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A global-scale horizontally- and vertically-resolved ozone climatology can provide a detailed assessment of ozone variability. Here, the Trajectory-mapped Ozonesonde dataset for the Stratosphere and Troposphere (TOST) ozone climatology is improved and updated to the recent decade (1970s–2010s) on a grid of 5° × 5° × 1 km (latitude, longitude, and altitude) from the surface to 26 km altitude, with the most recent ozonesonde data re-evaluated following the ASOPOS-2 guidelines (GAW Report No. 268, 2021). Comparison between independent ozonesonde and trajectory-derived ozone shows good agreement in each decade, altitude, and station, with relative differences (RD) of 2–4 % in the troposphere and 0.5 % in the stratosphere. Comparisons of TOST with aircraft and two satellite datasets, the Satellite Aerosol and Gas Experiment (SAGE) and the Microwave Limb Sounder (MLS), show comparable overall agreement. The updated TOST outperforms the previous version with higher data coverage in all latitude bands and altitudes and 14–17 % lower RD compared to independent ozonesondes, employing twice as many ozonesonde profiles and an updated trajectory simulation model. Higher uncertainties in TOST are where data are sparse, i.e., over the southern high latitudes and the tropics, and before the 1980s, and where variability is high, i.e., at the surface and upper troposphere and lower stratosphere (UTLS). Caution should therefore be taken when using TOST in these spaces and times. TOST captures global ozone distributions and temporal variations, showing an overall insignificant change of stratospheric ozone after 1998. TOST offers users a long record, global coverage, and high vertical resolution. [ABSTRACT FROM AUTHOR]

This study presents an updated and homogenised ozone time series covering 34 years (1987–2020) of ozonesonde measurements at Lauder, New Zealand, and derived vertically resolved ozone trends. Over the period of 1987–1999, the ozone trends in the homogenised ozone data are predominantly negative from the surface to ∼30 km, ranging from −2 to −10 % decade−1, maximising at around 12–13 km. These negative trends are statistically significant at 95 % confidence level below 5 km and above 17 km. For the post-2000 period, ozone at Lauder shows negative trends in the stratosphere (but the trends are only statistically significant above 17 km), maximising just below 20 km (∼ −5 % decade−1), despite stratospheric chlorine and bromine from ozone-depleting substances (ODSs) both declining in this period. In the troposphere, the ozone trends change from negative for 1987–1999 to positive in the post-2000 period. The post-2000 ozone trends from the ozonesonde measurements compare well with those from a low-vertical resolution Fourier-transform infrared spectroscopy (FTIR) ozone time series. A multiple-linear regression analysis indicates that anthropogenic forcing plays a significant role in driving the significant negative trend in the stratospheric ozone at Lauder, in which the effect of greenhouse gas (GHG)-driven dynamical and chemical changes is reflected in the significant positive trends in tropopause height and tropospheric temperature, and significant negative trends of stratospheric temperature observed at Lauder. The interannual variation in lower stratospheric ozone is largely explained by the variation in tropopause height at Lauder, which is highly anti-correlated with stratospheric temperature and correlated with tropospheric temperature. Furthermore, the impact of ODSs and GHGs on ozone over Lauder is assessed in a chemistry-climate model using a series of single forcing simulations. The model simulations show that the predominantly negative modelled trend in ozone for the 1987–1999 period is driven not only by ODSs, but also by increases in GHGs with large but opposing impacts from methane (positive) and CO2 (negative), respectively. Over the 2000–2020 period, although the model underestimates the observed negative ozone trend in the lower stratosphere but clearly shows that CO2-driven dynamical changes have had an increasingly important role in driving ozone trends in this region. [ABSTRACT FROM AUTHOR]

