Andrea Orfanoz-Cheuquelaf, Alexei Rozanov, M. Weber, C. Arosio, A. Ladstätter-weißenmayer, J. Burrows
Abstract. A scientific total ozone column product from the Ozone Mapping and Profiler Suite Nadir Mapper (OMPS-NM) observations and its retrieval algorithm are presented. The retrieval employs the Weighting Function Fitting Approach (WFFA), a modification of the Weighting Function Differential Optical Absorption Spectroscopy (WFDOAS) technique. The total ozone columns retrieved with WFFA are in very good agreement with other datasets. A mean difference of 0.6 % with respect to ground-based Brewer and Dobson measurements is observed. Seasonal and latitudinal variations are well represented and in agreement with other satellite datasets. The comparison of our product with the scientific product of OMPS-NM indicate a mean bias of around 0.1 %. The comparison with the Tropospheric Monitoring Instrument products (S5P/TROPOMI) OFFL and WFDOAS, shows a persistent negative bias of about −0.5 % for OFFL and –2 % for WFDOAS. Larger differences are only observed in the polar regions. This data product is intended to be used for trend analysis and the retrieval of tropospheric ozone combined with the OMPS limb profiler data.
摘要本文提出了一种科学的臭氧总柱产品，该产品是由臭氧制图和Profiler Suite Nadir Mapper (OMPS-NM)观测得到的。检索采用加权函数拟合方法(WFFA)，这是对加权函数微分光吸收光谱(WFDOAS)技术的改进。WFFA反演的总臭氧柱数与其他数据集吻合良好。与地面布鲁尔和多布森测量值相比，平均差异为0.6%。季节和纬度变化得到了很好的体现，并与其他卫星数据集一致。我们的产品与OMPS-NM的科学产品的比较表明，平均偏差约为0.1%。与对流层监测仪器产品(S5P/TROPOMI) OFFL和WFDOAS相比，OFFL和WFDOAS的持续负偏差约为- 0.5%和- 2%。较大的差异只在两极地区观察到。该数据产品拟用于趋势分析和对流层臭氧检索，并结合OMPS翼面剖面仪数据。
{"title":"Total ozone column retrieval from OMPS-NM measurements","authors":"Andrea Orfanoz-Cheuquelaf, Alexei Rozanov, M. Weber, C. Arosio, A. Ladstätter-weißenmayer, J. Burrows","doi":"10.5194/AMT-2021-61","DOIUrl":"https://doi.org/10.5194/AMT-2021-61","url":null,"abstract":"Abstract. A scientific total ozone column product from the Ozone Mapping and Profiler Suite Nadir Mapper (OMPS-NM) observations and its retrieval algorithm are presented. The retrieval employs the Weighting Function Fitting Approach (WFFA), a modification of the Weighting Function Differential Optical Absorption Spectroscopy (WFDOAS) technique. The total ozone columns retrieved with WFFA are in very good agreement with other datasets. A mean difference of 0.6 % with respect to ground-based Brewer and Dobson measurements is observed. Seasonal and latitudinal variations are well represented and in agreement with other satellite datasets. The comparison of our product with the scientific product of OMPS-NM indicate a mean bias of around 0.1 %. The comparison with the Tropospheric Monitoring Instrument products (S5P/TROPOMI) OFFL and WFDOAS, shows a persistent negative bias of about −0.5 % for OFFL and –2 % for WFDOAS. Larger differences are only observed in the polar regions. This data product is intended to be used for trend analysis and the retrieval of tropospheric ozone combined with the OMPS limb profiler data.","PeriodicalId":441110,"journal":{"name":"Atmospheric Measurement Techniques Discussions","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114889574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Oue, P. Kollias, S. Matrosov, A. Battaglia, A. Ryzhkov
Abstract. Radar dual wavelength ratio (DWR) measurements from the Stony Brook Radar Observatory Ka-band Scanning Polarimetric Radar (KASPR, 35 GHz), a profiling W-band (94 GHz) and a next generation K-band (24-GHz) Micro Rain Radar (MRRPro) were exploited for ice particle identification using triple frequency approaches. The results indicated that two of the radar frequencies (K- and Ka-band) are not sufficiently separated, thus, the triple radar frequency approaches had limited success. On the other hand, a joint analysis of DWR, mean vertical Doppler velocity (MDV), and polarimetric radar variables indicated potential in identifying ice particle types and distinguishing among different ice growth processes and even in revealing additional microphysical details.We investigated all DWR pairs in conjunction with MDV from the KASPR profiling measurements and differential reflectivity (ZDR) and specific differential phase (KDP) from the KASPR quasi-vertical profiles. The DWR-versus-MDV diagrams coupled with the polarimetric observables exhibited distinct separations of particle populations attributed to different rime degrees and particle growth processes. In fallstreaks, the 35–94 GHz DWR pair increased with the magnitude of MDV corresponding to the scattering calculations for aggregates with lower degrees of riming. The DWR values further increased at lower altitudes while ZDR slightly decreased, indicating further aggregation. Particle populations with higher rime degrees had a similar increase of DWR, but the 1–1.5 m s−1 larger magnitude of MDV and rapid decreases in KDP and ZDR. The analysis also depicted the early stage of riming where ZDR increased with the MDV magnitude collocated with small increases of DWR. This approach will improve quantitative estimations of snow amount and microphysical quantities such as rime mass fraction.
