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At least 613 records · Page 34

Local Group timing argument and virial theorem mass estimators from cosmological simulations

ABSTRACT We identify Local Group (LG) analogues in the IllustrisTNG cosmological simulation, and use these to study two-mass estimators for the LG: One based on the timing argument (TA) and one based on the virial theorem (VT). Including updated measurements of the Milky Way-M31 tangential velocity and the cosmological constant, we show that the TA mass estimator slightly overestimates the true median LG-mass, though the ratio of the TA to the true mass is consistent at the approximate 90 per cent c.l.. These are in broad agreement with previous results using dark matter-only simulations. We show that the VT estimator better estimates the true LG-mass, though there is a larger scatter in the virial mass to true mass ratio relative to the corresponding ratio for the TA. We attribute the broader scatter in the VT estimator to several factors, including the predominantly radial orbits for LG satellite galaxies, which differs from the VT assumption of isotropic orbits. With the systematic uncertainties we derive, the updated measurements of the LG mass at 90 per cent c.l. are $4.75_{-2.41}^{+2.22} \times 10^{12}$ M⊙ from the TA and $2.0_{-1.5}^{+2.1} \times 10^{12}$ M⊙ from the VT. We consider the LMC’s effect on the TA and VT LG mass estimates, and do not find exact LMC–MW–M31 analogues in the Illustris simulations. However, in LG simulations with satellite companions as massive as the LMC, we find that the effect on the TA and VT estimators is small, though we need further studies on a larger sample of LMC–MW–M31 systems to confirm these results.

79 ASTRONOMY AND ASTROPHYSICS↗

Accurate shear estimation with fourth-order moments

ABSTRACT As imaging surveys progress in exploring the large-scale structure of the Universe through the use of weak gravitational lensing, achieving sub-per cent accuracy in estimating shape distortions caused by lensing, or shear, is imperative for precision cosmology. In this paper, we extend the Fourier power function shapelets (FPFS) shear estimator using fourth-order shapelet moments and combine it with the original second-order shear estimator to reduce galaxy shape noise. We calibrate this novel shear estimator analytically to a sub-per cent level-accuracy using the AnaCal framework. This higher order shear estimator is tested with realistic image simulations, and after analytical correction for the detection/selection bias and noise bias, the multiplicative shear bias $|m|$ is below $3\times 10^{-3}$ (99.7 per cent confidence interval) for both isolated and blended galaxies. Once combined with the second-order FPFS shear estimator, the shape noise is reduced by $\sim 35~{{\ \rm per\ cent}}$ for isolated galaxies in simulations with Hyper Suprime-Cam and Vera C. Rubin Observatory Legacy Survey of Space and Time observational conditions. However, for blended galaxies, the effective number density does not significantly improve with the combination of the two estimators. Based on these results, we recommend exploration of how this framework can further reduce the systematic uncertainties in shear due to point spread function leakage and modelling error, and potentially provide improved precision in shear inference in high-resolution space-based images.

79 ASTRONOMY AND ASTROPHYSICS↗

A Hybrid Method to Estimate Specific Differential Phase and Rainfall With Linear Programming and Physics Constraints

A hybrid method of combining linear programming (LP) and physical constraints is developed to estimate specific differential phase (K DP ) and to improve rain estimation. Moreover, the hybrid K DP estimator and the existing estimators of LP, least squares fitting, and a self-consistent relation of polarimetric radar variables are evaluated and compared using simulated data. Our simulation results indicate the new estimator's superiority, particularly in regions where backscattering phase (δ hv ) dominates. Further, a quantitative comparison between auto-weather-station rain-gauge observations and K DP -based radar rain estimates for a Meiyu event also demonstrate the superiority of the hybrid K DP estimator over existing methods.

