Cyclone Amphan effects on the copepods of Ganges estuary

Tropical cyclones are increasingly affecting the estuarine communities. Impacts of category-5 tropical cyclone Amphan (landfall on 20 May 2020 near Ganges estuary mouth) on the copepod community of Muriganga section of Ganges estuary was studied by sampling the copepod assemblages before (February to December 2019), shortly after (31 May to 12 June 2020) and post (September to November 2020) cyclone. Hypothesis was shortly after Amphan a relatively homogenous community consists of a few estuarine specialist copepods would succeed but within months that community would be replaced by a heterogenous one but those estuarine specialists would continue their dominance. Shortly after Amphan, species richness declined but the recovery process completed within months led by herbivorous Paracalanus parvus, omnivorous Bestiolina similis, Acartia spinicauda, Acartiella tortaniformis, and carnivorous Oithona brevicornis. Spatial homogeneity of the community that prevailed in Muriganga in pre-Amphan and shorty after Amphan was lost in post-Amphan. Community composition changed from pre- to shortly after to post-Amphan. Unilateral dominance of B. similis observed in pre-Amphan was challenged by P. parvus, A. spinicauda, A. tortaniformis and O. brevicornis shortly after Amphan and in post-Amphan. Acartia spinicauda proliferated shortly after Amphan and co-dominated the estuary along with A. tortaniformis but the latter replaced the former in post-Amphan. Copepods did rebuild their community within a few months from Amphan but experienced rearrangements of species composition, abundance, dominance hierarchy and feeding guilds, which may strain benthic-pelagic linkages of Ganges estuary so shall be monitored regularly by coastal institutions following uniform methods and best practises.

Dealing with spatial heterogeneity in pointwise-to-gridded- data comparisons

Most studies on validation of satellite trace gas retrievals or atmospheric chemical transport models assume that pointwise measurements, which roughly represent the element of space, should compare well with satellite (model) pixels (grid box). This assumption implies that the field of interest must possess a high degree of spatial homogeneity within the pixels (grid box), which may not hold true for species with short atmospheric lifetimes or in the proximity of plumes. Results of this assumption often lead to a perception of a nonphysical discrepancy between data, resulting from different spatial scales, potentially making the comparisons prone to overinterpretation. Semivariogram is a mathematical expression of spatial variability in discrete data. Modeling the semivariogram behavior permits carrying out spatial optimal linear prediction of a random process field using kriging. Kriging can extract the spatial information (variance) pertaining to a specific scale, which in turn translates pointwise data to a gridded space with quantified uncertainty such that a grid-to-grid comparison can be made. Here, using both theoretical and real-world experiments, we demonstrate that this classical geostatistical approach can be well adapted to solving problems in evaluating model-predicted or satellite-derived atmospheric trace gases. This study suggests that satellite validation procedures using the present method must take kriging variance and satellite spatial response functions into account. We present the comparison of Ozone Monitoring Instrument (OMI) tropospheric NO2 columns against 11 Pandora spectrometer instrument (PSI) systems during the DISCOVER-AQ campaign over Houston. The least-squares fit to the paired data shows a low slope (OMI=0.76×PSI+1.18×1015 molecules cm−2, r2=0.66), which is indicative of varying biases in OMI. This perceived slope, induced by the problem of spatial scale, disappears in the comparison of the convolved kriged PSI and OMI (0.96×PSI+0.66×1015 molecules cm−2, r2=0.72), illustrating that OMI possibly has a constant systematic bias over the area. To avoid gross errors in comparisons made between gridded data vs. pointwise measurements, we argue that the concept of semivariogram (or spatial autocorrelation) should be taken into consideration, particularly if the field exhibits a strong degree of spatial heterogeneity at the scale of satellite and/or model footprints.

Theories on the Formation and Evolution of ~2D Planar Celestial Kinematics

The existence of essentially 2-dimensional planar solar systems and galaxies would seem to be a contradiction to the 2nd Law of Thermodynamics relating to the tendency of natural processes toward spatial homogeneity of matter and energy. During the formation process of celestial systems, an equal dispersion of matter throughout 3-dimensional space would have been a more logical result to satisfy entropy/disorder increasing at all times. Conventional belief is that the ~2D planar geometry of galaxies and solar systems is largely due to rotational kinetic forces and matter collapsing due to its own gravity; this project seeks to expand and enhance the potential forces to explain ~2D planar celestial kinematics. Computational mathematics utilizing programming in C# will analyze various potential forces and relative magnitudes to determine proposed force-balances during these formation processes. A better understanding of the formation process (and the forces that govern them) of galaxies and solar systems can help explain their evolutions to steady state; for this, the derived mathematical models will be computed and translated to visual models in 4-D space-time over various time frames.

