Storm‐time configuration of the inner magnetosphere: Lyon‐Fedder‐Mobarry MHD code, Tsyganenko model, and GOES observations

[1] We compare global magnetohydrodynamic (MHD) simulation results with an empirical model and observations to understand the magnetic field configuration and plasma distribution in the inner magnetosphere, especially during geomagnetic storms. The physics-based Lyon-Fedder-Mobarry (LFM) code simulates Earth\u27s magnetospheric topology and dynamics by solving the equations of ideal MHD. Quantitative comparisons of simulated events with observations reveal strengths and possible limitations and suggest ways to improve the LFM code. Here we present a case study that compares the LFM code to both a semiempirical magnetic field model and to geosynchronous measurements from GOES satellites. During a magnetic cloud event, the simulation and model predictions compare well qualitatively with observations, except during storm main phase. Quantitative statistical studies of the MHD simulation shows that MHD field lines are consistently under-stretched, especially during storm time (Dst \u3c −20 nT) on the nightside, a likely consequence of an insufficient representation of the inner magnetosphere current systems in ideal MHD. We discuss two approaches for improving the LFM result: increasing the simulation spatial resolution and coupling LFM with a ring current model based on drift physics (i.e., the Rice Convection Model (RCM)). We show that a higher spatial resolution LFM code better predicts geosynchronous magnetic fields (not only the average Bz component but also higher-frequency fluctuations driven by the solar wind). An early version of the LFM/RCM coupled code, which runs so far only for idealized events, yields a much-improved ring current, quantifiable by decreased field strengths at all local times compared to the LFM-only code

Modulation parameter estimation of LFM interference for direct sequence spread spectrum communication system in alpha-stable noise

The linear frequency modulation (LFM) interference is one of the typical broadband interferences in direct sequence spread spectrum (DSSS) communication system. In this article, a novel modulation parameter estimation method of LFM interference is proposed for the DSSS communication system in alpha-stable noise. To accurately estimate the modulation parameters, the alpha-stable noise should be eliminated first. Thus, we formulate a new generalized extended linear chirplet transform to suppress the alpha-stable noise, for a robust time-frequency, transformation of LFM interference is realized. Then, using the Radon transform, the maximum value after transformation and the chirp rate according to the angle related to the maximum value are estimated. In addition, a generalized Fourier transform is introduced to estimate the initial frequency of the LFM interference. For the performance analysis, the Cramér-Rao lower bounds of the estimated chirp rate and the initial frequency of the LFM interference in the presence of alpha-stable noise are derived. Moreover, the asymptotic properties of the modulation parameter estimator are analyzed. Simulation results demonstrate that the performance of the proposed parameter estimation method significantly outperforms existing methods, especially in a low SNR regime

Mimicking portfolios, economic risk premia, and tests of multi-beta models

This paper considers two alternative formulations of the linear factor model (LFM) with nontraded factors. The first formulation is the traditional LFM, where the estimation of risk premia and alphas is performed by means of a cross-sectional regression of average returns on betas. The second formulation (LFM*) replaces the factors with their projections on the span of excess returns. This formulation requires only time-series regressions for the estimation of risk premia and alphas. We compare the theoretical properties of the two approaches and study the small-sample properties of estimates and test statistics. Our results show that when estimating risk premia and testing multi-beta models, the LFM* formulation should be considered in addition to, or even instead of, the more traditional LFM formulation.

Monitoring live fuel moisture using soil moisture and remote sensing proxies

Live fuel moisture (LFM) is an important fuel property controlling fuel ignition and fire propagation. LFM varies seasonally, and is controlled by precipitation, soil moisture, evapotranspiration, and plant physiology. LFM is typically sampled manually in the field, which leads to sparse measurements in space and time. Use of LFM proxies could reduce the need for field sampling while potentially improving spatial and temporal sampling density. This study compares soil moisture and remote sensing data to field-sampled LFM for Gambel oak (Quercus gambelii Nutt) and big sagebrush (Artemisia tridentata Nutt) in northern Utah. Bivariate linear regression models were constructed between LFM and four independent variables. Soil moisture was more strongly correlated with LFM than remote sensing measurements, and produced the lowest mean absolute error (MAE) in predicted LFM values at most of the sites. When sites were pooled, canopy water content (CWC) had stronger correlations with LFM than normalized difference vegetation index (NDVI) or normalized difference water index (NDWI). MAE values for all proxies were frequently above 20 % LFM at individual sites. Despite this relatively large error, remote sensing and soil moisture data may still be useful for improving understanding of spatial and temporal trends in LFM

