Hasil untuk "Earthwork. Foundations"

M. Liu, W. Han, W. Han et al.

<p>To investigate the application of deep learning in satellite remote sensing, this study employs brightness temperature observations from the remapped Micro-Wave Radiation Imager-Rainfall Mission (MWRI-RM) onboard the Fengyun-3G (FY-3G) satellite as input data, while temperature and relative humidity profiles (ranging from 1000 to 100 hPa) obtained from ERA5 reanalysis data are used as label data. An Advanced Residual Convolutional Neural Network (AR-CNN) model was developed to retrieve atmospheric temperature and relative humidity profile data. The results show that: (1) The retrieval of temperature profiles achieves a root-mean-square error (RMSE) of approximately 1.24 K, and the RMSE for relative humidity profiles is 12.98 %. (2) A comparison between retrieved and labeled samples reveals consistent results for temperature retrieval but some discrepancies in extreme high and low humidity regions, suggesting the need for further refinement. (3) Gradient-based analyses and perturbation experiments confirm that 118 GHz oxygen channels are critical for mid-to-upper tropospheric temperature (500–200 hPa), indirectly impacting upper-level humidity (200–100 hPa) through thermal coupling, while 183 GHz water vapor channels dominate lower-to-mid tropospheric humidity retrievals (1000–500 hPa) and constrain temperature via moisture-radiation feedbacks. (4) Additional channel ablation experiments demonstrate that channels with smaller frequency offsets mainly affect upper atmospheric layers, whereas larger-offset channels have stronger impacts on lower layers, supporting the spectral contribution patterns identified in previous studies. These findings highlight the model's ability to capture temperature-humidity coupling and confirm the complementary roles of 118 and 183 GHz channels in improving vertical profile retrievals.</p>

J. Girdwood, J. Girdwood, J. Girdwood et al.

<p>Calibrations of optical particle spectrometers (OPSs) are non-trivial and conventionally involve aerosolisation techniques, which are challenging for larger particles. In this paper, we present a new technique for OPS calibration that involves mounting a static fibre within the instrument sample area, measuring the scattering cross section (SCS), and then comparing the SCS with a calculated value. In addition, we present a case for the use of generalised Lorenz–Mie theory (GLMT) simulations to account for deviations in both minor- and major-axis beam intensity, which has a significant effect on particles that are large compared with the beam waist, in addition to reducing the need for a “top-hat” spatial intensity profile. The described technique is OPS independent and could be applied to a field calibration tool that could be used to verify the calibration of instruments before they are deployed. In addition to this, the proposed calibration technique would be suited for applications involving the mass production of low-cost OPSs.</p>

D. Garcia Santos, M. E. Parés

<p>Effective monitoring of air pollution is essential for the development of environmental and public health policies. Comprehensive air quality management requires precise tools and strategies to assess the spatial and temporal distribution of pollutants. This study investigates the correlation between nitrogen dioxide (<span class="inline-formula">NO₂</span>) concentrations detected by the Sentinel-5 Precursor (S5p) satellite and those measured at ground stations by Catalonia's official air quality monitoring network during 2022 and 2023. The methodology integrates satellite and surface data aligned in space and time. The relationship between both measurements is analyzed under different frameworks: (i) global, considering the entire territory; (ii) by geographic zone (urban, suburban, and rural, as well as inside and outside the Barcelona Metropolitan Area (BMA)); (iii) according to the type of stations (traffic, background, industrial); and (iv) at a seasonal level, covering different quarters of the year. Statistical tools are then used to identify patterns and differences based on zones, typology, and seasonality characteristics. The results show a moderate positive correlation at the global level, with <span class="inline-formula"><i>r</i>=0.66</span>. By zones, the analysis reveals that suburban (<span class="inline-formula"><i>r</i>=0.66</span>) and non-BMA zones (<span class="inline-formula"><i>r</i>=0.67</span>) present stronger correlations compared to urban zones (<span class="inline-formula"><i>r</i>=0.55</span>), traffic typology (<span class="inline-formula"><i>r</i>=0.61</span>), or stations located in the BMA zone (<span class="inline-formula"><i>r</i>=0.42</span>). Seasonally, the correlation peaks in winter (<span class="inline-formula"><i>r</i>=0.70</span>) and autumn (<span class="inline-formula"><i>r</i>=0.66</span>), periods with more stable atmospheric conditions for <span class="inline-formula">NO₂</span> concentrations, while it is lowest in spring (<span class="inline-formula"><i>r</i>=0.61</span>) and summer (<span class="inline-formula"><i>r</i>=0.57</span>). These findings highlight the utility of the S5p satellite as a complement to ground-based networks in <span class="inline-formula">NO₂</span> monitoring, while revealing the limitations of applying a direct relationship between both types of data at the regional level and across different geographic zones.</p>