Patch individual filter layers in CNNs to harness the spatial homogeneity of neuroimaging data

Convolutional neural networks (CNNs)—as a type of deep learning—have been specifically designed for highly heterogeneous data, such as natural images. Neuroimaging data, however, is comparably homogeneous due to (1) the uniform structure of the brain and (2) additional efforts to spatially normalize the data to a standard template using linear and non-linear transformations. To harness spatial homogeneity of neuroimaging data, we suggest here a new CNN architecture that combines the idea of hierarchical abstraction in CNNs with a prior on the spatial homogeneity of neuroimaging data. Whereas early layers are trained globally using standard convolutional layers, we introduce patch individual filters (PIF) for higher, more abstract layers. By learning filters in individual latent space patches without sharing weights, PIF layers can learn abstract features faster and specific to regions. We thoroughly evaluated PIF layers for three different tasks and data sets, namely sex classification on UK Biobank data, Alzheimer’s disease detection on ADNI data and multiple sclerosis detection on private hospital data, and compared it with two baseline models, a standard CNN and a patch-based CNN. We obtained two main results: First, CNNs using PIF layers converge consistently faster, measured in run time in seconds and number of iterations than both baseline models. Second, both the standard CNN and the PIF model outperformed the patch-based CNN in terms of balanced accuracy and receiver operating characteristic area under the curve (ROC AUC) with a maximal balanced accuracy (ROC AUC) of 94.21% (99.10%) for the sex classification task (PIF model), and 81.24% and 80.48% (88.89% and 87.35%) respectively for the Alzheimer’s disease and multiple sclerosis detection tasks (standard CNN model). In conclusion, we demonstrated that CNNs using PIF layers result in faster convergence while obtaining the same predictive performance as a standard CNN. To the best of our knowledge, this is the first study that introduces a prior in form of an inductive bias to harness spatial homogeneity of neuroimaging data.

Estimating Mean Surface Backscatter from GPM Surface Backscatter Observations

The radar on the Global Precipitation Measurement (GPM) mission observes precipitation at 13.6 GHz (Ku-band) and 35.6 GHz (Ka-band) and also receives echoes from the earth’s surface. Statistics of surface measurements for non-raining conditions are saved in a database for later use in estimating the precipitation path-integrated attenuation. Previous work by Meneghini and Jones (2011) showed that while averaging over larger latitude/longitude bins increase the number of samples, it can also increase sample variance due to spatial inhomogeneity in the data. As a result, Meneghini and Kim (2017) proposed a new, adaptive method of database construction, in which the number of measurements averaged depends on the spatial homogeneity. The purpose of this work is to re-visit previous, single-frequency results using dual-frequency data and optimal interpolation (kriging). Results include that (1) temporal inhomogeneity can create similar results as spatial, (2) Ka-band behavior is similar to Ku-band, (3) the Ku-/Ka-band difference has less spatial inhomogeneity than either band by itself, and (4) kriging and the adaptive method can reduce the sample variance. The author concludes that finer spatial and temporal resolution is necessary in constructing the database for single frequencies but less so for the Ku-/Ka-band difference. The adaptive approach reduces sample standard deviation with a relatively modest computational increase.

Exploration of a singular fluid spacetime

We investigate the properties of a special class of singular solutions for a self-gravitating perfect fluid in general relativity: the singular isothermal sphere. For arbitrary constant equation-of-state parameter $$w=p/\rho $$ w = p / ρ , there exist static, spherically-symmetric solutions with density profile $$\propto 1/r^2$$ ∝ 1 / r 2 , with the constant of proportionality fixed to be a special function of w. Like black holes, singular isothermal spheres possess a fixed mass-to-radius ratio independent of size, but no horizon cloaking the curvature singularity at $$r=0$$ r = 0 . For $$w=1$$ w = 1 , these solutions can be constructed from a homogeneous dilaton background, where the metric spontaneously breaks spatial homogeneity. We study the perturbative structure of these solutions, finding the radial modes and tidal Love numbers, and also find interesting properties in the geodesic structure of this geometry. Finally, connections are discussed between these geometries and dark matter profiles, the double copy, and holographic entropy, as well as how the swampland distance conjecture can obscure the naked singularity.

Combining Navigator and Optical Prospective Motion Correction for High-Quality 500 μm Resolution Quantitative Multi-Parameter Mapping at 7T

