Hasil untuk "Earthwork. Foundations"

P. Dong, P. Dong, X. Jiang et al.

<p>The eastern Tibetan Plateau has a high elevation, with a cold and dry atmospheric background. The features of the raindrop size distributions (DSD) in this region have notable differences from those in the plains. The general empirical relationships for retrieving parameters of precipitation from radar observations are not applicable in the eastern Tibetan Plateau. In this study, we developed a new method based on optimal estimation theory to retrieve the vertical profiles of <span class="inline-formula"><i>N</i>₀</span> and <span class="inline-formula"><i>D</i>_m</span> from a Ka-band zenith-pointing Doppler radar. Validation by a field campaign during the summer of 2024 indicates that the mean bias in the <span class="inline-formula">log ₁₀(<i>N</i>₀)</span> and <span class="inline-formula"><i>D</i>_m</span> derived from the PARSIVEL² disdrometer and the retrieved values are 0.12 and <span class="inline-formula">−0.1</span> mm respectively, demonstrating the effectiveness of the retrieved DSD parameters in this region. Based on the retrieved vertical profiles of DSD parameters, some unique characteristics are found. The heavy precipitation (the maximum value in the reflectivity profile exceeding 30 dBZ) exhibits a higher particle number concentration above 2 km and larger raindrop size in the bottom of the rainfall on average. The mean values of <span class="inline-formula"><i>D</i>_m</span> above 2 km are approximately 0.5 mm, for heavy precipitation, the value increase as the raindrops fall, reaching a peak at around 0.5 km. Precipitation that occurs after the nighttime cooling usually has higher particle concentrations and smaller particle sizes. Based on the above research, empirical relationships for the quantitative precipitation estimates (QPE) and attenuation correction using Ka-band radar in the eastern Tibetan Plateau are established.</p>

D. Zhang, J. Comstock, C. Sivaraman et al.

<p>Remote sensing measurements have been widely used to estimate the planetary boundary layer height (PBLHT). Each remote sensing approach offers unique strengths and faces different limitations. In this study, we use machine learning (ML) methods to produce a best-estimate PBLHT (PBLHT-BE-ML) by integrating four PBLHT estimates derived from remote sensing measurements at the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) observatory. Three ML models – random forest (RF) classifier, RF regressor, and light gradient-boosting machine (LightGBM) – were trained on a dataset from 2017 to 2023 that included radiosonde, various remote sensing PBLHT estimates, and atmospheric meteorological conditions. Evaluations indicated that PBLHT-BE-ML from all three models improved alignment with the PBLHT derived from radiosonde data (PBLHT-SONDE), with LightGBM demonstrating the highest accuracy under both stable and unstable boundary layer conditions. Feature analysis revealed that the most influential input features at the SGP site were the PBLHT estimates derived from (a) potential temperature profiles retrieved using Raman lidar (RL) and atmospheric emitted radiance interferometer (AERI) measurements (PBLHT-THERMO), (b) vertical velocity variance profiles from Doppler lidar (PBLHT-DL), and (c) aerosol backscatter profiles from micropulse lidar (PBLHT-MPL). The trained models were then used to predict PBLHT-BE-ML at a temporal resolution of 10 min, effectively capturing the diurnal evolution of PBLHT and its significant seasonal variations, with the largest diurnal variation observed over summer at the SGP site. We applied these trained models to data from the ARM Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE) field campaign (EPC), where the PBLHT-BE-ML, particularly with the LightGBM model, demonstrated improved accuracy against PBLHT-SONDE. Analyses of model performance at both the SGP and EPC sites suggest that expanding the training dataset to include various surface types, such as ocean and ice-covered areas, could further enhance ML model performance for PBLHT estimation across varied geographic regions.</p>

