scholarly journals Impact of sampling frequency in the analysis of tropospheric ozone observations

2011 ◽  
Vol 11 (10) ◽  
pp. 27107-27137
Author(s):  
M. Saunois ◽  
L. Emmons ◽  
J.-F. Lamarque ◽  
S. Tilmes ◽  
C. Wespes ◽  
...  
Keyword(s):  
Atmospheric Chemistry ◽  
Tropospheric Ozone ◽  
Sampling Error ◽  
Data Sets ◽  
Vertical Profiles ◽  
Sampling Uncertainty ◽  
Data Set ◽  
Northern Hemispheric ◽  
The One ◽  
Typical Frequency

Abstract. The measurements of the ozone vertical profiles are valuable for the evaluation of atmospheric chemistry models and contribute to the understanding of the processes controlling the distribution of tropospheric ozone. The longest record of the ozone vertical profiles is provided by ozone sondes, which have a low time resolution with a typical frequency of 12 or 4 profiles a month. Here we discuss and quantify the uncertainty in the analysis of such data sets using high frequency MOZAIC (Measurements of OZone, water vapor, carbon monoxide and nitrogen oxides by in-service AIrbus airCraft) profiles data sets, such as the one over Frankfurt. We subsampled the MOZAIC data set at the two typical ozone sonde frequencies. We find that the uncertainty introduced by the coarser sampling is around 8% for a 12 profiles a month frequency (14% for a 4 profiles a month frequency) in the free troposphere over Frankfurt. As a consequence, this uncertainty at the lowest frequency is higher than the typical 10% accuracy of the ozone sondes and should be carefully considered for observation comparison and model evaluation. We found that the average intra-seasonal variability represented in the samples is similar to the sampling uncertainty and could also be used as an estimate of the sampling error in some Northern Hemisphere cases. The sampling impacts substantially the inter annual variability and the trend derived over the period 1995–2008 both in magnitude and in sign throughout the troposphere. Therefore, the sampling effect could be part of the observed discrepancies between European sites. Similar results regarding the sampling uncertainty are found at five other Northern Hemispheric sites. Also, a tropical case is discussed using the MOZAIC profiles taken over Windhoek, Namibia between 2005 and 2008.

scholarly journals Impact of sampling frequency in the analysis of tropospheric ozone observations

2012 ◽  
Vol 12 (15) ◽  
pp. 6757-6773 ◽  
Author(s):  
M. Saunois ◽  
L. Emmons ◽  
J.-F. Lamarque ◽  
S. Tilmes ◽  
C. Wespes ◽  
...  
Keyword(s):  
Atmospheric Chemistry ◽  
Tropospheric Ozone ◽  
High Frequency ◽  
Low Frequency ◽  
Sampling Frequency ◽  
Vertical Profiles ◽  
Sampling Uncertainty ◽  
Frequency Sampling ◽  
Typical Frequency

Abstract. Measurements of ozone vertical profiles are valuable for the evaluation of atmospheric chemistry models and contribute to the understanding of the processes controlling the distribution of tropospheric ozone. The longest record of ozone vertical profiles is provided by ozone sondes, which have a typical frequency of 4 to 12 profiles a month. Here we quantify the uncertainty introduced by low frequency sampling in the determination of means and trends. To do this, the high frequency MOZAIC (Measurements of OZone, water vapor, carbon monoxide and nitrogen oxides by in-service AIrbus airCraft) profiles over airports, such as Frankfurt, have been subsampled at two typical ozone sonde frequencies of 4 and 12 profiles per month. We found the lowest sampling uncertainty on seasonal means at 700 hPa over Frankfurt, with around 5% for a frequency of 12 profiles per month and 10% for a 4 profile-a-month frequency. However the uncertainty can reach up to 15 and 29% at the lowest altitude levels. As a consequence, the sampling uncertainty at the lowest frequency could be higher than the typical 10% accuracy of the ozone sondes and should be carefully considered for observation comparison and model evaluation. We found that the 95% confidence limit on the seasonal mean derived from the subsample created is similar to the sampling uncertainty and suggest to use it as an estimate of the sampling uncertainty. Similar results are found at six other Northern Hemisphere sites. We show that the sampling substantially impacts on the inter-annual variability and the trend derived over the period 1998–2008 both in magnitude and in sign throughout the troposphere. Also, a tropical case is discussed using the MOZAIC profiles taken over Windhoek, Namibia between 2005 and 2008. For this site, we found that the sampling uncertainty in the free troposphere is around 8 and 12% at 12 and 4 profiles a month respectively.

