An icon-based synoptic visualization of fully polarimetric radar data

The visualization of fully polarimetric radar data is hindered by traditional remote sensing methodologies for displaying data due to the large number of parameters per pixel in such data, and the non-scalar nature of variables such as phase difference. In this paper, a new method is described that uses icons instead of image pixels to represent the image data so that polarimetric properties and geographic context can be visualized together. The icons are parameterized using the alpha-entropy decomposition of polarimetric data. The resulting image allows the following five variables to be displayed simultaneously: unpolarized power, alpha angle, polarimetric entropy, anisotropy and orientation angle. Examples are given for both airborne and laboratory-based imaging

Visualisation of polarimetric radar data

This thesis examines the application of scientific visualisation to the analysis of polarimetric radar data sets. The research contained herein forms part of a larger body of work that studies the application of scientific visualisation to the analysis of large multi-valued datasets. Visualisation techniques have historically assumed a fundamental role in the analysis of patterns in geographic datasets. This is particularly apparent in the analysis of remotely sensed data, which, since the advent of aerial photography, has utilised the intensity of visible (and invisible) electromagnetic energy as a means of producing synoptic map-like images. Progress in remote sensing technology, however, has led to the development of systems which measure very large numbers of intensity 'channels', or require the analysis of variables other than intensity values. Current visualisation strategies are insufficient to adequately represent such datasets, whilst retaining the synoptic perspective. In response to this, two new visualisation techniques are presented for the analysis of polarimetric radar data. Both techniques demonstrate how it is possible to produce synoptic image suitable for the analysis of spatial patterns without relying on pixel based intensity images. This allows a large number of variables to be ascribed to a single geographic location, and thus encourages the rapid identification of patterns and anomalies within datasets. The value of applying the principals of scientific visualisation to exploratory data analysis is subsequently demonstrated with reference to a number of case studies that highlight the potential of the newly developed techniques

How organized is deep convection over Germany?

Deep moist convection shows a tendency to organize into mesoscale structures. To be able to understand the potential effect of convective organization on the climate, one needs first to characterize organization. In this study, we systematically characterize the organizational state of convection over Germany based on two years of cloud-top observations derived from the Meteosat Second Generation satellite and of precipitation cores detected by the German C-band radar network. The organizational state of convection is characterized by commonly employed organization indices, which are mostly based on the object numbers, sizes and nearest-neighbour distances. According to the organization index Iorg, cloud tops and precipitation cores are found to be in an organized state for 69% and 92% of the time, respectively. There is an increase in rainfall when the number of objects and their sizes increase, independently of the organizational state. Case-studies of specific days suggest that convectively organized states correspond to either local multi-cell clusters, with less numerous, larger objects close to each other, or to scattered clusters, with more numerous, smaller organized objects spread out over the domain. For those days, simulations are performed with the large-eddy model ICON with grid spacings of 625, 312 and 156?m. Although the model underestimates rainfall and shows a too large cold cloud coverage, the organizational state is reasonably well represented without significant differences between the grid spacings

Improving Wishart Classification of Polarimetric SAR Data Using the Hopfield Neural Network Optimization Approach.

This paper proposes the optimization relaxation approach based on the analogue Hopfield Neural Network (HNN) for cluster refinement of pre-classified Polarimetric Synthetic Aperture Radar (PolSAR) image data. We consider the initial classification provided by the maximum-likelihood classifier based on the complex Wishart distribution, which is then supplied to the HNN optimization approach. The goal is to improve the classification results obtained by the Wishart approach. The classification improvement is verified by computing a cluster separability coefficient and a measure of homogeneity within the clusters. During the HNN optimization process, for each iteration and for each pixel, two consistency coefficients are computed, taking into account two types of relations between the pixel under consideration and its corresponding neighbors. Based on these coefficients and on the information coming from the pixel itself, the pixel under study is re-classified. Different experiments are carried out to verify that the proposed approach outperforms other strategies, achieving the best results in terms of separability and a trade-off with the homogeneity preserving relevant structures in the image. The performance is also measured in terms of computational central processing unit (CPU) times

