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Many institutions struggle to tap into the potential of their large archives of radar reflectivity: these data are often affected by miscalibration, yet the bias is typically unknown and temporally volatile. Still, relative calibration techniques can be used to correct the measurements a posteriori. For that purpose, the usage of spaceborne reflectivity observations from the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement (GPM) platforms has become increasingly popular: the calibration bias of a ground radar (GR) is estimated from its average reflectivity difference to the spaceborne radar (SR). Recently, Crisologo et al. (2018) introduced a formal procedure to enhance the reliability of such estimates: each match between SR and GR observations is assigned a quality index, and the calibration bias is inferred as a quality-weighted average of the differences between SR and GR. The relevance of quality was exemplified for the Subic S-band radar in the Philippines, which is greatly affected by partial beam blockage.
The present study extends the concept of quality-weighted averaging by accounting for path-integrated attenuation (PIA) in addition to beam blockage. This extension becomes vital for radars that operate at the C or X band. Correspondingly, the study setup includes a C-band radar that substantially overlaps with the S-band radar. Based on the extended quality-weighting approach, we retrieve, for each of the two ground radars, a time series of calibration bias estimates from suitable SR overpasses. As a result of applying these estimates to correct the ground radar observations, the consistency between the ground radars in the region of overlap increased substantially. Furthermore, we investigated if the bias estimates can be interpolated in time, so that ground radar observations can be corrected even in the absence of prompt SR overpasses. We found that a moving average approach was most suitable for that purpose, although limited by the absence of explicit records of radar maintenance operations.
RainNet v1.0
(2020)
In this study, we present RainNet, a deep convolutional neural network for radar-based precipitation nowcasting. Its design was inspired by the U-Net and SegNet families of deep learning models, which were originally designed for binary segmentation tasks. RainNet was trained to predict continuous precipitation intensities at a lead time of 5min, using several years of quality-controlled weather radar composites provided by the German Weather Service (DWD). That data set covers Germany with a spatial domain of 900km × 900km and has a resolution of 1km in space and 5min in time. Independent verification experiments were carried out on 11 summer precipitation events from 2016 to 2017. In order to achieve a lead time of 1h, a recursive approach was implemented by using RainNet predictions at 5min lead times as model inputs for longer lead times. In the verification experiments, trivial Eulerian persistence and a conventional model based on optical flow served as benchmarks. The latter is available in the rainymotion library and had previously been shown to outperform DWD's operational nowcasting model for the same set of verification events.
RainNet significantly outperforms the benchmark models at all lead times up to 60min for the routine verification metrics mean absolute error (MAE) and the critical success index (CSI) at intensity thresholds of 0.125, 1, and 5mm h⁻¹. However, rainymotion turned out to be superior in predicting the exceedance of higher intensity thresholds (here 10 and 15mm h⁻¹). The limited ability of RainNet to predict heavy rainfall intensities is an undesirable property which we attribute to a high level of spatial smoothing introduced by the model. At a lead time of 5min, an analysis of power spectral density confirmed a significant loss of spectral power at length scales of 16km and below. Obviously, RainNet had learned an optimal level of smoothing to produce a nowcast at 5min lead time. In that sense, the loss of spectral power at small scales is informative, too, as it reflects the limits of predictability as a function of spatial scale. Beyond the lead time of 5min, however, the increasing level of smoothing is a mere artifact – an analogue to numerical diffusion – that is not a property of RainNet itself but of its recursive application. In the context of early warning, the smoothing is particularly unfavorable since pronounced features of intense precipitation tend to get lost over longer lead times. Hence, we propose several options to address this issue in prospective research, including an adjustment of the loss function for model training, model training for longer lead times, and the prediction of threshold exceedance in terms of a binary segmentation task. Furthermore, we suggest additional input data that could help to better identify situations with imminent precipitation dynamics. The model code, pretrained weights, and training data are provided in open repositories as an input for such future studies.
RainNet v1.0
(2020)
In this study, we present RainNet, a deep convolutional neural network for radar-based precipitation nowcasting. Its design was inspired by the U-Net and SegNet families of deep learning models, which were originally designed for binary segmentation tasks. RainNet was trained to predict continuous precipitation intensities at a lead time of 5min, using several years of quality-controlled weather radar composites provided by the German Weather Service (DWD). That data set covers Germany with a spatial domain of 900km × 900km and has a resolution of 1km in space and 5min in time. Independent verification experiments were carried out on 11 summer precipitation events from 2016 to 2017. In order to achieve a lead time of 1h, a recursive approach was implemented by using RainNet predictions at 5min lead times as model inputs for longer lead times. In the verification experiments, trivial Eulerian persistence and a conventional model based on optical flow served as benchmarks. The latter is available in the rainymotion library and had previously been shown to outperform DWD's operational nowcasting model for the same set of verification events.
