A document by Sarah C Kavassalis. Click on the document to view its contents.

As the climate warms and the transition from a perennial to a seasonal Arctic sea-ice cover becomes imminent, knowledge of melt ponding is central to understanding changes in the new Arctic. The Density Dimension Algorithm for Bifurcating Sea-Ice Reflectors (DDA-bifurcate-seaice), applied to NASA’s Ice, Cloud and land Elevation (ICESat-2) photon data, has the ability to provide measurements and monitoring of the onset of melt in the Arctic and on melt progression through its ability to detect multiple surfaces within the photon cloud. The DDA-bifurcate-seaice detects and characterizes melt ponds by retrieving two surface heights, pond surface and bottom, and measurements of depth and width of melt ponds at near sensor resolution. Ponds are generally missed in the standard ICESat-2 Sea Ice Height data product (ATL07) due to its lower resolution and inability to detect multiple surfaces.

A document by Chenyang Wei. Click on the document to view its contents.

We developed a data-driven prediction system for precipitation (Pr) and near-surface temperature (T2m) over the global domain by utilizing NASA’s satellite observations and the associated reanalysis products, with the focus on S2S hydrologic prediction. Our approach is based on a well-established methodology of linear inverse modeling modified and adapted by our science team for high-resolution modeling of precipitation. The key element of this new methodology is the usage of a so-called pseudo-precipitation (PP) variable, equal to the actual Pr in the case the precipitation is occurring and, otherwise, equal to the (negative) air-column integrated water-vapor saturation deficit (the amount of water vapor to be added to the air column to achieve saturation at each vertical level). The model Pr and T2m forecasts are then validated against the observed fields as usual. It is in part through the T2m modeling that the S2S predictability associated with low-frequency dynamical climate modes would filter in the potential S2S precipitation prediction skill within the proposed framework. The system above was shown to be an efficient tool for simulating independent sequencies of daily evolution of the global T2m and Pr fields with spatiotemporal characteristics strikingly similar to the observed characteristics. We are currently evaluating its predictive capabilities. We hypothesize that: (i) the resulting data-driven prediction system may rival the S2S skill of state-of-the-art NWP models in predicting extreme events in widely diverse precipitation environments; and that (ii) it will do so due to its ability to capture tropical low-frequency modes, as well as tropical-to-extratropical teleconnections in the context of a globally connected, seamless multi-scale model. Our goals are to demonstrate both the proposed model skill and its internal dynamical sources, as well as to map out the ensuing implications of these results for hydrologic predictions throughout the globe.

This study characterizes the changes produced by tropical cyclones (TCs) over the North West shelf of Australia on the ocean structure and ocean thermal energy budget using a 20-year composite of cyclone data (1996-2016) created from Bluelink ReANalysis data (BRAN). Part 2 focuses on assessing the effects of season and TC intensity and translation speed on the development of temperature anomalies and the associated ocean heat content (OHC) changes. Stronger cold anomalies develop at the surface over locations with a shallow barrier layer, for strong and slow-moving TCs. The period of the year influences the recovery of the surface anomalies, which happens faster between December and February. The warm anomalies in the subsurface are stronger for more intense TCs and over locations with a shallow barrier layer, as the vertical mixing processes are more efficient. During the passage of the TC the majority of the OHC losses are located beneath the TC core and in the surface layer, while the OHC increases in the subsurface layer between 500 and 1000 km from the TC centre. When the temperature anomalies start to recover, the OHC increases at a stronger rate in the surface layer due to the air-sea heat fluxes and in the TC core region where the strong upwelling relaxes to downwelling. A month after the TC passage there is an overall increase in the OHC of the region after strong TCs and TCs that happen later in the season, while weak TCs cause an overall decrease in the OHC.

The Gridded Surface Subsurface Hydrologic Analysis (GSSHA) model is an integrated system of physics-based hydrologic process models. This approach of using a system of physics-based models allows GSSHA to be a good fit for many watershed management decision support applications, such as understanding changing flood dynamics from urban growth as well as using natural and nature-based solutions, such as wetlands or artificial beaver dams, to ameliorate flooding potential. One of the key roles GSSHA has played is modeling compound coastal storm flooding – extreme rainfall combined with storm surge from tropical systems. Simulating such events requires coupling of overland, riverine, coastal, and weather modeling systems. A challenge facing the USACE modeling community is enabling the programmatic connections between models to reduce manual input errors and increase the throughput in time-sensitive situations. In this talk we will discuss how GSSHA has been modernized to enable programmatic-level access to GSSHA. This allows GSSHA to be used in systems of computational models as well as opens up GSSHA to a wide level of data science integration applications, particularly as a part of the NextGen Framework.

