In-Orbit Attitude Determination of the UVSQ-SAT CubeSat Using TRIAD and MEKF Methods.

Ultraviolet and infrared sensors at high quantum efficiency on-board a small satellite (UVSQ-SAT) is a CubeSat dedicated to the observation of the Earth and the Sun. This satellite has been in orbit since January 2021. It measures the Earth's outgoing shortwave and longwave radiations. The satellite does not have an active pointing system. To improve the accuracy of the Earth's radiative measurements and to resolve spatio-temporal fluctuations as much as possible, it is necessary to have a good knowledge of the attitude of the UVSQ-SAT CubeSat. The attitude determination of small satellites remains a challenge, and UVSQ-SAT represents a real and unique example to date for testing and validating different methods to improve the in-orbit attitude determination of a CubeSat. This paper presents the flight results of the UVSQ-SAT's attitude determination. The Tri-Axial Attitude Determination (TRIAD) method was used, which represents one of the simplest solutions to the spacecraft attitude determination problem. Another method based on the Multiplicative Extended Kalman Filter (MEKF) was used to improve the results obtained with the TRIAD method. In sunlight, the CubeSat attitude is determined at an accuracy better than 3° (at one σ) for both methods. During eclipses, the accuracy of the TRIAD method is 14°, while it reaches 10° (at one σ) for the recursive MEKF method. Many future satellites could benefit from these studies in order to validate methods and configurations before launch.

Tentative detection of clear-air turbulence using a ground-based Rayleigh lidar.

Atmospheric gravity waves and turbulence generate small-scale fluctuations of wind, pressure, density, and temperature in the atmosphere. These fluctuations represent a real hazard for commercial aircraft and are known by the generic name of clear-air turbulence (CAT). Numerical weather prediction models do not resolve CAT and therefore provide only a probability of occurrence. A ground-based Rayleigh lidar was designed and implemented to remotely detect and characterize the atmospheric variability induced by turbulence in vertical scales between 40 m and a few hundred meters. Field measurements were performed at Observatoire de Haute-Provence (OHP, France) on 8 December 2008 and 23 June 2009. The estimate of the mean squared amplitude of bidimensional fluctuations of lidar signal showed excess compared to the estimated contribution of the instrumental noise. This excess can be attributed to atmospheric turbulence with a 95% confidence level. During the first night, data from collocated stratosphere-troposphere (ST) radar were available. Altitudes of the turbulent layers detected by the lidar were roughly consistent with those of layers with enhanced radar echo. The derived values of turbulence parameters Cn2 or CT2 were in the range of those published in the literature using ST radar data. However, the detection was at the limit of the instrumental noise and additional measurement campaigns are highly desirable to confirm these initial results. This is to our knowledge the first successful attempt to detect CAT in the free troposphere using an incoherent Rayleigh lidar system. The built lidar device may serve as a test bed for the definition of embarked CAT detection lidar systems aboard airliners.

Methodological uncertainties in multi-regression analyses of middle-atmospheric data series.

Multi-regression analyses have often been used recently to detect trends, in particular in ozone or temperature data sets in the stratosphere. The confidence in detecting trends depends on a number of factors which generate uncertainties. Part of these uncertainties comes from the random variability and these are what is usually considered. They can be statistically estimated from residual deviations between the data and the fitting model. However, interferences between different sources of variability affecting the data set, such as the Quasi-Biennal Oscillation (QBO), volcanic aerosols, solar flux variability and the trend can also be a critical source of errors. This type of error has hitherto not been well quantified. In this work an artificial data series has been generated to carry out such estimates. The sources of errors considered here are: the length of the data series, the dependence on the choice of parameters used in the fitting model and the time evolution of the trend in the data series. Curves provided here, will permit future studies to test the magnitude of the methodological bias expected for a given case, as shown in several real examples. It is found that, if the data series is shorter than a decade, the uncertainties are very large, whatever factors are chosen to identify the source of the variability. However the errors can be limited when dealing with natural variability, if a sufficient number of periods (for periodic forcings) are covered by the analysed dataset. However when analysing the trend, the response to volcanic eruption induces a bias, whatever the length of the data series. The signal to noise ratio is a key factor: doubling the noise increases the period for which data is required in order to obtain an error smaller than 10%, from 1 to 3-4 decades. Moreover, if non-linear trends are superimposed on the data, and if the length of the series is longer than five years, a non-linear function has to be used to estimate trends. When applied to real data series, and when a breakpoint in the series occurs, the study reveals that data extending over 5 years are needed to detect a significant change in the slope of the ozone trends at mid-latitudes.