M. Reuter, M. Hilker, S. Noël et al.

<p>Carbon dioxide (CO<span class="inline-formula">₂</span>) and methane (CH<span class="inline-formula">₄</span>) are the most important anthropogenic greenhouse gases and the main drivers of climate change. Monitoring their concentrations from space helps detect and quantify anthropogenic emissions, supporting the mitigation efforts urgently needed to meet the primary objective of the Paris Agreement, adopted at the 21st Conference of the Parties to the United Nations Framework Convention on Climate Change (UNFCCC) in 2015, to limit the global average temperature increase to well below 2 °C above pre-industrial levels. In addition, satellite observations can be used to quantify natural sources and sinks, improving our understanding of the carbon cycle. Advancing these goals is one key motivation for the European Copernicus CO<span class="inline-formula">₂</span> monitoring mission CO2M. The necessary accuracy and precision requirements for the measured quantities XCO<span class="inline-formula">₂</span> and XCH<span class="inline-formula">₄</span> (the column-averaged dry-air mole fractions of CO<span class="inline-formula">₂</span> and CH<span class="inline-formula">₄</span>) are demanding. According to the CO2M mission requirements, the spatial and temporal variability of the systematic errors (or spatio-temporal systematic errors) of XCO<span class="inline-formula">₂</span> and XCH<span class="inline-formula">₄</span> must not exceed 0.5 ppm and 5 ppb, respectively. The stochastic errors due to instrument noise must not exceed 0.7 ppm for XCO<span class="inline-formula">₂</span> and 10 ppb for XCH<span class="inline-formula">₄</span>. Conventional so-called full-physics algorithms for retrieving XCO<span class="inline-formula">₂</span> and/or XCH<span class="inline-formula">₄</span> from satellite-based measurements of reflected solar radiation are typically computationally intensive and still usually require empirical bias corrections based on supervised machine learning methods. Here we present the retrieval algorithm Neural networks for Remote sensing of Greenhouse gases from CO2M (NRG-CO2M), which derives XCO<span class="inline-formula">₂</span> and XCH<span class="inline-formula">₄</span> from CO2M radiance measurements with minimal computational effort using artificial neural networks (ANNs). In addition, NRG-CO2M also provides estimates of both the noise-driven uncertainties and the averaging kernels of XCO<span class="inline-formula">₂</span> and XCH<span class="inline-formula">₄</span> for each sounding. Since CO2M will not be launched until 2026, our study exploits simulated measurements over land surfaces from a comprehensive observing system simulation experiment (OSSE) that includes realistic meteorology, aerosols, surface bidirectional reflectance distribution function (BRDF), solar-induced chlorophyll fluorescence (SIF), and CO<span class="inline-formula">₂</span> and CH<span class="inline-formula">₄</span> concentrations. We created a novel hybrid learning approach that combines advantages of simulation-based and measurement-based training data to ensure coverage of a wide range of XCO<span class="inline-formula">₂</span> and XCH<span class="inline-formula">₄</span> values, making the training data representative of future concentrations as well. The algorithm's postprocessing is designed to achieve a high data yield of about 80 % of all cloud-free soundings. The spatio-temporal systematic errors of XCO<span class="inline-formula">₂</span> and XCH<span class="inline-formula">₄</span> are 0.44 ppm and 2.45 ppb, respectively. The average single sounding precision is 0.41 ppm for XCO<span class="inline-formula">₂</span> and 2.74 ppb for XCH<span class="inline-formula">₄</span>. Therefore, the presented retrieval method has the potential to meet the demanding CO2M mission requirements for XCO<span class="inline-formula">₂</span> and XCH<span class="inline-formula">₄</span>. While the presented results are a solid proof of concept, the actual achievable quality can only be determined once NRG-CO2M is trained on real data, where it is confronted, e.g., with unknown instrument effects and systematic errors in the training truth.</p>

