Observational Uncertainties Research Articles

Missing Road Condition Imputation Using a Multi-View Heterogeneous Graph Network From GPS Trajectory

How to generate road conditions from urban GPS trajectory is an important problem in transportation systems. However, this computation process usually suffers from serious missing value problem due to the observation uncertainty or limited reports from crowdsourcing systems. Conventional tensor factorization approaches learn the spatio-temporal dependencies in a collaborative filtering way, which ignores the complex road network structure information and temporal heterogeneity. In this study, we propose a multi-view model with multiple aspects of prior knowledge to impute traffic state computed from a real-world trajectory dataset. More specifically, in the spatial view, rather than focusing on a specific type of road segment, we take the heterogeneity of road network into consideration and model the multiple relations of adjacent road segments. Meanwhile, the temporal pattern is also viewed as a heterogeneous graphical structure that discriminates the weekly/hourly adjacency in the temporal view. Finally, we fuse the above spatio-temporal features to provide a robust estimation under different sparse conditions. Intensive experiments on two types of missing scenarios (i.e., random and non-random) demonstrate that the proposed imputation method outperforms all the other state-of-the-art approaches. In addition, our model represents interpretable patterns for spatio-temporal graph analysis.

The emergence of the Gulf Stream and interior western boundary as key regions to constrain the future North Atlantic carbon uptake

Abstract. In recent years, the growing number of available climate models and future scenarios has led to emergent constraints becoming a popular tool to constrain uncertain future projections. However, when emergent constraints are applied over large areas, it is unclear (i) if the well-performing models simulate the correct dynamics within the considered area, (ii) which key dynamical features the emerging constraint is stemming from, and (iii) if the observational uncertainty is low enough to allow for a considerable reduction in the projection uncertainties. We therefore propose to regionally optimize emergent relationships with the twofold goal to (a) identify key model dynamics associated with the emergent constraint and model inconsistencies around them and (b) provide key areas where a narrow observational uncertainty is crucial for constraining future projections. Here, we consider two previously established emergent constraints of the future carbon uptake in the North Atlantic (Goris et al., 2018). For the regional optimization, we use a genetic algorithm and pre-define a suite of shapes and size ranges for the desired regions. Independent of pre-defined shape and size range, the genetic algorithm persistently identifies the Gulf Stream region centred around 30∘ N as optimal as well as the region associated with broad interior southward volume transport centred around 26∘ N. Close to and within our optimal regions, observational data of volume transport are available from the RAPID array with relative low observational uncertainty. Yet, our regionally optimized emergent constraints show that additional measures of specific biogeochemical variables along the array will fundamentally improve our estimates of the future carbon uptake in the North Atlantic. Moreover, our regionally optimized emergent constraints demonstrate that models that perform well for the upper-ocean volume transport and related key biogeochemical properties do not necessarily reproduce the interior-ocean volume transport well, leading to inconsistent gradients of key biogeochemical properties. This hampers the applicability of emergent constraints over large areas and highlights the need to additionally evaluate spatial model features.

Line lists for the ground states of GaF and InF

Rotation-vibrational line lists for 69,71GaF and 113,115InF in their ground electronic states are calculated. Empirical Hamiltonians determined by a direct potential-fitting approach using observed transition frequencies are used in line lists calculations. The reproduced transition frequencies are within the observation uncertainties in the range of experimental data. The dipole moment functions calculated using ab initio method at equilibrium nuclear separations are almost identical to the experimental results. The calculated intensities agree well with the laboratory observations. Transitions of Δv=0–4, ΔJ=±1 in the range of v = 0–30, Jmax=131–251 are predicted for 69GaF and 71GaF. For 113InF and 115InF, transitions of Δv=0–5, ΔJ=±1 in the range of v = 0–30 and Jmax=151–259 are predicted. The corresponding Einstein A coefficients and oscillator strengths are predicted. Partition functions for temperatures up to 6000 K have been calculated. The line lists can be used for spectroscopic measurements of GaF and InF in hot environments of various temperatures.

