Cloud Pressure Research Articles

A novel physics-based cloud retrieval algorithm based on neural networks (CRANN) from hyperspectral measurements in the O2-O2 band

Clouds play a crucial role in the Earth's climate system and their properties can be detected by hyperspectral measurements from space. With the increasing spectral resolution, traditional retrieval methods based on look-up tables (LUT) and optimal estimation are limited in both efficiency and accuracy compared with machine learning methods. However, the machine learning techniques used to establish the relationships between spectral measurements and cloud properties often lack physical explainability and universality. Additionally, traditional physical retrieval methods based on oxygen A-band are not applicable to instruments without the O2-A band like the ozone monitoring instrument (OMI). Therefore, we have proposed a novel physics-based deep neural networks (DNN) retrieval method––the cloud retrieval algorithm based on neural networks (CRANN)––which incorporates a deep neural network model with radiative transfer model to retrieve cloud fraction and cloud-top pressure from the oxygen–oxygen collision-induced (O4) absorption band. Validation using simulated test data supported the superior accuracy of CRANN, with the correlation coefficients for cloud fraction and cloud-top pressure are 0.989 and 0.993, respectively, whereas the correlation coefficients for cloud fraction and cloud-top pressure of the LUT method are 0.928 and 0.865, respectively. In comparison with the OMCLDO2 cloud product from the OMI, the CRANN results retrieved from OMI observations exhibit substantial consistency, boasting correlation coefficients surpassing 0.95 for cloud fraction and 0.83 for cloud pressure. As compared with the tropospheric monitoring instrument (TROPOMI) official products, the CRANN retrieval results from TROPOMI exhibit a high level of consistency with correlation coefficients exceeding 0.8 for cloud fraction and 0.73 for cloud pressure. Additionally, a promising agreement is observed between the CRANN retrievals from TROPOMI and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data, yielding RMSEs of 127.3, 134.6 and 106.4 hPa for the validation dataset, respectively.

A prediction method for aircraft icing severity forecast based on probability model and meteorological observation data

Aircraft flight performance and safety are inevitably affected by adverse meteorological conditions, one such weather is icing. Aircraft icing can cause severe aerodynamic and flight mechanical effects, thus threatens aircraft flight safety. This study presents an aircraft icing severity prediction method with probabilistic model, in which meteorological observation data are described as random variables and the airplane icing severity level is defined as fuzzy variables with membership function. When the information of aircraft flying cloud type, altitude, speed, temperature, pressure and dew point temperature is known, the probability of aircraft icing severity during aircraft fly can be effectively predicted by a weighted icing forecasting method. Using the proposed method into two aircraft icing cases, the analysis results show that the proposed method is accurate and valid. It can provide technical support for aircraft icing forecasting.

Experimental validation on the phase separation for the 2-(Diisopropylamino)ethyl methacrylate and Poly[2-(diisopropylamino)ethyl methacrylate] in supercritical CO2

Using an intense pressure flexible volume apparatus, the phase transition equilibrium of the two-component mixture consisting of 2-(diisopropylamino)ethyl methacrylate (2DIPAEMA) + supercritical carbon dioxide (Sc CO2), as well as poly(2-(diisopropylamino)ethyl methacrylate) [P(2DIPAEMA)] with various co-solvents (propylene, propane, 1-butene, butane, and dimethyl ether), was investigated. Additionally, the three-component system P(2DIPAEMA) + 2DIPAEMA (0.0, 0.0245, 0.0523, 0.1036 mol fraction) + Sc CO2 was studied at a specific temperature range of 313.2 K to 454.5 K and an upper-pressure limit of 171.03 MPa. The dew-point, critical-point, and bubble-point equilibria data for the 2DIPAEMA + Sc CO2 binary system at a temperature interval of 313.2–393.2 K and a pressure limit of 21.61 MPa were measured. At low co-solvent concentrations, the solubility transition curve in the three-component P(2DIPAEMA) + Sc CO2 + co-solvent system (comprising propylene, propane, 1-butene, butane, and dimethyl ether) shows a cloud pressure curve that increases as temperature decreases. However, at higher concentrations, cloud pressure decreased rapidly, but a minor change in cloud pressure with temperature was recorded, indicating a homogeneous single-phase behavior resembling a type-I phase diagram. The van der Waals one-fluid mixing rule was employed to compare the experimentally calculated data with the computed and modelled data of the Peng–Robinson mathematical equation. It was discovered that the computed values were consistent with the experimental values. The RMSD for the (Sc CO2 + 2DIPAEMA) system was 4.37 % at five different temperatures. The critical parameter of the pure component, 2DIPAEMA, was estimated using the Joback-Lyderson approach.

