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Water vapour is the most abundant natural greenhouse gas in the Earth's atmosphere, and global data sets are required for meteorological applications and climate research. The Tropospheric Monitoring Instrument (TROPOMI) on board Sentinel-5 Precursor (S5P) launched on 13 October 2017 has a high spatial resolution of around 5 km and a daily global coverage. Currently, there is no operational total water vapour product for S5P measurements. Here, we present first results of a new scientific total column water vapour (TCWV) product for S5P using the so-called air-mass-corrected differential optical absorption spectroscopy (AMC-DOAS) scheme. This method analyses spectral data between 688 and 700 nm and has already been successfully applied to measurements from the Global Monitoring Experiment (GOME) on ERS-2, the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) on Envisat and GOME-2 on MetOp. The adaptation of the AMC-DOAS method to S5P data requires an additional post-processing procedure to correct the influences of surface albedo, cloud height and cloud fraction. The quality of the new AMC-DOAS S5P water vapour product is assessed by comparisons with data from GOME-2 on MetOp-B retrieved also with the AMC-DOAS algorithm and with four independent data sets, namely reanalysis data from the European Centre for Medium range Weather Forecast (ECMWF ERA5), data obtained by the Special Sensor Microwave Imager and Sounder (SSMIS) flown on the Defense Meteorological Satellite Program (DMSP) platform 16 and two scientific S5P TCWV products derived from TROPOMI measurements. Both are recently published TCWV products for S5P provided by the Max Planck Institute for Chemistry (MPIC) in Mainz and the Netherlands Institute for Space Research (SRON), Utrecht. The SRON TCWV is limited to clear-sky scenes over land. These comparisons reveal a good agreement between the various data sets but also some systematic differences between all of them. On average, the daily derived offset between AMC-DOAS S5P TCWV and AMC-DOAS GOME-2B TCWV is negative (around −1.5 kg m−2) over land and positive over ocean surfaces (more than 1.5 kg m−2). In contrast, SSMIS TCWV is on average lower than AMC-DOAS S5P TCWV by about 3 kg m−2. Monthly averaged ERA5 TCWV and AMC-DOAS S5P TCWV comparison shows spatial features over both land and water surface. Over land, there are systematic spatial structures. There are larger differences between AMC-DOAS S5P TCWV and ERA5 TCWV in tropical regions. Over sea, AMC-DOAS S5P TCWV is slightly lower than ERA5 TCWV by around 2 kg m−2. The AMC-DOAS S5P TCWV and S5P TCWV from MPIC agree on average within 1 kg m−2 over both land and ocean. TCWV from SRON shows daily global averaged differences to AMC-DOAS S5P TCWV of around 1.2 kg m−2. All of these differences are in line with the accuracy of these products and with the typical range of differences of 5 kg m−2 obtained when comparing different TCWV data sets. The AMC-DOAS TCWV product for S5P provides therefore a valuable new and independent data set for atmospheric applications which also has a higher spatial coverage than the other S5P TCWV products.

An effort is made to examine the cloud parameters over few stations in India for one year from October 2016 to September 2017. This study includes the investigation of cloud base height, cloud top height, zero degree isotherm height, number of cloud layers, cloud thickness, number of days cloud detected, cloud liquid water content and cloud attenuation at 6 GHz, 12.5 GHz and 24 GHz. Salonen Uppala (SU) model and Water Vapor Pressure-Nanyang Technological University (WVP-NTU) model are used for cloud detection using the radiosonde data at Bangalore, Hyderabad, Mumbai, Thiruvananthapuram and Chennai. The cloud parameters found using both the models are compared. WVP-NTU model shows enhanced values for most of the cloud parameters over SU model. Cloud base height calculated using these models are compared with METAR data and found that Cloud base height from WVP-NTU model matches well with METAR data. Cloud liquid water content and cloud attenuation values are calculated using the ITU-R model. The cumulative distributions (CDF) of cloud attenuation for one year are compared for the places considered. The seasonal CDFs of cloud attenuation using the WVP model are compared with the rain statistics. In this study, the maximum cloud attenuation of 1.67 dB/km is measured in Hyderabad by WVP-NTU combined ITU-R model at 24 GHz.