Long-term ground-based ozone measurements are crucial to study the recovery of stratospheric ozone as well as the trends of tropospheric ozone. This study is performed in the context of the LOTUS (Long-term Ozone Trends and Uncertainties in the Stratosphere) and TOAR-II (Tropospheric Ozone Assessment Report, phase II) initiatives. We perform an intercomparison study of total column ozone and multiple partial ozone columns between the ground-based measurements available at the Lauder station from 2000 to 2022, which are the Fourier transform infrared (FTIR) spectrometer, Dobson Umkehr, ozonesonde, lidar, and the microwave radiometer. We compare partial columns, defined to provide independent information: one tropospheric and three stratospheric columns. The intercomparison is analyzed using the median of relative differences (the bias) of FTIR with each of the other measurements, the scaled Median Absolute deviation (MAD s ), and a trend of these differences (measurement drift). The total column shows a bias and strong scatter well within the combined systematic and random uncertainties respectively. There is however a drift of 0.6±0.5 %/decade if we consider the full time series. In the troposphere we find a low bias of -1.9 % with the ozonesondes. No drift is found between the three instruments in the troposphere, which is good for trend studies within TOAR-II. In both the lower and upper stratosphere, we get a negative bias for all instruments with respect to FTIR (between -1.2 % and -6.8 %), but all are within the range of the systematic uncertainties. In the middle stratosphere we seem to find a negative bias of around -5.2 to -6.6 %, pointing towards too high values for FTIR in this partial column. We find no significant drift in the stratosphere between ozonesonde and FTIR for all partial columns. We do observe drift between the FTIR and Umkehr partial columns in the lower and upper stratospheres (2.6±1.1 %/decade and -3.2±0.9 %/decade), with lidar in the midle and upper stratosphere (2.1±0.8 %/decade and -3.7±1.2 %/decade), and with MWR in the midle stratosphere (3.1±1.7 %/decade). These drifts point to the fact that the different observed trends in LOTUS are not due to different sampling, vertical sensitivity or time periods and gaps. However, the difference in trends in LOTUS is reduced by applying a new FTIR retrieval strategy, which changes inputs such as the choice of microwindows, spectroscopy from HITRAN2008 to HITRAN2020, and the regularization method. [ABSTRACT FROM AUTHOR]

Although in principle the ECC (Electrochemical Concentration Cell) ozonesonde is an absolute measuring device, in practice it has several "artefacts" which change over the course of a flight. Most of the artefacts have been corrected in the recommendations of the Assessment of Standard Operating Procedures for Ozone Sondes Report (GAW Report No. 268), giving an overall uncertainty of 5–10 % throughout the profile. However, the conversion of sampled ozone into the measured cell current has not been fully quantified, resulting in time-varying background current and pump efficiencies. We describe an updated methodology for ECC sonde data processing that is based on JOSIE 2009/2010 and JOSIE 2017-SHADOZ test chamber data. The stoichiometry (O3/I2) factors and their uncertainties along with the fast and the slow reaction pathways for the different sensing solution types used in the global ozonesonde network are determined. Experimental evidence is given for treating the background current of the ECC-sensor as the superposition of a constant ozone independent component (I B0, measured before ozone exposure in the sonde preparation protocol) and a slow time-variant ozone-dependent current determined from the initial measured ozone current using a first-order numerical convolution. The fast sensor current is refined using the time response determined in sonde preparation with a first order deconvolution scheme. Practical procedures for initializing the numerical deconvolution and convolution schemes to determine the slow and fast ECC currents are given. Calibration functions for specific ozonesondes and sensing solution type combinations were determined by comparing JOSIE 2009/2010 and JOSIE-2017-SHADOZ profiles with the JOSIE ozone reference UV-photometer (OPM). With fast and slow currents resolved and the new calibration functions, a full uncertainty budget is obtained. The time responses correction methodology makes every ozonesonde record traceable to one standard, i.e. the OPM of JOSIE, enabling the goal of a 5 % relative uncertainty to be met throughout the global ozone network. [ABSTRACT FROM AUTHOR]

The Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) is employed as an intercomparison tool for validating Tropospheric Monitoring Instrument (TROPOMI) satellite NO2 retrievals against high-resolution Airborne Prism EXperiment (APEX) remote sensing observations performed in June 2019 in the region of Antwerp, a major hotspot of NO2 pollution in Europe. The model is first evaluated using meteorological and chemical observations in this area. Sensitivity simulations varying the model planetary layer boundary (PBL) parameterization were conducted for a 3-day period in June 2019, indicating a general good performance of most parameterizations against meteorological data (namely ceilometer, surface meteorology and balloon measurements), except for a moderate overestimation (~1 m s-1) of near-surface wind speed. On average, all but one PBL schemes reproduce fairly well the surface NO2 measurements at stations of the Belgian Interregional Environmental Agency, although surface NO2 is generally underestimated during the day (between -4.3 and -25.1 % on average) and overestimated at night (8.2–77.3 %). This discrepancy in the diurnal evolution arises despite (1) implementing a detailed representation of the diurnal cycle of emissions (Crippa et al., 2020), and (2) correcting the modelled concentrations to account for measurement interferences due to NOy reservoir species, which increases NO2 concentrations by about 20 % during the day. The model is further evaluated by comparing a 15-day simulation with surface NO2, NO, CO and O3 data in the Antwerp region. The modelled daytime NO2 concentrations are more negatively biased during weekdays than during weekends, indicating a misrepresentation of the weekly temporal profile applied to the emissions, obtained from Crippa et al. (2020). Using a mass-balance approach, we determined a new weekly profile of NOx emissions, leading to a homogenization of the relative bias among the different weekdays. The ratio of weekend to weekday emissions is significantly lower in this updated profile (0.6) than in the profile based on Crippa et al. (2020) (0.84). Comparisons with remote sensing observations generally show a good reproduction of the spatial patterns of NO2 columns by the model. Both APEX and TROPOMI columns are underestimated on the 27/6, whereas no significant bias is found on the 29/6. The two datasets are intercompared by using the model as an intermediate platform to account for differences in vertical sensitivity through the application of averaging kernels. The derived bias of TROPOMI v1.3.1 NO2 with respect to APEX is about -10 % for columns between (6–12)x1015 molec. cm-2. The obtained bias for TROPOMI v1.3.1 increases with the NO2 column, following C APEX = 1.217 C v1.3 - 0.783x1015 molec. Cm-2, in line with previous validation campaigns. The bias is slightly lower for the reprocessed TROPOMI v2.3.1, with C APEX = 1.055 C PAL - 0.437x1015 molec. cm-2 (PAL). Finally, a mass balance approach was used to perform a crude inversion of NOx emissions, based on 15-day averaged TROPOMI columns. The emission correction is conducted only in regions with high columns and high sensitivity to emission changes, in order to minimize the errors due to wind transport. The results suggest emissions increases over Brussels-Antwerp (+20 %), Ruhr Valley (13 %), and especially Paris (+39 %), and emission decreases above a cluster of power plants in West Germany. [ABSTRACT FROM AUTHOR]

Starting in 1969, and with three launches a week, the Uccle (Brussels, Belgium) ozonesonde dataset is one of longest and densest of the world. Moreover, as the only major change was the switch from Brewer-Mast to Electrochemical Concentration Cell (ECC) ozonesonde types in 1997 (when the emissions of ozone depleting substances peaked), the Uccle time series is very homogenous. In this paper, we briefly describe which efforts have been taken during the first three decades of the 50 years of ozonesonde observations to guarantee the homogeneity between ascent and descent profiles, under changing environmental conditions (e.g. SO2), and between the different ozonesonde types. This paper focuses on the 50 years long Uccle ozonesonde dataset and aims to demonstrate its past, present and future relevance to ozone research in two application areas: (i) the assessment of the temporal evolution of ozone from the surface to the (middle) stratosphere, and (ii) as backbone for validation and stability analysis of both stratospheric as well as tropospheric satellite ozone retrievals. Using the Long-term Ozone Trends and Uncertainties in the Stratosphere (LOTUS) multiple linear regression model (SPARC/IO3C/GAW, 2019), we found that the stratospheric ozone concentrations at Uccle declined at a significant rate of around 2%/decade since 1969, rather consistently over the different stratospheric levels. This overall decrease can mainly be assigned to the 1969-1996 period with a rather consistent decline rate around -4%/decade. Since 2000, a recovery between +1 to +3%/decade of the stratospheric ozone levels above Uccle is observed, although not significant and not for the upper stratospheric levels measured by ozonesondes. Throughout the entire free troposphere, a very consistent increase of the ozone concentrations at 2 to 3%/decade is measured since both 1969 and 1995, the latter trend being in almost perfect agreement with the trends derived from the In-service Aircraft for a Global Observing System (IAGOS) ascent/descent profiles at Frankfurt. As the amount of tropopause folding events in the Uccle time series increased significantly over time, increased stratosphere-to-troposphere transport of recovering stratospheric ozone might partly explain these increasing tropospheric ozone concentrations, despite the levelling off in (tropospheric) ozone precursor emissions and notwithstanding the continued increase of mean surface ozone concentrations. Furthermore, we illustrate the crucial role of ozonesonde measurements for validation of satellite ozone profile retrievals. With the operational validation of Global Ozone Monitoring Experiment GOME-2, we show how the Uccle dataset can be used to evaluate the performance of a degradation correction for the MetOp-A/GOME-2 UV sensors. In another example, we illustrate that the Microwave Limb Sounder (MLS) overpass ozone profiles in the stratosphere agree within ±5% with the Uccle ozone profiles between 10 and 70 hPa. Another instrument on the same AURA satellite platform, Tropospheric Emission Spectrometer (TES), is generally positively biased with respect to the Uccle ozonesondes in the troposphere by up to ~10 ppbv, corresponding to relative differences up to ~15%. Using the Uccle ozonesonde time series as reference, we also demonstrate that the temporal stability of those last two satellite retrievals is excellent. [ABSTRACT FROM AUTHOR]

