Observational Uncertainties Research Articles

Observational Evaluation of Estimated Air Exchange Flux Between Atmospheric Boundary Layer and Free Troposphere With WRF Model

AbstractVertical exchange between the atmospheric boundary layer (ABL) and free troposphere (FT) is a key link in coupling the earth's surface and upper atmosphere. This process is usually quantified by numerical simulations, while its reliability is not well assessed until now. Using space‐time intensified ABL observations, we evaluate the ABL‐FT air mass exchange flux derived from the Weather Research and Forecast (WRF) model. A six‐site sounding experiment is conducted in the North China Plain during the wintertime of 2019. The measured data is processed to provide enough information to derive the vertical exchange flux corresponding to the model‐based result, so that a systematic comparison is conducted. Three physical processes involved in ABL‐FT vertical exchange are quantitatively evaluated, that is, temporal variation of ABL height, advection across the inclined ABL top, and vertical motion at the ABL‐FT interface. Results show that the model‐based and observation‐based fluxes are generally agreed in temporal evolution (R = 0.67, p < 0.01), both characterized by 4–6 days periodicity and diurnal cycle. Their relative mean error was about 45% during the whole study period, mainly stemming from the vertical motion term and the advection crossing term. The model inaccuracy in representing these relevant processes at the ABL top is largely responsible for the discrepancy. Besides, the difference may also be attributed to the observational uncertainty (∼22%) that is caused by the measurement's difficulties in determining ABL spatial variation and acquiring vertical velocity. Through this study, the credibility and limitation of the WRF model in deriving ABL‐FT exchange flux are quantified.

Variable-fidelity sensors and observer uncertainty using touring multi-body periodic orbits to conduct cislunar SSA: preliminary study

An accelerating interest in cislunar space and lunar orbit for civilian, commercial, and scientific missions requires a space situational awareness (SSA) architecture extending beyond geosynchronous orbit to promote space traffic management and safety. Space-based SSA in cislunar space is challenging due to difficulties associated with accurately estimating the position of the surveillance satellite, which is a foundational requirement for effectively performing the general SSA mission. Using multiple surveillance satellites with lower-fidelity sensors helps alleviate these concerns by aggregating multiple data sets with higher variance to achieve the same level or potentially improved accuracy as compared to fewer higher-quality sensors. A subset of Earth–Moon periodic orbits, herein identified as “touring” orbits, are used for an optical surveillance constellation with a target resident space object (RSO) in a L1 Halo orbit. Angles-only measurement data are processed utilizing an extended Kalman filter to estimate the position of the RSO. The analysis focuses on assessing the effectiveness of different numbers of surveillance satellites using touring cislunar periodic orbits for conducting the SSA mission relative to L1. Overall, this study finds that the use of an SSA constellation with low-fidelity sensors can match the performance achieved by a constellation featuring higher-fidelity sensors and reduced observer uncertainty for the observer orbits examined.

Two-step AI-aided Bayesian source identification of urban-scale pollution

Poor air quality produces detrimental effects worldwide; hence, it is vital to thoroughly characterize air pollution sources and effectively address and mitigate such effects. Due to the complexity of the underlying physical processes, and uncertainties in the available observations, the air pollution source identification problem is typically cast within a Bayesian inversion framework. The latter incorporates prior knowledge and observations to characterize a release event through the posterior distribution of the source parameters. In this study, we rely on two-dimensional (2D) pollutant concentration distributions as observations, and adopt the Wasserstein (W2) distance to model the likelihood probability distribution for given emission parameters. Since the posterior distribution is estimated via random sampling that involves many forward model runs, the Bayesian framework can be computationally prohibitive for realistic urban air pollution problems that are driven by computationally demanding micro-scale flow simulations. Furthermore, computing the W2 distance is resource-intensive . In this context, we develop a computationally efficient Bayesian framework by following (i) a two-stage approach that reduces the cost of the Bayesian inversion, and (ii) an artificial intelligence (AI) approximation of the W2 distance. In the two-stage approach, a low-resolution dispersion model is run in the first stage to propose representative samples of emission parameters for final selection by the original high-resolution model in the second stage. In addition, we approximate the W2 distance using a deep neural network (DNN) to achieve an appreciable reduction in the computational cost with negligible loss in the inversion performance. We design numerical experiments to test the sensitivity of the inverse solution to the characteristics of the approximative model. The results indicate that pairing the two-stage approach with the DNN approximation of the W2 distance preserves the quality of the inverse solution, while achieving at least threefold reductions in the computational cost.

