skip to main content

Attention:

The NSF Public Access Repository (NSF-PAR) system and access will be unavailable from 5:00 PM ET until 11:00 PM ET on Friday, June 21 due to maintenance. We apologize for the inconvenience.


Title: Universal power law of the gravity wave manifestation in the AIM CIPS polar mesospheric cloud images
We aim to extract a universal law that governs the gravity wave manifestation in polar mesospheric clouds (PMCs). Gravity wave morphology and the clarity level of display vary throughout the wave population manifested by the PMC albedo data. Higher clarity refers to more distinct exhibition of the features, which often correspond to larger variances and a better-organized nature. A gravity wave tracking algorithm based on the continuous Morlet wavelet transform is applied to the PMC albedo data at 83 km altitude taken by the Aeronomy of Ice in the Mesosphere (AIM) Cloud Imaging and Particle Size (CIPS) instrument to obtain a large ensemble of the gravity wave detections. The horizontal wavelengths in the range of  ∼ 20–60 km are the focus of the study. It shows that the albedo (wave) power statistically increases as the background gets brighter. We resample the wave detections to conform to a normal distribution to examine the wave morphology and display clarity beyond the cloud brightness impact. Sample cases are selected at the two tails and the peak of the normal distribution to represent the full set of wave detections. For these cases the albedo power spectra follow exponential decay toward smaller scales. The high-albedo-power category has the most rapid decay (i.e., exponent  =  −3.2) and corresponds to the most distinct wave display. The wave display becomes increasingly blurrier for the medium- and low-power categories, which hold the monotonically decreasing spectral exponents of −2.9 and −2.5, respectively. The majority of waves are straight waves whose clarity levels can collapse between the different brightness levels, but in the brighter background the wave signatures seem to exhibit mildly turbulent-like behavior.  more » « less
Award ID(s):
1651394
NSF-PAR ID:
10056536
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
Atmospheric Chemistry and Physics
Volume:
18
Issue:
2
ISSN:
1680-7324
Page Range / eLocation ID:
883 to 899
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract

    A gravity wave (GW) model that includes influences of temperature variations and large‐scale advection on polar mesospheric cloud (PMC) brightness having variable dependence on particle radius is developed. This Complex Geometry Compressible Atmosphere Model for PMCs (CGCAM‐PMC) is described and applied here for three‐dimensional (3‐D) GW packets undergoing self‐acceleration (SA) dynamics, breaking, momentum deposition, and secondary GW (SGW) generation below and at PMC altitudes. Results reveal that GW packets exhibiting strong SA and instability dynamics can induce significant PMC advection and large‐scale transport, and cause partial or total PMC sublimation. Responses modeled include PMC signatures of GW propagation and SA dynamics, “voids” having diameters of ∼500–1,200 km, and “fronts” with horizontal extents of ∼400–800 km. A number of these features closely resemble PMC imaging by the Cloud Imaging and Particle Size (CIPS) instrument aboard the Aeronomy of Ice in the Mesosphere (AIM) satellite. Specifically, initial CGCAM‐PMC results closely approximate various CIPS images of large voids surrounded by smaller void(s) for which dynamical explanations have not been offered to date. In these cases, the GW and instabilities dynamics of the initial GW packet are responsible for formation of the large void. The smaller void(s) at the trailing edge of a large void is (are) linked to the lower‐ or higher‐altitude SGW generation and primary mean‐flow forcing. We expect an important benefit of such modeling to be the ability to infer local forcing of the mesosphere and lower thermosphere (MLT) over significant depths when CGCAM‐PMC modeling is able to reasonably replicate PMC responses.

     
    more » « less
  2. null (Ed.)
    Abstract The spatial distribution of population affects disease transmission, especially when shelter in place orders restrict mobility for a large fraction of the population. The spatial network structure of settlements therefore imposes a fundamental constraint on the spatial distribution of the population through which a communicable disease can spread. In this analysis we use the spatial network structure of lighted development as a proxy for the distribution of ambient population to compare the spatiotemporal evolution of COVID-19 confirmed cases in the USA and China. The Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band sensor on the NASA/NOAA Suomi satellite has been imaging night light at ~ 700 m resolution globally since 2012. Comparisons with sub-kilometer resolution census observations in different countries across different levels of development indicate that night light luminance scales with population density over ~ 3 orders of magnitude. However, VIIRS’ constant ~ 700 m resolution can provide a more detailed representation of population distribution in peri-urban and rural areas where aggregated census blocks lack comparable spatial detail. By varying the low luminance threshold of VIIRS-derived night light, we depict spatial networks of lighted development of varying degrees of connectivity within which populations are distributed. The resulting size distributions of spatial network components (connected clusters of nodes) vary with degree of connectivity, but maintain consistent scaling over a wide range (5 × to 10 × in area & number) of network sizes. At continental scales, spatial network rank-size distributions obtained from VIIRS night light brightness are well-described by power laws with exponents near −2 (slopes near −1) for a wide range of low luminance thresholds. The largest components (10 4 to 10 5 km 2 ) represent spatially contiguous agglomerations of urban, suburban and periurban development, while the smallest components represent isolated rural settlements. Projecting county and city-level numbers of confirmed cases of COVID-19 for the USA and China (respectively) onto the corresponding spatial networks of lighted development allows the spatiotemporal evolution of the epidemic (infection and detection) to be quantified as propagation within networks of varying connectivity. Results for China show rapid nucleation and diffusion in January 2020 followed by rapid decreases in new cases in February. While most of the largest cities in China showed new confirmed cases approaching zero before the end of February, most of these cities also showed distinct second waves of cases in March or April. Whereas new cases in Wuhan did not approach zero until mid-March, as of December 2020 it has not yet experienced a second wave of cases. In contrast, the results for the USA show a wide range of trajectories, with an abrupt transition from slow increases in confirmed cases in a small number of network components in January and February, to rapid geographic dispersion to a larger number of components shortly before mobility reductions occurred in March. Results indicate that while most of the upper tail of the network had been exposed by the end of March, the lower tail of the component size distribution has only shown steep increases since mid-June. 
    more » « less
  3. Abstract