The effects of thermodynamic and moisture factors on cloud-to-ground (CG) lightning polarity in the warm season were discussed. Small convective available potential energy (CAPE) represents relatively shallow convection, which is beneficial to the generation of positive lightning. Large vertical wind shear results in the displacement of upper-level positive ice crystals and promotes the initiation of +CG lightning from positive ice crystals. The dry low- to midlevel troposphere and the high cloud base in the plateau region favor +CG lightning, while the strong thermodynamic conditions in the basin region offset the influence of these moisture factors. In the plateau region, due to the limited cloud thickness, high total column liquid water may mean high cloud water content in the warm cloud region rather than high liquid water content in the mixed-phase region, which is unfavorable for the middle-level positive graupel and thus is unfavorable for the initiation of +CG lightning. In the basin region, the cloud thickness is relatively thicker, the high total column liquid water means that the liquid water content in the warm cloud and the mixed-phase region is both high, which is conducive to the middle-level positive graupel and the +CG lightning.

We present results of 3D hydrodynamical simulations of HD209458b including a coupled, radiatively-active cloud model ({\sc EddySed}). We investigate the role of the mixing by replacing the default convective treatment used in previous works with a more physically relevant mixing treatment ($K_{zz}$) based on global circulation. We find that uncertainty in the efficiency of sedimentation through the sedimentation factor $f_\mathrm{sed}$ plays a larger role in shaping cloud thickness and its radiative feedback on the local gas temperatures -- e.g. hot spot shift and day-to-night side temperature gradient -- than the switch in mixing treatment. We demonstrate using our new mixing treatments that simulations with cloud scales which are a fraction of the pressure scale height improve agreement with the observed transmission spectra, the emission spectra, and the Spitzer 4.5 $\mathrm{\mu m}$ phase curve, although our models are still unable to reproduce the optical and UV transmission spectra. We also find that the inclusion of cloud increases the transit asymmetry in the optical between the east and west limbs, although the difference remains small ($\lesssim 1\%$)., Comment: 17 pages. 14 figures. 1 table

Although lightning-generated oxides of nitrogen (LNOx) account for only approximately 10 % of the global NOx source, they have a disproportionately large impact on tropospheric photochemistry due to the conducive conditions in the tropical upper troposphere where lightning is mostly discharged. In most global composition models, lightning flash rates used to calculate LNOx are expressed in terms of convective cloud-top height via the Price and Rind (1992) (PR92) parameterisations for land and ocean, where the oceanic parameterisation is known to greatly underestimate flash rates. We conduct a critical assessment of flash-rate parameterisations that are based on cloud-top height and validate them within the Australian Community Climate and Earth System Simulator – United Kingdom Chemistry and Aerosol (ACCESS-UKCA) global chemistry–climate model using the Lightning Imaging Sensor and Optical Transient Detector (LIS/OTD) satellite data. While the PR92 parameterisation for land yields satisfactory predictions, the oceanic parameterisation, as expected, underestimates the observed flash-rate density severely, yielding a global average over the ocean of 0.33 flashes s−1 compared to the observed 9.16 flashes s−1 and leading to LNOx being underestimated proportionally. We formulate new flash-rate parameterisations following Boccippio's (2002) scaling relationships between thunderstorm electrical generator power and storm geometry coupled with available data. The new parameterisation for land performs very similarly to the corresponding PR92 one, as would be expected, whereas the new oceanic parameterisation simulates the flash-rate observations much more accurately, giving a global average over the ocean of 8.84 flashes s−1. The use of the improved flash-rate parameterisations in ACCESS-UKCA changes the modelled tropospheric composition – global LNOx increases from 4.8 to 6.6 Tg N yr−1; the ozone (O3) burden increases by 8.5 %; there is an increase in the mid- to upper-tropospheric NOx by as much as 40 pptv, a 13 % increase in the global hydroxyl radical (OH), a decrease in the methane lifetime by 6.7 %, and a decrease in the lower-tropospheric carbon monoxide (CO) by 3 %–7 %. Compared to observations, the modelled tropospheric NOx and ozone in the Southern Hemisphere and over the ocean are improved by this new flash-rate parameterisation.

The Edition 2 (Ed2) cloud property retrieval algorithm system was upgraded and applied to the MODerate-resolution Imaging Spectroradiometer (MODIS) data for the Clouds and the Earth’s Radiant Energy System (CERES) Edition 4 (Ed4) products. New calibrations for solar channels and the use of the 1.24- $\mu \text{m}$ channel for cloud optical depth (COD) over snow improve the daytime consistency between Terra and Aqua MODIS retrievals. Use of additional spectral channels and revised logic enhanced the cloud-top phase retrieval accuracy. A new ice crystal reflectance model and a CO2-channel algorithm retrieved higher ice clouds, while a new regional lapse rate technique produced more accurate water cloud heights than in Ed2. Ice cloud base heights are more accurate due to a new cloud thickness parameterization. Overall, CODs increased, especially over the polar (PO) regions. The mean particle sizes increased slightly for water clouds, but more so for ice clouds in the PO areas. New experimental parameters introduced in Ed4 are limited in utility, but will be revised for the next CERES edition. As part of the Ed4 retrieval evaluation, the average properties are compared with those from other algorithms and the differences between individual reference data and matched Ed4 retrievals are explored. Part II of this article provides a comprehensive, objective evaluation of selected parameters. More accurate interpretation of the CERES radiation measurements has resulted from the use of the Ed4 cloud properties.