Peter Kirgis

Large language models are increasingly being used in critical domains of politics, business, and education, but the nature of their normative ethical judgment remains opaque. Alignment research has, to date, not sufficiently utilized perspectives and insights from the field of moral psychology to inform training and evaluation of frontier models. I perform a synthetic experiment on a wide range of models from most major model providers using Jonathan Haidt's influential moral foundations theory (MFT) to elicit diverse value judgments from LLMs. Using multiple descriptive statistical approaches, I document the bias and variance of large language model responses relative to a human baseline in the original survey. My results suggest that models rely on different moral foundations from one another and from a nationally representative human baseline, and these differences increase as model capabilities increase. This work seeks to spur further analysis of LLMs using MFT, including finetuning of open-source models, and greater deliberation by policymakers on the importance of moral foundations for LLM alignment.

R. Wu, S. R. Zorn, S. Kang et al.

<p>Oxidation of volatile organic compounds (VOCs) can lead to the formation of secondary organic aerosol (SOA), a significant component of atmospheric fine particles, which can affect air quality, human health, and climate change. However, the current understanding of the formation mechanism of SOA is still incomplete, which is not only due to the complexity of the chemistry but also relates to analytical challenges in SOA precursor detection and quantification. Recent instrumental advances, especially the development of high-resolution time-of-flight chemical ionization mass spectrometry (CIMS), greatly improved both the detection and quantification of low- and extremely low-volatility organic molecules (LVOCs/ELVOCs), which largely facilitated the investigation of SOA formation pathways. However, analyzing and interpreting complex mass spectrometric data remain a challenging task. This necessitates the use of dimension reduction techniques to simplify mass spectrometric data with the purpose of extracting chemical and kinetic information of the investigated system. Here we present an approach to apply fuzzy <span class="inline-formula"><i>c</i></span>-means clustering (FCM) to analyze CIMS data from a chamber experiment, aiming to investigate the gas phase chemistry of the nitrate-radical-initiated oxidation of isoprene.</p> <p>The performance of FCM was evaluated and validated. By applying FCM to measurements, various oxidation products were classified into different groups, based on their chemical and kinetic properties, and the common patterns of their time series were identified, which provided insight into the chemistry of the investigated system. The chemical properties of the clusters are described by elemental ratios and the average carbon oxidation state, and the kinetic behaviors are parameterized with a generation number and effective rate coefficient (describing the average reactivity of a species) using the gamma kinetic parameterization model. In addition, the fuzziness of FCM algorithm provides a possibility for the separation of isomers or different chemical processes that species are involved in, which could be useful for mechanism development. Overall, FCM is a technique that can be applied well to simplify complex mass spectrometric data, and the chemical and kinetic properties derived from clustering can be utilized to understand the reaction system of interest.</p>

Michael Wornow, Yizhe Xu, Rahul Thapa et al.

The successes of foundation models such as ChatGPT and AlphaFold have spurred significant interest in building similar models for electronic medical records (EMRs) to improve patient care and hospital operations. However, recent hype has obscured critical gaps in our understanding of these models' capabilities. We review over 80 foundation models trained on non-imaging EMR data (i.e. clinical text and/or structured data) and create a taxonomy delineating their architectures, training data, and potential use cases. We find that most models are trained on small, narrowly-scoped clinical datasets (e.g. MIMIC-III) or broad, public biomedical corpora (e.g. PubMed) and are evaluated on tasks that do not provide meaningful insights on their usefulness to health systems. In light of these findings, we propose an improved evaluation framework for measuring the benefits of clinical foundation models that is more closely grounded to metrics that matter in healthcare.

Adrianos E. F. Athanasiadis, Michal K. Budzik, Dilum Fernando et al.

