Steven J. Greybush

This study presents DustCast, an ensemble machine learning model developed to forecast monthly atmospheric dust concentrations across the Arabian Peninsula. Motivated by the increasing frequency and intensity of dust storms in the region and their associated adverse impacts on health, agriculture, and the environment, the model integrates multiple meteorological and aerosol datasets, including ERA5 reanalysis, MERRA‐2 aerosol diagnostics, and the Indian Ocean Dipole index. The methodology employs a heterogeneous parallel ensemble framework that combines four machine learning techniques: multiple linear regression, K‐nearest neighbors, decision tree, and random forest, with weights assigned based on each model's performance as evaluated by root mean squared error (RMSE). Spatial aggregation facilitates efficient and precise data binning and analysis. Results indicate that multiple linear regression and random forest exhibit superior predictive capabilities among the individual models on the surface, with the aggregated ensemble prediction achieving an RMSE of 0.00972 μg/m ³ and an R ² of 0.887, outperforming each base learner. When applied to the atmospheric column, DustCast derives the majority of the predictive contributions from decision tree and random forest, with the ensemble prediction achieving an RMSE of 0.00550 mg/m ² and an R ² of 0.984. The DustCast ensemble model captures seasonal patterns of dust mobilization across the Arabian Peninsula for both near‐surface and atmospheric column dust concentrations. The model performs particularly well during the summer months (June July and August) when the Shamal wind strongly influences sand and dust storms throughout the region. It also captures seasonal dust events associated with frontal systems and dynamic pressure gradients throughout the year.

Mathematical models have provided a general framework for understanding the dynamics and control of infectious disease. Many compartmental models are limited in that they do not account for the range of behavioral feedbacks that have been observed in the response to emerging infections. Here we expand on the SIR compartmental model framework by introducing a general class of behavioral feedbacks that encompasses both individual responses and nonpharmaceutical interventions. By linking transmission dynamics and behavior, this class of models can capture the interplay of disease incidence, behavioral response, and controls such as vaccination. We prove mathematically the existence of two endemic equilibria depending on the vaccination rate: one in the presence of low vaccination but with reduced societal activity (the "new normal"), and one with return to normal activity but with vaccination rate below that required for disease elimination. Establishing the existence and stability of these equilibria is a precursor to designing control strategies that may exploit them.

Optimization of wind energy integration requires knowing the relationship between weather patterns and winds they cause. For a region with less-studied weather such as the Middle East, climatology becomes more vital. The Shagaya Renewable Energy Park in development in Kuwait experiences regional wind regimes that affect wind power production. Weather Research and Forecasting (WRF) model output allowed investigation into the weather regimes most likely to impact Shagaya Park. The self-organizing maps (SOMs) machine-learning method clustered the WRF output into six primary weather regimes experienced by the Middle East. According to the wind regimes mapped by the SOM, two of the six regimes have average wind speeds of approximately 9.9 and 8.6 m s−1 at 80 m near Shagaya Park, as well as wind speed and estimated wind power distributions that are more favorable to wind power production in Kuwait. One regime depicts a strong northwesterly wind called the summer shamal, and the second is associated with strong westerlies. Regimes less favorable for Kuwaiti wind power production are represented by the remaining four SOM nodes: local weak southeasterlies, an African nocturnal low-level jet, a daytime planetary boundary layer, and local northwesterlies from autumn to spring. The remaining four SOM nodes have average wind speeds of 5.7–7.2 m s−1 and wind speed and estimated wind power distributions which indicate regimes less favorable for wind power production in Kuwait.