Reflected Spectroscopy of Small Exoplanets. III. Probing the UV Band to Measure Biosignature Gases

Direct-imaging observations of terrestrial exoplanets will enable their atmospheric characterization and habitability assessment. Considering Earth, the key atmospheric signatures for the biosphere are O2 and the photochemical product O3. However, this O2–O3 biosignature is not detectable in the visible wavelengths for most of the time after the emergence of oxygenic photosynthesis life (i.e., Proterozoic Earth). Here we demonstrate spectroscopic observations in the ultraviolet wavelengths for detecting and characterizing O2 and O3 in Proterozoic-Earth-like planets, using ExoReL . For an O2 mixing ratio 2–3 orders of magnitude less than the present-day Earth and an O3 mixing ratio of 10−7 to 10−6, we find that O3 can be detected and its mixing ratio can be measured precisely (within 1 order of magnitude) in the ultraviolet (0.25–0.4 μm), in addition to visible-wavelength spectroscopy. With modest spectral resolution (R = 7) and signal-to-noise ratio (∼10) in the ultraviolet, the O3 detection is robust against other potential gases absorbing in the ultraviolet (e.g., H2S and SO2), as well as the short-wavelength cutoff between 0.2 and 0.25 μm. While the O3 detection does not rely on the near-infrared spectra, extending the wavelength coverage to the near-infrared (1–1.8 μm) would provide essential information to interpret the O3 biosignature, including the mixing ratio of H2O, the cloud pressure, and the determination of the dominant gas of the atmosphere. The ultraviolet and near-infrared capabilities should thus be evaluated as critical components for future missions aiming at imaging and characterizing terrestrial exoplanets, such as the Habitable Worlds Observatory.

The extremely sharp transition between molecular and ionized gas in the Horsehead nebula

Massive stars can determine the evolution of molecular clouds by eroding and photo-evaporating their surfaces with strong ultraviolet (UV) radiation fields. Moreover, UV radiation is relevant in setting the thermal gas pressure in star-forming clouds, whose influence can extend across various spatial scales, from the rims of molecular clouds to entire star-forming galaxies. Probing the fundamental structure of nearby molecular clouds is therefore crucial to understand how massive stars shape their surrounding medium and how fast molecular clouds are destroyed, specifically at their UV-illuminated edges, where models predict an intermediate zone of neutral atomic gas between the molecular cloud and the surrounding ionized gas whose size is directly related to the exposed physical conditions. We present the highest angular resolution (~0.″5, corresponding to 207 au) and velocity-resolved images of the molecular gas emission in the Horsehead nebula, using CO J = 3–2 and HCO+ J = 4−3 observations with the Atacama Large Millimeter/submillimeter Array (ALMA). We find that CO and HCO+ are present at the edge of the cloud, very close to the ionization (H+/H) and dissociation fronts (H/H2), suggesting a very thin layer of neutral atomic gas (<650 au) and a small amount of CO-dark gas (AV = 0.006–0.26 mag) for stellar UV illumination conditions typical of molecular clouds in the Milky Way. The new ALMA observations reveal a web of molecular gas filaments with an estimated thermal gas pressure of Pth = (2.3 – 4.0) × 106 K cm−3, and the presence of a steep density gradient at the cloud edge that can be well explained by stationary isobaric photo-dissociation region (PDR) models with pressures consistent with our estimations. However, in the H II region and PDR interface, we find Pth,PDR > Pth,H II suggesting the gas is slightly compressed. Therefore, dynamical effects cannot be completely ruled out and even higher angular observations will be needed to unveil their role.

