundary on freshwater use appears intriguing, the line of arguments presented so far remains speculative and implicatory. As long as Earth system science does not present compelling evidence, the exercise of assigning actual numbers to such a boundary is arbitrary, premature, and misleading. Taken as a basis for water-related policy and management decisions, though, the idea transforms from misleading to dangerous, as it implies that we can globally offset water-related environmental impacts. A planetary boundary on freshwater use should thus be disapproved and actively refuted by the hydrological and water resources community.
An Overview of Using Weather Radar for Climatological Studies: Successes, Challenges, and Potential
(2019)
Weather radars have been widely used to detect and quantify precipitation and nowcast severe weather for more than 50 years. Operational weather radars generate huge three-dimensional datasets that can accumulate to terabytes per day. So it is essential to review what can be done with existing vast amounts of data, and how we should manage the present datasets for the future climatologists. All weather radars provide the reflectivity factor, and this is the main parameter to be archived. Saving reflectivity as volumetric data in the original spherical coordinates allows for studies of the three-dimensional structure of precipitation, which can be applied to understand a number of processes, for example, analyzing hail or thunderstorm modes. Doppler velocity and polarimetric moments also have numerous applications for climate studies, for example, quality improvement of reflectivity and rain rate retrievals, and for interrogating microphysical and dynamical processes. However, observational data alone are not useful if they are not accompanied by sufficient metadata. Since the lifetime of a radar ranges between 10 and 20 years, instruments are typically replaced or upgraded during climatologically relevant time periods. As a result, present metadata often do not apply to past data. This paper outlines the work of the Radar Task Team set by the Atmospheric Observation Panel for Climate (AOPC) and summarizes results from a recent survey on the existence and availability of long time series. We also provide recommendations for archiving current and future data and examples of climatological studies in which radar data have already been used.
High precipitation quantiles tend to rise with temperature, following the so-called Clausius–Clapeyron (CC) scaling. It is often reported that the CC-scaling relation breaks down and even reverts for very high temperatures. In our study, we investigate this reversal using observational climate data from 142 stations across Germany. One of the suggested meteorological explanations for the breakdown is limited moisture supply. Here we argue that, instead, it could simply originate from undersampling. As rainfall frequency generally decreases with higher temperatures, rainfall intensities as dictated by CC scaling are less likely to be recorded than for moderate temperatures. Empirical quantiles are conventionally estimated from order statistics via various forms of plotting position formulas. They have in common that their largest representable return period is given by the sample size. In small samples, high quantiles are underestimated accordingly. The small-sample effect is weaker, or disappears completely, when using parametric quantile estimates from a generalized Pareto distribution (GPD) fitted with L moments. For those, we obtain quantiles of rainfall intensities that continue to rise with temperature.
We explore the potential of spaceborne radar (SR) observations from the Ku-band precipitation radars onboard the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement (GPM) satellites as a reference to quantify the ground radar (GR) reflectivity bias. To this end, the 3-D volume-matching algorithm proposed by Schwaller and Morris (2011) is implemented and applied to 5 years (2012–2016) of observations. We further extend the procedure by a framework to take into account the data quality of each ground radar bin. Through these methods, we are able to assign a quality index to each matching SR–GR volume, and thus compute the GR calibration bias as a quality-weighted average of reflectivity differences in any sample of matching GR–SR volumes. We exemplify the idea of quality-weighted averaging by using the beam blockage fraction as the basis of a quality index. As a result, we can increase the consistency of SR and GR observations, and thus the precision of calibration bias estimates. The remaining scatter between GR and SR reflectivity as well as the variability of bias estimates between overpass events indicate, however, that other error sources are not yet fully addressed. Still, our study provides a framework to introduce any other quality variables that are considered relevant in a specific context. The code that implements our analysis is based on the wradlib open-source software library, and is, together with the data, publicly available to monitor radar calibration or to scrutinize long series of archived radar data back to December 1997, when TRMM became operational.
In a recent BAMS article, it is argued that community-based Open Source Software (OSS) could foster scientific progress in weather radar research, and make weather radar software more affordable, flexible, transparent, sustainable, and interoperable.
Nevertheless, it can be challenging for potential developers and users to realize these benefits: tools are often cumbersome to install; different operating systems may have particular issues, or may not be supported at all; and many tools have steep learning curves.
To overcome some of these barriers, we present an open, community-based virtual machine (VM). This VM can be run on any operating system, and guarantees reproducibility of results across platforms. It contains a suite of independent OSS weather radar tools (BALTRAD, Py-ART, wradlib, RSL, and Radx), and a scientific Python stack. Furthermore, it features a suite of recipes that work out of the box and provide guidance on how to use the different OSS tools alone and together. The code to build the VM from source is hosted on GitHub, which allows the VM to grow with its community.
We argue that the VM presents another step toward Open (Weather Radar) Science. It can be used as a quick way to get started, for teaching, or for benchmarking and combining different tools. It can foster the idea of reproducible research in scientific publishing. Being scalable and extendable, it might even allow for real-time data processing.