T. Han, C. Xie, Y. Liu et al.

<p>The development in uncrewed aerial vehicle (UAV) technologies over the past decade has led to a plethora of platforms that can potentially enable greenhouse gas emission quantification. Here, we report the development of a new air sampler, consisting of a pumped stainless coiled tube of 150 m in length with controlled time stamping, and its deployment from an industrial UAV to quantify CO<span class="inline-formula">₂</span> and CH<span class="inline-formula">₄</span> emissions from the main coking plant stacks of a major steel maker in eastern China. Laboratory tests show that the time series of CO<span class="inline-formula">₂</span> and CH<span class="inline-formula">₄</span> measured using the sampling system is smoothed when compared to online measurement by the cavity ring-down spectrometer (CRDS) analyzer. Further analyses show that the smoothing is akin to a convolution of the true time series signals with a heavy-tailed digital filter. For field testing, the air sampler was mounted on the UAV and flown in virtual boxes around two stacks in the coking plant of the Shagang Group (steel producer). Mixing ratios of CO<span class="inline-formula">₂</span> and CH<span class="inline-formula">₄</span> in air and meteorological parameters were measured from the UAV during the test flight. A mass-balance computational algorithm was used on the data to estimate the CO<span class="inline-formula">₂</span> and CH<span class="inline-formula">₄</span> emission rates from the stacks. Using this algorithm, the emission rates for the two stacks from the coking plant were calculated to be <span class="inline-formula">0.12±0.014</span> t h<span class="inline-formula">⁻¹</span> for CH<span class="inline-formula">₄</span> and <span class="inline-formula">110±18</span> t h<span class="inline-formula">⁻¹</span> for CO<span class="inline-formula">₂</span>, the latter being in excellent agreement with material-balance-based estimates. A Gaussian plume inversion approach was also used to derive the emission rates, and the results were compared with those derived using the mass-balance algorithm, showing a good agreement between the two methods.</p>

S. J. Munchak, S. J. Munchak, R. S. Schrom et al.

<p>A method is developed to use both polarimetric and dual-frequency radar measurements to retrieve microphysical properties of falling snow. It is applied to the Ku- and Ka-band measurements of the NASA dual-polarization, dual-frequency Doppler radar (D3R) obtained during the International Collaborative Experiments for PyeongChang 2018 Olympic and Paralympic winter games (ICE-POP 2018) field campaign and incorporates the Atmospheric Radiative Transfer Simulator (ARTS) microwave single-scattering property database for oriented particles. The retrieval uses optimal estimation to solve for several parameters that describe the particle size distribution (PSD), relative contribution of pristine, aggregate, and rimed ice species, and the orientation distribution along an entire radial simultaneously. Examination of Jacobian matrices and averaging kernels shows that the dual-wavelength ratio (DWR) measurements provide information regarding the characteristic particle size, and to a lesser extent, the rime fraction and shape parameter of the size distribution, whereas the polarimetric measurements provide information regarding the mass fraction of pristine particles and their characteristic size and orientation distribution. Thus, by combining the dual-frequency and polarimetric measurements, some ambiguities can be resolved that should allow a better determination of the PSD and bulk microphysical properties (e.g., snowfall rate) than can be retrieved from single-frequency polarimetric measurements or dual-frequency, single-polarization measurements.</p> <p>The D3R ICE-POP retrievals were validated using Precipitation Imaging Package (PIP) and Pluvio weighing gauge measurements taken nearby at the May Hills ground site. The PIP measures the snow PSD directly, and its measurements can be used to derived the snowfall rate (volumetric and water equivalent), mean volume-weighted particle size, and effective density, as well as particle aspect ratio and orientation. Four retrieval experiments were performed to evaluate the utility of different measurement combinations: Ku-only, DWR-only, Ku-pol, and All-obs. In terms of correlation, the volumetric snowfall rate (<span class="inline-formula"><i>r</i>=0.95</span>) and snow water equivalent rate (<span class="inline-formula"><i>r</i>=0.92</span>) were best retrieved by the Ku-pol method, while the DWR-only method had the lowest magnitude bias for these parameters (<span class="inline-formula">−31</span> % and <span class="inline-formula">−8</span> %, respectively). The methods that incorporated DWR also had the best correlation to particle size (<span class="inline-formula"><i>r</i>=0.74</span> and <span class="inline-formula"><i>r</i>=0.71</span> for DWR-only and All-obs, respectively), although none of the methods retrieved density particularly well (<span class="inline-formula"><i>r</i>=0.43</span> for All-obs). The ability of the measurements to retrieve mean aspect ratio was also inconclusive, although the polarimetric methods (Ku-pol and All-obs) had reduced biases and mean absolute error (MAE) relative to the Ku-only and DWR-only methods. The significant biases in particle size and snowfall rate appeared to be related to biases in the measured DWR, emphasizing the need for accurate DWR measurements and frequent calibration in future D3R deployments.</p>