The modelling of heterogeneous and architected materials poses a significant challenge, demanding advanced homogenisation techniques. However, the complexity of this task can be considerably simplified through the application of micropolar elasticity. Conversely, elastic foundation theory is widely employed in fracture mechanics and the analysis of delamination propagation in composite materials. This study aims to amalgamate these two frameworks, enhancing the elastic foundation theory to accommodate materials exhibiting micropolar behaviour. Specifically, we present a novel theory of elastic foundation for micropolar materials, employing stress potentials formulation and a unique normalisation approach. Closed-form solutions are derived for stress and couple stress reactions inherent in such materials, along with the associated restoring stiffness. The validity of the proposed theory is established through verification using the double cantilever beam configuration. Concluding our study, we elucidate the benefits and limitations of the developed theory by quantifying the derived parameters for materials known to exhibit micropolar behaviour. This integration of micropolar elasticity into the elastic foundation theory not only enhances our understanding of material responses but also provides a versatile framework for the analysis of heterogeneous materials in various engineering applications.

Xueyan Zou, Linjie Li, Jianfeng Wang et al.

Foundation models possess strong capabilities in reasoning and memorizing across modalities. To further unleash the power of foundation models, we present FIND, a generalized interface for aligning foundation models' embeddings with unified image and dataset-level understanding spanning modality and granularity. As shown in the teaser figure, a lightweight transformer interface without tuning any foundation model weights is enough for segmentation, grounding, and retrieval in an interleaved manner. The proposed interface has the following favorable attributes: (1) Generalizable. It applies to various tasks spanning retrieval, segmentation, etc., under the same architecture and weights. (2) Interleavable. With the benefit of multi-task multi-modal training, the proposed interface creates an interleaved shared embedding space. (3) Extendable. The proposed interface is adaptive to new tasks, and new models. In light of the interleaved embedding space, we introduce FIND-Bench, which introduces new training and evaluation annotations to the COCO dataset for interleaved segmentation and retrieval. We are the first work aligning foundations models' embeddings for interleave understanding. Meanwhile, our approach achieves state-of-the-art performance on FIND-Bench and competitive performance on standard retrieval and segmentation settings.

M. Šavli, V. Pourret, C. Payan et al.

<p>The retrieval of wind from the first Doppler wind lidar of European Space Agency (ESA) launched in space in August 2018 is based on a series of corrections necessary to provide observations of a quality useful for numerical weather prediction (NWP). In this paper we examine the properties of the Rayleigh–Brillouin correction necessary for the retrieval of horizontal line-of-sight wind (HLOS) from a Fabry–Pérot interferometer. This correction is taking into account the atmospheric stratification, namely temperature and pressure information that are provided by a NWP model as suggested prior to launch. The main goal of the study is to evaluate the impact of errors in simulated atmospheric temperature and pressure information on the HLOS sensitivity by comparing the Integrated Forecast System (IFS) and Action de Recherche Petite Echelle Grande Echelle (ARPEGE) global model temperature and pressure short-term forecasts collocated with the Aeolus orbit. These errors are currently not taken into account in the computation of the HLOS error estimate since its contribution is believed to be small. This study largely confirms this statement to be a valid assumption, although it also shows that model errors could locally (i.e. jet-stream regions, below 700 <span class="inline-formula">hPa</span> over both earth poles and in stratosphere) be significant. For future Aeolus follow-on missions this study suggests considering realistic estimations of errors in the HLOS retrieval algorithms, since this will lead to an improved estimation of the Rayleigh–Brillouin sensitivity uncertainty contributing to the HLOS error estimate and better exploitation of space lidar winds in NWP systems.</p>

