Mitigation of bias sources for atmospheric temperature and humidity in the mobile Raman Weather and Aerosol Lidar (WALI)

Lidars using vibrational and rotational Raman scattering to continuously monitor both the water vapor and temperature profiles in the low and middle troposphere offer enticing perspectives for applications in weather prediction and studies of aerosol–cloud–water vapor interactions by simultaneously deriving relative humidity and atmospheric optical properties. Several heavy systems exist in European laboratories, but only recently have they been downsized and ruggedized for deployment in the field. In this paper, we describe in detail the technical choices made during the design and calibration of the new Raman channels for the mobile Weather and Aerosol Lidar (WALI), going over the important sources of bias and uncertainty on the water vapor and temperature profiles stemming from the different optical elements of the instrument. For the first time, the impacts of interference filters and non-common-path differences between Raman channels, and their mitigation, in particular are investigated, using horizontal shots in a homogeneous atmosphere. For temperature, the magnitude of the highlighted biases can be much larger than the targeted absolute accuracy of 1 ∘C defined by the WMO (up to 6 ∘C bias below 300 m range). Measurement errors are quantified using simulations and a number of radiosoundings launched close to the laboratory. After de-biasing, the remaining mean differences are below 0.1 g kg−1 on water vapor and 1 ∘C on temperature, and rms differences are consistent with the expected error from lidar noise, calibration uncertainty, and horizontal inhomogeneities of the atmosphere between the lidar and radiosondes.

Unified Parameterization and Calibration of Serial, Parallel, and Hybrid Manipulators

In this work, we present methods allowing parallel, hybrid, and serial manipulators to be analyzed, calibrated, and controlled with the same analytical tools. We introduce a general approach to describe any robotic manipulator using established serial-link representations. We use this framework to generate analytical kinematic and calibration Jacobians for general manipulator constructions using null space constraints and extend the methods to hybrid manipulator types with complex geometry. We leverage the analytical Jacobians to develop detailed expressions for post-calibration pose uncertainties that are applied to describe the relationship between data set size and post-calibration uncertainty. We demonstrate the calibration of a hybrid manipulator assembled from high precision calibrated industrial components resulting in 91.1 μm RMS position error and 71.2 μrad RMS rotation error, representing a 46.7% reduction compared to the baseline calibration of assembly offsets.

Automated Low-Cost LED-Based Sun Photometer for City Scale Distributed Measurements

We propose a monochromatic low-cost automatic sun photometer (LoCo-ASP) to perform distributed aerosol optical depth (AOD) measurements at the city scale. This kind of network could fill the gap between current automatic ground instruments—with good temporal resolution and accuracy, but few devices per city and satellite products—with global coverage, but lower temporal resolution and accuracy-. As a first approach, we consider a single equivalent wavelength around 408 nm. The cost of materials for the instrument is around 220 dollars. Moreover, we propose a calibration transfer for a pattern instrument, and estimate the uncertainties for several units and due to the internal differences and the calibration process. We achieve a max MAE of 0.026 for 38 sensors at 408 nm compared with AERONET Cimel; a mean standard deviation of 0.0062 among our entire sensor for measurement and a calibration uncertainty of 0.01. Finally, we perform city-scale measurements to show the dynamics of AOD. Our instrument can measure unsupervised, with an expected error for AOD between 0.02 and 0.03.