Uncertainties In Observational Data Research Articles

Unpacking some of the linkages between uncertainties in observational data and the simulation of different hydrological processes using the Pitman model in the data scarce Zambezi River basin

AbstractThe main objective of this study was to use an uncertainty version of a widely used monthly time step, semi‐distributed model (the Pitman model) to explore the equifinalities in the way in which the main hydrological processes are simulated and any identifiable linkages with uncertainties in the available observational data. The study area is the Zambezi River basin and 17 gauged sub‐basins have been included in the analyses. Unfortunately, it is not generally possible to quantify some of the observational uncertainties in such a data scarce area and mostly we are limited to identifying where these data are clearly deficient (i.e., erroneous or non‐representative). The overall conclusion is that the equifinalities in the model are hugely dominant in terms of the uncertainties in the relative occurrence of different runoff generating processes, although water use uncertainties in the semi‐arid parts of the basin can contribute to these uncertainties. The identification of landscape features that suggest the occurrence of saturation excess surface runoff provides some information to constrain the model. Improved independent estimates of groundwater recharge is also identified as a key source of observational data that would help a great deal in constraining the model parameter space and therefore reducing some of the model equifinality.

Selection and validation of predictive models of radiation effects on tumor growth based on noninvasive imaging data

The use of mathematical and computational models for reliable predictions of tumor growth and decline in living organisms is one of the foremost challenges in modern predictive science, as it must cope with uncertainties in observational data, model selection, model parameters, and model inadequacy, all for very complex physical and biological systems.In this paper, large classes of parametric models of tumor growth in vascular tissue are discussed including models for radiation therapy. Observational data is obtained from MRI of a murine model of glioma and observed over a period of about three weeks, with X-ray radiation administered 14.5 days into the experimental program. Parametric models of tumor proliferation and decline are presented based on the balance laws of continuum mixture theory, particularly mass balance, and from accepted biological hypotheses on tumor growth. Among these are new model classes that include characterizations of effects of radiation and simple models of mechanical deformation of tumors.The Occam Plausibility Algorithm (OPAL) is implemented to provide a Bayesian statistical calibration of the model classes, 39 models in all, as well as the determination of the most plausible models in these classes relative to the observational data, and to assess model inadequacy through statistical validation processes. Discussions of the numerical analysis of finite element approximations of the system of stochastic, nonlinear partial differential equations characterizing the model classes, as well as the sampling algorithms for Monte Carlo and Markov chain Monte Carlo (MCMC) methods employed in solving the forward stochastic problem, and in computing posterior distributions of parameters and model plausibilities are provided. The results of the analyses described suggest that the general framework developed can provide a useful approach for predicting tumor growth and the effects of radiation.

Uncertainties in observational data on organic aerosol: An annual perspective of sampling artifacts in Beijing, China

Current understanding of organic aerosol (OA) is challenged by the large gap between simulation results and observational data. Based on six campaigns conducted in a representative mega city in China, this study provided an annual perspective of the uncertainties in observational OA data caused by sampling artifacts. Our results suggest that for the commonly-used sampling approach that involves collection of particles on a bare quartz filter, the positive artifact could result in a 20–40 % overestimation of OA concentrations. Based on an evaluation framework that includes four criteria, an activated carbon denuder was demonstrated to be able to effectively eliminate the positive artifact with a long useful time of at least one month, and hence it was recommended to be a good choice for routine measurement of carbonaceous aerosol.

The Caspian Sea Level forced by the atmospheric circulation, as observed and modelled

The Caspian Sea Level (CSL) has experienced large fluctuations with wide-reaching impacts on the population on the coastal regions and on the economy. The CSL variability is dominated by the variability of precipitation over the Volga River basin. The precipitation during summer plays a dominant role and can explain the two major events that happened in the 1930s (drop) and after 1977 (rise). Impacts are expected from global warming due to enhanced greenhouse gas concentrations; especially the precipitation over the Volga River basin is expected to increase. It is, however, compensated more or less by increased evaporation over the Caspian Sea (CS) itself. It is shown that the Max Planck Institute for Meteorology (Hamburg) models are able to simulate most processes relevant for the CSL variability quite realistically, i.e., within the uncertainty of observational data. The simulations suggest a slight increase of the CSL in the 21st century; but due to a large variability of precipitation over the Volga River basin a statement concerning the future development of the CSL cannot be made with confidence at the moment.

Simulation of the hydrological cycle over Europe: Model validation and impacts of increasing greenhouse gases

Different methods of estimating precipitation area means, based on observations, are compared with each other to investigate their usefulness for model validation. For the applications relevant to this study the ECMWF reanalyses provide a good and comprehensive data set for validation. The uncertainties of precipitation analyses, based on observed precipitation or from numerical weather forecasting schemes, are generally in the range of 20% but regionally much larger. The MPI atmospheric general circulation model is able to reproduce long term means of the main features of the hydrological cycle within the range of uncertainty of observational data, even for relatively small areas such as the Rhine river basin. Simulations with the MPI coupled general circulation model, assuming a further increase of anthropogenic greenhouse gases, show clear trends in temperature and precipitation for the next century which would have significant implications for human activity, e.g. a further increase of the sea level of the Caspian Sea and less water in the Rhine and the Danube. We have gained confidence in these results because trends in the temperature and precipitation in the coupled model simulations up to the present are partly confirmed by an atmospheric model simulation forced with observed SSTs and by observational data. We gained further confidence because the simulations with the same coupled model but using constant greenhouse gases do not show such trends. However, doubts arise from the fact that these trends are strong where the systematic errors of the model are large.

Semianalytic Predictions for Statistical Properties of X-Ray Clusters of Galaxies in Cold Dark Matter Universes

Temperature and luminosity functions of X-ray clusters are computed semi-analytically, combining a simple model for the cluster gas properties with the distribution functions of halo formation epochs proposed by Lacey & Cole (1993) and Kitayama & Suto (1996). In contrast to several previous approaches which apply the Press--Schechter mass function in a straightforward manner, our method can explicitly take into account the temperature and luminosity evolution of clusters. In order to make quantitative predictions in a specific cosmological context, we adopt cold dark matter (CDM) universes. Assuming the baryon density parameter $\Omega_{\rm B}=0.0125 h^{-2}$ ($h$ is the Hubble constant in units of 100 km$\cdot$sec$^{-1}\cdot$Mpc$^{-1}$) and the {\it COBE} normalization of matter fluctuations, temperature and luminosity functions of X-ray clusters depend sensitively on the density parameter $\Omega_0$. Allowing for several uncertainties in observational data as well as in our simplified assumptions, we conclude that $\Omega_0 \sim 0.2-0.5$ and $h\sim 0.7$ CDM models with/without the cosmological constant reproduce simultaneously the observed temperature and luminosity functions of X-ray clusters at redshift $z\sim0$.