Jessica Keiser

I. Kaur, P. Eriksson, S. Pfreundschuh et al.

<p>A methodology based on quantile regression neural networks (QRNNs) is presented that identifies and corrects the cloud impact on microwave humidity sounder radiances at 183 GHz. This approach estimates the posterior distributions of noise-free clear-sky (NFCS) radiances, providing nearly bias-free estimates of clear-sky radiances with a full posterior error distribution. It is first demonstrated by application to a present sensor, the MicroWave Humidity Sounder 2 (MWHS-2); then the applicability to sub-millimetre (sub-mm) sensors is also analysed. The QRNN results improve upon what operational cloud filtering techniques like a scattering index can achieve but are ultimately imperfect due to limited information content on cirrus impact from traditional microwave channels – the negative departures associated with high cloud impact are successfully corrected, but thin cirrus clouds cannot be fully corrected. In contrast, when sub-mm observations are used, QRNN successfully corrects most cases with cloud impact, with only 2 %–6 % of the cases left partially corrected. The methodology works well even if only one sub-mm channel (325 GHz) is available. When using sub-mm observations, cloud correction usually results in error distributions with a standard deviation less than typical channel noise values. Furthermore, QRNN outputs predicted quantiles for case-specific uncertainty estimates, successfully representing the uncertainty of cloud correction for each observation individually. In comparison to deterministic correction or filtering approaches, the corrected radiances and attendant uncertainty estimates have great potential to be used efficiently in assimilation systems due to being largely unbiased and adding little further uncertainty to the measurements.</p>

Y. An, Y. An, Y. An et al.

<p>The hydroxyl radical (OH) determines the capability of atmospheric self-cleansing and is one of the significant oxidants in atmospheric photochemistry reactions. Global OH has been monitored by satellites with the traditional limb mode in the past decades. This observed mode can achieve the acquisition of high-resolution vertical OH data but cannot obtain enough horizontal OH data for inverting high-precision OH concentrations because OH has a high reactivity that makes OH concentrations extremely low and distributions complicated. The double spatial heterodyne spectrometer (DSHS) is designed to obtain higher-resolution and more detailed OH data. This sensor can measure OH by the three-dimensional limb mode to obtain comprehensive OH data in the atmosphere. Here we propose a new tomographic retrieval algorithm based on the simulated observation data because the DSHS will work officially on the orbit in the future. We build up an accurate forward model. The main part of it is the SCIATRAN radiative transfer model which is modified according to the radiation transmission theory. The error in results obtained by the forward model is <span class="inline-formula">±44.30</span>&thinsp;% in the lower atmosphere such as at a 21&thinsp;<span class="inline-formula">km</span> height and decreases gradually until the limit of observation altitude. We also construct the tomographic retrieval algorithm of which the core is a lookup table method. A tomographic-observation database is built up through the atmospheric model, the spatial information (the position of the target area and satellite position), the date parameters, the observation geometries, OH concentrations, and simulated observation data. The OH concentrations can be found from it directly. If there are no corresponding query conditions in the tomographic-observation database, the cubic spline interpolation is used to obtain the OH concentrations. The inversion results are given, and the errors in them increase as the altitudes rise until about a 41&thinsp;<span class="inline-formula">km</span> height then start to decrease. The errors in the inversion results reach the maximum of about <span class="inline-formula">±25.03</span>&thinsp;% at the 41&thinsp;<span class="inline-formula">km</span> height and decrease to <span class="inline-formula">±8.09</span>&thinsp;% at the limited observation height. They are also small in the lower atmosphere at <span class="inline-formula">±12.96</span>&thinsp;% at 21&thinsp;<span class="inline-formula">km</span>. In summary, the tomographic retrieval algorithm can obtain more accurate OH concentrations even in the lower atmosphere where the OH data are not high quality and avoids the setting of initial guess values for solving the iteration problems. Our research not only provides support for the scientific theory of the construction of the DSHS but also gives a new retrieval algorithm idea for other radicals.</p>

