Atmospheric Measurement Techniques Discussions最新文献

{"title":"Inter-comparison of retrievals of Integrated Precipitable Water Vapour IPWV) made by INSAT-3DR satellite-borne Infrared Radiometer Sounding and CAMS reanalysis data \u0000with ground-based Indian GNSS data","authors":"Ramashray Yadav, R. Giri, V. Singh","doi":"10.5194/AMT-2021-4","DOIUrl":"https://doi.org/10.5194/AMT-2021-4","url":null,"abstract":"Abstract. The spatiotemporal variations of integrated precipitable water vapor (IPWV) are very important to understand the regional variability of water vapour. Traditional in-situ measurements of IPWV in Indian region are limited and therefore the performance of satellite and Copernicus Atmosphere Meteorological Service (CAMS) retrievals with Indian Global Navigation Satellite System (GNSS) taking as reference has been analyzed. In this study the CAMS reanalysis retrieval one year (2018), Indian GNSS and INSAT-3DR sounder retrievals data for one & half years (January-2017 to June-2018) has been utilized and computed statistics. It is noticed that seasonal correlation coefficient (CC) values between INSAT-3DR and Indian GNSS data mainly lie within the range of 0.50 to 0.98 for all the selected 19 stations except Thiruvanathpuram (0.1), Kanyakumari (0.31), Karaikal (0.15) during monsoon and Panjim (0.2) during post monsoon season respectively. The seasonal CC values between CAMS and INSAT-3DR IPWV are ranges 0.73 to .99 except Jaipur (0.16) & Bhubneshwar (0.29) during pre-monsoon season, Panjim (0.38) during monsoon, Nagpur (0.50) during post-monsoon and Dibrugarh (0.49) Jaipur (0.58) & Bhubneshwar (0.16) during winter season respectively .The root mean square error (RMSE) values are higher under the wet conditions (Pre Monsoon & Monsoon season) than under dry conditions (Post Monsoon & Winter season) and found differences in magnitude and sign of bias of INSAT-3DR, CAMS with respect to GNSS IPWV from station to station and season to season. This study will help to improve understanding and utilization of CASMS and INSAT-3DR data more effectively along with GNSS data over land, coastal and desert locations in term of seasonal flow of IPWV which is an essential integrated variable in forecasting applications.\u0000","PeriodicalId":441110,"journal":{"name":"Atmospheric Measurement Techniques Discussions","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133513622","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}

{"title":"Retrieval of UV-Visible aerosol absorption using AERONET and\u0000OMI-MODIS synergy: Spatial and temporal variability across major\u0000aerosol environments","authors":"Vinay Kayetha, O. Torres, H. Jethva","doi":"10.5194/AMT-2021-8","DOIUrl":"https://doi.org/10.5194/AMT-2021-8","url":null,"abstract":"Abstract. Measuring spectral aerosol absorption remains a challenging task in aerosol studies, especially in the UV region, where the ground and airborne measurements are sparse. In this research, we introduce an algorithm that synergizes ground measurements with satellite observations for the derivation of spectral single scattering albedo (SSA, ωo) of aerosols in the UV to visible range (340–670 nm). The approach consists in explaining satellite measured near-UV radiances (340, 354 and 388 nm) by the Ozone Monitoring Instrument (OMI), and visible radiances (466 and 646 nm) by MODerate Imaging Spectrometer (MODIS), in terms of ground-based Aerosol Robotic Network (AERONET) measurements of total column extinction aerosol optical depth (AOD, τ), and retrieved total column wavelength dependent SSA using radiative transfer calculations. Required information on aerosol particle size distribution is taken from an AERONET-based climatology specifically developed for this project. This inversion procedure is applied over 110 AERONET sites distributed worldwide, for which continuous, long-term AERONET measurements are available. Using the derived data set we present seasonal and regional climatology of ωo(λ) for carbonaceous, dust and urban/industrial aerosol types. The UV-Visible spectral dependence of ωo obtained for the three major aerosol types from the synergy algorithm is found to be consistent with the in situ measurements reported in the literature. A comparison to standard AERONET SSA product at 646 nm shows absolute differences within 0.03 (0.05) for 40 % (59 %) of the compared observations. The derived aerosol ωo(λ) data set provides a valuable addition to the existing aerosol absorption record from AERONET by extending the absorption retrieval capability to the near-UV region. The combined UV-Visible data set, in addition to improving our understanding of spectral aerosol absorption properties, also offers wavelength-dependent dynamic aerosol absorption models for use in the satellite-based aerosol retrieval algorithms.\u0000","PeriodicalId":441110,"journal":{"name":"Atmospheric Measurement Techniques Discussions","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129354591","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}