A Machine Learning-Based Multiple Cloud Vertical Structure Parameter Prediction Algorithm Only Using OCO-2 Oxygen A-Band Measurements

Measurements of the global cloud vertical structure (CVS) are critical to better understanding the effects of the CVS on climate. Current CVS algorithms based on OCO-2 have to be combined with cloud top height products from CALIPSO and CloudSat, which are no longer available after these two satellites left A-Train in 2018. In this paper, we derive a machine learning-based algorithm using only OCO-2 oxygen A-band hyperspectral measurements to simultaneously predict the cloud optical depth (COD), cloud top pressure (p_top), and cloud pressure thickness (CPT) of single-layer liquid clouds. For validation of real observations, the root mean square errors (RMSEs) of the COD, p_top, and CPT are 7.31 (versus the MYD06_L2), 35.06 hPa, and 26.66 hPa (versus the 2B-CLDCLASS-LIDAR). The new algorithm can also predict CVS parameters trained with p_tops from CALIPSO/CloudSat or CODs from MODIS. Controlled experiments show that known p_tops are more conducive to CPT prediction than known CODs, and experiments with both known CODs and p_tops obtain the best accuracy of RMSE = 20.82 hPa. Moreover, a comparison with OCO2CLD-LIDAR-AUX products that rely on CALIPSO shows that our CVS predictions only using OCO-2 measurements have better CODs for all clouds, better p_tops for clouds with a p_top < 900 hPa, and better CPTs for clouds with a CPT > 30 hPa.

On Nanobubble Dynamics under an Oscillating Pressure Field during Salting-out Effects and Its DLVO Potential

We have investigated the origin, stability, and nanobubble dynamics under an oscillating pressure field followed by the salting-out effects. The higher solubility ratio (salting-out parameter) of the dissolved gases and pure solvent nucleates nanobubbles during the salting-out effect, and the oscillating pressure field enhances the nanobubble density further as solubility varies linearly with gas pressure by Henry's law. A novel method for refractive index estimation is developed to differentiate nanobubbles and nanoparticles based on the scattering intensity of light. The electromagnetic wave equations have been numerically solved and compared with the Mie scattering theory. The scattering cross-section of the nanobubbles was estimated to be smaller than the nanoparticles. The DLVO potentials of the nanobubbles predict the stable colloidal system. The zeta potential of nanobubbles varied by generating nanobubbles in different salt solutions, and it is characterized by particle tracking, dynamic light scattering, and cryo-TEM. The size of nanobubbles in salt solutions was reported to be higher than that in pure water. The novel mechanical stability model is proposed by considering both ionic cloud and electrostatic pressure at the charged interface. The ionic cloud pressure is derived by electric flux balance, and it is found to be twice the electrostatic pressure. The mechanical stability model for a single nanobubble predicts the existence of stable nanobubbles in the stability map.

Using Model Cloud Information to Reassign Low-Level Atmospheric Motion Vectors in the ECMWF Assimilation System

Abstract This paper investigates the use of model cloud information in the assimilation of low-level atmospheric motion vectors (AMVs) in the ECMWF global data assimilation system, with the aim to characterize and address issues encountered in the assimilation of these observations. An analysis of background departure statistics (comparison of observations with the model background) shows that AMVs placed above the model cloud show larger deviations from the model background relative to those placed unrealistically close to the surface. Reassigning the pressure of AMVs diagnosed above the model cloud layer to either the model cloud top, cloud base, or average pressure leads to improvements in root-mean-square vector difference (RMSVD) and speed bias against the background wind fields. In assimilation experiments, reassigning AMVs placed above the model cloud to the model cloud top, cloud base, or average pressure results overall in a positive impact on subsequent forecasts. The reassignment to an average model cloud pressure performs best in this respect, and this approach has been implemented in the operational ECMWF system in October 2021.

