Observational Uncertainties Research Articles

Estimating the gain of increasing the ensemble size from analytical considerations

AbstractModel ensembles may provide estimates of uncertainties arising from unknown initial conditions and model deficiencies. Often, the ensemble mean is taken as the best estimate, and quantities such as the mean‐squared error between model mean and observations decrease with the number of ensemble members. But the ensemble size is often limited by available resources, and so some idea of how many ensemble members that are needed before the error has saturated would be advantageous. The behaviour with ensemble size is often estimated by producing subsamples from a large ensemble. But this strategy requires that this large ensemble is already available. Fortunately, in many situations, the dependence on ensemble size follows simple analytical relations when the quantity under interest (such as the mean‐squared error between ensemble mean and observations) is calculated over many grid points or time points. This holds both for ensemble means and the related sampling variance. Here, we present such relations and demonstrate how they can be used to estimate the gain of increasing the ensemble. Whereas previous work has mainly focused on the size of the model ensemble, we recognize that uncertainties in observations play a role. We therefore also study the effect of using the mean of an ensemble of reanalyses. We show how the analytical relations can be used to estimate the point where the gain of increasing the size of the model ensemble is dwarfed by the gain of increasing the number of reanalyses. We demonstrate these points using two climate model ensembles: a large multimodel ensemble and a large single‐model initial‐condition ensemble.

Uncertainty in continuous ΔCO-based ΔffCO2 estimates derived from 14C flask and bottom-up ΔCO ∕ ΔffCO2 ratios

Abstract. Measuring the 14C / C depletion in atmospheric CO2 compared with a clean-air reference is the most direct way to estimate the recently added CO2 contribution from fossil fuel (ff) combustion (ΔffCO2) in ambient air. However, as 14CO2 measurements cannot be conducted continuously nor remotely, there are only very sparse 14C-based ΔffCO2 estimates available. Continuously measured tracers, like carbon monoxide (CO), that are co-emitted with ffCO2 can be used as proxies for ΔffCO2, provided that the ΔCO / ΔffCO2 ratios can be determined correctly (here, ΔCO refers to the CO excess compared with a clean-air reference). In the present study, we use almost 350 14CO2 measurements from flask samples collected between 2019 and 2020 at the urban site Heidelberg, Germany, and corresponding analyses from more than 50 afternoon flasks collected between September 2020 and March 2021 at the rural ICOS site Observatoire pérenne de l'environnement (OPE), France, to calculate average 14C-based ΔCO / ΔffCO2 ratios for those sites. For this, we constructed a clean-air reference from the 14CO2 and CO measurements of Mace Head, Ireland. By dividing the hourly ΔCO excess observations by the averaged flask ratio, we calculate continuous proxy-based ΔffCO2 records. The mean bias between the proxy-based ΔffCO2 and the direct 14C-based ΔffCO2 estimates from the flasks is – with 0.31 ± 3.94 ppm for the urban site Heidelberg and −0.06 ± 1.49 ppm for the rural site OPE – only ca. 3 % at both sites. The root-mean-square deviation (RMSD) between proxy-based ΔffCO2 and 14C-based ΔffCO2 is about 4 ppm for Heidelberg and 1.5 ppm for OPE. While this uncertainty can be explained by observational uncertainties alone at OPE, about half of the uncertainty is caused by the neglected variability in the ΔCO / ΔffCO2 ratios at Heidelberg. We further show that modeled ratios based on a bottom-up European emission inventory would lead to substantial biases in the ΔCO-based ΔffCO2 estimates for both Heidelberg and OPE. This highlights the need for an ongoing observational calibration and/or validation of inventory-based ratios if they are to be applied for large-scale ΔCO-based ΔffCO2 estimates, e.g., from satellites.