M. Fencl, M. Dohnal, P. Valtr et al.

<p>Opportunistic sensing of rainfall and water vapor using commercial microwave links operated within cellular networks was conceived more than a decade ago. It has since been further investigated in numerous studies, predominantly concentrating on the frequency region of 15–40&thinsp;GHz. This article provides the first evaluation of rainfall and water vapor sensing with microwave links operating at E-band frequencies (specifically 71–76 and 81–86&thinsp;GHz). These microwave links are increasingly being updated (and are frequently replacing) older communication infrastructure. Attenuation–rainfall relations are investigated theoretically on drop size distribution data. Furthermore, quantitative rainfall estimates from six microwave links, operated within cellular backhaul, are compared with observed rainfall intensities. Finally, the capability to detect water vapor is demonstrated on the longest microwave link measuring 4.86&thinsp;km in path length. The results show that E-band microwave links are markedly more sensitive to rainfall than devices operating in the 15–40&thinsp;GHz range and can observe even light rainfalls, a feat practically impossible to achieve previously. The E-band links are, however, substantially more affected by errors related to variable drop size distribution. Water vapor retrieval might be possible from long E-band microwave links; nevertheless, the efficient separation of gaseous attenuation from other signal losses will be challenging in practice.</p>

M. Zhou, P. Wang, P. Wang et al.

<p>In this study, we present <span class="inline-formula">O₃</span> retrievals from ground-based Fourier transform infrared (FTIR) solar absorption measurements between June 2018 and December 2019 at Xianghe, China (39.75<span class="inline-formula">^∘</span>&thinsp;N, 116.96<span class="inline-formula">^∘</span>&thinsp;E). The FTIR spectrometer at Xianghe is operated with indium gallium arsenide (InGaAs) and indium antimonide (InSb) detectors, recording the spectra between 1800 and 11&thinsp;000&thinsp;cm<span class="inline-formula">⁻¹</span>. As the harmonized FTIR <span class="inline-formula">O₃</span> retrieval strategy <span class="cit" id="xref_paren.1">(<a href="#bib1.bibx39">Vigouroux et al.</a>, <a href="#bib1.bibx39">2015</a>)</span> within the Network for the Detection of Atmospheric Composition Change (NDACC) uses the 1000&thinsp;cm<span class="inline-formula">⁻¹</span> spectral range, we apply the <span class="inline-formula">O₃</span> retrieval in the 3040&thinsp;cm<span class="inline-formula">⁻¹</span> spectral range at Xianghe.</p> <p>The retrieved <span class="inline-formula">O₃</span> profile is mainly sensitive to the vertical range between 10 and 40&thinsp;km, and the degrees of freedom for signal is <span class="inline-formula">2.4±0.3</span> (<span class="inline-formula">1<i>σ</i></span>), indicating that there are two individual pieces of information in partial columns between the surface and 20&thinsp;km and between 20 and 40&thinsp;km. According to the optimal estimation method, the systematic and random uncertainties of the FTIR <span class="inline-formula">O₃</span> total columns are about 13.6&thinsp;% and 1.4&thinsp;%, respectively. The random uncertainty is consistent with the observed daily standard deviation of the FTIR retrievals.