Characterizing the specific energy and pressure in near-Earth magnetic clouds

Context.The pressure and energy density of the gas and magnetic field inside solar coronal mass ejections (in relation to that in the ambient solar wind) is thought to play an important role in determining their dynamics as they propagate through the heliosphere.Aims.We compare the specific energy (erg g−1), comprising kinetic (Hk), thermal (Hth) and magnetic field (Hmag) contributions, inside magnetic clouds (MCs) and the solar wind background. We examine whether the excess thermal+magnetic pressure and specific energy inside MCs (relative to the background) are correlated with their propagation and internal expansion speeds. We consider whether the excess thermal+magnetic specific energy inside MCs might cause them to resemble rigid bodies in the context of aerodynamic drag.Methods.We used near-Earth in situ data from the WIND spacecraft to identify a sample of 152 well-observed interplanetary coronal mass ejections and their MC counterparts. We compared various metrics based on these data to address our questions.Results.We find that the total specific energy (H) inside MCs is approximately equal to that in the background solar wind. We find that the excess (thermal+magnetic) pressure and specific energy are not well correlated with the near-Earth propagation and expansion speeds. We find that the excess thermal+magnetic specific energy is greater or equivalent to the specific kinetic energy of the solar wind incident in 81–89% of the MCs we study. This might explain how MCs retain their structural integrity and resist deformation by the solar wind bulk flow.

Analysis of the planetary mass uncertainties on the accuracy of atmospherical retrieval

Context. Characterising the properties of exoplanet atmospheres relies on several interconnected parameters, which makes it difficult to determine them independently. Planetary mass plays a role in determining the scale height of atmospheres, similarly to the contribution from the average molecular weight of the gas. Analogously, the clouds masking the real atmospheric scale height make it difficult to correctly derive the atmospheric properties. Aims. We investigate the relevance of planetary mass knowledge in spectral retrievals, identifying cases where mass measurements are needed for clear or cloudy and primary or secondary atmospheres, along with the relevant precision, in the context of the ESA M4 Ariel Mission. Methods. We used TauREx to simulate the Ariel transmission spectra of representative targets of the Ariel mission reference sample, assuming different scenarios: a primordial cloudy atmosphere of a hot Jupiter and a hot Neptune, as well as the secondary atmosphere of a super-Earth that also exhibits a cloud presence. We extracted information on the various properties of the atmospheres for the cases of unknown mass or mass with different uncertainties. We also tested how the signal-to-noise ratio impacts atmospheric retrieval for different wavelength ranges. Results. We accurately retrieved the primordial atmospheric composition independently from mass uncertainties for clear atmospheres, while we found that the uncertainties increased for high altitude clouds. We highlight the importance of the signal-to-noise ratio in the Rayleigh scattering region of the spectrum, which is crucial to retrieving the cloud pressure and to accurately retrieving all other relevant parameters. For the secondary atmosphere cases, a mass uncertainty no larger than 50% is sufficient to retrieve the atmospheric parameters, even in the presence of clouds. Conclusions. Our analysis suggests that even in the worst-case scenario, a 50% mass precision level is enough for producing reliable retrievals, while an atmospheric retrieval without any knowledge of a planetary mass could lead to biases in cloudy primary atmospheres as well as in secondary atmospheres.

Cloud parameter retrieval for Environmental trace gases Monitoring Instrument (EMI) using the O2-O2 absorption band

This study proposes an improved cloud parameter retrieval algorithm for the existing cloud parameter algorithm of the Environmental trace gases Monitoring Instrument (EMI) using the collision complex of the oxygen (O2-O2) absorption feature. The main improvements are the evaluation of the impact of temperature on the O2-O2 slant column densities (SCDs), and the well-corrected EMI measured reflectance. The cloud parameters will help improve the accuracy of EMI trace gas retrievals. The O2-O2 SCDs were fitted based on EMI-observed radiances and solar irradiance, and the impact of temperature on the O2-O2 SCD was evaluated. A simulated reflectance look-up table (LUT), as well as the O2-O2 SCD LUT, were established through the SCIATRAN radiative transfer model. Reflectance and O2-O2 SCD LUTs are functions of effective cloud fraction (ECF) and cloud pressure (CP), which can be inverted to ECF and CP LUTs. On this basis, the EMI ECF and CP were obtained using the EMI observation geometric information, EMI measured reflectance, and O2-O2 SCD to interpolate the ECF and CP LUTs. To evaluate the results, we performed an inter-comparison between the EMI cloud parameters, TROPOspheric Monitoring Instrument (TROPOMI) cloud products, Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) CP, and ground-based lidar cloud altitude. Of them, the one-day global ECF and CP of EMI and TROPOMI exhibited good agreement; the correlation coefficients were 0.925 and 0.866, respectively. Additionally, EMI was closer to the CALIOP results when the cloud pressure was >600 hPa. The comparison with the ground-based lidar cloud altitude also yielded good agreement during the continuous observation period from September 21 to November 10, 2019. The results prove that the improved EMI cloud retrieval algorithm could provide effective cloud products for EMI trace gas inversion.