Effect of cosmic rays and ionizing radiation on observational ultraviolet plasma diagnostics in the circumgalactic medium

ABSTRACT The relevance of some galactic feedback mechanisms, in particular cosmic-ray (CR) feedback and the hydrogen ionizing radiation field, has been challenging to definitively describe in a galactic context, especially far outside the galaxy in the circumgalactic medium (CGM). Theoretical and observational uncertainties prevent conclusive interpretations of multiphase CGM properties derived from ultraviolet (UV) diagnostics. We conduct three-dimensional magnetohydrodynamic simulations of a section of a galactic disc with star formation and feedback, including radiative heating from stars, a UV background, and CR feedback. We utilize the temperature phases present in our simulations to generate Cloudy models to derive spatially and temporally varying synthetic UV diagnostics. We find that radiative effects without additional heating mechanisms are not able to produce synthetic diagnostics in the observed ranges. For low CR diffusivity $\kappa _{\rm {cr}}=10^{28} \rm {cm}^2 \rm {s}^{-1}$, CR streaming heating in the outflow helps our synthetic line ratios roughly match observed ranges by producing transitional temperature gas (T ∼ 105–106 K). High CR diffusivity $\kappa _{\rm {cr}}=10^{29} \rm {cm}^2 \rm {s}^{-1}$, with or without CR streaming heating, produced transitional temperature gas. The key parameter controlling the production of this gas phase remains unclear, as the different star formation history and outflow evolution itself influences these diagnostics. Our work demonstrates the use of UV plasma diagnostics to differentiate between galactic/circumgalactic feedback models.

A novel epidemiologically informed particle filter for assessing epidemic phenomena. Application to the monkeypox outbreak of 2022

Contagious diseases are constantly affecting more and more people every day, resulting in widespread health crises especially in developing nations. Previous studies have developed deterministic and stochastic mathematical models to investigate the spread of epidemics. In the present study, a hybrid particle filtering epidemiological model is proposed, which combines the elements of a deterministic susceptible-exposed-infectious-recovered-deceased model with the inclusion of stochastic and penalty factors, in order to efficiently evaluate the dynamics of the disease. The inclusion of penalty factors stands out as the main novelty of the proposed methodology, guaranteeing estimations that align with the unique aspects of the examined natural phenomenon. The model is applied to the monkeypox data of the United States from 25 June to 21 November 2022. Our approach is compared to four alternatives, corresponding to deterministic and stochastic approaches that are associated with either fixed or time-varying parameters. In all cases, the particle filtering models displayed better characteristics in terms of infectious cases and deaths compared to their deterministic counterpart. The final version of the proposed epidemiologically informed particle filtering model exhibited significant potential and provided the best fitting/predictive performance compared to other examined methodologies. The predictive effectiveness of the proposed methodology has been thoroughly evaluated across various time intervals. Moreover, the inclusion of additional penalty factors in the weight computation procedure, assists in reducing fitting and prediction errors while simultaneously providing increased likelihood estimates. This modeling approach can be readily applied to other epidemics, both existing and emerging, where uncertainties in system dynamics and real-time observations hinder the accurate capture of the epidemic’s progression.

Uncertainty and outliers in high‐resolution gridded precipitation products over eastern North America

AbstractSeveral observational precipitation products that provide high temporal (≤3 h) and spatial (≤0.25°) resolution gridded estimates are available, although no single product can be assumed worldwide to be closest to the (unknown) “reality.” Here, we propose and apply a methodology to quantify the uncertainty of a set of precipitation products and to identify, at individual grid points, the products that are likely wrong (i.e., outliers). The methodology is applied over eastern North America for the 2015–2019 period for eight high‐resolution gridded precipitation products: CMORPH, ERA5, GSMaP, IMERG, MSWEP, PERSIANN, STAGE IV and TMPA. Four difference metrics are used to quantify discrepancies in different aspects of the precipitation time series, such as the total accumulation, two characteristics of the intensity‐frequency distribution, and the timing of precipitating events. Large regional and seasonal variations in the observational uncertainty are found across the ensemble. The observational uncertainty is higher in Canada than in the United States, reflecting large differences in the density of precipitation gauge measurements. In northern midlatitudes, the uncertainty is highest in winter, demonstrating the difficulties of satellite retrieval algorithms in identifying precipitation in snow‐covered areas. In southern midlatitudes, the uncertainty is highest in summer, probably due to the more discontinuous nature of precipitation. While the best product cannot be identified due to the lack of an absolute reference, our study is able to identify products that are likely wrong and that should be excluded depending on the specific application.