scholarly journals Key Parts of Transmission Line Detection Using Improved YOLO v3

2021 ◽  
Vol 18 (6) ◽  
Author(s):  
Tu Renwei ◽  
Zhu Zhongjie ◽  
Bai Yongqiang ◽  
Gao Ming ◽  
Ge Zhifeng
Keyword(s):  
Transmission Line ◽  
Detection Accuracy ◽  
Data Sets ◽  
Feature Maps ◽  
Data Set ◽  
Detection Model ◽  
The Neural Network ◽  
Low Efficiency ◽  
The One ◽  
Detection Speed

Unmanned Aerial Vehicle (UAV) inspection has become one of main methods for current transmission line inspection, but there are still some shortcomings such as slow detection speed, low efficiency, and inability for low light environment. To address these issues, this paper proposes a deep learning detection model based on You Only Look Once (YOLO) v3. On the one hand, the neural network structure is simplified, that is the three feature maps of YOLO v3 are pruned into two to meet specific detection requirements. Meanwhile, the K-means++ clustering method is used to calculate the anchor value of the data set to improve the detection accuracy. On the other hand, 1000 sets of power tower and insulator data sets are collected, which are inverted and scaled to expand the data set, and are fully optimized by adding different illumination and viewing angles. The experimental results show that this model using improved YOLO v3 can effectively improve the detection accuracy by 6.0%, flops by 8.4%, and the detection speed by about 6.0%.

Data Sets

2013 ◽  
Author(s):  
James B. Elsner ◽  
Thomas H. Jagger
Keyword(s):  
Model Building ◽  
Careful Analysis ◽  
Logical Structure ◽  
Fortran Program ◽  
Data Sets ◽  
Data Set ◽  
Near Surface ◽  
Wind Speeds ◽  
The North ◽  
The One

Hurricane data originate from careful analysis of past storms by operational meteorologists. The data include estimates of the hurricane position and intensity at 6-hourly intervals. Information related to landfall time, local wind speeds, damages, and deaths, as well as cyclone size, are included. The data are archived by season. Some effort is needed to make the data useful for hurricane climate studies. In this chapter, we describe the data sets used throughout this book. We show you a work flow that includes importing, interpolating, smoothing, and adding attributes. We also show you how to create subsets of the data. Code in this chapter is more complicated and it can take longer to run. You can skip this material on first reading and continue with model building in Chapter 7. You can return here when you have an updated version of the data that includes the most recent years. Most statistical models in this book use the best-track data. Here we describe these data and provide original source material. We also explain how to smooth and interpolate them. Interpolations are needed for regional hurricane analyses. The best-track data set contains the 6-hourly center locations and intensities of all known tropical cyclones across the North Atlantic basin, including the Gulf of Mexico and Caribbean Sea. The data set is called HURDAT for HURricane DATa. It is maintained by the U.S. National Oceanic and Atmospheric Administration (NOAA) at the National Hurricane Center (NHC). Center locations are given in geographic coordinates (in tenths of degrees) and the intensities, representing the one-minute near-surface (∼10 m) wind speeds, are given in knots (1 kt = .5144 m s−1) and the minimum central pressures are given in millibars (1 mb = 1 hPa). The data are provided in 6-hourly intervals starting at 00 UTC (Universal Time Coordinate). The version of HURDAT file used here contains cyclones over the period 1851 through 2010 inclusive. Information on the history and origin of these data is found in Jarvinen et al (1984). The file has a logical structure that makes it easy to read with a FORTRAN program. Each cyclone contains a header record, a series of data records, and a trailer record.

scholarly journals Quantifying uncertainties of climate signals in chemistry climate models related to the 11-year solar cycle – Part 1: Annual mean response in heating rates, temperature, and ozone

2020 ◽  
Vol 20 (11) ◽  
pp. 6991-7019
Author(s):  
Markus Kunze ◽  
Tim Kruschke ◽  
Ulrike Langematz ◽  
Miriam Sinnhuber ◽  
Thomas Reddmann ◽  
...  
Keyword(s):  
Atmospheric Chemistry ◽  
Solar Minimum ◽  
Climate Model ◽  
Solar Maximum ◽  
Sunspot Cycle ◽  
Spectral Irradiance ◽  
Data Sets ◽  
Reference Spectrum ◽  
Heating Rates ◽  
Data Set

