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Aerosol lidar networks can play an important role in revealing structural characteristics of the atmospheric boundary layer, the urban heat island effect, and the spatial distribution of aerosols, especially in relation to the monitoring of atmospheric pollution in megacities. To fulfill the need of the monitoring and numerical forecasting of atmospheric pollution, an aerosol lidar network is proposed by the China Meteorological Administration which serves as an important part of the "MegaCity Experiment on Integrated Meteorological Observation in China" (MEMO). To ensure a high standard of data quality and traceability of measurement error, an inter-comparison campaign, dedicated to the quality assessment of lidar systems from different institutes and manufacturers, was designed and performed at Beijing Southern Suburb Observatory in September 2021. Six Mie–Rayleigh lidar systems at 1064 nm were involved in this campaign. The strategies for lidar self-evaluations and inter-comparisons were predefined. A lidar system at 1064 nm, which was developed by the Atmospheric Remote Sensing group at Wuhan University, was selected as the reference lidar system after passing all strict self-evaluation quality checks. The reference lidar system serves as the cornerstone for evaluating the performance of other lidar systems. After using the Rayleigh fit and signal-to-noise evaluation self-tests for each individual lidar system as a fast check of the data quality, the range-corrected signal and backscatter coefficient obtained from all the lidar systems were inter-compared with a reference lidar system. In the end, the lidar systems passed the quality control/assurance, ensuring that the standard deviation of range-corrected signal could be controlled within 5 % at 500–2000 m and 10 % at 2000–5000 m. For the derived aerosol backscatter coefficients, standard deviations can be controlled within 10 % at 500–2000 and 2000–5000 m. The quality assurance strategy lays down a solid basis for atmospheric lidar at near-infrared wavelengths and will be applied in Chinese lidar network development. [ABSTRACT FROM AUTHOR]

The main in situ database for numerical weather prediction currently relies on radiosonde and airliner observations, with large systematic data gaps: horizontally in certain countries, above the oceans and in polar regions, and vertically in the rapidly changing atmospheric boundary layer, as well as up to the tropopause in areas with low air traffic. These gaps might be patched by measurements with drones. They provide a significant improvement towards environment-friendly additional data, avoiding waste and without the need for helium. So far, such systems have not been regarded as a feasible alternative for performing measurements up to the upper troposphere. In this article, the development of a drone system that is capable of sounding the atmosphere up to an altitude of 10 km with its own propulsion is presented, for which Antarctic and mid-European ambient conditions were taken into account: after an assessment of the environmental conditions at two exemplary radiosounding sites, the design of the system and the instrumentation are presented. Further, the process to get permissions for such flight tests even in the densely populated continent of Europe is discussed, and methods to compare drone and radiosonde data for quality assessment are presented. The main result is the technical achievement of demonstrating the feasibility of reaching an altitude of 10 km with a small meteorologically equipped drone using its own propulsion. The first data are compared to radiosonde measurements, demonstrating an accuracy comparable to other aircraft-based observations, despite the simplistic sensor package deployed. A detailed error discussion is given. The article closes with an outlook on the potential use of drones for filling data gaps in the troposphere. [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 NO 2 retrievals against high-resolution Airborne Prism EXperiment (APEX) remote sensing observations performed in June 2019 in the region of Antwerp, a major hotspot of NO 2 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 d period in June 2019, indicating a generally 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 of the PBL schemes reproduce the surface NO 2 measurements at stations of the Belgian Interregional Environmental Agency fairly well, although surface NO 2 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 modeled concentrations to account for measurement interferences due to NO y reservoir species, which increases NO 2 concentrations by about 20 % during the day. The model is further evaluated by comparing a 15 d simulation with surface NO 2 , NO, CO, and O 3 data in the Antwerp region. The modeled daytime NO 2 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 NO x 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 NO 2 columns by the model. The model underestimated both APEX (by ca. -37 %) and TROPOMI columns (ca. -25 %) on 27 June, whereas no significant bias is found on 29 June. 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 NO 2 with respect to APEX is about -10 % for columns between (6–12) × 10 15 molec. cm -2. The obtained bias for TROPOMI v1.3.1 increases with the NO 2 column, following CAPEX=1.217Cv1.3-0.783 × 10 15 molec. cm -2 , in line with previous validation campaigns. The bias is slightly lower for the reprocessed TROPOMI v2.3.1, with CAPEX=1.055CPAL-0.437 × 10 15 molec. cm -2 (PAL). Finally, a mass balance approach was used to perform a crude inversion of NO x emissions based on 15 d 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 that emissions increase over Brussels–Antwerp (+20 %), the Ruhr Valley (13 %), and especially Paris (+39 %), and emissions decrease above a cluster of power plants in western Germany. [ABSTRACT FROM AUTHOR]