Integration of decentralized solar collectors into a district heating system

• Development of the prototype of the prosumer heat substation with solar collectors. • Experimentally validated TRNSYS model of the prosumer heat substation. • Key factors influencing stability of the feed-in temperature from the solar collectors. • Proposals for DH operators for integration of prosumers with solar collectors. This study focuses on the integration of small capacity prosumers in district heating networks. The challenge for installations with solar collectors lies in the fact that fluctuations in cloud cover and pressure drops in the network are unpredictable. Therefore, it is more difficult to ensure a stable feed-in temperature that meets the technical requirements of the district heating network. For this, an experimental prototype with flat plate and vacuum thermal collectors was developed and the TRNSYS simulation model was created. The model has been validated using data from the test facility. Multiple simulations have been performed to analyse various options for improving the stability of the feed-in temperature. An analysis of the change in the temperature of the heat carrier on sunny and cloudy days in three control modes was carried out. It showed that the temperature fluctuation depends both on weather conditions and "idling" and "rotating" time intervals of the three-way valve. Results of the study show that the cloud cover has a strong influence on the stability of the collector outlet temperature. Fluctuations in the feed-in temperature need to be compensated for by changing the flow rate and different strategies for operating the three-way valve, which become a challenge in real-life conditions with fluctuating differential pressure in the district heating network.

Sentinel-5P TROPOMI NO<sub>2</sub> retrieval: impact of version v2.2 improvements and comparisons with OMI and ground-based data

Abstract. Nitrogen dioxide (NO2) is one of the main data products measured by the Tropospheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor (S5P) satellite, which combines a high signal-to-noise ratio with daily global coverage and high spatial resolution. TROPOMI provides a valuable source of information to monitor emissions from local sources such as power plants, industry, cities, traffic and ships, and variability of these sources in time. Validation exercises of NO2 v1.2–v1.3 data, however, have revealed that TROPOMI's tropospheric vertical column densities (VCDs) are too low by up to 50 % over highly polluted areas. These findings are mainly attributed to biases in the cloud pressure retrieval, the surface albedo climatology and the low resolution of the a priori profiles derived from global simulations of the TM5-MP chemistry model. This study describes improvements in the TROPOMI NO2 retrieval leading to version v2.2, operational since 1 July 2021. Compared to v1.x, the main changes are the following. (1) The NO2-v2.2 data are based on version-2 level-1b (ir)radiance spectra with improved calibration, which results in a small and fairly homogeneous increase in the NO2 slant columns of 3 % to 4 %, most of which ends up as a small increase in the stratospheric columns. (2) The cloud pressures are derived with a new version of the FRESCO cloud retrieval already introduced in NO2-v1.4, which led to a lowering of the cloud pressure, resulting in larger tropospheric NO2 columns over polluted scenes with a small but non-zero cloud coverage. (3) For cloud-free scenes a surface albedo correction is introduced based on the observed reflectance, which also leads to a general increase in the tropospheric NO2 columns over polluted scenes of order 15 %. (4) An outlier removal was implemented in the spectral fit, which increases the number of good-quality retrievals over the South Atlantic Anomaly region and over bright clouds where saturation may occur. (5) Snow/ice information is now obtained from ECMWF weather data, increasing the number of valid retrievals at high latitudes. On average the NO2-v2.2 data have tropospheric VCDs that are between 10 % and 40 % larger than the v1.x data, depending on the level of pollution and season; the largest impact is found at mid and high latitudes in wintertime. This has brought these tropospheric NO2 closer to Ozone Monitoring Instrument (OMI) observations. Ground-based validation shows on average an improvement of the negative bias of the stratospheric (from −6 % to −3 %), tropospheric (from −32 % to −23 %) and total (from −12 % to −5 %) columns. For individual measurement stations, however, the picture is more complex, in particular for the tropospheric and total columns.