PURPOSEHigh-resolution quantitative multi-parameter mapping shows promise for non-invasively characterizing human brain microstructure but is limited by physiological artifacts. We implemented corrections for rigid head movement and respiration-related B0-fluctuations and evaluated them in healthy volunteers and dementia patients.METHODSCamera-based optical prospective motion correction (PMC) and free-induction decay (FID) navigator correction were implemented in a gradient and RF-spoiled multi-echo 3D gradient echo sequence for mapping proton density (PD), longitudinal relaxation rate (R1) and effective transverse relaxation rate (R2*). We studied their effectiveness separately and in concert in young volunteers and then evaluated the navigator correction (NAVcor) with PMC in a group of elderly volunteers and dementia patients. We used spatial homogeneity within white matter (WM) and gray matter (GM) and scan-rescan measures as quality metrics.RESULTSNAVcor and PMC reduced artifacts and improved the homogeneity and reproducibility of parameter maps. In elderly participants, NAVcor improved scan-rescan reproducibility of parameter maps (coefficient of variation decreased by 14.7% and 11.9% within WM and GM respectively). Spurious inhomogeneities within WM were reduced more in the elderly than in the young cohort (by 9% vs 2%). PMC increased regional GM/WM contrast and was especially important in the elderly cohort, which moved twice as much as the young cohort. We did not find a significant interaction between the two corrections.CONCLUSIONNavigator correction and PMC significantly improved the quality of PD, R1 and R2* maps, particularly in less compliant elderly volunteers and dementia patients.

Source and variability of formaldehyde (HCHO) at northern high latitude: an integrated satellite, ground/aircraft, and model study

Here we use satellite observations of HCHO vertical column densities (VCD) from the TROPOspheric Monitoring Instrument (TROPOMI), ground-based and aircraft measurements, combined with a nested regional chemical transport model (GEOS-Chem at 0.5° × 0.625° resolution), to understand the variability and sources of summertime HCHO better in Alaska. We first evaluate GEOS-Chem with in-situ airborne measurements during Atmospheric Tomography Mission 1 (ATom-1) aircraft campaign and ground-based measurements from Multi-AXis Differential Optical Absorption Spectroscopy (MAX-DOAS). We show reasonable agreement between observed and modeled HCHO, isoprene and monoterpenes. In particular, HCHO profiles show spatial homogeneity in Alaska, suggesting a minor contribution of biogenic emissions to HCHO VCD. We further examine the TROPOMI HCHO product in Alaska during boreal summer, which is in good agreement with GEOS-Chem model results. We find that HCHO VCDs are dominated by free-tropospheric background in wildfire-free regions. During the summer of 2018, the model suggests that the background HCHO column, resulting from methane oxidation, contributes to 66–80 % of the HCHO VCD, while wildfires contribute to 14 % and biogenic VOC contributes to 5–9 % respectively. For the summer of 2019, which had intense wildfires, the model suggests that wildfires contribute to 40 to 65 %, and the background column accounts for 30 to 50 % of HCHO VCD in June and July. In particular, the model indicates a major contribution of wildfires from direct emissions of HCHO, instead of secondary production of HCHO from oxidation of larger VOCs. We find that the column contributed by biogenic VOC is often small and below the TROPOMI detection limit. The source and variability of HCHO VCD above Alaska during summer is mainly driven by background methane oxidation and wildfires. This work discusses challenges for quantifying HCHO and its precursors in remote pristine regions.

Dealing with Spatial Heterogeneity in Pointwise to Gridded Data Comparisons

Atmospheric modelers and the trace gas retrieval community typically presuppose that pointwise measurements, which roughly represent the element of space, should compare well with satellite (model) pixels (grids). This assumption implies that the field of interest must possess a high degree of spatial homogeneity within the pixels (grids), which may not hold true for species with short atmospheric lifetimes or in the proximity of plumes. Results of this assumption often lead to a perception of a nonphysical discrepancy between data, resulting from different spatial scales, potentially making the comparisons prone to overinterpretation. Semivariogram is a mathematical expression of spatial variability in discrete data. Modeling the semivariogram behavior permits carrying out spatial optimal linear prediction of a random process field using kriging. Kriging can extract the spatial information (variance) pertaining to a specific scale, which in turn translating pointwise data to a grid space with quantified uncertainty such that a grid-to-grid comparison can be made. Here, using both theoretical and real-world experiments, we demonstrate that this classical geostatistical approach can be well adapted to solving problems in evaluating model-predicted or satellite-derived atmospheric trace gases. This study demonstrates that satellite validation procedures must take kriging variance and satellite spatial response functions into account. We present the comparison of Ozone Monitoring Instrument (OMI) tropospheric NO2 columns against 11 Pandora Spectrometer Instrument (PSI) systems during the DISCOVER-AQ campaign over Houston. The least-squares fit to the paired data shows a low slope (OMI=0.76×PSI+1.18×1015 molecules cm−2, r2=0.67) which is indicative of varying biases in OMI. This perceived slope, induced by the problem of spatial scale, disappears in the comparison of the convolved kriged PSI and OMI (0.96×PSI+0.66×1015 molecules cm−2, r2=0.72) illustrating that OMI possibly has a constant systematic bias over the area. To avoid gross errors in comparisons made between gridded data versus pointwise measurements, we argue that the concept of semivariogram (or spatial auto-correlation) should be taken into consideration, particularly if the field exhibits a strong degree of spatial heterogeneity at the scale of satellite and/or model footprints.