Doppler wind lidars (DWLs) have increasingly been used over the last decade to derive the mean wind in the atmospheric boundary layer. DWLs allow the determination of wind vector profiles with high vertical resolution and provide an alternative to classic meteorological tower observations. They also receive signals from altitudes higher than a tower and can be set up flexibly in any power-supplied location. In this work, we address the question of whether and how wind gusts can be derived from DWL observations. The characterization of wind gusts is one central goal of the Field Experiment on Sub-Mesoscale Spatio-Temporal Variability in Lindenberg (FESSTVaL). Obtaining wind gusts from a DWL is not trivial because a monostatic DWL provides only a radial velocity per line of sight, i.e., only one component of a three-dimensional vector, and measurements in at least three linearly independent directions are required to derive the wind vector. Performing them sequentially limits the achievable time resolution, while wind gusts are short-lived phenomena. This study compares different DWL configurations in terms of their potential to derive wind gusts. For this purpose, we develop a new wind retrieval method that is applicable to different scanning configurations and various time resolutions. We test eight configurations with StreamLine DWL systems from HALO Photonics and evaluate gust peaks and mean wind over 10 min at 90 m a.g.l. against a sonic anemometer at the meteorological tower in Falkenberg, Germany. The best-performing configuration for retrieving wind gusts proves to be a fast continuous scanning mode (CSM) that completes a full observation cycle within 3.4 s. During this time interval, about 11 radial Doppler velocities are measured, which are then used to retrieve single gusts. The fast CSM configuration was successfully operated over a 3-month period in summer 2020. The CSM paired with our new retrieval technique provides gust peaks that compare well to classic sonic anemometer measurements from the meteorological tower. [ABSTRACT FROM AUTHOR]

Doppler wind lidars (DWLs) have increasingly been used over the last decade to derive the mean wind in the atmospheric boundary layer. DWLs allow the determination of wind vector profiles with high vertical resolution and provide an alternative to classic meteorological tower observations. They also receive signals from higher altitudes than a tower and can be set up flexibly in any power-supplied location. In this work, we address the question of whether and how wind gusts can be derived from DWL observations. The characterization of wind gusts is one central goal of the Field Experiment on Sub-Mesoscale Spatio-Temporal Variability in Lindenberg (FESSTVaL). Obtaining wind gusts from a DWL is not trivial because a monostatic DWL provides only a radial velocity per line-of-sight, i.e., only one component of a three-dimensional vector, and measurements in at least three linearly independent directions are required to derive the wind vector. Performing them sequentially limits the achievable time resolution, while wind gusts are short-lived phenomena. This study compares different DWL configurations in terms of their potential to derive wind gusts. For this purpose, we develop a new wind retrieval method that is applicable to different scanning configurations and various time resolutions. We test eight configurations with StreamLine DWL systems from Halo Photonics and evaluate gust peaks and mean wind over 10 minutes at 90 m a.g.l. against a sonic anemometer at the meteorological tower in Falkenberg, Germany. The best performing configuration for retrieving wind gusts proves to be a fast continuous scanning mode (CSM) that completes a full observation cycle within 3.4 s. During this time interval, about eleven radial Doppler velocities are measured, which are then used to retrieve single gusts. The fast CSM configuration was successfully operated over a three-month period in summer 2020. The CSM paired with our new retrieval technique provides gust peaks that compare well to classic sonic anemometer measurements from the meteorological tower. [ABSTRACT FROM AUTHOR]

