Traditional validation of atmospheric profiles is based on the intercomparison of two or more data sets in predefined ranges or classes of a given observational characteristic such as latitude or ...solar zenith angle. In this study we trained a self-organising map (SOM) with a full time series of relative difference profiles of SCIAMACHY limb v5.02 and lidar ozone profiles from seven observation sites. Each individual observation characteristic was then mapped to the obtained SOM to investigate to which degree variation in this characteristic is explanatory for the variation seen in the SOM map. For the studied data sets, altitude-dependent relations for the global data set were found between the difference profiles and studied variables. From the lowest altitude studied (18 km) ascending, the most influencing factors were found to be longitude, followed by solar zenith angle and latitude, sensor age and again solar zenith angle together with the day of the year at the highest altitudes studied here (up to 45 km). After accounting for both latitude and longitude, residual partial correlations with a reduced magnitude are seen for various factors. However, (partial) correlations cannot point out which (combination) of the factors drives the observed differences between the ground-based and satellite ozone profiles as most of the factors are inter-related. Clustering into three classes showed that there are also some local dependencies, with for instance one cluster having a much stronger correlation with the sensor age (days since launch) between 36 and 42 km. The proposed SOM-based approach provides a powerful tool for the exploration of differences between data sets without being limited to a priori defined data subsets.
Transformation of endocrine active compounds (EACs) by either chlorination (Cl-D) or UV disinfection (UV-D) was studied by field sampling and bench-scale validation studies. Field testing assessed ...concentration of 13 EACs in effluent at two Chicago area 250 MGD wastewater reclamation plants (WRP) over two years. One WRP uses chlorination/dechlorination while the other employs UV disinfection. Target compounds included bupropion, carbamazepine, citalopram, duloxetine, estradiol, estrone, fluoxetine, nonylphenol, norfluoxetine, norsertraline, paroxetine, sertraline, and venlafaxine. Concentrations of 9/13 target compounds were partially reduced after disinfection (5–65% reduction). None of the target compounds were fully transformed by either chlorination or UV treatment at the WRP scale. In bench-scale experiments each compound was spiked into deionized water or effluent and treated in a process mimicking plant-scale disinfection to validate transformations. Correlation was observed between compounds that were transformed in bench-testing and those that decreased in concentration in post-disinfection WRP effluent (10/13 compounds). A survey of potential reaction products was made. Chlorination of some amine containing compounds produced chloramine by-products that reverted to the initial form after dechlorination. Transformation products produced upon simulated UV disinfection were more diverse. Laboratory UV-induced transformation was generally more effective under stirred conditions, suggesting that indirect photo-induced reactions may predominate over direct photolysis.
•Representative contaminants were measured in wastewater pre- and post-disinfection.•Both UV and chlorination were studied at two Chicago treatment plants.•Bench-scale measurements mimicking the disinfection processes were conducted.•UV and chlorination both partially decrease the concentration of some compounds.•Neither process leads to elimination of the representative contaminants.
Highly unusual open fires burned in western Greenland between 31 July and
21 August 2017, after a period of warm, dry and sunny weather. The fires
burned on peatlands that became vulnerable to fires ...by permafrost thawing.
We used several satellite data sets to estimate that the total area burned
was about 2345 ha. Based on assumptions of typical burn depths and
emission factors for peat fires, we estimate that the fires consumed a fuel
amount of about 117 kt C and emitted about 23.5 t of black carbon (BC) and
731 t of organic carbon (OC), including 141 t of brown carbon (BrC). We used
a Lagrangian particle dispersion model to simulate the atmospheric transport
and deposition of these species. We find that the smoke plumes were often
pushed towards the Greenland ice sheet by westerly winds, and thus a large
fraction of the emissions (30 %) was deposited on snow- or ice-covered
surfaces. The calculated deposition was small compared to the deposition from
global sources, but not entirely negligible. Analysis of aerosol optical
depth data from three sites in western Greenland in August 2017 showed strong
influence of forest fire plumes from Canada, but little impact of the
Greenland fires. Nevertheless, CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) lidar data showed that our model
captured the presence and structure of the plume from the Greenland fires.
The albedo changes and instantaneous surface radiative forcing in Greenland
due to the fire emissions were estimated with the SNICAR model and the uvspec
model from the libRadtran radiative transfer software package. We estimate
that the maximum albedo change due to the BC and BrC deposition was about
0.007, too small to be measured. The average instantaneous surface radiative
forcing over Greenland at noon on 31 August was 0.03–0.04 W m−2, with
locally occurring maxima of 0.63–0.77 W m−2 (depending on the studied
scenario). The average value is up to an order of magnitude smaller than the
radiative forcing from other sources. Overall, the fires burning in Greenland
in the summer of 2017 had little impact on the Greenland ice sheet, causing a
small extra radiative forcing. This was due to the – in a global context –
still rather small size of the fires. However, the very large fraction of the
emissions deposited on the Greenland ice sheet from these fires could
contribute to accelerated melting of the Greenland ice sheet if these fires
become several orders of magnitude larger under future climate.
