As Global Navigation Satellite System (GNSS) signals travel through the troposphere, a tropospheric delay occurs due to a change in the refractive index of the medium. The Precise Point Positioning (PPP) technique can achieve centimeter/millimeter positioning accuracy with only one GNSS receiver. The Zenith Tropospheric Delay (ZTD) is estimated alongside with the position unknowns in PPP. Estimated ZTD can be very useful for meteorological applications, an example is the estimation of water vapor content in the atmosphere from the estimated ZTD. PPP is implemented with different algorithms and models in online services and software packages. In this study, a performance assessment with analysis of ZTD estimates from three PPP online services and three software packages is presented. The main contribution of this paper is to show the accuracy of ZTD estimation achievable in PPP. The analysis also provides the GNSS users and researchers the insight of the processing algorithm dependence and impact on PPP ZTD estimation. Observation data of eight whole days from a total of nine International GNSS Service (IGS) tracking stations spread in the northern hemisphere, the equatorial region and the southern hemisphere is used in this analysis. The PPP ZTD estimates are compared with the ZTD obtained from the IGS tropospheric product of the same days. The estimates of two of the three online PPP services show good agreement (<1 cm) with the IGS ZTD values at the northern and southern hemisphere stations. The results also show that the online PPP services perform better than the selected PPP software packages at all stations.

We propose differential optical feedback interferometry, a technique able to measure nanometer-size amplitude displacements by comparing the optical power of two lasers subject to optical feedback. In this letter, the principles of the technique are explained in detail, and its limits are explored by simulation. Theoretical results are presented showing that the technique can measure nanometer scale displacements with resolution within the angstrom scale. An experimental setup for validation has been built, and a series of experimental tests were performed using a capacitive sensor as a reference. Results show good agreement between theory and experiment with a reasonable reduction in performance due to mechanical coupling and signal noise. The proposed technique, thus, provides measurements of a very high resolution using an extremely simple and robust experimental setup.

The urban heat island (UHI) effect has been widely studied because of its impacts on the environment and human’s wellbeing. The surface UHI (SUHI) is typically monitored with remote sensing products, whereas the air UHI (AUHI) is typically monitored using temperature sensor networks. The first technique requires clear days to obtain good images, and the latter is limited by the number of weather stations in an urban and rural area. An alternative to monitor the AUHI is the usage of global navigation satellite system (GNSS) data which is available in all weather conditions and there are well-developed GNSS receiver networks around the world. In this article, we propose a machine learning (ML) approach to estimating the air temperature from GNSS data to take advantage of the quantity of data. By training a support vector regressor with three datasets, GNSS-derived zenith tropospheric delay (ZTD), temperature, and day-of-the year, we can estimate temperature from GNSS data without the need for other environmental variables. The regressor presented in this article is trained with data from 2016 to 2019 and tested with data from 2020 in five Japanese metropolitan areas. The results show that it is possible to predict temperature from only the ZTD of GNSS signals with high precision, as an $R^{2}$ between 0.82 and 0.90 is found for the predictions. Predicted temperatures are then used to study the diurnal cycle of the AUHI in Sapporo, Tokyo, Nagoya, Osaka–Kyoto, and Fukuoka with an average precision of 0.8 °C.

The urban heat island (UHI) effect can contribute to extreme heat exposure. This can be detrimental to human health. In this paper, we propose a method to estimate air temperature to evaluate the spatial distribution and to monitor the intensity of the air urban heat island (AUHI) from existing GPS infrastructure. The proposed algorithm is based on the relationship between the refractivity of the troposphere and environmental variables, as well as the relationships between the zenith tropospheric delay (ZTD), a by-product of the precise point positioning technique, and the refractivity of the troposphere. The advantage of GPS data is its high temporal resolution and the availability of embedded GPS receivers. In this paper, GPS-derived ZTD data from stations in the Hong Kong Special Administrative Region (HKSAR) of China and Tokyo in Japan are processed to estimate the hourly AUHI intensity. The results derived from this technique are validated using meteorological data in the same cities. Mean absolute error values of 0.79 °C in Hong Kong and 0.22 °C in Tokyo are found from data from the summer. Moreover, an overall accuracy of 0.51 °C is found.