The Deep Blue (DB) algorithm has been used to retrieve aerosol optical depth (AOD) and Ångström exponent (AE) over land from multiple satellite instruments, including the Moderate Resolution Imaging ...Spectroradiometers (MODIS) aboard the Terra and Aqua platforms and the Visible Infrared Imaging Radiometer Suite (VIIRS). This study first validates the latest MODIS (Collection 6.1) and VIIRS (Version 1) DB data products against Aerosol Robotic Network observations. On global average, the typical level of uncertainty in AOD is slightly better than ±(0.05 + 20%) relative to Aerosol Robotic Network. AE is quantitatively more uncertain but qualitatively shows skill at distinguishing between fine‐mode and coarse‐mode dominated aerosol columns. Results are also compared with the previous MODIS Collection 6. The stability of the three DB data sets ranges from 0.005–0.01 AOD per decade. Second, spatial and temporal patterns in AOD and AE are compared between the three data sets. It is found that they all show similar patterns of spatial coverage, which is predominantly linked to cloud cover, snow, and polar night. Regional time series of AOD also show highly consistent seasonal and interannual variations and are strongly correlated, although have offsets in some regions due to a combination of algorithmic and sensor‐related differences.
Plain Language Summary
Aerosols are small particles in the atmosphere like desert dust, volcanic ash, smoke, industrial haze, and sea spray. Understanding them is important for applications such as hazard avoidance, air quality and human health, and climate studies. Satellite instruments provide an important tool to study aerosol loading over the world. However, individual satellites do not last forever, and newer satellites often have improved capabilities compared to older ones. This paper evaluates the latest version of the Deep Blue algorithm for monitoring aerosols as applied to the Moderate Resolution Imaging Spectroradiometers (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) satellite instruments. The two MODIS sensors provide data from 2000 and 2002 onward, while the first VIIRS was launched in late 2011, and VIIRS will carry on the MODIS data records into the future. The evaluation is performed by comparing to ground‐truth data which are part of (National Aeronautics and Space Administration) NASA's global Aerosol Robotic Network. The stability in time and consistency between the MODIS and VIIRS data sets are also examined.
Key Points
VIIRS and MODIS Deep Blue show very similar validation results against AERONET
Decadal stability in retrieved AOD is about 0.01 per decade or better
The data sets show consistent seasonal and interannual variations in regional AOD
Aerosol forcing uncertainty represents the largest climate forcing uncertainty overall. Its magnitude has remained virtually undiminished over the past 20 years despite considerable advances in ...understanding most of the key contributing elements. Recent work has produced modest increases only in the confidence of the uncertainty estimate itself. This review summarizes the contributions toward reducing the uncertainty in the aerosol forcing of climate made by satellite observations, measurements taken within the atmosphere, as well as modeling and data assimilation. We adopt a more measurement-oriented perspective than most reviews of the subject in assessing the strengths and limitations of each; gaps and possible ways to fill them are considered. Currently planned programs supporting advanced, global-scale satellite and surface-based aerosol, cloud, and precursor gas observations, climate modeling, and intensive field campaigns aimed at characterizing the underlying physical and chemical processes involved, are all essential. But in addition, new efforts are needed: (a) to obtain systematic aircraft in situ measurements capturing the multi-variate probability distribution functions of particle optical, microphysical, and chemical properties (and associated uncertainty estimates), as well as co-variability with meteorology, for the major aerosol airmass types; (b) to conceive, develop, and implement a suborbital (aircraft plus surface-based) program aimed at systematically quantifying the cloud-scale microphysics, cloud optical properties, and cloud-related vertical velocities associated with aerosol-cloud interactions; and (c) to focus much more research on integrating the unique contributions of satellite observations, suborbital measurements, and modeling, to reduce the persistent uncertainty in aerosol climate forcing.