We expect the VM to catalyze progress toward interoperability, and to lower the barrier for new users and developers, thus extending the weather radar community and user base.
Optical flow models as an open benchmark for radar-based precipitation nowcasting (rainymotion v0.1)
(2019)
Quantitative precipitation nowcasting (QPN) has become an essential technique in various application contexts, such as early warning or urban sewage control. A common heuristic prediction approach is to track the motion of precipitation features from a sequence of weather radar images and then to displace the precipitation field to the imminent future (minutes to hours) based on that motion, assuming that the intensity of the features remains constant (“Lagrangian persistence”). In that context, “optical flow” has become one of the most popular tracking techniques. Yet the present landscape of computational QPN models still struggles with producing open software implementations. Focusing on this gap, we have developed and extensively benchmarked a stack of models based on different optical flow algorithms for the tracking step and a set of parsimonious extrapolation procedures based on image warping and advection. We demonstrate that these models provide skillful predictions comparable with or even superior to state-of-the-art operational software. Our software library (“rainymotion”) for precipitation nowcasting is written in the Python programming language and openly available at GitHub (https://github.com/hydrogo/rainymotion, Ayzel et al., 2019). That way, the library may serve as a tool for providing fast, free, and transparent solutions that could serve as a benchmark for further model development and hypothesis testing – a benchmark that is far more advanced than the conventional benchmark of Eulerian persistence commonly used in QPN verification experiments.
Flood generation in mountainous headwater catchments is governed by rainfall intensities, by the spatial distribution of rainfall and by the state of the catchment prior to the rainfall, e. g. by the spatial pattern of the soil moisture, groundwater conditions and possibly snow. The work presented here explores the limits and potentials of measuring soil moisture with different methods and in different scales and their potential use for flood simulation. These measurements were obtained in 2007 and 2008 within a comprehensive multi-scale experiment in the Weisseritz headwater catchment in the Ore-Mountains, Germany. The following technologies have been applied jointly thermogravimetric method, frequency domain reflectometry (FDR) sensors, spatial time domain reflectometry (STDR) cluster, ground-penetrating radar (GPR), airborne polarimetric synthetic aperture radar (polarimetric SAR) and advanced synthetic aperture radar (ASAR) based on the satellite Envisat. We present exemplary soil measurement results, with spatial scales ranging from point scale, via hillslope and field scale, to the catchment scale. Only the spatial TDR cluster was able to record continuous data. The other methods are limited to the date of over-flights (airplane and satellite) or measurement campaigns on the ground. For possible use in flood simulation, the observation of soil moisture at multiple scales has to be combined with suitable hydrological modelling, using the hydrological model WaSiM-ETH. Therefore, several simulation experiments have been conducted in order to test both the usability of the recorded soil moisture data and the suitability of a distributed hydrological model to make use of this information. The measurement results show that airborne-based and satellite-based systems in particular provide information on the near-surface spatial distribution. However, there are still a variety of limitations, such as the need for parallel ground measurements (Envisat ASAR), uncertainties in polarimetric decomposition techniques (polarimetric SAR), very limited information from remote sensing methods about vegetated surfaces and the non-availability of continuous measurements. The model experiments showed the importance of soil moisture as an initial condition for physically based flood modelling. However, the observed moisture data reflect the surface or near-surface soil moisture only. Hence, only saturated overland flow might be related to these data. Other flood generation processes influenced by catchment wetness in the subsurface such as subsurface storm flow or quick groundwater drainage cannot be assessed by these data. One has to acknowledge that, in spite of innovative measuring techniques on all spatial scales, soil moisture data for entire vegetated catchments are still today not operationally available. Therefore, observations of soil moisture should primarily be used to improve the quality of continuous, distributed hydrological catchment models that simulate the spatial distribution of moisture internally. Thus, when and where soil moisture data are available, they should be compared with their simulated equivalents in order to improve the parameter estimates and possibly the structure of the hydrological model.
Hydrologic modelers often need to know which method of quantitative precipitation estimation (QPE) is best suited for a particular catchment. Traditionally, QPE methods are verified and benchmarked against independent rain gauge observations. However, the lack of spatial representativeness limits the value of such a procedure. Alternatively, one could drive a hydrological model with different QPE products and choose the one which best reproduces observed runoff. Unfortunately, the calibration of conceptual model parameters might conceal actual differences between the QPEs. To avoid such effects, we abandoned the idea of determining optimum parameter sets for all QPE being compared. Instead, we carry out a large number of runoff simulations, confronting each QPE with a common set of random parameters. By evaluating the goodness-of-fit of all simulations, we obtain information on whether the quality of competing QPE methods is significantly different. This knowledge is inferred exactly at the scale of interest-the catchment scale. We use synthetic data to investigate the ability of this procedure to distinguish a truly superior QPE from an inferior one. We find that the procedure is prone to failure in the case of linear systems. However, we show evidence that in realistic (nonlinear) settings, the method can provide useful results even in the presence of moderate errors in model structure and streamflow observations. In a real-world case study on a small mountainous catchment, we demonstrate the ability of the verification procedure to reveal additional insights as compared to a conventional cross validation approach.