Sources of predictability for deep convection

The advent of convection-permitting ensemble prediction systems at most operational weather centers within the last 15 years constitutes a step-change in our ability to forecast convection. This is a fundamental task for weather services, as not only the general public demands good and reliable forecasts of convection, but they can also be accompanied by heavy precipitation and destructive hail, and so comprise a risk to human life. Unfortunately, their prediction is challenging and their predictability limited by the chaotic nature of the atmosphere, so there is an intrinsic limit to their prediction in the order of a few hours. But for all that, the possible existence of sources of predictability that are able to extend the forecast horizon on the convective scales is being discussed. More precisely, those scales might inherit predictability from larger-scale features, such as orography or the prevailing weather regime. Furthermore, radar data will be operationally assimilated at \acf{DWD} within the year and provide similar information as the natural sources of predictability---the position of convection. The identification of predictability limits of convection poses significant challenges, which we address within a hierarchy of model configurations, combined with radar DA and two sets of \acfp{IC}. First, we reduce the complexity in an idealized setup with simplified \acp{IC} and orography before we reintroduce complex orography and natural variability of the synoptic weather regime. We apply a combination of sophisticated verification metrics to address specific facets of practical predictability, namely the predictability of the model state and the model predictability of the atmospheric state. In the idealized setup, we find increased predictability of convection in the presence of orography if the \acp{IC} depict only small-scale errors, representing perfect large-scale predictability. In the presence of large-scale errors, the beneficial effect of the orography is greatly diminished. However, the assimilation of radar observations proved its ability to account for these errors and provide high-quality analyses. In both sets of \acp{IC}, the forecast horizon is extended by \SI{6}{h}, where scales up to \SI{50}{km} remain predictable for small-scale errors, and scales up to \SI{100}{km} do so with additional large-scale uncertainty. The experiments also highlight increased predictability for convection with a high level of organization. We were also able to confirm these results in the pre-operational COSMO-KENDA system of the \ac{DWD}. More specifically, we found increased predictability of the model state and model predictability of the atmospheric state in the orographically more influenced South than in the comparatively plain North of Germany. Throughout three summers in an older \ac{DWD} system, we found locally forced weather situations to be less predictable than those forced by the synoptic weather regime and no significant effect of orography.Die Einführung von Konvektion auflösenden Vorhersagemodellen in den meisten operationellen Wetterzentren während der letzten 15 Jahre markiert einen Fortschritt in der Vorhersagbarkeit von Konvektion. Ein öffentliches Interesse an akkuraten Gewittervorhersagen ebenso wie die mit Gewittern verbundenen Sicherheitsrisiken machen diese zu einer zentrale Aufgabe für Wetterdienste. Eine Herausforderung ergibt sich hierbei aus der natürlichen Begrenztheit der Vorhersage auf wenige Stunden aufgrund der chaotischen Eigenschaften der Atmosphäre. Jedoch gibt es Hypothesen zu möglichen Quellen von Vorhersagbarkeit, die den Vorhersagehorizont auf den konvektiven Skalen erweitern können. Diese Skalen könnten Vorhersagbarkeit von anderen Merkmalen, wie z.B. Orographie oder dem Wetterregime, übernehmen. Darüberhinaus plant der Deutsche Wetterdienst noch 2019 operationell Radardaten zu assimilieren, was ebenso wie Orographie die Position von Konvektion beeinflusst. Wir nähern uns der Ermittlung der Grenzen von Gewittervorhersagbarkeit mittels einer Reihe von Modellkonfigurationen, kombiniert mit Radardatenassimilation und zweierlei Arten von Anfangsbedingungen, an. Wir verringern die Komplexität in einem idealisierten Setup mit vereinfachten Anfangsbedingungen (AB) und Orographie, ehe wir komplexe Orographie und die natürlich Variabilität des synoptischen Wetterregimes erneut einführen. Wir wenden eine Kombination von Verifikationsmethoden an und berücksichtigen so spezifische Facetten der praktischen Vorhersagbarkeit, d.h. der Modellvorhersagbarkeit und derjenigen der Atmosphäre. Im idealisierten Setup finden wir gesteigerte Vorhersagbarkeit von Konvektion unter Einfluss von Orographie, sofern die AB nur kleinskalige Fehler aufweisen, die eine perfekte synoptische Vorhersage repräsentieren. Wenn synoptische Fehler auftreten, wird die positive Wirkung der Orographie verringert. Jedoch kann die Assimilation von Radardaten diese Fehler kompensieren und hochwertige Analysen liefern. Der Vorhersagehorizont wird unter beiderlei AB um 6 Stunden erweitert, wobei Skalen bis zu 50 km vorhersagbar bleiben, wenn nur kleinskalige Fehler vorliegen. Wenn synoptische Unsicherheiten auftreten, sind Skalen bis 100 km vorhersagbar. Die Experimente zeigen ferner die gestiegene Vorhersagbarkeit bei Konvektion mit hohem Organisationsgrad. Auch konnten wir diese Ergebnisse im ab Mai 2019 operationellen COSMO-KENDA-System des DWD bestätigen. Des weiteren fanden wir eine höhere Vorhersagbarkeit von Regimen mit synoptischem Einfluss sowie im bergigen Süden Deutschlands verglichen mit dem flacheren Norden. Über drei Sommer hinweg fanden wir in einem älteren DWD-Modell, das Wetterlagen, die mehr von lokalen Prozessen abhängen und weniger vorhersagbar sind als diejenigen, die einem synoptischen Einfluss unterliegen. Beide Wetterlagen zeigen keinen signifikanten Effekt der Orographie

