Automated Detection of Dry, Well-Mixed Layers in North Africa

1. Objective Algorithm Automates the Detection of Dry, Well-Mixed Layers in North Africa Stephen D. Nicholls (JCET/GSFC) and Karen I. Mohr (NASA/GSFC) Our open-source algorithm objectively identifies and characterizes dry, well-mixed layers (WMLs) using thermodynamic profiles (temperature and moisture) derived from (left) weather balloons, (center) satellite, and (right) weather model data. While the satellite and model product profiles are not capable of capturing small-scale vertical changes (< 300 m thick), the detection methodology was sensitive enough to identify WMLs from those products in the same region as the weather balloon observations. Despite these limitations, our algorithm expands upon our capability to observe the existence and evolution of WMLs, which are a key influence on both Atlantic tropical cyclone initiation and convective system generation in North Africa.

2. Name: Stephen D. Nicholls, JCET/UMBC, Code 612 • E-mail: stephen.d.nicholls@nasa.gov • Phone: 301-614-6337 • References: • Nicholls, S.D. and K.I. Mohr, 2019: An Automated Detection Methodology for Dry Well-Mixed Layers. J. Atmos. Oceanic Technol., 36, 761–779, https://doi.org/10.1175/JTECH-D-18-0149.1 • Supported Input Data Sources: • National Center for Environmental Investigation (NCEI): Rawinsondes (RAOB) • National Center for Atmospheric Research (NCAR) Research Data Archive: European Centre for Medium Range Weather Forecasts (ECMWF) Reanalysis – Interim (ERA-I; ds627.0); Japanese 55-year reanalysis (JRA-55; ds628.0) • NASA Earthdata Search: Atmosphere Infrared Sounder Version 6 Support Product (AIRS; AIRS2SUP); AIRS + Atmospheric Microwave Sounding Unit Version 6 Support Product (AIRS+AMSU, AIRX2SUP); Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2, M2I6NPANA) • Technical Description of Figures: • Skew-T log-P diagrams showing algorithm-detected dry, well-mixed layer (WML) detections at In-Salah, Algeria on 27 August 2003 at 12UTC derived from weather balloons (RAOB, left), AIRS+AMSU (center), and MERRA-2 (right). This diagram represents profile air temperature (red) and dewpoint temperature (green) with black wind barbs (m s-1). For example, at 600 hPa (y-axis) the ROAB Skew-T shows an air temperature of 0°C (main x-axis), a dewpoint temperature of -11°C, a potential temperature of 42°C, a mixing ratio of 2.4 g kg-1(secondary x-axis), and a wind speed of 5 m s-1from the east southeast. Each algorithm-detected WML is represented as a different layer color (orange, grey) where the potential temperature and mixing ratio lapse rates were simultaneously nearly constant. Values shown below the x-axis denote interpolated station elevation, the number of valid observations below 500 hPa, and the number of detected WMLs. Wind shifts do often, but not always, occur near the base and top of WML layers (e.g., ROAB around 600 hPa). Despite sharing common WML detection criteria, differences in the native resolution amongst the data products, vertical observation density variation (i.e., ROAB vs AIRS+AMSU), and RAOB drift can result in environmental profiles from the model and satellite products that both diverge from and lack the complexity of ROAB observations. Despite these inter-product differences, each WML detection is consistent with our WML criteria (specified below) and clearly demonstrates that the detection algorithm is sensitive enough to identify WMLs across a wide array of data products. Future work will investigate the accuracy of WML detections and layer properties between each data product and ROABs. • Scientific significance, societal relevance, and relationships to future missions: • Particularly during the boreal summer, deep, dry, and potentially dusty WMLs are a nearly ubiquitous climatological feature of the Sahara Desert associated with strong surface heating and dry convection. We define dry WMLs as layers with water vapor mixing ratios at or below 7 g kg-1, which a constitutes a Saharan-like dry airmass, and where the potential temperature and water vapor mixing ratio lapse rates are both nearly constant. The algorithm achieves its best results (relative to hand analysis) when the maximum permitted change in potential temperature is 1 K km-1 and maximum change in water vapor mixing ratio scales from 0.8 kg kg-1 km-1 using the ratio of the water vapor mixing ratio at the current level relative to a reference value (7 g kg-1). Relative to a fixed maximum allowable change in mixing ratio, the scaled limits reduce false alarms at low (<0.1 g kg-1) mixing ratios. These WMLs can retain their unique mid-tropospheric structure for days and be transported thousands of kilometers downstream (along with their dust) by the prevailing synoptic wind flow, potentially acting to enhance or weaken convection (including tropical cyclones) depending upon layer height, dust content, and other factors. More locally, elevated WMLs can indirectly enhance convection and precipitation in West Africa by augmenting and promoting stronger updrafts (>10 m s-1) at the expense of weaker updrafts (<1 m s-1). Results from this study are particularly relevant to NASA‘s on-going efforts with MERRA-2 and the Goddard Earth Observing System Model, Version 5 (GEOS-5), where accurately characterizing WMLs and their impact upon convection remain an on-going challenge. Additionally, both the upcoming Earth Surface Mineral Dust Source Investigation (EMIT) EV-I 4 and the 2020 Aeolus instrument validation field campaign which aim to investigation dust transport (include that within WMLs) and its role on global radiative balance. Earth Sciences Division - Atmospheres

3. Finding the World’s Ship-tracks with a Deep Learning Model Tianle Yuan, Chenxi Wang, Hua Song, Steven Platnick, Kerry Meyer, Lazaros Oreopoulos Code 613, NASA/GSFC JCET-UMBC, ESSIC-UMD, SSAI Input Model ‘Truth’ We developed a deep learning model to identify in satellite images marine clouds modified by ship emissions, also known as ship-tracks. This paves the way for routine automatic detection of ship-tracks worldwide and the creation of a unique dataset for detailed aerosol-cloud interactions studies. They can also be used to identify shipping activities around the world.