A document by Kavya Johny. Click on the document to view its contents.

A document by Nicole Read. Click on the document to view its contents.

A comprehensive full-waveform inversion model of the crust and mantle covering nearly the entire tectonic domain of the western Pacific (FWP24) is developed by the using optimized many-core version of SPECFEM-GLOBE on the new generation Sunway supercomputer. Taking the global adjoint tomography model GLAD-M25 as the initial model, the three-component seismograms from 1,228 earthquakes recorded at 3,687 stations are employed in iterative gradient-based inversions for three period bands: 40-100s, 17-40s, and 10-60s. A total of 36 iterations are carried out using the conjugate gradient method to update the velocities of horizontally and vertically polarized P-waves and S-waves (Vph, Vpv, Vsh, and Vsv) in the FWP24 model. This process systematically reduces the phase difference between the synthetic and observed seismograms within the phase measurements. Compared with the existing inversion results, the FWP24 model realizes a wider, more continuous and higher resolution inversion range, including all subduction zones in the western Pacific (e.g., Kurile-Japan, Izu-Bonin-Mariana, New-Britanin-Solomon, New-Hebrides, Tonga-Kermadec, etc.). Furthermore, it reveals more detailed structures particularly in oceanic regions around the Philippine Sea Plate, the Caroline Sea Plate and the Ontong-Java Plateau by applying more seismic rays. Overall, the high-resolution and wide-range FWP24 model not only supports for the study of the deep structure of the mantle in the study area, but also offers reliable insights for the study of the subduction dynamics in the western Pacific.

Lidar is an established bathymetric technique but separating the shallow-water surface and bottom returns remains difficult. The Montana State University Fish Lidar is a dual-polarization lidar instrument that was developed primarily to survey freshwater fisheries. In May 2024 we flew this lidar at Neets Bay outside Ketchikan, Alaska to investigate using polarization retrieval to improve bathymetric retrievals in shallow water. The surface return appears primarily in the co-polarized channel, while appearing only weakly in the cross-polarized channel. Conversely, the bottom return typically appears most strongly in the cross-polarized channel. Thus, polarization presents an intriguing and relatively straightforward technique to resolve overlapping surface and bottom returns. Initial results will be presented and findings discussed.

Prado Dam, located near Los Angeles California, is used to control flows for water supply and flood control in the Sana Ana River with a basin area of 6,216 km2. Existing operations at Prado Dam are supported by streamflow forecasts issued by the California Nevada River Forecast Center (CNRFC). The CNRFC uses a well-tested set of semi-lumped models developed in the 1970s to simulate and predict flows upstream of Prado as well as Prado reservoir inflows. The models are calibrated to current watershed conditions and executed in a modern forecasting framework. Nonetheless, there remains a potential to improve streamflow forecasts using a contemporary physics-based, gridded hydrologic model that can be coupled with a mesoscale weather forecast model run on a similar scale (West-WRF), which may perform better in an arid region like the Santa Ana River Watershed where there are a limited number of events for model tuning. Additional benefits for reservoir operations may be derived from integrated simulation of the watershed, streams, and reservoir. To test this hypothesis, the Santa Ana River watershed and Prado Dam were simulated with the Gridded Surface Subsurface Hydrologic Analyses (GSSHA) model, as an alternative to other watershed models such as those used by the CNRFC. The model is calibrated/verified to observed streamflows and changes in reservoir volume using observed rainfall data, then tested against observed reservoir inflows for an extended simulation period. Utilizing changes in reservoir volume in the calibration allowed for better estimates of reservoir volume without significantly deteriorating flow estimates. The GSSHA model, developed for larger flood events, outperformed the current system for larger events, while performing comparable for other events, indicating it might provide additional information in support of reservoir operations An experimental operational model was developed to run on the Center for Western Weather and Water Extremes (CW3E) computer resources using a West-WRF forecast; output is downloaded to and displayed on the US Army Corps of Engineers UMIP system, which shows all types of measured and simulated hydrologic data online. Pending funding, this system could be operationalized to provide additional reservoir operational guidance, especially during extreme events.

