Although pressure plays a vital role in the dynamics of the turbulent plasmas, pressure statistics have not been studied as extensively as other plasma properties. The studies that have focused on pressure are mostly based on hydrodynamic turbulence or have been formulated in the nearly incompressible magnetohydrodynamics (NIMHD) framework. However, less attention has been paid to the scaling properties of pressure in kinetic plasma. In this study, we explore the statistics of magnetic, thermal, and total pressure fluctuation in kinetic collisionless turbulence. A 2.5D kinetic particle-in-cell (PIC) simulation of turbulence is used to investigate pressure balance via the evolution of thermal and magnetic pressure. Further, the behavior of thermal, magnetic, and total pressure structure function and their corresponding spectrum is explored. Finally, we evaluate higher-order total pressure structure functions to discuss intermittency and compare the power exponents with higher-order structure functions of velocity and magnetic fluctuations.

The low viscosity of mafic magmas generally promotes effusive to mildly explosive eruptions; however, highly explosive mafic volcanic activity does occur. Recent models suggest that rapid ascent rates leading to significant disequilibrium drastically impact the rheology leading to fragmentation of the mafic magma. However, the magmatic conditions preceding magma ascent initiation are primarily unknown. There is a rich rock and mineral record for understanding melt generation in the mantle wedge and open system processes stored in the juvenile products of these highly explosive mafic eruptions. This study uses the pyroclasts from the Curacautin ignimbrite, a large volume (3.5–4.5 km3 DRE), mafic (50.4-57.3 wt% SiO2) ignimbrite at Llaima volcano in Chile.Of the 68 ignimbrite samples collected and previously analyzed, we targeted four for small volume (5 mg vs. standard 50 mg) characterization via ICPMS: (1) the top and (2) base of the ignimbrite, and the most (3) depleted and (4) enriched samples. We prepped and analyzed ten randomly selected pyroclasts from the highlighted samples (a total of 40 analyses). This study aimed to determine how sampling (e.g., composite vs. individual and the number of samples) impacts the signal of geological processes in trace element compositions. We used the χ2 distribution to test if the observed standard deviation (corrected for noise by subtracting analytical error) of all 40 samples, as well as each sample set’s ten analyzed pyroclasts, is larger than the analytical error. We identified true variability for at least 13 of the 30 trace elements measured for the base of the ignimbrite and enriched end member sample sets and the combined 40 analyses. For the top of the ignimbrite sample and the depleted end member sample, there is true variability in almost all 30 trace elements. Small volume whole rock geochemical characterization of individual pyroclasts detects previously unseen small-scale geochemical variability in compatible and REE elements and constrains new enrichment end-member compositions for geochemical models. Using principal component analysis, we investigate melt source heterogeneity and fractional crystallization as controls on the observed variability.

The number of orbiting satellites has increased significantly and in an unrestricted and unregulated manner in recent years. This trend is expected to continue with ongoing plans from the commercial space sector to build mega-constellations of microsatellites. However, the effect of satellite demise during reentry on Earth’s atmosphere has only been lightly studied, and the long-term impact remains unknown.  This poster presents, to our knowledge, the first Reactive Force Field (ReaxFF) Molecular Dynamics (MD) simulation study on the atmospheric chemical mechanisms and byproducts generated by satellite reentry. Simulations are carried out to resolve chemical reactions and byproducts for Aluminum – a typical satellite structure constituent – under reentry conditions. MD simulation results are used to estimate the presence of oxides and predict the accumulated increase of reentry byproducts in the mesosphere when compared with that from meteoroids entering atmosphere and other natural sources. A methodology to estimate the residence time of these substances in the atmosphere is also presented so as to evaluate the polluting potential of mega-constellations reentry events.

A document by Andrea Corpolongo. Click on the document to view its contents.