Accurate knowledge of cloud properties is essential to the measurement of atmospheric composition from space. In this work we assess the quality of the cloud data from three Copernicus Sentinel-5 Precursor (S5P) TROPOMI cloud products: (i) S5P OCRA/ROCINN_CAL (Optical Cloud Recognition Algorithm/Retrieval of Cloud Information using Neural Networks;Clouds-As-Layers), (ii) S5P OCRA/ROCINN_CRB (Clouds-as-Reflecting Boundaries), and (iii) S5P FRESCO-S (Fast Retrieval Scheme for Clouds from Oxygen absorption bands – Sentinel). Target properties of this work are cloud-top height and cloud optical thickness (OCRA/ROCINN_CAL), cloud height (OCRA/ROCINN_CRB and FRESCO-S), and radiometric cloud fraction (all three algorithms). The analysis combines (i) the examination of cloud maps for artificial geographical patterns, (ii) the comparison to other satellite cloud data (MODIS, NPP-VIIRS, and OMI O2–O2), and (iii) ground-based validation with respect to correlative observations (30 April 2018 to 27 February 2020) from the Cloudnet network of ceilometers, lidars, and radars. Zonal mean latitudinal variation of S5P cloud properties is similar to that of other satellite data. S5P OCRA/ROCINN_CAL agrees well with NPP VIIRS cloud-top height and cloud optical thickness and with Cloudnet cloud-top height, especially for the low (mostly liquid) clouds. For the high clouds, S5P OCRA/ROCINN_CAL cloud-top height is below the cloud-top height of VIIRS and of Cloudnet, while its cloud optical thickness is higher than that of VIIRS. S5P OCRA/ROCINN_CRB and S5P FRESCO cloud height are well below the Cloudnet cloud mean height for the low clouds but match on average better with the Cloudnet cloud mean height for the higher clouds. As opposed to S5P OCRA/ROCINN_CRB and S5P FRESCO, S5P OCRA/ROCINN_CAL is well able to match the lowest CTH mode of the Cloudnet observations. Peculiar geographical patterns are identified in the cloud products and will be mitigated in future releases of the cloud data products.

This study investigates the characteristics of cold clouds and snowfall in both the Yeongdong coastal and mountainous regions under different meteorological conditions based on the integration of numerical modeling and three-hourly rawinsonde observations with snow crystal photographs for a snowfall event that occurred on 29–30 January 2016. We found that rimed particles predominantly observed turned into dendrite particles in the latter period of the episode when the 850 hPa temperature decreased at the coastal site, whereas the snow crystal habits at the mountainous site were largely needle or rimed needle. Rawinsonde soundings showed a well-defined, two-layered cloud structure along with distinctive wind-directional shear, and an inversion in the equivalent potential temperature above the low-level cloud layer. The first experiment with a decrease in lower-layer temperature showed that the low-level cloud thickness was reduced to less than 1.5 km, and the accumulated precipitation was decreased by 87% compared with the control experiment. The difference in precipitation amount between the single-layered experiment and control experiment (two-layered) was not so significant to attribute it to the effect of the seeder-feeder mechanism. The precipitation in the last experiment by weakening wind-directional shear was increased by 1.4 times greater than the control experiment specifically at the coastal site, with graupel particles accounting for the highest proportion (∼62%). The current results would improve snowfall forecasts in complicated geographical environments such as Yeongdong in terms of snow crystal habit as well as snowfall amount in both time and space domains.

The hydrodynamics of floating and sinking particles mixing process in a stirred tank with four pitched-blade impellers and fractal impellers were investigated employing computational fluid dynamics (CFD) simulation. Axial solid concentration profile, solid concentration distribution, power consumption, trailing vortex, cloud height, water integrated velocity, just suspension speed (Njs) and just drawdown speed (Njd) in the stirred tank were predicted. Results showed that the mixing quality of floating and sinking particles increased with the increase of impeller speed. Smaller density difference between solid-liquid two phases and smaller particle diameter resulted in higher suspension degree. Fractal impeller can enhance water integrated velocity and improve suspension quality compared with four pitched-blade impeller under the constant power consumption, and increasingly so with fractal iteration number. Meanwhile, fractal impeller can reduce the size of trailing vortex and decrease the power consumption based on four pitched-blade impeller at the same impeller speed. In addition, the just suspension speed (Njs), just drawdown speed (Njd) and correlation equation for Njs and Njd in the three different impeller systems were obtained. Fractal impeller could decrease the Njs and Njd on the basis of four pitched-blade impeller, and decreasingly so with the fractal iteration number.