54 ENVIRONMENTAL SCIENCES↗

Bootstrapping outperforms community‐weighted approaches for estimating the shapes of phenotypic distributions

Abstract Estimating phenotypic distributions of populations and communities is central to many questions in ecology and evolution. These distributions can be characterized by their moments (mean, variance, skewness and kurtosis) or diversity metrics (e.g. functional richness). Typically, such moments and metrics are calculated using community‐weighted approaches (e.g. abundance‐weighted mean). We propose an alternative bootstrapping approach that allows flexibility in trait sampling and explicit incorporation of intraspecific variation, and show that this approach significantly improves estimation while allowing us to quantify uncertainty. We assess the performance of different approaches for estimating the moments of trait distributions across various sampling scenarios, taxa and datasets by comparing estimates derived from simulated samples with the true values calculated from full datasets. Simulations differ in sampling intensity (individuals per species), sampling biases (abundance, size), trait data source (local vs. global) and estimation method (two types of community‐weighting, two types of bootstrapping). We introduce the traitstrap R package, which contains a modular and extensible set of bootstrapping and weighted‐averaging functions that use community composition and trait data to estimate the moments of community trait distributions with their uncertainty. Importantly, the first function in the workflow, trait_fill , allows the user to specify hierarchical structures (e.g. plot within site, experiment vs. control, species within genus) to assign trait values to each taxon in each community sample. Across all taxa, simulations and metrics, bootstrapping approaches were more accurate and less biased than community‐weighted approaches. With bootstrapping, a sample size of 9 or more measurements per species per trait generally included the true mean within the 95% CI. It reduced average percent errors by 26%–74% relative to community‐weighting. Random sampling across all species outperformed both size‐ and abundance‐biased sampling. Our results suggest randomly sampling ~9 individuals per sampling unit and species, covering all species in the community and analysing the data using nonparametric bootstrapping generally enable reliable inference on trait distributions, including the central moments, of communities. By providing better estimates of community trait distributions, bootstrapping approaches can improve our ability to link traits to both the processes that generate them and their effects on ecosystems.

Maitner, Brian S.↗

NMR Logging to Estimate Hydraulic Conductivity in Unconsolidated Aquifers

Abstract Nuclear magnetic resonance ( NMR ) logging provides a new means of estimating the hydraulic conductivity ( K ) of unconsolidated aquifers. The estimation of K from the measured NMR parameters can be performed using the Schlumberger‐Doll Research ( SDR ) equation, which is based on the Kozeny–Carman equation and initially developed for obtaining permeability from NMR logging in petroleum reservoirs. The SDR equation includes empirically determined constants. Decades of research for petroleum applications have resulted in standard values for these constants that can provide accurate estimates of permeability in consolidated formations. The question we asked: Can standard values for the constants be defined for hydrogeologic applications that would yield accurate estimates of K in unconsolidated aquifers? Working at 10 locations at three field sites in Kansas and Washington, USA, we acquired NMR and K data using direct‐push methods over a 10‐ to 20‐m depth interval in the shallow subsurface. Analysis of pairs of NMR and K data revealed that we could dramatically improve K estimates by replacing the standard petroleum constants with new constants, optimal for estimating K in the unconsolidated materials at the field sites. Most significant was the finding that there was little change in the SDR constants between sites. This suggests that we can define a new set of constants that can be used to obtain high resolution, cost‐effective estimates of K from NMR logging in unconsolidated aquifers. This significant result has the potential to change dramatically the approach to determining K for hydrogeologic applications.

Knight, Rosemary↗

Model-based PSF and MTF estimation and validation from skeletal clinical CT images

Purpose: A method was developed to correct for systematic errors in estimating the thickness of thin bones due to image blurring in CT images using bone interfaces to estimate the point-spread-function (PSF). This study validates the accuracy of the PSFs estimated using said method from various clinical CT images featuring cortical bones. Methods: Gaussian PSFs, characterized by a different extent in the z (scan) direction than in the x and y directions were obtained using our method from 11 clinical CT scans of a cadaveric craniofacial skeleton. These PSFs were estimated for multiple combinations of scanning parameters and reconstruction methods. The actual PSF for each scan setting was measured using the slanted-slit technique within the image slice plane and the longitudinal axis. The Gaussian PSF and the corresponding modulation transfer function (MTF) are compared against the actual PSF and MTF for validation. Results: The differences (errors) between the actual and estimated full-width half-max (FWHM) of the PSFs were 0.09 ± 0.05 and 0.14 ± 0.11 mm for the xy and z axes, respectively. The overall errors in the predicted frequencies measured at 75%, 50%, 25%, 10%, and 5% MTF levels were 0.06 ± 0.07 and 0.06 ± 0.04 cycles/mm for the xy and z axes, respectively. The accuracy of the estimates was dependent on whether they were reconstructed with a standard kernel (Toshiba's FC68, mean error of 0.06 ± 0.05 mm, MTF mean error 0.02 ± 0.02 cycles/mm) or a high resolution bone kernel (Toshiba's FC81, PSF FWHM error 0.12 ± 0.03 mm, MTF mean error 0.09 ± 0.08 cycles/mm). Conclusions: The method is accurate in 3D for an image reconstructed using a standard reconstruction kernel, which conforms to the Gaussian PSF assumption but less accurate when using a high resolution bone kernel. The method is a practical and self-contained means of estimating the PSF in clinical CT images featuring cortical bones, without the need phantoms or any prior knowledge about the scanner-specific parameters.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