Sensitivity of thermodynamic profiles retrieved from ground-based microwave and infrared observations to additional input data from active remote sensing instruments and numerical weather prediction models

Abstract. Accurate and continuous estimates of the thermodynamic structure of the lower atmosphere are highly beneficial to meteorological process understanding and its applications, such as weather forecasting. In this study, the Tropospheric Remotely Observed Profiling via Optimal Estimation (TROPoe) physical retrieval is used to retrieve temperature and humidity profiles from various combinations of input data collected by passive and active remote sensing instruments, in situ surface platforms, and numerical weather prediction models. Among the employed instruments are microwave radiometers (MWRs), infrared spectrometers (IRSs), radio acoustic sounding systems (RASSs), ceilometers, and surface sensors. TROPoe uses brightness temperatures and/or radiances from MWRs and IRSs, as well as other observational inputs (virtual temperature from the RASS, cloud-base height from the ceilometer, pressure, temperature, and humidity from the surface sensors) in a physical iterative retrieval approach. This starts from a climatologically reasonable profile of temperature and water vapor, with the radiative transfer model iteratively adjusting the assumed temperature and humidity profiles until the derived brightness temperatures and radiances match those observed by the MWR and/or IRS instruments within a specified uncertainty, as well as within the uncertainties of the other observations, if used as input. In this study, due to the uniqueness of the dataset that includes all the abovementioned sensors, TROPoe is tested with different observational input combinations, some of which also include information higher than 4 km above ground level (a.g.l.) from the operational Rapid Refresh numerical weather prediction model. These temperature and humidity retrievals are assessed against independent collocated radiosonde profiles under non-cloudy conditions to assess the sensitivity of the TROPoe retrievals to different input combinations.

Selecting and weighting dynamical models using data-driven approaches

Abstract. In geosciences, multi-model ensembles are helpful to explore the robustness of a range of results. To obtain a synthetic and improved representation of the studied dynamic system, the models are usually weighted. The simplest method, namely the model democracy, gives equal weights to all models, while more advanced approaches base weights on agreement with available observations. Here, we focus on determining weights for various versions of an idealized model of the Atlantic Meridional Overturning Circulation. This is done by assessing their performance against synthetic observations (generated from one of the model versions) within a data assimilation framework using the ensemble Kalman filter (EnKF). In contrast to traditional data assimilation, we implement data-driven forecasts using the analog method based on catalogs of short-term trajectories. This approach allows us to efficiently emulate the model's dynamics while keeping computational costs low. For each model version, we compute a local performance metric, known as the contextual model evidence, to compare observations and model forecasts. This metric, based on the innovation likelihood, is sensitive to differences in model dynamics and considers forecast and observation uncertainties. Finally, the weights are calculated using both model performance and model co-dependency and then evaluated on averages of long-term simulations. Results show good performance in identifying numerical simulations that best replicate observed short-term variations. Additionally, it outperforms benchmark approaches such as strategies based on model democracy or climatology when reconstructing missing distributions. These findings encourage the application of the proposed methodology to more complex datasets in the future, like climate simulations.

Are Atmospheric Models Too Cold in the Mountains? The State of Science and Insights from the SAIL Field Campaign

Abstract Mountains play an outsized role in water resource availability, and the amount and timing of water they provide depend strongly on temperature. To that end, we ask the question: How well are atmospheric models capturing mountain temperatures? We synthesize results showing that high-resolution, regionally relevant climate models produce 2-m air temperature (T2m) measurements colder than what is observed (a “cold bias”), particularly in snow-covered midlatitude mountain ranges during winter. We find common cold biases in 44 studies across global mountain ranges, including single-model and multimodel ensembles. We explore the factors driving these biases and examine the physical mechanisms, data limitations, and observational uncertainties behind T2m. Our analysis suggests that the biases are genuine and not due to observation sparsity or resolution mismatches. Cold biases occur primarily on mountain peaks and ridges, whereas valleys are often warm biased. Our literature review suggests that increasing model resolution does not clearly mitigate the bias. By analyzing data from the Surface Atmosphere Integrated Field Laboratory (SAIL) field campaign in the Colorado Rocky Mountains, we test various hypotheses related to cold biases and find that local wind circulations, longwave (LW) radiation, and surface-layer parameterizations contribute to the T2m biases in this particular location. We conclude by emphasizing the value of coordinated model evaluation and development efforts in heavily instrumented mountain locations for addressing the root cause(s) of T2m biases and improving predictive understanding of mountain climates.