F. Tack, A. Merlaud, M.-D. Iordache et al.

<p>Sentinel-5 Precursor (S-5P), launched in October 2017, carrying the TROPOspheric Monitoring Instrument (TROPOMI) nadir-viewing spectrometer, is the first mission of the Copernicus Programme dedicated to the monitoring of air quality, climate, and ozone. In the presented study, the TROPOMI tropospheric nitrogen dioxide (<span class="inline-formula">NO₂</span>) level-2 (L2) product (OFFL v1.03.01; 3.5 <span class="inline-formula">km</span> <span class="inline-formula">×</span> 7 <span class="inline-formula">km</span> at nadir observations) has been validated over strongly polluted urban regions by comparison with coincident high-resolution Airborne Prism EXperiment (APEX) remote sensing observations (<span class="inline-formula">∼</span> 75 <span class="inline-formula">m</span> <span class="inline-formula">×</span> 120 <span class="inline-formula">m</span>). Satellite products can be optimally assessed based on (APEX) airborne remote sensing observations, as a large amount of satellite pixels can be fully mapped at high accuracy and in a relatively short time interval, reducing the impact of spatiotemporal mismatches. In the framework of the S-5P validation campaign over Belgium (S5PVAL-BE), the APEX imaging spectrometer has been deployed during four mapping flights (26–29 June 2019) over the two largest urban regions in Belgium, i.e. Brussels and Antwerp, in order to map the horizontal distribution of tropospheric <span class="inline-formula">NO₂</span>. For each flight, 10 to 20 TROPOMI pixels were fully covered by approximately 2700 to 4000 APEX measurements within each TROPOMI pixel. The TROPOMI and APEX <span class="inline-formula">NO₂</span> vertical column density (VCD) retrieval schemes are similar in concept. Overall, for the ensemble of the four flights, the standard TROPOMI <span class="inline-formula">NO₂</span> VCD product is well correlated (<span class="inline-formula"><i>R</i></span> <span class="inline-formula">=</span> 0.92) but biased negatively by <span class="inline-formula">−</span>1.2 <span class="inline-formula">±</span> 1.2 <span class="inline-formula">×</span> 10<span class="inline-formula">¹⁵</span> <span class="inline-formula">molec cm⁻²</span> or <span class="inline-formula">−</span>14 <span class="inline-formula">±</span> 12 %, on average, with respect to coincident APEX <span class="inline-formula">NO₂</span> retrievals. When replacing the coarse 1<span class="inline-formula">^∘</span> <span class="inline-formula">×</span> 1<span class="inline-formula">^∘</span> the massively parallel (MP) version of the Tracer Model version 5 (TM5) a priori <span class="inline-formula">NO₂</span> profiles by <span class="inline-formula">NO₂</span> profile shapes from the Copernicus Atmospheric Monitoring Service (CAMS) regional chemistry transport model (CTM) ensemble at 0.1<span class="inline-formula">^∘</span> <span class="inline-formula">×</span> 0.1<span class="inline-formula">^∘</span>, <span class="inline-formula"><i>R</i></span> is 0.94 and the slope increases from 0.82 to 0.93. The bias is reduced to <span class="inline-formula">−</span>0.1 <span class="inline-formula">±</span> 1.0 <span class="inline-formula">×</span> 10<span class="inline-formula">¹⁵</span> <span class="inline-formula">molec cm⁻²</span> or <span class="inline-formula">−</span>1.0 <span class="inline-formula">±</span> 12 %. The absolute difference is on average 1.3 <span class="inline-formula">×</span> 10<span class="inline-formula">¹⁵</span> <span class="inline-formula">molec cm⁻²</span> (16 %) and 0.7 <span class="inline-formula">×</span> 10<span class="inline-formula">¹⁵</span> <span class="inline-formula">molec cm⁻²</span> (9 %), when comparing APEX <span class="inline-formula">NO₂</span> VCDs with TM5-MP-based and CAMS-based <span class="inline-formula">NO₂</span> VCDs, respectively. Both sets of retrievals are well within the mission accuracy requirement of a maximum bias of 25 %–50 % for the TROPOMI tropospheric <span class="inline-formula">NO₂</span> product for all individual compared pixels. Additionally, the APEX data set allows the study of TROPOMI subpixel variability and impact of signal smoothing due to its finite satellite pixel size, typically coarser than fine-scale gradients in the urban <span class="inline-formula">NO₂</span> field. For a case study in the Antwerp region, the current TROPOMI data underestimate localized enhancements and overestimate background values by approximately 1–2 <span class="inline-formula">×</span> 10<span class="inline-formula">¹⁵</span> <span class="inline-formula">molec cm⁻²</span> (10 %–20 %).</p>