The spectral distribution of marine remote sensing reflectance, R(rs), is the fundamental measurement of ocean color science, from which a host of bio-optical and biogeochemical properties of the ...water column can be derived. Estimation of uncertainty in these derived properties is thus dependent on knowledge of the uncertainty in satellite-retrieved R(rs) (u(c)(R(rs))) at each pixel. Uncertainty in R(rs), in turn, is dependent on the propagation of various uncertainty sources through the R(rs) retrieval process, namely the atmospheric correction (AC). A derivative-based method for uncertainty propagation is established here to calculate the pixel-level uncertainty in R(rs), as retrieved using NASA’s multiple-scattering epsilon (MSEPS) AC algorithm and verified using Monte Carlo (MC) analysis. The approach is then applied to measurements from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite, with uncertainty sources including instrument random noise, instrument systematic uncertainty, and forward model uncertainty. The uc(Rrs) is verified by comparison with statistical analysis of coincident retrievals from MODIS and in situ Rrs measurements, and our approach performs well in most cases. Based on analysis of an example 8-day global products, we also show that relative uncertainty in R(rs) at blue bands has a similar spatial pattern to the derived concentration of the phytoplankton pigment chlorophyll-a (chl-a), and around 7.3%, 17.0%, and 35.2% of all clear water pixels (chl-a ≤ 0.1 mg/cu.m) with valid u(c)(R(rs)) have a relative uncertainty ≤ 5% at bands 412 nm, 443 nm, and 488 nm respectively, which is a common goal of ocean color retrievals for clear waters. While the analysis shows that u(c)(R(rs)) calculated from our derivative-based method is reasonable, some issues need further investigation, including improved knowledge of forward model uncertainty and systematic uncertainty in instrument calibration.
This study validates aerosol properties retrieved using a Satellite Ocean Aerosol Retrieval (SOAR) algorithm applied to Visible Infrared Imaging Radiometer Suite (VIIRS) measurements, from Version 1 ...of the VIIRS Deep Blue data set. SOAR is the over‐water complement to the over‐land Deep Blue algorithm and has two processing paths: globally, 95% of pixels are processed with the full retrieval algorithm, while the 5% of pixels in shallow or turbid (mostly coastal) waters are processed with a backup algorithm. Aerosol Robotic Network (AERONET) data are used to validate and compare the midvisible (550 nm) aerosol optical depth (AOD), Ångström exponent (AE), and fine mode fraction of AOD at 550 nm (FMF). AOD uncertainty is shown to be approximately ±(0.03 + 10%) for the full and ±(0.03 + 15%) for the backup algorithms, with a small positive median bias around 0.02. When AOD is below about 0.2, the AE and FMF have small negative offsets from AERONET around −0.15 and −0.04, respectively. For higher AOD, AE is less offset and the magnitudes of differences versus AERONET are about ±0.2 and ±0.14, respectively. Aerosol‐type classifications provided by SOAR are found to be reasonable, matching optical‐based classifications from AERONET over 80% of the time. Spatial and temporal patterns of AOD and AE are also compared with those of other contemporary over‐water satellite aerosol data sets; dependent on region, the satellite data sets show varying levels of consistency, with SOAR broadly in‐family, and the largest discrepancies in regions with persistent heavy cloud cover.
Plain Language Summary
Aerosols are small particles in the atmosphere like desert dust, volcanic ash, smoke, industrial haze, and sea spray. Understanding them is important for applications such as hazard avoidance, air quality and human health, and climate studies. Satellite instruments provide an important tool to study aerosol loading over the world. However, individual satellites do not last forever and newer satellites often have improved capabilities compared to older ones. This paper evaluates an extension of an algorithm, originally designed to monitor aerosols from an older satellite instrument, to a new satellite instrument called Visible Infrared Imaging Radiometer Suite. The evaluation is performed by comparing to ground truth data which are part of the National Aeronautics and Space Administration's global Aerosol Robotic Network, as well as to other satellite‐based aerosol data sets from different spaceborne instruments.
