Suborbital Measurements of Spectral Aerosol Optical Depth and Its Variability at Subsatellite Grid Scales in Support of CLAMS 2001

2005 ◽  
Vol 62 (4) ◽  
pp. 993-1007 ◽  
Author(s):  
J. Redemann ◽  
B. Schmid ◽  
J. A. Eilers ◽  
R. Kahn ◽  
R. C. Levy ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Large Scale ◽  
Spatial Scales ◽  
The United States ◽  
Horizontal Distance ◽  
Airborne Measurements ◽  
Low Level ◽  
Modis Aod

Abstract As part of the Chesapeake Lighthouse and Aircraft Measurements for Satellites (CLAMS) experiment, 10 July–2 August 2001, off the central East Coast of the United States, the 14-channel NASA Ames Airborne Tracking Sunphotometer (AATS-14) was operated aboard the University of Washington’s Convair 580 (CV-580) research aircraft during 10 flights (∼45 flight hours). One of the main research goals in CLAMS was the validation of satellite-based retrievals of aerosol properties. The goal of this study in particular was to perform true over-ocean validations (rather than over-ocean validation with ground-based, coastal sites) at finer spatial scales and extending to longer wavelengths than those considered in previous studies. Comparisons of aerosol optical depth (AOD) between the Aerosol Robotic Network (AERONET) Cimel instrument at the Chesapeake Lighthouse and airborne measurements by AATS-14 in its vicinity showed good agreement with the largest r-square correlation coefficients at wavelengths of 0.38 and 0.5 μm (>0.99). Coordinated low-level flight tracks of the CV-580 during Terra overpass times permitted validation of over-ocean Moderate Resolution Imaging Spectroradiometer (MODIS) level 2 (MOD04_L2) multiwavelength AOD data (10 km × 10 km, nadir) in 16 cases on three separate days. While the correlation between AATS-14- and MODIS-derived AOD was weak with an r square of 0.55, almost 75% of all MODIS AOD measurements fell within the prelaunch estimated uncertainty range Δτ = ±0.03 ± 0.05τ. This weak correlation may be due to the small AODs (generally less than 0.1 at 0.5 μm) encountered in these comparison cases. An analogous coordination exercise resulted in seven coincident over-ocean matchups between AATS-14 and Multiangle Imaging Spectroradiometer (MISR) measurements. The comparison between AATS-14 and the MISR standard algorithm regional mean AODs showed a stronger correlation with an r square of 0.94. However, MISR AODs were systematically larger than the corresponding AATS values, with an rms difference of ∼0.06. AATS data collected during nine extended low-level CV-580 flight tracks were used to assess the spatial variability in AOD at horizontal scales up to 100 km. At UV and midvisible wavelengths, the largest absolute gradients in AOD were 0.1–0.2 per 50-km horizontal distance. In the near-IR, analogous gradients rarely reached 0.05. On any given day, the relative gradients in AOD were remarkably similar for all wavelengths, with maximum values of 70% (50 km)−1 and more typical values of 25% (50 km)−1. The implications of these unique measurements of AOD spatial variability for common validation practices of satellite data products and for comparisons to large-scale aerosol models are discussed.

scholarly journals Assessment of the Representativeness of MODIS Aerosol Optical Depth Products at Different Temporal Scales Using Global AERONET Measurements

2020 ◽  
Vol 12 (14) ◽  
pp. 2330
Author(s):  
Yan Tong ◽  
Lian Feng ◽  
Kun Sun ◽  
Jing Tang
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Radiative Forcing ◽  
Large Scale ◽  
Data Availability ◽  
Temporal Scales ◽  
Modis Aod ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer

