Time series inversion of spectra from ground-based radiometers

Retrieving time series of atmospheric constituents from ground-based spectrometers often requires different temporal averaging depending on the altitude region in focus. This can lead to several datasets existing for one instrument, which complicates validation and comparisons between instruments. This paper puts forth a possible solution by incorporating the temporal domain into the maximum a posteriori (MAP) retrieval algorithm. The state vector is increased to include measurements spanning a time period, and the temporal correlations between the true atmospheric states are explicitly specified in the a priori uncertainty matrix. This allows the MAP method to effectively select the best temporal smoothing for each altitude, removing the need for several datasets to cover different altitudes. The method is compared to traditional averaging of spectra using a simulated retrieval of water vapour in the mesosphere. The simulations show that the method offers a significant advantage compared to the traditional method, extending the sensitivity an additional 10 km upwards without reducing the temporal resolution at lower altitudes. The method is also tested on the Onsala Space Observatory (OSO) water vapour microwave radiometer confirming the advantages found in the simulation. Additionally, it is shown how the method can interpolate data in time and provide diagnostic values to evaluate the interpolated data

New insights on polar mesospheric cloud particle size distributions from a two-satellite common volume study

The particle size distribution of Polar Mesospheric Clouds (PMC) is closely related to the fundamental processes of cloud formation and evolution. Still, despite substantial observational efforts, specific details about the particle size distribution have remained obscure. In this study, we aim at deriving more constraints on PMC size distributions by combining optical measurements from two satellite instruments observing a common PMC volume. We use a special set of 2D tomographic limb observations from the Optical Spectrograph and Infrared Imager System (OSIRIS) on the Odin satellite from 2010 to 2011 in the latitude range 78\ub0 N to 80\ub0 N and compare these to simultaneous PMC observations from the nadir-viewing Cloud Imaging and Particle Size (CIPS) instrument on the AIM satellite. A key goal is to find the assumption on the mathematical shape of the particle size distribution that should be applied to a vertically resolving limb-viewing instrument to reach consistent size results compared to the column-integrated ice distribution as seen by a nadir-viewing instrument. Our results demonstrate that viewing geometry and sampling volume of each instrument must be carefully considered and that the same size distribution assumption cannot simultaneously describe a column-integrated and a local height-resolved size distribution. In particular, applying the standard Gaussian assumption, used by many earlier PMC studies, to both limb and nadir observation leads to an overestimate of particle sizes seen by OSIRIS by about 10 nm as compared to CIPS. We show that the agreement can be improved if a Log-normal assumption with a broad distribution width around σ = 1.42 is adopted for OSIRIS. A reason for this broad distribution best describing the OSIRIS observations we suggest the large retrieval volume of the limb measurement. Gravity waves and other small-scale processes can cause horizontal variations and a co-existence of a wide range of particle populations in the sampling volume. Horizontal integration then leads to apparently much broader size distributions than encountered in a small horizontal sampling volume

Anomaly Detection for Agricultural Vehicles Using Autoencoders

The safe in-field operation of autonomous agricultural vehicles requires detecting all objects that pose a risk of collision. Current vision-based algorithms for object detection and classification are unable to detect unknown classes of objects. In this paper, the problem is posed as anomaly detection instead, where convolutional autoencoders are applied to identify any objects deviating from the normal pattern. Training an autoencoder network to reconstruct normal patterns in agricultural fields makes it possible to detect unknown objects by high reconstruction error. Basic autoencoder (AE), vector-quantized variational autoencoder (VQ-VAE), denoising autoencoder (DAE) and semisupervised autoencoder (SSAE) with a max-margin-inspired loss function are investigated and compared with a baseline object detector based on YOLOv5. Results indicate that SSAE with an area under the curve for precision/recall (PR AUC) of 0.9353 outperforms other autoencoder models and is comparable to an object detector with a PR AUC of 0.9794. Qualitative results show that SSAE is capable of detecting unknown objects, whereas the object detector is unable to do so and fails to identify known classes of objects in specific cases

Retrieval of daytime mesospheric ozone using OSIRIS observations of O2 (a1Δg) emission

This work is distributed under the Creative Commons Attribution 4.0 License. Improving knowledge of the ozone global distributions in the mesosphere-lower thermosphere (MLT) is a crucial step in understanding the behaviour of the middle atmosphere. However, the concentration of ozone under sunlit conditions in the MLT is often so low that its measurement requires instruments with very high sensitivity. Fortunately, the bright oxygen airglow can serve as a proxy to retrieve the daytime ozone density indirectly, due to the strong connection to ozone photolysis in the Hartley band. The OSIRIS IR imager (hereafter, IRI), one of the instruments on the Odin satellite, routinely measures the oxygen infrared atmospheric band (IRA band) at 1.27 μm. In this paper, we will primarily focus on the detailed description of the steps done for retrieving the calibrated IRA band limb radiance (with <10 % random error), the volume emission rate of O2 (a1i"g) (with <25 % random error) and finally the ozone number density (with <20 % random error). This retrieval technique is applied to a 1-year sample from the IRI dataset. The resulting product is a new ozone dataset with very tight along-track sampling distance (<20 km). The feasibility of the retrieval technique is demonstrated by a comparison of coincident ozone measurements from other instruments aboard the same spacecraft, as well as zonal mean and monthly average comparisons between Odin-OSIRIS (both spectrograph and IRI), Odin-SMR and Envisat-MIPAS. We find that IRI appears to have a positive bias of up to 25 % below 75 km, and up to 50 % in some regions above. We attribute these differences to uncertainty in the IRI calibration as well as uncertainties in the photochemical constants. However, the IRI ozone dataset is consistent with the compared dataset in terms of the overall atmospheric distribution of ozone between 50 and 100 km. If the origin of the bias can be identified before processing the entire dataset, this will be corrected and noted in the dataset description. The retrieval technique described in this paper can be further applied to all the measurements made throughout the 19 year mission, leading to a new, long-term high-resolution ozone dataset in the middle atmosphere