摘要利用石溪雷达观测站ka波段扫描极化雷达(KASPR, 35 GHz)、w波段剖面雷达(94 GHz)和下一代k波段微雨雷达(MRRPro)的雷达双波长比(DWR)测量数据，利用三频方法识别冰粒。结果表明，两个雷达频率(K波段和ka波段)没有充分分离，因此，三雷达频率接近的成功有限。另一方面，对DWR、平均垂直多普勒速度(MDV)和极化雷达变量的联合分析表明，在识别冰粒类型和区分不同的冰生长过程，甚至揭示额外的微物理细节方面具有潜力。我们将所有DWR对与来自KASPR剖面测量的MDV以及来自KASPR准垂直剖面的差反射率(ZDR)和比差相位(KDP)结合起来进行了研究。与极化观测相结合的dwr - vs - mdv图显示出由于不同的时间度和颗粒生长过程而导致的颗粒群的明显分离。在降条纹中，35-94 GHz DWR对随MDV的大小而增加，对应于低边缘度聚集体的散射计算。低海拔DWR值进一步增大，而ZDR值略有减小，表明进一步聚集。高龄期粒子群的DWR增幅相似，但1 ~ 1.5 m s−1的MDV幅度较大，KDP和ZDR下降较快。分析还描述了轮蚀的早期阶段，其中ZDR随着MDV的大小而增加，而DWR则小幅增加。这种方法将改进雪量和微物理量(如雾凇质量分数)的定量估计。
{"title":"Combination Analysis of Multi-Wavelength, Multi-Parameter Radar Measurements for Snowfall","authors":"M. Oue, P. Kollias, S. Matrosov, A. Battaglia, A. Ryzhkov","doi":"10.5194/AMT-2021-78","DOIUrl":"https://doi.org/10.5194/AMT-2021-78","url":null,"abstract":"Abstract. Radar dual wavelength ratio (DWR) measurements from the Stony Brook Radar Observatory Ka-band Scanning Polarimetric Radar (KASPR, 35 GHz), a profiling W-band (94 GHz) and a next generation K-band (24-GHz) Micro Rain Radar (MRRPro) were exploited for ice particle identification using triple frequency approaches. The results indicated that two of the radar frequencies (K- and Ka-band) are not sufficiently separated, thus, the triple radar frequency approaches had limited success. On the other hand, a joint analysis of DWR, mean vertical Doppler velocity (MDV), and polarimetric radar variables indicated potential in identifying ice particle types and distinguishing among different ice growth processes and even in revealing additional microphysical details.We investigated all DWR pairs in conjunction with MDV from the KASPR profiling measurements and differential reflectivity (ZDR) and specific differential phase (KDP) from the KASPR quasi-vertical profiles. The DWR-versus-MDV diagrams coupled with the polarimetric observables exhibited distinct separations of particle populations attributed to different rime degrees and particle growth processes. In fallstreaks, the 35–94 GHz DWR pair increased with the magnitude of MDV corresponding to the scattering calculations for aggregates with lower degrees of riming. The DWR values further increased at lower altitudes while ZDR slightly decreased, indicating further aggregation. Particle populations with higher rime degrees had a similar increase of DWR, but the 1–1.5 m s−1 larger magnitude of MDV and rapid decreases in KDP and ZDR. The analysis also depicted the early stage of riming where ZDR increased with the MDV magnitude collocated with small increases of DWR. This approach will improve quantitative estimations of snow amount and microphysical quantities such as rime mass fraction.\u0000","PeriodicalId":441110,"journal":{"name":"Atmospheric Measurement Techniques Discussions","volume":"4 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129071472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract. Some aircraft temperature observations, retrieved through the Aircraft Meteorological Data Relay (AMDAR), suffer from a significant warm bias when comparing observations with numerical weather prediction (NWP) model. In this manuscript we show that this warm bias of AMDAR temperature can be characterized and consequently reduced substantially. The characterization of this warm bias is based on the methodology of measuring temperature with a moving sensor and can be split into two separate processes. The first process depends on the flight phase of the aircraft and relates to difference of timing, as it appears that the time of measurement of altitude and temperature differ. When an aircraft is ascending or descending this will result in small bias in temperature due to the (on average) presence of an atmospheric temperature lapse rate. The second process is related to internal corrections applied to pressure altitude without feedback to temperature observation measurement. Based on NWP model temperature data combined with additional information on Mach number and true airspeed, we were able to estimate corrections using an 18 months period from January 2017 to July 2018. Next, the corrections were applied on AMDAR observations over the period from September 2018 to mid-December 2019. Comparing these corrected temperatures with (independent) radiosonde temperature observations demonstrates a reduction of the temperature bias from 0.5 K to around zero and reduction of standard deviation of almost 10 %.