C. Li, H. Wang, H. Wang et al.

<p>We developed thermal dissociation cavity-enhanced absorption spectroscopy (TD-CEAS) for the in situ measurement of NO<span class="inline-formula">₂</span>, total peroxy nitrates (PNs, RO<span class="inline-formula">₂</span>NO<span class="inline-formula">₂</span>), and total alkyl nitrates (ANs, RONO<span class="inline-formula">₂</span>) in the atmosphere. PNs and ANs were thermally converted to NO<span class="inline-formula">₂</span> at the corresponding pyrolytic temperatures and detected by CEAS at 435–455 nm. The instrument sampled sequentially from three channels at ambient temperature, 453 and 653 K, with a cycle of 3 min, to measure NO<span class="inline-formula">₂</span>, NO<span class="inline-formula">₂+</span> PNs, and NO<span class="inline-formula">₂+</span> PNs <span class="inline-formula">+</span> ANs. The absorptions between the three channels were used to derive the mixing ratios of PNs and ANs by spectral fitting. The detection limit (LOD, 1<span class="inline-formula"><i>σ</i></span>) for retrieving NO<span class="inline-formula">₂</span> was 97 parts per trillion by volume (pptv) in 6 s. The measurement uncertainty of NO<span class="inline-formula">₂</span> was 9 %, while the uncertainties of PN and AN detection were larger than those of NO<span class="inline-formula">₂</span> due to chemical interferences that occurred in the heated channels, such as the reaction of NO (or NO<span class="inline-formula">₂</span>) with the peroxy radicals produced by the thermal dissociation of organic nitrates. Based on laboratory experiments and numerical simulations, we created a lookup table method to correct these interferences in PN and AN channels under various ambient organic nitrates, NO, and NO<span class="inline-formula">₂</span>. Finally, we present the first field deployment and compare it with other instruments during a field campaign in China. The advantages and limitations of this instrument are outlined.</p>

E. A. Wendt, C. Quinn, C. L'Orange et al.

<p>Atmospheric particulate matter smaller than 2.5 <span class="inline-formula">µm</span> in diameter (PM<span class="inline-formula">_2.5</span>) has a negative impact on public health, the environment, and Earth's climate. Consequently, a need exists for accurate, distributed measurements of surface-level PM<span class="inline-formula">_2.5</span> concentrations at a global scale. Existing PM<span class="inline-formula">_2.5</span> measurement infrastructure provides broad PM<span class="inline-formula">_2.5</span> sampling coverage but does not adequately characterize community-level air pollution at high temporal resolution. This motivates the development of low-cost sensors which can be more practically deployed in spatial and temporal configurations currently lacking proper characterization. Wendt et al. (2019) described the development and validation of a first-generation device for low-cost measurement of AOD and PM<span class="inline-formula">_2.5</span>: the Aerosol Mass and Optical Depth (AMODv1) sampler. Ford et al. (2019) describe a citizen-science field deployment of the AMODv1 device. In this paper, we present an updated version of the AMOD, known as AMODv2, featuring design improvements and extended validation to address the limitations of the AMODv1 work. The AMODv2 measures AOD and PM<span class="inline-formula">_2.5</span> at 20 min time intervals. The sampler includes a motorized Sun tracking system alongside a set of four optically filtered photodiodes for semicontinuous, multiwavelength (current version at 440, 500, 675, and 870 nm) AOD sampling. Also included are a Plantower PMS5003 sensor for time-resolved optical PM<span class="inline-formula">_2.5</span> measurements and a pump/cyclone system for time-integrated gravimetric filter measurements of particle mass and composition. AMODv2 samples are configured using a smartphone application, and sample data are made available via data streaming to a companion website (<span class="uri">https://csu-ceams.com/</span>, last access: 16 July 2021). We present the results of a 9 d AOD validation campaign where AMODv2 units were co-located with an AERONET (Aerosol Robotics Network) instrument as the reference method at AOD levels ranging from 0.02 <span class="inline-formula">±</span> 0.01 to 1.59 <span class="inline-formula">±</span> 0.01. We observed close agreement between AMODv2s and the reference instrument with mean absolute errors of 0.04, 0.06, 0.03, and 0.03 AOD units at 440, 500, 675, and 870 nm, respectively. We derived empirical relationships relating the reference AOD level to AMODv2 instrument error and found that the mean absolute error in the AMODv2 deviated by less than 0.01 AOD units between clear days and elevated-AOD days and across all wavelengths. We identified bias from individual units, particularly due to calibration drift, as the primary source of error between AMODv2s and reference units. In a test of 15-month calibration stability performed on 16 AMOD units, we<span id="page6024"/> observed median changes to calibration constant values of <span class="inline-formula">−</span>7.14 %, <span class="inline-formula">−</span>9.64 %, <span class="inline-formula">−</span>0.75 %, and <span class="inline-formula">−</span>2.80 % at 440, 500, 675, and 870 nm, respectively. We propose annual recalibration to mitigate potential errors from calibration drift. We conducted a trial deployment to assess the reliability and mechanical robustness of AMODv2 units. We found that 75 % of attempted samples were successfully completed in rooftop laboratory testing. We identify several failure modes in the laboratory testing and describe design changes that we have since implemented to reduce failures. We demonstrate that the AMODv2 is an accurate, stable, and low-cost platform for air pollution measurement. We describe how the AMODv2 can be implemented in spatial citizen-science networks where reference-grade sensors are economically impractical and low-cost sensors lack accuracy and stability.</p>