We present version 3 (V3) of the Cloud_cci Along-Track Scanning Radiometer (ATSR) and Advanced ATSR (AATSR) data set. The data set was created for the European Space Agency (ESA) Cloud_cci (Climate Change Initiative) programme. The cloud properties were retrieved from the second ATSR (ATSR-2) on board the second European Remote Sensing Satellite (ERS-2) spanning 1995–2003 and the AATSR on board Envisat, which spanned 2002–2012. The data are comprised of a comprehensive set of cloud properties: cloud top height, temperature, pressure, spectral albedo, cloud effective emissivity, effective radius, and optical thickness, alongside derived liquid and ice water path. Each retrieval is provided with its associated uncertainty. The cloud property retrievals are accompanied by high-resolution top- and bottom-of-atmosphere shortwave and longwave fluxes that have been derived from the retrieved cloud properties using a radiative transfer model. The fluxes were generated for all-sky and clear-sky conditions. V3 differs from the previous version 2 (V2) through development of the retrieval algorithm and attention to the consistency between the ATSR-2 and AATSR instruments. The cloud properties show improved accuracy in validation and better consistency between the two instruments, as demonstrated by a comparison of cloud mask and cloud height with co-located CALIPSO data. The cloud masking has improved significantly, particularly in its ability to detect clear pixels. The Kuiper Skill score has increased from 0.49 to 0.66. The cloud top height accuracy is relatively unchanged. The AATSR liquid water path was compared with the Multisensor Advanced Climatology of Liquid Water Path (MAC-LWP) in regions of stratocumulus cloud and shown to have very good agreement and improved consistency between ATSR-2 and AATSR instruments. The correlation with MAC-LWP increased from 0.4 to over 0.8 for these cloud regions. The flux products are compared with NASA Clouds and the Earth's Radiant Energy System (CERES) data, showing good agreement within the uncertainty. The new data set is well suited to a wide range of climate applications, such as comparison with climate models, investigation of trends in cloud properties, understanding aerosol–cloud interactions, and providing contextual information for co-located ATSR-2/AATSR surface temperature and aerosol products. The following new digital identifier has been issued for the Cloud_cci ATSR-2/AATSRv3 data set: https://doi.org/10.5676/DWD/ESA_Cloud_cci/ATSR2-AATSR/V003 (Poulsen et al., 2019).

Author(s): Zapata, Monica Zamora; Norris, Joel R; Kleissl, Jan | Abstract: Abstract The impact of initial states and meteorological variables on stratocumulus cloud dissipation time over coastal land is investigated using a mixed-layer model. A large set of realistic initial conditions and forcing parameters are derived from radiosonde observations and numerical weather prediction model outputs, including total water mixing ratio and liquid water potential temperature profiles (within the boundary layer, across the capping inversion, and at 3 km), inversion-base height and cloud thickness, large-scale divergence, wind speed, Bowen ratio, sea surface fluxes, sky effective radiative temperature, shortwave irradiance above the cloud, and sea level pressure. We study the sensitivity of predicted dissipation time using two analyses. In the first, we simulate 195 cloudy days (all variables covary as observed in nature). We caution that simulated predictions correlate only weakly to observations of dissipation time, but the simulation approach is robust and facilitates covariability testing. In the second, a single variable is varied around an idealized reference case. While both analyses agree in that initial conditions influence dissipation time more than forcing parameters, some results with covariability differ greatly from the more traditional sensitivity analysis and with previous studies: opposing trends are observed for boundary layer total water mixing ratio and Bowen ratio, and covariability diminishes the sensitivity to cloud thickness and inversion height by a factor of 5. With covariability, the most important features extending predicted cloud lifetime are (i) initially thicker clouds, higher inversion height, and stronger temperature inversion jumps, and (ii) boundary forcings of lower sky effective radiative temperature.