Abstract. Variations in the solar spectral irradiance (SSI) with the 11-year sunspot cycle have been shown to have a significant impact on temperatures and the mixing ratios of atmospheric constituents in the stratosphere and mesosphere. Uncertainties in modelling the effects of SSI variations arise from uncertainties in the empirical models reconstructing the prescribed SSI data set as well as from uncertainties in the chemistry–climate model (CCM) formulation. In this study CCM simulations with the ECHAM/MESSy Atmospheric Chemistry (EMAC) model and the Community Earth System Model 1 (CESM1)–Whole Atmosphere Chemistry Climate Model (WACCM) have been performed to quantify the uncertainties of the solar responses in chemistry and dynamics that are due to the usage of five different SSI data sets or the two CCMs. We apply a two-way analysis of variance (ANOVA) to separate the influence of the SSI data sets and the CCMs on the variability of the solar response in shortwave heating rates, temperature, and ozone. The solar response is derived from climatological differences of time slice simulations prescribing SSI for the solar maximum in 1989 and near the solar minimum in 1994. The SSI values for the solar maximum of each SSI data set are created by adding the SSI differences between November 1994 and November 1989 to a common SSI reference spectrum for near-solar-minimum conditions based on ATLAS-3 (Atmospheric Laboratory of Applications and Science-3). The ANOVA identifies the SSI data set with the strongest influence on the variability of the solar response in shortwave heating rates in the upper mesosphere and in the upper stratosphere–lower mesosphere. The strongest influence on the variability of the solar response in ozone and temperature is identified in the upper stratosphere–lower mesosphere. However, in the region of the largest ozone mixing ratio, in the stratosphere from 50 to 10 hPa, the SSI data sets do not contribute much to the variability of the solar response when the Spectral And Total Irradiance REconstructions-T (SATIRE-T) SSI data set is omitted. The largest influence of the CCMs on variability of the solar responses can be identified in the upper mesosphere. The solar response in the lower stratosphere also depends on the CCM used, especially in the tropics and northern hemispheric subtropics and mid-latitudes, where the model dynamics modulate the solar responses. Apart from the upper mesosphere, there are also regions where the largest fraction of the variability of the solar response is explained by randomness, especially for the solar response in temperature.

scholarly journals A reassessment of the discrepancies in the annual variation of <i>δ</i>D-H<sub>2</sub>O in the tropical lower stratosphere between the MIPAS and ACE-FTS satellite data sets

2020 ◽  
Vol 13 (1) ◽  
pp. 287-308
Author(s):  
Stefan Lossow ◽  
Charlotta Högberg ◽  
Farahnaz Khosrawi ◽  
Gabriele P. Stiller ◽  
Ralf Bauer ◽  
...  
Keyword(s):  
Atmospheric Chemistry ◽  
Annual Variation ◽  
Lower Stratosphere ◽  
Michelson Interferometer ◽  
Tape Recorder ◽  
Data Sets ◽  
Altitude Effect ◽  
Data Set ◽  
Tropical Tropopause ◽  
Tropopause Region