William DeMeo

The Agda Universal Algebra Library (UALib) is a library of types and programs (theorems and proofs) we developed to formalize the foundations of universal algebra in dependent type theory using the Agda programming language and proof assistant. The UALib includes a substantial collection of definitions, theorems, and proofs from general algebra and equational logic, including many examples that exhibit the power of inductive and dependent types for representing and reasoning about relations, algebraic structures, and equational theories. In this paper we discuss the logical foundations on which the library is built, and describe the types defined in the first 13 modules of the library. Special attention is given to aspects of the library that seem most interesting or challenging from a type theory or mathematical foundations perspective.

Valerio Capraro, Matjaz Perc

One-shot anonymous unselfishness in economic games is commonly explained by social preferences, which assume that people care about the monetary payoffs of others. However, during the last ten years, research has shown that different types of unselfish behaviour, including cooperation, altruism, truth-telling, altruistic punishment, and trustworthiness are in fact better explained by preferences for following one's own personal norms - internal standards about what is right or wrong in a given situation. Beyond better organising various forms of unselfish behaviour, this moral preference hypothesis has recently also been used to increase charitable donations, simply by means of interventions that make the morality of an action salient. Here we review experimental and theoretical work dedicated to this rapidly growing field of research, and in doing so we outline mathematical foundations for moral preferences that can be used in future models to better understand selfless human actions and to adjust policies accordingly. These foundations can also be used by artificial intelligence to better navigate the complex landscape of human morality.

Flavio Del Santo, Emanuel Schwarzhans

Vienna today is one of the capitals for the research on foundations of quantum physics. In this paper we reconstruct the main historical steps of the development of modern physics in Vienna, with an emphasis on quantum foundations. We show that the two main intuitive reasons, namely the influence of E. Schrödinger and the initiatives of A. Zeilinger in more recent years, cannot alone be held accountable for today's outstanding research landscape on foundation of quantum mechanics in Vienna. We instead show that the connection between physics and philosophy in Vienna always had an exceptional strength, and that this played a major role in establishing the prolific field of quantum foundations.

