The need for high precision calculations with 64-bit or 32-bit floating-point arithmetic for weather and climate models is questioned. Lower precision numbers can accelerate simulations and are increasingly supported by modern computing hardware. This paper investigates the potential of 16-bit arithmetic when applied within a shallow water model that serves as a medium complexity weather or climate application. There are several 16-bit number formats that can potentially be used (IEEE half precision, BFloat16, posits, integer and fixed-point). It is evident that a simple change to 16-bit arithmetic will not be possible for complex weather and climate applications as it will degrade model results by intolerable rounding errors that cause a stalling of model dynamics or model instabilities. However, if the posit number format is used as an alternative to the standard floating-point numbers the model degradation can be reduced to a tolerable minimum. Furthermore, a number of mitigation methods, such as rescaling, reordering and mixed-precision, are available to make model simulations resilient against a precision reduction. If mitigation methods are applied, 16-bit floating-point arithmetic can be used successfully within the shallow water model. The results show the potential of 16-bit formats for at least parts of complex weather and climate models where rounding errors would be entirely masked by initial condition, model or discretization error.

Estimation of velocity profile through mud depth is a long-standing and essential problem in debris-flow dynamics. Until now, various velocity profiles have been proposed based on the regression of experimental measurements, but these are often limited by the observation conditions, such as the number of the configured sensors. Therefore, the resulting linear velocity profiles exhibit limitations in reproducing the nonlinear behavior and its temporal variation during the debris-flow process. In this study, we present a novel approach to explore debris-flow velocity profile in detail upon our previous 3D-HBP-SPH numerical model, i.e., the three-dimensional Smoothed Particle Hydrodynamic model incorporating with the Herschel-Bulkley-Papanastasiou rheology. Specifically, we propose a stratification statistical algorithm for interpreting the details of SPH particles, which enables the recording of temporal velocities of debris flow at different mud depths. To regress the velocity profile, we introduce a logarithmic-based nonlinear function with two empirical parameters, that controlling the shape of velocity profile and concerning its temporal evolution. We verify the proposed velocity profile and explore its sensitivity using 34 sets of velocity data from three individual flume experiments in previous literatures. Our results demonstrate that the proposed temporal-varying and depth-nonlinear velocity profile outperforms the previous ones.

It is now well-established through multiple event and statistical studies that the solar wind at 1 AU contains contains periodic, mesoscale (L~100-1000Mm) structures in the proton density. Composition variations observed within these structures and remote sensing observations of similar structures in the young solar wind indicate that at least some of these periodic structures originate in the solar atmosphere as a part of solar wind formation. Viall et al. [2008] analyzed 11 years of data from the Wind spacecraft near L1 and demonstrated a recurrence to the observed length scales of periodic structures in the solar wind proton density. In the time since that study, Wind has collected 14 additional years of solar wind data, new moment analysis of the Wind SWE data is available, and new methods for spectral background approximation have been developed. In this study, we analyze 25 years of Wind data collected near L1 and produce occurrence distributions of statistically significant periodic length scales in proton density. The results significantly expand upon the Viall et al. [2008] study, and further shows a possible relation of the length scales to solar “termination” events.

Subtle elastic rock deformation during aquifer testing may bear hydraulic parameter (permeability and compressibility) information owing to the poroelastic hydromechanical coupling effect. Here we report that such in situ rock deformations (~50 µε) during an aquifer pumping test are successfully measured along a vertical well by a high-resolution fiber optic distributed strain sensing (DSS) tool with an accuracy of 0.5 µε. We investigate the feasibility of hydraulic parameter estimation at meter scale using DSS data through a coupled hydromechanical model. Both synthetic and field cases are tested with sensitivity analysis. The results indicate that the simultaneous estimation of permeability and compressibility using DSS data is possible at low noise levels. However, only non-global near-optimal solutions can be obtained using the applied gradient-based inversion algorithm, because of parameter crosstalk and sensitivity problems when the data contain large noise. In particular, estimation is difficult for zones with relatively low permeability due to the low sensitivity to the strain changes. The estimated permeability/compressibility structures for the field test are largely consistent with other geological information from well logs. Our study suggests that DSS data can be quite useful in aquifer characterization and fluid flow profiling in addition to geomechanical monitoring. The obtained hydraulic information is beneficial for the optimized reservoir management of water and oil/gas storage.

