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Secondary Ice Production - current state of the science and recommendations for the future

Measured ice crystal concentrations in natural clouds at modest supercooling (temperature ~>−10°C) are often orders of magnitude greater than the number concentration of primary ice nucleating particles. Therefore, it has long been proposed that a secondary ice production process must exist that is able to rapidly enhance the number concentration of the ice population following initial primary ice

Sensitivity of Arctic Clouds to Ice Microphysical Processes in the NorESM2 Climate Model

Ice formation remains one of the most poorly represented microphysical processes in climate models. While primary ice production (PIP) parameterizations are known to have a large influence on the modeled cloud properties, the representation of secondary ice production (SIP) is incomplete and its corresponding impact is therefore largely unquantified. Furthermore, ice aggregation is another importa

Convergence Properties of Iteratively Coupled Surface-Subsurface Models

Surface-subsurface flow models for hydrological applications solve a coupled multiphysics problem. This usually consists of some form of the Richards and shallow water equations. A typical setup couples these two nonlinear partial differential equations in a partitioned approach via boundary conditions. Full interaction between the subsolvers is ensured by an iterative coupling procedure. This can

Chironomidae-based inference model for mean July air temperature reconstructions in the eastern Baltic area

Here we present a new eastern Baltic Chironomidae training set (TS) containing 35 sites that was collected and merged with neighbouring published Finnish (82 lakes) and northern part of the Polish (nine lakes) TSs. Chironomidae, non-biting midges, are known to be strongly responsive to the July air temperature and are widely used to infer palaeotemperature. Several modern analogue-based TSs necess

Deep Distributional Temporal Difference Learning for Game Playing

We compare classic scalar temporal difference learning with three new distributional algorithms for playing the game of 5-in-a-row using deep neural networks: distributional temporal difference learning with constant learning rate, and two distributional temporal difference algorithms with adaptive learning rate. All these algorithms are applicable to any two-player deterministic zero sum game and

Detailed 3D human body reconstruction from multi-view images combining voxel super-resolution and learned implicit representation

The task of reconstructing detailed 3D human body models from images is interesting but challenging in computer vision due to the high freedom of human bodies. This work proposes a coarse-to-fine method to reconstruct detailed 3D human body from multi-view images combining Voxel Super-Resolution (VSR) based on learning the implicit representation. Firstly, the coarse 3D models are estimated by lea

Non-attracting regions of local minima in deep and wide neural networks

Understanding the loss surface of neural networks is essential for the design of models with predictable performance and their success in applications. Experimental results suggest that sufficiently deep and wide neural networks are not negatively impacted by suboptimal local minima. Despite recent progress, the reason for this outcome is not fully understood. Could deep networks have very few, if

Sensor Networks Tdoa Self-Calibration : 2d Complexity Analysis and Solutions

Given a network of receivers and transmitters, the process of determining their positions from measured pseudoranges is known as network self-calibration. In this paper we consider 2D networks with synchronized receivers but unsynchronized transmitters and the corresponding calibration techniques, known as Time-Difference-Of-Arrival (TDOA) techniques. Despite previous work, TDOA self-calibration i

A time-frequency-shift invariant parameter estimator for oscillating transient functions using the matched window reassignment

In this paper we present the matched window reassignment method, generalizing the results to complex valued signals in multiple dimensions. For an oscillating transient signal with an envelope shape described by an arbitrary twice differentiable function, the reassigned spectrogram, with a matched window, concentrates all energy into one single time-frequency point. An estimator for the parameters

Towards Precise Localisation : Subsample Methods, Efficient Estimation and Merging of Maps

Over the last couple of years audio and radio sensors have become cheaper and more common in our everyday life. Such sensors can be used to form a network, from which one can obtain distance measures by correlating the different received signals. One example of such distance measures is time-difference of arrival measurements (TDoA), which can be used to estimate the positions of the senders and r

The smoothed reassigned spectrogram for robust energy estimation

The matched window reassigned spectrogram relocates all signal energy of an oscillating transient to the time- and frequency locations, resulting in a sharp peak in the time-frequency plane. However, previous research has shown that the method may result in split energy peaks for close components and in high noise levels, and the peak energy is then erroneously estimated. With use of novel knowled

Range-based radar model structure selection

In this work, we study under which circumstances it is appropriate to use simplified models for range determination using radar. Typically, pulsed radar systems result in the backscattered, demodulated, and matched signal having a chirp signal structure, with the frequency rate being related to the range to the reflecting target and the relative velocity of the transmitter and reflector. Far from

Optimal microphone placement for localizing tonal sound sources

This work is concerned with determining optimal microphone placements that allow for an accurate location estimate of the sound sources, taking into account the expected signal structure of voiced speech, as well as the expected location areas and the typical range of the fundamental frequencies of the speakers. To determine preferable microphone placements, we propose a scheme that minimizes a th

Comparing machine learning-derived global estimates of soil respiration and its components with those from terrestrial ecosystem models

The CO2 efflux from soil (soil respiration (SR)) is one of the largest fluxes in the global carbon (C) cycle and its response to climate change could strongly influence future atmospheric CO2 concentrations. Still, a large divergence of global SR estimates and its autotrophic (AR) and heterotrophic (HR) components exists among process based terrestrial ecosystem models. Therefore, alternatively de