Data from rawinsondes launched during intensive observation periods (IOPs) of the Ontario Winter Lake-Effect Systems (OWLeS) field project reveal that elevated mixed layers (EMLs) in the lower troposphere were relatively common near Lake Ontario during OWLeS lake-effect events. Conservatively, EMLs exist in 193 of the 290 OWLeS IOP soundings. The distribution of EML base pressure derived from the OWLeS IOP soundings reveals two classes of EML, one that has a relatively low-elevation base (900–750 hPa) and one that has a relatively high-elevation base (750–500 hPa). It is hypothesized that the former class of EML, which is the focus of this research, is, at times, the result of mesoscale processes related to individual Great Lakes. WRF reanalysis fields from a case study during the OWLeS field project provide evidence of two means by which low-elevation base EMLs can originate from the lake-effect boundary layer convection and associated mesoscale circulations. First, such EMLs can form within the upper-level outflow branches of mesoscale solenoidal circulations. Evacuated Great Lakes–modified convective boundary layer air aloft then lies above ambient air of a greater static stability, forming EMLs. Second, such EMLs can form in the absence of a mesoscale solenoidal circulation when Great Lake–modified convective boundary layers overrun ambient air of a greater density. The reanalysis fields show that EMLs and layers of reduced static stability tied to Great Lakes–modified convective boundary layers can extend downwind for hundreds of kilometers from their areas of formation. Operational implications and avenues for future research are discussed.

Rainfall is an important variable to be able to monitor and forecast across Africa, due to its impact on agriculture, food security, climate‐related diseases and public health. Numerical weather models (NWMs) are an important component of this work, due to their complete spatial coverage, high resolution and ability to forecast into the future. In this study, the spatio‐temporal skill of short‐term forecasts of rainfall across Africa from 2016 through 2018 is evaluated. Specifically, the European Centre for Medium‐Range Weather Forecasts (ECMWF) and the National Centers for Environmental Prediction‐Global Forecast System (NCEP‐GFS) forecast models are verified by Rainfall Estimates 2.0 (RFE2) and African Rainfall Climatology Version 2 (ARC2), which are fused products of satellite and in situ observations and are commonly used in analysis of African rainfall. Model rainfall forecasts show good consistency with the satellite rainfall observations in spatial distribution over Africa on the seasonal timescale. Evaluation metrics of daily and weekly forecasts show high spatial and seasonal variations over the African continent, including a strong link to the location of the inter‐tropical convergence zone (ITCZ) and topographically enhanced precipitation. The rainfall forecasts at 1 week aggregation time are improved against daily forecasts.

Data assimilation (DA) has been applied to estimate the time‐mean state, such as annual mean surface temperature for paleoclimate reconstruction. There are two types of DA for this purpose: online‐DA and offline‐DA. The online‐DA estimates both time‐mean states (analyses) and initial conditions for subsequent DA cycles, while the offline‐DA only estimates the time‐mean analyses. If there is sufficiently long predictability in the system of interest compared to the temporal resolution of the observations, online‐DA is expected to outperform offline‐DA by utilizing information in the initial conditions. However, previous studies failed to show the superiority of online‐DA when time‐averaged observations are assimilated, and the reason has not been investigated thoroughly. This study compares online‐DA and offline‐DA and investigates the relation to the predictability using an intermediate complexity general circulation model with perfect‐model observing system simulation experiments. The result shows that the online‐DA outperforms offline‐DA when the length of predictability is longer than the averaging time of the observations. We also found that the longer the predictability, the more skillful the online‐DA. Here, the ocean plays a crucial role in extending predictability, which helps online‐DA to outperform offline‐DA. Interestingly, the observations of near‐surface air temperature over land are highly valuable to update the ocean variables in the analysis steps, suggesting the importance of using cross‐domain covariance information between the atmosphere and the ocean when online‐DA is applied to reconstruct paleoclimate.

Significance: We created a strategy for understanding the evolution of the COVID-19 pandemic throughout the African continent. Because high-quality mobility data are challenging to obtain across Africa, the approach provides the ability to distinguish cases arising from within a country or from its neighbors. The results further show how testing capacity and social and health policy contribute to the dynamics of cases, and generate short-term prediction of the evolution of the pandemic on a country-by-country basis. This framework improves the ability to interpret and act upon real-time complex COVID-19 data from the African continent. These findings emphasize that regional efforts to coordinate country-specific strategies in transmission suppression should be a continental priority to control the COVID-19 pandemic in Africa.