Key Points
Based on comparisons to AERONET, the AOD uncertainty is consistent with expectations
Retrieved parameters related to aerosol particle size and type are also demonstrated to have skill
SOAR shows similar regional and temporal patterns to other satellite data sets
This paper provides the theoretical basis and simulated retrievals for the Cloud Height Retrieval from O2 Molecular Absorption (CHROMA) algorithm. Simulations are performed for the Ocean Color ...Instrument (OCI), which is the primary payload on the forthcoming NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, and the Ocean Land Colour Instrument (OLCI) currently flying on the Sentinel 3 satellites. CHROMA is a Bayesian approach which simultaneously retrieves cloud optical thickness (COT), cloud-top pressure and height (CTP and CTH respectively), and (with a significant prior constraint) surface albedo. Simulated retrievals suggest that the sensor and algorithm should be able to meet the PACE mission goal for CTP error, which is ±60 mb for 65 % of opaque (COT ≥3) single-layer clouds on global average. CHROMA will provide pixel-level uncertainty estimates, which are demonstrated to have skill at telling low-error situations from high-error ones. CTP uncertainty estimates are well-calibrated in magnitude, although COT uncertainty is overestimated relative to observed errors. OLCI performance is found to be slightly better than OCI overall, demonstrating that it is a suitable proxy for the latter in advance of PACE's launch. CTP error is only weakly sensitive to correct cloud phase identification or assumed ice crystal habit/roughness. As with other similar algorithms, for simulated retrievals of multi-layer systems consisting of optically thin cirrus clouds above liquid clouds, retrieved height tends to be underestimated because the satellite signal is dominated by the optically thicker lower layer. Total (liquid plus ice) COT also becomes underestimated in these situations. However, retrieved CTP becomes closer to that of the upper ice layer for ice COT ≈3 or higher.
Recent years have seen the increasing inclusion of per-retrieval prognostic (predictive) uncertainty estimates within satellite aerosol optical depth (AOD) data sets, providing users with ...quantitative tools to assist in the optimal use of these data. Prognostic estimates contrast with diagnostic (i.e. relative to some external truth) ones, which are typically obtained using sensitivity and/or validation analyses. Up to now, however, the quality of these uncertainty estimates has not been routinely assessed. This study presents a review of existing prognostic and diagnostic approaches for quantifying uncertainty in satellite AOD retrievals, and it presents a general framework to evaluate them based on the expected statistical properties of ensembles of estimated uncertainties and actual retrieval errors. It is hoped that this framework will be adopted as a complement to existing AOD validation exercises; it is not restricted to AOD and can in principle be applied to other quantities for which a reference validation data set is available. This framework is then applied to assess the uncertainties provided by several satellite data sets (seven over land, five over water), which draw on methods from the empirical to sensitivity analyses to formal error propagation, at 12 Aerosol Robotic Network (AERONET) sites. The AERONET sites are divided into those for which it is expected that the techniques will perform well and those for which some complexity about the site may provide a more severe test. Overall, all techniques show some skill in that larger estimated uncertainties are generally associated with larger observed errors, although they are sometimes poorly calibrated (i.e. too small or too large in magnitude). No technique uniformly performs best. For powerful formal uncertainty propagation approaches such as optimal estimation, the results illustrate some of the difficulties in appropriate population of the covariance matrices required by the technique. When the data sets are confronted by a situation strongly counter to the retrieval forward model (e.g. potentially mixed land–water surfaces or aerosol optical properties outside the family of assumptions), some algorithms fail to provide a retrieval, while others do but with a quantitatively unreliable uncertainty estimate. The discussion suggests paths forward for the refinement of these techniques.
Atmospheric aerosols and their impact on cloud properties remain the largest uncertainty in the human forcing of the climate system. By increasing the concentration of cloud droplets (Nd), aerosols ...reduce droplet size and increase the reflectivity of clouds (a negative radiative forcing). Central to this climate impact is the susceptibility of cloud droplet number to aerosol (β), the diversity of which explains much of the variation in the radiative forcing from aerosol–cloud interactions (RFaci) in global climate models. This has made measuring β a key target for developing observational constraints of the aerosol forcing.