Sample size requirements for estimating effective dose from computed tomography using solid-state metal-oxide-semiconductor field-effect transistor dosimetry

Purpose: Effective dose (ED) is a widely used metric for comparing ionizing radiation burden between different imaging modalities, scanners, and scan protocols. In computed tomography (CT), ED can be estimated by performing scans on an anthropomorphic phantom in which metal-oxide-semiconductor field-effect transistor (MOSFET) solid-state dosimeters have been placed to enable organ dose measurements. Here a statistical framework is established to determine the sample size (number of scans) needed for estimating ED to a desired precision and confidence, for a particular scanner and scan protocol, subject to practical limitations. Methods: The statistical scheme involves solving equations which minimize the sample size required for estimating ED to desired precision and confidence. It is subject to a constrained variation of the estimated ED and solved using the Lagrange multiplier method. The scheme incorporates measurement variation introduced both by MOSFET calibration, and by variation in MOSFET readings between repeated CT scans. Sample size requirements are illustrated on cardiac, chest, and abdomen–pelvis CT scans performed on a 320-row scanner and chest CT performed on a 16-row scanner. Results: Sample sizes for estimating ED vary considerably between scanners and protocols. Sample size increases as the required precision or confidence is higher and also as the anticipated ED is lower. For example, for a helical chest protocol, for 95% confidence and 5% precision for the ED, 30 measurements are required on the 320-row scanner and 11 on the 16-row scanner when the anticipated ED is 4 mSv; these sample sizes are 5 and 2, respectively, when the anticipated ED is 10 mSv. Conclusions: Applying the suggested scheme, it was found that even at modest sample sizes, it is feasible to estimate ED with high precision and a high degree of confidence. As CT technology develops enabling ED to be lowered, more MOSFET measurements are needed to estimate ED with the same precision and confidence.

61 RADIATION PROTECTION AND DOSIMETRY↗

Size-specific dose estimate (SSDE) provides a simple method to calculate organ dose for pediatric CT examinations

Purpose: To investigate the correlation of size-specific dose estimate (SSDE) with absorbed organ dose, and to develop a simple methodology for estimating patient organ dose in a pediatric population (5–55 kg). Methods: Four physical anthropomorphic phantoms representing a range of pediatric body habitus were scanned with metal oxide semiconductor field effect transistor (MOSFET) dosimeters placed at 23 organ locations to determine absolute organ dose. Phantom absolute organ dose was divided by phantom SSDE to determine correlation between organ dose and SSDE. Organ dose correlation factors (CF{sub SSDE}{sup organ}) were then multiplied by patient-specific SSDE to estimate patient organ dose. The CF{sub SSDE}{sup organ} were used to retrospectively estimate individual organ doses from 352 chest and 241 abdominopelvic pediatric CT examinations, where mean patient weight was 22 kg ± 15 (range 5–55 kg), and mean patient age was 6 yrs ± 5 (range 4 months to 23 yrs). Patient organ dose estimates were compared to published pediatric Monte Carlo study results. Results: Phantom effective diameters were matched with patient population effective diameters to within 4 cm; thus, showing appropriate scalability of the phantoms across the entire pediatric population in this study. IndividualCF{sub SSDE}{sup organ} were determined for a total of 23 organs in the chest and abdominopelvic region across nine weight subcategories. For organs fully covered by the scan volume, correlation in the chest (average 1.1; range 0.7–1.4) and abdominopelvic region (average 0.9; range 0.7–1.3) was near unity. For organ/tissue that extended beyond the scan volume (i.e., skin, bone marrow, and bone surface), correlation was determined to be poor (average 0.3; range: 0.1–0.4) for both the chest and abdominopelvic regions, respectively. A means to estimate patient organ dose was demonstrated. Calculated patient organ dose, using patient SSDE and CF{sub SSDE}{sup organ}, was compared to previously published pediatric patient doses that accounted for patient size in their dose calculation, and was found to agree in the chest to better than an average of 5% (27.6/26.2) and in the abdominopelvic region to better than 2% (73.4/75.0). Conclusions: For organs fully covered within the scan volume, the average correlation of SSDE and organ absolute dose was found to be better than ±10%. In addition, this study provides a complete list of organ dose correlation factors (CF{sub SSDE}{sup organ}) for the chest and abdominopelvic regions, and describes a simple methodology to estimate individual pediatric patient organ dose based on patient SSDE.