A Simple Model of Flood Peak Attenuation

AbstractA simple analytical model was developed for evaluating the attenuation of flood wave peak discharge. The physically‐based model represents the flood wave along its trajectory, based on the diffusive model. Relative peak discharge decreases along the downstream distance according to a power function. The distance is scaled by the attenuation factor related to river hydrodynamics (flow rating, hydraulic diffusivity, celerity, and floodplain storage) and input hydrograph (initial peak discharge, hydrograph volume, and its relative curvature). It also informs the attenuation length, which is a practical indicator of the river distance in which discharge decreases by a given factor. Sensitivity analyses indicate that initial peak discharge, volume, floodplain storage, and slope are the governing factors of attenuation. Model's validity and accuracy were demonstrated by reproducing data from (a) numerical solutions of the Saint‐Venant equations covering a wide range of conditions, (b) 29 observations from 11 historical dam‐breaks, (c) 15 observations of natural floods in seven rivers and (d) a detailed hydrodynamic model. The model errors were generally lower than 10% and not larger than the typical uncertainty of flood observations. The accuracy is higher than simplified empirical models and analogous to a detailed hydrodynamic model that is representative of current practice. The proposed flood attenuation model can be easily applied using a few common parameters and a simple equation in a basic spreadsheet. It is suitable for practical applications such as first assessments of natural and dam‐break floods, engineering design, and analyses of large river networks supported by remote sensing data.

Inferring chemical disequilibrium biosignatures for Proterozoic Earth-like exoplanets

AbstractChemical disequilibrium quantified using the available free energy has previously been proposed as a potential biosignature. However, researchers remotely sensing exoplanet biosignatures have not yet investigated how observational uncertainties impact the ability to infer a life-generated available free energy. We pair an atmospheric retrieval tool to a thermodynamics model to assess the detectability of chemical disequilibrium signatures of Earth-like exoplanets, focusing on the Proterozoic eon when the atmospheric abundances of oxygen–methane disequilibrium pairs may have been relatively high. Retrieval model studies applied across a range of gas abundances revealed that order-of-magnitude constraints on the disequilibrium energy are achieved with simulated reflected-light observations for the high-abundance scenario and high signal-to-noise ratios (50), whereas weak constraints are found for moderate signal-to-noise ratios (20–30) and medium- to low-abundance cases. Furthermore, the disequilibrium-energy constraints are improved by using the modest thermal information encoded in water vapour opacities at optical and near-infrared wavelengths. These results highlight how remotely detecting chemical disequilibrium biosignatures can be a useful and metabolism-agnostic approach to biosignature detection.

Quantum relative entropy uncertainty relation.

For classic systems, the thermodynamic uncertainty relation (TUR) states that the fluctuations of a current have a lower bound in terms of the entropy production. Some TURs are rooted in information theory, particularly derived from relations between observations (mean and variance) and dissimilarities, such as the Kullback-Leibler divergence, which plays the role of entropy production in stochastic thermodynamics. We generalize this idea for quantum systems, where we find a lower bound for the uncertainty of quantum observables given in terms of the quantum relative entropy. We apply the result to obtain a quantum thermodynamic uncertainty relation in terms of the quantum entropy production, valid for arbitrary dynamics and nonthermal environments.

