Estimating the uncertainty of areal precipitation using data assimilation

We present a method to estimate spatially and temporally variable uncertainty of areal precipitation data. The aim of the method is to merge measurements from different sources, remote sensing and in situ, into a combined precipitation product and to provide an associated dynamic uncertainty estimate. This estimate should provide an accurate representation of uncertainty both in time and space, an adjustment to additional observations merged into the product through data assimilation, and flow dependency. Such a detailed uncertainty description is important for example to generate precipitation ensembles for probabilistic hydrological modelling or to specify accurate error covariances when using precipitation observations for data assimilation into numerical weather prediction models. The presented method uses the Local Ensemble Transform Kalman Filter and an ensemble nowcasting model. The model provides information about the precipitation displacement over time and is continuously updated by assimilation of observations. In this way, the precipitation product and its uncertainty estimate provided by the nowcasting ensemble evolve consistently in time and become flow-dependent. The method is evaluated in a proof of concept study focusing on weather radar data of four precipitation events. The study demonstrates that the dynamic areal uncertainty estimate outperforms a constant benchmark uncertainty value in all cases for one of the evaluated scores, and in half the number of cases for the other score. Thus, the flow dependency introduced by the coupling of data assimilation and nowcasting enables a more accurate spatial and temporal distribution of uncertainty. The mixed results achieved in the second score point out the importance of a good probabilistic nowcasting scheme for the performance of the method

Deterministic quantum state transfer from an electronic charge qubit to a photonic polarization qubit

Building on an earlier proposal for the production of polarization-entangled microwaves by means of intraband transitions in a pair of quantum dots, we show how this device can be used to transfer an unknown single-qubit state from electronic charge to photonic polarization degrees of freedom. No postselection is required, meaning that the quantum state transfer happens deterministically. Decoherence of the charge qubit causes a non-monotonic decay of the fidelity of the transferred state with increasing decoherence rate.Comment: 3 pages, 3 figure

Convective cold pools in long-term boundary layer mast observations

Cold pools are mesoscale features that are key for understanding the organization of convection, but are insufficiently captured in conventional observations. This study conducts a statistical characterization of cold-pool passages observed at a 280-m-high boundary layer mast in Hamburg (Germany) and discusses factors controlling their signal strength. During 14 summer seasons 489 cold-pool events are identified from rapid temperature drops below 22K associated with rainfall. The cold-pool activity exhibits distinct annual and diurnal cycles peaking in July and midafternoon, respectively. The median temperature perturbation is -3.3K at 2-m height and weakens above. Also the increase in hydrostatic air pressure and specific humidity is largest near the surface. Extrapolation of the vertically weakening pressure signal suggests a characteristic cold-pool depth of about 750 m. Disturbances in the horizontal and vertical wind speed components document a lifting-induced circulation of air masses prior to the approaching cold-pool front. According to a correlation analysis, the near-surface temperature perturbation is more strongly controlled by the pre-event saturation deficit (r = -0.71) than by the event-accumulated rainfall amount (r = -0.35). Simulating the observed temperature drops as idealized wet-bulb processes suggests that evaporative cooling alone explains 64 of the variability in cold-pool strength. This number increases to 92 for cases that are not affected by advection of midtropospheric low-Qe air masses under convective downdrafts. © 2021 American Meteorological Society

The Hamburg Tornado (7 June 2016) from the perspective of low-cost high-resolution radar data and weather forecast model

A tornado hit the northeastern suburbs of Hamburg, Germany, on 7 June 2016. It had an estimated strength of upper end F1 on the Fujita scale and was short-lived with an approximate duration of only 13 min and a path length of just about 1.3 km. We demonstrate that such a small-scale, extreme event can be observed and forecasted accurately by a low-cost radar and by an atmospheric model with low computational costs, respectively. Observations from a low-cost single polarized X-band radar covering the urban area of Hamburg with 60 m spatial and 30 s temporal resolution are analyzed with respect to their ability to capture the development as well as the track of the tornado. In contrast to the national C-band radar network, the X-band radar is capable of capturing the hook echo of the tornado as well as the circular pattern in rain rates, because of its higher resolution in space and time. High-resolution forecasts of the tornado event are conducted with the computational efficient Conformal Cubic Atmosphere Model (CCAM) in order to test the capability of predicting the tornado with a lead time of a few hours. A three step downscaling method is used to obtain a spatial resolution of 1 km with initial conditions taken from the NCEP analysis. Calculated severe weather indices clearly indicate a potential for a tornado. CCAM cannot explicitly resolve small scale tornadic features but the model simulates a strong convective cell only a few kilometers apart from the tornadic thunderstorm observed by the radar

Two adaptive radiative transfer schemes for numerical weather prediction models

Radiative transfer calculations in atmospheric models are computationally expensive, even if based on simplifications such as the δ-two-stream approximation. In most weather prediction models these parameterisation schemes are therefore called infrequently, accepting additional model error due to the persistence assumption between calls. This paper presents two so-called adaptive parameterisation schemes for radiative transfer in a limited area model: A perturbation scheme that exploits temporal correlations and a local-search scheme that mainly takes advantage of spatial correlations. Utilising these correlations and with similar computational resources, the schemes are able to predict the surface net radiative fluxes more accurately than a scheme based on the persistence assumption. An important property of these adaptive schemes is that their accuracy does not decrease much in case of strong reductions in the number of calls to the δ-two-stream scheme. It is hypothesised that the core idea can also be employed in parameterisation schemes for other processes and in other dynamical models

An Empirical Analysis of Synergies and Tradeoffs between Sustainable Development Goals

The UN Sustainable Development Goals (SDGs) represent a universal agenda that nations have committed to achieving by 2030. The challenge is substantial, with no country excelling across all SDGs. Using global UN data, we assess patterns of positive and negative correlations between indicators of SDG status and progress. For nearly 70% of SDG indicators, status is positively associated with GDP/capita. Progress on SDG indicators, however, occurs in both poorer and wealthier countries. When GDP/capita is controlled for, positive associations remain between health, environment and energy usage indicators. Economic growth is negatively associated with changes in some health and environment indicators. For SDGs targets to be achieved, major opportunities and conflicts will need to be identified, prioritized and acted upon

Electrostatic attraction of nanoobjects - a versatile strategy towards mesostructured transition metal compounds

This highlight summarizes current challenges of mesostructuring and focuses on the scope and the potential of the ELAN – (electrostatic attraction of nanoobjects) strategy in mesostructuring of transition metal compounds. It discusses the limitations of this concept and highlights prominent examples. ELAN exploits the Coulomb attraction between inorganic precursors and polymeric templates in order to prevent macrophase separation. Essential requirements for ELAN are tailor-made, mesoscopic polyelectrolytic templates and charged molecular oligo-ions or stable colloids carrying a surface charge. The ELAN-strategy is highly reliable and opens the way to crystalline, mesoporous transition metal compounds with predefined polymorphism. It also provides the possibility to adjust wall chemistry and reactivity as well as the flexibility to synthesise different mesostructures (spheres, non-woven arrays or hexagonally ordered phases)