{"title":"Characterizing and correcting the warm bias observed in AMDAR\u0000temperature observations","authors":"S. Haan, P. M. Jong, J. V. D. Meulen","doi":"10.5194/AMT-2020-519","DOIUrl":"https://doi.org/10.5194/AMT-2020-519","url":null,"abstract":"Abstract. Some aircraft temperature observations, retrieved through the Aircraft Meteorological Data Relay (AMDAR), suffer from a significant warm bias when comparing observations with numerical weather prediction (NWP) model. In this manuscript we show that this warm bias of AMDAR temperature can be characterized and consequently reduced substantially. The characterization of this warm bias is based on the methodology of measuring temperature with a moving sensor and can be split into two separate processes. The first process depends on the flight phase of the aircraft and relates to difference of timing, as it appears that the time of measurement of altitude and temperature differ. When an aircraft is ascending or descending this will result in small bias in temperature due to the (on average) presence of an atmospheric temperature lapse rate. The second process is related to internal corrections applied to pressure altitude without feedback to temperature observation measurement. Based on NWP model temperature data combined with additional information on Mach number and true airspeed, we were able to estimate corrections using an 18 months period from January 2017 to July 2018. Next, the corrections were applied on AMDAR observations over the period from September 2018 to mid-December 2019. Comparing these corrected temperatures with (independent) radiosonde temperature observations demonstrates a reduction of the temperature bias from 0.5 K to around zero and reduction of standard deviation of almost 10 %.\u0000","PeriodicalId":441110,"journal":{"name":"Atmospheric Measurement Techniques Discussions","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121723110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yann Fabel, B. Nouri, S. Wilbert, N. Blum, Rudolph Triebel, M. Hasenbalg, Pascal Kuhn, L. Zarzalejo, R. Pitz-Paal
Abstract. Semantic segmentation of ground-based all-sky images (ASIs) can provide high-resolution cloud coverage information of distinct cloud types, applicable for meteorology, climatology and solar energy-related applications. Since the shape and appearance of clouds is variable and there is high similarity between cloud types, a clear classification is difficult. Therefore, most state-of-the-art methods focus on the distinction between cloudy- and cloudfree-pixels, without taking into account the cloud type. On the other hand, cloud classification is typically determined separately on image-level, neglecting the cloud's position and only considering the prevailing cloud type. Deep neural networks have proven to be very effective and robust for segmentation tasks, however they require large training datasets to learn complex visual features. In this work, we present a self-supervised learning approach to exploit much more data than in purely supervised training and thus increase the model's performance. In the first step, we use about 300,000 ASIs in two different pretext tasks for pretraining. One of them pursues an image reconstruction approach. The other one is based on the DeepCluster model, an iterative procedure of clustering and classifying the neural network output. In the second step, our model is fine-tuned on a small labeled dataset of 770 ASIs, of which 616 are used for training and 154 for validation. For each of them, a ground truth mask was created that classifies each pixel into clear sky, low-layer, mid-layer or high-layer cloud. To analyze the effectiveness of self-supervised pretraining, we compare our approach to randomly initialized and pretrained ImageNet weights, using the same training and validation sets. Achieving 85.8 % pixel-accuracy on average, our best self-supervised model outperforms the conventional approaches of random (78.3 %) and pretrained ImageNet initialization (82.1 %). The benefits become even more evident when regarding precision, recall and intersection over union (IoU) on the respective cloud classes, where the improvement is between 5 and 20 % points. Furthermore, we compare the performance of our best model on binary segmentation with a clear-sky library (CSL) from the literature. Our model outperforms the CSL by over 7 % points, reaching a pixel-accuracy of 95 %.
