Contrail cirrus constitute the largest radiative forcing (RF) component to the total aviation effect on climate. However, the microphysical properties and radiative effects of contrail cirrus and natural cirrus clouds in the same meteorological conditions are still not completely resolved. Motivated by these uncertainties, we investigate an extended cirrus region perturbed by aviation in the North Atlantic region (NAR) on 26 March 2014 during the Midlatitude Cirrus (ML-CIRRUS) experiment. On that day, high air traffic density in the NAR combined with large scale cold and humid ambient conditions favored the formation of a contrail cirrus outbreak situation. In addition, low coverage by low-level water clouds and the homogeneous oceanic albedo increased the sensitivity for retrieving cirrus properties and their radiative effect from satellite remote sensing. This allowed us to extend the current knowledge on contrail cirrus by combining airborne in situ, lidar and satellite observations. In the synoptic context of a ridge cirrus, an extended thin ice cloud with many persistent contrails and contrail cirrus has been observed for many hours with the geostationary Meteosat Second Generation (MSG)/Spinning Enhanced Visible and InfraRed Imager (SEVIRI) from the early morning hours until dissipation after noon. Airborne lidar observations aboard the German High Altitude and LOng Range Research Aircraft (HALO) suggest that this cirrus has a significant anthropogenic contribution from aviation. A new method based on in situ measurements was used to distinguish between contrails, contrail cirrus and natural cirrus based on ice number and gas phase NO concentrations. Results show that contrail effective radii (R_eff) reach at most 11 µm, while contrail cirrus R_eff can be as large as 51 µm. Contrail and contrail cirrus mean R_eff is 18 % smaller than that of natural cirrus. We find that a difference in R_eff between contrail cirrus and natural cirrus survives in this contrail cirrus outbreak event. As for radiative effects, a new method to estimate top-of-atmosphere instantaneous RF in the solar and thermal range is developed based on radiative transfer model simulations exploiting in situ and lidar measurements, satellite observations and ERA5 reanalysis data for both cirrus and cirrus-free regions. Broadband irradiances estimated from our simulations compare well with satellite observations from MSG, indicating that our method provides a good representation of the real atmosphere and can thus be used to determine the RF of ice clouds. For a larger spatial area around the flight path, we find that the contrail cirrus outbreak is warming in the early morning and cooling during the day. The methods presented here and the results will be valuable for future research to constrain uncertainties in the assessment of radiative impacts of contrail cirrus and natural cirrus and for the formulation and evaluation of contrail mitigation options.

Knowledge of humidity in the upper troposphere and lower stratosphere (UTLS) is of special interest due to its importance for cirrus cloud formation and its climate impact. However, the UTLS water vapor distribution in current weather models is subject to large uncertainties. Here, we develop a dynamic-based humidity correction method using an artificial neural network (ANN) to improve the relative humidity over ice (RHi) in ECMWF numerical weather predictions. The model is trained with time-dependent thermodynamic and dynamical variables from ECMWF ERA5 and humidity measurements from the In-service Aircraft for a Global Observing System (IAGOS). Previous and current atmospheric variables within ±2 ERA5 pressure layers around the IAGOS flight altitude are used for ANN training. RHi, temperature, and geopotential exhibit the highest impact on ANN results, while other dynamical variables are of low to moderate or high importance. The ANN shows excellent performance, and the predicted RHi in the UT has a mean absolute error (MAE) of 5.7 % and a coefficient of determination (R²) of 0.95, which is significantly improved compared to ERA5 RHi (MAE of 15.8 %; R² of 0.66). The ANN model also improves the prediction skill for all-sky UT/LS and cloudy UTLS and removes the peak at RHi = 100 %. The contrail predictions are in better agreement with Meteosat Second Generation (MSG) observations of ice optical thickness than the results without humidity correction for a contrail cirrus scene over the Atlantic. The ANN method can be applied to other weather models to improve humidity predictions and to support aviation and climate research applications.

The detection of supercooled water clouds (SWCs) is essential for artificial rain enhancement, the prevention of aircraft ice accretion, and better understanding of radiative energy balance. However, it is challenging to identify SWCs using only passive satellite measurements. We adopt measurements from the Advanced Himawari Imager, which is onboard the new‐generation, high temporal, spatial, and spectral resolution geostationary Himawari‐8 satellite, to develop a time‐continuous Himawari‐8 SWC (HSWC) algorithm. The HSWC algorithm includes a group of tests using comprehensive cloud properties (e.g., cloud phase [CPH], cloud top temperature, cloud optical thickness, and cloud effective radius [CER]). Unlike previous SWC detection algorithms, which are based on cloud top temperature and cloud optical thickness properties, we introduce CER and CPH information into the HSWC algorithm because the distribution of SWCs is sensitive to CER values, and SWCs may appear in mixed‐phase clouds identified by satellites. Our analyses indicate that the additions of the CER and CPH tests could improve the performance of SWC detection by 15.07% and 4.75%, respectively. The full disk SWC detection results identified by the HSWC algorithm in January, May, August, and October of 2017 are validated using lidar measurements. The hit rate and false alarm rate are 93.52% and 25.27%, respectively. Our study provides potential SWC regions for the implementation of artificial rain enhancement.

A silicon nanochannel system with integrated transverse electrodes was designed and fabricated by combining micro-electro-mechanical systems (MEMS) micromachining and atomic force microscopy (AFM)-based nanolithography. The fabrication process began with the patterning of microscale reservoirs and electrodes on an oxidised silicon chip using conventional MEMS techniques. A nanochannel, approximately 30 µm long with a small semi-circular cross-sectional area of 20 nm×200 nm, was then mechanically machined on the oxide surface between the micro reservoirs by applying AFM nanolithography with an all-diamond probe. Anodic bonding was used to seal off the nanochannel with a matching Pyrex cover. Continuous flow in the nanochannel was verified by pressurising a solution of fluorescein isothiocyanate in ethanol through the nanochannel in a vacuum chamber. It was further demonstrated by translocating negatively charged nanobeads (diameter ∼20 nm) through the nanochannel by using an external DC electric field. The passage of the nanobeads caused a sharp increase in the transverse electrical conductivity of the nanochannel.

The supercooled liquid fraction (SLF) in mixed-phase clouds (MPCs) is an essential variable of cloud microphysical processes and climate sensitivity. However, the SLF is currently calculated in spaceborne remote sensing only as the cloud phase–frequency ratio of adjacent pixels, which results in a loss of the original resolution in observations of cloud liquid or ice content within MPCs. Here, we present a novel method for retrieving the SLF in MPCs based on the differences in radiative properties of supercooled liquid droplets and ice particles at visible (VIS) and shortwave infrared (SWI) channels of the geostationary Himawari-8. Liquid and ice water paths are inferred by assuming that clouds are composed of only liquid or ice, with the real cloud water path (CWP) expressed as a combination of these two water paths (SLF and 1-SLF as coefficients), and the SLF is determined by referring to the CWP from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). The statistically relatively small cloud phase spatial inhomogeneity at a Himawari-8 pixel level indicates an optimal scene for cloud retrieval. The SLF results are comparable to global SLF distributions observed by active instruments, particularly for single-layered cloud systems. While accessing the method's feasibility, SLF averages are estimated between 74 % and 78 % in Southern Ocean (SO) stratocumulus across seasons, contrasting with a range of 29 % to 32 % in northeastern Asia. The former exhibits a minimum SLF around midday in summer and a maximum in winter, while the latter trend differs. This novel algorithm will be valuable for research to track the evolution of MPCs and constrain the related climate impact.