{"title":"New correction method of scattering coefficient measurements of a\u0000three-wavelength nephelometer","authors":"J. Qiu, Wangshu Tan, Gang Zhao, Yingli Yu, Chunsheng Zhao","doi":"10.5194/AMT-2020-412","DOIUrl":"https://doi.org/10.5194/AMT-2020-412","url":null,"abstract":"Abstract. The aerosol scattering coefficient is a significant parameter for estimating aerosol direct radiative forcing, which can be measured by nephelometers. Currently, nephelometers have the problem of non-ideal Lambertian light source and angle truncation. Hence, the observed raw scattering coefficient data need to be corrected. In this study, based on the random forest machine learning model, we have proposed a new method to correct the scattering coefficient measurements of a three-wavelength nephelometer, Aurora 3000, under different relative humidity conditions. The result shows that the empirical corrected values match Mie-calculation values very well at all the three wavelengths and under all the measured relative humidity conditions, with more than 85 % of the corrected values in error by less than 2 %. The correction method is valid to obtain scattering coefficient with high accuracy and there is no need for additional observation data.\u0000","PeriodicalId":441110,"journal":{"name":"Atmospheric Measurement Techniques Discussions","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115962098","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}

{"title":"First ground-based FTIR observations of HFC-23 at Rikubetsu,\u0000Japan, and Syowa Station, Antarctica","authors":"M. Takeda, H. Nakajima, I. Murata, T. Nagahama, I. Morino, G. Toon, R. Weiss, J. Mühle, P. Krummel, P. Fraser, Hsiang J. Wang","doi":"10.5194/AMT-2020-505","DOIUrl":"https://doi.org/10.5194/AMT-2020-505","url":null,"abstract":"Abstract. We have developed a procedure for retrieving atmospheric abundances of HFC-23 (CHF3) with a ground-based Fourier transform infrared spectrometer (FTIR) and analysed the spectra observed at Rikubetsu, Japan (43.5° N, 143.8° E), and at Syowa Station, Antarctica (69.0° S, 39.6° E). The FTIR retrievals were carried out with the SFIT4 retrieval program, and the two spectral windows of 1138.5–1148.0 cm−1 and 1154.0–1160.0 cm−1 in the overlapping ν2 and ν5 vibrational-rotational transition bands of HFC-23 were used to avoid strong H2O absorption features. We considered O3, N2O, CH4, H2O, HDO, CFC-12 (CCl2F2), HCFC-22 (CHClF2), PAN (CH3C(O)OONO2), HCFC-141b (CH3CCl2F), and HCFC-142b (CH3CClF2) as interfering species. Vertical profiles of H2O, HDO, and CH4 are preliminarily retrieved with other independent spectral windows because these profiles may induce large uncertainties in the HFC-23 retrieval. Each HFC-23 retrieval has only one piece of vertical information with sensitivity to HFC-23 in the troposphere and the lower stratosphere. The retrieval errors mainly arise from the systematic uncertainties of the spectroscopic parameters used to obtain the HFC-23, H2O, HDO, and CH4 abundances. For comparison between FTIR-retrieved HFC-23 total columns and surface dry-air mole fractions provided by AGAGE (Advanced Global Atmospheric Gases Experiment), the FTIR-retrieved HFC-23 dry-air column-averaged mole fractions (XHFC-23) were calculated. The FTIR-retrieved XHFC-23 at Rikubetsu and Syowa Station have negative biases compared to AGAGE datasets. The trend derived from the FTIR-retrieved XHFC-23 data at Rikubetsu for December to February (DJF) data over the 1997–2010 period is 0.817 ± 0.087 ppt (parts per trillion) year−1, which is in good agreement with the trend derived from the annual global mean datasets of the AGAGE 12-box model for the same period (0.820 ± 0.011 ppt year−1). The trend of the FTIR-retrieved XHFC-23 data at Rikubetsu for DJF data over the 2007–2020 period is 0.894 ± 0.099 ppt year−1, which is smaller than the trend in the AGAGE in-situ measurements at Trinidad Head (41.1° N, 124.2° W) for the 2007–2019 period (0.984 ± 0.002 ppt year−1). The trend computed from the XHFC-23 datasets at Syowa Station over the 2007–2016 period is 0.823 ± 0.075 ppt year−1, which is consistent with that derived from the AGAGE in-situ measurements at Cape Grim (40.7° S, 144.7° E) for the same period (0.874 ± 0.002 ppt year−1). Although there are systematic biases on the FTIR-retrieved XHFC-23 at both sites, these results indicate that ground-based FTIR observations have the capability to monitor the trend of atmospheric HFC-23.","PeriodicalId":441110,"journal":{"name":"Atmospheric Measurement Techniques Discussions","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128403406","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}