摘要地面全天空图像的语义分割可以提供不同云类型的高分辨率云覆盖信息，适用于气象、气候学和太阳能相关应用。由于云的形状和外观是多变的，而且云的类型之间有很高的相似性，因此很难进行明确的分类。因此，大多数最先进的方法侧重于区分有云像素和无云像素，而不考虑云的类型。另一方面，云的分类通常是在图像级别上单独确定的，忽略了云的位置，只考虑流行云的类型。深度神经网络已经被证明是非常有效和鲁棒的分割任务，但他们需要大量的训练数据集来学习复杂的视觉特征。在这项工作中，我们提出了一种自监督学习方法，可以利用比纯监督训练更多的数据，从而提高模型的性能。在第一步中，我们在两个不同的借口任务中使用了大约30万个ASIs进行预训练。其中一个研究的是图像重建方法。另一种是基于DeepCluster模型，这是一种对神经网络输出进行聚类和分类的迭代过程。在第二步中，我们的模型在770个ASIs的小标记数据集上进行微调，其中616个用于训练，154个用于验证。对于每一个图像，我们都创建了一个ground truth mask，将每个像素划分为晴空、低层、中层或高层云。为了分析自监督预训练的有效性，我们将我们的方法与随机初始化和预训练的ImageNet权重进行比较，使用相同的训练集和验证集。我们最好的自监督模型平均达到85.8%的像素精度，优于随机(78.3%)和预训练ImageNet初始化(82.1%)的传统方法。当考虑到各自云类上的精度、召回率和交集(IoU)时，这些好处变得更加明显，其中的改进在5%到20%之间。此外，我们将我们的最佳模型与文献中的晴空库(CSL)在二值分割上的性能进行了比较。我们的模型比CSL高出7%以上，达到95%的像素精度。
{"title":"Applying self-supervised learning for semantic cloud segmentation\u0000of all-sky images","authors":"Yann Fabel, B. Nouri, S. Wilbert, N. Blum, Rudolph Triebel, M. Hasenbalg, Pascal Kuhn, L. Zarzalejo, R. Pitz-Paal","doi":"10.5194/AMT-2021-1","DOIUrl":"https://doi.org/10.5194/AMT-2021-1","url":null,"abstract":"Abstract. Semantic segmentation of ground-based all-sky images (ASIs) can provide high-resolution cloud coverage information of distinct cloud types, applicable for meteorology, climatology and solar energy-related applications. Since the shape and appearance of clouds is variable and there is high similarity between cloud types, a clear classification is difficult. Therefore, most state-of-the-art methods focus on the distinction between cloudy- and cloudfree-pixels, without taking into account the cloud type. On the other hand, cloud classification is typically determined separately on image-level, neglecting the cloud's position and only considering the prevailing cloud type. Deep neural networks have proven to be very effective and robust for segmentation tasks, however they require large training datasets to learn complex visual features. In this work, we present a self-supervised learning approach to exploit much more data than in purely supervised training and thus increase the model's performance. In the first step, we use about 300,000 ASIs in two different pretext tasks for pretraining. One of them pursues an image reconstruction approach. The other one is based on the DeepCluster model, an iterative procedure of clustering and classifying the neural network output. In the second step, our model is fine-tuned on a small labeled dataset of 770 ASIs, of which 616 are used for training and 154 for validation. For each of them, a ground truth mask was created that classifies each pixel into clear sky, low-layer, mid-layer or high-layer cloud. To analyze the effectiveness of self-supervised pretraining, we compare our approach to randomly initialized and pretrained ImageNet weights, using the same training and validation sets. Achieving 85.8 % pixel-accuracy on average, our best self-supervised model outperforms the conventional approaches of random (78.3 %) and pretrained ImageNet initialization (82.1 %). The benefits become even more evident when regarding precision, recall and intersection over union (IoU) on the respective cloud classes, where the improvement is between 5 and 20 % points. Furthermore, we compare the performance of our best model on binary segmentation with a clear-sky library (CSL) from the literature. Our model outperforms the CSL by over 7 % points, reaching a pixel-accuracy of 95 %.\u0000","PeriodicalId":441110,"journal":{"name":"Atmospheric Measurement Techniques Discussions","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131895419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Raphael Dörich, Philipp G. Eger, J. Lelieveld, J. Crowley
Abstract. Chemical Ionisation Mass Spectrometry (CIMS) using I− (the iodide anion) as primary chemi-ion has previously been used to measure NO3 and N2O5 both in laboratory and field experiments. We show that reports of the large daytime mixing ratios of NO3 and N2O5 (usually only present in detectable amounts at night-time) are likely to be heavily biased by the ubiquitous presence of HNO3 in the troposphere and lower stratosphere. We demonstrate in a series of laboratory experiments that the CIMS detection of HNO3 at m/z 62 using I− ions is efficient in the presence of PAN or peracetic acid (PAA) and especially O3. We have characterised the dependence of the sensitivity to HNO3 detection on the presence of acetate anions (CH3CO2−, m/z 59, from either PAN or PAA). The loss of CH3CO2− via conversion to NO3− in the presence of HNO3 may represent a significant bias in I-CIMS measurements of PAN and CH3C(O)OOH. The largest sensitivity to HNO3 at m/z 62 is achieved in the presence of ambient levels of O3 whereby the thermodynamically disfavoured, direct reaction of I− with HNO3 to form NO3− is bypassed by the formation of IOX− which react with HNO3 to form e.g. iodic acid and NO3−. The ozone and humidity dependence of the detection of HNO3 at m/z 62 was characterised in laboratory experiments and applied to daytime, airborne measurements in which very good agreement with measurements of the I−(HNO3) cluster-ion (specific for HNO3 detection) was obtained.