This study presents an analysis of TROPOMI cloud heights as a proxy for volcanic plume heights in the presence of absorbing aerosols and sulfur dioxide for the 19 February 2018 eruption plume of the Sinabung volcano on Sumatra, Indonesia. Comparison with CALIPSO satellite data shows that all three TROPOMI cloud height data products based on oxygen absorption which are considered here (FRESCO, ROCINN, O22CLD) provide volcanic ash cloud heights comparable to heights measured by CALIPSO for optically thick volcanic ash clouds. FRESCO and ROCINN heights are very similar, with the only differences for FRESCO cloud top heights above 14 km altitude. O22CLD cloud top heights unsurprisingly fall below those of FRESCO and ROCINN, as the O22CLD retrieval is less sensitive to cloud top heights above 10 km altitude. For optically thin volcanic ash clouds, i.e., when Earth's surface or clouds at lower altitudes shine through the volcanic ash cloud, retrieved heights fall below the volcanic ash cloud heights derived from CALIPSO data. Evaluation of corresponding Himawari-8 geostationary infrared (IR) brightness temperature differences (ΔBTs) – a signature for detection of volcanic ash clouds in geostationary satellite data and widely used as input for quantitative volcanic ash cloud retrievals – reveals that for this particular eruption the ΔBT volcanic ash signature changes to a ΔBT ice crystal signature for the part of the ash plume reaching the upper troposphere beyond 10 km altitude several hours after the start of the eruption and which TROPOMI clearly characterizes as volcanic (SO2 > 1 DU – Dobson units – and AAI > 4 – absorbing aerosol index – or, more conservatively, SO2 > 10). The presence of ice in volcanic ash clouds is known to prevent the detection of volcanic ash clouds based on broadband geostationary satellite data. TROPOMI does not suffer from this effect and can provide valuable and accurate information about volcanic ash clouds and ash top heights in cases where commonly used geostationary IR measurements of volcanic ash clouds fail.

Aerosol-cloud-precipitation interactions (ACIs) provide the greatest source of uncertainties in predicting changes in Earth’s energy budget due to poor representation of marine stratocumulus and the associated ACIs in climate models. Using in situ data from 329 cloud profiles across 24 research flights from the NASA ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) field campaign in September 2016, August 2017, and October 2018, it is shown that contact between above-cloud biomass-burning aerosols and marine stratocumulus over the southeast Atlantic Ocean was associated with precipitation suppression and a decrease in the precipitation susceptibility (So) to aerosols. The 173 “contact” profiles with aerosol concentration (Na) greater than 500 cm−3 within 100 m above cloud tops had 50 % lower precipitation rate (Rp) and 20 % lower So, on average, compared to 156 “separated” profiles with Na less than 500 cm−3 up to at least 100 m above cloud tops. Contact and separated profiles had statistically significant differences in droplet concentration (Nc) and effective radius (Re) (95 % confidence intervals from a two-sample t-test are reported). Contact profiles had 84 to 90 cm−3 higher Nc and 1.4 to 1.6 μm lower Re compared to separated profiles. In clean boundary layers (below-cloud Na less than 350 cm−3), contact profiles had 25 to 31 cm−3 higher Nc and 0.2 to 0.5 μm lower Re. In polluted boundary layers (below-cloud Na exceeding 350 cm−3), contact profiles had 98 to 108 cm−3 higher Nc and 1.6 to 1.8 μm lower Re. On the other hand, contact and separated profiles had statistically insignificant differences between the average liquid water path, cloud thickness, and meteorological parameters like surface temperature, lower tropospheric stability, and estimated inversion strength. These results suggest the changes in cloud properties were driven by ACIs rather than meteorological effects, and the existing relationships between Rp and Nc must be adjusted to account for the role of ACIs.

Lightning flash rate is strongly influenced by cloud microphysics, such as cloud ice properties, but this relationship is poorly constrained. Here we analyze 20 years of satellite-derived lightning flash rate data and cloud water data from the ERA-Interim reanalysis above continental and ocean regions at a global scale. We find a robust modified gamma function relationship between cloud ice fraction and lightning rate. Lightning rate increases initially with increasing cloud ice fraction in stratocumulus, liquid clouds. Maximum flash rates are reached at a critical cloud ice fraction value that is associated with high top, large optical thickness, deep convective clouds. Beyond the critical value, lightning rate decreases as the ice fraction increases to values representative of cirrus, ice clouds. We find consistent critical ice fraction values over continental and oceanic regions, respectively, with a lower value over the continent due to greater cloud thickness at similar cloud top height. We suggest that our findings may help improve the accuracy of lightning forecast and hazard prediction. Lightning rate varies non-linearly with cloud ice fraction and between ocean and continent regions due to differences in convection depth, according to analyses of 20-years of lightning observations and cloud water data.