Abstract. The annual variation of δD in the tropical lower stratosphere is a critical indicator for the relative importance of different processes contributing to the transport of water vapour through the cold tropical tropopause region into the stratosphere. Distinct observational discrepancies of the δD annual variation were visible in the works of Steinwagner et al. (2010) and Randel et al. (2012). Steinwagner et al. (2010) analysed MIPAS (Michelson Interferometer for Passive Atmospheric Sounding) observations retrieved with the IMK/IAA (Institut für Meteorologie und Klimaforschung in Karlsruhe, Germany, in collaboration with the Instituto de Astrofísica de Andalucía in Granada, Spain) processor, while Randel et al. (2012) focused on ACE-FTS (Atmospheric Chemistry Experiment Fourier Transform Spectrometer) observations. Here we reassess the discrepancies based on newer MIPAS (IMK/IAA) and ACE-FTS data sets, also showing for completeness results from SMR (Sub-Millimetre Radiometer) observations and a ECHAM/MESSy (European Centre for Medium-Range Weather Forecasts Hamburg and Modular Earth Submodel System) Atmospheric Chemistry (EMAC) simulation (Eichinger et al., 2015b). Similar to the old analyses, the MIPAS data set yields a pronounced annual variation (maximum about 75 ‰), while that derived from the ACE-FTS data set is rather weak (maximum about 25 ‰). While all data sets exhibit the phase progression typical for the tape recorder, the annual maximum in the ACE-FTS data set precedes that in the MIPAS data set by 2 to 3 months. We critically consider several possible reasons for the observed discrepancies, focusing primarily on the MIPAS data set. We show that the δD annual variation in the MIPAS data up to an altitude of 40 hPa is substantially impacted by a “start altitude effect”, i.e. dependency between the lowermost altitude where MIPAS retrievals are possible and retrieved data at higher altitudes. In itself this effect does not explain the differences with the ACE-FTS data. In addition, there is a mismatch in the vertical resolution of the MIPAS HDO and H2O data (being consistently better for HDO), which actually results in an artificial tape-recorder-like signal in δD. Considering these MIPAS characteristics largely removes any discrepancies between the MIPAS and ACE-FTS data sets and shows that the MIPAS data are consistent with a δD tape recorder signal with an amplitude of about 25 ‰ in the lowermost stratosphere.

scholarly journals An assessment of Bayesian bias estimator for numerical weather prediction

2008 ◽  
Vol 15 (6) ◽  
pp. 1013-1022 ◽  
Author(s):  
J. Son ◽  
D. Hou ◽  
Z. Toth
Keyword(s):  
Time Series ◽  
Numerical Weather Prediction ◽  
Sampling Error ◽  
Weather Prediction ◽  
Training Data ◽  
Statistical Characteristics ◽  
Forecast Errors ◽  
Data Sets ◽  
Data Set ◽  
Numerical Weather

Abstract. Various statistical methods are used to process operational Numerical Weather Prediction (NWP) products with the aim of reducing forecast errors and they often require sufficiently large training data sets. Generating such a hindcast data set for this purpose can be costly and a well designed algorithm should be able to reduce the required size of these data sets. This issue is investigated with the relatively simple case of bias correction, by comparing a Bayesian algorithm of bias estimation with the conventionally used empirical method. As available forecast data sets are not large enough for a comprehensive test, synthetically generated time series representing the analysis (truth) and forecast are used to increase the sample size. Since these synthetic time series retained the statistical characteristics of the observations and operational NWP model output, the results of this study can be extended to real observation and forecasts and this is confirmed by a preliminary test with real data. By using the climatological mean and standard deviation of the meteorological variable in consideration and the statistical relationship between the forecast and the analysis, the Bayesian bias estimator outperforms the empirical approach in terms of the accuracy of the estimated bias, and it can reduce the required size of the training sample by a factor of 3. This advantage of the Bayesian approach is due to the fact that it is less liable to the sampling error in consecutive sampling. These results suggest that a carefully designed statistical procedure may reduce the need for the costly generation of large hindcast datasets.

scholarly journals The SPARC water vapour assessment II: Profile-to-profile and climatological comparisons of stratospheric <i>δ</i>D(H<sub>2</sub>O) observations from satellite

2018 ◽  
Author(s):  
Charlotta Högberg ◽  
Stefan Lossow ◽  
Ralf Bauer ◽  
Kaley A. Walker ◽  
Patrick Eriksson ◽  
...  
Keyword(s):  
Water Vapour ◽  
Atmospheric Chemistry ◽  
Lower Stratosphere ◽  
Isotopic Ratio ◽  
Michelson Interferometer ◽  
Data Sets ◽  
Data Set ◽  
Spectroscopic Database ◽  
Temporal And Spatial ◽  
Chemistry Experiment