Assessments of long-term changes of air quality and global radiative forcing at a large scale heavily rely on satellite aerosol optical depth (AOD) datasets, particularly their temporal binning products. Although some attempts focusing on the validation of long-term satellite AOD have been conducted, there is still a lack of comprehensive quantification and understanding of the representativeness of satellite AOD at different temporal binning scales. Here, we evaluated the performances of the Moderate Resolution Imaging Spectroradiometer (MODIS) AOD products at various temporal scales by comparing the MODIS AOD datasets from both the Terra and Aqua satellites with the entire global AErosol RObotic NETwork (AERONET) observation archive between 2000 and 2017. The uncertainty levels of the MODIS hourly and daily AOD products were similarly high, indicating that MODIS AOD retrievals could be used to represent daily aerosol conditions. The MODIS data showed the reduced quality when integrated from the daily to monthly scale, where the relative mean bias (RMB) changed from 1.09 to 1.21 for MODIS Terra and from 1.04 to 1.17 for MODIS Aqua, respectively. The limitation of valid data availability within a month appeared to be the primary reason for the increased uncertainties in the monthly binning products, and the monthly data associated uncertainties could be reduced when the number of valid AOD retrievals reached 15 times in one month. At all three temporal scales, the uncertainty levels of satellite AOD products decreased with increasing AOD values. The results of this study could provide crucial information for satellite AOD users to better understand the reliability of different temporal AOD binning products and associated uncertainties in their derived long-term trends.

scholarly journals Climatology of Severe Local Storm Environments and Synoptic-Scale Features over North America in ERA5 Reanalysis and CAM6 Simulation

2020 ◽  
Vol 33 (19) ◽  
pp. 8339-8365 ◽  
Author(s):  
Funing Li ◽  
Daniel R. Chavas ◽  
Kevin A. Reed ◽  
Daniel T. Dawson II
Keyword(s):  
United States ◽  
North America ◽  
Great Plains ◽  
Large Scale ◽  
Extreme Values ◽  
Convective Available Potential Energy ◽  
The United States ◽  
Synoptic Scale ◽  
Low Level ◽  
High Bias

AbstractSevere local storm (SLS) activity is known to occur within specific thermodynamic and kinematic environments. These environments are commonly associated with key synoptic-scale features—including southerly Great Plains low-level jets, drylines, elevated mixed layers, and extratropical cyclones—that link the large-scale climate to SLS environments. This work analyzes spatiotemporal distributions of both extreme values of SLS environmental parameters and synoptic-scale features in the ERA5 reanalysis and in the Community Atmosphere Model, version 6 (CAM6), historical simulation during 1980–2014 over North America. Compared to radiosondes, ERA5 successfully reproduces SLS environments, with strong spatiotemporal correlations and low biases, especially over the Great Plains. Both ERA5 and CAM6 reproduce the climatology of SLS environments over the central United States as well as its strong seasonal and diurnal cycles. ERA5 and CAM6 also reproduce the climatological occurrence of the synoptic-scale features, with the distribution pattern similar to that of SLS environments. Compared to ERA5, CAM6 exhibits a high bias in convective available potential energy over the eastern United States primarily due to a high bias in surface moisture and, to a lesser extent, storm-relative helicity due to enhanced low-level winds. Composite analysis indicates consistent synoptic anomaly patterns favorable for significant SLS environments over much of the eastern half of the United States in both ERA5 and CAM6, though the pattern differs for the southeastern United States. Overall, our results indicate that both ERA5 and CAM6 are capable of reproducing SLS environments as well as the synoptic-scale features and transient events that generate them.

scholarly journals Airborne observation during KORUS-AQ show aerosol optical depth are more spatially self-consistent than aerosol intensive properties

2022 ◽  
Author(s):  
Samuel E. LeBlanc ◽  
Michal Segal-Rozenhaimer ◽  
Jens Redemann ◽  
Connor J. Flynn ◽  
Roy R. Johnson ◽  
...  
Keyword(s):  
Air Quality ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Temporal Variability ◽  
Spatial Scales ◽  
Aerosol Emission ◽  
Fine Mode ◽  
Predominant Factor ◽  
Spatio Temporal ◽  
Microphysical Processes