Observations of Arctic low-level mixed-phase clouds at Ny-Ålesund: Characterization and insights gained by high-resolution Doppler radar

Low-level mixed-phase clouds (MPCs) play an important role in the Arctic climate system by contributing to the surface warming. The complexities of the mixed-phase microphysics combined with the multitude of ways the low-level MPCs interact with the surface and the boundary layer make these clouds difficult to represent in climate models, which contributes to the uncertainties in predicting future climate change in the Arctic. Observations are needed to provide constrains for model parameterizations on one hand, and to improve process understanding on the other hand. However, continuous observations in the high Arctic are sparse, particularly on the eastern hemisphere. This dissertation presents the first work investigating a multi-year dataset of remote sensing observations of persistent low-level mixed-phase clouds (P-MPC) above Ny-Ålesund, Svalbard. A state-of-the-art Doppler cloud radar providing highly vertically and temporally resolved cloud measurements was utilized in combination with further remote sensing and standard meteorological observations. Two complimentary approaches for addressing the observational gaps in measuring Arctic low-level mixed-phase clouds have been considered. The first study investigated the P-MPCs above Ny-Ålesund in the context of the complex fjord environment. The occurrence and properties of P-MPC in different seasons and under different regional free-tropospheric and surface wind conditions were analyzed. Furthermore, the influence of thermodynamical coupling with the surface was investigated considering both its effect on cloud properties and how coupling is related to the local wind in the fjord. P-MPCs were found to occur most commonly with westerly winds (from the direction of the sea), and these clouds had a lower liquid base height and higher mean liquid and ice water paths compared to the clouds associated with easterly winds (from the direction of the interior of the island). The increased height and rarity of P-MPCs with easterly free-tropospheric winds suggest the island and its orography have an influence on the studied clouds. Most P-MPCs were found at least partially decoupled from the surface, and the decoupled cases where found to have on average a lower liquid water path than the coupled P-MPCs. Decoupling was more common with surface wind directions associated with katabatic winds. The second study explored the potential of the cloud radar Doppler spectrum skewness for gaining insights in the microphysical properties of the P-MPCs. Combining case studies and statistical analysis of a 3-year dataset, a conceptual model relating the reflectivities of supercooled liquid and ice to the skewness profile in the mixed-phase layer at P-MPC top was developed and tested. The change from liquid dominated reflectivity at cloud top to ice dominating below was found to be associated with a skewness profile turning from positive to negative (when defining positive Doppler velocity down towards the radar), thus skewness is providing a reflectivity weighted measure of phase-partitioning in the mixed-phase layer. Although the approach is limited to profiles where the amount of liquid is sufficient to produce a clear signal in the Doppler spectrum, a third of all radar profiles obtained from P-MPCs were found to exhibit the described skewness feature. The analysis indicated that the height where skewness changes sign is to a large extend defined by the reflectivity of the ice phase, and skewness could therefore be useful for studying the early stages of precipitation formation. The statistical analysis carried out further revealed steady relationships between skewness and other cloud parameters, that could provide observational constrain for the evaluation of microphysical parameterizations applied in numerical models