Lake-effect snowstorms are often observed to manifest as dominant bands, commonly produce heavy localized snowfall, and may extend large distances inland, resulting in hazards and high societal impact. Some studies of dominant bands have documented concomitant environmental baroclinity (i.e., baroclinity occurring at a scale larger than the width of the parent lake), but the interaction of this baroclinity with the inland structure of dominant bands has been largely unexplored. In this study, the thermodynamic environment and thermodynamic and kinematic structure of simulated dominant bands are examined using WRF reanalyses at 3-km horizontal resolution and an innovative technique for selecting the most representative member from the WRF ensemble. Three reanalysis periods are selected from the Ontario Winter Lake-effect Systems (OWLeS) field campaign, encompassing 185 simulation hours, including 155 h in which dominant bands are identified. Environmental baroclinity is commonly observed during dominant-band periods and occurs in both the north–south and east–west directions. Sources of this baroclinity are identified and discussed. In addition, case studies are conducted for simulation hours featuring weak and strong along-band environmental baroclinity, resulting in weak and strong inland extent, respectively. These contrasting cases offer insight into one mechanism by which along-band environmental baroclinity can influence the inland structure and intensity of dominant bands: in the case with strong environmental baroclinity, inland portions of this band formed under weak instability and therefore exhibit slow overturning, enabling advection far inland under strong winds, whereas the nearshore portion forms under strong instability, and the enhanced overturning eventually leads to the demise of the inland portion of the band.

The Ensemble Mars Atmosphere Reanalysis System (EMARS) dataset version 1.0 contains hourly gridded atmospheric variables for the planet Mars, spanning Mars Year (MY) 24 through 33 (1999 through 2017). A reanalysis represents the best estimate of the state of the atmosphere by combining observations that are sparse in space and time with a dynamical model and weighting them by their uncertainties. EMARS uses the Local Ensemble Transform Kalman Filter (LETKF) for data assimilation with the GFDL/NASA Mars Global Climate Model (MGCM). Observations that are assimilated include the Thermal Emission Spectrometer (TES) and Mars Climate Sounder (MCS) temperature retrievals. The dataset includes gridded fields of temperature, wind, surface pressure, as well as dust, water ice, CO₂ surface ice and other atmospheric quantities. Reanalyses are useful for both science and engineering studies, including investigations of transient eddies, the polar vortex, thermal tides and dust storms, and during spacecraft operations.

The ensemble predictability of the January 2015 and 2016 East Coast winter storms is assessed, with model precipitation forecasts verified against observational datasets. Skill scores and reliability diagrams indicate that the large ensemble spread produced by operational forecasts was warranted given the actual forecast errors imposed by practical predictability limits. For the 2015 storm, uncertainties along the western edge’s sharp precipitation gradient are linked to position errors of the coastal low, which are traced to the positioning of the preceding 500-hPa wave pattern using the ensemble sensitivity technique. Predictability horizon diagrams indicate the forecast lead time in terms of initial detection, emergence of a signal, and convergence of solutions for an event. For the 2016 storm, the synoptic setup was detected at least 6 days in advance by global ensembles, whereas the predictability of mesoscale features is limited to hours. Convection-permitting WRF ensemble forecasts downscaled from the GEFS resolve mesoscale snowbands and demonstrate sensitivity to synoptic and mesoscale ensemble perturbations, as evidenced by changes in location and timing. Several perturbation techniques are compared, with stochastic techniques [the stochastic kinetic energy backscatter scheme (SKEBS) and stochastically perturbed parameterization tendency (SPPT)] and multiphysics configurations improving performance of both the ensemble mean and spread over the baseline initial conditions/boundary conditions (IC/BC) perturbation run. This study demonstrates the importance of ensembles and convective-allowing models for forecasting and decision support for east coast winter storms.