Abstract. Aerosol particles can be emitted, transported, removed, or transformed, leading to aerosol variability at scales impacting the climate (days to years and over hundreds of kilometers) or the air quality (hours to days and from meters to hundreds of kilometers). We present the temporal and spatial scales of changes in AOD (Aerosol Optical Depth), and aerosol size (using Angstrom Exponent; AE, and Fine-Mode-Fraction; FMF) over Korea during the 2016 KORUS-AQ (KORea-US Air Quality) atmospheric experiment. We use measurements and retrievals of aerosol optical properties from airborne instruments for remote sensing (4STAR; Spectrometers for Sky-Scanning Sun Tracking Atmospheric Research) and in situ (LARGE; NASA Langley Aerosol Research Group Experiment) on board the NASA DC-8, geostationary satellite (GOCI; Geostationary Ocean Color Imager; Yonsei aerosol retrieval (YAER) version 2) and reanalysis (MERRA-2; Modern-Era Retrospective Analysis for Research and Applications, version 2). Measurements from 4STAR when flying below 500 m, show an average AOD at 501 nm of 0.43 and an average AE of 1.15 with large standard deviation (0.32 and 0.26 for AOD and AE respectively) likely due to mixing of different aerosol types (fine and coarse mode). The majority of AODs due to fine mode aerosol is observed at altitudes lower than 2 km. Even though there are large variations, for 18 out of the 20 flight days, the column AOD measurements by 4STAR along the NASA DC-8 flight trajectories matches the south-Korean regional average derived from GOCI. We also observed that, contrary to prevalent understanding, AE and FMF are more spatially variable than AOD during KORUS-AQ, even when accounting for potential sampling biases by using Monte Carlo resampling. Averaging between measurements and model for the entire KORUS-AQ period, a reduction in correlation by 15 % is 65.0 km for AOD and shorter at 22.7 km for AE. While there are observational and model differences, the predominant factor influencing spatial-temporal homogeneity is the meteorological period. High spatio-temporal variability occur during the dynamic period (25–31 May), and low spatio-temporal variability occur during blocking Rex pattern (01–07 June). The changes in spatial variability scales between AOD and FMF/AE, while inter-related, indicate that microphysical processes that impact mostly the dominant aerosol size, like aerosol particle formation, growth, and coagulation, vary at shorter scales than the aerosol concentration processes that mostly impact AOD, like aerosol emission, transport, and removal.

scholarly journals Sampling density and date influence spatial representation of tree ring reconstructions

2020 ◽  
Author(s):  
Justin T. Maxwell ◽  
Grant L. Harley ◽  
Trevis J. Matheus ◽  
Brandon M. Strange ◽  
Kayla Van Aken ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Spatial Variability ◽  
Tree Ring ◽  
Tree Rings ◽  
Large Scale ◽  
The United States ◽  
Severe Drought ◽  
Ring Network ◽  
Tree Ring Data ◽  
The Us

Abstract. Our understanding of the natural variability of hydroclimate before the instrumental period (ca. 1900 in the United States; US) is largely dependent on tree-ring-based reconstructions. Large-scale soil moisture reconstructions from a network of tree-ring chronologies have greatly improved our understanding of the spatial and temporal variability in hydroclimate conditions, particularly extremes of both drought and pluvial (wet) events. However, certain regions within these large-scale reconstructions in the US have a sparse network of tree-ring chronologies. Further, several chronologies were collected in the 1980s and 1990s, thus our understanding of the sensitivity of radial growth to soil moisture in the US is based on a period that experienced multiple extremely severe droughts and neglects the impacts of recent, rapid global change. In this study, we expanded the tree-ring network of the Ohio River Valley in the US, a region with sparse coverage. We used a total of 72 chronologies across 15 species to examine how increasing the density of the tree-ring network influences the representation of reconstructing the Palmer Meteorological Drought Index (PMDI). Further, we tested how the sampling date influenced the reconstruction models by creating reconstructions that ended in the year 1980 and compared them to reconstructions ending in 2010 from the same chronologies. We found that increasing the density of the tree-ring network resulted in reconstructed values that better matched the spatial variability of instrumentally recorded droughts and to a lesser extent, pluvials. By sampling tree in 2010 compared to 1980, the sensitivity of tree rings to PMDI decreased in the southern portion of our region where severe drought conditions have been absent over recent decades. We emphasize the need of building a high-density tree-ring network to better represent the spatial variability of past droughts and pluvials. Further, chronologies on the International Tree-Ring Data Bank need updating regularly to better understand how the sensitivity of tree rings to climate may vary through time.

