Estimating the Acute Health Impacts of Fire‐Originated PM 2.5 Exposure During the 2017 California Wildfires: Sensitivity to Choices of Inputs
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Stephanie E. Cleland↗
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Rapid Assessment of Contrast Sensitivity with Mobile Touch-screens
The availability of low-cost high-quality touch-screen displays in modern mobile devices has created opportunities for new approaches to routine visual measurements. Here we describe a novel method in which subjects use a finger swipe to indicate the transition from visible to invisible on a grating which is swept in both contrast and frequency. Because a single image can be swiped in about a second, it is practical to use a series of images to zoom in on particular ranges of contrast or frequency, both to increase the accuracy of the measurements and to obtain an estimate of the reliability of the subject. Sensitivities to chromatic and spatio-temporal modulations are easily measured using the same method. We will demonstrate a prototype for Apple Computer's iPad-iPod-iPhone family of devices, implemented using an open-source scripting environment known as QuIP (QUick Image Processing,
mobile devices↗
Discovery of candidate cataclysmic variables in the post-core-collapse globular cluster NGC 6397
A photometric search for objects with H alpha emission in the globular cluster NGC 6397 has been carried out with the Hubble Space Telescope (HST) Planetary Camera. Images were obtained through the F675W (similar to Johnson R) and F656N (H alpha) filters, and photometry carried out on approximately 900 stars detected in the central approximately 38 x 35 arcseconds of the cluster. Limiting magnitudes of R approximately 21 and 19.5 were reached in the F675W and F656N images, respectively. Three H alpha-bright stars are found which have apparent magnitudes in the range R = 17.8-19.5. The corresponding absolute magnitudes (M(sub R) = 5.6-7.3) and inferred H alpha emission-line strengths (EW(H alpha) greater than or approximately equal to 15-26 A) make these objects good candidates to be cataclysmic variables (CVs) in this nearby, post-collapse cluster. All three H alpha-bright objects are found to be UV-bright in a photometric comparison of the F675W images with archival HST Faint Object Camera (FOC) images of the cluster center through the F220W and F346M filters. Such UV excesses further support the identification of these objects as candidates to be CVs in the cluster. The H alpha-bright stars are within the error circles of three X-ray sources previously found with the ROSAT High Resoltion Imager (HRI) and are likley to be the dominant source of the X-ray emission. The implied X-Ray to optical flux ratios (f(sub x)/f(sub v) approximately 0.8-4.2) are consistent with CVs known elsewhere in the Galaxy. Two additional stars with possible H alpha emission are also discussed, along the stars identified on the basis of UV emisssion. Preliminary completeness estimates suggest that these observations should be sensitive to approximately one-third to two-thirds of most varieties of CVs in the cluster, and that therefore greater than or approximately 5-10 CVs are likely to be present in the observed section of NGC 6397. Scaling these numbers to the cluster as a whole would increase these numbers by up to a factor of 2. A total CV population on the order of 5-20 is in reasonable with previous estimates made on the basis of X-ray observations, and with theoretical predictions scaled to this cluster.