60 APPLIED LIFE SCIENCES↗

SU-E-T-95: Delivery Time Estimator

Purpose: The development and testing of a tool for the inclusion of delivery time as a parameter in plan optimization. Methods: We developed an algorithm that estimates the time required for the machine and personnel movements required to deliver a treatment plan on a linear accelerator. We included dose rate, leaf motion, collimator motion, gantry motion, and couch motions (including time to enter the room to rotate the couch safely). Vault-specific parameters to account for time to enter and perform couch angle adjustments were also included. This algorithm works for static, step and shoot IMRT, and VMAT beams photon beams and for fixed electron beams. This was implemented as a script in our treatment planning system. We validated the estimator against actual recorded delivery time from our R and V system as well as recorded times from our IMRT QA delivery. Results: Data was collected (Figure 1) for 12 treatment plans by examining the R and V beam start times, and by manually timing the QA treatment for a reference, but the QA measurements were only significant to the nearest minute. The average difference between the estimated and R and V times was 15%, and 11% when excluding the major outliers. Outliers arose due to respiratory aides and gating techniques which could not be accounted for in the estimator. Conclusion: Non-mechanical factors such as the time a therapist needs to walk in and out of the room to adjust the couch needed to be fine-tuned and cycled back into the algorithm to improve the estimate. The algorithm has been demonstrated to provide reasonable and useful estimates for delivery time. This estimate has provided a useful additional input for clinical decision-making when comparing several potential radiation treatment options.

07 ISOTOPE AND RADIATION SOURCES↗

SU-E-T-86: A Systematic Method for GammaKnife SRS Fetal Dose Estimation

Purpose: Estimating fetal dose is critical to the decision-making process when radiation treatment is indicated during pregnancy. Fetal doses less than 5cGy confer no measurable non-cancer developmental risks but can produce a threefold increase in developing childhood cancer. In this study, we estimate fetal dose for a patient receiving Gamma Knife stereotactic radiosurgery (GKSRS) treatment and develop a method to estimate dose directly from plan details. Methods: A patient underwent GKSRS on a Perfexion unit for eight brain metastases (two infratentorial and one brainstem). Dose measurements were performed using a CC13, head phantom, and solid water. Superficial doses to the thyroid, sternum, and pelvis were measured using MOSFETs during treatment. Because the fetal dose was too low to accurately measure, we obtained measurements proximally to the isocenter, fitted to an exponential function, and extrapolated dose to the fundus of the uterus, uterine midpoint, and pubic synthesis for both the preliminary and delivered plans. Results: The R-squared fit for the delivered doses was 0.995. The estimated fetal doses for the 72 minute preliminary and 138 minute delivered plans range from 0.0014 to 0.028cGy and 0.07 to 0.38cGy, respectively. MOSFET readings during treatment were just above background for the thyroid and negligible for all inferior positions. The method for estimating fetal dose from plan shot information was within 0.2cGy of the measured values at 14cm cranial to the fetal location. Conclusion: Estimated fetal doses for both the preliminary and delivered plan were well below the 5cGy recommended limit. Due to Pefexion shielding, internal dose is primarily governed by attenuation and drops off exponentially. This is the first work that reports fetal dose for a GK Perfexion unit. Although multiple lesions were treated and the duration of treatment was long, the estimated fetal dose remained very low.

60 APPLIED LIFE SCIENCES↗

SU-E-T-129: Are Knowledge-Based Planning Dose Estimates Valid for Distensible Organs?