The Neutron Mean Life and Big Bang Nucleosynthesis

We explore the effect of neutron lifetime and its uncertainty on standard big bang nucleosynthesis (BBN). BBN describes the cosmic production of the light nuclides, 1H, D, 3H+3He, 4He, and 7Li+7Be, in the first minutes of cosmic time. The neutron mean life τn has two roles in modern BBN calculations: (1) it normalizes the matrix element for weak n↔p interconversions, and (2) it sets the rate of free neutron decay after the weak interactions freeze-out. We review the history of the interplay between τn measurements and BBN, and present a study of the sensitivity of the light element abundances to the modern neutron lifetime measurements. We find that τn uncertainties dominate the predicted 4He error budget, but these theory errors remain smaller than the uncertainties in 4He observations, even with the dispersion in recent neutron lifetime measurements. For the other light element predictions, τn contributes negligibly to their error budget. Turning the problem around, we combine present BBN and cosmic microwave background (CMB) determinations of the cosmic baryon density to predict a “cosmologically preferred” mean life of τn(BBN+CMB)=870±16s, which is consistent with experimental mean life determinations. We show that if future astronomical and cosmological helium observations can reach an uncertainty of σobs(Yp)=0.001 in the 4He mass fraction Yp, this could begin to discriminate between the mean life determinations.

Thermodynamic Data for Sn(IV) Species in Aqueous Solution: A Matter of Controversy and Error

Critically assessed data regarding Sn(IV) dioxides and hydroxy complexes have recently been challenged. Differences as large as nine orders of magnitude occur in certain of the published solubility products and other equilibrium constants, despite supposedly being derived from the same ‘reliable’ measurements. We show how these differing conclusions depend on the assignments of uncertainty in the respective experimental observations and that the divergence is due to error propagation in identifiable thermodynamic analyses. The use of Sn4+ as a ‘basis’/‘master’ species in thermodynamic modelling is deprecated. Automatic methods which enable the necessary calculations to be properly evaluated, as well as easily repeated, help uncover such mistakes. The results from the comprehensive NEA review are substantially confirmed.

Surface impacts and associated mechanisms of a moisture intrusion into the Arctic observed in mid-April 2020 during MOSAiC

Distinct events of warm and moist air intrusions (WAIs) from mid-latitudes have pronounced impacts on the Arctic climate system. We present a detailed analysis of a record-breaking WAI observed during the MOSAiC expedition in mid-April 2020. By combining Eulerian with Lagrangian frameworks and using simulations across different scales, we investigate aspects of air mass transformations via cloud processes and quantify related surface impacts. The WAI is characterized by two distinct pathways, Siberian and Atlantic. A moist static energy transport across the Arctic Circle above the climatological 90th percentile is found. Observations at research vessel Polarstern show a transition from radiatively clear to cloudy state with significant precipitation and a positive surface energy balance (SEB), i.e., surface warming. WAI air parcels reach Polarstern first near the tropopause, and only 1–2 days later at lower altitudes. In the 5 days prior to the event, latent heat release during cloud formation triggers maximum diabatic heating rates in excess of 20 K d-1. For some poleward drifting air parcels, this facilitates strong ascent by up to 9 km. Based on model experiments, we explore the role of two key cloud-determining factors. First, we test the role moisture availability by reducing lateral moisture inflow during the WAI by 30%. This does not significantly affect the liquid water path, and therefore the SEB, in the central Arctic. The cause are counteracting mechanisms of cloud formation and precipitation along the trajectory. Second, we test the impact of increasing Cloud Condensation Nuclei concentrations from 10 to 1,000 cm-3 (pristine Arctic to highly polluted), which enhances cloud water content. Resulting stronger longwave cooling at cloud top makes entrainment more efficient and deepens the atmospheric boundary layer. Finally, we show the strongly positive effect of the WAI on the SEB. This is mainly driven by turbulent heat fluxes over the ocean, but radiation over sea ice. The WAI also contributes a large fraction to precipitation in the Arctic, reaching 30% of total precipitation in a 9-day period at the MOSAiC site. However, measured precipitation varies substantially between different platforms. Therefore, estimates of total precipitation are subject to considerable observational uncertainty.