Cool, Adrienne M.↗
Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications
Abstract Robust quantification of predictive uncertainty is a critical addition needed for machine learning applied to weather and climate problems to improve the understanding of what is driving prediction sensitivity. Ensembles of machine learning models provide predictive uncertainty estimates in a conceptually simple way but require multiple models for training and prediction, increasing computational cost and latency. Parametric deep learning can estimate uncertainty with one model by predicting the parameters of a probability distribution but does not account for epistemic uncertainty. Evidential deep learning, a technique that extends parametric deep learning to higher-order distributions, can account for both aleatoric and epistemic uncertainties with one model. This study compares the uncertainty derived from evidential neural networks to that obtained from ensembles. Through applications of the classification of winter precipitation type and regression of surface-layer fluxes, we show evidential deep learning models attaining predictive accuracy rivaling standard methods while robustly quantifying both sources of uncertainty. We evaluate the uncertainty in terms of how well the predictions are calibrated and how well the uncertainty correlates with prediction error. Analyses of uncertainty in the context of the inputs reveal sensitivities to underlying meteorological processes, facilitating interpretation of the models. The conceptual simplicity, interpretability, and computational efficiency of evidential neural networks make them highly extensible, offering a promising approach for reliable and practical uncertainty quantification in Earth system science modeling. To encourage broader adoption of evidential deep learning, we have developed a new Python package, Machine Integration and Learning for Earth Systems (MILES) group Generalized Uncertainty for Earth System Science (GUESS) (MILES-GUESS) ( https://github.com/ai2es/miles-guess ), that enables users to train and evaluate both evidential and ensemble deep learning. Significance Statement This study demonstrates a new technique, evidential deep learning, for robust and computationally efficient uncertainty quantification in modeling the Earth system. The method integrates probabilistic principles into deep neural networks, enabling the estimation of both aleatoric uncertainty from noisy data and epistemic uncertainty from model limitations using a single model. Our analyses reveal how decomposing these uncertainties provides valuable insights into reliability, accuracy, and model shortcomings. We show that the approach can rival standard methods in classification and regression tasks within atmospheric science while offering practical advantages such as computational efficiency. With further advances, evidential networks have the potential to enhance risk assessment and decision-making across meteorology by improving uncertainty quantification, a longstanding challenge. This work establishes a strong foundation and motivation for the broader adoption of evidential learning, where properly quantifying uncertainties is critical yet lacking.
Schreck, John S.↗
Towards a Model Climatology of Relative Humidity in the Upper Troposphere for Estimation of Contrail and Contrail-Induced Cirrus
The formation of contrails and contrail cirrus is very sensitive to the relative humidity of the upper troposphere. To reduce uncertainty in an estimate of the radiative impact of aviation-induced cirrus, a model must therefore be able to reproduce the observed background moisture fields with reasonable and quantifiable fidelity. Here we present an upper tropospheric moisture climatology from a 26-year ensemble of simulations using the GEOS CCM. We compare this free-running model's moisture fields to those obtained from the MLS and AIRS satellite instruments, our most comprehensive observational databases for upper tropospheric water vapor. Published comparisons have shown a substantial wet bias in GEOS-5 assimilated fields with respect to MLS water vapor and ice water content. This tendency is clear as well in the GEOS CCM simulations. The GEOS-5 moist physics in the GEOS CCM uses a saturation adjustment that prevents supersaturation, which is unrealistic when compared to in situ moisture observations from MOZAIC aircraft and balloon sondes as we will show. Further, the large-scale satellite datasets also consistently underestimate super-saturation when compared to the in-situ observations. We place these results in the context of estimates of contrail and contrail cirrus frequency.
Selkirk, Henry B.↗
Performance of an INTEGRAL spectrometer model
Model calculations for the INTEGRAL spectrometer (SPI) onboard the future INTErnational Gamma Ray Astrophysics Laboratory (INTEGAL) are presented, where the sensitivity for narrow lines is based on estimates of the background level and the detection efficiency. The instrumental background rates are explained as the sum of various components that depend on the cosmic ray intensity and the spectrometer characteristics, such as the mass distribution around the Ge detectors, the passive material, the characteristics of the detector system and the background reduction techniques. Extended background calculations were performed with Monte Carlo simulations and using semi-empirical and calculated neutron and proton cross sections. In order to improve the INTEGRAL spectrometer sensitivity, several designs and background reduction techniques were compared for an instrument with a fixed detector volume.