The SAR Handbook: Comprehensive Methodologies for Forest Monitoring and Biomass Estimation

This Synthetic Aperture Radar (SAR) handbook of applied methods for forest monitoring and biomass estimation has been developed by SERVIR in collaboration with SilvaCarbon to address pressing needs in the development of operational forest monitoring services. Despite the existence of SAR technology with all-weather capability for over 30 years, the applied use of this technology for operational purposes has proven difficult. This handbook seeks to provide understandable, easy-to-assimilate technical material to remote sensing specialists that may not have expertise on SAR but are interested in leveraging SAR technology in the forestry sector

Improving the Understanding and Simulation of Precipitation Forming Processes through Combined Analysis of Microphysical Models and Multi-Frequency Doppler Radar Observations

The society is strongly influenced by precipitation, which forms by cloud microphysical processes, e.g., sedimentation and aggregation. These processes determine where and how clouds precipitate relevant for the global water cycle, freshwater availability, and flooding. However, the precipitation forming processes are poorly understood and pose a significant challenge to earth system modeling. Challenges arise from the difficulties of deriving parameterizations from laboratory experiments or observations. Even if accurate process parameterizations could be derived, implementing them into numerical models poses additional challenges due to computational cost and unresolved scales. In the last decades, rapid progress has been made in modeling and observing microphysical processes, which enables or even necessitates further studies that exploit the synergy between both fields. In this thesis, microphysical models are employed that either resolve the microphysical processes up to the single particle level (3D snowflake model and Lagrangian particle model) or are computationally efficient (bulk scheme). The explicit models are used to derive parameterizations and provide detailed insights into the processes that can be used in the less explicit models. Improving the less explicit but computationally efficient bulk schemes is particularly important, as they are indispensable for weather and climate prediction. Output from all models is compared to observations that provide information either on individual particle properties (in situ particle observations) or average properties of large particle ensembles (multi-frequency Doppler radar observations). These model-observation combinations are used to improve the knowledge about the microphysical processes and their representation in the microphysical models. 3D snowflake models simulate the complex shape of ice particles, the representation of which presents a major difficulty for microphysical schemes. In Study I, such a 3D snowflake model is used to derive parameterizations of particle properties, such as mass as a function of size, monomer number and shape. Hydrodynamic models are used to additionally derive the particle velocity. The most detailed parameterizations are used to assess the effect of aggregate composition on the particle properties, which is challenging to do with observations alone. It is found that aggregate properties change smoothly with increasing monomer number but differ substantially depending on the monomer shapes that constitute the aggregates. Other, less detailed parameterizations can be readily applied in bulk microphysical schemes to improve the physical consistency of these schemes. In simulations with a Lagrangian particle model, it can be shown that these less detailed parameterisations are very accurate even if they only distinguish between the two classes of monomers and aggregates. Comparing the parameterization with in situ observations ensures that they are physically realistic in size ranges where observations are available. In addition, the physical principles of the 3D snowflake and hydrodynamic models help to ensure that the parameterizations are realistic even in size ranges for which it is difficult to obtain observations. In Study II, parameters that are important for the microphysical description of sedimentation and aggregation in a two-moment scheme bulk microphysics scheme are constrained by