The FORUM instrument will look at the Earth's atmosphere from a satellite, covering a spectral range responsible for about 95 % of the radiation lost by our planet. FORUM helps to measure the imbalance between incoming and outgoing radiation that is responsible for the increasing average temperatures on Earth. The end-to-end simulator is a chain of codes that simulates the FORUM measurement process. The goal of the project is to study how the instrument reacts to different retrieval conditions.

HALO is a recently developed lidar system that demonstrates new technologies and advanced algorithms for profiling water vapor as well as aerosol and cloud properties. The high-resolution, high-accuracy measurements have unique advantages within the suite of atmospheric instrumentation, such as directly trading water vapor measurement resolution for precision. This paper provides the methodology and first water vapor results, showing agreement with in situ and spaceborne sounder measurements.

A continuous source for the generation of pure HONO mixtures was developed and characterized, which is based on the Henry's law solubility of HONO in acidic aqueous solutions. The source shows a fast time response and an excellent long-term stability and can be easily adjusted to HONO mixing ratios in the range 0.05–500 ppb. A general equation based on Henry's law is developed, whereby the HONO concentration of the source can be absolutely calculated with an accuracy of better than 10 %.

Aerosols are tiny particles suspended in the atmosphere. A fraction of these particles can form clouds and are called cloud condensation nuclei (CCN). Measurements of such aerosol particles are necessary to study the aerosol–cloud interactions and reduce the uncertainty in our future climate predictions. We present a novel methodology to estimate global 3D CCN concentrations from the CALIPSO satellite measurements. The final data set will be used to study the aerosol–cloud interactions.

We show that the low-cost PurpleAir sensor can be characterized as a cell-reciprocal nephelometer. At two very different locations (Mauna Loa Observatory in Hawaii and the Table Mountain rural site in Colorado), the PurpleAir measurements are highly correlated with the submicrometer aerosol scattering coefficient measured by a research-grade integrating nephelometer. These results imply that, with care, PurpleAir data may be used to evaluate climate and air quality models.

We test a novel method to remotely measure ice particles in clouds. This is important because such measurements are required to improve climate and weather models. The method combines a radar with newly developed sensors measuring microwave radiation at very short wavelengths. We use observations made from aircraft flying above the cloud and compare them to real measurements from inside the cloud. This works well given that one can model the ice particles in the cloud sufficiently well.

In this study, an analysis has been performed to understand the improvements in observational coverage over Canada in the new MOPITT V9 product. Temporal and spatial analysis of V9 indicates a general coverage gain of 15–20 % relative to V8, which varies regionally and seasonally; e.g., the number of successful MOPITT retrievals in V9 was doubled over Canada in winter. Also, comparison with the corresponding IASI instrument indicated generally good agreement, with about a 5–10 % positive bias.

We developed a machine learning model to detect plumes of nitrogen dioxide satellite observations over 2 years. We find over 310 000 plumes, mainly over cities, industrial regions, and areas of oil and gas production. Our model performs well in comparison to other datasets and in some cases finds emissions that are not included in other datasets. This method could be used to help locate and measure emission hotspots across the globe and help inform climate policies.

This paper proposes a method for evaluating canopy UHI intensity (CUHII) at high resolution by using remote sensing data and machine learning with a random forest (RF) model. The spatial distribution of CUHII was evaluated at 30 m resolution based on the output of the RF model. The present RF model framework for real-time monitoring and assessment of high-resolution CUHII provides scientific support for studying the changes and causes of CUHII.

Our research has determined the possible relationship between Weibull natural wind mesoscale parameter c and shape factor k with height under the conditions of a desert steppe terrain in northern China, which has great potential in wind power generation. We have gained an enhanced understanding of the seasonal changes in the surface roughness of the desert grassland and the changes in the incoming wind direction.

An analysis of airborne triple-frequency radar and almost perfectly co-located coincident in situ data from an Arctic storm confirms the main findings of modeling work with radar dual-frequency ratios (DFRs) at different zones of the DFR plane associated with different ice habits. High-resolution CPI images provide accurate identification of rimed particles within the DFR plane. The relationships between the triple-frequency signals and cloud microphysical properties are also presented.

This work presents a new approach to exploit unlabeled image data from ground-based sky observations to train neural networks. We show that our model can detect cloud classes within images more accurately than models trained with conventional methods using small, labeled datasets only. Novel machine learning techniques as applied in this work enable training with much larger datasets, leading to improved accuracy in cloud detection and less need for manual image labeling.

AMDAR temperatures suffer from a bias, which can be related to a difference in the timing of height and measurement and to internal corrections applied to pressure altitude. Based on NWP model temperature data, combined with Mach number and true airspeed, we could estimate corrections. Comparing corrected temperatures with (independent) radiosonde observations demonstrates a reduction in the bias, from 0.5 K to around zero, and standard deviation, of almost 10 %.

We made a humidity generator that adds water vapor to a flowing gas. Its range of humidity is useful for calibrating balloon-borne probes to the Earth's stratosphere. The generator’s novel feature is a saturator that comprises 5 m of silicone tubing immersed in water. The length was enough to ensure that the saturator’s output was independent of the dimensions and permeability of the tube. This simple, low-cost design provides an accuracy that is acceptable for many applications.