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David N. Whiteman, Kofi Boateng, Sara Harbison, Hadijat Oke, Audrey Rappaport, Monique Watson, Ayomiposi Ajayi, Oluwafisayo Okunuga, Ricardo Forno, and Marcos Andrade

Abstract

For the past four years, four different cohorts of students from the Science and Technology program at Eleanor Roosevelt High School in Greenbelt, MD have performed their senior research projects at the Howard University Beltsville Research Campus in Beltsville, MD. The projects have focused generally on the testing and correction of low-cost sensors and development of instrumentation for use in profiling the lower atmosphere. Specifically, we have developed a low-cost tethersonde system and used it to carry aloft a low-cost instrument that measures particulate matter (PM) as well as a standard radiosonde measuring temperature, pressure and relative humidity. The low-cost PM sensor was found to provide artificially high values of PM under conditions of elevated relative humidity likely due to the presence of hygroscopic aerosols. Reference measurements of PM were used to develop a correction technique for the low-cost PM sensor. Profiling measurements of temperature and PM during the breakdown of a nocturnal inversion were performed using the tethersonde system on August 30, 2019. The evolution of temperature during the breakdown of the inversion was studied and compared with model forecasts. The attempt to measure PM during the tethersonde experiment was not successful due, we believe, to the packaging of the low cost sensor. Future cohorts of students from Eleanor Roosevelt High School students will work on improving the instrumentation and measurements shown here as we continue the collaboration between the Howard University Beltsville Campus and the local school system.

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Ophélia Miralles, Daniel Steinfield, Olivia Martius, and Anthony C. Davison

Abstract

Near-surface wind is difficult to estimate using global numerical weather and climate models, as airflow is strongly modified by underlying topography, especially that of a country such as Switzerland. In this article, we use a statistical approach based on deep learning and a high-resolution Digital Elevation Model to spatially downscale hourly near-surface wind fields at coarse resolution from ERA5 reanalysis from their original 25 km to a 1.1 km grid. A 1.1 km resolution wind dataset for 2016–2020 from the operational numerical weather prediction model COSMO-1 of the national weather service, MeteoSwiss, is used to train and validate our model, a generative adversarial network (GAN) with gradient penalized Wasserstein loss aided by transfer learning. The results are realistic-looking high-resolution historical maps of gridded hourly wind fields over Switzerland and very good and robust predictions of the aggregated wind speed distribution. Regionally averaged image-specific metrics show a clear improvement in prediction compared to ERA5, with skill measures generally better for locations over the flatter Swiss Plateau than for Alpine regions. The downscaled wind fields demonstrate higher-resolution, physically plausible orographic effects, such as ridge acceleration and sheltering, which are not resolved in the original ERA5 fields.

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John R. Albers, Matthew Newman, Andrew Hoell, Melissa L. Breeden, Yan Wang, and Jiale Lou

Abstract

The sources of predictability for the February 2021 cold air outbreak (CAO) over the central United States, which led to power grid failures and water delivery shortages in Texas, are diagnosed using a machine learning-based prediction model called a Linear Inverse Model (LIM). The flexibility and low computational cost of the LIM allows its forecasts to be used for identifying and assessing the predictability of key physical processes. The LIM may also be run as a climate model for sensitivity and risk analysis for the same reasons. The February 2021 CAO was a subseasonal forecast of opportunity, as the LIM confidently predicted the CAO’s onset and duration four weeks in advance, up to two weeks earlier than other initialized numerical forecast models. The LIM shows that the February 2021 CAO was principally caused by unpredictable internal atmospheric variability and predictable La Niña teleconnections, with nominally predictable contributions from the previous month’s sudden stratospheric warming and the Madden-Julian Oscillation. When run as a climate model, the LIM estimates that the February 2021 CAO was in the top 1% of CAO severity and suggests that similarly extreme CAOs could be expected to occur approximately every 20-30 years.