Climate change is a major threat to food security. The global population is increasing at a stimulated rate. Wheat and maize are the globally important crops. There is a need to focus on the methods that help to improve crop production. Since, conventional tillage (CT) is the major tillage practice in rain-fed areas. Conservation tillage methods are practiced to conserve soil moisture in order to increase crop productivity. However, the effects of conservation tillage methods under varying soil textures, precipitation and temperature patterns are still unknown. We took data from 119 peer-reviewed published articles and carried a meta-analysis to assess the effects of 3 conservation tillage practices including no tillage (NT), reduced tillage (RT) and subsoil tillage (ST) on precipitation storage efficiency (PSE), soil water storage at crop planting (SWSp), grain yield, evapotranspiration (ET) and water use efficiency (WUE) under varying precipitation and temperature patterns and soil textures in dry land wheat and maize cropping systems. We took conventional tillage as a control treatment and compared it with different types of fallow conservation tillage systems. Compared to conventional tillage (CT), conservation tillage methods overall increased PSE, SWSp, grain yield, ET and WUE by 22.6%, 17.8%, 24.1%, 6.5% and 12.1%, respectively in winter wheat. Among conservation tillage methods, NT had a better performance on SWSp, grain yield and WUE compared to RT and ST. Fine-textured soils showed better response of tillage methods on PSE, SWSp and ET than medium and coarse-textured soils, while medium-textured soils showed greater positive response ratio (RR) of conservation tillage methods on grain yield and WUE. The enhancement of conservation tillage on PSE and grain yield was greater in the regions having mean annual precipitation (MAP) of >600 mm, while crop yield, ET and WUE were greater when MAP was <400 mm. Conservation tillage methods also increased PSE, grain yield and WUE in the regions where mean annual temperature (MAT) was 8-15 ℃, while SWSp was greater when MAT was <8 ℃. In dryland spring maize, conservation tillage overall increased PSE, SWSp, grain yield, ET and WUE by 38.1%, 20.6%, 29.6%, 16.9% and 11.0%, respectively. The regions having medium-textured soils showed better response of tillage methods on PSE, SWSp, ET and WUE, while coarse-textured soils showed greater positive response ratio (RR) of tillage methods on grain yield. Compared to CT, the RR of conservation tillage on PSE, grain yield, ET and WUE was greater when MAP was <400 mm, while SWSp was greater when MAP was 400-600 mm. Conservation tillage also increased PSE, SWSp and ET in the regions where MAT was <8 ℃, while grain yield and WUE were greater when MAT was >15 ℃. We conclude that NT is a global promising practice among all conservation tillage methods to increase soil water storage and crop production under varying precipitation and temperature patterns and soil textures in both winter wheat and spring maize cropping systems.

Cyst nematodes comprise a group of soil-born pests that globally cause severe damage to cash crops like soybeans, cereals, potatoes and sugar beet. Density of nematode cysts in the soil is a key factor to determine the risk for crop cultivation. The nematodes threaten crop production they colonize crop roots and parasitize them, resulting in significant yield losses. Vice versa, development of nematode resistant cultivars require determination of cysts in-vitro to discriminate resistant from susceptible plant genotypes. Determining cysts per soil sample can be achieved by imaging and image processing. After extraction of cysts from soil samples, they are displayed on filter papers, which are traditionally scored by human eye. By developing an imaging setup for this sample type and a related image processing procedure, it was possible to optimize this scoring process. Imaging and image processing is faster, more comparable, and better documented than visual scoring by human operators. The imaging and image processing method was referenced with the traditional rating method as it is common practice in the German agricultural monitoring lab at the Julius Kühn Institute. The counting accuracy was highly comparable to the accuracy of visual counting. The optical sensing method delivers a set of additional data beyond the cyst count for phenotyping nematode populations. These comprise length, width, and area of the cyst that enable analyzing cyst dimensions. Sensing measured dimensions were found to be in accordance to literature data. Moreover, image processing enables analyzing cyst colors, a feature that could be important for age determination.

