Biomass Burning in the Conterminous United States: A Comparison and Fusion of Active Fire Observations from Polar-Orbiting and Geostationary Satellites for Emissions Estimation

Biomass burning is an important source of atmospheric greenhouse gases and aerosol emissions that significantly influence climate and air quality. Estimation of biomassburning emissions (BBE) has been limited to the conventional method in which parameters (i.e., burned area and fuel load) can be challenging to quantify accurately. Recent studies have demonstrated that the rate of biomass combustion is a linear function of fire radiative power (FRP), the instantaneous radiative energy released from actively burning fires, which provides a novel pathway to estimate BBE. To obtain accurate and timely BBE estimates for near real-time applications (i.e., air quality forecast), the satellite FRP-based method first requires a reliable biomass combustion coefficient that converts fire radiative energy (FRE), the temporal integration of FRP, to biomass consumption. The combustion coefficient is often derived in controlled small-scale fire experiments and is assumed a constant, whereas the coefficient based on satellite retrievals of FRP and atmospheric optical depth is suggested varying in a wide range. Undoubtedly, highly variable combustion coefficient results in large uncertainty of BBE estimates. Further, the FRP-based method also depends on high-spatiotemporalresolution FRP retrievals that, however, are not available in any active fire products from current polar-orbiting and geostationary satellites due to their sampling limitations. To address these challenges, this study first investigates the combustion coefficient for landscape-scale wildfires in the Conterminous United States (CONUS) by comparing FRE from the polar-orbiting Moderate Resolution Imaging Spectroradiometer (MODIS) and the Geostationary Operational Environmental Satellite system (GOES) with the Landsat-based biomass consumption. The results confirms that biomass consumption is a linear function of FRE for wildfires. The derived combustion coefficient is 0.374 kg · MJ- 1 for GOES FRE, 0.266 kg · MJ-1 for MODIS FRE, and 0.320 kg · MJ-1 considering both GOES and MODIS FRE in the CONUS. Limited sensitivity analyses indicate that the combustion coefficient varies from 0.301 to 0.458 kg · MJ-1, which is similar to the reported values in small fire experiments. Then, this study reconstructs diurnal FRP cycle to derive high-spatiotemporal-resolution FRP by fusing MODIS and GOES FRP retrievals and estimates hourly BBE at a 0.25°×0.3125° grid across the CONUS. The results indicate that the reconstructed diurnal FRP cycle varies significantly in magnitude and shape among 45 CONUS ecosystems. In the CONUS, the biomass burning annually releases approximately 690 Gg particulate matter (smaller than 2.5 μm in diameter, PM2.5). The diurnal-FRP-cycle-based BBE estimates compare well with BBE derived from Landsat burned areas in the western CONUS and with the hourly carbon monoxide emissions simulated using a biogeochemical model over the Rim Fire in California. Moreover, the BBE estimates show a similar seasonal variation to six existing BBE inventories but with variable magnitude. Finally, this study examines potential improvements in fires characterization capability of the Visible Infrared Imaging Radiometer Suite (VIIRS), which is the follow-on sensor of the MODIS sensor, for integrating VIIRS FRP retrievals into the FRP-based method for BBE estimation in future work. The results indicate that the VIIRS fire characterization capability is similar across swath, whereas MODIS is strongly dependent on satellite view zenith angle. VIIRS FRP is generally comparable with contemporaneous MODIS FRP at continental scales and in most fire clusters. At 1-degree grid cells, the FRP difference between the two sensors is, on average, approximately 20% in fire-prone regions but varies significantly in fire-limited regions. In summary, this study attempts to enhance the capability of the FRP-based method by addressing challenges in its two parameters (combustion coefficient and FRP), which should help to improve estimation of BBE and advance our understanding of the effects of BBE on climate and air quality. This research has resulted in two published papers and one paper to be submitted to a peer-reviewed journal so far

Numerical Studies of the Generalized \u3cem\u3el\u3c/em\u3e₁ Greedy Algorithm for Sparse Signals