Improved retrievals of the optical properties of cirrus clouds by a combination of lidar methods.

We focus on improvement of the retrieval of optical properties of cirrus clouds by combining two lidar methods. We retrieve the cloud's optical depth by using independently the molecular backscattering profile below and above the cloud [molecular integration (MI) method] and the backscattering profile inside the cloud with an a priori effective lidar ratio [particle integration (PI) method]. When the MI method is reliable, the combined MI-PI method allows us to retrieve the optimal effective lidar ratio. We compare these results with Raman lidar retrievals. We then use the derived optimal effective lidar ratio for retrieval with the PI method for situations in which the MI method cannot be applied.

Stratospheric temperature monitoring using a vibrational Raman lidar. Part 1: aerosols and ozone interferences.

Lidar measurements of temperature for the upper troposphere and lower stratosphere are commonly derived by the Raman technique. Lidar signals derived from vibrational Raman processes have been subjected to numerous simulation tests to examine their sensitivity to the presence of aerosols and ozone in the atmosphere. The influence of aerosols characteristics (wavelength dependence of aerosol extinction and particle phase function) and of ozone concentration on Raman temperature profiles is estimated. Simulations indicate large temperature deviations for post-volcanic conditions. For a Raman backscatter at 607 nm, bias is below 1 K for a total optical depth less than 9 x 10(-3) in the case of a stratospheric contamination and less than 6 x 10(-3) for a tropospheric contamination. The effect of aerosols depends on phase function and a few parameters such as altitude, optical depth and the shape of the high-altitude cloud. The wavelength dependence of aerosol extinction has some influence only for severe post-volcanic conditions (Scattering Ratio, SR >2). For a Raman backscatter at 387 nm, bias is larger and can be significant even in background aerosol conditions. Changes in the ozone density profile lead to significant Raman temperature deviations only for some specific conditions. Results suggest that both aerosol and ozone corrections are necessary to obtain an accuracy better than the 1 K requested for most atmospheric applications.

Review of ozone and temperature lidar validations performed within the framework of the Network for the Detection of Stratospheric Change.

The use of assimilation tools for satellite validation requires true estimates of the accuracy of the reference data. Since its inception, the Network for Detection of Stratospheric Change (NDSC) has provided systematic lidar measurements of ozone and temperature at several places around the world that are well adapted for satellite validations. Regular exercises have been organised to ensure the data quality at each individual site. These exercises can be separated into three categories: large scale intercomparisons using multiple instruments, including a mobile lidar; using satellite observations as a geographic transfer standards to compare measurements at different sites; and comparative investigations of the analysis software. NDSC is a research network, so each system has its own history, design, and analysis, and has participated differently in validation campaigns. There are still some technological differences that may explain different accuracies. However, the comparison campaigns performed over the last decade have always proved to be very helpful in improving the measurements. To date, more efforts have been devoted to characterising ozone measurements than to temperature observations. The synthesis of the published works shows that the network can potentially be considered as homogeneous within +/-2% between 20-35 km for ozone and +/-1 K between 35-60 km for temperature. Outside this altitude range, larger biases are reported and more efforts are required. In the lower stratosphere, Raman channels seem to improve comparisons but such capabilities were not systematically compared. At the top of the profiles, more investigations on analysis methodologies are still probably needed. SAGE II and GOMOS appear to be excellent tools for future ozone lidar validations but need to be better coordinated and take more advantage of assimilation tools. Also, temperature validations face major difficulties caused by atmospheric tides and therefore require intercomparisons with the mobile systems, at all sites.