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Walker S. Ashley, Alex M. Haberlie, and Vittorio A. Gensini

Abstract

A supercell is a distinct type of intense, long-lived thunderstorm that is defined by its quasi-steady, rotating updraft. Supercells are responsible for most damaging hail and deadly tornadoes, causing billions of dollars in losses and hundreds of casualties annually. This research uses high-resolution, convection-permitting climate simulations across 15-yr epochs that span the 21st century to assess how supercells may change across the United States. Specifically, the study explores how late 20th century supercell populations compare with their late 21st century counterparts for two—intermediate and pessimistic—anthropogenic climate change trajectories. An algorithm identifies, segments, and tracks supercells in the simulation output using updraft helicity, which measures the magnitude of corkscrew flow through a storm’s updraft and is a common proxy for supercells. Results reveal that supercells will be more frequent and intense in future climates, with robust spatiotemporal shifts in their populations. Supercells are projected to become more numerous in regions of the eastern United States, while decreasing in frequency in portions of the Great Plains. Supercell risk is expected to escalate outside of the traditional severe storm season, with supercells and their perils likely to increase in late winter and early spring months under both emissions scenarios. Conversely, the latter part of the severe storm season may be curtailed, with supercells expected to decrease midsummer through early fall. These results suggest the potential for more significant tornadoes, hail, and extreme rainfall that, when combined with an increasingly vulnerable society, may produce disastrous consequences.

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David A. Peterson, Laura H. Thapa, Pablo E. Saide, Amber J. Soja, Emily M. Gargulinski, Edward J. Hyer, Bernadett Weinzierl, Maximilian Dollner, Manuel Schöberl, Philippe P. Papin, Shobha Kondragunta, Christopher P. Camacho, Charles Ichoku, Richard H. Moore, Johnathan W. Hair, James H. Crawford, Philip E. Dennison, Olga V. Kalashnikova, Christel E. Bennese, Thaopaul P. Bui, Joshua P. DiGangi, Glenn S. Diskin, Marta A. Fenn, Hannah S. Halliday, Jose Jimenez, John B. Nowak, Claire Robinson, Kevin Sanchez, Taylor J. Shingler, Lee Thornhill, Elizabeth B. Wiggins, Edward Winstead, and Chuanyu Xu

Abstract

The 2019 Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) field experiment obtained a diverse set of in situ and remotely sensed measurements before and during a pyrocumulonimbus (pyroCb) event over the Williams Flats fire in Washington State. This unique dataset confirms that pyroCb activity is an efficient vertical smoke transport pathway into the upper troposphere and lower stratosphere (UTLS). The magnitude of smoke plumes observed in the UTLS has increased significantly in recent years, following unprecedented wildfire and pyroCb activity observed worldwide. The FIREX-AQ pyroCb dataset is therefore extremely relevant to a broad community, providing the first measurements of fresh smoke exhaust in the upper troposphere, including from within active pyroCb cloud tops. High-resolution remote sensing reveals that three plume cores linked to localized fire fronts, burning primarily in dense forest fuels, contributed to four total pyroCb “pulses.” Rapid changes in fire geometry and spatial extent dramatically influenced the magnitude, behavior, and duration of pyroCb activity. Cloud probe measurements and weather radar identify the presence of large ice particles within the pyroCb and hydrometers below cloud base, indicating precipitation development. The resulting feedbacks suggest that vertical smoke transport efficiency was reduced slightly when compared with intense pyroCb events reaching the lower stratosphere. Physical and optical aerosol property measurements in pyroCb exhaust are compared with previous assumptions. A large suite of aerosol and gas-phase chemistry measurements sets a foundation for future studies aimed at understanding the composition of smoke plumes lifted by pyroconvection into the UTLS and their role in the climate system.

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Caren Marzban, Jueyi Liu, and Philippe Tissot

Abstract

Resampling methods such as cross-validation or bootstrap are often employed to estimate the uncertainty in a loss function due to sampling variability, usually for the purpose of model selection. But in models that require nonlinear optimization, the existence of local minima in the loss function landscape introduces an additional source of variability which is confounded with sampling variability. In other words, some portion of the variability in the loss function across different resamples is due to local minima. Given that statistically-sound model selection is based on an examination of variance, it is important to disentangle these two sources of variability. To that end, a methodology is developed for estimating each, specifically in the context of K-fold cross-validation, and Neural Networks (NN) whose training leads to different local minima. Random effects models are used to estimate the two variance components - due to sampling and due to local minima. The results are examined as a function of the number of hidden nodes, and the variance of the initial weights, with the latter controlling the “depth” of local minima. The main goal of the methodology is to increase statistical power in model selection and/or model comparison. Using both simulated and realistic data it is shown that the two sources of variability can be comparable, casting doubt on model selection methods that ignore the variability due to local minima. Furthermore, the methodology is sufficiently flexible so as to allow assessment of the effect of other/any NN parameters on variability.