Purpose: Knowledge-based planning programs have become available to assist treatment planning in radiation therapy. Such programs can be used to generate estimated DVHs and planning constraints for organs at risk (OARs), based upon a model generated from previous plans. These estimates are based upon the planning CT scan. However, for distensible OARs like the bladder and rectum, daily variations in volume may make the dose estimates invalid. The purpose of this study is to determine whether knowledge-based DVH dose estimates may be valid for distensible OARs. Methods: The Varian RapidPlan™ knowledge-based planning module was used to generate OAR dose estimates and planning objectives for 10 prostate cases previously planned with VMAT, and final plans were calculated for each. Five weekly setup CBCT scans of each patient were then downloaded and contoured (assuming no change in size and shape of the target volume), and rectum and bladder DVHs were recalculated for each scan. Dose volumes were then compared at 75, 60,and 40 Gy for the bladder and rectum between the planning scan and the CBCTs. Results: Plan doses and estimates matched well at all dose points., Volumes of the rectum and bladder varied widely between planning CT and the CBCTs, ranging from 0.46 to 2.42 for the bladder and 0.71 to 2.18 for the rectum, causing relative dose volumes to vary between planning CT and CBCT, but absolute dose volumes were more consistent. The overall ratio of CBCT/plan dose volumes was 1.02 ±0.27 for rectum and 0.98 ±0.20 for bladder in these patients. Conclusion: Knowledge-based planning dose volume estimates for distensible OARs are still valid, in absolute volume terms, between treatment planning scans and CBCT’s taken during daily treatment. Further analysis of the data is being undertaken to determine how differences depend upon rectum and bladder filling state. This work has been supported by Varian Medical Systems.

60 APPLIED LIFE SCIENCES↗

Improved frame-based estimation of head motion in PET brain imaging

Purpose: Head motion during PET brain imaging can cause significant degradation of image quality. Several authors have proposed ways to compensate for PET brain motion to restore image quality and improve quantitation. Head restraints can reduce movement but are unreliable; thus the need for alternative strategies such as data-driven motion estimation or external motion tracking. Herein, the authors present a data-driven motion estimation method using a preprocessing technique that allows the usage of very short duration frames, thus reducing the intraframe motion problem commonly observed in the multiple frame acquisition method. Methods: The list mode data for PET acquisition is uniformly divided into 5-s frames and images are reconstructed without attenuation correction. Interframe motion is estimated using a 3D multiresolution registration algorithm and subsequently compensated for. For this study, the authors used 8 PET brain studies that used F-18 FDG as the tracer and contained minor or no initial motion. After reconstruction and prior to motion estimation, known motion was introduced to each frame to simulate head motion during a PET acquisition. To investigate the trade-off in motion estimation and compensation with respect to frames of different length, the authors summed 5-s frames accordingly to produce 10 and 60 s frames. Summed images generated from the motion-compensated reconstructed frames were then compared to the original PET image reconstruction without motion compensation. Results: The authors found that our method is able to compensate for both gradual and step-like motions using frame times as short as 5 s with a spatial accuracy of 0.2 mm on average. Complex volunteer motion involving all six degrees of freedom was estimated with lower accuracy (0.3 mm on average) than the other types investigated. Preprocessing of 5-s images was necessary for successful image registration. Since their method utilizes nonattenuation corrected frames, it is not susceptible to motion introduced between CT and PET acquisitions. Conclusions: The authors have shown that they can estimate motion for frames with time intervals as short as 5 s using nonattenuation corrected reconstructed FDG PET brain images. Intraframe motion in 60-s frames causes degradation of accuracy to about 2 mm based on the motion type.

60 APPLIED LIFE SCIENCES↗

SU-F-P-44: A Direct Estimate of Peak Skin Dose for Interventional Fluoroscopy Procedures