Abstract. Within the framework of the second SPARC (Stratosphere-troposphere Processes And their Role in Climate) water vapour assessment (WAVAS-II), we have evaluated five data sets of δD(H2O) obtained from observations of Odin/SMR (Sub-Millimetre Radiometer), Envisat/MIPAS (Environmental Satellite/Michelson Interferometer for Passive Atmospheric Sounding) and SCISAT/ACE-FTS (Science Satellite/Atmospheric Chemistry Experiment-Fourier Transform Spectrometer) using profile-to-profile and climatological comparisons. Our focus is on stratospheric altitudes, but results from the upper troposphere to the lower mesosphere are provided. There are clear quantitative differences in the measurements of the isotopic ratio, which primarily concerns the comparisons to the SMR data set. In the lower stratosphere, this data set shows a higher depletion than the MIPAS and ACE-FTS data sets. The differences maximise close to 50 hPa and exceed 200 per mille. With increasing altitude, the biases typically decrease. Above 4 hPa, the SMR data set shows a lower depletion than the MIPAS data sets, on occasion exceeding 100 per mille. Overall, the δD biases of the SMR data set are driven by HDO biases in the lower stratosphere and by H2O biases in the upper stratosphere and lower mesosphere. In between, in the middle stratosphere, the biases in δD are a combination of deviations in both HDO and H2O. These biases are attributed to issues with the calibration, in particular in terms of the sideband filtering for H2O, and uncertainties in spectroscopic parameters. The MIPAS and ACE-FTS data sets agree rather well between about 100 hPa and 10 hPa. The MIPAS data sets show less depletion below about 15 hPa (up to about 30 per mille), due to differences in both HDO and H2O. Higher up the picture is reversed, and towards the upper stratosphere the biases typically increase. This is driven by increasing biases in H2O and on occasion the differences in δD exceed 80 per mille. Below 100 hPa, the differences between the MIPAS and ACE-FTS data sets are even larger. In the climatological comparisons, the MIPAS data sets continue to show less depletion than the ACE-FTS data sets below 15 hPa during all seasons, with some variations in magnitude. The differences between the MIPAS and ACE-FTS data come from different aspects, such as differences in the temporal and spatial sampling (except for the profile-to-profile comparisons), cloud influence, vertical resolution, and the microwindows and spectroscopic database chosen. Differences between data sets from the same instrument are typically small in the stratosphere.

The implications of the revised code of corporate governance on firm performance

2015 ◽  
Vol 5 (3) ◽  
pp. 350-380 ◽  
Author(s):  
Abdifatah Ahmed Haji ◽  
Sanni Mubaraq
Keyword(s):  
Corporate Governance ◽  
Firm Performance ◽  
Ownership Structure ◽  
Market Performance ◽  
Negative Relationship ◽  
Future Research ◽  
Data Sets ◽  
Data Set ◽  
Content Type ◽  
The One

Purpose – The purpose of this paper is to examine the impact of corporate governance and ownership structure attributes on firm performance following the revised code on corporate governance in Malaysia. The study presents a longitudinal assessment of the compliance and implications of the revised code on firm performance. Design/methodology/approach – Two data sets consisting of before (2006) and after (2008-2010) the revised code are examined. Drawing from the largest companies listed on Bursa Malaysia (BM), the first data set contains 92 observations in the year 2006 while the second data set comprises of 282 observations drawn from the largest companies listed on BM over a three-year period, from 2008-2010. Both accounting (return on assets and return on equity) and market performance (Tobin’s Q) measures were used to measure firm performance. Multiple and panel data regression analyses were adopted to analyze the data. Findings – The study shows that there were still cases of non-compliance to the basic requirements of the code such as the one-third independent non-executive director (INDs) requirement even after the revised code. While the regression models indicate marginal significance of board size and independent directors before the revised code, the results indicate all corporate governance variables have a significant negative relationship with at least one of the measures of corporate performance. Independent chairperson, however, showed a consistent positive impact on firm performance both before and after the revised code. In addition, ownership structure elements were found to have a negative relationship with either accounting or market performance measures, with institutional ownership showing a consistent negative impact on firm performance. Firm size and leverage, as control variables, were significant in determining corporate performance. Research limitations/implications – One limitation is the use of separate measures of corporate governance attributes, as opposed to a corporate governance index (CGI). As a result, the study constructs a CGI based on the recommendations of the revised code and proposes for future research use. Practical implications – Some of the largest companies did not even comply with basic requirements such as the “one-third INDs” mandatory requirement. Hence, the regulators may want to reinforce the requirements of the code and also detail examples of good governance practices. The results, which show a consistent positive relationship between the presence of an independent chairperson and firm performance in both data sets, suggest listed companies to consider appointing an independent chairperson in the corporate leadership. The regulatory authorities may also wish to note this phenomenon when drafting any future corporate governance codes. Originality/value – This study offers new insights of the implications of regulatory changes on the relationship between corporate governance attributes and firm performance from the perspective of a developing country. The development of a CGI for future research is a novel approach of this study.