The observation range adjusted method: a novel approach to accounting for observation uncertainty in model evaluation

Model evaluations are performed by comparing a modelled quantity with an observation of that quantity and any deviation from this observed quantity is considered an error. We know that all observing systems have uncertainties, and multiple observational products for the same quantity can provide equally plausible ‘truths’. Thus, model errors depend on the choice of observation used in the evaluation exercise. We propose a method that considers models to be indistinguishable from observations when they lie within the range of observations, and hence are not assigned any error. Errors are assigned when models are outside the observational range. Errors calculated in this way can be used within traditional statistics to calculate the Observation Range Adjusted (ORA) version of that statistic. The ORA statistics highlight the measurable errors of models, provide more robust model performance rankings, and identify areas of the model where further model development is likely to lead to consistent model improvements.

Action and energy clustering of stellar streams in deforming Milky Way dark matter haloes

ABSTRACT We investigate the non-adiabatic effect of time-dependent deformations in the Milky Way (MW) halo potential on stellar streams. Specifically, we consider the MW’s response to the infall of the Large Magellanic Cloud (LMC) and how this impacts our ability to recover the spherically averaged MW mass profile from observation using stream actions. Previously, action clustering methods have only been applied to static or adiabatic MW systems to constrain the properties of the host system. We use a time-evolving MW–LMC simulation described by basis function expansions. We find that for streams with realistic observational uncertainties on shorter orbital periods and without close encounters with the LMC, e.g. GD-1, the radial action distribution is sufficiently clustered to locally recover the spherical MW mass profile across the stream radial range within a $2\sigma$ confidence interval determined using a Fisher information approach. For streams with longer orbital periods and close encounters with the LMC, e.g. Orphan–Chenab (OC), the radial action distribution disperses as the MW halo has deformed non-adiabatically. Hence, for OC streams generated in potentials that include an MW halo with any deformations, action clustering methods will fail to recover the spherical mass profile within a $2\sigma$ uncertainty. Finally, we investigate whether the clustering of stream energies can provide similar constraints. Surprisingly, we find for OC-like streams, the recovered spherically averaged mass profiles demonstrate less sensitivity to the time-dependent deformations in the potential.

Uncertainty in Sea State Observations from Satellite Altimeters and Buoys during the Jason-3/Sentinel-6 MF Tandem Experiment

The Copernicus Sentinel-6 Michael Freilich (S6-MF) and Jason-3 (J3) Tandem Experiment (S6-JTEX) provided over 12 months of closely collocated altimeter sea state measurements, acquired in “low-resolution” (LR) and synthetic aperture radar “high-resolution” (HR) modes onboard S6-MF. The consistency and uncertainties associated with these measurements of sea state are examined in a region of the eastern North Pacific. Discrepancies in mean significant wave height (Hs, 0.01 m) and root-mean-square deviation (0.06 m) between J3 and S6-MF LR are found to be small compared to differences with buoy data (0.04, 0.29 m). S6-MF HR data are found to be highly correlated with LR data (0.999) but affected by a nonlinear sea state-dependent bias. However, the bias can be explained robustly through regression modelling based on Hs. Subsequent triple collocation analysis (TCA) shows very little difference in measurement error (0.18 ± 0.03 m) for the three altimetry datasets, when analysed with buoy data (0.22 ± 0.02 m) and ERA5 reanalysis (0.27 ± 0.02 m), although statistical precision, limited by total collocations (N = 535), both obscures interpretation and motivates the use of a larger dataset. However, we identify uncertainties in the collocation methodology, with important consequences for methods such as TCA. Firstly, data from some commonly used buoys are found to be statistically questionable, possibly linked to erroneous buoy operation. Secondly, we develop a methodology based on altimetry data to show how statistically outlying data also arise due to sampling over local sea state gradients. This methodology paves the way for accurate collocation closer to the coast, bringing larger collocation sample sizes and greater statistical robustness.