Densely rewarded reinforcement learning for robust low-thrust trajectory optimization

To overcome the time-consuming training caused by the sparse reward function in reinforcement learning, an efficient dense reward framework for robust low-thrust trajectory optimization is proposed. The dense reward functions are designed separately for the deterministic and considered uncertain scenarios, including state uncertainties, observation uncertainties and execution uncertainties. For the uncertainties, the dense reward function is designed to diminish the deviation with respect to the isochronous nominal state along the corresponding deterministic optimal trajectory at each step, rendering the reward function no longer sparse and suitable for complex multirevolution problems. In addition, a multistage reward function of the terminal constraints for the rendezvous missions is designed by incorporating some exponential acceleration terms, enabling significant improvement in training efficiency as the terminal errors become low. In addition, a dense reward function for the deterministic scenario is also proposed via the introduction of empirical forbidden zones and an exponential term. The effectiveness and efficiency of the proposed method is demonstrated in a simple Earth-Mars mission and a complex Earth-Venus multirevolution mission. The promising results verify the significant effect of the proposed method in speeding up the process of training an initial incapable agent to an ‘expert’ while guaranteeing or even improving the performance.

Uncertainties in Models Predicting Critical Bed Shear Stress of Cohesionless Particles

Our data show large scatter in the critical Shields stress values for initial sediment motion. The main sources of dispersion are related to the methodological procedures defining the inception of movement (i.e., visual observations or extrapolation of sediment transport rate) and to the estimation of the bed shear stress. The threshold for sediment motion varies with many factors related not only to grain size but also to bed composition (e.g., presence of fine sediments in a coarse matrix), arrangement (e.g., bed roughness, grain orientation, and characteristic lengths of bed structures) and slope. New models to estimate the critical Shields number are proposed using grain size or bed slope or a combination of both. Model parameters and uncertainty are estimated through Bayesian inference using prior knowledge of these parameters and measured data. Apart from the uncertainty in observations, two types of uncertainty can be evaluated: one related to the parameter estimation (i.e., parametric) and one related to the choice of the model (i.e., structural). Finally, a four-parameter model based on the grain size and bed slope yields the best results and demonstrates potential interaction between these two parameters. Model uncertainty, however, remains large, which indicates that other input parameters may be needed to improve the proposed model.

Exoplanet characterization using conditional invertible neural networks

Context. The characterization of the interior of an exoplanet is an inverse problem. The solution requires statistical methods such as Bayesian inference. Current methods employ Markov chain Monte Carlo (MCMC) sampling to infer the posterior probability of the planetary structure parameters for a given exoplanet. These methods are time-consuming because they require the evaluation of a planetary structure model ~105 times. Aims. To speed up the inference process when characterizing an exoplanet, we propose to use conditional invertible neural networks to calculate the posterior probability of the planetary structure parameters. Methods. Conditional invertible neural networks (cINNs) are a special type of neural network that excels at solving inverse problems. We constructed a cINN following the framework for easily invertible architectures (FreIA). This neural network was then trained on a database of 5.6 × 106 internal structure models to recover the inverse mapping between internal structure parameters and observable features (i.e., planetary mass, planetary radius, and elemental composition of the host star). We also show how observational uncertainties can be accounted for. Results. The cINN method was compared to a commonly used Metropolis-Hastings MCMC. To do this, we repeated the characterization of the exoplanet K2-111 b, using both the MCMC method and the trained cINN. We show that the inferred posterior probability distributions of the internal structure parameters from both methods are very similar; the largest differences are seen in the exoplanet water content. Thus, cINNs are a possible alternative to the standard time-consuming sampling methods. cINNs allow infering the composition of an exoplanet that is orders of magnitude faster than what is possible using an MCMC method. The computation of a large database of internal structures to train the neural network is still required, however. Because this database is only computed once, we found that using an invertible neural network is more efficient than an MCMC when more than ten exoplanets are characterized using the same neural network.

