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https://scholarworks.seattleu.edu/lightning-feb2021/1005/thumbnail.jp

Ensemble model output statistics for wind vectors

A bivariate ensemble model output statistics (EMOS) technique for the postprocessing of ensemble forecasts of two-dimensional wind vectors is proposed, where the postprocessed probabilistic forecast takes the form of a bivariate normal probability density function. The postprocessed means and variances of the wind vector components are linearly bias-corrected versions of the ensemble means and ensemble variances, respectively, and the conditional correlation between the wind components is represented by a trigonometric function of the ensemble mean wind direction. In a case study on 48-hour forecasts of wind vectors over the North American Pacific Northwest with the University of Washington Mesoscale Ensemble, the bivariate EMOS density forecasts were calibrated and sharp, and showed considerable improvement over the raw ensemble and reference forecasts, including ensemble copula coupling

Seasonal variation in light response of polar phytoplankton

The seasonality of light response curves was observed in phytoplankton samples taken in the Eastern Bering Sea (EBS) shortly before and during the spring bloom. Under-ice samples were found to have lower values of both the maximum growth rate (μ0) and the initial slope (α) of the photosynthesis-irradiance (PE) curve. This trend in α was also noted in a literature review of photoacclimation studies that looked at acclimation periods of 30 days or more. A trade-off is proposed between α and maintenance respiration such that below the compensation intensity EC it becomes advantageous to decrease α to mitigate the costs of respiration. An existing NPZD model of the EBS was then extended to reflect this trade-off with a seasonal transition from low to high α, and likewise μ0, at the point where available light is greater than EC. A parameter analysis found that with this seasonal plasticity the model could accurately reproduce the timing and magnitude of the 2009 spring bloom using parameter combinations within realistic ranges. Without this seasonality, no parameter set could be found that reasonably reproduced the observations. This strongly suggests that ecosystem models of phytoplankton should consider the effects of seasonality within parameters, including α which may be lower in over-wintering populations

Improving shoestring surveys for off-grid humanitarian power projects : kilowatts for humanity and KoboCollect

Field surveys are commonplace and essential for off-grid power projects in developing countries where availability of data may be scarce. Critical decisions such as site selection, technology choice, business models employed, and approach to community engagement are all greatly assisted by data that can be gathered through field surveys. Paper-based field surveys, the de facto standard approach, are prone to error, slow to deploy and adjust, and have other practical challenges despite the obvious advantage of having fewer technological dependencies. Over recent years, improvement in freely available surveying software, smartphones and tablets, as well as good cellular coverage throughout the world offers humanitarian organizations an opportunity to implement digital field surveys with relative ease. This article presents the experience implementing KoboCollect by Kilowatts for Humanity (KWH), a non-profit that implements sustainable energy kiosks in developing countries. KoboCollect is an open-source data collection software platform designed to support humanitarian and research organizations. In this paper, limitations of paper-based field surveys from previous KWH projects, as well as from the extant literature, are considered with respect to their ultimate impact on the implementation of the development project. A new approach is presented in which survey questions are refined based on past experience and are directly related to pre-defined project indicators. Key benefits and challenges are identified from the adoption of the new approach and methodological questions around sampling and decision-making following data collection are discussed. The new method is discussed in the context of a KWH survey project being conducted in the summer of 2018 in three locations in the Philippines. A major goal of this work is to open a discussion about the successes and failures of the shoestring, paper-based survey methodology and point to current best practices

Stochastic partial differential equation based modelling of large space-time data sets

Increasingly larger data sets of processes in space and time ask for statistical models and methods that can cope with such data. We show that the solution of a stochastic advection-diffusion partial differential equation provides a flexible model class for spatio-temporal processes which is computationally feasible also for large data sets. The Gaussian process defined through the stochastic partial differential equation has in general a nonseparable covariance structure. Furthermore, its parameters can be physically interpreted as explicitly modeling phenomena such as transport and diffusion that occur in many natural processes in diverse fields ranging from environmental sciences to ecology. In order to obtain computationally efficient statistical algorithms we use spectral methods to solve the stochastic partial differential equation. This has the advantage that approximation errors do not accumulate over time, and that in the spectral space the computational cost grows linearly with the dimension, the total computational costs of Bayesian or frequentist inference being dominated by the fast Fourier transform. The proposed model is applied to postprocessing of precipitation forecasts from a numerical weather prediction model for northern Switzerland. In contrast to the raw forecasts from the numerical model, the postprocessed forecasts are calibrated and quantify prediction uncertainty. Moreover, they outperform the raw forecasts, in the sense that they have a lower mean absolute error