Presenting Author: [email protected] roll is the most common coherent noise observed in the land seismic data, mainly characterized by low frequency and low velocities. In this study, we are demonstrating the application of a model-based surface wave attenuation technique for the efficient removal of ground roll from 2D seismic data. The method employs inversion and adaptive subtraction filtering of surface waves. A 1D viscoelastic model is characterized by considering layer thickness, P & Sv wave velocities, density, and P & Sv wave quality factors (Qp & Qs) for each layer, to compute a synthetic seismogram. A linear Radon transform is used to generate dispersion spectra of input shot gather and synthetic shot gather generated from the model. The misfit between the dispersion spectra of input shot gather and synthetic shot gather is given by an objective function ‘J’,\(J\left(m\right)=\ {|\left|s-o\right||}_{2}\), where ‘s’ is the dispersion spectrum of the synthetic data generated by the model, ‘o’ is the dispersion spectrum of the shot gather and \({||.||}_{2}\)is the L2-norm of the difference between ‘s’ and ‘o’. The main focus is to minimize the objective function J. The local minima always seems to be the bottleneck for most of the non-linear inversion problems. To overcome this constraint, we implemented the Genetic Algorithm (GA) method in the surface-wave inversion process using the tool available in a commercial software. GA is a derivative-free search approach towards global minima, for the solution of the inversion problem. Once an optimized model is achieved from GA, synthetic data is generated and is adaptively subtracted from the shot gather. As the 1D model obtained cannot represent any lateral variations of physical parameters in the earth, adaptive subtraction and GA are implemented several times to mitigate this limitation.The proposed technique is applied to process a 2D shot gather data (Fig. 1-a) accessed from the SEG wiki Open data set collection of oz Yilmaz-40 shots. In this data, ground roll is masking the reflection events in the shot gather. After implementation of the proposed technique, it is observed that the ground roll is efficiently attenuated and reflections became more prominent by restoring the actual amplitudes, as shown in Fig. (1-b). The dispersion spectrum of both shot gathers is shown in Fig. 1 (c-d), it is observed that the ground roll present in the low-frequency zone (0-15 Hz) is attenuated. The results of the study suggest that the proposed method is quite efficient for surface wave attenuation.

Rapid urbanization has caused extensive land use changes, which may impact the local climatology and trigger extreme rainfall events. Wind, another important factor in rainfall, has been examined to determine its role in generating extreme rainfall in conjunction with land use changes. This study evaluates the variability of extreme rainfall in the Dallas Fort-Worth (DFW) area. The study area has been divided into 256 equal sized grids and grouped into 4 clusters. Spatial Synoptic Classification (SSC) technique has been adopted to select days with benign synoptic activity so that the role of large-scale weather patterns is reduced. Extreme rainfall days are defined as those exceeding the 95th percentile threshold of the daily rainfall observed at the DFW airport. After filtering data based on SSC and extreme rainfall criteria, 114 days are selected for the study period from 2000-2016. The Multi-Radar Multi-Sensor rainfall product has been used to examine the variability of extreme rainfall in grids and clusters. The variation in extreme rainfall for different land covers (e.g., high, medium, and low intensity developed land, and open space) has also been examined. The results reveal that the “Urban Core” cluster receives maximum rainfall across the study period. Also, the difference in cumulative maximum rainfall between highly urbanized grids and the lowest urbanized grid is highest in the “Urban Core” cluster. The grid with the highest percentage of “High intensity developed land” received the most rainfall. The wind regime associated with the most rainfall across the study period is from the South-West. Less urbanized grids located downwind of the highest urbanized grids received relatively more rainfall. Urbanization and the magnitude of extreme rainfall is proportional in most of the grids. The role of the Urban Heat Island will also be examined to determine its impact on extreme rainfall variability.  