We developed the WRF-GC model, an online coupling of the Weather Research and Forecasting (WRF) mesoscale meteorological model and the GEOS-Chem atmospheric chemistry model, for regional atmospheric chemistry and air quality modeling. Both WRF and GEOS-Chem are open-source and community-supported. WRF-GC provides regional chemistry modellers easy access to the GEOS-Chem chemical module, which is stably-configured, state-of-the-science, well-documented, traceable, benchmarked, actively developed by a large international user base, and centrally managed by a dedicated support team. At the same time, WRF-GC gives GEOS-Chem users the ability to perform high-resolution forecasts and hindcasts for any location and time of interest. WRF-GC is designed to be easy to use, massively parallel, extendable, and easy to update. The WRF-GC coupling structure allows future versions of either one of the two parent models to be immediately integrated into WRF-GC. This enables WRF-GC to stay state-of-the-science with traceability to parent model versions. Physical and chemical state variables in WRF and in GEOS-Chem are managed in distributed memory and translated between the two models by the WRF-GC Coupler at runtime. We used the WRF-GC model to simulate surface PM2.5 concentrations over China during January 22 to 27, 2015 and compared the results to surface observations and the outcomes from a GEOS-Chem nested-grid simulation. Both models were able to reproduce the observed spatiotemporal variations of regional PM2.5, but the WRF-GC model (r = 0.68, bias = 29%) reproduced the observed daily PM2.5 concentrations over Eastern China better than the GEOS-Chem model did (r = 0.72, bias = 55%). This was mainly because our WRF-GC simulation, nudged with surface and upper-level meteorological observations, was able to better represent the spatiotemporal variability of the planetary boundary layer heights over China during the simulation period. Both parent models and the WRF-GC Coupler are parallelized across computational cores and can scale to massively parallel architectures. The WRF-GC simulation was three times more efficient than the GEOS-Chem nested-grid simulation at similar resolutions and for the same number of computational cores, owing to the more efficient transport algorithm and the MPI-based parallelization provided by the WRF software framework. WRF-GC scales nearly perfectly up to a few hundred cores on a variety of computational platforms. Version 1.0 of the WRF-GC model supports one-way coupling only, using WRF-simulated meteorological fields to drive GEOS-Chem with no feedbacks from GEOS-Chem. The development of two-way coupling capabilities, i.e., the ability to simulate radiative and microphysical feedbacks of chemistry to meteorology, is under-way. The WRF-GC model is open-source and freely available from http://wrf.geos-chem.org. [ABSTRACT FROM AUTHOR]

The Yongxing air–sea flux tower (YXASFT), which was specially designed for air–sea boundary layer observations, was constructed on Yongxing Island in the South China Sea (SCS). Surface bulk variable measurements were collected during a 1-year period from 1 February 2016 to 31 January 2017. The sensible heat flux (SHF) and latent heat flux (LHF) were further derived via the Coupled Ocean–Atmosphere Response Experiment version 3.0 (COARE3.0). This study employed the YXASFT in situ observations to evaluate the Woods Hole Oceanographic Institute (WHOI) Objectively Analyzed Air–Sea Fluxes (OAFlux) reanalysis data products. First, the reliability of COARE3.0 data in the SCS was validated using direct turbulent heat flux measurements via an eddy covariance flux (ECF) system. The LHF data derived from COARE3.0 are highly consistent with the ECF with a coefficient of determination (R2) of 0.78. Second, the overall reliabilities of the bulk OAFlux variables were diminished in the order of Ta (air temperature), U (wind speed), Qa (air humidity) and Ts (sea surface temperature) based on a combination of R2 values and biases. OAFlux overestimates (underestimates) U (Qa) throughout the year and provides better estimates for winter and spring than in the summer–autumn period, which seems to be highly correlated with the monsoon climate in the SCS. The lowest R2 is between the OAFlux-estimated and YXASFT-observed Ts , indicating that Ts is the least reliable dataset and should thus be used with considerable caution. In terms of the heat fluxes, OAFlux considerably overestimates LHF with an ocean heat loss bias of 52 w m -2 in the spring, and the seasonal OAFlux LHF performance is consistent with U and Qa. The OAFlux-estimated SHF appears to be a poor representative, with enormous overestimations in the spring and winter, while its performance is much better during the summer–autumn period. Third, analysis reveals that the biases in Qa are the most dominant factor on the LHF biases in the spring and winter, and that the biases in both Qa and U are responsible for controlling the biases in LHF during the summer–autumn period. The biases in Ts are responsible for controlling the SHF biases, and the effects of biases in Ts on the biases in SHF during the spring and winter are much greater than that in the summer–autumn period. [ABSTRACT FROM AUTHOR]