O. Coopmann, V. Guidard, N. Fourrié et al.

<p>The Infrared Atmospheric Sounding Interferometer (IASI) is an essential instrument for numerical weather prediction (NWP). It measures radiances at the top of the atmosphere using 8461 channels. The huge amount of observations provided by IASI has led the community to develop techniques to reduce observations while conserving as much information as possible. Thus, a selection of the 300 most informative channels was made for NWP based on the concept of information theory. One of the main limitations of this method was to neglect the covariances between the observation errors of the different channels. However, many centres have shown a significant benefit for weather forecasting to use them. Currently, the observation-error covariances are only estimated on the current IASI channel selection, but no studies to make a new selection of IASI channels taking into account the observation-error covariances have yet been carried out.</p> <p>The objective of this paper was therefore to perform a new selection of IASI channels by taking into account the observation-error covariances. The results show that with an equivalent number of channels, accounting for the observation-error covariances, a new selection of IASI channels can reduce the analysis error on average in temperature by 3&thinsp;%, humidity by 1.8&thinsp;% and ozone by 0.9&thinsp;% compared to the current selection. Finally, we go one step further by proposing a robust new selection of 400 IASI channels to further reduce the analysis error for NWP.</p>

G. Roversi, P. P. Alberoni, A. Fornasiero et al.

<p>There is a growing interest in emerging opportunistic sensors for precipitation, motivated by the need to improve its quantitative estimates at the ground. The scope of this work is to present a preliminary assessment of the accuracy of commercial microwave link (CML) retrieved rainfall rates in Northern Italy. The CML product, obtained by the open-source RAINLINK software package, is evaluated on different scales (single link, <span class="inline-formula">5 km×5 km</span> grid, river basin) against the precipitation products operationally used at Arpae-SIMC, the regional weather service of Emilia-Romagna, in Northern Italy. The results of the 15&thinsp;<span class="inline-formula">min</span> single-link validation with nearby rain gauges show high variability, which can be caused by the complex physiography and precipitation patterns. Known sources of errors (e.g. the attenuation caused by the wetting of the antennas or random fluctuations in the baseline) are particularly hard to mitigate in these conditions without a specific calibration, which has not been implemented. However, hourly cumulated spatially interpolated CML rainfall maps, validated with respect to the established regional gauge-based reference, show similar performance (<span class="inline-formula"><i>R</i>²</span> of 0.46 and coefficient of variation, CV, of 0.78) to adjusted radar-based precipitation gridded products and better performance than satellite-based ones. Performance improves when basin-scale total precipitation amounts are considered (<span class="inline-formula"><i>R</i>²</span> of 0.83 and CV of 0.48). Avoiding regional-specific calibration therefore does not preclude the algorithm from working but has some limitations in probability of detection (POD) and accuracy. A widespread underestimation is evident at both the grid box scale (mean error of <span class="inline-formula">−0.26</span>) and the basin scale (multiplicative bias of 0.7), while the number of false alarms is generally low and becomes even lower as link coverage increases. Also taking into account delays in the availability of the data (latency of 0.33&thinsp;<span class="inline-formula">h</span> for CML against 1&thinsp;<span class="inline-formula">h</span> for the adjusted radar and 24&thinsp;<span class="inline-formula">h</span> for the quality-controlled rain gauges), CML appears as a valuable data source in particular from a local operational framework perspective. Finally, results show complementary strengths for CMLs and radars, encouraging joint exploitation.</p>

N. J. Gingerysty, H. D. Osthoff

<p>A well-characterized source of nitrous acid vapour (HONO) is essential for accurate ambient air measurements by instruments requiring external calibration. In this work, a compact HONO source is described in which gas streams containing dilute concentrations of HONO are generated by flowing hydrochloric acid (HCl) vapour emanating from a permeation tube over continuously agitated dry sodium nitrite (<span class="inline-formula">NaNO₂</span>) heated to 50&thinsp;<span class="inline-formula">^∘</span>C. Mixing ratios of HONO and potential by-products including NO, <span class="inline-formula">NO₂</span>, and nitrosyl chloride (ClNO) were quantified by Fourier transform infrared (FTIR) and thermal-dissociation cavity ring-down spectroscopy (TD-CRDS). A key parameter is the concentration of HCl, which needs to be kept small (<span class="inline-formula">&lt;4</span>&thinsp;ppmv) to avoid ClNO formation. The source produces gas streams containing HONO in air in <span class="inline-formula">&gt;95</span>&thinsp;% purity relative to other nitrogen oxides. The source output is rapidly tuneable and stabilizes within 90&thinsp;min. Combined with its small size and portability, this source is highly suitable for calibration of HONO instruments in the field.</p>