scholarly journals Eurasia large-scale hazes in summer 2016

2019 ◽  
Vol 55 (3) ◽  
pp. 41-51
Author(s):  
G. I. Gorchakov ◽  
S. A. Sitnov ◽  
A. V. Karpov ◽  
I. A. Gorchakova ◽  
R. A. Gushchin ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Radiative Forcing ◽  
Large Scale ◽  
Empirical Distribution ◽  
Growth Period ◽  
Maximum Growth ◽  
Qualitative Change ◽  
Smoke Aerosol

Using maximum aerosol optical depth (MAOD) spatial distribution formation technique the optically dense haze expansion scales in period from 15 to 31 July 2016 over Eurasia are estimated in during great Siberian smoke haze (SSH) with the area 16 mln km2 about, smog over the Northern China Plain (2 mln km2), dust haze in Takla Makan desert (0.8 mln km2) and hazes in India and Pakistan (1 mln km2 approximately). Empirical distribution function (EDF) MAOD is received which is approximated by linear function of MAOD logarithm. Aerosol optical depth (AOD) spatial distribution at wavelength 550 nm in SSH is analyzed. Total smoke aerosol mass assessment in SSH (3.2 mln tons) is evaluated. Smoke aerosol (SA) mass during maximum growth period from 22 July to 26 July 2016 over Siberia (50°-70°, 60°-120 °E) was equal 2 mln tons approximately. Aerosol index (AI) temporal variability is illustrated visually SA composition qualitative change in SSH during long-range transport. It is shown that AI variations are correlated with AOD variations. Aerosol radiative forcing (ARF) at the top and the bottom of the atmosphere over Siberia from 22 July to 26 July 2016 is estimated (average ARF are equal –68 and –98 W/m2). EDF AOD and EDF ARF at the top of the atmosphere are approximated by exponential and power function of AOD correspondingly.

scholarly journals Flood Risk Assessment and Quantification at the Community and Property Level in the State of Iowa

2021 ◽  
Author(s):  
Enes Yildirim ◽  
Ibrahim Demir
Keyword(s):  
Risk Assessment ◽  
High Resolution ◽  
Flood Risk ◽  
Large Scale ◽  
Spatial Scales ◽  
The United States ◽  
Flood Risk Assessment ◽  
Study Region ◽  
Study Results ◽  
Flood Maps

Flood risk assessment contributes to identifying at-risk communities and supports mitigation decisions to maximize benefits from the investments. Large-scale risk assessments generate invaluable inputs for prioritizing regions for the distribution of limited resources. High-resolution flood maps and accurate parcel information are critical for flood risk analysis to generate reliable outcomes for planning, preparedness, and decision-making applications. Large-scale damage assessment studies in the United States often utilize the National Structure Inventory (NSI) or HAZUS default dataset, which results in inaccurate risk estimates due to the low geospatial accuracy of these datasets. On the other hand, some studies utilize higher resolution datasets, however they are limited to focus on small scales, for example, a city or a Hydrological United Code (HUC)-12 watershed. In this study, we collected extensive detailed flood maps and parcel datasets for many communities in Iowa to carry out a large-scale flood risk assessment. High-resolution flood maps and the most recent parcel information are collected to ensure the accuracy of risk products. The results indicate that the Eastern Iowa communities are prone to a higher risk of direct flood losses. Our model estimates nearly $10 million in average annualized losses, particularly in large communities in the study region. The study highlights that existing risk products based on FEMA's flood risk output underestimate the flood loss, specifically in highly populated urban communities such as Bettendorf, Cedar Falls, Davenport, Dubuque, and Waterloo. Additionally, we propose a flood risk score methodology for two spatial scales (e.g., HUC-12 watershed, property) to prioritize regions and properties for mitigation purposes. Lastly, the watershed-scale study results are shared through a web-based platform to inform the decision-makers and the public.

scholarly journals Using the OMI Aerosol Index and Absorption Aerosol Optical Depth to evaluate the NASA MERRA Aerosol Reanalysis