Single-column models (SCMs) have been used as tools to help develop numerical weather prediction and global climate models for several decades. SCMs decouple small-scale processes from large-scale forcing, which allows the testing of physical parameterisations in a controlled environment with reduced computational cost. Typically, either the ocean, sea ice or atmosphere is fully modelled and assumptions have to be made regarding the boundary conditions from other subsystems, adding a potential source of error. Here, we present a fully coupled atmosphere-ocean SCM (AOSCM), which is based on the global climate model EC-Earth3. The initial configuration of the AOSCM consists of the Nucleus for European Modelling of the Ocean (NEMO3.6) (ocean), the Louvain-la-Neuve Sea Ice Model (LIM3) (sea ice), the Open Integrated Forecasting System (OpenIFS) cycle 40r1 (atmosphere), and OASIS3-MCT (coupler). Results from the AOSCM are presented at three locations: the tropical Atlantic, the midlatitude Pacific and the Arctic. At all three locations, in situ observations are available for comparison. We find that the coupled AOSCM can capture the observed atmospheric and oceanic evolution based on comparisons with buoy data, soundings and ship-based observations. The model evolution is sensitive to the initial conditions and forcing data imposed on the column. Comparing coupled and uncoupled configurations of the model can help disentangle model feedbacks. We demonstrate that the AOSCM in the current set-up is a valuable tool to advance our understanding in marine and polar boundary layer processes and the interactions between the individual components of the system (atmosphere, sea ice and ocean). [ABSTRACT FROM AUTHOR]

The retrieval of humidity profiles from wind profiler radars has already been documented in the past 30 years and is known to be neither as straightforward and nor as robust as the retrieval of the wind velocity. The main constraint to retrieve the humidity profile is the necessity to combine measurements from the wind profiler and additional measurements (such as observations from radiosoundings at a coarser time resolution). Furthermore, the method relies on some assumptions and simplifications that restrict the scope of its application. The first objective of this paper is to identify the obstacles and limitations and solve them, or at least define the field of applicability. To improve the method, we propose using the radar capacity to detect transition levels, such as the top level of the boundary layer, marked by a maximum in the radar reflectivity. This forces the humidity profile from the free troposphere and from the boundary layer to coincide at this level, after an optimization of the calibration coefficients, and reduces the error. The resulting mean bias affecting the specific humidity profile never exceeds 0.25 g kg-1. The second objective is to explore the capability of the algorithm to retrieve the humidity vertical profiles for an operational purpose by comparing the results with observations from a Raman lidar. [ABSTRACT FROM AUTHOR]