{"title":"Iodide-CIMS and m/z 62: The detection of HNO3 as NO3− in the presence of PAN, peracetic acid and O3","authors":"Raphael Dörich, Philipp G. Eger, J. Lelieveld, J. Crowley","doi":"10.5194/AMT-2021-57","DOIUrl":"https://doi.org/10.5194/AMT-2021-57","url":null,"abstract":"Abstract. Chemical Ionisation Mass Spectrometry (CIMS) using I− (the iodide anion) as primary chemi-ion has previously been used to measure NO3 and N2O5 both in laboratory and field experiments. We show that reports of the large daytime mixing ratios of NO3 and N2O5 (usually only present in detectable amounts at night-time) are likely to be heavily biased by the ubiquitous presence of HNO3 in the troposphere and lower stratosphere. We demonstrate in a series of laboratory experiments that the CIMS detection of HNO3 at m/z 62 using I− ions is efficient in the presence of PAN or peracetic acid (PAA) and especially O3. We have characterised the dependence of the sensitivity to HNO3 detection on the presence of acetate anions (CH3CO2−, m/z 59, from either PAN or PAA). The loss of CH3CO2− via conversion to NO3− in the presence of HNO3 may represent a significant bias in I-CIMS measurements of PAN and CH3C(O)OOH. The largest sensitivity to HNO3 at m/z 62 is achieved in the presence of ambient levels of O3 whereby the thermodynamically disfavoured, direct reaction of I− with HNO3 to form NO3− is bypassed by the formation of IOX− which react with HNO3 to form e.g. iodic acid and NO3−. The ozone and humidity dependence of the detection of HNO3 at m/z 62 was characterised in laboratory experiments and applied to daytime, airborne measurements in which very good agreement with measurements of the I−(HNO3) cluster-ion (specific for HNO3 detection) was obtained.\u0000","PeriodicalId":441110,"journal":{"name":"Atmospheric Measurement Techniques Discussions","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126859515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Etienne Cheynet, M. Flügge, J. Reuder, J. B. Jakobsen, Y. Heggelund, Benny Svardal, Pablo Saavedra Garfias, C. Obhrai, N. Daniotti, J. Berge, C. Duscha, N. Wildmann, I. Onarheim, M. Godvik
Abstract. The paper presents the measurement strategy and dataset collected during the COTUR (COherence of TURbulence with lidars) campaign. This field experiment took place from February 2019 to April 2020 on the southwestern coast of Norway. The coherence quantifies the spatial correlation of eddies and is little known in the marine atmospheric boundary layer. The study was motivated by the need to better characterize the lateral coherence, which partly governs the dynamic wind load on multi-megawatt offshore wind turbines. During the COTUR campaign, the coherence was studied using land-based remote sensing technology. The instrument setup consisted of three long-range scanning Doppler wind lidars, one Doppler wind lidar profiler and one passive microwave radiometer. Both the WindScanner software and Lidar Planner software were used jointly to simultaneously orient the three scanner heads into the mean wind direction, which was provided by the lidar wind profiler. The radiometer instrument complemented these measurements by providing temperature and humidity profiles in the atmospheric boundary layer. The preliminary results show an undocumented variation of the lateral coherence with the distance from the coast. The scanning beams were pointed slightly upwards to record turbulence characteristics both within and above the surface layer, providing further insight on the applicability of surface-layer scaling to model the turbulent wind load on offshore wind turbines.