Aerosols have significant and complex impacts on regional climate in East Asia. Cloud‐aerosol‐precipitation interactions (CAPI) remain most challenging in climate studies. The quantitative understanding of CAPI requires good knowledge of aerosols, ranging from their formation, composition, transport, and their radiative, hygroscopic, and microphysical properties. A comprehensive review is presented here centered on the CAPI based chiefly, but not limited to, publications in the special section named EAST‐AIRcpc concerning (1) observations of aerosol loading and properties, (2) relationships between aerosols and meteorological variables affecting CAPI, (3) mechanisms behind CAPI, and (4) quantification of CAPI and their impact on climate. Heavy aerosol loading in East Asia has significant radiative effects by reducing surface radiation, increasing the air temperature, and lowering the boundary layer height. A key factor is aerosol absorption, which is particularly strong in central China. This absorption can have a wide range of impacts such as creating an imbalance of aerosol radiative forcing at the top and bottom of the atmosphere, leading to inconsistent retrievals of cloud variables from space‐borne and ground‐based instruments. Aerosol radiative forcing can delay or suppress the initiation and development of convective clouds whose microphysics can be further altered by the microphysical effect of aerosols. For the same cloud thickness, the likelihood of precipitation is influenced by aerosols: suppressing light rain and enhancing heavy rain, delaying but intensifying thunderstorms, and reducing the onset of isolated showers in most parts of China. Rainfall has become more inhomogeneous and more extreme in the heavily polluted urban regions.

As a basic property of cloud, accurate identification of cloud type is useful in forecasting the evolution of landfalling typhoons. Millimeter-wave cloud radar is an important means of identifying cloud type. Here, we develop a fuzzy logic algorithm that depends on radar range-height-indicator (RHI) data and takes into account the fundamental physical features of different cloud types. The algorithm is applied to a ground-based Ka-band millimeter-wave cloud radar. The input parameters of the algorithm include average reflectivity factor intensity, ellipse long axis orientation, cloud base height, cloud thickness, presence/absence of precipitation, ratio of horizontal extent to vertical extent, maximum echo intensity, and standard variance of intensities. The identified cloud types are stratus (St), stratocumulus (Sc), cumulus (Cu), cumulonimbus (Cb), nimbostratus (Ns), altostratus (As), altocumulus (Ac) and high cloud. The cloud types identified using the algorithm are in good agreement with those identified by a human observer. As a case study, the algorithm was applied to typhoon Khanun (1720), which made landfall in south-eastern China in October 2017. Sequential identification results from the algorithm clearly reflected changes in cloud type and provided indicative information for forecasting of the typhoon.

Global advocacy to mitigate climate change impacts on pristine environments, wildlife, ecology, and health has led scientists to design technologies that harness solar energy with remotely sensed, freely available data. This paper presents a study that designed a regionally adaptable and predictively efficient extreme learning machine (ELM) model to forecast long-term incident solar radiation (ISR) over Australia. The relevant satellite-based input data extracted from the Moderate Resolution Imaging Spectroradiometer (i.e., normalized vegetation index, land-surface temperature, cloud top pressure, cloud top temperature, cloud effective emissivity, cloud height, ozone and near infrared-clear water vapour), enriched by geo-temporal input variables (i.e., periodicity, latitude, longitude and elevation) are applied for a total of 41 study sites distributed approximately uniformly and paired with ground-based ISR (target). Of the 41 sites, 26 are incorporated in an ELM algorithm for the design of a universal model, and the remainder are used for model cross-validation. A universally-trained ELM (with training data as a global input matrix) is constructed, and the spatially-deployable model is applied at 15 test sites. The optimal ELM model is attained by trial and error to optimize the hidden layer activation functions for feature extraction and is benchmarked with competitive artificial intelligence algorithms: random forest (RF), M5 Tree, and multivariate adaptive regression spline (MARS). Statistical metrics show that the universally-trained ELM model has very good accuracy and outperforms RF, M5 Tree, and MARS models. With a distinct geographic signature, the ELM model registers a Legates & McCabe's Index of 0.555–0.896 vs. 0.411–0.858 (RF), 0.434–0.811 (M5 Tree), and 0.113–0.868 (MARS). The relative root-mean-square (RMS) error of ELM is low, ranging from approximately 3.715–7.191% vs. 4.907–10.784% (RF), 7.111–11.169% (M5 Tree) and 4.591–18.344% (MARS). Taylor diagrams that illustrate model preciseness in terms of RMS centred difference, error analysis, and boxplots of forecasted vs. observed ISR also confirmed the versatility of the ELM in generating forecasts over heterogeneous, remote spatial sites. This study ascertains that the proposed methodology has practical implications for regional energy modelling, particularly at national scales by utilizing remotely-sensed satellite data, and thus, may be useful for energy feasibility studies at future solar-powered sites. The approach is also important for renewable energy exploration in data-sparse or remote regions with no established measurement infrastructure but with a rich and viable satellite footprint.