An ensemble-based approach for pumping optimization in an island aquifer considering parameter, observation and climate uncertainty

Abstract. In coastal zones, a major objective of groundwater management is often to determine sustainable pumping rates which avoid well salinization. Understanding how model and climate uncertainties affect optimal management solutions is essential for providing groundwater managers with information about salinization risk and is facilitated by the use of optimization under uncertainty (OUU) methods. However, guidelines are missing for the widespread implementation of OUU in real-world coastal aquifers and for the incorporation of climate uncertainty into OUU approaches. An ensemble-based OUU approach was developed considering parameter, observation and climate uncertainty and was implemented in a real-world island aquifer in the Magdalen Islands (Quebec, Canada). A sharp-interface seawater intrusion model was developed using MODFLOW-SWI2 and a prior parameter ensemble was generated containing multiple equally plausible realizations. Ensemble-based history matching was conducted using an iterative ensemble smoother which yielded a posterior parameter ensemble conveying both parameter and observation uncertainty. Sea level and recharge ensembles were generated for the year 2050 and were then used to generate a predictive parameter ensemble conveying parameter, observation and climate uncertainty. Multi-objective OUU was then conducted, aiming to both maximize pumping rates and minimize the probability of well salinization. As a result, the optimal trade-off between pumping and the probability of salinization was quantified considering parameter, historical observation and future climate uncertainty simultaneously. The multi-objective, ensemble-based OUU led to optimal pumping rates that were very different from a previous deterministic OUU and close to the current and projected water demand for risk-averse stances. Incorporating climate uncertainty into the OUU was also critical since it reduced the maximum allowable pumping rates for users with a risk-averse stance. The workflow used tools adapted to very high-dimensional, nonlinear models and optimization problems to facilitate its implementation in a wide range of real-world settings.

Determining cosmological-model-independent H0 and post-Newtonian parameter with time-delay lenses and supernovae

ABSTRACT Strong gravitational lensing provides a natural opportunity to test general relativity (GR). We propose a model-independent method for simultaneous constraining on Hubble constant (H0) and post-Newtonian parameter (γPPN) using strong lensing systems and observational Type Ia supernovae. The time-delay measurements from strong lensing can directly determine the Hubble constant, and the lens distance inferred from the spectroscopic measurement of the stellar kinematics of the deflector galaxy can help us to constrain the post-Newtonian parameter. We seek the Pantheon data set and reconstruct unanchored distances using Gaussian process regression to achieve the cosmological-model-independent GR testing instead of assuming a specific model, which can reduce possible bias on GR testing and measurement of Hubble constant. Combining the reconstructed unanchored distances and the four H0LiCOW lens data sets, our results are $H_0=72.9^{+2.0}_{-2.3} \, \mathrm{km \, s^{-1} \, Mpc^{-1}}$ and $\gamma _{\rm {PPN}}=0.89^{+0.17}_{-0.15}$. All the lenses show that there is no obvious evidence to support GR deviation within observational uncertainties. In the subsequent analysis, we consider a ratio of distance ${D_{\Delta t}}/{D^{^{\prime }}_{\mathrm{ d}}}$ method to further avoid the influence of H0 on GR testing. The results show that, except J1206 within the ∼1.2σ observational uncertainty, the remaining three lenses support that GR holds within the 1σ observational uncertainties.

Activity Discovered on Mars-Crossing Jupiter Family Comet 2018 OR by Citizen Scientists

We present the discovery of cometary activity on 2018 OR as part of our Active Asteroids project, a NASA Partner Program fueled by Zooniverse Citizen Scientists. Volunteers found 2018 OR with a long, diffuse tail in archival images from the Dark Energy Camera on the Blanco 4 m telescope at the Cerro Tololo Inter-American Observatory in Chile. Our team identified additional Canada–France–Hawaii–Telescope MegaCam and Zwicky Transient Facility archival data after classification by Citizen Scientists. Activity originating from 2018 OR and directed in the anti-solar and anti-velocity directions was visible in archival images between UT 2018 September 5–18. Our dynamical simulations indicate 2018 OR experiences close encounters with Jupiter over hundred-year timescales. The orbital period and dynamics suggest 2018 OR is a Jupiter Family Comet, and we recommend further observations from the community to reduce observational uncertainties and investigate activity patterns.