Purpose: There is an increasing demand for medical physicist to calculate peak skin dose (PSD) for interventional fluoroscopy procedures. The dose information (Dose-Area-Product and Air Kerma) displayed in the console cannot directly be used for this purpose. Our clinical experience shows that the use of the existing methods may overestimate or underestimate PSD. This study attempts to develop a direct estimate of PSD from the displayed dose metrics. Methods: An anthropomorphic torso phantom was used for dose measurements for a common fluoroscopic procedure. Entrance skin doses were measured with a Piranha solid state point detector placed on the table surface below the torso phantom. An initial “reference dose rate” (RE) measurement was conducted by comparing the displayed dose rate (mGy/min) to the dose rate measured. The distance from table top to focal spot was taken as the reference distance (RD at the RE. Table height was then adjusted. The displayed air kerma and DAP were recorded and sent to three physicists to estimate PSD. An inverse square correction was applied to correct displayed air kerma at various table heights. The PSD estimated by physicists and the PSD by the proposed method were then compared with the measurements. The estimated DAPs were compared to displayed DAP readings (mGycm2). Results: The difference between estimated PSD by the proposed method and direct measurements was less than 5%. For the same set of data, the estimated PSD by each of three physicists is different from measurements by ±52%. The DAP calculated by the proposed method and displayed DAP readings in the console is less than 20% at various table heights. Conclusion: PSD may be simply estimated from displayed air kerma or DAP if the distance between table top and tube focal spot or if x-ray beam area on table top is available.

60 APPLIED LIFE SCIENCES↗

TH-CD-206-12: Image-Based Motion Estimation for Plaque Visualization in Coronary Computed Tomography Angiography

Purpose: Visualization and quantification of coronary artery calcification and atherosclerotic plaque benefits from coronary artery motion (CAM) artifact elimination. This work applies a rigid linear motion model to a Volume of Interest (VoI) for estimating motion estimation and compensation of image degradation in Coronary Computed Tomography Angiography (CCTA). Methods: In both simulation and testbench experiments, translational CAM was generated by displacement of the imaging object (i.e. simulated coronary artery and explanted human heart) by ∼8 mm, approximating the motion of a main coronary branch. Rotation was assumed to be negligible. A motion degraded region containing a calcification was selected as the VoI. Local residual motion was assumed to be rigid and linear over the acquisition window, simulating motion observed during diastasis. The (negative) magnitude of the image gradient of the reconstructed VoI was chosen as the motion estimation objective and was minimized with Covariance Matrix Adaptation Evolution Strategy (CMAES). Results: Reconstruction incorporated the estimated CAM yielded signification recovery of fine calcification structures as well as reduced motion artifacts within the selected local region. The compensated reconstruction was further evaluated using two image similarity metrics, the structural similarity index (SSIM) and Root Mean Square Error (RMSE). At the calcification site, the compensated data achieved a 3% increase in SSIM and a 91.2% decrease in RMSE in comparison with the uncompensated reconstruction. Conclusion: Results demonstrate the feasibility of our image-based motion estimation method exploiting a local rigid linear model for CAM compensation. The method shows promising preliminary results for the application of such estimation in CCTA. Further work will involve motion estimation of complex motion corrupted patient data acquired from clinical CT scanner.

60 APPLIED LIFE SCIENCES↗

Nonpareil 3: Fast Estimation of Metagenomic Coverage and Sequence Diversity

Estimations of microbial community diversity based on metagenomic data sets are affected, often to an unknown degree, by biases derived from insufficient coverage and reference database-dependent estimations of diversity. For instance, the completeness of reference databases cannot be generally estimated since it depends on the extant diversity sampled to date, which, with the exception of a few habitats such as the human gut, remains severely undersampled. Further, estimation of the degree of coverage of a microbial community by a metagenomic data set is prohibitively time-consuming for large data sets, and coverage values may not be directly comparable between data sets obtained with different sequencing technologies. Here, we extend Nonpareil, a database-independent tool for the estimation of coverage in metagenomic data sets, to a high-performance computing implementation that scales up to hundreds of cores and includes, in addition, a k -mer-based estimation as sensitive as the original alignment-based version but about three hundred times as fast. Further, we propose a metric of sequence diversity ( N d ) derived directly from Nonpareil curves that correlates well with alpha diversity assessed by traditional metrics. We use this metric in different experiments demonstrating the correlation with the Shannon index estimated on 16S rRNA gene profiles and show that N d additionally reveals seasonal patterns in marine samples that are not captured by the Shannon index and more precise rankings of the magnitude of diversity of microbial communities in different habitats. Therefore, the new version of Nonpareil, called Nonpareil 3, advances the toolbox for metagenomic analyses of microbiomes.