The generalized l1 greedy algorithm was recently introduced and used to reconstruct medical images in computerized tomography in the compressed sensing framework via total variation minimization. Experimental results showed that this algorithm is superior to the reweighted l1-minimization and l1 greedy algorithms in reconstructing these medical images. In this paper the effectiveness of the generalized l1 greedy algorithm in finding random sparse signals from underdetermined linear systems is investigated. A series of numerical experiments demonstrate that the generalized l1 greedy algorithm is superior to the reweighted l1-minimization and l1 greedy algorithms in the successful recovery of randomly generated Gaussian sparse signals from data generated by Gaussian random matrices. In particular, the generalized l1 greedy algorithm performs extraordinarily well in recovering random sparse signals with nonzero small entries. The stability of the generalized l1 greedy algorithm with respect to its parameters and the impact of noise on the recovery of Gaussian sparse signals are also studied

Exploring the GLIDE model for Human Action-effect Prediction

We address the following action-effect prediction task. Given an image depicting an initial state of the world and an action expressed in text, predict an image depicting the state of the world following the action. The prediction should have the same scene context as the input image. We explore the use of the recently proposed GLIDE model for performing this task. GLIDE is a generative neural network that can synthesize (inpaint) masked areas of an image, conditioned on a short piece of text. Our idea is to mask-out a region of the input image where the effect of the action is expected to occur. GLIDE is then used to inpaint the masked region conditioned on the required action. In this way, the resulting image has the same background context as the input image, updated to show the effect of the action. We give qualitative results from experiments using the EPIC dataset of ego-centric videos labelled with actions

The observation of quantum fluctuations in a kagome Heisenberg antiferromagnet

The search for the experimental evidence of quantum spin liquid (QSL) states is critical but extremely challenging, as the quenched interaction randomness introduced by structural imperfection is usually inevitable in real materials. YCu$_3$(OH)$_{6.5}$Br$_{2.5}$ (YCOB) is a spin-1/2 kagome Heisenberg antiferromagnet (KHA) with strong coupling of $\langle J_1\rangle\sim$ 51 K but without conventional magnetic freezing down to 50 mK $\sim$ 0.001$\langle J_1\rangle$. Here, we report a Br nuclear magnetic resonance (NMR) study of the local spin susceptibility and dynamics on the single crystal of YCOB. The temperature dependence of NMR main-line shifts and broadening can be well understood within the frame of the KHA model with randomly distributed hexagons of alternate exchanges, compatible with the formation of a randomness-induced QSL state at low temperatures. The in-plane spin fluctuations as measured by the spin-lattice relaxation rates ($1/T_1$) exhibit a weak temperature dependence down to $T$ $\sim$ 0.03$\langle J_1\rangle$. Our results demonstrate that the majority of spins remain highly fluctuating at low temperatures despite the quenched disorder in YCOB.Comment: NMR work on YCu3(OH)6.5Br2.5, accepted in Communications Physic

A Transmissive X-ray Polarimeter Design For Hard X-ray Focusing Telescopes

The X-ray Timing and Polarization (XTP) is a mission concept for a future space borne X-ray observatory and is currently selected for early phase study. We present a new design of X-ray polarimeter based on the time projection gas chamber. The polarimeter, placed above the focal plane, has an additional rear window that allows hard X-rays to penetrate (a transmission of nearly 80% at 6 keV) through it and reach the detector on the focal plane. Such a design is to compensate the low detection efficiency of gas detectors, at a low cost of sensitivity, and can maximize the science return of multilayer hard X-ray telescopes without the risk of moving focal plane instruments. The sensitivity in terms of minimum detectable polarization, based on current instrument configuration, is expected to be 3% for a 1mCrab source given an observing time of 10^5 s. We present preliminary test results, including photoelectron tracks and modulation curves, using a test chamber and polarized X-ray sources in the lab

Online Estimation of ARW Coefficient of Fiber Optic Gyro

As a standard method for noise analysis of fiber optic gyro (FOG), Allan variance has too large offline computational burden and data storages to be applied to online estimation. To overcome the barriers, the state space model is firstly established for FOG. Then the Sage-husa adaptive Kalman filter (SHAKF) is introduced in this field. Through recursive calculation of measurement noise covariance matrix, SHAKF can avoid the storage of large amounts of history data. However, the precision and stability of this method are still the primary matters that needed to be addressed. Based on this point, a new online method for estimation of the coefficient of angular random walk is proposed. In the method, estimator of measurement noise is constructed by the recursive form of Allan variance at the shortest sampling time. Then the estimator is embedded into the SHAKF framework resulting in a new adaptive filter. The estimations of measurement noise variance and Kalman filter are independent of each other in this method. Therefore, it can address the problem of filtering divergence and precision degrading effectively. Test results of both digital simulation and experimental data of FOG verify the validity and feasibility of the proposed method