Shipborne wind observations by a coherent Doppler lidar (CDL) have been conducted to study the structure of the marine atmospheric boundary layer (MABL) during the 2014 Yellow Sea campaign. This paper evaluates uncertainties associated with the ship motion and presents the correction methodology regarding lidar velocity measurement based on modified 4-Doppler beam swing (DBS) solution. The errors of calibrated measurement, both for the anchored and the cruising shipborne observations, are comparable to those of ground-based measurements. The comparison between the lidar and radiosonde results in a bias of -0.23 ms-1 and a standard deviation of 0.87 ms-1 for the wind speed measurement, and 2:48, 8:84° for the wind direction. The biases of horizontal wind speed and random errors of vertical velocity are also estimated using the error propagation theory and frequency spectrum analysis, respectively. The results show that the biases are mainly related to the measuring error of the ship velocity and lidar pointing error, and the random errors are mainly determined by the signal-to-noise ratio (SNR) of the lidar backscattering spectrum signal. It allows for the retrieval of vertical wind, based on one measurement, with random error below 0.15 ms-1 for an appropriate SNR threshold and bias below 0.02 ms-1. The combination of the CDL attitude correction system and the accurate motion correction process has the potential of continuous long-term high temporal and spatial resolution measurement for the MABL thermodynamic and turbulence process. [ABSTRACT FROM AUTHOR]

The meteorological input parameters for urban and local scale dispersion models can be evaluated by pre-processing meteorological observations, using a boundary-layer parametrization model. This study presents a sensitivity analysis of a meteorological pre-processor model (MPP-FMI) that utilises readily available meteorological data as input. The sensitivity of the pre-processor to meteorological input was analysed using algorithmic differentiation (AD). The AD tool used was TAPENADE. The AD method numerically evaluates the partial derivatives of functions that are implemented in a computer program. In this study, we focus on the evaluation of vertical fluxes in the atmosphere, and in particular on the sensitivity of the predicted inverse Obukhov length and friction velocity on the model input parameters. The study shows that the estimated inverse Obukhov length and friction velocity are most sensitive to wind speed, and second most sensitive to solar irradiation. The dependency on wind speed is most pronounced at low wind speeds. The presented results have implications for improving the meteorological pre-processing models. AD is shown to be an efficient tool for studying the ranges of sensitivities of the predicted parameters on the model input values quantitatively. A wider use of such advanced sensitivity analysis methods could potentially be very useful in analysing and improving the models used in atmospheric sciences. [ABSTRACT FROM AUTHOR]

The micro-RPAS (remotely piloted aircraft system) SUMO (Small Unmanned Meteorological Observer) equipped with a five-hole-probe (5HP) system for turbulent flow measurements was operated in 49 flight missions during the BLLAST (Boundary-Layer Late Afternoon and Sunset Turbulence) field campaign in 2011. Based on data sets from these flights, we investigate the potential and limitations of airborne velocity variance and TKE (turbulent kinetic energy) estimations by an RPAS with a take-off weight below 1 kg. The integration of the turbulence probe in the SUMO system was still in an early prototype stage during this campaign, and therefore extensive post-processing of the data was required. In order to be able to calculate the three-dimensional wind vector, flow probe measurements were first synchronized with the autopilot's attitude and velocity data. Clearly visible oscillations were detected in the resulting vertical velocity, w, even after correcting for the aircraft motion. The oscillations in w were identified as the result of an internal time shift between the inertial measurement unit (IMU) and the GPS sensors, leading to insufficient motion correction, especially for the vertical wind component, causing large values of σw. Shifting the IMU 1-1.5 s forward in time with respect to the GPS yields a minimum for σw and maximum covariance between the IMU pitch angle and the GPS climb angle. The SUMO data show a good agreement to sonic anemometer data from a 60 m tower for σu, but show slightly higher values for σv and σw. Vertical TKE profiles, obtained from consecutive flight legs at different altitudes, show reasonable results, both with respect to the overall TKE level and the temporal variation. A thorough discussion of the methods used and the identified uncertainties and limitations of the system for turbulence measurements is included and should help the developers and users of other systems with similar problems. [ABSTRACT FROM AUTHOR]

The article presents a research study which focuses on the meteorological observation condition of the coastal boundary layer structure in the Black Sea coastal region of Bulgaria. It mentions the implementation of the turbulence and continuous wind profile measurements to determine the climatic conditions of the said coastal region. The accuracy of the measurement program is also noted.