Velocity selection problem for combined motion of melting and solidification fronts

We discuss a free boundary problem for two moving solid-liquid interfaces that strongly interact via the diffusion field in the liquid layer between them. This problem arises in the context of liquid film migration (LFM) during the partial melting of solid alloys. In the LFM mechanism the system chooses a more efficient kinetic path which is controlled by diffusion in the liquid film, whereas the process with only one melting front would be controlled by the very slow diffusion in the mother solid phase. The relatively weak coherency strain energy is the effective driving force for LFM. As in the classical dendritic growth problems, also in this case an exact family of steady-state solutions with two parabolic fronts and an arbitrary velocity exists if capillary effects are neglected. We develop a velocity selection theory for this problem, including anisotropic surface tension effects. The strong diffusion interaction and coherency strain effects in the solid near the melting front lead to substantial changes compared to classical dendritic growth.Comment: submitted to PR

Estimating Live Fuel Moisture Using SMAP L-Band Radiometer Soil Moisture for Southern California, USA

Live fuel moisture (LFM) is a field-measured indicator of vegetation water content and a crucial observation of vegetation flammability. This study presents a new multi-variant regression model to estimate LFM in the Mediterranean ecosystem of Southern California, USA, using the Soil Moisture Active Passive (SMAP) L-band radiometer soil moisture (SMAP SM) from April 2015 to December 2018 over 12 chamise (Adenostoma fasciculatum) LFM sites. The two-month lag between SMAP SM and LFM was utilized either as steps to synchronize the SMAP SM to the LFM series or as the leading time window to calculate the accumulative SMAP SM. Cumulative growing degree days (CGDDs) were also employed to address the impact from heat. Models were constructed separately for the green-up and brown-down periods. An inverse exponential weight function was applied in the calculation of accumulative SMAP SM to address the different contribution to the LFM between the earlier and present SMAP SM. The model using the weighted accumulative SMAP SM and CGDDs yielded the best results and outperformed the reference model using the Moderate Resolution Imaging Spectroradiometer (MODIS) Visible Atmospherically Resistance Index. Our study provides a new way to empirically estimate the LFM in chaparral areas and extends the application of SMAP SM in the study of wildfire risk

Investigating the Lagged Relationship between Smap Soil Moisture and Live Fuel Moisture in California, USA

Live fuel moisture (LFM), defined as the ratio between water in the fresh biomass out of the dry biomass, is a vital measurement of vegetation water content and flammability. In this study, we investigated the dynamics of in-situ measurement of LFM at all the active sites in California, USA and revealed the difference between evergreen forest and shrub/scrub, the two dominant land cover types in California\u27s fire-prone regions. We found that LFM of evergreen forest responses to soil moisture increase later than shrub/scrub, due to a later occurrence of major precipitation, a lower air temperature, and the different plant physiology. The comparison between SMAP L-band radiometer soil moisture and LFM showed that the lag between the rise in soil moisture and the response from LFM was much longer in evergreen forest. Compared with the evergreen forest, LFM of shrub/scrub was more sensitive to the inter-annual variability of soil moisture due to plant physiology and air temperature

LambdaFM: Learning Optimal Ranking with Factorization Machines Using Lambda Surrogates

State-of-the-art item recommendation algorithms, which apply Factorization Machines (FM) as a scoring function and pairwise ranking loss as a trainer (PRFM for short), have been recently investigated for the implicit feedback based context-aware recommendation problem (IFCAR). However, good recommenders particularly emphasize on the accuracy near the top of the ranked list, and typical pairwise loss functions might not match well with such a requirement. In this paper, we demonstrate, both theoretically and empirically, PRFM models usually lead to non-optimal item recommendation results due to such a mismatch. Inspired by the success of LambdaRank, we introduce Lambda Factorization Machines (LambdaFM), which is particularly intended for optimizing ranking performance for IFCAR. We also point out that the original lambda function suffers from the issue of expensive computational complexity in such settings due to a large amount of unobserved feedback. Hence, instead of directly adopting the original lambda strategy, we create three effective lambda surrogates by conducting a theoretical analysis for lambda from the top-N optimization perspective. Further, we prove that the proposed lambda surrogates are generic and applicable to a large set of pairwise ranking loss functions. Experimental results demonstrate LambdaFM significantly outperforms state-of-the-art algorithms on three real-world datasets in terms of four standard ranking measures