We have simulated 0–5 Hz deterministic wave propagation for a suite of 17 models of the 2014 Mw 5.1 La Habra, CA, earthquake with the Southern California Earthquake Center Community Velocity Model Version S4.26-M01 using a finite-fault source. Strong motion data at 259 sites within a 148 km × 140 km area are used to validate our simulations. Our simulations quantify the effects of statistical distributions of small-scale crustal heterogeneities (SSHs), frequency-dependent attenuation Q(f), surface topography and near-surface low-velocity material (via a 1-D approximation) on the resulting ground motion synthetics. The shear wave quality factor QS(f) is parametrized as QS, 0 and QS, 0fγ for frequencies less than and higher than 1 Hz, respectively. We find the most favourable fit to data for models using ratios of QS, 0 to shear wave velocity VS of 0.075–1.0 and γ values less than 0.6, with the best-fitting amplitude drop-off for the higher frequencies obtained for γ values of 0.2–0.4. Models including topography and a realistic near-surface weathering layer tend to increase peak velocities at mountain peaks and ridges, with a corresponding decrease behind the peaks and ridges in the direction of wave propagation. We find a clear negative correlation between the effects on peak ground velocity amplification and duration lengthening, suggesting that topography redistributes seismic energy from the large-amplitude first arrivals to the adjacent coda waves. A weathering layer with realistic near-surface low velocities is found to enhance the amplification at mountain peaks and ridges, and may partly explain the underprediction of the effects of topography on ground motions found in models. Our models including topography tend to improve the fit to data, as compared to models with a flat free surface, while our distributions of SSHs with constraints from borehole data fail to significantly improve the fit. Accuracy of the velocity model, particularly the near-surface low velocities, as well as the source description, controls the resolution with which the anelastic attenuation can be determined. Our results demonstrate that it is feasible to use fully deterministic physics-based simulations to estimate ground motions for seismic hazard analysis up to 5 Hz. Here, the effects of, and trade-offs with, near-surface low-velocity material, topography, SSHs and Q(f) become increasingly important as frequencies increase towards 5 Hz, and should be included in the calculations. Future improvement in community velocity models, wider access to computational resources, more efficient numerical codes and guidance from this study are bound to further constrain the ground motion models, leading to more accurate seismic hazard analysis.
Accurate characterization of precipitation P at subdaily temporal resolution is important for a wide range of hydrological applications, yet large-scale gridded observational datasets primarily contain daily total P. Unfortunately, a widely used deterministic approach that disaggregates P uniformly over the day grossly mischaracterizes the diurnal cycle of P, leading to potential biases in simulated runoff Q. Here we present Precipitation Isosceles Triangle (PITRI), a two-parameter deterministic approach in which the hourly hyetograph is modeled with an isosceles triangle with prescribed duration and time of peak intensity. Monthly duration and peak time were derived from meteorological observations at U.S. Climate Reference Network (USCRN) stations and extended across the United States, Mexico, and southern Canada at 6-km resolution via linear regression against historical climate statistics. Across the USCRN network (years 2000–13), simulations using the Variable Infiltration Capacity (VIC) model, driven by P disaggregated via PITRI, yielded nearly unbiased estimates of annual Q relative to simulations driven by observed P. In contrast, simulations using the uniform method had a Q bias of −11%, through overestimating canopy evaporation and underestimating throughfall. One limitation of the PITRI approach is a potential bias in snow accumulation when a high proportion of P falls on days with a mix of temperatures above and below freezing, for which the partitioning of P into rain and snow is sensitive to event timing within the diurnal cycle. Nevertheless, the good overall performance of PITRI suggests that a deterministic approach may be sufficiently accurate for large-scale hydrologic applications.
more » « less- NSF-PAR ID:
- 10086579
- Publisher / Repository:
- American Meteorological Society
- Date Published:
- Journal Name:
- Journal of Hydrometeorology
- Volume:
- 20
- Issue:
- 2
- ISSN:
- 1525-755X
- Page Range / eLocation ID:
- p. 297-317
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract We explore the potential of feed‐forward deep neural networks (DNNs) for emulating cloud superparameterization in realistic geography, using offline fits to data from the superparameterized community atmospheric model. To identify the network architecture of greatest skill, we formally optimize hyperparameters using ∼250 trials. Our DNN explains over 70% of the temporal variance at the 15‐min sampling scale throughout the mid‐to‐upper troposphere. Autocorrelation timescale analysis compared against DNN skill suggests the less good fit in the tropical, marine boundary layer is driven by neural network difficulty emulating fast, stochastic signals in convection. However, spectral analysis in the temporal domain indicates skillful emulation of signals on diurnal to synoptic scales. A closer look at the diurnal cycle reveals correct emulation of land‐sea contrasts and vertical structure in the heating and moistening fields, but some distortion of precipitation. Sensitivity tests targeting precipitation skill reveal complementary effects of adding positive constraints versus hyperparameter tuning, motivating the use of both in the future. A first attempt to force an offline land model with DNN emulated atmospheric fields produces reassuring results further supporting neural network emulation viability in real‐geography settings. Overall, the fit skill is competitive with recent attempts by sophisticated Residual and Convolutional Neural Network architectures trained on added information, including memory of past states. Our results confirm the parameterizability of superparameterized convection with continents through machine learning and we highlight the advantages of casting this problem locally in space and time for accurate emulation and hopefully quick implementation of hybrid climate models.
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