{"title":"The high frequency response correction of eddy covariance fluxes. Part 2: the empirical approach and its interdependence with the time-lag estimation","authors":"O. Peltola, Toprak Aslan, A. Ibrom, E. Nemitz, Ü. Rannik, I. Mammarella","doi":"10.5194/AMT-2020-479","DOIUrl":"https://doi.org/10.5194/AMT-2020-479","url":null,"abstract":"Abstract. The eddy covariance (EC) technique has emerged as the prevailing method to observe ecosystem - atmosphere exchange of gases, heat and momentum. EC measurements require rigorous data processing to derive the fluxes that can be used to analyse exchange processes at the ecosystem - atmosphere interface. Here we show that two common post-processing steps (time-lag estimation via cross-covariance maximisation, and correction for limited frequency response of the EC measurement system) are interrelated and this should be accounted for when processing EC gas flux data. These findings are applicable to EC systems employing closed- or enclosed-path gas analysers which can be approximated to be linear first-order sensors. These EC measurement systems act as a low-pass filters on the time-series of the scalar χ (e.g. CO2, H2O) and this induces a time-lag (tlpf) between vertical wind speed (w) and scalar χ time series which is additional to the travel time of the gas signal in the sampling line (tube, filters). Time-lag estimation via cross-covariance maximisation inadvertently accounts also for tlpf and hence overestimates the travel time in the sampling line. This results in a phase shift between the time-series of w and χ, which distorts the measured cospectra between w and χ and hence has an effect on the correction for dampening of EC flux signal at high frequencies. This distortion can be described with a transfer function related to the phase shift (Hp) which is typically neglected when processing EC flux data. Based on analyses using EC data from two contrasting measurement sites, we show that the low-pass filtering induced time-lag increases approximately linearly with the time constant of the low-pass filter, and hence the importance of Hp in describing the high frequency flux loss increases as well. Incomplete description of these processes in EC data processing algorithms results in flux biases of up to 10 %, with the largest biases observed for short towers due to prevalence of small scale turbulence. Based on these findings, it is suggested that spectral correction methods implemented in EC data processing algorithms are revised to account for the influence of low-pass filtering induced time-lag.\u0000","PeriodicalId":441110,"journal":{"name":"Atmospheric Measurement Techniques Discussions","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129449440","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}

{"title":"Advanced method for estimating the number concentration of cloud water and liquid water content observed by cloud particle sensor sondes","authors":"J. Inoue, Kazutoshi Sato, Y. Tobo, F. Taketani, M. Maturilli","doi":"10.5194/AMT-2020-476","DOIUrl":"https://doi.org/10.5194/AMT-2020-476","url":null,"abstract":"Abstract. A cloud particle sensor (CPS) sonde is an observing system attached with a radiosonde sensor to observe the vertical structure of cloud properties. The signals obtained from CPS sondes are related to the phase, size, and number of cloud particles. The system offers economic advantages including human resource and simple operation costs compared with aircraft measurements and land-/satellite-based remote sensing. However, because CPS systems are limited for data downlink to land stations, the observed information should be appropriately corrected. We launched approximately 40 CPS sondes in the Arctic region between 2018 and 2020 and use these data sets to develop correction methods that exclude unreliable data, estimate the effective cloud water droplet radius, and determine a correction factor for the total cloud particle count. We apply this method to data obtained in October 2019 over the Arctic Ocean and March 2020 at Ny-Alesund, Svalbard, Norway to compare with a particle counter onboard a tethered balloon and liquid water content retrieved by a microwave radiometer. The estimated total particle count and liquid water content from the CPS sondes generally agree with those data, which exemplifies the promising advantages of this approach to retrieve quantitative and meaningful information on the vertical distribution of cloud microphysics.","PeriodicalId":441110,"journal":{"name":"Atmospheric Measurement Techniques Discussions","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117314147","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}