Near Surface Air Temperature is an important climatic variable that affects the hydrological response of a river basin, and forms an input to most of the hydrological models. General Circulation Models (GCMs) simulate the response of temperature and other climate variables to the variations in emission concentrations, but their outputs are too coarse to be used in most hydrological models. A multi stage statistical downscaling approach is proposed for downscaling GCM predicted temperatures. In the first stage the Relevance Vector Machine (RVM) is used to develop a statistical model between the GCM simulated historical climate variables and the observed historical temperature for spatially downscaling monthly GCM simulations. A weather generator is then used to generate daily data from the spatially downscaled temperature data. On fine scales, lack of correlation between precipitation and temperature data used for hydrological modelling can lead to large uncertainties in the generated hydrological series. Thus, a distribution free post processing is performed for reproducing the observed regional correlation between temperature and precipitation, in the generated temperature data. The methodology is then applied to the Bharathapuzha catchment in Kerala, India, to downscale temperature from the climate models BNU-ESM, CESM1-BGC, CMCC-ESM2, FGOALS-G2, FIO-ESM-2.0 and MIROC4h. The statistical models set up using RVM show consistent performance during the calibration (1969-1980) and validation (1981-2005) phases, with Nash-Sutcliffe efficiency (NSE) between 0.64 to 0.83. The weather generator is then run to generate daily temperature data from the monthly downscaled series. Across the different climate models, daily maximum temperature is generated with RMSE between  2.5°C to 3.3°C, while the minimum temperature has RMSE ranging from 1.7°C to 2.0°C. The probabilistic nature of the procedure enables the generation of multiple series from the same set of predictors. The simulation band from the multiple GCMs is studied for the period 2016 to 2021 to understand the deviation in predicted temperature for the future scenario. The prediction band for maximum temperature has an average band width of 6.7°C and for minimum temperature, the average band width is 4.9°C.

A document by PERUMALA RAJU. Click on the document to view its contents.

The Planetary Data System (PDS) maintains archives of data collected by NASA missions that explore our solar system. The PDS Cartography and Imaging Sciences Node (Imaging Node) provides access to millions of images of planets, moons, and other bodies. Given the large and continually growing volume of data, there is a need for tools that enable users to quickly search for images of interest. Each image archived at the PDS Imaging Node is described by a rich set of searchable metadata properties, such as the time it was collected and the instrument used. However, users often wish to search on the content of the image to find those images most relevant to their scientific investigation or individual curiosity.  To enable the content-based search of the large image archives, we utilized machine learning techniques to create convolution neural network (CNN) classification models. The initial CNN classification results for rover missions (i.e., Mars Science Laboratory and Mars Exploration Rover) and orbiter missions (i.e., Mars Reconnaissance Orbiter, Cassini, and Galileo) were deployed at the PDS Image Atlas (https://pds-imaging.jpl.nasa.gov/search) in 2017. With the content-based search capability, users of the PDS Image Atlas can search using a list of pre-defined classes and quickly find relevant images. For example, users can search “Impact ejecta” and find the images containing impact ejecta from the archive of the Mars Reconnaissance Orbiter mission.  All of the CNN classification models were trained using the transfer learning approach, in which we adapted a CNN model pretrained on Earth images to classify planetary images. Over the past several years, we employed the following three techniques to improve the efficiency of collecting labeled data sets, the accuracy of the models, and the interpretability of the classification results:·      First, we used the marginal-probability based active learning (MP-AL) algorithm to improve the efficiency of collecting labeled data sets.·      Second, we used the classifier chain and ensemble approaches to improve the accuracy of the classification results. ·      Third, we incorporated the prototypical part network (ProtoPNet) architecture to improve the interpretability of the classification results.

Roots are the "hidden half" of the plants, which play an essential role in plant development by drawing water and nutrients from the soil and providing anchorage to the plants. Global warming alters soil properties that limit root development and produces drought-like conditions that reduce water and nutrient uptake by the plant roots, ultimately affecting plant growth. It is, therefore, necessary to study Root System Architecture (RSA) traits and breed plants with better root architecture capable of resisting these conditions. RSA comprises various root traits including total root length, root angle, number of lateral roots, and many other traits. Due to the challenges in phenotyping these traits, the research on RSA is lacking behind. In this project, we are studying RSA traits in 500 late-maturity soybean accessions based on germinating the seeds on germination papers, transferring them to blue blotting papers, and imaging the roots 21 days after transfer. Image analysis for the root traits will be carried out using RootNav2.0 software. These phenotypic data and SNPs selected from Illumina Infinium SoySNP50K iselect SNP Beadchip will be used to perform Genome-wide Association Studies (GWAS) using Genome Association and Prediction Integrated Tool (GAPIT) package in R to identify genomic loci associated with each trait. The results from this study will help in discovery of new QTLS or genes that play a key role in root system architecture and help in breeding novel cultivars that are resistance to changing climate.