Jean, P.↗
An Update on Oceanic Precipitation Rate and its Zonal Distribution in Light of Advanced Observations from Space
This study contributes to the estimation of the global mean and zonal distribution of oceanic precipitation rate using complementary information from advanced precipitation measuring sensors and provides an independent reference to assess current precipitation products. Precipitation estimates from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and CloudSat cloud profiling radar (CPR) were merged, as the two complementary sensors yield an unprecedented range of sensitivity to quantify rainfall from drizzle through the most intense rates. At higher latitudes, where TRMM PR does not exist, precipitation estimates from Aqua's Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) complemented CloudSat CPR to capture intense precipitation rates. The high sensitivity of CPR allows estimation of snow rate, an important type of precipitation at high latitudes, not directly observed in current merged precipitation products. Using the merged precipitation estimate from the CloudSat, TRMM, and Aqua platforms (this estimate is abbreviated to MCTA), the authors' estimate for 3-yr (2007-09) nearglobal (80degS-80degN) oceanic mean precipitation rate is approx. 2.94mm/day. This new estimate of mean global ocean precipitation is about 9% higher than that of the corresponding Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) value (2.68mm/day) and about 4% higher than that of the Global Precipitation Climatology Project (GPCP; 2.82mm/day). Furthermore, MCTA suggests distinct differences in the zonal distribution of precipitation rate from that depicted in GPCPand CMAP, especially in the Southern Hemisphere.
profiling radar↗
On the measurement of pilot perceptual workload - A comparison of assessment techniques addressing sensitivity and intrusion issues
A flight simulator-based study was conducted to examine fourteen distinct mental workload estimation measures, including opinion, secondary task, physiological, and primary task measures. Both the relative sensitivity of the measures to changes in mental workload and the differential intrusion of the changes on primary task performance were assessed. The flight task was varied in difficulty by manipulation of the presentation rate and complexity of a hazard-perception task that required each of 48 licensed pilots to rely heavily on their perceptual abilities. Three rating scales (Modified Cooper-Harper, Multi-descriptor, and Workload-Compensation-Interference/Technical Effectiveness), two secondary task measures (time estimation and tapping regularity), one physiological measure (respiration frequency), and one primary task measure (danger-condition response time) were reliable indicants of workload changes. Recommendations for applying the workload measures are presented.
Casali, J. G.↗
Observations of cloud liquid water path over oceans: Optical and microwave remote sensing methods
Published estimates of cloud liquid water path (LWP) from satellite-measured microwave radiation show little agreement, even about the relative magnitudes of LWP in the tropics and midlatitudes. To understand these differences and to obtain more reliable estimate, optical and microwave LWP retrieval methods are compared using the International Satellite Cloud Climatology Project (ISCCP) and special sensor microwave/imager (SSM/I) data. Errors in microwave LWP retrieval associated with uncertainties in surface, atmosphere, and cloud properties are assessed. Sea surface temperature may not produce great LWP errors, if accurate contemporaneous measurements are used in the retrieval. An uncertainty of estimated near-surface wind speed as high as 2 m/s produces uncertainty in LWP of about 5 mg/sq cm. Cloud liquid water temperature has only a small effect on LWP retrievals (rms errors less than 2 mg/sq cm), if errors in the temperature are less than 5 C; however, such errors can produce spurious variations of LWP with latitude and season. Errors in atmospheric column water vapor (CWV) are strongly coupled with errors in LWP (for some retrieval methods) causing errors as large as 30 mg/sq cm. Because microwave radiation is much less sensitive to clouds with small LWP (less than 7 mg/sq cm) than visible wavelength radiation, the microwave results are very sensitive to the process used to separate clear and cloudy conditions. Different cloud detection sensitivities in different microwave retrieval methods bias estimated LWP values. Comparing ISCCP and SSM/I LWPs, we find that the two estimated values are consistent in global, zonal, and regional means for warm, nonprecipitating clouds, which have average LWP values of about 5 mg/sq cm and occur much more frequently than precipitating clouds. Ice water path (IWP) can be roughly estimated from the differences between ISCCP total water path and SSM/I LWP for cold, nonprecipitating clouds. IWP in the winter hemisphere is about 3 times the LWP but only half the LWP in the summer hemisphere. Precipitating clouds contribute significantly to monthly, zonal mean LWP values determined from microwave, especially in the intertropical convergence zone (ITCZ), because they have almost 10 times the liquid water (cloud plus precipitation) of nonprecipitating clouds on average. There are significant differences among microwave LWP estimates associated with the treatment of precipitating clouds.