Impact of a ‘reverse keystone species’ on the temporal dynamics of bird communities in Australia

Interference competition by noisy miners Manorina melanocephala threatens small woodland bird communities in the highly modified agricultural landscapes of southeastern Australia. Noisy miners aggressively exclude small passerines from their territories promoting the biotic homogenization of communities. Here, we addressed the impact of this native species on the temporal dynamics of bird communities over a 7-years period (2010/2011–2016/2017). We explored the differences in community dynamic metrics (synchrony, stability, rate of change) between assemblages with and without presence of noisy miners. We also tested for differences in temporal beta diversity between the two categories of community. We first applied a multi-species abundance model to account for the imperfect observation process and uncertainty in species abundance. Yearly variation in species occurrence (but not in abundance) was higher in communities with presence of noisy miners, suggesting that this hyper-aggressive competitor excludes rare species and contributes to increased level of temporal turnover. In line with this, species loss was also significantly higher in communities with this competitor. These communities are also subject to greater variation in composition across years despite consisting of a smaller number of species in comparison to those without noisy miners. This translates into instability, where communities with noisy miners showed a loose equilibrium and generally exhibited little directional change. Our findings suggest that in the short term, noisy miners act as a filter, resulting in a predictable homogenization of species assemblages (small-bodied species are excluded while large-bodied ones are favored) in their presence. However, over a longer period, this species contributes to increased community instability. This study adds to the growing body of literature showing evidence of the ecological relevance that the impact of noisy miners can have on Australian woodlands.

Incorporation of observation uncertainty in stock assessment using spatio-temporal modeling of catch-at-length and age-at-length survey data

Abstract Spatio-temporal models are essential tools for estimating abundance indices and quantifying the associated uncertainty. Time series of index uncertainties can be used to objectively determine the influence each index has on the assessment. This can involve reducing the influence of indices in years with limited data. However, incorporating uncertainty in age-length conversion into assessment models has remained a challenge. In this research, we propose an index estimation approach that combines an abundance-at-length model with a model for age-at-length to generate age-specific abundance indices. By jointly modeling abundance-at-length and age-at-length, we address uncertainties in both components of the index-at-age. Using North East Arctic haddock (Melanogrammus aeglefinus) as a case study, we validate the uncertainty of the indices by integrating them into the state space assessment model SAM. The results indicate that the uncertainty estimates are realistic, and we further demonstrate that incorporating uncertainty in age conversion has effects on the assessment results. Our case study demonstrates that incorporating the uncertainty in age-at-length data improves the characterization of uncertainty in stock assessment, and hence better accounts for risk in precautionary management.

Characteristic times for radiation belt drift phase mixing

Impulsive radial transport events occurring in the radiation belts leave lasting marks in the form of drift echoes, that is, energy-dependent drift phase structures in the radiation belts that evolve at the drift frequency. Drift echoes are known to be transient structures that dissipate due to phase mixing. The objective of this paper is to discuss how much time it takes for drift echoes to dissipate, and what drives this phase-mixing process. While any uncertainty or perturbation in the variables controlling trapped particles’ drift frequency contributes to phase mixing, we highlight two main drivers: the observational uncertainty associated with the finite size of the instrument energy channels, and the natural field fluctuations driving perturbations in trapped particles’ drift frequency. It is the combination of both instrumental and natural sources of phase mixing that determines the observed dissipation and lifetime of drift echoes. This means that the observed magnitude and lifetime of a drift echo are always underestimations of the natural magnitude and lifetime of the structure. This calls into question the applicability of the standard, drift-averaged formulation of radial diffusion. The three key points of the study are the following: First, the time it takes for particles initially localized in local time to phase-mix is measured in hours in the Earth’s radiation belts. Second, phase mixing at the drift scale is primarily due to uncertainties in measured kinetic energy and field perturbations. Third, our analysis can be utilized to set an energy resolution requirement for future particle instruments.