The OH (3-1) nightglow volume emission rate retrieved from OSIRIS measurements: 2001 to 2015

The OH airglow has been used to investigate the chemistry and dynamics of the mesosphere and the lower thermosphere (MLT) for a long time. The infrared imager (IRI) aboard the Odin satellite has been recording the night-time 1.53 mu m OH (3-1) emission for more than 15 years (2001-2015), and we have recently processed the complete data set. The newly derived data products contain the volume emission rate profiles and the Gaussian-approximated layer height, thickness, peak intensity and zenith intensity, and their corresponding error estimates. In this study, we describe the retrieval steps for these data products. We also provide data screening recommendations. The monthly zonal averages depict the well-known annual oscillation and semi-annual oscillation signatures, which demonstrate the fidelity of the data set (https://doi.org/10.5281/zenodo.4746506, Li et al., 2021). The uniqueness of this Odin IRI OH long-term data set makes it valuable for studying various topics, for instance, the sudden stratospheric warming events in the polar regions and solar cycle influences on the MLT

Urokinase-type plasminogen activator receptor (uPAR) on tumor-associated macrophages is a marker of poor prognosis in colorectal cancer

Patients were identified from a population-based prospective study of 4990 individuals with symptoms associated with colorectal cancer (CRC). A total of 244 CRC tissue samples were available for immunohistochemical staining of uPAR, semiquantitatively scored at the invasive front, and in the tumor core on cancer cells, macrophages, and myofibroblasts. In addition, the levels of the intact and cleaved uPAR-forms in blood from the same patients are evaluated in this study. In a univariate analysis, the number of uPAR-positive versus uPAR-negative macrophages (HR = 2.26, [95% CI: 1.39–3.66, P = 0.0009]) and cancer cells (HR=1.49, [95% CI: 1.01–2.20, P = 0.047]) located in the tumor core were significantly associated to overall survival. In a multivariate analysis, uPAR-positive versus uPAR-negative macrophages located in the tumor core showed the best separation of patients with positive score associated to poor prognosis (HR = 1.84 [95% CI: 1.12–3.04, P = 0.017]). In a multivariate analysis including clinical covariates and soluble uPAR(I), the latter was significantly associated to overall survival (HR = 2.68 [95% CI: 1.90–3.79, P < 0.0001]) and uPAR-positive macrophages in the tumor core remained significantly associated to overall survival (HR = 1.81 [95% CI: 1.08–3.01, P = 0.023]). Membrane-bound uPAR showed additive effects with the circulating uPAR(I) and stage, giving a hazard ratio of 12 between low and high scores. Thus, combining stage, uPAR(I) in blood and uPAR on macrophages in the tumor core increase the prognostic precision more than tenfold, as compared to stage alone

Flight model characterization of the wide-field off-axis telescope for the MATS satellite

We present optical characterization, calibration, and performance tests of the Mesospheric Airglow/Aerosol Tomography Spectroscopy (MATS) satellite, which for the first time for a satellite applies a linear-astigmatism-free confocal off-axis reflective optical design. Mechanical tolerances of the telescope were investigated using Monte-Carlo methods and single-element perturbations. The sensitivity analysis results indicate that tilt errors of the tertiary mirror and a surface RMS error of the secondary mirror mainly degrade optical performance. From the Monte-Carlo simulation, the tolerance limits were calculated to $\pm$0.5 mm, $\pm$1 mm, and $\pm$0.15$^\circ$ for decenter, despace, and tilt, respectively. We performed characterization measurements and optical tests with the flight model of the satellite. Multi-channel relative pointing, total optical system throughput, and distortion of each channel were characterized for end-users. Optical performance was evaluated by measuring modulation transfer function (MTF) and point spread function (PSF). The final MTF performance is 0.25 MTF at 20 lp/mm for the ultraviolet channel (304.5 nm), and 0.25 - 0.54 MTF at 10 lp/mm for infrared channels. The salient fact of the PSF measurement of this system is that there is no noticeable linear astigmatism detected over wide field of view (5.67$^\circ$ $\times$ 0.91$^\circ$). All things considered, the design method showed great advantages in wide field of view observations with satellite-level optical performance.Comment: 21 pages, 11 figure