{"title":"The COTUR project: Remote sensing of offshore turbulence for\u0000wind energy application","authors":"Etienne Cheynet, M. Flügge, J. Reuder, J. B. Jakobsen, Y. Heggelund, Benny Svardal, Pablo Saavedra Garfias, C. Obhrai, N. Daniotti, J. Berge, C. Duscha, N. Wildmann, I. Onarheim, M. Godvik","doi":"10.5194/AMT-2020-511","DOIUrl":"https://doi.org/10.5194/AMT-2020-511","url":null,"abstract":"Abstract. The paper presents the measurement strategy and dataset collected during the COTUR (COherence of TURbulence with lidars) campaign. This field experiment took place from February 2019 to April 2020 on the southwestern coast of Norway. The coherence quantifies the spatial correlation of eddies and is little known in the marine atmospheric boundary layer. The study was motivated by the need to better characterize the lateral coherence, which partly governs the dynamic wind load on multi-megawatt offshore wind turbines. During the COTUR campaign, the coherence was studied using land-based remote sensing technology. The instrument setup consisted of three long-range scanning Doppler wind lidars, one Doppler wind lidar profiler and one passive microwave radiometer. Both the WindScanner software and Lidar Planner software were used jointly to simultaneously orient the three scanner heads into the mean wind direction, which was provided by the lidar wind profiler. The radiometer instrument complemented these measurements by providing temperature and humidity profiles in the atmospheric boundary layer. The preliminary results show an undocumented variation of the lateral coherence with the distance from the coast. The scanning beams were pointed slightly upwards to record turbulence characteristics both within and above the surface layer, providing further insight on the applicability of surface-layer scaling to model the turbulent wind load on offshore wind turbines.\u0000","PeriodicalId":441110,"journal":{"name":"Atmospheric Measurement Techniques Discussions","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123823377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Libby Koolik, M. Roesch, Lesly J. Franco Deloya, Chuanyang Shen, A. Hallar, I. McCubbin, D. Cziczo
Abstract. A new inlet for studying the aerosol particles and hydrometeor residuals that compose mixed-phase clouds – the phaSe seParation Inlet for Droplets icE residuals and inteRstitial aerosol particles (SPIDER) – is described here. SPIDER combines a Large-Pumped Counterflow Virtual Impactor (L-PCVI), a flow tube evaporation chamber, and a Pumped Counterflow Virtual Impactor (PCVI) to separate droplets, ice crystals, and interstitial aerosol particles for simultaneous sampling. Laboratory verification tests of each individual component and then the composite SPIDER system were conducted. SPIDER was then deployed at Storm Peak Laboratory (SPL), a mountain-top research facility at 3210 m a.s.l. in the Rocky Mountains. SPIDER performance as a field instrument is presented with data that demonstrates its capability of separating cloud elements and interstitial aerosol particle. Possible design improvements of SPIDER are also suggested.
{"title":"A Phase Separation Inlet for Droplets, Ice Residuals, and Interstitial\u0000Aerosol Particles","authors":"Libby Koolik, M. Roesch, Lesly J. Franco Deloya, Chuanyang Shen, A. Hallar, I. McCubbin, D. Cziczo","doi":"10.5194/AMT-2021-26","DOIUrl":"https://doi.org/10.5194/AMT-2021-26","url":null,"abstract":"Abstract. A new inlet for studying the aerosol particles and hydrometeor residuals that compose mixed-phase clouds – the phaSe seParation Inlet for Droplets icE residuals and inteRstitial aerosol particles (SPIDER) – is described here. SPIDER combines a Large-Pumped Counterflow Virtual Impactor (L-PCVI), a flow tube evaporation chamber, and a Pumped Counterflow Virtual Impactor (PCVI) to separate droplets, ice crystals, and interstitial aerosol particles for simultaneous sampling. Laboratory verification tests of each individual component and then the composite SPIDER system were conducted. SPIDER was then deployed at Storm Peak Laboratory (SPL), a mountain-top research facility at 3210 m a.s.l. in the Rocky Mountains. SPIDER performance as a field instrument is presented with data that demonstrates its capability of separating cloud elements and interstitial aerosol particle. Possible design improvements of SPIDER are also suggested.\u0000","PeriodicalId":441110,"journal":{"name":"Atmospheric Measurement Techniques Discussions","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115834279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract. From August 4th to 30th, 2020 and from November 27th to December 25th, 2020, a self-developed radiosonde balloon system was used to observe high-altitude atmospheric optical turbulence at three sites in northwestern China, and an improved model based on the observational data was established. Through comparative analysis of the observational data and the improved model, the distribution characteristics of atmospheric optical turbulence under the combined action of different meteorological parameters and different landform features in different seasons were obtained. The improved model can show the variation of the detailed characteristics of turbulence with the height distribution, and the degree of correlation with the measured values is above 0.82. The improved model can provide a theoretical basis and supporting data for turbulence estimation and forecasting in northwestern China.