scholarly journals GLEAM v3: satellite-based land evaporation and root-zone soil moisture

2016 ◽  
Author(s):  
Brecht Martens ◽  
Diego G. Miralles ◽  
Hans Lievens ◽  
Robin van der Schalie ◽  
Richard A. M. de Jeu ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Eddy Covariance ◽  
Optical Depth ◽  
Root Zone ◽  
European Space Agency ◽  
Data Sets ◽  
Data Set ◽  
Previous Version ◽  
The One

Abstract. The Global Land Evaporation Amsterdam Model (GLEAM) is a set of algorithms dedicated to the estimation of terrestrial evaporation and root-zone soil moisture from satellite data. Ever since its development in 2011, the model has been regularly revised aiming at the optimal incorporation of new satellite-observed geophysical variables, and improving the representation of physical processes. In this study, the next version of this model (v3) is presented. Key changes relative to the previous version include: (1) a revised formulation of the evaporative stress, (2) an optimized drainage algorithm, and (3) a new soil moisture data assimilation system. GLEAM v3 is used to produce three new data sets of terrestrial evaporation and root-zone soil moisture, including a 35-year data set spanning the period 1980–2014 (v3.0a, based on satellite-observed soil moisture, vegetation optical depth and snow water equivalents, reanalysis air temperature and radiation, and a multi-source precipitation product), and two fully satellite-based data sets. The latter two share most of their forcing, except for the vegetation optical depth and soil moisture products, which are based on observations from different passive and active C- and L-band microwave sensors (European Space Agency Climate Change Initiative data sets) for the first data set (v3.0b, spanning the period 2003–2015) and observations from the Soil Moisture and Ocean Salinity satellite in the second data set (v3.0c, spanning the period 2011–2015). These three data sets are described in detail, compared against analogous data sets generated using the previous version of GLEAM (v2), and validated against measurements from 64 eddy-covariance towers and 2338 soil moisture sensors across a broad range of ecosystems. Results indicate that the quality of the v3 soil moisture is consistently better than the one from v2: average correlations against in situ surface soil moisture measurements increase from 0.61 to 0.64 in case of the v3.0a data set and the representation of soil moisture in the second layer improves as well, with correlations increasing from 0.47 to 0.53. Similar improvements are observed for the two fully satellite-based data sets. Despite regional differences, the quality of the evaporation fluxes remains overall similar as the one obtained using the previous version of GLEAM, with average correlations against eddy-covariance measurements between 0.78 and 0.80 for the three different data sets. These global data sets of terrestrial evaporation and root-zone soil moisture are now openly available at http://GLEAM.eu and may be used for large-scale hydrological applications, climate studies and research on land-atmosphere feedbacks.

scholarly journals Global validation of improved SCIAMACHY scientific ozone limb data using ozonesonde measurements

2015 ◽  
Vol 8 (5) ◽  
pp. 4817-4858
Author(s):  
J. Jia ◽  
A. Rozanov ◽  
A. Ladstätter-Weißenmayer ◽  
J. P. Burrows
Keyword(s):  
Global Scale ◽  
Data Sets ◽  
Vertical Profiles ◽  
Data Set ◽  
Latitude Range ◽  
Ozone Profile ◽  
The Tropics ◽  
Relative Differences ◽  
Significant Underestimation ◽  
Good Agreement

Abstract. In this manuscript, the latest SCIAMACHY limb ozone scientific vertical profiles, namely the current V2.9 and the upcoming V3.0, are extensively compared with ozone sonde data from the WOUDC database. The comparisons are made on a global scale from 2003 to 2011, involving 61 sonde stations. The retrieval processors used to generate V2.9 and V3.0 data sets are briefly introduced. The comparisons are discussed in terms of vertical profiles and stratospheric partial columns. Our results indicate that the V2.9 ozone profile data between 20–30 km is in good agreement with ground based measurements with less than 5% relative differences in the latitude range of 90° S–40° N (with exception of the tropical Pacific region where an overestimation of more than 10% is observed), which corresponds to less than 5 DU partial column differences. In the tropics the differences are within 3%. However, this data set shows a significant underestimation northwards of 40° N (up to ~15%). The newly developed V3.0 data set reduces this bias to below 10% while maintaining a good agreement southwards of 40° N with slightly increased relative differences of up to 5% in the tropics.

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