Typhoon In-Fa hit continental China in July 2021 and caused an unprecedented rainfall amount, making it a typical case to examine the ability of numerical models in forecasting landfalling typhoons. The record-breaking storm was simulated using a 3-km-resolution weather research and forecast (WRF) model with spectral bin microphysics scheme (BIN) and two-moment seven-class bulk parameterization scheme (BULK). The simulations were then separated into three different typhoon landfall periods (i.e., pre-landfall, landfall, and post-landfall). At present, the ability of WRF and other mesoscale models to accurately simulate the typhoon precipitation hydrometeors is still limited. To evaluate the performances of BIN and BULK schemes of WRF model in simulating the condensed water in Typhoon In-Fa, the observed microwave brightness temperature and radar reflectivity from the core observatory of Global Precipitation Mission (GPM) satellite are directly used for validation with the help of a satellite simulator. It is suggested that BIN scheme has better performance in estimating the spatial structure, overall amplitude, and precise location of the condensed water in typhoons before landfall. During typhoon landfall, the performance of BIN scheme in simulating the structure and location of the condensate is close to that of BULK scheme, but the condensate intensity prediction by BIN scheme is still better; BULK scheme performs even better than BIN scheme in the prediction of condensate structure and location after typhoon landfall. Both schemes seem to have poorer performances in simulating the spatial structure of precipitation hydrometeors during typhoon landfall than before/after typhoon landfall. Moreover, BIN scheme simulates more (less) realistic warm (cold) rain processes than BULK scheme, especially after typhoon landfall. BULK scheme simulates more cloud water and larger convective updraft than BIN scheme, and this is also reported in many model studies comparing BIN and BULK schemes.

A document by Balaji Senapati. Click on the document to view its contents.

Mapping global vertical vegetation structure (VS) is critical for the quantification of global carbon stocks. While orbital LIDAR measurements of NASA’s Global Ecosystem Dynamics Investigation (GEDI) mission provide direct estimates of vertical VS, they only cover 4% of the global land surface. Recent works have produced global contiguous maps of canopy height using convolutional neural network (CNN) models and satellite imagery. However, these models faced some challenges estimating high canopy heights (>30 m) and identified some tiling artifacts (Lang et al., 2022; Potapov et al., 2020).We present various methods to address these limitations. First, we remove tiling artifacts by using overlapping tiles when producing maps and removing zero-padding from CNN architectures. Second, we compare the benefits and limitations of a few different methods to improve high canopy height estimation. Among the methods is histogram matching predicted heights to nearby LIDAR measurements. Another is learning a calibration model to correct each pixel based on similar known measurements nearby. Finally, we borrow recent advances in deep depth completion from the autonomous driving field to create an integrated model that uses known values to improve the prediction map. To demonstrate these methods, we map global vertical VS with a CNN model at 1km resolution using observations from Landsat, L-band synthetic aperture radar observations from the Advanced Land Observing Satellite (ALOS) PALSAR-2, and surface topography. For model training, GEDI relative height metrics are filtered and aggregated into 1km grids. We also use measurements from the Ice, Cloud and land Elevation Satellite 2 (ICESat-2), inter-calibrated with GEDI using co-located measurements. Finally, we apply the aforementioned corrective methods to the product, reporting global RMSE and MAE metrics, as well as visual qualitative observations. These results open the path for unsaturated global vertical VS products at higher 500 m, 200 m, and 100 m resolutions.

