A Data-driven Approach For Winter Precipitation Classification Using Weather Radar And NWP Data

This study describes a framework that provides qualitative weather information on winter precipitation types using a data-driven approach. The framework incorporates the data retrieved from weather radars and the numerical weather prediction (NWP) model to account for relevant precipitation microphysics. To enable multimodel-based ensemble classification, we selected six supervised machine learning models: k-nearest neighbors, logistic regression, support vector machine, decision tree, random forest, and multi-layer perceptron. Our model training and cross-validation results based on Monte Carlo Simulation (MCS) showed that all the models performed better than our baseline method, which applies two thresholds (surface temperature and atmospheric layer thickness) for binary classification (i.e., rain/snow). Among all six models, random forest presented the best classification results for the basic classes (rain, freezing rain, and snow) and the further refinement of the snow classes (light, moderate, and heavy). Our model evaluation, which uses an independent dataset not associated with model development and learning, led to classification performance consistent with that from the MCS analysis. Based on the visual inspection of the classification maps generated for an individual radar domain, we confirmed the improved classification capability of the developed models (e.g., random forest) compared to the baseline one in representing both spatial variability and continuity

Der Arbeitsmarkt in Nordkorea am Beispiel des Industriekomplexes in Kaesong

Die Umweltbedingungen auf der koreanischen Halbinsel sind alles andere als ideal für ein einfaches Überleben. Dabei erfordert die landwirtschaftliche Produktion viel Mühe. Obwohl sie im Lauf ihrer Geschichte schwere Zeiten durchstehen mussten, konnten die Südkoreaner, nach Erlangung ihrer politischen Freiheit der Welt zeigen, zu welchen Leistungen sie fähig sind. Auch Nordkoreaner gelten im Allgemeinen als fleißig und belastbar. Deshalb sind sie z.B. in China beliebte Arbeitskräfte. Nordkoreanische Kinder werden früh eingeschult und sind verpflichtet, bis zum Abschluss der gymnasialen Mittelstufe an der Ausbildung teilzunehmen. Um die Produktivität des einzelnen Arbeiters zu erhöhen, muss jeder Schüler und Student mindestens eine technische Ausbildung absolvieren. Seit 1980 wurde die Wichtigkeit der Ausbildung qualifizierter Ingenieure und Techniker in Nordkorea häufig hervorgehoben. Auch bei der Ausbil-dung technischer Fachkräfte ist stets das Prinzip der sozialistischen Erziehung maßgebend. Kim Jong-Un kontrolliert darüber hinaus als alleiniger Befehlshaber die Ausbildung und den persönlichen Charakter der Schüler und die Entwicklung eigener Ideen. Dabei wird die Entfaltung individueller Fähigkeiten der Arbeiter vernachlässigt. Dies wird am Beispiel der Arbeitskräfte im Kaesong Industriekomplex deutlich gemacht

Scale Dependence Of Radar Rainfall Uncertainty: Initial Evaluation Of NEXRAD\u27s New Super-resolution Data For Hydrologic Applications

This study explores the scale effects of radar rainfall accumulation fields generated using the new super-resolution level II radar reflectivity data acquired by the Next Generation Weather Radar (NEXRAD) network of the Weather Surveillance Radar-1988 Doppler (WSR-88D) weather radars. Eleven months (May 2008-August 2009, exclusive of winter months) of high-density rain gauge network data are used to describe the uncertainty structure of radar rainfall and rain gauge representativeness with respect to five spatial scales (0.5, 1, 2, 4, and 8 km). While both uncertainties of gauge representativeness and radar rainfall show simple scaling behavior, the uncertainty of radar rainfall is characterized by an almost 3 times greater standard error at higher temporal and spatial resolutions (15 min and 0.5 km) than at lower resolutions (1 h and 8 km). These results may have implications for error propagation through distributed hydrologic models that require high-resolution rainfall input. Another interesting result of the study is that uncertainty obtained by averaging rainfall products produced from the super-resolution reflectivity data is slightly lower at smaller scales than the uncertainty of the corresponding resolution products produced using averaged (recombined) reflectivity data. © 2010 American Meteorological Society

TempNet -- Temporal Super Resolution of Radar Rainfall Products with Residual CNNs

The temporal and spatial resolution of rainfall data is crucial for environmental modeling studies in which its variability in space and time is considered as a primary factor. Rainfall products from different remote sensing instruments (e.g., radar, satellite) have different space-time resolutions because of the differences in their sensing capabilities and post-processing methods. In this study, we developed a deep learning approach that augments rainfall data with increased time resolutions to complement relatively lower resolution products. We propose a neural network architecture based on Convolutional Neural Networks (CNNs) to improve the temporal resolution of radar-based rainfall products and compare the proposed model with an optical flow-based interpolation method and CNN-baseline model. The methodology presented in this study could be used for enhancing rainfall maps with better temporal resolution and imputation of missing frames in sequences of 2D rainfall maps to support hydrological and flood forecasting studies

Efficient Episodic Memory Utilization of Cooperative Multi-Agent Reinforcement Learning

In cooperative multi-agent reinforcement learning (MARL), agents aim to achieve a common goal, such as defeating enemies or scoring a goal. Existing MARL algorithms are effective but still require significant learning time and often get trapped in local optima by complex tasks, subsequently failing to discover a goal-reaching policy. To address this, we introduce Efficient episodic Memory Utilization (EMU) for MARL, with two primary objectives: (a) accelerating reinforcement learning by leveraging semantically coherent memory from an episodic buffer and (b) selectively promoting desirable transitions to prevent local convergence. To achieve (a), EMU incorporates a trainable encoder/decoder structure alongside MARL, creating coherent memory embeddings that facilitate exploratory memory recall. To achieve (b), EMU introduces a novel reward structure called episodic incentive based on the desirability of states. This reward improves the TD target in Q-learning and acts as an additional incentive for desirable transitions. We provide theoretical support for the proposed incentive and demonstrate the effectiveness of EMU compared to conventional episodic control. The proposed method is evaluated in StarCraft II and Google Research Football, and empirical results indicate further performance improvement over state-of-the-art methods.Comment: Accepted at ICLR 202