Virial Clumps in Central Molecular Zone Clouds

Abstract CMZoom survey observations with the Submillimeter Array are analyzed to describe the virial equilibrium (VE) and star-forming potential of 755 clumps in 22 clouds in the Central Molecular Zone (CMZ) of the Milky Way. In each cloud, nearly all clumps follow the column density–mass trend N ∝ M s , where s = 0.38 ± 0.03 is near the pressure-bounded limit s p = 1/3. This trend is expected when gravitationally unbound clumps in VE have similar velocity dispersion and external pressure. Nine of these clouds also harbor one or two distinctly more massive clumps. These properties allow a VE model of bound and unbound clumps in each cloud, where the most massive clump has the VE critical mass. These models indicate that 213 clumps have velocity dispersion 1–2 km s−1, mean external pressure (0.5–4) × 108 cm−3 K, bound clump fraction 0.06, and typical virial parameter α = 4–15. These mostly unbound clumps may be in VE with their turbulent cloud pressure, possibly driven by inflow from the Galactic bar. In contrast, most Sgr B2 clumps are bound according to their associated sources and N–M trends. When the CMZ clumps are combined into mass distributions, their typical power-law slope is analyzed with a model of stopped accretion. It also indicates that most clumps are unbound and cannot grow significantly, due to their similar timescales of accretion and dispersal, ∼0.2 Myr. Thus, virial and dynamical analyses of the most extensive clump census available indicate that star formation in the CMZ may be suppressed by a significant deficit of gravitationally bound clumps.

Importance of Pressure Changes in High Cloud Area Feedback Due to Global Warming

AbstractHigh clouds have large impacts on the Earth's radiation balance, and a better understanding of their change in warmer climates is essential for climate projection science. Previous studies have focused on the role of large‐scale circulations in high cloud change, and little attention is paid to the local processes in high clouds. Here, we show that the evolution of ice clouds in the upper troposphere due to the cloud microphysical processes is dominated by the collection, sublimation, deposition, and sedimentation using a model with an explicit cloud microphysics scheme. Furthermore, we show that these processes can be enhanced and can lead to the reduction of high clouds in warmer climates through the pressure dependencies of fall speeds of ice particles and the molecular diffusivity of water vapor. Our results highlight the importance of change of the high cloud pressure level in the high cloud area feedback.

Explicit and consistent aerosol correction for visible wavelength satellite cloud and nitrogen dioxide retrievals based on optical properties from a global aerosol analysis

Abstract. We discuss an explicit and consistent aerosol correction for cloud and NO2 retrievals that are based on the mixed Lambertian-equivalent reflectivity (MLER) concept. We apply the approach to data from the Ozone Monitoring Instrument (OMI) for a case study over northeastern China. The cloud algorithm reports an effective cloud pressure, also known as cloud optical centroid pressure (OCP), from oxygen dimer (O2−O2) absorption at 477 nm after determining an effective cloud fraction (ECF) at 466 nm. The retrieved cloud products are then used as inputs to the standard OMI NO2 algorithm. A geometry-dependent Lambertian-equivalent reflectivity (GLER), which is a proxy of surface bidirectional reflectance, is used for the ground reflectivity in our implementation of the MLER approach. The current standard OMI cloud and NO2 algorithms implicitly account for aerosols by treating them as nonabsorbing particulate scatters within the cloud retrieval. To explicitly account for aerosol effects, we use a model of aerosol optical properties from a global aerosol assimilation system and radiative transfer computations. This approach allows us to account for aerosols within the OMI cloud and NO2 algorithms with relatively small changes. We compare the OMI cloud and NO2 retrievals with implicit and explicit aerosol corrections over our study area.