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Xiping Zeng, Andrew J. Heymsfield, Zbigniew Ulanowski, Ryan R. Neely III, Xiaowen Li, Jie Gong, and Dong L. Wu

Abstract

Cloud representation is one of the largest uncertainties in the current weather and climate models. In this article, the observations and modeling of the radiative effect on (cloud) microphysics (REM) from the Arctic to the tropics are overviewed, providing a new direction to meet the challenge of cloud representation. REM deals with the radiation-induced temperature difference between cloud particles and air. It leads to two common phenomena observed at the surface—dew and frost—and impacts clouds aloft significantly, which is noticed via the wide occurrence of horizontally oriented ice crystals (HOICs). However, REM has been overlooked by all of the operational weather and climate models. Based on the bin model of REM and the global distribution of radiative cooling/warming, the observations of REM from several platforms (e.g., aircrafts, field campaigns, and satellites) are coordinated in this article, yielding a global picture on REM. As a result, the picture is compatible with the global distribution of HOICs and other ice crystal characteristics obtained from various clouds on the globe, such as diamond dust (or clear-sky precipitation) in the Arctic, subvisual cirrus clouds in the tropical tropopause layer, and other cirrus clouds from the low to high latitudes. In addition, ice crystals possess relatively strong REM compared to liquid drops because their aspect ratio is usually not one. The global picture on REM can be used by the weather and climate modelers to diagnose their cloud representation biases. It can also be used to improve the atmospheric ice retrieval algorithm from satellite observations.

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Maxim Shrestha, Abhishek Upadhyay, Sugat Bajracharya, Mona Sharma, Amina Maharjan, Kamala Gurung, Omair Ahmad, Bhupesh Adhikary, Bidya Banmali Pradhan, Philippus Wester, and Siva Praveen Puppala
Open access
Kathy Pegion, Emily J. Becker, and Ben P. Kirtman

Abstract

We investigate the predictability of the sign of daily South-East US (SEUS) precipitation anomalies associated with simultaneous predictors of large-scale climate variability using machine learning models. Models using index-based climate predictors and gridded fields of large-scale circulation as predictors are utilized. Logistic regression (LR) and fully connected neural networks using indices of climate phenomena as predictors produce neither accurate nor reliable predictions, indicating that the indices themselves are not good predictors. Using gridded fields as predictors, a LR and convolutional neural network (CNN) are more accurate than the index based models. However, only the CNN can produce reliable predictions which can be used to identify forecasts of opportunity. Using explainable machine learning we identify which variables and gridpoints of the input fields are most relevant for confident and correct predictions in the CNN. Our results show that the local circulation is most important as represented by maximum relevance of 850 hPa geopotential heights and zonal winds to making skillful, high probability predictions. Corresponding composite anomalies identify connections with the El-Niño Southern Oscillation during winter and the Atlantic Multidecadal Oscillation and North Atlantic Subtropical High during summer.

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Kazuya Kusahara, Hiroaki Tatebe, Tomohiro Hajima, Fuyuki Saito, and Michio Kawamiya

Abstract

Changes in the Antarctic ice sheet play a critical role in the Southern Ocean and global climates. Although many studies have pointed out that enhanced ocean heat delivery onto the Antarctic continental shelf regions can cause significant changes in Antarctic ice-shelf basal melting, the associated physical mechanisms require further research. Here, we perform numerical experiments using an ocean-sea ice model with an ice-shelf component to simulate future projections in Antarctic ice-shelf basal melting in a warming climate, focusing on the driving mechanism and the physical linkages with the seasonal Antarctic sea-ice fields and coastal water masses. The model projects a distinct superlinear response of ice-shelf basal melting to future atmospheric warming, demonstrating that future projections of the Antarctic and Southern Ocean climate bifurcate with the level of global warming. Detailed examinations of sea ice and water masses show that in an extreme warming scenario, a combination of enhanced intrusions of warm deep water and warm summertime surface water can cause the nonlinear response of Antarctic ice-shelf basal melting. A large reduction in Antarctic coastal sea ice and the associated ocean freshening by decreasing coastal sea-ice production in winter provide favorable conditions for summertime warm surface water formation and warm deep water intrusions onto some continental shelves. The model results demonstrate that disappearing summertime sea ice along the Antarctic coastal margins in a warming climate heralds the nonlinear increase in Antarctic ice-shelf basal melting, presumably contributing to the negative mass balance of the Antarctic ice sheet and the sea-level rise.

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