Lin, Bing↗
SO2 on Venus: A final cross-calibration with Pioneer Venus
In the present reporting period we have met with the Pioneer Venus PI to collaborate on the recalibration of the UV spectrometer of Pioneer Venus. The associated data reduction and analysis activities have been completed. The sensitivity of the UV spectrometer has been steadily declining since orbit insertion of Pioneer Venus in 1978 due to aging of the detector tubes. The sensitivity decline is a strong function of wavelength and the rate of decline is also a function of time. Measures were taken to reduce the light dose received by the instrument to slow down the sensitivity decline. The stellar calibration using the bright UV star Hadar in 1990 indicates that the sensitivity decline may have slowed down more than have been previously estimated. The derived amount of SO2 from Pioneer Venus depends on the accuracy of the absolute sensitivity of the UV spectrometer. The previous cross calibration between IUE and Pioneer Venus led to the use of the same solar flux data for reducing and modelling data from both IUE and Pioneer Venus. The comparison between the 1991 IUE results and the Pioneer Venus stellar calibration carried out in 1990 will allow a more accurate determination of sensitivity decline of the PV UV spectrometer. The result of this comparison will be crucial in determining the trend of SO2 in the Venus atmosphere.
Stern, S. Alan↗
Regional Oil and gas Aerial Methane Synthesis model (ROAMS) v2.0
The Regional Oil and gas Aerial Methane Synthesis model is a tool to convert the results of wide-area, source-resolved aerial methane remote sensing surveys of oil and natural gas infrastructure in a given region into methane emissions inventories (estimates of the magnitude and breakdown of methane emissions from the surveyed infrastructure). The tool leverages databases of source-resolved methane emissions detected in aerial surveys, aerial survey coverage information (which areas were measured and when), data summarizing surveyed oil and natural gas infrastructure and production (derived from third-party databases), as well as state-of-the-art mechanistic emissions simulation tools to characterize emissions too small for the aerial system to see. The regional methane emissions estimates produced by this tool are much more granular in both space and asset type than common satellite- or flux tower-based regional estimates. Unlike other tools for converting site-level measurements into regional emissions estimates, our unique geostatistical approach integrates aerially measured emissions with limited need for statistical extrapolation, which can be highly sensitive to modeler assumptions. As a result, ROAMS-based estimates of regional methane emissions from oil and gas activity are widely viewed as highly credible, as evidenced by the success of Dr. Sherwin's recent paper in Nature.
Sherwin, Evan [Lawrence Berkeley National Laborato↗
Regional Oil and gas Aerial Methane Synthesis model (Analytica) (ROAMS Analytica) v1.5.2
The Regional Oil and gas Aerial Methane Synthesis model (Analytica) is a tool to convert the results of wide-area, source-resolved aerial methane remote sensing surveys of oil and natural gas infrastructure in a given region into methane emissions inventories (estimates of the magnitude and breakdown of methane emissions from the surveyed infrastructure). This version is written in the Analytica programming language, and this version accompanies a correction in preparation for submission to Sherwin et al. 2024 (Nature). The tool leverages databases of source-resolved methane emissions detected in aerial surveys, aerial survey coverage information (which areas were measured and when), data summarizing surveyed oil and natural gas infrastructure and production (derived from third-party databases), as well as state-of-the-art mechanistic emissions simulation tools to characterize emissions too small for the aerial system to see. The regional methane emissions estimates produced by this tool are much more granular in both space and asset type than common satellite- or flux tower-based regional estimates. Unlike other tools for converting site-level measurements into regional emissions estimates, our unique geostatistical approach integrates aerially measured emissions with limited need for statistical extrapolation, which can be highly sensitive to modeler assumptions. As a result, ROAMS-based estimates of regional methane emissions from oil and gas activity are widely viewed as highly credible, as evidenced by the success of Dr. Sherwin's recent paper in Nature.
