Abstract The interactions between the atmosphere and land surface are characterized by complex, non‐linear processes on varying time scales. Noah‐MP is a medium complexity land‐surface model (LSM), which was recently selected as new default LSM for hydrologically enhanced Weather Research Forecasting modelling system (WRF‐Hydro). Compared to its predecessor, several parameterizations were considerably improved ones added, inter alia more sophisticated groundwater descriptions, aim replace...

Abstract The combination of a conventional Markov chain model (zero and first order) gamma distribution are found to be applicable derive meaningful agricultural features from precipitation in the Volta Basin (West Africa). Since analysis monthly or annual amount does not provide any adequate information on rainfall timing sufficiency crop water requirement, modelling was performed daily time scale for 29 stations. modelled follow distinct spatial patterns, which will presented as maps of(1)...

An increase in the spatial resolution of regional climate model simulations improves representation land surface characteristics and may allow explicit calculation important physical processes such as convection. The present study investigates further potential benefits with respect to precipitation, based on a small ensemble high-resolution WRF grid spacings up 1 km. skill each experiment is evaluated regarding temporal performance simulation precipitation one year over both mountainous...

A model for residual demand is proposed, which extends structural electricity price models to account renewable infeed in the market. Infeed from wind and solar modeled explicitly withdrawn total demand. The methodology separates impact of weather capacity. Efficiency as a stochastic process. Installed capacity deterministic function time. applied German day-ahead Price trajectories show typical features seen market prices recent years. able closely reproduce structure magnitude prices....

Electricity prices strongly depend on seasonality of different time scales, therefore any forecasting electricity has to account for it. Neural networks have proven successful in short-term price-forecasting, but complicated architectures like LSTM are used integrate the seasonal behavior. This paper shows that simple neural network DNNs with an embedding layer information can generate a competitive forecast. The embedding-based processing calendar additionally opens up new applications...

The class of arithmetic factor models is flexible enough to model all stylized facts occurring in electricity markets, including negative prices, while still yielding tractable derivative prices. In this paper we conduct a thorough review the requirements and possibilities models. We compare different seasonality functions study their power deseasonalise day-ahead spot prices from EPEX Germany/Austria market. Furthermore, introduce an alternative method estimate mean reversion speed based on...

Abstract The COVID-19 pandemic interrupts the relatively steady trend of improving longevity observed in many countries over last decades. We claim that this needs to be addressed explicitly mortality modelling applications, for example, life insurance industry. To support position, we provide a descriptive analysis development several up and including year 2020. Furthermore, perform an empirical theoretical investigation impact jump has on parameters, forecasts implied present values...

Abstract. First results of radar derived climatology have emerged over the last years, as datasets appropriate extent are becoming available. Usually, these statistics based on time series lasting up to ten years continuous storage data was often not achieved before. This kind demands a high level quality. Small deviations or minor systematic under- overestimations in single images become major cause error statistical analysis. Extensive corrections crucial prerequisite for climatology. We...

The assimilation of observations in limited area models (LAMs) allows to find the best possible estimate a region’s meteorological state. Water vapor is crucial constituent terms cloud and precipitation formation. Its highly variable nature space time often insufficiently represented models. This study investigates improvement simulated water content within Weather Research Forecasting model (WRF) every season by assimilating temperature, relative humidity, surface pressure obtained from...

Abstract. Tropospheric water vapor is one of the most important trace gases Earth's climate system, and its temporal spatial distribution critical for genesis clouds precipitation. Due to pronounced dynamics atmosphere nonlinear relation air temperature saturated pressure, it highly variable, which hampers development high-resolution three-dimensional maps regional extent. With their complementary high resolutions, Global Navigation Satellite Systems (GNSS) meteorology Interferometric...

This paper employs machine learning algorithms to forecast German electricity spot market prices. The forecasts utilize in particular bid and ask order book data from the but also fundamental like renewable infeed expected demand. Appropriate feature extraction for is developed. Using cross-validation optimise hyperparameters, neural networks random forests are proposed compared statistical reference models. models outperform traditional approaches.

Air quality is a highly relevant issue for any developed economy. The high incidence of pollution levels and their impact on human health has attracted the attention machine-learning scientific community. We present study using several methods to forecast NO2 concentration historical data meteorological variables apply them city Erfurt, Germany. propose modelling time dependency embedding variables, which enable model learn implicit behaviour traffic offers possibility elaborate local...

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A model for residual demand is proposed, which extends structural electricity price models to account renewable infeed in the market. Infeed from wind and solar modelled explicitly withdrawn total demand. The methodology separates impact of weather capacity. Efficiency as a stochastic process. Installed capacity deterministic function time. applied German day-ahead Price trajectories show typical features seen market prices recent years. able closely reproduce structure magnitude prices....

Abstract Mortality shocks such as the one induced by COVID-19 pandemic have substantial impact on mortality models. We describe how to deal with them in period effect of Lee–Carter model. The main idea is not rely usual normal distribution assumption it always justified. consider a mixture model based peaks-over-threshold method, jump model, and regime switching introduce modified calibration procedure account for fact that varying amounts data are necessary calibrating different parts these...

We characterize the distributions of short cycles in a large metabolic network previously shown to have small world characteristics and power law degree distribution. Compared with three classes random networks, including Erdoes-Renyi graphs synthetic networks same connectivity, has particularly number triangles deficit cycles. Short reduce length detours when connection is clipped, so we propose that long metabolism may been selected against order shorten transition times likelihood...

In this work we use the Parsimonious Multi–Asset Heston model recently developed in [Dimitroff et al., 2009] at Fraunhofer ITWM, Department Financial Mathematics, Kaiserslautern (Germany) and apply it to Quanto options. We give a summary of its calibration scheme. A suitable transformation option payoff is explained used price Quantos within new framework. Simulated prices are given compared market Black–Scholes prices. find that approach underprices chosen options, but gives better results...

In this paper, we introduce a model for the pricing of German intraday cap/floor futures, introduced by EEX in 2015. We give thorough overview market and particular ID3 price index, which is underlying futures. To these derivatives, propose Hull-White from interest rate theory with seasonality futures prices. apply our theoretical results to data conduct an empirical analysis involving initial fit distribution cap