Based on The Gate Bottom Edge Structures Specific Numerical Simulation of Flow Pattern of Gate

The bottom edge structure is a key factor affecting the flow pattern under the gate. The reasonable selection of bottom edge configuration plays an important role in the safe and stable operation of the gate. RNG k-ɛ model is used to simulate the gate water area. Through numerical simulation, the pressure cloud map and pressure data of monitoring points of water area under the gate are fully simulated. Influence of bottom edge structure on flow pattern. The results show that only the flat bottom gate always has negative pressure zone directly below the bottom edge, and the other three structures have no obvious negative pressure zone and move around the downward inclination Angle with time. According to the analysis of the characteristics of upper support and suction, it can be concluded that the upper support force increases with the increase of the front rake angle, and the upper support force of the flat-bottom gate is negative, showing the characteristics of suction.

Evaluating Sentinel-5P TROPOMI tropospheric NO2 column densities with airborne and Pandora spectrometers near New York City and Long Island Sound.

Airborne and ground-based Pandora spectrometer NO2 column measurements were collected during the 2018 Long Island Sound Tropospheric Ozone Study (LISTOS) in the New York City/Long Island Sound region, which coincided with early observations from the Sentinel-5P TROPOspheric Monitoring Instrument (TROPOMI) instrument. Both airborne- and ground-based measurements are used to evaluate the TROPOMI NO2 Tropospheric Vertical Column (TrVC) product v1.2 in this region, which has high spatial and temporal heterogeneity in NO2. First, airborne and Pandora TrVCs are compared to evaluate the uncertainty of the airborne TrVC and establish the spatial representativeness of the Pandora observations. The 171 coincidences between Pandora and airborne TrVCs are found to be highly correlated (r2 =0.92 and slope of 1.03), with the largest individual differences being associated with high temporal and/or spatial variability. These reference measurements (Pandora and airborne) are complementary with respect to temporal coverage and spatial representativity. Pandora spectrometers can provide continuous long-term measurements but may lack areal representativity when operated in direct-sun mode. Airborne spectrometers are typically only deployed for short periods of time, but their observations are more spatially representative of the satellite measurements with the added capability of retrieving at subpixel resolutions of 250m×250m over the entire TROPOMI pixels they overfly. Thus, airborne data are more correlated with TROPOMI measurements (r2 = 0.96) than Pandora measurements are with TROPOMI (r2 = 0.84). The largest outliers between TROPOMI and the reference measurements appear to stem from too spatially coarse a priori surface reflectivity (0.5°) over bright urban scenes. In this work, this results during cloud-free scenes that, at times, are affected by errors in the TROPOMI cloud pressure retrieval impacting the calculation of tropospheric air mass factors. This factor causes a high bias in TROPOMI TrVCs of 4%–11%. Excluding these cloud-impacted points, TROPOMI has an overall low bias of 19%–33% during the LISTOS timeframe of June–September 2018. Part of this low bias is caused by coarse a priori profile input from the TM5-MP model; replacing these profiles with those from a 12 km North American Model–Community Multiscale Air Quality (NAMCMAQ) analysis results in a 12%–14% increase in the TrVCs. Even with this improvement, the TROPOMI-NAMCMAQ TrVCs have a 7%–19% low bias, indicating needed improvement in a priori assumptions in the air mass factor calculation. Future work should explore additional impacts of a priori inputs to further assess the remaining low biases in TROPOMI using these datasets.