Climate change is known to increase the frequency and intensity of hot days (daily maximum temperature ≥ 30°C), both globally and locally. Exposure to extreme heat is associated with numerous adverse human health outcomes. This study estimated the burden of heat-related illness (HRI) attributable to anthropogenic climate change in North Carolina physiographic divisions (Coastal and Piedmont) during the summer months from 2011-2016. Additionally, assuming intermediate and high greenhouse gas emission scenarios, future HRI morbidity burden attributable to climate change was estimated. The association between daily maximum temperature and the rate of HRI was evaluated using the Generalized Additive Model. The rate of HRI assuming natural simulations (i.e., absence of greenhouse gas emissions) and future greenhouse gas emission scenarios were predicted to estimate the HRI attributable to climate change. During 2011-2016, we observed a significant decrease in the rate of HRI assuming natural simulations compared to the observed. About 15% of HRI is attributable to anthropogenic climate change in Coastal (13.40% (IQR: -34.90,95.52)) and Piedmont (16.39% (IQR: -35.18,148.26)) regions. During the future periods, the median rate of HRI was significantly higher (78.65%:Coastal and 65.85%:Piedmont), assuming a higher emission scenario than the intermediate emission scenario. We observed significant associations between anthropogenic climate change and adverse human health outcomes. Our findings indicate the need for evidence-based public health interventions to protect human health from climate-related exposures, like extreme heat, while minimizing greenhouse gas emissions.

A document by Triparna Sett. Click on the document to view its contents.

Fluid migration in subduction zones is a key controlling factor of slow and megathrust earthquakes at plate boundaries. During the migration, seismic velocity and heterogeneous structures in its pathways may be temporarily varied, preferably triggering slow earthquakes. Here, we show that transient changes of seismic heterogeneity occurred 0-9 months before shallow slow earthquakes in the Nankai subduction zone, Japan, using very long-term (6–10 y) records of ambient seafloor noise. The heterogeneity changes preceding to shallow slow earthquakes were observed near the margin of the source region, while concurrent changes primarily occurred in the source region. We propose that the heterogeneity changes are attributed to dynamic fluid migration, and the difference in timings reflects the pore pressure level in the corresponding source region. When fluids are supplied to a source region under relatively low pressure, fluids are leaked out from its downdip or updip side, and slow earthquakes occur not immediately but with a time delay of at most 9 months. In the high pore pressure case, slow earthquakes occur immediately with fluid migration from the source region. This study suggests that the heterogeneous seismic structure is possibly changed by fluid migration before slow earthquakes in the Nankai subduction zone.

Auroral brightness and color ratio imagery, captured using the Juno mission’s Ultraviolet Spectrograph, display intense emissions poleward of Jupiter’s northern main emission, and these are split into two distinctly different spectral or “color ratio” regimes. The most poleward region, designated the “swirl region” by Grodent et al. (2003), exhibits a high color ratio, while low color ratio emissions are found within the collar around the swirl region but still poleward of the main emission. We confirm the apparent strong magnetospheric local time control within the polar collar (Grodent et al., 2003), with the dusk side bright “active region” emissions extending from ~11 to 22 hr of magnetospheric local time. These bright emissions dim by at least an order of magnitude between ~0 and 11 hr magnetospheric local time, in the midnight to dawn side “dark region”. This magnetospheric local time structure holds true even when the entire northern oval is located on the night side of the planet (in ionospheric local time), a geometry unstudied prior to Juno, as it is unobservable from Earth. The swirl region brightens at ionospheric dawn (~5-7 ionospheric local time) and diminishes or completely disappears at ionospheric local times of ~20 to 22 hrs. Finally, the southern auroral polar emissions appear to share all of the local time dependencies of its northern counterpart, but at a reduced intensity.

Mohan [email protected]

The Fletcher-Ponnambalam (FP) method is an explicit stochastic optimization method for design and operations management of storage systems. It has been applied successfully in many real-world operations optimization problems (for example, the Great Lakes system and the Parambikulam-Aliyar project) and groundwater management problems. The FP method faces no curse of dimensionality unlike stochastic dynamic programming (SDP) and no need for scenarios generation as in implicit stochastic programming (ISP) methods. The paper introduces a novel implementation for the FP method by removing the need for nonlinear constraints and by decreasing the number of decision variables to just one third of its original value, significantly reducing solving time (~27 times faster than the original formulation). Additionally, new expressions derived for first and second moments of both reservoir release deficit and spill terms and the already-derived expression for second moments of reservoir storage are incorporated into the new formulation enabling the FP method to reach an improved optimality for a nonlinear objective function. The enhanced procedure is applied to solving a water reservoir operation optimization problem for a major dam in Brazil. The result comparisons made with SDP, two-stage stochastic programming and ISP along with a thorough analysis of release operation policies for both non-Gaussian correlated and Gaussian independent inflows prove the optimality of this highly numerically efficient and convenient-to-use FP method.