All-sky imager based systems can be used to measure a number of cloud properties. Configurations consisting of two all-sky imagers can be used to derive cloud heights for weather stations, aviation and nowcasting of solar irradiance. One key question for such systems is the optimal distance between the all-sky imagers. This problem has not been studied conclusively in the literature. To the best of our knowledge, no previous in-field study of the optimal camera distance was performed. Also, comprehensive modeling is lacking. Here, we address this question with an in-field study on 93 days using 7 camera distances between 494 m and 2562 m and one specific cloud height estimation approach. We model the findings and draw conclusions for various configurations with different algorithmic methods and camera hardware. The camera distance is found to have a major impact on the accuracy of cloud height determinations. For the used 3 megapixel cameras, cloud heights up to 12,000 m and the used algorithmic approaches, an optimal camera distance of approximately 1500 m is determined. Optimal camera distances can be reduced to less than 1000 m if higher camera resolutions (e.g. 6 megapixel) are deployed. A step-by-step guide to determine the optimal camera distance is provided.

Atmospheric temperature and humidity retrievals from ground-based microwave remote sensing are useful in a variety of meteorological and environmental applications. Though the influence of clouds is usually considered in current retrieval algorithms, the resulting temperature and humidity estimates are still biased high in overcast conditions compared to radiosonde observations. Therefore, there is a need to improve the quality of retrievals in cloudy conditions. This paper presents an approach to make brightness temperature (TB) correction for cloud influence before the data can be used in the inversion of vertical profiles of atmospheric temperature and humidity. A three-channel method is proposed to make cloud parameter estimation, i.e., of the total 22 channels of the ground-based radiometer, three are adopted to set up a relationship between cloud parameters and brightness temperatures, so that the observations from the three channels can be used to estimate cloud thickness and water content and complete the cloud correction for the rest of the channels used in the retrieval. Based on two years of data from the atmosphere in Beijing, a comparison of the retrievals with radiosonde observations (RAOB) shows: (1) the temperature retrievals from this study have a higher correlation with RAOB and are notably better than in the vendor-provided LV2. The bias of the temperature retrievals from this study is close to zero at all heights, and the RMSE is greatly reduced from &gt, 5 °C to &lt, 2 °C in the layer, from about 1.5 km up to 5 km. The temperature retrievals from this study have higher correlation with RAOB data compared to the vendor-provided LV2, especially at and above a 2 km height. (2) The bias of the water vapor density profile from this study is near to zero, while the LV2 has a positive bias as large as 4 g/m3. The RMSE of the water vapor density profile from this study is &lt, 2 g/m3, while the RMSE for LV2 is as large as 10 g/m3. That is, both the bias and RMSE from this study are evidently less than the LV2, with a greater improvement in the lower troposphere below 5 km. Correlation with RAOB is improved even more for the water vapor density. The correlation of the retrievals from this study increases to one within the boundary layer, but the correlation of LV2 with RAOB is only 0.8 at 0.5 km height, 0.7 at 1 km, and even less than 0.5 at 2 km. (3) A parameter named the Cloud Impact index, determined by cloud water concentration and cloud thickness, together with the cloud base height, has been defined to show that both BIAS and RMSE of “high-CI subsample” are larger than those of the “low-CI subsample”, indicating that high-CI cloud has a higher impact on the retrievals and the correction for cloud influence is more necessary.