Evidence of human influence on Northern Hemisphere snow loss

Documenting the rate, magnitude and causes of snow loss is essential to benchmark the pace of climate change and to manage the differential water security risks of snowpack declines1–4. So far, however, observational uncertainties in snow mass5,6 have made the detection and attribution of human-forced snow losses elusive, undermining societal preparedness. Here we show that human-caused warming has caused declines in Northern Hemisphere-scale March snowpack over the 1981–2020 period. Using an ensemble of snowpack reconstructions, we identify robust snow trends in 82 out of 169 major Northern Hemisphere river basins, 31 of which we can confidently attribute to human influence. Most crucially, we show a generalizable and highly nonlinear temperature sensitivity of snowpack, in which snow becomes marginally more sensitive to one degree Celsius of warming as climatological winter temperatures exceed minus eight degrees Celsius. Such nonlinearity explains the lack of widespread snow loss so far and augurs much sharper declines and water security risks in the most populous basins. Together, our results emphasize that human-forced snow losses and their water consequences are attributable—even absent their clear detection in individual snow products—and will accelerate and homogenize with near-term warming, posing risks to water resources in the absence of substantial climate mitigation.

Iterative ensemble smoothing scheme for reservoir history matching with unknown observation error covariance

Ensemble-based smoothers are well-known data assimilation tools in reservoir applications, including history matching and geophysical inversion. They entail the knowledge of the covariance of observation errors, which, however, is often poorly known in real applications and commonly estimated subjectively on the basis of a diagonal structure. This neglects the correlations within the observation errors, which may deteriorate the quality of the model estimates. Herein, we relaxed the independent observational error assumption by considering a nondiagonal structure for the covariance. Given that all the elements in the covariance matrix could not be estimated given a limited amount of data, which is typically the case in practice, we reduced the number of degrees of freedom by parameterizing this covariance with two scalar parameters, one for error variance and the other for error correlation length–scale. We then estimated these parameters together with the model variables using a hybrid algorithm that combined a variational Bayesian approach with particle filters and an ensemble-based smoother. The proposed approach was validated with a linear Gaussian model and a nonlinear reservoir flow model. The results clearly demonstrate the potential of the proposed method in effectively addressing the uncertainty of observations in the history-matching process.

Uncertainties in the observational reference: Implications in skill assessment and model ranking of seasonal predictions

AbstractThe probabilistic skill of seasonal prediction systems is often inferred using reanalysis data, assuming these benchmark observations to be error free. However, an increasing number of studies report non‐negligible levels of uncertainty affecting reanalysis observations, especially when it comes to variables like precipitation or wind speed. We consider different possibilities to account for such error in forecast quality assessment, either exploiting the newly produced ensemble reanalyses (e.g., European Centre for Medium‐Range Weather Forecasts Reanalysis version 5–Ensemble of Data Assimilations, ERA5 ‐ EDA) or applying methodologies that use scores that take observational uncertainty into account. We illustrate the benefits of employing ensemble reanalyses over traditional reanalyses and show how the true skill can be approximated, whatever the observational reference. We ultimately emphasise the perils and quantify the error committed when the observational reference, either reanalysis or point dataset, is selected arbitrarily for verifying a seasonal prediction system.

On the Development of Fuzzy Estimators for Life Time Distributions based on Censored Fuzzy Life Times

Lifetime analyses comprise the techniques dealing with observations obtained from the occurrence of a specified event(s). In most of the situations dealing with lifetime observations, some units are recorded as censored observations. Dealing with censored observations makes these techniques unique. Countless standard statistical tools are available for inference based on censored lifetime observations. These classical techniques consider lifetime observations as precise numbers and ignore the uncertainty of single observations. Whereas in practical applications it is not possible to measure life times as precise numbers, they are always more or less nonprecise. The imprecision in measurements can be covered by neutrosophic set. Fuzzy estimators for life time distributions potentially use neutrosophic system to model and analyze the inherent uncertainties and neutalities present in the data and the parameter estimates. This study aimed to obtain estimators for the Weibull parameters and two exponential parameters based on the up-to-date fuzzy number approach, a special case for neutrosophic set. The suggested estimators incorporate fuzziness in addition to random variation, which makes these estimators more realistic. The same techniques need to be extended to fuzzy and neutrosophic sets.