{"title":"Atmospheric Optical Turbulence Profile Measurement and Model \u0000Improvement over Arid and Semi-arid regions","authors":"Hao Yang, Zhiyuan Fang, Cheng Li, X. Deng, K. Xing, Chenbo Xie","doi":"10.5194/AMT-2021-55","DOIUrl":"https://doi.org/10.5194/AMT-2021-55","url":null,"abstract":"Abstract. From August 4th to 30th, 2020 and from November 27th to December 25th, 2020, a self-developed radiosonde balloon system was used to observe high-altitude atmospheric optical turbulence at three sites in northwestern China, and an improved model based on the observational data was established. Through comparative analysis of the observational data and the improved model, the distribution characteristics of atmospheric optical turbulence under the combined action of different meteorological parameters and different landform features in different seasons were obtained. The improved model can show the variation of the detailed characteristics of turbulence with the height distribution, and the degree of correlation with the measured values is above 0.82. The improved model can provide a theoretical basis and supporting data for turbulence estimation and forecasting in northwestern China.","PeriodicalId":441110,"journal":{"name":"Atmospheric Measurement Techniques Discussions","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134289025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
P. Fung, M. A. Zaidan, Ola M. Surakhi, S. Tarkoma, T. Petäjä, T. Hussein
Abstract. In air quality research, often only particle mass concentrations as indicators of aerosol particles are considered. However, the mass concentrations do not provide sufficient information to convey the full story of fractionated size distribution, which are able to deposit differently on respiratory system and cause various harm. Aerosol size distribution measurements rely on a variety of techniques to classify the aerosol size and measure the size distribution. From the raw data the ambient size distribution is determined utilising a suite of inversion algorithms. However, the inversion problem is quite often ill-posed and challenging to invert. Due to the instrumental insufficiency and inversion limitations, models for fractionated particle size distribution are of great significance to fill the missing gaps or negative values. The study at hand involves a merged particle size distribution, from a scanning mobility particle sizer (NanoSMPS) and an optical particle sizer (OPS) covering the aerosol size distributions from 0.01 to 0.42 μm (electrical mobility equivalent size) and 0.3 μm to 10 μm (optical equivalent size) and meteorological parameters collected at an urban background region in Amman, Jordan in the period of 1st Aug 2016–31st July 2017. We develop and evaluate feed-forward neural network (FFNN) models to estimate number concentrations at particular size bin with (1) meteorological parameters, (2) number concentration at other size bins, and (3) both of the above as input variables. Two layers with 10–15 neurons are found to be the optimal option. Lower model performance is observed at the lower edge (0.01
{"title":"Neural network modelling to estimate particle size distribution \u0000based on other particle sections and meteorological parameters","authors":"P. Fung, M. A. Zaidan, Ola M. Surakhi, S. Tarkoma, T. Petäjä, T. Hussein","doi":"10.5194/AMT-2021-37","DOIUrl":"https://doi.org/10.5194/AMT-2021-37","url":null,"abstract":"Abstract. In air quality research, often only particle mass concentrations as indicators of aerosol particles are considered. However, the mass concentrations do not provide sufficient information to convey the full story of fractionated size distribution, which are able to deposit differently on respiratory system and cause various harm. Aerosol size distribution measurements rely on a variety of techniques to classify the aerosol size and measure the size distribution. From the raw data the ambient size distribution is determined utilising a suite of inversion algorithms. However, the inversion problem is quite often ill-posed and challenging to invert. Due to the instrumental insufficiency and inversion limitations, models for fractionated particle size distribution are of great significance to fill the missing gaps or negative values. The study at hand involves a merged particle size distribution, from a scanning mobility particle sizer (NanoSMPS) and an optical particle sizer (OPS) covering the aerosol size distributions from 0.01 to 0.42 μm (electrical mobility equivalent size) and 0.3 μm to 10 μm (optical equivalent size) and meteorological parameters collected at an urban background region in Amman, Jordan in the period of 1st Aug 2016–31st July 2017. We develop and evaluate feed-forward neural network (FFNN) models to estimate number concentrations at particular size bin with (1) meteorological parameters, (2) number concentration at other size bins, and (3) both of the above as input variables. Two layers with 10–15 neurons are found to be the optimal option. Lower model performance is observed at the lower edge (0.01 \u0000","PeriodicalId":441110,"journal":{"name":"Atmospheric Measurement Techniques Discussions","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124371267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