Sherwin, Evan [Lawrence Berkeley National Laborato↗
Study of synthesis techniques for insensitive aircraft control systems
Insensitive flight control system design criteria was defined in terms of maximizing performance (handling qualities, RMS gust response, transient response, stability margins) over a defined parameter range. Wing load alleviation for the C-5A was chosen as a design problem. The C-5A model was a 79-state, two-control structure with uncertainties assumed to exist in dynamic pressure, structural damping and frequency, and the stability derivative, M sub w. Five new techniques (mismatch estimation, uncertainty weighting, finite dimensional inverse, maximum difficulty, dual Lyapunov) were developed. Six existing techniques (additive noise, minimax, multiplant, sensitivity vector augmentation, state dependent noise, residualization) and the mismatch estimation and uncertainty weighting techniques were synthesized and evaluated on the design example. Evaluation and comparison of these six techniques indicated that the minimax and the uncertainty weighting techniques were superior to the other six, and of these two, uncertainty weighting has lower computational requirements. Techniques based on the three remaining new concepts appear promising and are recommended for further research.
Harvey, C. A.↗
Reconstructing Hanford worker external doses from photons for epidemiology
The accurate reconstruction of external photon doses is essential for credible radiation epidemiology. This article presents the methodology used to derive dose estimates for 37 012 Hanford Site workers included in the Million Person Study. The approach employs historical dose records from the Hanford Radiation Exposure database and a previous epidemiology study. Bias correction factors specific to dosimeter type and period of use were applied and missing annual doses were estimated using a hierarchical nearby method to estimate deep dose equivalent for each worker. For early years with limited detection sensitivity, missed doses were quantified based on expected time-period-specific, low-dose statistical distributions. The revised dose estimates resulted in lower median and mean career doses than unadjusted data, while increasing the number of person-years with nonzero dose. Sensitivity analyses assessed the influence of bias in dosimetry measurements, missed doses and gap years on dose estimates. Differences in cumulative dose estimates between unadjusted and revised annual estimates are most prominent in the early operational years due to the highest bias during that time period.
dose reconstruction↗
Space Telescope Sensitivity and Controls for Exoplanet Imaging
Herein we address design considerations and outline requirements for space telescopes with capabilities for high contrast imaging of exoplanets. The approach taken is to identify the span of potentially detectable Earth-sized terrestrial planets in the habitable zone of the nearest stars within 30 parsecs and estimate their inner working angles, flux ratios, SNR, sensitivities, wavefront error requirements and sensing and control times parametrically versus aperture size. We consider 1, 2, 4, 8 and 16-meter diameter telescope apertures. The achievable science, range of telescope architectures, and the coronagraphic approach are all active areas of research and are all subject to change in a rapidly evolving field. Thus, presented is a snapshot of our current understanding with the goal of limiting the choices to those that appear currently technically feasible. We describe the top-level metrics of inner working angle, contrast and photometric throughput and explore how they are related to the range of target stars. A critical point is that for each telescope architecture and coronagraphic choice the telescope stability requirements have differing impacts on the design for open versus closed-loop sensing and control.
Wavefront control↗
Combining Satellite and in Situ Data with Models to Support Climate Data Records in Ocean Biology
The satellite ocean color data record spans multiple decades and, like most long-term satellite observations of the Earth, comes from many sensors. Unfortunately, global and regional chlorophyll estimates from the overlapping missions show substantial biases, limiting their use in combination to construct consistent data records. SeaWiFS and MODIS-Aqua differed by 13% globally in overlapping time segments, 2003-2007. For perspective, the maximum change in annual means over the entire Sea WiFS mission era was about 3%, and this included an El NinoLa Nina transition. These discrepancies lead to different estimates of trends depending upon whether one uses SeaWiFS alone for the 1998-2007 (no significant change), or whether MODIS is substituted for the 2003-2007 period (18% decline, P less than 0.05). Understanding the effects of climate change on the global oceans is difficult if different satellite data sets cannot be brought into conformity. The differences arise from two causes: 1) different sensors see chlorophyll differently, and 2) different sensors see different chlorophyll. In the first case, differences in sensor band locations, bandwidths, sensitivity, and time of observation lead to different estimates of chlorophyll even from the same location and day. In the second, differences in orbit and sensitivities to aerosols lead to sampling differences. A new approach to ocean color using in situ data from the public archives forces different satellite data to agree to within interannual variability. The global difference between Sea WiFS and MODIS is 0.6% for 2003-2007 using this approach. It also produces a trend using the combination of SeaWiFS and MODIS that agrees with SeaWiFS alone for 1998-2007. This is a major step to reducing errors produced by the first cause, sensor-related discrepancies. For differences that arise from sampling, data assimilation is applied. The underlying geographically complete fields derived from a free-running model is unaffected by solar zenith angle requirements and obscuration from clouds and aerosols. Combined with in situ dataenhanced satellite data, the model is forced into consistency using data assimilation. This approach eliminates sampling discrepancies from satellites. Combining the reduced differences of satellite data sets using in situ data, and the removal of sampling biases using data assimilation, we generate consistent data records of ocean color. These data records can support investigations of long-term effects of climate change on ocean biology over multiple satellites, and can improve the consistency of future satellite data sets.