{"title":"Comparative characterization of bio-aerosol nebulizers in connection\u0000to atmospheric simulation chambers","authors":"S. G. Danelli, M. Brunoldi, D. Massabò, F. Parodi, V. Vernocchi, P. Prati","doi":"10.5194/AMT-2020-490","DOIUrl":"https://doi.org/10.5194/AMT-2020-490","url":null,"abstract":"Abstract. The interplay of bio-aerosol dispersion and impact, meteorology, air quality is gaining increasing interest in the wide spectrum of atmospheric science. Experiments conducted inside confined artificial environments, such as the Atmospheric Simulations Chambers (ASCs), where atmospheric conditions and composition are controlled, can provide valuable information on bio-aerosol viability, dispersion, and impact. We focus here on the reproducible aerosolization and injection of viable microorganisms into an ASC, the first and crucial step of any experimental protocol to expose bio-aerosol at different atmospheric conditions. We compare the performance of three nebulizers specifically designed for bioaerosol applications: the Collison nebulizer, the Blaustein Atomizing Modules (BLAM) and the Sparging Liquid Aerosol Generator (SLAG), all manufactured and commercialized by CH TECHNOLOGIES. The comparison refers to operating conditions and the concentration of viable bacteria at the nebulizer outlet, with the final goal to measure the reproducibility of the nebulization procedure and assess the application in experiments at ASCs. A typical bacterial test model, Escherichia coli (ATCC® 25922™), was selected for such characterization. Bacteria suspensions, with a concentration around 108 CFU ml−1, were first aerosolized at different air pressures and collected by a Liquid Impinger, to obtain a correlation curve between airflow and nebulized bacteria, for each generator. Afterwards, bacteria were aerosolized inside the atmospheric simulation chamber ChAMBRe (Chamber for Aerosol Modelling and Bio-aerosol Research) to measure the reproducibility of the whole procedure. An overall reproducibility of 11 % was obtained with each nebulizer through a set of baseline experiments.","PeriodicalId":441110,"journal":{"name":"Atmospheric Measurement Techniques Discussions","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126152317","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}

{"title":"Distributed wind measurements with multiple quadrotor UAVs in the atmospheric boundary layer","authors":"Tamino Wetz, N. Wildmann, F. Beyrich","doi":"10.5194/AMT-2020-471","DOIUrl":"https://doi.org/10.5194/AMT-2020-471","url":null,"abstract":"<p>A swarm of quadrotor UAVs is presented as a system to measure the spatial distribution of atmospheric boundary layer flow. The big advantage of this approach is, that multiple and flexible measurement points in space can be sampled synchronously. The algorithm to obtain horizontal wind speed and direction is designed for hovering flight phases and is based on the principle of aerodynamic drag and the related quadrotor dynamics using only on-board sensors.</p><p>During the FESST@MOL campaign at the Boundary Layer Field Site (Grenzschichtmessfeld, GM) Falkenberg of the Lindenberg Meteorological Observatory - Richard-A&#223;mann-Observatory (MOL-RAO), 76 calibration and validation flights were performed. The 99 m tower equipped with cup and sonic anemometers at the site is used as the reference for the calibration of the wind measurements. The validation with an independent dataset against the tower anemometers reveals that an average accuracy of &#963;_rms< 0.3 m s^-1 for the wind speed and &#963;_rms,_&#936;< 8&#176; for the wind direction was achieved.</p><p>Furthermore, we compare the spatial distribution of wind measurements with the swarm to the tower vertical profiles and Doppler wind lidar scans. We show that the observed shear in the vertical profiles matches well with the tower and the fluctuations on short time scales agree between the systems. Flow structures that appear in the time series of a line-of-sight measurement and a two-dimensional vertical scan of the lidar can be observed with the swarm and are even sampled with a higher resolution than the deployed lidar can provide.</p><p>In addition to the intercomparison of the mean wind velocity measurements, turbulence data of the UAV-swarm measurements are analyzed and a comparison to sonic anemometer measurements is provided.</p>","PeriodicalId":441110,"journal":{"name":"Atmospheric Measurement Techniques Discussions","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129866297","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}