Bis[2-(2-amino­ethyl)-1H-benzimidazole-κ2 N 2,N 3]zinc(II) bis­(perchlorate)

In the title compound, [Zn(C9H11N3)2](ClO4)2, the ZnII atom resides on a crystallographic twofold axis and is coordinated by two benzimidazole N [Zn⋯N = 1.993 (4) Å] and two amine N atoms [Zn⋯N = 2.036 (4) Å] in a distorted tetra­hedral geometry. The crystal packing is dominated by N—H⋯O inter­actions involving the perchlorate anions and π–π stacking inter­actions with an inter­planar separation of 3.42 Å. A weak C—H⋯O inter­action is also present

Chilling Stress—The Key Predisposing Factor for Causing Alternaria alternata Infection and Leading to Cotton (Gossypium hirsutum L.) Leaf Senescence

Leaf senescence plays a vital role in nutrient recycling and overall capacity to assimilate carbon dioxide. Cotton premature leaf senescence, often accompanied with unexpected short-term low temperature, has been occurring with an increasing frequency in many cotton-growing areas and causes serious reduction in yield and quality of cotton. The key factors for causing and promoting cotton premature leaf senescence are still unclear. In this case, the relationship between the pre-chilling stress and Alternaria alternata infection for causing cotton leaf senescence was investigated under precisely controlled laboratory conditions with four to five leaves stage cotton plants. The results showed short-term chilling stress could cause a certain degree of physiological impairment to cotton leaves, which could be recovered to normal levels in 2–4 days when the chilling stresses were removed. When these chilling stress injured leaves were further inoculated with A. alternata, the pronounced appearance and development of leaf spot disease, and eventually the pronounced symptoms of leaf senescence, occurred on these cotton leaves. The onset of cotton leaf senescence at this condition was also reflected in various physiological indexes such as irreversible increase in malondialdehyde (MDA) content and electrolyte leakage, irreversible decrease in soluble protein content and chlorophyll content, and irreversible damage in leaves' photosynthesis ability. The presented results demonstrated that chilling stress acted as the key predisposing factor for causing A. alternata infection and leading to cotton leaf senescence. It could be expected that the understanding of the key factors causing and promoting cotton leaf senescence would be helpful for taking appropriate management steps to prevent cotton premature leaf senescence

Pronounced Increases in Nitrogen Emissions and Deposition Due to the Historic 2020 Wildfires in the Western U.S.

Wildfire outbreaks can lead to extreme biomass burning (BB) emissions of both oxidized (e.g., nitrogen oxides; NOx= NO+NO2) and reduced form(e.g., ammonia; NH3) nitrogen (N) compounds. High N emissions aremajor concerns for air quality, atmospheric deposition, and consequential human and ecosystemhealth impacts. In this study, we use both satellite-based observations and modeling results to quantify the contribution of BB to the total emissions, and approximate the impact on total N deposition in the western U.S. Our results show that during the 2020 wildfire season of August–October, BB contributes significantly to the total emissions, with a satellite-derived fraction of NH3 to the total reactiveN emissions (median~40%) in the range of aircraft observations. During the peak of the western August Complex Fires in September, BB contributed to~55%(for the contiguous U.S.) and~83%(for thewestern U.S.) of the monthly total NOx and NH3 emissions. Overall, there is good model performance of the George Mason University- Wildfire Forecasting System(GMU-WFS) used in this work. The extreme BB emissions lead to significant contributions to the total N deposition for different ecosystems in California, with an average August – October 2020 relative increase of~78%(from7.1 to 12.6 kg ha−1 year−1) in deposition rate tomajor vegetation types (mixed forests+grasslands/ shrublands/savanna) compared to the GMU-WFS simulations without BB emissions. For mixed forest types only, the average N deposition rate increases (from 6.2 to 16.9 kg ha−1 year−1) are even larger at ~173%. Such large N deposition due to extreme BB emissions are much (~6-12 times) larger than low-end critical load thresholds for major vegetation types (e.g., forests at 1.5-3 kg ha−1 year−1), and thus may result in adverse N deposition effects across larger areas of lichen communities found in California\u27s mixed conifer forests