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A direct-detection Rayleigh–Mie Doppler lidar for measuring horizontal wind speed in the middle atmosphere (10 to 50 km altitude) has been deployed at Observatoire de Haute-Provence (OHP) in southern France starting from 1993. After a recent upgrade, the instrument gained the capacity of wind profiling between 5 and 75 km altitude with vertical resolution up to 115 m and temporal resolution up to 5 min. The lidar comprises a monomode Nd:Yag laser emitting at 532 nm, three telescope assemblies and a double-edge Fabry–Pérot interferometer for detection of the Doppler shift in the backscattered light. In this article, we describe the instrument design, recap retrieval methodology and provide an updated error estimate for horizontal wind. The evaluation of the wind lidar performance is done using a series of 12 time-coordinated radiosoundings conducted at OHP. A point-by-point intercomparison shows a remarkably small average bias of 0.1 m s−1 between the lidar and the radiosonde wind profiles with a standard deviation of 2.3 m s−1. We report examples of a weekly and an hourly observation series, reflecting various dynamical events in the middle atmosphere, such as a sudden stratospheric warming event in January 2019 and an occurrence of a stationary gravity wave, generated by the flow over the Alps. A qualitative comparison between the wind profiles from the lidar and the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System is also discussed. Finally, we present an example of early validation of the European Space Agency (ESA) Aeolus space-borne wind lidar using its ground-based predecessor.

The objective of this paper and its companion (Wing et al., 2018) is to show that ground-based lidar temperatures are a stable, accurate, and precise data set for use in validating satellite temperatures at high vertical resolution. Long-term lidar observations of the middle atmosphere have been conducted at the Observatoire de Haute-Provence (OHP), located in southern France (43.93° N, 5.71° E), since 1978. Making use of 20 years of high-quality co-located lidar measurements, we have shown that lidar temperatures calculated using the Rayleigh technique at 532 nm are statistically identical to lidar temperatures calculated from the non-absorbing 355 nm channel of a differential absorption lidar (DIAL) system. This result is of interest to members of the Network for the Detection of Atmospheric Composition Change (NDACC) ozone lidar community seeking to produce validated temperature products. Additionally, we have addressed previously published concerns of lidar–satellite relative warm bias in comparisons of upper-mesospheric and lower-thermospheric (UMLT) temperature profiles. We detail a data treatment algorithm which minimizes known errors due to data selection procedures, a priori choices, and initialization parameters inherent in the lidar retrieval. Our algorithm results in a median cooling of the lidar-calculated absolute temperature profile by 20 K at 90 km altitude with respect to the standard OHP NDACC lidar temperature algorithm. The confidence engendered by the long-term cross-validation of two independent lidars and the improved lidar temperature data set is exploited in Wing et al. (2018) for use in multi-year satellite validations.

In the frame of designing a knowledge discovery system, we have developed stochastic models based on high-order hidden Markov models. These models are capable to map sequences of data into a Markov chain in which the transitions between the states depend on the n previous states according to the order of the model. We study the process of achieving information extraction from spatial and temporal data by means of an unsupervised classification. We use therefore a French national database related to the land use of a region, named Ter Uti, which describes the land use both in the spatial and temporal domain. Land-use categories (wheat, corn, forest, ...) are logged every year on each site regularly spaced in the region. They constitute a temporal sequence of images in which we look for spatial and temporal dependencies. [ABSTRACT FROM AUTHOR]