observations. Traditionally, microphysical parameterizations are tuned to improve the prediction of few variables of interest, such as the precipitation rate. This procedure likely introduces compensating errors, since adjusting one parameter may improve the prediction of these variables even if that change leads away from the most physically meaningful value of the parameters. Therefore, a different approach is used in this study that uses several variables from multi-frequency Doppler radars simultaneously and focuses on single or few processes to avoid this issue of underdetermined parameters. First, the observed statistics are used to evaluate microphysical parameters in an idealized 1D model, which allows efficient testing of all key parameters. These simulations reveal that the simulation of aggregation is most sensitive to the aggregate particle properties, the aggregation kernel formulation and the size distribution width and less sensitive to the monomer habit and the sticking efficiency. A statistical comparison between 3D large-eddy simulations with the default and the new scheme setup and the observations show that previously existing large biases of too fast and too large particles in the scheme could be substantially reduced. This bias reduction can be attributed to the improved simulation of sedimentation and aggregation. Since a large portion of precipitation reaches the ground as rain but forms in the ice phase, processes in the melting layer are an essential part of precipitation modeling. In Study III, an approach is used to infer the dominance of growth or shrinkage processes through the relationship of reflectivity flux at the melting layer boundaries. In addition, radar Doppler spectra and multi-frequency observations are used to evaluate assumptions of the approach and to classify profiles according to the degree of riming. For unrimed profiles, growth processes increase the mean mass only slightly. For rimed profiles, shrinking processes lead to a substantial decrease the mean mass probably caused by particle breakup. Simulations using a Lagrangian particle model reveal that breakup processes for which parameterizations are available can not reproduce the observed decrease of the mean mass for rimed profiles and suggest that further laboratory studies of collisional breakup of melting particles are needed

Increasing Resolution and Resolving Convection Improve the Simulation of Cloud-Radiative Effects Over the North Atlantic

Clouds interact with atmospheric radiation and substantially modify the Earth's energy budget. Cloud formation processes occur over a vast range of spatial and temporal scales, which make their thorough numerical representation challenging. Therefore, the impact of parameter choices for simulations of cloud-radiative effects is assessed in the current study. Numerical experiments are carried out using the ICOsahedral Nonhydrostatic (ICON) model with varying grid spacings between 2.5 and 80 km and with different subgrid-scale parameterization approaches. Simulations are performed over the North Atlantic with either one-moment or two-moment microphysics and with convection being parameterized or explicitly resolved by grid-scale dynamics. Simulated cloud-radiative effects are compared to products derived from Meteosat measurements. Furthermore, a sophisticated cloud classification algorithm is applied to understand the differences and dependencies of simulated and observed cloud-radiative effects. The cloud classification algorithm developed for the satellite observations is also applied to the simulation output based on synthetic infrared brightness temperatures, a novel approach that is not impacted by changing insolation and guarantees a consistent and fair comparison. It is found that flux biases originate equally from clear-sky and cloudy parts of the radiation field. Simulated cloud amounts and cloud-radiative effects are dominated by marine, shallow clouds, and their behavior is highly resolution dependent. Bias compensation between shortwave and longwave flux biases, seen in the coarser simulations, is significantly diminished for higher resolutions. Based on the analysis results, it is argued that cloud-microphysical and cloud-radiative properties have to be adjusted to further improve agreement with observed cloud-radiative effects. © 2020. The Authors