The ILRS contribution to ITRF2020 is a time series of weekly/bi-weekly SINEXs with station position estimates and EOP, from 7-day arcs (1993.0 – 2021.0) and 15-day arcs over 1983.0- 1993.0. Each solution was obtained as the combination of loosely constrained individual solutions from the seven ILRS Analysis Centers: ASI, BKG, DGFI, ESA, GFZ, JCET and NSGF. Everyone followed strict standards agreed within the ILRS Analysis Standing Committee (ASC) and used SLR data from LAGEOS, LAGEOS-2, Etalon-1 and Etalon-2, (LAGEOS-only from 1983 to 1992). The ILRS ASC devised an innovative approach in handling systematic errors in the network, never before utilized. After a 5-year pilot-project documented in Luceri et al., (2019). The Station Systematic Error Monitoring PP (SSEM), delivered a series of long-term mean bias estimates for each station, the time intervals of applicability and their statistics. They were derived from freely adjusted station position and EOP solutions for the period 1993.0 to 2020.5, using the latest satellite CoG model. The simultaneous estimation of the station heights and measurement biases resulted in a self-consistent set of weekly bias estimates for each site. Breaks and “jumps” were used to decne the periods of applicability and to calculate the mean bias and its standard deviation. The mean biases were pre-applied in the re- analysis, limiting the remaining jitter of the bias to negligible level. This approach strengthened the estimation process without a compromise of the cnal results’ accuracy. As a result, the ILRS contribution to ITRF2020 minimized the scale difference between SLR and VLBI to below 2 mm (ITRF2014 ~9 mm). We present an overview of the procedures, models, the improvement over previous ILRS products, focusing especially on the Core ILRS sites, and an overview of how the new model has been implemented in support of the ILRS ogcial products.

Identification of global methane (CH4) sources is critical to the quantification and mitigation of this greenhouse gas. Future imaging spectrometer missions, such as Carbon Mapper, will provide global, spatially resolved observations that will make it possible to accurately map methane sources. However, the sheer data volume of these missions make manual source identification infeasible, and expected artifacts in matched filter methane plume identification challenge simple thresholding. Recent works have demonstrated the feasibility of Convolutional Neural Networks (CNNs) for plume detection; however, in the past, these models have suffered from high false positive rates and were limited in their training and evaluation to individual flight campaigns.We have assembled quality-controlled tiled datasets from three Airborne Visible/Infrared Imaging Spectrometer Next Generation (AVIRIS-NG) campaigns: a 2020 California campaign (“COVID”), a 2019 Texas Permian Basin campaign (“Permian”), and another 2018 California campaign (“CACH4”). These datasets are notable for their diversity of surface conditions, spatial resolutions, and source types (oil & gas, energy, waste, livestock). Labeled methane sources in these datasets have been manually verified, and flightlines with systematic artifacts have been filtered out. We trained a GoogLeNet CNN classifier model on each of these campaigns to evaluate intra- and inter- campaign performance. We also trained a model on all three campaigns and evaluated its performance on each dataset. We observed an F1 performance of 0.7 or greater for each model trained and evaluated on its own dataset. We also observed that the model trained on all three datasets often outperforms individual models on multiple metrics. Finally, we converted the model into a fully convolutional network (FCN) for methane plume saliency map generation. We plan to extend this work to datasets acquired by the Global Airborne Observatory (GAO) and prepare a model for deployment for the Carbon Mapper orbital data product pipeline.

The Gravity Recovery and Climate Experiment (GRACE) data help to determine the total water storage anomalies (TWS) across the global scale. The various other important components such as Groundwater storage (GWS) and evapotranspiration for the region of South –East Asia have been determined. With the study of the gravity variation across the globe the long-term changes in the hydrological cycle can be determined which can be related to climate science or the influence of anthropogenic activities. The variation between the Groundwater storage (GWS) and the Total water storage (TWS) of the study area has been calculated for the pre and post-monsoon season of the study area. The variation between groundwater storage and total water storage can be visualized through geospatial analysis. Therefore, the regions with a substantial decrease in water storage can be related to various climate and anthropogenic factors hence implying a sustainable use of groundwater as a resource. Keywords: Machine Learning, Remote Sensing, Groundwater Recharge, Climate science.