RainNet significantly outperforms the benchmark models at all lead times up to 60min for the routine verification metrics mean absolute error (MAE) and the critical success index (CSI) at intensity thresholds of 0.125, 1, and 5mm h⁻¹. However, rainymotion turned out to be superior in predicting the exceedance of higher intensity thresholds (here 10 and 15mm h⁻¹). The limited ability of RainNet to predict heavy rainfall intensities is an undesirable property which we attribute to a high level of spatial smoothing introduced by the model. At a lead time of 5min, an analysis of power spectral density confirmed a significant loss of spectral power at length scales of 16km and below. Obviously, RainNet had learned an optimal level of smoothing to produce a nowcast at 5min lead time. In that sense, the loss of spectral power at small scales is informative, too, as it reflects the limits of predictability as a function of spatial scale. Beyond the lead time of 5min, however, the increasing level of smoothing is a mere artifact – an analogue to numerical diffusion – that is not a property of RainNet itself but of its recursive application. In the context of early warning, the smoothing is particularly unfavorable since pronounced features of intense precipitation tend to get lost over longer lead times. Hence, we propose several options to address this issue in prospective research, including an adjustment of the loss function for model training, model training for longer lead times, and the prediction of threshold exceedance in terms of a binary segmentation task. Furthermore, we suggest additional input data that could help to better identify situations with imminent precipitation dynamics. The model code, pretrained weights, and training data are provided in open repositories as an input for such future studies.
Many institutions struggle to tap into the potential of their large archives of radar reflectivity: these data are often affected by miscalibration, yet the bias is typically unknown and temporally volatile. Still, relative calibration techniques can be used to correct the measurements a posteriori. For that purpose, the usage of spaceborne reflectivity observations from the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement (GPM) platforms has become increasingly popular: the calibration bias of a ground radar (GR) is estimated from its average reflectivity difference to the spaceborne radar (SR). Recently, Crisologo et al. (2018) introduced a formal procedure to enhance the reliability of such estimates: each match between SR and GR observations is assigned a quality index, and the calibration bias is inferred as a quality-weighted average of the differences between SR and GR. The relevance of quality was exemplified for the Subic S-band radar in the Philippines, which is greatly affected by partial beam blockage.
The present study extends the concept of quality-weighted averaging by accounting for path-integrated attenuation (PIA) in addition to beam blockage. This extension becomes vital for radars that operate at the C or X band. Correspondingly, the study setup includes a C-band radar that substantially overlaps with the S-band radar. Based on the extended quality-weighting approach, we retrieve, for each of the two ground radars, a time series of calibration bias estimates from suitable SR overpasses. As a result of applying these estimates to correct the ground radar observations, the consistency between the ground radars in the region of overlap increased substantially. Furthermore, we investigated if the bias estimates can be interpolated in time, so that ground radar observations can be corrected even in the absence of prompt SR overpasses. We found that a moving average approach was most suitable for that purpose, although limited by the absence of explicit records of radar maintenance operations.
In this study we examine the tonal organization of a series of recordings of liturgical chants, sung in 1966 by the Georgian master singer Artem Erkomaishvili. This dataset is the oldest corpus of Georgian chants from which the time synchronous F0-trajectories for all three voices have been reliably determined (Müller et al. 2017). It is therefore of outstanding importance for the understanding of the tuning principles of traditional Georgian vocal music.