Assessment of Large-Scale Indices of Surface Temperature during the Historical Period in the CMIP6 Ensemble

Abstract We develop a statistical method to assess CMIP6 simulations of large-scale surface temperature change during the historical period (1850–2014), considering all time scales, allowing for the different unforced variability of each model and the observations, observational uncertainty, and variable ensemble size. The generality of this method, and the fact that it incorporates information about the unforced variability, makes it a useful model assessment tool. We apply this method to the historical simulations of the CMIP6 multimodel ensemble. We use three indices that measure different aspects of large-scale surface air temperature change: global mean, hemispheric gradient, and a recently developed index that captures the sea surface temperature (SST) pattern in the tropics (SST#; see Fueglistaler and Silvers). We use the following observations: HadCRUT5 for the first two indices, and AMIPII and ERSSTv5 for SST#. In each case, we test the hypothesis that the model’s forced response is compatible with the observations, accounting for unforced variability in both models and observations as well as measurement uncertainty. This hypothesis is accepted more often (75% of the models) for the hemispheric gradient than for the global mean, for which half of the models fail the test. The tropical SST pattern is poorly simulated in all models. Given that the tropical SST pattern can strongly modulate the relationship between energy imbalance and global-mean surface temperature anomalies on annual to decadal time scales (short-term feedback parameter), we suggest this should be a focus area for future improvements due to its potential implications for the global-mean temperature evolution in decadal time scales.

Barium stars as tracers ofs-process nucleosynthesis in AGB stars

Context.Barium (Ba) stars are characterised by an abundance of heavy elements made by the slow neutron capture process (s-process). This peculiar observed signature is due to the mass transfer from a stellar companion, bound in a binary stellar system, to the Ba star observed today. The signature is created when the stellar companion is an asymptotic giant branch (AGB) star.Aims.We aim to analyse the abundance pattern of 169 Ba stars using machine learning techniques and the AGB final surface abundances predicted by the FRUITYand Monash stellar models.Methods.We developed machine learning algorithms that use the abundance pattern of Ba stars as input to classify the initial mass and metallicity of each Ba star’s companion star using stellar model predictions. We used two algorithms. The first exploits neural networks to recognise patterns, and the second is a nearest-neighbour algorithm that focuses on finding the AGB model that predicts the final surface abundances closest to the observed Ba star values. In the second algorithm, we included the error bars and observational uncertainties in order to find the best-fit model. The classification process was based on the abundances of Fe, Rb, Sr, Zr, Ru, Nd, Ce, Sm, and Eu. We selected these elements by systematically removings-process elements from our AGB model abundance distributions and identifying the elements whose removal had the biggest positive effect on the classification. We excluded Nb, Y, Mo, and La. Our final classification combined the output of both algorithms to identify an initial mass and metallicity range for each Ba star companion.Results.With our analysis tools, we identified the main properties for 166 of the 169 Ba stars in the stellar sample. The classifications based on both stellar sets of AGB final abundances show similar distributions, with an average initial mass ofM= 2.23M⊙and 2.34M⊙and an average [Fe/H] = −0.21 and −0.11, respectively. We investigated why the removal of Nb, Y, Mo, and La improves our classification and identified 43 stars for which the exclusion had the biggest effect. We found that these stars have statistically significant and different abundances for these elements compared to the other Ba stars in our sample. We discuss the possible reasons for these differences in the abundance patterns.