4. Name: Tianle Yuan, NASA/GSFC, Code 613 and UMBC-JCET E-mail: tianle.yuan@nasa.gov Phone: 301-614-6195 References: Yuan, T., Wang, C., Song, H., Platnick, S., Meyer, K. and Oreopoulos, L., 2019. Automatically Finding Ship‐tracks to Enable Large‐scale Analysis of Aerosol‐Cloud Interactions. Geophysical Research Letters. doi: 10.1029/2019GL083441. Data Sources: MODIS Level1b radiances available from the NASA LAADS DAAC, https://ladsweb.modaps.eosdis.nasa.gov. Funding was provided by NASA’s MEaSUREs program. Technical Description of Figures: Graphic 1: Input data (leftmost column), model outputs of ship-track masks from our model, and “truth” according to human experts. In the first scene (top row), out model, not only finds ship-track pixels labelled by human experts, but also detects a ship-track missed by humans Graphic 2: Applying our model to data off coast of California produces a ship-track density map that agrees the ship traffic patterns in this region, offering an additional validation of the technique. This example shows that at regional scales, ships can affect more than 8% of all low clouds which is significant enough to be considered in climate studies. We can also identify major shipping routes in the figure, e.g. between Asia to the Americas, region traffic along the West Coast. Scientific significance, societal relevance, and relationships to future missions: Ship-tracks are one of the best examples of anthropogenically-induced aerosol-cloud interaction, one of the most uncertain issues in climate science. The ability to detect them automatically at global scales will help us reduce uncertainty about this phenomenon through better understanding of its extent (our test run has already found more than 10 times the number of samples than all previous studies combined) and variability under different conditions. Our technique can be applied to any future imagers similar to MODIS and VIIRS and can potentially provide a wealth of data to advance aerosol-cloud interaction studies. In addition, our methodology can be used to study climate change impacts of specific ship-traffic and trade patterns. Earth Sciences Division - Atmospheres

5. Changes in the Diurnal Cycle of Surface Ozone Sarah Strode, Jerald Ziemke, Luke Oman, et al. NASA GSFC Code 614 & GESTAR 2) 2005-2015 Trend in OMI NO2 Column 1) Change in the Magnitude of the Surface Ozone Diurnal Cycle: 1980s vs. present 3) Change in Eastern U.S. Surface Ozone 1990s Obs 2000s Obs 1990s Model 2000s Model The diurnal cycle of ozone affects metrics that quantify how ozone impacts human health and vegetation. The MERRA2-GMI model simulation shows decreases in the magnitude of the ozone diurnal cycle in the eastern U.S. and Europe, where OMI shows that NO2 decreased, and increases in Asia, where NO2 increased. The reduced magnitude over the eastern U.S. agrees with surface ozone observations.

6. Name: Sarah Strode, NASA/GSFC, Code 614 and GESTAR E-mail: sarah.a.strode@nasa.gov Phone: 301-614-5547 References: Strode, Sarah A.1,2, Jerald R. Ziemke1,3, Luke D. Oman1, Lok N. Lamsal1,2, Mark A. Olsen1,3, and Junhua Liu1,2. "Global changes in the diurnal cycle of surface ozone." Atmospheric Environment 199 (2019): 323-333, doi:10.1016/j.atmosenv.2018.11.028. 1NASA GSFC, 2USRA, 3Morgan State University Data Sources: The MERRA2-GMI model simulation was conducted with the Goddard Earth Observing System (GEOS) model using the Global Modeling Initiative (GMI) chemistry mechanism with meteorology constrained by MERRA2. Tropospheric column NO2 data from OMI were gridded at the model resolution. Surface ozone observations are from EPA’s Clean Air Status and Trends Network (CASTNET). Technical Description of Figures: Graphic 1: The change in the peak-to-peak magnitude of the diurnal cycle of July surface ozone between the 1980s and 2006-2015 is calculated from the MERRA2-GMI model simulation. Positive values, indicating an increase in the amplitude of the diurnal cycle, occur where NOx emissions increased, while negative values occur where NOx emissions decreased. Areas where the change is not statistically significant are grayed out. Graphic 2: Trends in the OMI tropospheric NO2 column for 2005-2015 are positive in regions such as East Asia where NOx emissions increased and negative in regions such as the eastern United States where emissions decreased. Only statistically significant trends are shown. The regions of NO2 increase or decrease seen by OMI show a strong correspondence with the simulated changes in the ozone diurnal cycle seen in Graphic 1. Graphic 3: The diurnal cycle of July surface ozone observed at CASTNET sites I the northeastern Unites States is weaker in the 2000s (green circles) compared to the 1990s (red circles) due to a decrease in the daytime peak concentrations. The MERRA2-GMI model simulation sampled at the same locations (triangles) shows a similar decrease between the 1990s and the 2000s, lending confidence to the simulated changes in the diurnal cycle shown in Graphic 1. Error bars represent the standard deviation for each time period. Scientific significance, societal relevance, and relationships to future missions: Metrics that relate ozone exposure to human health, such as the maximum 8-hour ozone concentration, typically focus on just a portion of the day. Consequently, changes in ozone’s diurnal cycle in addition to changes in the daily average concentration can impact the trends in these metrics. Since surface observations are not available in parts of the world, the MERRA2-GMI simulation provides a global estimate of how the diurnal cycle has changed in different regions. In the future, geostationary satellite observations of ozone precursors will provide valuable data for constraining the diurnal cycle of surface ozone. Earth Sciences Division - Atmospheres