MMG 3DOF model identification with uncertainty of observation and hydrodynamic maneuvering coefficients using MCMC method

The trajectory prediction using ship maneuverability mathematical models is one of the essential technologies implemented in autonomous surface ship. Several ship maneuverability mathematical models and each one with a particular hydrodynamic coefficient approximation using towing tank tests are existed. However, it is presented difficult to directly inverse estimate the hydrodynamic maneuvering coefficients of a ship maneuverability mathematical model from operational data consisting of ship trajectory and maneuvering operation records. This paper proposed a method for estimating the hydrodynamic maneuvering coefficients of the MMG 3DOF model using three types of time-series ship motions (surge, sway, and yaw velocity) as observed data. In the assumption of this paper, there is uncertainty in observations and the hydrodynamic maneuvering coefficients of the MMG 3DOF model. The proposed method outputs samples of the simultaneous posterior probability distribution of the hydrodynamic maneuvering coefficients by the MCMC method using the observed data and stochastic model. A robust trajectory with a wide range can be presented by conducting ship maneuvering simulations using these samples. To verify the feasibility of the proposed method, this paper conducted observation system simulation experiments (OSSE) using the KVLCC2 L7 model and applied the proposed method to several free-running model ship tests. Results showed that on the assumption that MMG 3DOF model can explain the ship's state and trajectory in real world, the proposed method can estimate the ship hydrodynamic maneuvering coefficients of the MMG 3DOF model corresponding to the observed ship trajectory and control data including the error of observed data.

Finding accreted stars in the Milky Way: clues from NIHAO simulations

ABSTRACT Exploring the marks left by galactic accretion in the Milky Way helps us understand how our Galaxy was formed. However, finding and studying accreted stars and the galaxies they came from has been challenging. This study uses a simulation from the Numerical Investigation of a Hundred Astronomical Objects project, which now includes a wider range of chemical compositions, to find better ways to spot these accreted stars. By comparing our findings with data from the GALAH spectroscopic survey, we confirm that the observationally established diagnostics of [Al/Fe] versus [Mg/Mn] also show a separation of in situ and accreted stars in the simulation, but stars from different accretion events tend to overlap in this plane even without observational uncertainties. Looking at the relationship between stellar age and linear or logarithmic abundances, such as [Fe/H], we can clearly separate different groups of these stars if the uncertainties in their chemical makeup are less than 0.15 dex and less than 20 per cent for their ages. This method shows promise for studying the history of the Milky Way and other galaxies. Our work highlights how important it is to have accurate measurements of stellar ages and chemical content. It also shows how simulations can help us understand the complex process of galaxies merging and suggest how these events might relate to the differences we see between our Galaxy’s thin and thick disc stars. This study provides a way to compare theoretical models with real observations, opening new paths for research in both our own Galaxy and beyond.

Robust modeling of material flows to end‐uses under uncertainty: UK wood flows and material efficiency opportunities

AbstractIn this study we quantify wood flows from raw materials to end‐uses for the United Kingdom in a robust way using a new material flow analysis (MFA) model with uncertainty. This is important to identify opportunities for efficiency given constraints on wood supply. We have developed a new “ProbPACTOT” MFA model which introduces a systematic method to handle mixed units during reconciliation and uncertainty in data observations, conversion factors, and process recipes. This makes it possible to track material flows all the way to end‐uses, which is otherwise difficult because the diverse materials, data types, and units used to quantify end‐products are hard to integrate into standard allocation‐based MFA models. We apply the model for the case of wood in the United Kingdom by defining 56 process recipes and reconciling 117 data observations from various sources. The results quantify upstream production and trade flows, through to 19 specific end‐uses of wood fibers. We use this to show the potential scale of savings by enhancing material efficiency; for example, if pallets were used 25% more intensively, 0.49 0.2 of wood fibers could be saved, corresponding to 4%–7% of the total soft sawnwood consumption of the United Kingdom. Judging the scale of opportunities for wood material efficiency in the United Kingdom is important domestically, and has global significance as the United Kingdom is the second largest net importer of wood products in the world. Moreover, this study proposes an important advancement in MFA giving a structure for modeling uncertain material flows up to end‐uses, applicable to any material. This article met the requirements for a gold‐gold JIE data openness badge described at http://jie.click/badges.