2014 ◽  
Vol 14 (23) ◽  
pp. 32177-32231 ◽  
Author(s):  
V. Buchard ◽  
A. M. da Silva ◽  
P. R. Colarco ◽  
A. Darmenov ◽  
C. A. Randles ◽  
...  
Keyword(s):  
Optical Properties ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Biomass Burning ◽  
Saharan Dust ◽  
Aerosol Layer ◽  
Anthropogenic Aerosols ◽  
Aerosol Absorption ◽  
Modis Aod ◽  
Aerosol Index

Abstract. A radiative transfer interface has been developed to simulate the UV Aerosol Index (AI) from the NASA Goddard Earth Observing System version 5 (GEOS-5) aerosol assimilated fields. The purpose of this work is to use the AI and Aerosol Absorption Optical Depth (AAOD) derived from the Ozone Monitoring Instrument (OMI) measurements as independent validation for the Modern Era Retrospective analysis for Research and Applications Aerosol Reanalysis (MERRAero). MERRAero is based on a version of the GEOS-5 model that is radiatively coupled to the Goddard Chemistry, Aerosol, Radiation, and Transport (GOCART) aerosol module and includes assimilation of Aerosol Optical Depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Since AI is dependent on aerosol concentration, optical properties and altitude of the aerosol layer, we make use of complementary observations to fully diagnose the model, including AOD from the Multi-angle Imaging SpectroRadiometer (MISR), aerosol retrievals from the Aerosol Robotic Network (AERONET) and attenuated backscatter coefficients from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) mission to ascertain potential misplacement of plume height by the model. By sampling dust, biomass burning and pollution events in 2007 we have compared model produced AI and AAOD with the corresponding OMI products, identifying regions where the model representation of absorbing aerosols was deficient. As a result of this study over the Saharan dust region, we have obtained a new set of dust aerosol optical properties that retains consistency with the MODIS AOD data that were assimilated, while resulting in better agreement with aerosol absorption measurements from OMI. The analysis conducted over the South African and South American biomass burning regions indicates that revising the spectrally-dependent aerosol absorption properties in the near-UV region improves the modeled-observed AI comparisons. Finally, during a period where the Asian region was mainly dominated by anthropogenic aerosols, we have performed a qualitative analysis in which the specification of anthropogenic emissions in GEOS-5 is adjusted to provide insight into discrepancies observed in AI comparisons.

Relationship between MODIS based Aerosol Optical Depth and PM10 over Croatia

2014 ◽  
Vol 6 (1) ◽  
Author(s):  
Sanja Grgurić ◽  
Josip Križan ◽  
Goran Gašparac ◽  
Oleg Antonić ◽  
Zdravko Špirić ◽  
...  
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Mass Concentration ◽  
Meteorological Data ◽  
Neural Network Approach ◽  
Data Set ◽  
Independent Variables ◽  
Data Variability ◽  
Modis Aod ◽  
The Relationship

AbstractThis study analyzes the relationship between Aerosol Optical Depth (AOD) obtained from Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) and ground-based PM10 mass concentration distribution over a period of 5 years (2008–2012), and investigates the applicability of satellite AOD data for ground PM10 mapping for the Croatian territory. Many studies have shown that satellite AOD data are correlated to ground-based PM mass concentration. However, the relationship between AOD and PM is not explicit and there are unknowns that cause uncertainties in this relationship.The relationship between MODIS AOD and ground-based PM10 has been studied on the basis of a large data set where daily averaged PM10 data from the 12 air quality stations across Croatia over the 5 year period are correlated with AODs retrieved from MODIS Terra and Aqua. A database was developed to associate coincident MODIS AOD (independent) and PM10 data (dependent variable). Additional tested independent variables (predictors, estimators) included season, cloud fraction, and meteorological parameters — including temperature, air pressure, relative humidity, wind speed, wind direction, as well as planetary boundary layer height — using meteorological data from WRF (Weather Research and Forecast) model.It has been found that 1) a univariate linear regression model fails at explaining the data variability well which suggests nonlinearity of the AOD-PM10 relationship, and 2) explanation of data variability can be improved with multivariate linear modeling and a neural network approach, using additional independent variables.

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