The aim of the present study is to use various computational methods to analyze what these recordings can contribute to the ongoing scientific dispute about traditional Georgian tuning systems. Starting point for the present analysis is the re-release of the original audio data together with estimated fundamental frequency (F0) trajectories for each of the three voices, beat annotations, and digital scores (Rosenzweig et al. 2020). We present synoptic models for the pitch and the harmonic interval distributions, which are the first of such models for which the complete Erkomaishvili dataset was used. We show that these distributions can be very compactly be expressed as Gaussian mixture models, anchored on discrete sets of pitch or interval values for the pitch and interval distributions, respectively. As part of our study we demonstrate that these pitch values, which we refer to as scale pitches, and which are determined as the mean values of the Gaussian mixture elements, define the scale degrees of the melodic sound scales which build the skeleton of Artem Erkomaishvili’s intonation. The observation of consistent pitch bending of notes in melodic phrases, which appear in identical form in a group of chants, as well as the observation of harmonically driven intonation adjustments, which are clearly documented for all pure harmonic intervals, demonstrate that Artem Erkomaishvili intentionally deviates from the scale pitch skeleton quite freely. As a central result of our study, we proof that this melodic freedom is always constrained by the attracting influence of the scale pitches. Deviations of the F0-values of individual note events from the scale pitches at one instance of time are compensated for in the subsequent melodic steps. This suggests a deviation-compensation mechanism at the core of Artem Erkomaishvili’s melody generation, which clearly honors the scales but still allows for a large degree of melodic flexibility. This model, which summarizes all partial aspects of our analysis, is consistent with the melodic scale models derived from the observed pitch distributions, as well as with the melodic and harmonic interval distributions. In addition to the tangible results of our work, we believe that our work has general implications for the determination of tuning models from audio data, in particular for non-tempered music.
Hydrometric networks play a vital role in providing information for decision-making in water resource management. They should be set up optimally to provide as much information as possible that is as accurate as possible and, at the same time, be cost-effective. Although the design of hydrometric networks is a well-identified problem in hydrometeorology and has received considerable attention, there is still scope for further advancement. In this study, we use complex network analysis, defined as a collection of nodes interconnected by links, to propose a new measure that identifies critical nodes of station networks. The approach can support the design and redesign of hydrometric station networks. The science of complex networks is a relatively young field and has gained significant momentum over the last few years in different areas such as brain networks, social networks, technological networks, or climate networks. The identification of influential nodes in complex networks is an important field of research. We propose a new node-ranking measure – the weighted degree–betweenness (WDB) measure – to evaluate the importance of nodes in a network. It is compared to previously proposed measures used on synthetic sample networks and then applied to a real-world rain gauge network comprising 1229 stations across Germany to demonstrate its applicability. The proposed measure is evaluated using the decline rate of the network efficiency and the kriging error. The results suggest that WDB effectively quantifies the importance of rain gauges, although the benefits of the method need to be investigated in more detail.
Hydrometric networks play a vital role in providing information for decision-making in water resource management. They should be set up optimally to provide as much information as possible that is as accurate as possible and, at the same time, be cost-effective. Although the design of hydrometric networks is a well-identified problem in hydrometeorology and has received considerable attention, there is still scope for further advancement. In this study, we use complex network analysis, defined as a collection of nodes interconnected by links, to propose a new measure that identifies critical nodes of station networks. The approach can support the design and redesign of hydrometric station networks. The science of complex networks is a relatively young field and has gained significant momentum over the last few years in different areas such as brain networks, social networks, technological networks, or climate networks. The identification of influential nodes in complex networks is an important field of research. We propose a new node-ranking measure – the weighted degree–betweenness (WDB) measure – to evaluate the importance of nodes in a network. It is compared to previously proposed measures used on synthetic sample networks and then applied to a real-world rain gauge network comprising 1229 stations across Germany to demonstrate its applicability. The proposed measure is evaluated using the decline rate of the network efficiency and the kriging error. The results suggest that WDB effectively quantifies the importance of rain gauges, although the benefits of the method need to be investigated in more detail.
Convergence between the Indian and Asian plates has reshaped large parts of Asia, changing regional climate and biodiversity, yet geodynamic models fundamentally diverge on how convergence was accommodated since the India-Asia collision. Here we report palaeomagnetic data from the Burma Terrane, which is at the eastern edge of the collision zone and is famous for its Cretaceous amber biota, to better determine the evolution of the India-Asia collision. The Burma Terrane was part of a Trans-Tethyan island arc and stood at a near-equatorial southern latitude at similar to 95 Ma, suggesting island endemism for the Burmese amber biota. The Burma Terrane underwent significant clockwise rotation between similar to 80 and 50 Ma, causing its subduction margin to become hyper-oblique. Subsequently, it was translated northward on the Indian Plate by an exceptional distance of at least 2,000 km along a dextral strike-slip fault system in the east. Our reconstructions are only compatible with geodynamic models involving an initial collision of India with a near-equatorial Trans-Tethyan subduction system at similar to 60 Ma, followed by a later collision with the Asian margin.