Nowadays, precipitable water vapor (PWV) retrieved from ground-based Global Navigation Satellite Systems (GNSS) tracking stations has heralded a new era of GNSS meteorological applications, especially for severe weather prediction. Among the existing models that use PWV timeseries to predict heavy precipitation, the "threshold-based" models, which are based on a set of predefined thresholds for the predictors used in the model for predictions, are effective in heavy precipitation nowcasting. In previous studies, monthly thresholds have been widely accepted due to the monthly patterns of different predictors being fully considered. However, the primary weakness of this type of thresholds lies in their poor prediction results in the transitional periods between two consecutive months. Therefore, in this study, a new method for the determination of an optimal set of diurnal thresholds by adopting a 31-day sliding window was first proposed. Both the monthly and diurnal variation characteristics of the predictors were taken into consideration in the new method. Then, on the strength of the new method, an improved PWV-based model for heavy precipitation prediction was developed using the optimal set of diurnal thresholds determined based on the hourly PWV and precipitation records for the summer over the period 2010–2017 at the co-located HKSC–KP (King's Park) stations in Hong Kong. The new model was evaluated by comparing its prediction results against the hourly precipitation records for the summer in 2018 and 2019. It is shown that 96.9% of heavy precipitation events were correctly predicted with a lead time of 4.86 h, and the false alarms resulting from the new model were reduced to 25.3%. These results suggest that the inclusion of the diurnal thresholds can significantly improve the prediction performance of the model. [ABSTRACT FROM AUTHOR]

The meteorological reanalysis data has been widely applied to derive zenith tropospheric delay (ZTD) with a high spatial and temporal resolution. With the rapid development of artificial intelligence, machine learning also begins as a high-efficiency tool to be employed in modeling and predicting ZTD. In this paper, we develop three new regional ZTD models based on the least squares support vector machine (LSSVM), using both the International GNSS Service (IGS)-ZTD products and European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) data over Europe throughout 2018. Among them, the ERA5 data is extended to ERA5S-ZTD and ERA5P-ZTD as the background data by the model method and integral method, respectively. Depending on different background data, three schemes are designed to construct ZTD models based on the LSSVM algorithm, including the without background data, with the ERA5S-ZTD, and with the ERA5P-ZTD. To investigate the advantage and feasibility of the proposed ZTD models, we evaluate the accuracy of two background data and three schemes by segmental comparison with the IGS-ZTD of 85 IGS stations in Europe. The results show that the overall average Root Mean Square Errors (RMSE) value of all sites is 30.1 mm for the ERA5S-ZTD, and 10.7 mm for the ERA5P-ZTD. The overall average RMSE is 25.8 mm, 22.9 mm, and 9 mm for the three schemes, respectively. Moreover, the overall improvement rate is 19.1% and 1.6% for the ZTD model with ERA5S-ZTD and ERA5P-ZTD, respectively. In order to explore the reason of the lower improvement for the ZTD model with ERA5P-ZTD, the loop verification is performed by estimating the ZTD values of each available IGS station. In actuality, the monthly improvement rate of estimated ZTD is positive for most stations, and the biggest improvement rate can even reach about 40%. The negative rate mainly comes from specific stations, these stations are located on the edge of the region, near the coast, as well as the lower similarity between the individual verified station and training stations. [ABSTRACT FROM AUTHOR]

The delays of radio signals transmitted by global navigation satellite system (GNSS) satellites and induced by neutral atmosphere, which are usually represented by zenith tropospheric delay (ZTD), are required as critical information both for GNSS positioning and navigation and GNSS meteorology. Establishing a stable and reliable ZTD model is one of the interests in GNSS research. In this study, we proposed a regional ZTD model that makes full use of the ZTD calculated from regional GNSS data and the corresponding ZTD estimated by global pressure and temperature 3 (GPT3) model, adopting the artificial neutral network (ANN) to construct the correlation between ZTD derived from GPT3 and GNSS observations. The experiments in Hong Kong using Satellite Positioning Reference Station Network (SatRet) were conducted and three statistical values, i.e., bias, root mean square error (RMSE), and compound relative error (CRE) were adopted for our comparisons. Numerical results showed that the proposed model outperformed the parameter ZTD model (Saastamoinen model) and the empirical ZTD model (GPT3 model), with an approximately 56%/52% and 52%/37% RMSE improvement in the internal and external accuracy verification, respectively. Moreover, the proposed method effectively improved the systematic deviation of GPT3 model and achieved better ZTD estimation in both rainy and rainless conditions. [ABSTRACT FROM AUTHOR]