Comparison of solar activity proxies: eigenvectors versus averaged sunspot numbers

ABSTRACTWe explore the links between the averaged sunspot numbers (SSN) and a modulus summary curve (MSC) of two largest eigenvectors of the solar background magnetic field (SBMF) derived from principal component analysis. MSC has rather close correspondence with the whole set of SSN revealing close cycle timings, duration, and maxima times for the cycles 12–24, 6, 7, and −4, −3, while for a few cycles in the mid-18th and mid-19th centuries there are discrepancies in the maximum amplitudes, durations, and times of the maxima. Possible reasons of these discrepancies related to uncertainties in the SSN observations in the 18th–19th centuries, in MSC definition and the different solar activity entities they represent: toroidal (SSN) and poloidal (MSC) magnetic fields, are discussed. Wavelet and Fourier spectral analysis of SSN and MSC series reveal within 95 per cent confidence levels the same prominent period of 10.7 yr, whereas SSN series show a period of 101 yr and MSC of 342 yr close to or above 95 per cent red-noise level. The correlation coefficients between SSN and MSC series vary from 0.25 for the whole SSN data set (from 1700), to 0.56 for the data sets from 1860, to 0.67 for the data sets from 1900 when all SSN restorations agree. These SSN and MSC data sets are confirmed to be closely but not identically related representing the solar activity in different entities of solar dynamo. Use of the summary curve and MSC of eigenvectors of SBMF can provide additional information to SSN for better understanding of solar activity.

Climate data uncertainty for agricultural impact assessments in West Africa

Reliable information on climate impacts can support planning processes to make the agricultural sector—which has cascading effects on food security, livelihoods and the security situation—more resilient. Subsequently, uncertainties in past and future climate data need to be decreased and better understood. In this study, we analysed the quality and limitations of different past and future climate data sets to be used for agricultural impact assessments in West Africa. The high differences between the three analysed past climate data sets underline the high observational uncertainty in West Africa and show the influence of selecting the observational data set for the bias-adjustment of climate model data. The ten CMIP6 (Coupled Model Inter-comparison Project Phase 6) models show regional and model-dependent biases with similar systematic biases as have been observed in earlier CMIP versions. Although the bias-adjusted version of this data (ISIMIP3b—Inter-Sectoral Impact Model Intercomparison Project) aligns overall well with observations, we could detect some regional strong deviations from observations for some agroclimatological indices. The use of the multi-model ensemble mean has resulted in an improved agreement of CMIP6 and the bias-adjusted ISIMIP3b data with observations. Choosing a sub-ensemble of bias-adjusted models could only improve the performance of the ensemble mean locally but not over the whole region. Therefore, our results suggest the use of the whole model ensemble for agricultural impact assessments in West Africa. While averaging the impact results over all climate models can serve as a best guess, the spread of the results over all models should be considered to give insights into the uncertainties. This study can support agricultural impact modelling in quantifying climate risk hotspots as well as suggesting suitable adaptation measures to increase the resilience of the agricultural sector in West Africa.

On the impact of spectral template uncertainties in synthetic stellar populations

ABSTRACTUncertainties in stellar population models, both in terms of stellar evolution and stellar spectra, translate into uncertainties in our interpretation of stellar populations in galaxies, since stars are the source of most of the light we receive from them. Observations by JWST are revealing high-redshift galaxies in great detail, which must then be compared to models. One significant source of uncertainty is in the stellar spectra used to generate composite spectra of stellar populations, which are then compared to data. Confidence in theoretical models is important to enable reliable determination of the properties of these galaxies such as their ages and star formation history. Here, we present a comparison of spectral synthesis carried out with six different stellar spectral libraries using the Binary Population and Spectral Synthesis framework. In photometric colours, the differences between theoretical libraries are relatively small (<0.10 mag), similar to typical observational uncertainties on individual galaxy observations. Differences become more pronounced when detailed spectroscopic properties are examined. Predictions for spectral line indices can vary significantly, with equivalent widths differing by a factor of 2 in some cases. With these index strengths, some of the libraries yield predictions of ages and metallicities which are unphysical. Many spectral libraries lack wavelength coverage in the ultraviolet, which is of growing importance in the era of JWST observations of distant galaxies, whose flux is dominated by hot, young stars.