The Isocon method is a widely used and efficient approach for mass-transfer calculations. However, it faces significant challenges, including the reliable identification of immobile elements, selection of parental source compositions, and effective visualization. This paper aims to establish a general principle for selecting reference immobile elements. It demonstrates that the Isocon diagram cannot guarantee the accurate identification of real immobile elements and clarifies the meanings of ∆𝐶𝐶 𝑚𝑚 and ∆𝐶𝐶 𝑚𝑚 𝐶𝐶 𝑚𝑚 𝑂𝑂. Importantly, mobile elements with identical mobility align along the same line in an Isocon plot, complicating the selection of immobile elements. For immobile elements, ∆𝐶𝐶 𝑚𝑚 and ∆𝐶𝐶 𝑚𝑚 𝐶𝐶 𝑚𝑚 𝑂𝑂 both equal zero, and their partition coefficients approach infinity. This study provides alternative masstransfer calculation methods without requiring knowledge of original concentrations, based on the equation ∆𝐶𝐶 𝑚𝑚 = (𝜀𝜀 − 1) 𝐶𝐶 𝑚𝑚 𝐴𝐴 𝐷𝐷 𝐴𝐴 dis = (𝜀𝜀 − 1)𝐶𝐶 𝑚𝑚 𝑑𝑑𝑑𝑑𝑑𝑑 = (𝜀𝜀 − 1)𝐶𝐶 𝑚𝑚 𝐴𝐴 𝐷𝐷 𝑑𝑑𝑑𝑑𝑑𝑑/𝐴𝐴. The principle for selecting immobile elements is to prioritize those with partition coefficients closest to infinity. To validate the proposed approach, datasets from magmatic, metamorphic, ore-forming, and theoretical environments were analyzed. The results confirm the approach's reliability and emphasize the critical role of high-quality chemical analyses in mass-transfer studies.

A document by Mary Keller. Click on the document to view its contents.

Variability of the Madden-Julian Oscillation (MJO) inferred from tropical in-situ observations during 1940-2023 is examined, and compared to that derived from reanalyses. MJO assessments are challenging because characterizing MJO behavior outside of the satellite era suffers from a lack of detailed information about its observed state, remote signals and often relies on climate models’ typically poor MJO representation. This study shows that, while tropical soundings are spatially and temporally sparse, they are still useful for better understanding multi-decadal MJO amplitude variability. The similarities between variability of the MJO derived from (imperfect) reanalysis products and (sparse) tropical station observations suggest that decadal changes during the last 60 years are physical and not necessarily related to changes in the observational network. The larger differences in MJO amplitude between station and reanalysis prior to 1960 suggest that sparse tropical observations are a barrier to fully characterizing long-term MJO evolution during the 20th century.

In the pursuit of advancing environmental monitoring technologies to measure toxic air contaminants, our research introduces a novel, cost-efficient Spark-Induced Breakdown Spectroscopy (SIBS) instrument engineered for the detection and quantification of toxic metal aerosols. This instrument, named TARTA 2.0 (Toxic metal Aerosol Real Time Analyzer version 2.0), has been developed recently with a comprehensive optimization of critical experimental parameters. Notably, our improvement includes a combined electrode holder and ground electrode with an increased surface area to improve particle collection efficiency. The intensity of the spark generated by this new system is strong enough to be directly captured by optical fibers, allowing us to remove the previously used optical lenses and create a more compact, user-friendly device. This compact device features an 8” x 10” footprint and weighs only 10 lbs, and it can be powered by a 12 VDC battery. With the new design, we have conducted experiments to optimize the observation time delay, electrode separation distance, and ablation voltage, all aimed at enhancing the instrument's analytical performance. Additionally, the efficacy of the instrument has been validated through the analysis of nebulized elemental standard solutions, showcasing its reliable capacity and sensitivity to detect critical metals with improved Limit of Detections (LODs) such as 1.736 ng/m3 for Co, 2.11ng/m3 for Cr, 1.97 ng/m3 for Li, 5.18ng/m3 for Be, 2.07ng/m3 for V, 1.037ng/m3 for Mn, 1.42 ng/m3 for Zn, 3.81 ng/m3 for Mg, and 5.9 ng/m3 for Fe in laboratory experiments. Our findings support the promising application of TARTA in field monitoring of air quality, heralding a new era of accessible and efficient monitoring of hazardous air pollutants, ultimately protecting public health against toxic metal exposure. Further research will be conducted to extend the spectrum of detectable pollutants, refine the quantification model, and validate the instrument across varied ambient environmental matrices. 