D. Singh, Spencer Donovan, E. Pardyjak, T. Garrett
Abstract. The Differential Emissivity Imaging Disdrometer (DEID) is a new evaporation-based optical and thermal instrument designed to measure the mass, size, density, and type of individual hydrometeors and their bulk properties. Hydrometeor spatial dimensions are measured on a heated metal plate using an infrared camera by exploiting the much higher thermal emissivity of water compared with metal. As a melted hydrometeor evaporates, its mass can be directly related to the loss of heat from the hotplate assuming energy conservation across the hydrometeor. The heat-loss required to evaporate a hydrometeor is found to be independent of environmental conditions including ambient wind velocity, moisture level, and temperature. The difference in heat loss for snow versus rain for a given mass offers a method for discriminating precipitation phase. The DEID measures hydrometeors at sampling frequencies up to 1 Hz with masses and effective diameters greater than 1 µg and 200 µm, respectively, determined by the size of the hotplate and the thermal camera specifications. Measurable snow water equivalent (SWE) precipitation rates range from 0.001 to 200 mm h−1, as validated against a standard weighing bucket. Preliminary field-experiment measurements of snow and rain from the winters of 2019 and 2020 provided continuous automated measurements of precipitation rate, snow density, and visibility. Measured hydrometeor size distributions agree well with canonical results described in the literature.
摘要差分发射率成像Disdrometer (DEID)是一种基于蒸发的新型光学和热仪器，用于测量单个水成物的质量、大小、密度和类型及其总体特性。水流星的空间尺寸是利用红外摄像机在加热的金属板上测量的，利用的是水比金属高得多的热发射率。当融化的水流星蒸发时，假设整个水流星的能量守恒，它的质量可以与热板的热量损失直接相关。发现水流星蒸发所需的热损失与环境条件无关，包括环境风速、湿度水平和温度。给定质量的雪和雨的热损失的差异提供了一种判别降水阶段的方法。根据热板的尺寸和热像仪的规格，DEID测量的水成物的采样频率可达1hz，其质量和有效直径分别大于1µg和200µm。可测量的雪水当量(SWE)降水率范围为0.001至200 mm h - 1，通过标准称重桶进行验证。2019年和2020年冬季的雪和雨的初步现场试验测量提供了降水率、雪密度和能见度的连续自动测量。测量的水流星尺寸分布与文献中描述的典型结果一致。
{"title":"A differential emissivity imaging technique for measuring\u0000hydrometeor mass and type","authors":"D. Singh, Spencer Donovan, E. Pardyjak, T. Garrett","doi":"10.5194/AMT-2021-44","DOIUrl":"https://doi.org/10.5194/AMT-2021-44","url":null,"abstract":"Abstract. The Differential Emissivity Imaging Disdrometer (DEID) is a new evaporation-based optical and thermal instrument designed to measure the mass, size, density, and type of individual hydrometeors and their bulk properties. Hydrometeor spatial dimensions are measured on a heated metal plate using an infrared camera by exploiting the much higher thermal emissivity of water compared with metal. As a melted hydrometeor evaporates, its mass can be directly related to the loss of heat from the hotplate assuming energy conservation across the hydrometeor. The heat-loss required to evaporate a hydrometeor is found to be independent of environmental conditions including ambient wind velocity, moisture level, and temperature. The difference in heat loss for snow versus rain for a given mass offers a method for discriminating precipitation phase. The DEID measures hydrometeors at sampling frequencies up to 1 Hz with masses and effective diameters greater than 1 µg and 200 µm, respectively, determined by the size of the hotplate and the thermal camera specifications. Measurable snow water equivalent (SWE) precipitation rates range from 0.001 to 200 mm h−1, as validated against a standard weighing bucket. Preliminary field-experiment measurements of snow and rain from the winters of 2019 and 2020 provided continuous automated measurements of precipitation rate, snow density, and visibility. Measured hydrometeor size distributions agree well with canonical results described in the literature.\u0000","PeriodicalId":441110,"journal":{"name":"Atmospheric Measurement Techniques Discussions","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115245823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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