Consequences of a two-time relativistic Bohmian model

Effects of a Bohmian type quantum-relativistic theory are explored. The model is obtained by introducing a new and independent time parameter whose relative motions are not directly observable and cause quantum uncertainties of the physical observables. Unlike the usual de Broglie–Bohm theories, the Quantum Potential does not directly affect the observable motion, but determines the one that is relative to the new time variable. It turns out that the Zitterbewegung of a free particle, of which a more general law is obtained, is the key example of these hidden motions and, through it, it seems possible to give physical reality to the Feynman’s paths. A relativistic revision of the uncertainty principle is also derived from the theory.

Can the allometric relationship between saturated water accumulation and dry mass be used to diagnose the water status of winter wheat?

ContextAgriculture is the major consumer of fresh water globally. However, climate change-induced water scarcity is challenging the freshwater resources to meet global crop water demands. Consequently, utilizing limited water resources efficiently is increasingly important in agriculture. Accurate diagnosis of crop water status and in-season demand is imperative for precision irrigation, as it allows growers to make informed decisions about fine-tuning in-season irrigation scheduling. Plant-based diagnostic approaches such as the critical saturated water accumulation (SWA) curve have been employed to diagnose the in-season crop water status in maize. However, it remains uncertain if this indicator applies to other crops including wheat. ObjectiveThe study aimed to develop and validate a model for quantifying the in-season water status of winter wheat using SWA curves and water diagnosis index (WDI). MethodsA four-year study was carried out by employing four water and two nitrogen rate treatments from 2019 to 2023 to establish the relationship between dry mass (DM) and SWA during the vegetative growth period of winter wheat. The effects of different water and nitrogen conditions on DM-SWA allometry were also analyzed. Besides, WDI was also derived as the ratio of the observed SWA value to the critical SWA value. ResultsThe allometric relationships between SWA and DM were used to construct critical SWA curves under sub-optimal and optimal nitrogen nutrition (N1: SWA = 5.34DM0.77; N2: SWA = 7.37DM0.83). The decrease in plant stem ratio and indirect soil nitrogen deficiency caused by water stress were the two main factors contributing to the decrease in plant WDI. However, uncertainties of observation and input data, model assumptions, model structure, and parameters led to a high degree of uncertainty in WDI, which affected its practical application. ConclusionsThe allometric relationships between SWA and DM can potentially be used as a diagnostic tool for assessing crop water status. Nitrogen deficiency would reduce the SWA without affecting its accumulation rate. Further research is required to employ WDI for plant water status diagnosis under field conditions. Significance/ImplicationsThe findings would assist in developing precision irrigation indicators for monitoring in-season plant water status, optimizing irrigation scheduling, selecting water-efficient cultivars, and enhancing wheat productivity.

Time-frequency relationship between heterogeneous uncertainties and stock market extreme risk: new evidence from China

ABSTRACT We empirically investigate the dynamic correlation between different uncertainties and stock market extreme risk across multiple time-frequency domains, providing novel evidence for the multiscale heterogeneity of popular uncertainty measures. Our findings reveal a more significant short-term relationship between the observable news-based economic uncertainty proposed by Baker et al. (2016) and China’s stock market extreme risk, while the latent economic uncertainty suggested by Jurado et al. (2015) dominates the long-term time horizon. Moreover, financial uncertainty is also a crucial source of stock market extreme risk that cannot be ignored.