    The spatial distribution of population affects disease transmission, especially when shelter in place orders restrict mobility for a large fraction of the population. The spatial network structure of settlements therefore imposes a fundamental constraint on the spatial distribution of the population through which a communicable disease can spread. In this analysis we use the spatial network structure of lighted development as a proxy for the distribution of ambient population to compare the spatiotemporal evolution of COVID-19 confirmed cases in the USA and China. The Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band sensor on the NASA/NOAA Suomi satellite has been imaging night light at ~ 700 m resolution globally since 2012. Comparisons with sub-kilometer resolution census observations in different countries across different levels of development indicate that night light luminance scales with population density over ~ 3 orders of magnitude. However, VIIRS’ constant ~ 700 m resolution can provide a more detailed representation of population distribution in peri-urban and rural areas where aggregated census blocks lack comparable spatial detail. By varying the low luminance threshold of VIIRS-derived night light, we depict spatial networks of lighted development of varying degrees of connectivity within which populations are distributed. The resulting size distributions of spatial network components (connected clusters of nodes) vary with degree of connectivity, but maintain consistent scaling over a wide range (5 × to 10 × in area & number) of network sizes. At continental scales, spatial network rank-size distributions obtained from VIIRS night light brightness are well-described by power laws with exponents near −2 (slopes near −1) for a wide range of low luminance thresholds. The largest components (104to 105km2) represent spatially contiguous agglomerations of urban, suburban and periurban development, while the smallest components represent isolated rural settlements. Projecting county and city-level numbers of confirmed cases of COVID-19 for the USA and China (respectively) onto the corresponding spatial networks of lighted development allows the spatiotemporal evolution of the epidemic (infection and detection) to be quantified as propagation within networks of varying connectivity. Results for China show rapid nucleation and diffusion in January 2020 followed by rapid decreases in new cases in February. While most of the largest cities in China showed new confirmed cases approaching zero before the end of February, most of these cities also showed distinct second waves of cases in March or April. Whereas new cases in Wuhan did not approach zero until mid-March, as of December 2020 it has not yet experienced a second wave of cases. In contrast, the results for the USA show a wide range of trajectories, with an abrupt transition from slow increases in confirmed cases in a small number of network components in January and February, to rapid geographic dispersion to a larger number of components shortly before mobility reductions occurred in March. Results indicate that while most of the upper tail of the network had been exposed by the end of March, the lower tail of the component size distribution has only shown steep increases since mid-June.

     
    more » « less
  4. Abstract

    The cloud imaging and particle size (CIPS) instrument onboard the Aeronomy of Ice in the Mesosphere satellite provides images of gravity waves (GWs) near the stratopause and lowermost mesosphere (altitudes of 50–55 km). GW identification is based on Rayleigh Albedo Anomaly (RAA) variances, which are derived from GW‐induced fluctuations in Rayleigh scattering at 265 nm. Based on 3 years of CIPS RAA variance data from 2019 to 2022, we report for the first time the seasonal distribution of GWs entering the mesosphere with high (7.5 km) horizontal resolution on a near‐global scale. Seasonally averaged GW variances clearly show spatial and temporal patterns of GW activity, mainly due to the seasonal variation of primary GW sources such as convection, the polar vortices and flow over mountains. Measurements of stratospheric GWs derived from Atmospheric InfraRed Sounder (AIRS) observations of 4.3 μm brightness temperature perturbations within the same 3‐year time range are compared to the CIPS results. The comparisons show that locations of GW hotspots are similar in the CIPS and AIRS observations. Variability in GW variances and the monthly changes in background zonal wind suggest a strong GW‐wind correlation. This study demonstrates the utility of the CIPS GW variance data set for statistical investigations of GWs in the lowermost mesosphere, as well as provides a reference for location/time selection for GW case studies.

     
    more » « less
  5. Abstract

    The Polar Mesospheric Cloud Turbulence (PMC Turbo) experiment was designed to observe and quantify the dynamics of small‐scale gravity waves (GWs) and instabilities leading to turbulence in the upper mesosphere during polar summer using instruments aboard a stratospheric balloon. The PMC Turbo scientific payload comprised seven high‐resolution cameras and a Rayleigh lidar. Overlapping wide and narrow camera field of views from the balloon altitude of ~38 km enabled resolution of features extending from ~20 m to ~100 km at the PMC layer altitude of ~82 km. The Rayleigh lidar provided profiles of temperature below the PMC altitudes and of the PMCs throughout the flight. PMCs were imaged during an ~5.9‐day flight from Esrange, Sweden, to Northern Canada in July 2018. These data reveal sensitivity of the PMCs and the dynamics driving their structure and variability to tropospheric weather and larger‐scale GWs and tides at the PMC altitudes. Initial results reveal strong modulation of PMC presence and brightness by larger‐scale waves, significant variability in the occurrence of GWs and instability dynamics on time scales of hours, and a diversity of small‐scale dynamics leading to instabilities and turbulence at smaller scales. At multiple times, the overall field of view was dominated by extensive and nearly continuous GWs and instabilities at horizontal scales from ~2 to 100 km, suggesting sustained turbulence generation and persistence. At other times, GWs were less pronounced and instabilities were localized and/or weaker, but not absent. An overview of the PMC Turbo experiment motivations, scientific goals, and initial results is presented here.

     
    more » « less