We investigate the inclusions hosted in peritectic garnet from metapelitic migmatites of the Kinzigite Formation (Ivrea Zone, NW Italy) to evaluate the starting composition of the anatectic melt and fluid regime during anatexis throughout the upper amphibolite facies, transition, and granulite facies zones. Inclusions have negative crystal shapes, sizes from 2 to 10 mu m and are regularly distributed in the core of the garnet. Microstructural and micro-Raman investigations indicate the presence of two types of inclusions: crystallized silicate melt inclusions (i.e., nanogranitoids, NI), and fluid inclusions (FI). Microstructural evidence suggests that FI and NI coexist in the same cluster and are primary (i.e., were trapped simultaneously during garnet growth). FI have similar compositions in the three zones and comprise variable proportions of CO2, CH4, and N-2, commonly with siderite, pyrophyllite, and kaolinite, suggesting a COHN composition of the trapped fluid. The mineral assemblage in the NI contains K-feldspar, plagioclase, quartz, biotite, muscovite, chlorite, graphite and, rarely, calcite. Polymorphs such as kumdykolite, cristobalite, tridymite, and less commonly kokchetavite, were also found. Rehomogenized NI from the different zones show that all the melts are leucogranitic but have slightly different compositions. In samples from the upper amphibolite facies, melts are less mafic (FeO + MgO = 2.0-3.4 wt%), contain 860-1700 ppm CO2 and reach the highest H2O contents (6.5-10 wt%). In the transition zone melts have intermediate H2O (4.8-8.5 wt%), CO2 (457-1534 ppm) and maficity (FeO + MgO = 2.3-3.9 wt%). In contrast, melts at granulite facies reach highest CaO, FeO + MgO (3.2-4.7 wt%), and CO2 (up to 2,400 ppm), with H2O contents comparable (5.4-8.3 wt%) to the other two zones. Our results represent the first clear evidence for carbonic fluid-present melting in the Ivrea Zone. Anatexis of metapelites occurred through muscovite and biotite breakdown melting in the presence of a COH fluid, in a situation of fluid-melt immiscibility. The fluid is assumed to have been internally derived, produced initially by devolatilization of hydrous silicates in the graphitic protolith, then as a result of oxidation of carbon by consumption of Fe3+-bearing biotite during melting. Variations in the compositions of the melts are interpreted to result from higher T of melting. The H2O contents of the melts throughout the three zones are higher than usually assumed for initial H2O contents of anatectic melts. The CO2 contents are highest at granulite facies, and show that carbon-contents of crustal magmas are not negligible at high T. The activity of H2O of the fluid dissolved in granitic melts decreases with increasing metamorphic grade. Carbonic fluid-present melting of the deep continental crust represents, together with hydrate-breakdown melting reactions, an important process in the origin of crustal anatectic granitoids.
Debate persists concerning the timing and geodynamics of intercontinental collision, style of syncollisional deformation, and development of topography and fold-and-thrust belts along the >1,700-km-long Izmir-Ankara-Erzincan suture zone (IAESZ) in Turkey. Resolving this debate is a necessary precursor to evaluating the integrity of convergent margin models and kinematic, topographic, and biogeographic reconstructions of the Mediterranean domain. Geodynamic models argue either for a synchronous or diachronous collision during either the Late Cretaceous and/or Eocene, followed by Eocene slab breakoff and postcollisional magmatism. We investigate the collision chronology in western Anatolia as recorded in the sedimentary archives of the 90-km-long Saricakaya Basin perched at shallow structural levels along the IAESZ. Based on new zircon U-Pb geochronology and depositional environment and sedimentary provenance results, we demonstrate that the Saricakaya Basin is an Eocene sedimentary basin with sediment sourced from both the IAESZ and Sogut Thrust fault to the south and north, respectively, and formed primarily by flexural loading from north-south shortening along the syncollisional Sogut Thrust. Our results refine the timing of collision between the Anatolides and Pontide terranes in western Anatolia to Maastrichtian-Middle Paleocene and Early Eocene crustal shortening and basin formation. Furthermore, we demonstrate contemporaneous collision, deformation, and magmatism across the IAESZ, supporting synchronous collision models. We show that regional postcollisional magmatism can be explained by renewed underthrusting instead of slab breakoff. This new IAESZ chronology provides additional constraints for kinematic, geodynamic, and biogeographic reconstructions of the Mediterranean domain.