SciKit-GStat Uncertainty: A software extension to cope with uncertain geostatistical estimates

This study is focused on an extension of a well established geostatistical software to enable one to effectively and interactively cope with uncertainty in geostatistical applications. The extension includes a rich component library, pre-built interfaces and an online application. We discuss the concept of replacing the empirical variogram with its uncertainty bound. This enables one to acknowledge uncertainties characterizing the underlying geostatistical datasets and typical methodological approaches. This allows for a probabilistic description of the variogram and its parameters at the same time. Our approach enables (1) multiple interpretations of a sample and (2) a multi-model context for geostatistical applications. We focus the sample application on propagating observation uncertainties into manual variogram parametrization and analyze its effects. Using two different datasets, we show how insights on uncertainty can be used to reject variogram models, thus constraining the space of formally equally probable models to tackle the issue of parameter equifinality.

Variations and drivers of evapotranspiration in the Tibetan Plateau during 1982–2015

Study regionThe Tibetan Plateau Study focusEvapotranspiration (ET) plays a critical role in the water balance, energy budget, and carbon cycle. However, the variations, trends, and controls of ET on the Tibetan Plateau (TP) are poorly understood because of uncertainties in ET estimates and sparse observations. In this study, the variations in ET and its components and their drivers and controls in the TP were analyzed at seasonal and annual scales during 1982–2015. New hydrological insights for the regionSpatially, the multiyear mean annual ET decreased from the southeastern to northwestern TP. Canopy transpiration (Ec) was the main component of ET (52.7%), followed by soil evaporation (Es) (34.4%) and interception (Ei) (10.7%). Regionally, the averaged ET and its components increased significantly at the seasonal and annual scales. Spatially, the controlling factor for ET changed from water to energy as the climatic zones transferred from aridity to humidity. The annual ET was controlled by soil moisture (SM) in arid and semi-arid zones, whereas Ta was the dominant factor in the other regions. The increased annual Es and Ei were primarily caused by SM, while the annual Ec was determined by Ta. In addition, NDVI played a certain role in regulating the annual Ec and Ei variations. This study improves our understanding of hydrological processes and water resource management under global climate change.

A comprehensive assessment of in situ and remote sensing soil moisture data assimilation in the APSIM model for improving agricultural forecasting across the US Midwest

Abstract. Today, the most popular approaches in agricultural forecasting leverage process-based crop models, crop monitoring data, and/or remote sensing imagery. Individually, each of these tools has its own unique advantages but is, nonetheless, limited in prediction accuracy, precision, or both. In this study we integrate in situ and remote sensing (RS) soil moisture observations with APSIM model through sequential data assimilation to evaluate the improvement in model predictions of downstream state variables across five experimental sites in the US Midwest. Four RS data products and in situ observations spanning 19 site years were used through two data assimilation approaches, namely ensemble Kalman filter (EnKF) and generalized ensemble filter (GEF), to constrain model states at observed time steps and estimate joint background and observation error matrices. Then, the assimilation's impact on estimates of soil moisture, yield, normalized difference vegetation index (NDVI), tile drainage, and nitrate leaching was assessed across all site years. When assimilating in situ observations, the accuracy of soil moisture forecasts in the assimilation layers was improved by reducing RMSE by an average of 17 % for 10 cm and ∼28 % for 20 cm depth soil layer across all site years. These changes also led to improved simulation of soil moisture in deeper soil layers by an average of 12 %. Although crop yield was improved by an average of 23 %, the greatest improvement in yield accuracy was demonstrated in site years with higher water stress, where assimilation served to increase available soil water for crop uptake. Alternatively, estimates of annual tile drainage and nitrate leaching were not well constrained across the study sites. Trends in drainage constraint suggest the importance of including additional data constraint such as evapotranspiration. The assimilation of RS soil moisture showed a weaker constraint of downstream model state variables when compared to the assimilation of in situ soil moisture. The median reduction in soil moisture RMSE for observed soil layers was lower, on average, by a factor of 5. However, crop yield estimates were still improved overall with a median RMSE reduction of 17.2 %. Crop yield prediction was improved when assimilating both in situ and remote sensing soil moisture observations, and there is strong evidence that yield improvement was higher when under water-stressed conditions. Comparisons of system performance across different combinations of remote sensing data products indicated the importance of high temporal resolution and accurate observation uncertainty estimates when assimilating surface soil moisture observations.