{"title":"Towards low-cost and high-performance air pollution measurements\u0000using machine learning calibration techniques","authors":"P. Nowack, Lev Konstantinovskiy, Hannah Gardiner, J. Cant","doi":"10.5194/amt-2020-473","DOIUrl":"https://doi.org/10.5194/amt-2020-473","url":null,"abstract":"Abstract. Air pollution is a key public health issue in urban areas worldwide. The development of low-cost air pollution sensors is consequently a major research priority. However, low-cost sensors often fail to attain sufficient measurement performance compared to state-of-the-art measurement stations, and typically require calibration procedures in expensive laboratory settings. As a result, there has been much debate about calibration techniques that could make their performance more reliable, while also developing calibration procedures that can be carried out without access to advanced laboratories. One repeatedly proposed strategy is low-cost sensor calibration through co-location with public measurement stations. The idea is that, using a regression function, the low-cost sensor signals can be calibrated against the station reference signal, to be then deployed separately with performances similar to the original stations. Here we test the idea of using machine learning algorithms for such regression tasks using hourly-averaged co-location data for nitrogen dioxide (NO2) and particulate matter of particle sizes smaller than 10 μm (PM10) at three different locations in the urban area of London, UK. Specifically, we compare the performance of Ridge regression, a linear statistical learning algorithm, to two non-linear algorithms in the form of Random Forest (RF) regression and Gaussian Process regression (GPR). We further benchmark the performance of all three machine learning methods to the more common Multiple Linear Regression (MLR). We obtain very good out-of-sample R2-scores (coefficient of determination) > 0.7, frequently exceeding 0.8, for the machine learning calibrated low-cost sensors. In contrast, the performance of MLR is more dependent on random variations in the sensor hardware and co-located signals, and is also more sensitive to the length of the co-location period. We find that, subject to certain conditions, GPR is typically the best performing method in our calibration setting, followed by Ridge regression and RF regression. However, we also highlight several key limitations of the machine learning methods, which will be crucial to consider in any co-location calibration. In particular, none of the methods is able to extrapolate to pollution levels well outside those encountered at training stage. Ultimately, this is one of the key limiting factors when sensors are deployed away from the co-location site itself. Consequently, we find that the linear Ridge method, which best mitigates such extrapolation effects, is typically performing as good as, or even better, than GPR after sensor re-location. Overall, our results highlight the potential of co-location methods paired with machine learning calibration techniques to reduce costs of air pollution measurements, subject to careful consideration of the co-location training conditions, the choice of calibration variables, and the features of the calibration algorithm.\u0000","PeriodicalId":441110,"journal":{"name":"Atmospheric Measurement Techniques Discussions","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122295794","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}

{"title":"Effects of the prewhitening method, the time granularity, and the time segmentation on the Mann–Kendall trend detection and the associated Sen's slope","authors":"M. Collaud Coen, E. Andrews, A. Bigi, G. Martucci, G. Romanens, F. Vogt, L. Vuilleumier","doi":"10.5194/AMT-13-6945-2020","DOIUrl":"https://doi.org/10.5194/AMT-13-6945-2020","url":null,"abstract":"Abstract. The most widely used non-parametric method for trend analysis is the Mann-Kendall test associated with the Sen's slope. The Mann-Kendall test requires serially uncorrelated time series, whereas most of the atmospheric processes exhibit positive autocorrelation. Several prewhitening methods have been designed to overcome the presence of lag-1 autocorrelation. These include a prewhitening, a detrending and/or a correction for the detrended slope and the original variance of the time series. The choice of which prewhitening method and temporal segmentation to apply has consequences for the statistical significance, the value of the slope and of the confidence limits. Here, the effects of various prewhitening methods are analyzed for seven time series comprising in-situ aerosol measurements (scattering coefficient, absorption coefficient, number concentration and aerosol optical depth), Raman Lidar water vapor mixing ratio and the tropopause and zero degree levels measured by radio-sounding. These time series are characterized by a broad variety of distributions, ranges and lag-1 autocorrelation values and vary in length between 10 and 60 years. A common way to work around the autocorrelation problem is to decrease it by averaging the data over longer time intervals than in the original time series. Thus, the second focus of this study is evaluation of the effect of time granularity on long-term trend analysis. Finally, a new algorithm involving three prewhitening methods is proposed in order to maximize the power of the test, to minimize the amount of erroneous detected trends in the absence of a real trend and to ensure the best slope estimate for the considered length of the time series.","PeriodicalId":441110,"journal":{"name":"Atmospheric Measurement Techniques Discussions","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125468554","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}