Assessment and weighting of meteorological ensemble forecast members based on supervised machine learning with application to runoff simulations and flood warning

Numerical weather forecasts, such as meteorological forecasts of precipitation, are inherently uncertain. These uncertainties depend on model physics as well as initial and boundary conditions. Since precipitation forecasts form the input into hydrological models, the uncertainties of the precipitation forecasts result in uncertainties of flood forecasts. In order to consider these uncertainties, ensemble prediction systems are applied. These systems consist of several members simulated by different models or using a single model under varying initial and boundary conditions. However, a too wide uncertainty range obtained as a result of taking into account members with poor prediction skills may lead to underestimation or exaggeration of the risk of hazardous events. Therefore, the uncertainty range of model-based flood forecasts derived from the meteorological ensembles has to be restricted. In this paper, a methodology towards improving flood forecasts by weighting ensemble members according to their skills is presented. The skill of each ensemble member is evaluated by comparing the results of forecasts corresponding to this member with observed values in the past. Since numerous forecasts are required in order to reliably evaluate the skill, the evaluation procedure is time-consuming and tedious. Moreover, the evaluation is highly subjective, because an expert who performs it makes his decision based on his implicit knowledge. Therefore, approaches for the automated evaluation of such forecasts are required. Here, we present a semi automated approach for the assessment of precipitation forecast ensemble members. The approach is based on supervised machine learning and was tested on ensemble precipitation forecasts for the area of the Mulde river basin in Germany. Based on the evaluation results of the specific ensemble members, weights corresponding to their forecast skill were calculated. These weights were then successfully used to reduce the uncertainties within rainfall-runoff simulations and flood risk predictions

Uncertainty analysis using Bayesian Model Averaging: a case study of input variables to energy models and inference to associated uncertainties of energy scenarios

Background Energy models are used to illustrate, calculate and evaluate energy futures under given assumptions. The results of energy models are energy scenarios representing uncertain energy futures. Methods The discussed approach for uncertainty quantification and evaluation is based on Bayesian Model Averaging for input variables to quantitative energy models. If the premise is accepted that the energy model results cannot be less uncertain than the input to energy models, the proposed approach provides a lower bound of associated uncertainty. The evaluation of model-based energy scenario uncertainty in terms of input variable uncertainty departing from a probabilistic assessment is discussed. Results The result is an explicit uncertainty quantification for input variables of energy models based on well-established measure and probability theory. The quantification of uncertainty helps assessing the predictive potential of energy scenarios used and allows an evaluation of possible consequences as promoted by energy scenarios in a highly uncertain economic, environmental, political and social target system. Conclusions If societal decisions are vested in computed model results, it is meaningful to accompany these with an uncertainty assessment. Bayesian Model Averaging (BMA) for input variables of energy models could add to the currently limited tools for uncertainty assessment of model-based energy scenarios

Improved Bayesian multimodeling: Integration of copulas and Bayesian model averaging

Bayesian Model Averaging (BMA) is a popular approach to combine hydrologic forecasts from individual models, and characterize the uncertainty induced by model structure. In the original form of BMA, the conditional probability density function (PDF) of each model is assumed to be a particular probability distribution (e.g. Gaussian, gamma, etc.). If the predictions of any hydrologic model do not follow certain distribution, a data transformation procedure is required prior to model averaging. Moreover, it is strongly recommended to apply BMA on unbiased forecasts, whereas it is sometimes difficult to effectively remove bias from the predictions of complex hydrologic models. To overcome these limitations, we develop an approach to integrate a group of multivariate functions, the so-called copula functions, into BMA. Here, we introduce a copula-embedded BMA (Cop-BMA) method that relaxes any assumption on the shape of conditional PDFs. Copula functions have a flexible structure and do not restrict the shape of posterior distributions. Furthermore, copulas are effective tools in removing bias from hydrologic forecasts. To compare the performance of BMA with Cop-BMA, they are applied to hydrologic forecasts from different rainfall-runoff and land-surface models. We consider the streamflow observation and simulations for ten river basins provided by the Model Parameter Estimation Experiment (MOPEX) project. Results demonstrate that the predictive distributions are more accurate and reliable, less biased, and more confident with small uncertainty after Cop-BMA application. It is also shown that the post-processed forecasts have better correlation with observation after Cop-BMA application