The synoptic controls on orographic precipitation during the Olympics Mountains Experiment (OLYMPEX) are investigated using observations and numerical simulations. Observational precipitation retrievals for six warm-frontal (WF), six warm-sector (WS), and six postfrontal (PF) periods indicate that heavy precipitation occurred in both WF and WS periods, but the latter saw larger orographic enhancements. Such enhancements extended well upstream of the terrain in WF periods but were focused over the windward slopes in both PF and WS periods. Quasi-idealized simulations, constrained by OLYMPEX data, reproduce the key synoptic sensitivities of the OLYMPEX precipitation distributions and thus facilitate physical interpretation. These sensitivities are largely explained by three upstream parameters: the large-scale precipitation rate [Formula: see text], the impinging horizontal moisture flux I, and the low-level static stability. Both WF and WS events exhibit large [Formula: see text] and I, and thus, heavy orographic precipitation, which is greatly enhanced in amplitude and areal extent by the seeder–feeder process. However, the stronger stability of the WF periods, particularly within the frontal inversion (even when it lies above crest level), causes their precipitation enhancement to weaken and shift upstream. In contrast, the small [Formula: see text] and I, larger static stability, and absence of stratiform feeder clouds in the nominally unsaturated and convective PF events yield much lighter time- and area-averaged precipitation. Modest enhancements still occur over the windward slopes due to the local development and invigoration of shallow convective showers.

Global Navigation Satellite System Reflectometry (GNSS-R) tide gauges are a promising alternative to traditional tide gauges. However, the precision of GNSS-R sea level measurements when compared to measurements from a co-located tide gauge is highly variable, with no clear indication of what causes the variability. Here we present a modelling technique to estimate the precision of GNSS-R sea level measurements that relies on creating and analyzing synthetic Signal-to-Noise-Ratio (SNR) data. The modelled value obtained from the synthetic SNR data is compared to observed RMSE between GNSS-R measurements and a co-located tide gauge at five sites and using two retrieval methods: spectral analysis and inverse modelling. We find that the inverse method is more precise than the spectral analysis method by up to 60% for individual measurements but the two methods perform similarly for daily and monthly means. We quantify the contribution of dominant effects to the variations in precision and find that noise is the dominant source of uncertainty for spectral analysis whereas the effect of the dynamic sea surface is the dominant source of uncertainty for the inverse method. Additionally, we test the sensitivity of sea level measurements to the choice of elevation angle interval and find that the spectral analysis method is more sensitive to the choice of elevation angle interval than the inverse method due to the effect of noise, which is greater at larger elevation angle intervals. Conversely, the effect of tropospheric delay increases for lower elevation angle intervals but is generally a minor contribution.

Abstract. We have developed a ground-based Global Navigation Satellite System Reflectometry (GNSS-R) technique for monitoring water levels with a comparable precision to standard tide gauges (e.g. pressure transducers) but at a fraction of the cost and using commercial products that are straightforward to assemble. As opposed to using geodetic-standard antennas that have been used in previous GNSS-R literature, we use multiple co-located low-cost antennas to retrieve water levels via inverse modelling of signal-to-noise ratio data. The low-cost antennas are advantageous over geodetic-standard antennas not only because they are much less expensive (even when using multiple antennas in the same location) but also because they can be used for GNSS-R analysis over a greater range of satellite elevation angles. We validate our technique using arrays of four antennas at three test sites with variable tidal forcing and co-located operational tide gauges. The root mean square error between the GNSS-R and tide gauge measurements ranges from 0.69–1.16 cm when using all four antennas at each site. We find that using four antennas instead of a single antenna improves the precision by 30 %–50 % and preliminary analysis suggests that four appears to be the optimum number of co-located antennas. In order to obtain precise measurements, we find that it is important for the antennas to track GPS, GLONASS and Galileo satellites over a wide range of azimuth angles (at least 140∘) and elevation angles (at least 30∘). We also provide software for analysing low-cost GNSS data and obtaining GNSS-R water level measurements.

Abstract. We have developed a ground-based Global Navigation Satellite System Reflectometry (GNSS-R) technique for monitoring water levels with a comparable precision to standard tide gauges (e.g., pressure transducers) but at a fraction of the cost and using commercial products that are straightforward to assemble. As opposed to using geodetic-standard antennas that have been used in previous GNSS-R literature, we use multiple co-located low-cost antennas to retrieve water levels via inverse modelling of Signal-to-Noise ratio data. The low-cost antennas are advantageous over geodetic-standard antennas because they are much less expensive (even when using multiple antennas in the same location) and they can be used for GNSS-R analysis over a greater range of satellite elevation angles. We validate our technique using arrays of four antennas at three test sites with variable tidal forcing and co-located operational tide gauges. The root mean square error between the GNSS-R and tide gauge measurements ranges from 0.7–1.2 cm when using all four antennas at each site. We find that using four antennas instead of a single antenna improves the precision by 30–50 % and preliminary analysis suggests that four appears to be the optimum number of co-located antennas. In order to obtain precise measurements, we find that it is important for the antennas to track GPS, GLONASS and Galileo satellites over a wide range of azimuth angles (at least 140 degrees) and elevation angles (at least 30 degrees).

<p>GNSS-Reflectometry (GNSS-R) is a promising new technique to monitor water levels due to easier and cheaper installation of instruments in remote environments compared to traditional acoustic sensors or pressure gauges. GNSS stations that have been used for reflectometry purposes thus far are designed for monitoring land motion and may cost more than 10,000 USD each. We have found that a low-cost GNSS antenna and receiver (10 USD) can be used to make equally precise water level measurements, with an RMSE of a few centimeters when compared to a collocated acoustic sensor. However, an RMSE of less than one centimeter is typical for water level sensors and this level of accuracy is desired for research purposes. Two of the dominant sources of error in GNSS-R measurements are the effects of random noise in the Signal-to-Noise Ratio (SNR) data and tropospheric delay. Modelling work suggests that these sources of error can be reduced by using multiple low-cost antennas in the same location. In light of this, we have installed an experimental setup of antennas at various locations along the Saint Lawrence River and Initial results show that multiple antennas can be used to provide more precise measurements than a single antenna. Our installations of multiple antennas are less than 5% of the cost of stations that have been used in previous GNSS-R literature. Hence this approach could be applied to install a dense network of water level sensors along rivers, lakes or coastlines at a relatively low cost. We expect that this approach could also be applied to GNSS-R soil moisture or snow depth measurements.</p>