Spectral remote sensing reflectance, Rrs(λ) (sr−1), is the fundamental quantity used to derive a host of bio-optical and biogeochemical properties of the water column from satellite ocean color ...measurements. Estimation of uncertainty in those derived geophysical products is therefore dependent on knowledge of the uncertainty in satellite-retrieved Rrs. Furthermore, since the associated algorithms require Rrs at multiple spectral bands, the spectral (i.e., band-to-band)error covariance in Rrs is needed to accurately estimate the uncertainty in those derived properties. This study establishes a derivative-based approach for propagating instrument random noise, instrument systematic uncertainty, and forward model uncertainty into R rs as retrieved using NASA’s multiple-scattering epsilon (MSEPS) atmospheric correction algorithm, to generate pixel-level error covariance in R rs. The approach is applied to measurements from Moderate Resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite and verified using Monte Carlo (MC) analysis. We also make use of this full spectral error covariance in R rs to calculate uncertainty in phytoplankton pigment chlorophyll-a concentration (chla, mg/m3) and diffuse attenuation coefficient of downwelling irradiance at 490 nm (K d(490), m-1). Accounting for the error covariance in Rrs generally reduces the estimated relative uncertainty in chla by ∼1-2% (absolute value) in waters with chla< 0.25 mg/m3 where the color index (CI) algorithm is used. The reduction is ∼5-10% in waters with chla> 0.35 mg/m3 where the blue-green ratio (OCX) algorithm is used. Such reduction can be higher than 30% in some regions. For K d(490), the reduction by error covariance is generally ∼2%, but can be higher than 20% in some regions. The error covariance in R rs is further verified through forward-calculating chla from MODIS-retrieved and in situ R rs and comparing estimated uncertainty with observed differences. An 8-day global composite of propagated uncertainty shows that the goal of 35% uncertainty in chla can be achieved over deep ocean waters (chla ≤ 0.1 mg/m3). While the derivative-based approach generates reasonable error covariance in R rs some assumptions should be updated as our knowledge improves. These include the inter-band error correlation in top-of-atmosphere reflectance, and uncertainties in the calibration of MODIS 869 nm band, in ancillary data, and in the in situ data used for system vicarious calibration.
The 100,000 Genomes Project is a U.K. government project that is sequencing the genomes of patients with cancer or rare or infectious diseases. This pilot study involving 4660 participants with rare ...diseases provided actionable diagnoses and identified three newly implicated disease genes and offers a road map for the larger implementation of genome sequencing in the setting of a national health service.
Background
Motor units (MUs) control the contraction of muscles and degenerate with age. It is therefore of interest to measure whole muscle and MU twitch profiles in aging skeletal muscle.
Purpose
...Apply phase contrast MU MRI (PC‐MUMRI) in a cohort of healthy adults to measure whole anterior compartment, individual muscles, and single MU twitch profiles in the calf. Assess the effect of age and sex on contraction and relaxation times.
Study Type
Prospective cross‐sectional study.
Subjects
Sixty‐one healthy participants (N = 32 male; age 55 ± 16 years range: 26–82).
Field Strength/Sequences
3 T, velocity encoded gradient echo and single shot spin echo pulsed gradient spin echo, echo‐planar imaging.
Assessment
Anterior shin compartment (N = 47), individual muscle (tibialis anterior, extensor digitorum longus, peroneus longus; N = 47) and single MU (N = 34) twitch profiles were extracted from the data to calculate contraction and relaxation times.
Statistical Tests
Multivariable linear regression to investigate relationships between age, sex and contraction and relaxation times of the whole anterior compartment. Pearson correlation to investigate relationships between age and contraction and relaxation times of individual muscles and single MUs. A P value <0.05 was considered statistically significant.
Results
Age and sex predicted significantly increased contraction and relaxation time for the anterior compartment. Females had significantly longer contraction times than males (females 86 ± 8 msec, males 80 ± 9 msec). Relaxation times were longer, not significant (females 204 ± 36 msec, males 188 ± 34 msec, P = 0.151). Contraction and relaxation times of single MUs showed no change with age (P = 0.462, P = 0.534, respectively).
Date Conclusion
Older participants had significantly longer contraction and relaxation times of the whole anterior compartment compared to younger participants. Females had longer contraction and relaxation times than males, significant for contraction time.
Evidence Level
2
Technical Efficacy
Stage 1
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