The ozone profile records of a large number of limb and occultation satellite instruments are widely used to address several key questions in ozone research. Further progress in some domains depends ...on a more detailed understanding of these data sets, especially of their long-term stability and their mutual consistency. To this end, we made a systematic assessment of fourteen limb and occultation sounders that, together, provide more than three decades of global ozone profile measurements. In particular, we considered the latest operational Level-2 records by SAGE II, SAGE III, HALOE, UARS MLS, Aura MLS, POAM II, POAM III, OSIRIS, SMR, GOMOS, MIPAS, SCIAMACHY, ACE-FTS and MAESTRO. Central to our work is a consistent and robust analysis of the comparisons against the ground-based ozonesonde and stratospheric ozone lidar networks. It allowed us to investigate, from the troposphere up to the stratopause, the following main aspects of satellite data quality: long-term stability, overall bias, and short-term variability, together with their dependence on geophysical parameters and profile representation. In addition, it permitted us to quantify the overall consistency between the ozone profilers. Generally, we found that between 20-40 km the satellite ozone measurement biases are smaller than ±5 %, the short-term variabilities are less than 5-12% and the drifts are at most ±5% decade
(or even ±3 % decade
for a few records). The agreement with ground-based data degrades somewhat towards the stratopause and especially towards the tropopause where natural variability and low ozone abundances impede a more precise analysis. In part of the stratosphere a few records deviate from the preceding general conclusions; we identified biases of 10% and more (POAM II and SCIAMACHY), markedly higher single-profile variability (SMR and SCIAMACHY), and significant long-term drifts (SCIAMACHY, OSIRIS, HALOE, and possibly GOMOS and SMR as well). Furthermore, we reflected on the repercussions of our findings for the construction, analysis and interpretation of merged data records. Most notably, the discrepancies between several recent ozone profile trend assessments can be mostly explained by instrumental drift. This clearly demonstrates the need for systematic comprehensive multi-instrument comparison analyses.
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.
Within the framework of the Network for the Detection of Stratospheric Change (NDSC), an intercomparison campaign of ground‐based zenith‐sky viewing UV‐visible spectrometers was held at the Andøya ...Rocket Range (69°N, 16°E) at Andenes, Norway, from February 12 to March 8, 2003. The chosen site is classified as a complementary NDSC site. Eight groups from seven countries participated in the campaign which focused on the measurements of slant columns of NO2, BrO, and OClO. This first campaign publication concentrates on measurements of the NO2 slant columns. Different analysis criteria were investigated during the campaign. These included the use of fitting parameters as chosen by each group to provide what they considered to be optimized retrievals. Additional sets of parameters, imposed for all the groups, were also used, including the wavelength interval, absorption cross sections, and species fitted. Each instrument's results were compared to the measurements of selected reference instruments, whose choice was based on a technique combining regression analysis and examination of the residuals with solar zenith angle. Considering the data obtained during the whole campaign for solar zenith angles between 75° and 95°, all instruments agreed within 5% in the case of NO2 with imposed analysis parameters in the 425–450 nm region. Measurements agree less well when retrieving the NO2 slant columns in the 400–418 nm region or when using parameters optimized by each investigator for their instrument.
Analysis of global meteorological assimilations between mid‐July and late‐August 2000 shows strong 5‐day planetary waves in the middle atmosphere. Observations of temperature, zonal wind, noctilucent ...clouds and polar mesosphere summer echoes (PMSE) near Kiruna, Sweden, all at heights between 80 and 95 km, show variations correlating with the passage of the 5‐day waves. Temperature variations correlated with the 5‐day wave reach 15 K peak‐to‐peak and correspond to modulation of PMSE occurrence by up to 50%. These observations appear to be the first experimental evidence of amplification of 5‐day waves at the summer mesosphere which was predicted theoretically in 1976. A close linear relation is found between mean daily temperature and mean daily occurrence of PMSE. This can be explained if temperature is the primary factor controlling PMSE occurrence and time and height variations within each day between 80 and 90 km altitude reach 30 – 50 K.
In early October 2020, northern Europe experienced an episode with poor air quality due to high concentrations of particulate matter (PM). At several sites in Norway, recorded weekly values exceeded ...historical maximum PM10 concentrations from the past 4 to 10 years. Daily mean PM10 values at Norwegian sites were up to 97 µgm-3 and had a median value of 59 µgm-3. We analysed this severe pollution episode caused by long-range atmospheric transport based on surface and remote sensing observations and transport model simulations to understand its causes. Samples from three sites in mainland Norway and the Arctic remote station Zeppelin (Svalbard) showed strong contributions from mineral dust to PM10 (23 %–36 % as a minimum and 31 %–45 % as a maximum) and biomass burning (8 %–16 % to 19 %–21 %). Atmospheric transport simulations indicate that Central Asia was the main source region for mineral dust observed in this episode. The biomass burning fraction can be attributed to forest fires in Ukraine and southern Russia, but we cannot exclude other sources contributing, like fires elsewhere, because the model underestimates observed concentrations. The combined use of remote sensing, surface measurements, and transport modelling proved effective in describing the episode and distinguishing its causes.
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