Ensemble Kalman Filters (EnKFs), which assimilate observations based on statistics derived from samples of ocean states called ensemble, have become the norm for ocean data assimilation (DA) and forecasting. These schemes are commonly implemented with inflation and localization techniques to increase their ensemble spread and to filter out spurious long-range correlations resulting from the limited-size ensembles imposed by computational burden constraints. Such ad hoc methods were found not necessary in ensemble DA experiments with simplified ocean/atmospheric models and large ensembles. Here, we conduct a series of 1-year-long ensemble experiments with a fully realistic EnKF-DA system in the Red Sea using tens-to-thousands of ensemble members. The system assimilates satellite and in-situ observations and accounts for model uncertainties by integrating a 4km-resolution ocean model with ECMWF atmospheric ensemble fields, perturbed internal physics and initial conditions for forecasting. Our results indicate that accounting for model uncertainties is more beneficial than simply increasing the ensemble size, with the improvements due to large ensemble leveling off at about 250 members. Besides, and in contrast to what is commonly observed with simplified models, the investigated ensemble DA system still required localization even when implemented with thousands of members. These findings are explained by (i) amplified spurious long-range correlations produced by the low-rank nature of the ECMWF atmospheric forcing ensemble, and (ii) non-Gaussianity generated by the perturbed internal physical parameterization schemes. Large ensemble forcing fields and non-Gaussian DA methods might be needed to take full benefits from large ensembles in ocean DA.

We search for repeating earthquakes (REs) in the northern San Francisco Bay Area in 1984-2016. By comparing over 670,000 waveforms from ~75,000 events, we identify candidate clusters of events whose waveforms have high cross-correlation coefficients at multiple stations. A key difference with our approach is that these ‘multi-station clusters’ do not require each event in a family be recorded at multiple common stations. We validate these candidate REs by estimating precise relative relocations for the events in each cluster. We identify 59 RE families whose relocated hypocenters are separated by less than one source radius. These are distributed throughout the Maacama fault zone, and along the northern Rodgers Creek and central Bartlett Springs faults, implying that widespread, pervasive creep occurs on those faults, at rates of 1-6 mm/yr. At either end of the Maacama fault, the RE pattern highlights structural complexity, suggesting that multiple subparallel strands may be active and creeping.

This study focuses on determining the orientation and constraining the magnitude of the present-day stress in the Dezful Embayment in Iran’s Zagros Fold and Thrust Belt. Two datasets are used: the first includes petrophysical data from 25 wells (3 to 4 km), and the second contains 108 earthquake focal plane mechanisms mostly occurring in blind active basement faults (5 to 20 km). Formal stress inversion analysis of the focal plane mechanism demonstrates that the major basement faults are reverse faults with ( =2.0-2.2). The seismologically determined SHmax direction is 37{degree sign}{plus minus}10{degree sign}, nearly perpendicular to the strike of most faults in the region. However, borehole geomechanics analysis using rock strength and drilling evidence leads to the counterintuitive result that the shallow state of stress is a normal/strike-slip regime. These results are consistent with the low seismicity level in the sedimentary cover in the Dezful Embayment, and may be evidence of stress decoupling due to the existence of salt layers. This finding also aligns with the Mohr-Coulomb faulting theory in that the N-S strike-slip basement Kazerun fault has an unfavourable orientation for slip in a reverse fault regime with an average SW-NE SHmax orientation. The stress state situation in the field was used to identify the optimally oriented fault planes and the fault friction factor. The results are useful for determining the origin of seismic activity in the basin and better assessing fault-associated seismic hazards in the area.