59 BASIC BIOLOGICAL SCIENCES↗

Estimation of Tropical Cyclone Intensity in the North Atlantic and Northeastern Pacific Basins Using TRMM Satellite Passive Microwave Observations

Here, a statistical passive microwave intensity estimation (PMW-IE) algorithm for estimating the intensity of tropical cyclones (TCs) in the North Atlantic and northeastern and central Pacific basins is developed and tested. The algorithm is derived from Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) 85-GHz brightness temperatures and near-surface rain-rate retrievals to provide objective estimates of current maximum sustained surface winds (Vmax) and 6-h future Vmax of TCs. The full record of TRMM data (1998–2013) including 2326 TMI overpasses of 503 TCs is separated into dependent samples (1998–2010) for model development and independent samples (2011–13) for model verification. The best track intensities are used as dependent variables in a stepwise multiple-regression approach. Separately for each basin, three regression models are derived using selected 1) 85-GHz-only variables, 2) rain-rate-only variables, and 3) combined 85-GHz and rain variables. The algorithms are assessed using independent samples and those with contemporaneous aircraft-reconnaissance measurements. Rain-only and combined models perform better than the 85-GHz-only model. Lower errors are found for estimating the 6-h future Vmax than estimating the current Vmax using all three models. This implies that it is optimal to use passive-microwave-retrieved rain variables observed a few hours earlier to estimate TC intensity. The MAE (RMSE) of 6-h future Vmax is 9 (12) kt (1 kt ≈ 0.51 m s -1 ) when testing the combined models with ATL and EPA independent samples. Aircraft-reconnaissance-based independent samples yields a MAE of 9.6 kt and RMSE of 12.6 kt for estimating 6-h future Vmax.

58 GEOSCIENCES↗

A Bayesian Approach to Regional Decadal Predictability: Sparse Parameter Estimation in High-Dimensional Linear Inverse Models of High-Latitude Sea Surface Temperature Variability

Stochastic reduced models are an important tool in climate systems whose many spatial and temporal scales cannot be fully discretized or underlying physics may not be fully accounted for. One form of reduced model, the linear inverse model (LIM), has been widely used for regional climate predictability studies—typically focusing more on tropical or midlatitude studies. However, most LIM fitting techniques rely on point estimation techniques deriving from fluctuation–dissipation theory. In this methodological study we explore the use of Bayesian inference techniques for LIM parameter estimation of sea surface temperature (SST), to quantify the skillful decadal predictability of Bayesian LIM models at high latitudes. We show that Bayesian methods, when compared to traditional point estimation methods for LIM-type models, provide better calibrated probabilistic skill, while simultaneously providing better point estimates due to the regularization effect of the prior distribution in high-dimensional problems. We compare the effect of several priors, as well as maximum likelihood estimates, on 1) estimating parameter values on a perfect model experiment and 2) producing calibrated 1-yr SST anomaly forecast distributions using a preindustrial control run of the Community Earth System Model (CESM). Finally, we employ a host of probabilistic skill metrics to determine the extent to which an LIM can forecast SST anomalies at high latitudes. We find that the choice of prior distribution has an appreciable impact on estimation outcomes, and priors that emphasize physically relevant properties enhance the model’s ability to capture variability of SST anomalies.

54 ENVIRONMENTAL SCIENCES↗

Forced Component Estimation Statistical Method Intercomparison Project (ForceSMIP)

Anthropogenic climate change is unfolding rapidly, yet its regional manifestation can be obscured by internal variability. A primary goal of climate science is to identify the externally forced climate response from among the noise of internal variability. Separating the forced response from internal variability can be addressed in climate models by using a large ensemble to average over different possible realizations of internal variability. However, with only one realization of the real world, it is a major challenge to isolate the forced response directly in observations. In the Forced Component Estimation Statistical Method Intercomparison Project (ForceSMIP), contributors used existing and newly developed statistical and machine learning methods to estimate the forced response over 1950–2022 within individual realizations of the climate system. Participants used neural networks, linear inverse models, fingerprinting methods, and low-frequency component analysis, among other approaches. These methods were trained using large ensembles from multiple climate models and then applied to observations. Here, we evaluate method performance within large ensembles and investigate the estimates of the forced response in observations. Our results show that many different types of methods are skillful for estimating the forced response in climate models, though the relative skill of individual methods varies depending on the variable and evaluation metric. Methods with comparable skill in models can give a wide range of estimates of the forced response pattern in observations, illustrating the epistemic uncertainty in forced response estimates. ForceSMIP gives new insights into the forced response in observations, its uncertainty, and methods for its estimation.

Climate attribution↗