M. Brast, P. Markmann

<p>A new method to determine the melting layer height using a micro rain radar (MRR) is presented. The MRR is a small vertically pointing frequency-modulated continuous-wave radar that measures Doppler spectra of precipitation. From these Doppler spectra, various variables such as Doppler velocity or spectral width can be derived. The melting layer is visible due to higher reflectivity and an acceleration of the falling particles, among others. These characteristics are fed to a neural network to determine the melting layer height. To train the neural network, the melting layer height is determined manually. The neural network is trained and tested using data from two sites that cover all seasons. For most cases, the neural network is able to detect the correct melting layer height well. Sensitivity studies show that the neural network is able to handle different MRR settings. Comparisons to radiosonde data and cloud radar data show a good agreement with respect to the melting layer heights.</p>

M. Radenz, J. Bühl, P. Seifert et al.

<p>Clouds are frequently composed of more than one particle population even at the smallest scales. Cloud radar observations frequently contain information on multiple particle species in the observation volume when there are distinct peaks in the Doppler spectrum. Multi-peaked situations are not taken into account by established algorithms, which only use moments of the Doppler spectrum. In this study, we propose a new algorithm that recursively represents the subpeaks as nodes in a binary tree. Using this tree data structure to represent the peaks of a Doppler spectrum, it is possible to drop all a priori assumptions on the number and arrangement of subpeaks. The approach is rigid, unambiguous and can provide a basis for advanced analysis methods. The applicability is briefly demonstrated in two case studies, in which the tree structure was used to investigate particle populations in Arctic multilayered mixed-phase clouds, which were observed during the research vessel <i>Polarstern</i> expedition PS106 and the Atmospheric Radiation Measurement Program BAECC campaign.</p>

Z. Li, A. Stoffelen, A. Verhoef

<p>Rotating-beam wind scatterometers exist in two types: rotating fan-beam and rotating pencil-beam. In our study, a generic simulation frame is established and verified to assess the wind retrieval skill of the three different scatterometers: SCAT on CFOSAT (China France Oceanography SATellite), WindRad (Chinese Wind Radar) on FY-3E, and SeaWinds on QuikSCAT. Besides the comparison of the so-called first rank solution retrieval skill of the input wind field, other figures of merit (FoMs) are applied to statistically characterize the associated wind retrieval performance from three aspects: wind vector root mean square error, ambiguity susceptibility, and wind biases. The evaluation shows that, overall, the wind retrieval quality of the three instruments can be ranked from high to low as WindRad, SCAT, and SeaWinds, where the wind retrieval quality strongly depends on the wind vector cell (WVC) location across the swath. Usually, the higher the number of views, the better the wind retrieval, but the effect of increasing the number of views reaches saturation, considering the fact that the wind retrieval quality at the nadir and sweet swath parts stays relatively similar for SCAT and WindRad. On the other hand, the wind retrieval performance in the outer swath of WindRad is improved substantially as compared to SCAT due to the increased number of views. The results may be generally explained by the different incidence angle ranges of SCAT and WindRad, mainly affecting azimuth diversity around nadir and number of views in the outer swath. This simulation frame can be used for optimizing the Bayesian wind retrieval algorithm, in particular to avoid biases around nadir but also to investigate resolution and accuracy through incorporating and analyzing the spatial response functions of the simulated Level-1B data for each WVC.</p>