Flow Regime-Dependent, Discharge Uncertainty Envelope for Uncertainty Analysis with Ensemble Methods

A discharge uncertainty envelope is presented that provides an observation error model for data assimilation (DA) using discharge observations derived from measurement of stage using a rating curve. It uniquely represents the rating curve representation error, which is due to scale and process incompatibility between the rating curve hydrodynamic model and “true” discharge, within the observation error model. Ensemble methods, specifically, the iterative ensemble smoother (IES) algorithms in PEST++, provide the DA framework for this observation error model. The purpose of the uncertainty envelope is to describe prior observation uncertainty for ensemble methods of DA. Envelope implementation goals are (1) limiting the spread of the envelope to avoid conditioning to extreme parameter values and producing posterior parameter distributions with increased variance, and (2) incorporating a representative degree of observation uncertainty to avoid overfitting, which will introduce bias into posterior parameter estimates and predicted model outcomes. The expected uncertainty envelope is flow regime dependent and is delineated using stochastic, statistical methods before undertaking history matching with IES. Analysis of the goodness-of-fit between stochastically estimated “true” discharge and observed discharge provides criteria for the selection of best-fit parameter ensembles from IES results.

From remotely-sensed solar-induced chlorophyll fluorescence to ecosystem structure, function, and service: Part II-Harnessing data.

Although our observing capabilities of solar-induced chlorophyll fluorescence (SIF) have been growing rapidly, the quality and consistency of SIF datasets are still in an active stage of research and development. As a result, there are considerable inconsistencies among diverse SIF datasets at all scales and the widespread applications of them have led to contradictory findings. The present review is the second of the two companion reviews, and data oriented. It aims to (1) synthesize the variety, scale, and uncertainty of existing SIF datasets, (2) synthesize the diverse applications in the sector of ecology, agriculture, hydrology, climate, and socioeconomics, and (3) clarify how such data inconsistency superimposed with the theoretical complexities laid out in (Sun et al., 2023) may impact process interpretation of various applications and contribute to inconsistent findings. We emphasize that accurate interpretation of the functional relationships between SIF and other ecological indicators is contingent upon complete understanding of SIF data quality and uncertainty. Biases and uncertainties in SIF observations can significantly confound interpretation of their relationships and how such relationships respond to environmental variations. Built upon our syntheses, we summarize existing gaps and uncertainties in current SIF observations. Further, we offer our perspectives on innovations needed to help improve informing ecosystem structure, function, and service under climate change, including enhancing in-situ SIF observing capability especially in "data desert" regions, improving cross-instrument data standardization and network coordination, and advancing applications by fully harnessing theory and data.

Measurement Uncertainty in Clinical Validation Studies of Sensors

Accurate clinical sensors and devices are essential to support optimal medical decision-making, and accuracy can be demonstrated through the conduct of clinical validation studies using validated reference sensors and/or devices for comparison. Typically unmeasurable, the true reference value can be substituted with an accepted physiological measurement with an associated uncertainty. We describe a basic model of measurement uncertainty that specifies the factors that may degrade the accuracy of an observed measurement value from a sensor, and we detail validation study design strategies that may be used to quantify and minimize these uncertainties. In addition, we describe a model that extends the observed measurement uncertainty to the resultant clinical decision and the factors that may impact the uncertainty of this decision. Clinical validation studies should be designed to estimate and minimize uncertainty that is unrelated to the sensor accuracy. The contribution of measurement observation uncertainty to clinical decision-making should be minimized but also acknowledged and incorporated into the clinical decision-making process.