Gregg, Watson↗
Effects of granular convection in the response of C I 5380 A to solar luminosity variations
The response of the weak solar photospheric neutral carbon line at 5380.3 A to percolations of photospheric granulation and the five-minute solar oscillations is investigated in order to calibrate the sensitivity of the line to temporal variations in solar luminosity. Line strength variations and simultaneous continuum variations in two granular regions were observed at 30 sec intervals using a 25-cm coude spectrograph. The response of line equivalent width to continuum intensity variations is found to be essentially uniform at time scales ranging from 5 min to 2 hr independently of whether the variations arise from granular convection or velocity field oscillations. The extent to which line strength varies with luminosity (sensitivity) is observed to be significantly smaller than estimates based on models of the perturbed Harvard-Smithsonian Reference Atmosphere of Gingerich et al. (1970), and it is proposed that the structure of the solar granulation is a major factor determining local luminosity variations and line sensitivity.
Lindsey, C. A.↗
Global Burned Area and Biomass Burning Emissions from Small Fires
In several biomes, including croplands, wooded savannas, and tropical forests, many small fires occur each year that are well below the detection limit of the current generation of global burned area products derived from moderate resolution surface reflectance imagery. Although these fires often generate thermal anomalies that can be detected by satellites, their contributions to burned area and carbon fluxes have not been systematically quantified across different regions and continents. Here we developed a preliminary method for combining 1-km thermal anomalies (active fires) and 500 m burned area observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) to estimate the influence of these fires. In our approach, we calculated the number of active fires inside and outside of 500 m burn scars derived from reflectance data. We estimated small fire burned area by computing the difference normalized burn ratio (dNBR) for these two sets of active fires and then combining these observations with other information. In a final step, we used the Global Fire Emissions Database version 3 (GFED3) biogeochemical model to estimate the impact of these fires on biomass burning emissions. We found that the spatial distribution of active fires and 500 m burned areas were in close agreement in ecosystems that experience large fires, including savannas across southern Africa and Australia and boreal forests in North America and Eurasia. In other areas, however, we observed many active fires outside of burned area perimeters. Fire radiative power was lower for this class of active fires. Small fires substantially increased burned area in several continental-scale regions, including Equatorial Asia (157%), Central America (143%), and Southeast Asia (90%) during 2001-2010. Globally, accounting for small fires increased total burned area by approximately by 35%, from 345 Mha/yr to 464 Mha/yr. A formal quantification of uncertainties was not possible, but sensitivity analyses of key model parameters caused estimates of global burned area increases from small fires to vary between 24% and 54%. Biomass burning carbon emissions increased by 35% at a global scale when small fires were included in GFED3, from 1.9 Pg C/yr to 2.5 Pg C/yr. The contribution of tropical forest fires to year-to-year variability in carbon fluxes increased because small fires amplified emissions from Central America, South America and Southeast Asia-regions where drought stress and burned area varied considerably from year to year in response to El Nino-Southern Oscillation and other climate modes.
Randerson, J. T.↗