</p> <p>To validate the FTIR <span class="inline-formula">O₃</span> total and partial columns, we apply the same <span class="inline-formula">O₃</span> retrieval strategy at Maïdo, Réunion (a.k.a. Reunion Island; 21.08<span class="inline-formula">^∘</span>&thinsp;N, 55.38<span class="inline-formula">^∘</span>&thinsp;E). The FTIR <span class="inline-formula">O₃</span> (3040&thinsp;cm<span class="inline-formula">⁻¹</span>) measurements at Xianghe and Maïdo are then compared with the nearby ozonesondes at Beijing (39.81<span class="inline-formula">^∘</span>&thinsp;N, 116.47<span class="inline-formula">^∘</span>&thinsp;E) and at Gillot (20.89<span class="inline-formula">^∘</span>&thinsp;S, 55.53<span class="inline-formula">^∘</span>&thinsp;E), respectively, as well as with co-located TROPOspheric Monitoring Instrument (TROPOMI) satellite measurements at both sites. In addition at Maïdo, we compare the FTIR <span class="inline-formula">O₃</span> (3040&thinsp;cm<span class="inline-formula">⁻¹</span>) retrievals with the standard NDACC FTIR <span class="inline-formula">O₃</span> measurements using the 1000&thinsp;cm<span class="inline-formula">⁻¹</span> spectral range. It was found that the total columns retrieved from the FTIR <span class="inline-formula">O₃</span> 3040&thinsp;cm<span class="inline-formula">⁻¹</span> measurements are underestimated by 5.5&thinsp;%–9.0&thinsp;%, which is mainly due to the systematic uncertainty in the partial column between 20 and 40&thinsp;km (about <span class="inline-formula">−10.4</span>&thinsp;%). The systematic uncertainty in the partial column between surface and 20&thinsp;km is relatively small (within 2.4&thinsp;%). By comparison with other measurements, it was found that the FTIR <span class="inline-formula">O₃</span> (3040&thinsp;cm<span class="inline-formula">⁻¹</span>) retrievals capture the seasonal and synoptic variations of the <span class="inline-formula">O₃</span> total and two partial columns very well. Therefore, the ongoing FTIR measurements at Xianghe can provide useful information on the <span class="inline-formula">O₃</span> variations and (in the future) long-term trends.</p>

R. Holzinger, W. J. F. Acton, W. J. Bloss et al.

<p>In September 2017, we conducted a proton-transfer-reaction mass-spectrometry (PTR-MS) intercomparison campaign at the CESAR observatory, a rural site in the central Netherlands near the village of Cabauw. Nine research groups deployed a total of 11 instruments covering a wide range of instrument types and performance. We applied a new calibration method based on fast injection of a gas standard through a sample loop. This approach allows calibrations on timescales of seconds, and within a few minutes an automated sequence can be run allowing one to retrieve diagnostic parameters that indicate the performance status. We developed a method to retrieve the mass-dependent transmission from the fast calibrations, which is an essential characteristic of PTR-MS instruments, limiting the potential to calculate concentrations based on counting statistics and simple reaction kinetics in the reactor/drift tube. Our measurements show that PTR-MS instruments follow the simple reaction kinetics if operated in the standard range for pressures and temperature of the reaction chamber (i.e. 1–4&thinsp;mbar, 30–120<span class="inline-formula">^∘</span>, respectively), as well as a reduced field strength <span class="inline-formula"><i>E</i>∕<i>N</i></span> in the range of 100–160&thinsp;Td. If artefacts can be ruled out, it becomes possible to quantify the signals of uncalibrated organics with accuracies better than <span class="inline-formula">±30</span>&thinsp;%. The simple reaction kinetics approach produces less accurate results at <span class="inline-formula"><i>E</i>∕<i>N</i></span><span id="page6194"/> levels below 100&thinsp;Td, because significant fractions of primary ions form water hydronium clusters. Deprotonation through reactive collisions of protonated organics with water molecules needs to be considered when the collision energy is a substantial fraction of the exoergicity of the proton transfer reaction and/or if protonated organics undergo many collisions with water molecules.</p>