The Measurements of Pollution in the Troposphere (MOPITT) satellite instrument has been measuring global tropospheric carbon monoxide (CO) since March 2000, providing the longest nearly continuous record of CO from space. During its long mission the data processing algorithms have been updated to improve the quality of CO retrievals and the sensitivity to the lower troposphere. Currently, MOPITT retrievals are only performed for clear-sky observations or over low clouds for ocean scenes. Compared to all observed radiances, successful retrieval rates are about 30 % and 40 % between 90° S–90° N and 60° S–60° N, respectively. Spatial seasonal variations show that while MOPITT data coverage in some places reaches 30 % in summer, this number can drop to less than 10 % in winter due to significantly increased cloud cover. Therefore, we investigate the current MOPITT cloud detection algorithm and consider approaches to increase the data coverage. The MOPITT CO total column (TC) data were modified by turning off the cloud detection scheme to allow a CO retrieval result regardless of their cloud status. Analyses of the standard CO TC product (cloud filtered) and non-standard product (non-cloud masked) were conducted for selected days. Results showed some coherent structures that were observed frequently in the non-masked CO product that were not present in the standard product and could potentially be actual CO features. A corresponding analysis of Moderate Resolution Imaging Spectroradiometer(MODIS) cloud height and cloud mask products along with MOPITT cloud flag descriptors was conducted in order to understand the cloud conditions present for these apparently physical CO features. Results show that a significant number of low cloud CO retrievals were rejected in the standard product. Those missing areas match the coherent patterns that were detected in the non-masked CO product. Many times, these structures were also seen in the Infrared Atmospheric Sounding Interferometer (IASI) CO TC product indicating actual CO plumes. Multi-angle Imaging SpectroRadiometer (MISR) data on the Terra satellite were also employed for cloud height comparison with MODIS. Comparisons of MODIS and MISR cloud height data indicate remarkable agreement which is encouraging for the possibility of incorporating MODIS cloud height in the MOPITT cloud detection scheme. Statistics of the global assessment of the potential use of MODIS cloud height shows that MOPITT data increases significantly when cloud heights less than 2 km in height are incorporated in the retrievals. However quality indices should be defined and produced to ensure sufficient retrieval quality.

Clouds and aerosols play an important role in the Earth’s energy budget through a complex interaction with solar, atmospheric, and terrestrial radiation, and air humidity. Optically thick clouds efficiently reflect the incoming solar radiation and, globally, clouds are responsible for about two thirds of the planetary albedo. Thin cirrus trap the outgoing longwave radiation and keep the planet warm. Aerosols scatter or absorb sunlight depending on their size and shape and interact with clouds in various ways.Due to the importance of clouds and aerosols for the Earth’s energy budget, global satellite observations of their properties are essential for climate studies, for constraining climate models, and for evaluating cloud parameterizations. Active sounding from space by lidars and radars is advantageous since it provides the vertically resolved information. This has been proven by CALIOP lidar which has been observing the Earth’s atmosphere since 2006. Another instrument of this kind, CATS lidar on-board ISS provided measurements for over 33 months starting from the beginning of 2015. The ALADIN lidar on-board ADM/Aeolus has been measuring horizontal winds and aerosols/clouds since August 2018. More lidars are planned – in 2022, the ATLID/EarthCare lidar will be launched and other space-borne lidars are in the development phase.In this work, we compare the scattering ratio products retrieved from ALADIN and CALIOP observations. The former is aimed at 35 deg from nadir, it measures the atmospheric backscatter at 355nm from nadir, is capable of separating the molecular and particular components (HSRL), and provides the profiles with a vertical resolution of ~1km up to 20km altitude. The latter, operating at 532nm is aimed at 3 deg from nadir and measures the total backscatter up to 40 km. Its natural vertical resolution is higher than that of ALADIN, but the scattering ratio product used in the comparison is provided at ~0.5km vertical grid.We have performed a search of nearly simultaneous common volume observations of atmosphere by these two instruments for the period from 28/06/2019 through 31/12/2019 and analyzed the collocated data. We present the zonal averages of scattering ratios as well as the instantaneous profile comparisons and the statistical analysis of cloud detection, cloud height agreement, and temporal evolution of these characteristics.The preliminary conclusion, which can be drawn from this analysis, is that the general agreement of scattering ratio profiles retrieved from ALADIN and CALIOP observations is good up to 6-7 km height whereas in the higher atmospheric layers ALADIN is less sensitive to clouds than the CALIOP. This lack of sensitivity might be compensated by further averaging of the input signals and/or by an updating of the retrieval algorithms using the collocated observations dataset provided in the present work.

A hydraulic fracturing site in Morgantown, West Virginia, USA was selected to be a research site for the Marcellus Shale Energy and Environment Laboratory (MSEEL) project which was funded by the U.S. Department of Energy (DOE). The field site has two previously drilled horizontal wells and two newly drilled horizontal wells for extracting natural gas. A separate exploratory vertical “science well” was also drilled and an array of geophones was used to extract important seismic/microseismic event information during hydraulic fracture propagation. Microseismic data collected from the geophones was then used to approximate the extent of hydraulic fractures. Numerical modeling was performed to determine the extent of all hydraulic fractures at the site. The 58 stages encompassing two wells were individually numerically modeled. Available geologic, geomechanical, and treatment data was utilized in the numerical modeling of all stages at both horizontal wells. Comparisons were then made with available microseismic data collected at the field site during hydraulic fracturing operations. Model calibration was based on a statistical methodology and available microseismic data. Results show a good match between estimates/measurements and model calculations of fracture/microseismic cloud height.