Constraining the helium-to-metal enrichment ratio deltaY/deltaZ from main-sequence binary stars. Theoretical analysis of the accuracy and precision of the age and helium abundance estimates

We aim to investigate the theoretical possibility of accurately determining the helium-to-metal enrichment ratio $ Y/ Z$ from precise observations of double-lined eclipsing binary systems. Using Monte Carlo simulations, we drew synthetic binary systems with masses between 0.85 and 1.00 $M_ sun $ from a grid of stellar models. Both stars were sampled from a grid with $ Y/ Z = 2.0$, with a primary star at 80<!PCT!> of its main-sequence evolution. Subsequently, a broader grid with $ Y/ Z$ from 1.0 to 3.0 was used in the fitting process. To account for observational uncertainties, two scenarios were explored: S1, with realistic uncertainties of 100 K in temperature and 0.1 dex in Fe/H ; and S2, with halved uncertainties. We repeated the simulation at two baseline metallicities: Fe/H = 0.0 and $-0.3$. The posterior distributions of $ Y/ Z$ revealed significant biases. The distributions were severely biased towards the edge of the allowable range in the S1 error scenario. The situation only marginally improved when considering the S2 scenario. The effect is due to the impact of changing $ Y/ Z$ in the stellar effective temperature and its interplay with Fe/H observational error, and it is therefore not restricted to the specific fitting method. Despite the presence of these systematic discrepancies, the age of the systems were recovered unbiased with 10<!PCT!> precision. Our findings indicate that the observational uncertainty in effective temperature and metallicity significantly hinders the accurate determination of the $ Y/ Z$ parameter from main-sequence binary systems.

A Prefire Approach for Probabilistic Assessments of Postfire Debris‐Flow Inundation

AbstractIncreases in wildfire activity and rainfall intensification are driving more postfire debris flows (PFDF) in many regions around the world. PFDFs are most common in the first postfire year and may even occur before a fire is fully controlled. This underscores the importance of assessing postfire hazards before a fire starts. Evaluation of PFDF hazards prior to fire can help strategize interventions lessening the negative effects of future fires. However, debris‐flow runout and inundation analyses are not routine in PFDF hazard assessments, partially due to time constraints and substantial uncertainties in boundary conditions. Here, we propose a prefire PFDF inundation assessment framework using a debris‐flow runout model based on the Herschel‐Bulkley (HB) rheology (HEC‐RAS v6.1). We constrain model inputs and parameters using Bayesian posterior analysis, rainfall‐runoff simulations, and a debris‐flow volume model. We use observations from recent PFDF incidents in northern Arizona, USA, to calibrate model components and then apply our prefire inundation assessment framework in a nearby unburned area. Specifically, we (a) identify yield stress as the most influential factor on inundation extent and arrival time in a HB model, (b) establish posterior distributions for model parameters suitable for forward modeling by leveraging uncertainties in field observations, and (c) implement a predictive forward analysis in an area that has not burned recently to evaluate PFDF inundation under several future fire scenarios. This study improves our ability to assess postfire debris‐flow hazards before a fire begins and provides guidance for future applications of single‐phase rheological models when assessing PFDF hazards.

On the Initial Spin Period Distribution of Neutron Stars

We derive the initial spin period distribution of neutron stars by studying the population of young pulsars associated with supernova remnants. Our hierarchical Bayesian approach accounts for the measurement uncertainties of individual observations and selection effects. Without correcting for selection effects, as done in previous studies, we find that pulsar initial spin periods follow a Weibull distribution, peaking at 40 ms, which is favored against a lognormal distribution with a Bayes factor of 200. The known selection effects in radio pulsar surveys, including pulse broadening and period-dependent beaming fraction, have been quantitatively investigated. We show that, based on measurements of pulsar luminosity and spin period from the ATNF Pulsar Catalogue, the impact of pulse broadening on the inference of the pulsar period distribution is likely to be insignificant. Correcting for the beaming selection effect, the Weibull distribution remains the preferred model, while its peak slightly shifts to longer periods at 50 ms. Our method will prove useful in constraining the birth properties of neutron stars in the Square Kilometre Array era.