J.-F. Ribaud, L. A. T. Machado, T. Biscaro

<p>The dominant hydrometeor types associated with Brazilian tropical precipitation systems are identified via research X-band dual-polarization radar deployed in the vicinity of the Manaus region (Amazonas) during both the GoAmazon2014/5 and ACRIDICON-CHUVA field experiments. The present study is based on an agglomerative hierarchical clustering (AHC) approach that makes use of dual polarimetric radar observables (reflectivity at horizontal polarization <span class="inline-formula"><i>Z</i>_H</span>, differential reflectivity <span class="inline-formula"><i>Z</i>_DR</span>, specific differential-phase <span class="inline-formula"><i>K</i>_DP</span>, and correlation coefficient <span class="inline-formula"><i>ρ</i>_HV</span>) and temperature data inferred from sounding balloons. The sensitivity of the agglomerative clustering scheme for measuring the intercluster dissimilarities (linkage criterion) is evaluated through the wet-season dataset. Both the weighted and Ward linkages exhibit better abilities to retrieve cloud microphysical species, whereas clustering outputs associated with the centroid linkage are poorly defined. The AHC method is then applied to investigate the microphysical structure of both the wet and dry seasons. The stratiform regions are composed of five hydrometeor classes: drizzle, rain, wet snow, aggregates, and ice crystals, whereas convective echoes are generally associated with light rain, moderate rain, heavy rain, graupel, aggregates, and ice crystals. The main discrepancy between the wet and dry seasons is the presence of both low- and high-density graupel within convective regions, whereas the rainy period exhibits only one type of graupel. Finally, aggregate and ice crystal hydrometeors in the tropics are found to exhibit higher polarimetric values compared to those at midlatitudes.</p>

D. Mamali, E. Marinou, E. Marinou et al.

In situ measurements using unmanned aerial vehicles (UAVs) and remote sensing observations can independently provide dense vertically resolved measurements of atmospheric aerosols, information which is strongly required in climate models. In both cases, inverting the recorded signals to useful information requires assumptions and constraints, and this can make the comparison of the results difficult. Here we compare, for the first time, vertical profiles of the aerosol mass concentration derived from light detection and ranging (lidar) observations and in situ measurements using an optical particle counter on board a UAV during moderate and weak Saharan dust episodes. Agreement between the two measurement methods was within experimental uncertainty for the coarse mode (i.e. particles having radii  &gt; 0.5 µm), where the properties of dust particles can be assumed with good accuracy. This result proves that the two techniques can be used interchangeably for determining the vertical profiles of aerosol concentrations, bringing them a step closer towards their systematic exploitation in climate models.

A. A. Turnipseed, P. C. Andersen, C. J. Williford et al.

A new solid-phase scrubber for use in conventional ozone (O₃) photometers was investigated as a means of reducing interferences from other UV-absorbing species and water vapor. It was found that when heated to 100–130 °C, a tubular graphite scrubber efficiently removed up to 500 ppb ozone and ozone monitors using the heated graphite scrubber were found to be less susceptible to interferences from water vapor, mercury vapor, and aromatic volatile organic compounds (VOCs) compared to conventional metal oxide scrubbers. Ambient measurements from a graphite scrubber-equipped photometer and a co-located Federal equivalent method (FEM) ozone analyzer showed excellent agreement over 38 days of measurements and indicated no loss in the scrubber's ability to remove ozone when operated at 130 °C. The use of a heated graphite scrubber was found to reduce the interference from mercury vapor to ≤ 3 % of that obtained using a packed-bed Hopcalite scrubber. For a series of substituted aromatic compounds (ranging in volatility and absorption cross section at 253.7 nm), the graphite scrubber was observed to consistently exhibit reduced levels of interference, typically by factors of 2.5 to 20 less than with Hopcalite. Conventional solid-phase scrubbers also exhibited complex VOC adsorption and desorption characteristics that were dependent upon the relative humidity (RH), volatility of the VOC, and the available surface area of the scrubber. This complex behavior involving humidity is avoided by use of a heated graphite scrubber. These results suggest that heated graphite scrubbers could be substituted in most ozone photometers as a means of reducing interferences from other UV-absorbing species found in the atmosphere. This could be particularly important in ozone monitoring for compliance with the United States (U.S.) Clean Air Act or for use in VOC-rich environments such as in smog chambers and monitoring indoor air quality.