New Observations of Upper Tropospheric NO 2 from TROPOMI

Abstract. Nitrogen oxides (NOx ≡ NO + NO2) in the NOx-limited upper troposphere (UT) are long-lived and so have a large influence on the oxidizing capacity of the troposphere and formation of the greenhouse gas ozone. Models misrepresent NOx in the UT and observations to address deficiencies in models are sparse. Here we obtain a year of near-global seasonal mean mixing ratios of NO2 in the UT (450–180 hPa) at 1 ° x 1° by applying cloud-slicing to partial columns of NO2 from TROPOMI. This follows refinement of the cloud-slicing algorithm with synthetic partial columns from the GEOS-Chem chemical transport model. We find that synthetic cloud-sliced UT NO2 are spatially consistent (R = 0.64) with UT NO2 calculated across the same cloud pressure range and scenes as are cloud-sliced (“true” UT NO2), but the cloud-sliced UT NO2 is 11–22 % more than the true all-sky seasonal mean. The largest contributors to differences between synthetic cloud-sliced and “true” UT NO2 are target resolution of the cloud-sliced product and uniformity of overlying stratospheric NO2. TROPOMI, prior to cloud-slicing, is corrected for a 13 % underestimate in stratospheric NO2 variance and a 50 % overestimate in free tropospheric NO2 determined by comparison to Pandora total columns at high-altitude sites in Mauna Loa, Izana and Altzomoni, and MAX-DOAS and Pandora tropospheric columns at Izana. Two cloud-sliced seasonal mean UT NO2 products for June 2019 to May 2020 are retrieved from corrected TROPOMI total columns using distinct TROPOMI cloud products that assume clouds are reflective boundaries (FRESCO-S) or water droplet layers (ROCINN-CAL). TROPOMI UT NO2 typically ranges from 20-30 pptv over remote oceans to > 80 pptv over locations with intense seasonal lightning. Spatial coverage is mostly in the tropics and subtropics with FRESCO-S and extends to the midlatitudes and polar regions with ROCINN-CAL, due to its greater abundance of optically thick clouds and wider cloud top altitude range. TROPOMI UT NO2 seasonal means are spatially consistent (R = 0.6–0.8) with an existing coarser spatial resolution (5° latitude x 8° longitude) UT NO2 product from the Ozone Monitoring Instrument (OMI). UT NO2 from TROPOMI is 12–26 pptv more than that from OMI due to increase in NO2 with altitude from the OMI pressure ceiling (280 hPa) to that for TROPOMI (180 hPa), but possibly also systematic altitude differences between the TROPOMI and OMI cloud products. The TROPOMI UT NO2 product offers potential to evaluate and improve representation of UT NOx in models and supplement aircraft observations that are sporadic and susceptible to large biases in the UT.

Development and Application of HECORA Cloud Retrieval Algorithm Based On the O2-O2 477 nm Absorption Band

In this paper, we present the Hefei EMI Cloud Retrieval Algorithm (HECORA), which uses information from the O2-O2 absorption band around 477 nm to retrieve effective cloud fraction and effective cloud pressure from satellite observations. The retrieved cloud information intends to improve the atmospheric trace gas products based on the Environment Monitoring Instrument (EMI) spectrometer. The HECORA method builds on OMCLDO2 and presents some evolutions. The Vector Linearized Discrete Ordinate Radiative Transfer (VLIDORT) model has been used to produce the Top of the Atmosphere (TOA) reflectance Look-up Tables (LUT) as a function of the cloud fraction and cloud pressure. Applying the Differential Optical Absorption Spectroscopy (DOAS) technique to the synthetic reflectance LUT, the reflectance spectra can be associated with O2-O2 geometrical vertical column densities (VCDgeo) and continuum reflectance. This is the core of the retrieval method, since there is a one-to-one relationship between O2-O2 VCDgeo and continuum reflectance, on the one hand, and effective cloud fraction and effective cloud pressure, on the other hand, for a given illumination and observing geometry and given surface height and surface albedo. We first used the VLIDORT synthetic spectra to verify the HECORA algorithm and obtained good results in both the Lambertian cloud model and the scattering cloud model. Secondly, HECORA is applied to OMI and TROPOMI and compared with OMCLDO2, FRESCO+, and OCRA/ROCINN cloud products. Later, the cloud pressure results from TROPOMI observations obtained using HECORA and FRESCO+ are compared with the CALIOP Cloud Layer product. HECORA is closer to the CALIOP results under low cloud conditions, while FRESCO+ is closer to high clouds due to the higher sensitivity of the O2 A-band to cloud vertical information. Finally, HECORA is applied to the TROPOMI NO2 retrieval. Validation of the tropospheric NO2 VCD with ground-based MAX-DOAS measurements shows that choosing HECORA cloud products to correct for photon path variations on the TROPOMI tropospheric NO2 VCD retrievals has better performance than using FRESCO+ under low cloud conditions. In conclusion, this paper shows that the HECORA cloud products are in good agreement with the well-established cloud products and that they are suitable for correcting the effect of cloud in trace gas retrievals. Therefore, HECORA has the potential to be applied to EMI.