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[en] Solar energy production is subject to variability in the solar resource - clouds and aerosols will reduce the available solar irradiance and inhibit power production. The fact that solar irradiance can vary by large amounts at small timescales and in an unpredictable way means that power utilities are reluctant to assign to their solar plants a large portion of future energy demand - the needed power might be unavailable, forcing the utility to make costly adjustments to its daily portfolio. The availability and predictability of solar radiation therefore represent important research topics for increasing the power produced by renewable sources.
[en] The Atmospheric Technology Group at SRNL developed a new method to detect signals from Weapons of Mass Destruction (WMD) activities in a time series of chemical measurements at a downwind location. This method was tested with radioxenon measured in Russia and Japan after the 2013 underground test in North Korea. This LDRD calculated the uncertainty in the method with the measured data and also for a case with the signal reduced to 1/10 its measured value. The research showed that the uncertainty in the calculated probability of origin from the NK test site was small enough to confirm the test. The method was also wellbehaved for small signal strengths.
[en] Concentration data collected from the 2013 H-Canyon effluent reprocessing experiment were reanalyzed to improve the source term estimate. When errors in the model-predicted wind speed and direction were removed, the source term uncertainty was reduced to 30% of the mean. This explained the factor of 30 difference between the source term size derived from data at 5 km and 10 km downwind in terms of the time history of dissolution. The results show a path forward to develop a sampling strategy for quantitative source term calculation.
[en] A method is outlined and tested to detect low level nuclear or chemical sources from time series of concentration measurements. The method uses a mesoscale atmospheric model to simulate the concentration signature from a known or suspected source at a receptor which is then regressed successively against segments of the measurement series to create time series of metrics that measure the goodness of fit between the signatures and the measurement segments. The method was applied to radioxenon data from the Comprehensive Test Ban Treaty (CTBT) collection site in Ussuriysk, Russia (RN58) after the Democratic People's Republic of Korea (North Korea) underground nuclear test on February 12, 2013 near Punggye. The metrics were found to be a good screening tool to locate data segments with a strong likelihood of origin from Punggye, especially when multiplied together to a determine the joint probability. Metrics from RN58 were also used to find the probability that activity measured in February and April of 2013 originated from the Feb 12 test. A detailed analysis of an RN58 data segment from April 3/4, 2013 was also carried out for a grid of source locations around Punggye and identified Punggye as the most likely point of origin. Thus, the results support the strong possibility that radioxenon was emitted from the test site at various times in April and was detected intermittently at RN58, depending on the wind direction. The method does not locate unsuspected sources, but instead, evaluates the probability of a source at a specified location. However, it can be extended to include a set of suspected sources. Extension of the method to higher resolution data sets, arbitrary sampling, and time-varying sources is discussed along with a path to evaluate uncertainty in the calculated probabilities.
[en] Ensemble modeling (EM), the creation of multiple atmospheric simulations for a given time period, has become an essential tool for characterizing uncertainties in model predictions. We explore two novel ensemble modeling techniques: (1) perturbation of model parameters (Adaptive Programming, AP), and (2) data assimilation (Ensemble Kalman Filter, EnKF). The current research is an extension to work from last year and examines transport on a small spatial scale (<100 km) in complex terrain, for more rigorous testing of the ensemble technique. Two different release cases were studied, a coastal release (SF6) and an inland release (Freon) which consisted of two release times. Observations of tracer concentration and meteorology are used to judge the ensemble results. In addition, adaptive grid techniques have been developed to reduce required computing resources for transport calculations. Using a 20- member ensemble, the standard approach generated downwind transport that was quantitatively good for both releases; however, the EnKF method produced additional improvement for the coastal release where the spatial and temporal differences due to interior valley heating lead to the inland movement of the plume. The AP technique showed improvements for both release cases, with more improvement shown in the inland release. This research demonstrated that transport accuracy can be improved when models are adapted to a particular location/time or when important local data is assimilated into the simulation and enhances SRNL's capability in atmospheric transport modeling in support of its current customer base and local site missions, as well as our ability to attract new customers within the intelligence community.