In September 2016 and again in November 2017, Korea witnessed some of the largest magnitude earthquakes occurred in-land in its recent history. They were located in the South-Eastern part of the peninsula, near the cities of Gyeongju and Pohang, with magnitude ML 5.8 and 5.4 respectively. The regional PRESTo Early Warning System has been in the testing phase at KIGAM (Korea Institute of Geoscience and Mineral Resources) since late 2013, using the nation-wide KIGAM and KMA (Korea Meteorological Agency) real-time accelerometric streams, after a calibration based on moderate-to-low seismicity. In this work the PRESTo algorithm parameters and regression laws were optimized and calibrated for the region of South Korea around the Gyeongju earthquake, specifically for a sub-network with a side of about 200 km. A waveform database spanning ten years was analyzed (99 earthquakes during 2007-2017, with M 2 ÷ 5.8, 15681 waveforms), to derive an optimized configuration for this network geometry and seismicity, including GMPEs and empirical regression laws for magnitude estimation from peak displacement, measured in short P-waves and early S-waves time-windows. This configuration enables a fast characterization of the local earthquake sources and effects, i.e. during the playback of onshore earthquakes with magnitude 3.0 and above the EW system produces, in 90% of the cases: a first alert within 6 seconds from the first pick; stable magnitude estimations (variations within 0.5 magnitude points); a final estimate performance against the reference bulletin of ∆M ≤ 0.5, ∆Repi ≤ 10 km.

Analyses of wildfire-climate relationships over North America were conducted using diverse data including ground-based measurements, satellite retrievals, and re-analyses for the period 1984-2014. Results show the western US (WUS) has experienced the most robust trend in increasing burned area, even though Alaska and central Canada possess comparable or even stronger warming trends compared to WUS. In addition to warming, the WUS has been under the influence of multi-decadal trends in tropospheric relative humidity deficit, reduced cloudiness, increased surface net insolation, enhanced adiabatic warming and drying from increased tropospheric subsidence, as well as drying from enhanced off-shore low-level flow, potentially leading to more abundant dry fuels and raging large wildfires. These trends are likely the manifestation of a regional climate feedback that is enabled by the intensification, and expansion of the North Pacific Subtropical High, associated with a widening of the subsiding branch of the Hadley circulation under greenhouse warming.

The Advanced Topographic Laser Altimeter System (ATLAS) is the sole science instrument on NASA’s ICESat-2 mission. It was designed to measure elevation simultaneously along six tracks on the earth’s surface with centimeter-level vertical precision, demanding a picosecond-level precision in photon time of flight. To ensure this precision requirement was met, we developed for the Level-1B ATLAS data product a careful verification procedure. To quantify the amount of acceptable error we needed to understand the effects of the various floating-point precisions at all steps of the calculations and the limitations of the programming languages used for the production software (Fortran) and for the verification software (Python). For example, we found in the 64-bit photon time-of-flight calculations that differences even at 13 or 14 decimal places often revealed that an incorrect calibration value had been selected. Without first characterizing the acceptable error, such small errors could be overlooked and could propagate into critical inaccuracies in the science products. We describe our methods and lessons learned in order to inform future remote sensing verification efforts.

The increasing frequency of devastating floods from heavy rainfall associated with climate change has made river stage prediction more important. For steep, forest-covered mountainous watersheds, deep learning models may improve prediction of river stages from rainfall. Here we use the framework of multilayer perceptron (MLP) neural networks to develop such a river stage model. The MLP is constructed for the Shimanto river, which lies in southwestern Japan under a mild, rain-heavy climate. Our input for stage estimation, as well as prediction, is long-term rainfall time series. With a one-year time series of rainfall, the model estimates the stage with 50 cm RMSE for about 10 m of stage peaks as well as accurately simulate stage-time fluctuations. Furthermore, the forecast model can predict the stage without rainfall forecasts up to three hours ahead. To estimate the base flow stages as well as flood peaks with high precision we find the rainfall time series should be at least one year. This indicates that the use of a long rainfall time series enables one to model the contributions of ground water and evaporation. Given that the delay between the arrival time of rainfall at a rain-gauge to the outlet change is well simulated, the physical concepts of runoff appear to be soundly embedded in the MLP.

We examine mid-lower crustal xenoliths erupted in Late Pleistocene to Holocene alkali basalt at San Quintin, Baja California, Mexico to investigate the role of partial melt in deep lithospheric deformation. Using major and trace element mineral chemistry data obtained from LA-ICP-MS and microstructural data obtained from EBSD, we cluster the crustal xenoliths into two groups based on textural and geochemical characteristics: Group (1) largely unfoliated two-pyroxene gabbros with minor amounts of olivine, and Group (2) gabbros with strong compositional foliation and up to 30% modal olivine. We interpret Group 1 xenoliths as representing underplated cumulates from basaltic magmas, whereas petrographic and quantitative microstructural data suggest Group 2 xenoliths record melt infiltration of pre-existing gabbros coeval with deformation, perhaps in a deep crustal shear zone. We propose this shear zone represents a deep lithospheric expression of the active transform plate boundary along the Baja California margin.