Comment on “The Treatment of Uncertainty in Hydrometric Observations: A Probabilistic Description of Streamflow Records” by de Oliveira and Vrugt

AbstractQuantifying uncertainties and errors in hydrometric observations is critical to improve our ability to predict streamflow. de Oliveira and Vrugt (2022, https://doi.org/10.1029/2022wr032263) expand on an earlier publication (Vrugt et al., 2005, https://doi.org/10.1029/2004wr003059) to describe a difference‐based variance estimation method that is, used to estimate the aleatory observation uncertainty in streamflow records directly from a time series of such data. The method is then applied to hourly data from 500+ catchments across the contiguous United States. This comment outlines our concerns that the assumptions needed to effectively use difference‐based variance estimation methods are not always met by hourly streamflow records. We illustrate our concerns through the use of a specific test case. We argue that more work is needed to quantify the individual sources of errors and uncertainties in hydrometric observations.

Entropy-Regularized Partially Observed Markov Decision Processes

We investigate partially observed Markov decision processes (POMDPs) with cost functions regularized by entropy terms describing state, observation, and control uncertainty. Standard POMDP techniques are shown to offer bounded-error solutions to these entropy-regularized POMDPs, with exact solutions possible when the regularization involves the joint entropy of the state, observation, and control trajectories. Our joint-entropy result is particularly surprising since it constitutes a novel, tractable formulation of active state estimation.

SatERR: A Community Error Inventory for Satellite Microwave Observation Error Representation and Uncertainty Quantification

Abstract Satellite observations are indispensable for weather forecasting, climate change monitoring, and environmental studies. Understanding and quantifying errors and uncertainties associated with satellite observations are essential for hardware calibration, data assimilation, and developing environmental and climate data records. Satellite observation errors can be classified into four categories: measurement, observation operator, representativeness, and preprocessing errors. Current methods for diagnosing observation errors still yield large uncertainties due to these complex errors. When simulating satellite errors, empirical errors are usually used, which do not always accurately represent the truth. We address these challenges by developing an error inventory simulator, the Satellite Error Representation and Realization (SatERR). SatERR can simulate a wide range of observation errors, from instrument measurement errors to model assimilation errors. Most of these errors are based on physical models, including existing and newly developed algorithms. SatERR takes a bottom-up approach: errors are generated from root sources and forward propagate through radiance and science products. This is different from, but complementary to, the top-down approach of current diagnostics, which inversely solves unknown errors. The impact of different errors can be quantified and partitioned, and a ground-truth testbed can be produced to test and refine diagnostic methods. SatERR is a community error inventory, open-source on GitHub, which can be expanded and refined with input from engineers, scientists, and modelers. This debut version of SatERR is centered on microwave sensors, covering traditional large satellites and small satellites operated by NOAA, NASA, and EUMETSAT.

The Tree-Crop Remote Sensing of Evapotranspiration Experiment (T-REX): A Science-Based Path for Sustainable Water Management and Climate Resilience

Abstract Water scarcity threatens agriculture in California. During the last two decades, historically severe droughts have led to severe water shortages. Under projected changes in climate, droughts of greater severity and duration will exacerbate this situation. California produces 80% of the world’s almonds, which require consistent water supplies for irrigation. Almonds are the most commonly grown crop in California, covering nearly 1.4 million acres over about 8,000 farms. In response to these challenges, almond growers are considering a myriad of management strategies to save water and mitigate climate change. The Tree-Crop Remote Sensing of Evapotranspiration Experiment (T-REX) aims to identify water and orchard management opportunities to maximize water use efficiency and carbon sequestration in almonds and other woody perennial tree crops. The project combines satellite, uncrewed aerial vehicles, and proximal sensing technologies to retrieve key variables used to model surface fluxes and biophysical properties. We aim to advance our understanding of water management and cultural practices on water–carbon relationships in tree-perennial agroecosystems. Through new methods, such as evapotranspiration-based irrigation scheduling, even a modest 10% decrease in almond orchard irrigation across the state equates to about a third of the water in Lake Oroville, California’s second-largest reservoir, at average levels. From a carbon perspective, almond orchards could sequester 8% of the state’s current greenhouse gas emissions by transitioning toward climate-smart practices. As such, the almond industry is uniquely positioned to curb water use and contribute to climate change mitigation while maintaining economic viability of almond production. An overview of initial results related to evapotranspiration observational and modeling uncertainty and carbon sequestration potential are presented in this article.