We have recently argued that a simple nonlinear law could be important for the origin of the Universe resulting in fractal or multifractal features [1]. Various fractal scaling models of the large-scale mass distribution have already been proposed. The expected universal multifractal function for galaxies is similar to that identified by NASA's Voyager mission in the Solar System [2]. Now we apply the similar method to determine a reliable multifractal spectrum of distribution of galaxies on cosmological scales, based on selected observations from a million of galaxies in the Redshift Catalog (June 2008) [3]. We show that the observed spectrum is consistent with the weighted oneor two-scale Cantor set models characteristic for turbulence in laboratory and inside the Sun's heliosphere immersed in the very local interstellar medium. However, the total degree of multifractality ∆ ≈ 0.2 is much smaller then that inside the heliosphere. This would be characteristic for a simple linear fractal scaling of galaxy distribution, but somewhat varying for nearby (∆ ≈ 0.1) and the most remote galaxies (∆ ≈ 0.2) receding from our Solar System. The parameters p ≈ 0.45 and λ ≤ 1/2 for one-scale model are apparently related to some voids in the large-scale distribution of matter. A possible asymmetry (A ≈ 0.75) of the total spectrum for two-scale weighted Cantor set (A ̸ = 1) could admittedly be attributed to some deviations from the Hubble's law for the ideal uniform expansion of the Universe.

EISCAT3D, which is under its final stage of construction, will be the first incoherent scatter radar (ISR) system to provide the three-dimensional ion velocity across hundreds of kms in vertical and horizontal directions. This presents a tremendous opportunity to study the three-dimensional nature of ionospheric electrodynamics. Here we present a data-driven regional model of the electric field based on the EISCAT3D observations, where the measured F-region ion velocity data are fitted to a regional electric potential produced by a grid of spherical elementary systems. Performance of the model is demonstrated using simulated ionospheric parameters obtained from the three-dimensional GEMINI model. To simulate realistic radar measurement of the ion velocity, error estimates obtained from the e3doubt package are added to the ground truth GEMINI data. Our model can be used either with multistatic or monostatic measurements of the ion velocity, and it can also integrate ion velocity data from other platforms, such as satellite sensors, into existing ISR measurements. The model captures the ground truth electric field including its complex spatial structure with average percentile differences of about 1.5%. Most accurate results are achieved with the multistatic data, but the general spatial structure of the electric field can be captured also with monostatic data, if optimal beam patterns and regularization are used. The modeling method is also applied using real monostatic line-of-sight ion velocity data measured by the Poker Flat ISR. The modeled electric field shows reasonably well-behaved variations in latitude and longitude within the radar’s field of view.

NASA’s Orbiting Carbon Observatory-2 (OCO-2) has the goal of accurately measuring column-averaged dry-air mole fractions of carbon dioxide (XCO2). In order to fit the measured radiances, many parameters besides CO2 are included in the optimal estimation state vector, including atmospheric water vapor and temperature. The current operational XCO2 retrieval algorithm (V11) solves for a multiplicative scaling factor on an a priori water vapor profile and an additive offset on an a priori temperature profile. However, simulations have indicated that water vapor and temperature each have 1.5-3 degrees of freedom in the vertical column. This means that the retrieval is limited in its ability to fit the true profiles of temperature and water vapor. Here, we use singular value decomposition (SVD) to determine the three most explanatory profile ”shapes” of water vapor and temperature error, then retrieve a single scaling factor applied to each shape. We assess retrieval errors by comparing to the Total Carbon Column Observing Network (TCCON) and a collection of CO2 models. We find that after applying quality filtering using Data Ordering Genetic Optimization (DOGO) and a custom bias correction, the scatter of the XCO2 error versus TCCON is reduced from 1.02 to 1.01 ppm (2.3% reduction in variance) for land glint observations, 1.04 to 0.96 ppm (14.5% reduction in variance) for land nadir observations, and 0.68 to 0.66 ppm (4.7% reduction in variance) for ocean glint observations. We also see a small improvement in the agreement between OCO-2 XCO2 and CO2 models over oceans and the Amazon.