Impact of GPS (Global Positioning System) data assimilation is assessed here using a high-resolution numerical weather prediction system at 2.5 km horizontal resolution. The Zenithal Tropospheric Delay (ZTD) GPS data from mesoscale networks are assimilated with the 3DVAR AROME data assimilation scheme. Data from more than 280 stations over the model domain have been assimilated during 15-day long assimilation cycles prior each of the two studied events. The results of these assimilation cycles show that the assimilation of GPS ZTD with the AROME system performs well in producing analyses closer to the ZTD observations in average. Then the impacts of assimilating GPS data on the precipitation forecast have been evaluated. For the first case, only the AROME runs starting a few hours prior the triggering of the convective system are able to simulate the convective precipitation. The assimilation of GPS ZTD observations improves the simulation of the spatial extent of the precipitation, but slightly underestimates the heaviest precipitation in that case compared with the experiment without GPS. The accuracy of the precipitation forecast for the second case is much better. The analyses from the control assimilation cycle provide already a good description of the atmosphere state that cannot be further improved by the assimilation of GPS observations. Only for the latest day (22 November 2007), significant differences have been found between the two parallel cycles. In that case, the assimilation of GPS ZTD allows to improve the first 6 to 12 h of the precipitation forecast.

In this study, we compare gridded snow depth estimates from the Snow Data Assimilation System (SNODAS) with snow depth observations derived from GPS interferometric reflectometry (GPS-IR) from roughly 100 Plate Boundary Observatory sites in the Western United States spanning four water-years (2010–2013). Data from these sites are not assimilated by SNODAS; thus, GPS-IR measurements provide an independent data set to evaluate SNODAS. Our results indicate that at 80% of the sites, SNODAS and GPS-IR snow depth agree to better than 15-cm root mean square error, with correlation coefficients greater than 0.6. Significant differences are found between GPS-IR and SNODAS for sites that are distant from other point measurements, are located in complex terrain or are in areas with strong vegetation heterogeneities. GPS-IR estimates of snow depth are shown to provide useful error characterization of SNODAS products across much of the Western United States and may have potential as an additional data assimilation source that could help improve SNODAS estimates. © 2014 The Authors. Hydrological Processes published by John Wiley & Sons Ltd.

This work aims to estimate soil moisture and vegetation height from Global Navigation Satellite System (GNSS) Signal to Noise Ratio (SNR) data using direct and reflected signals by the land surface surrounding a ground-based antenna. Observations are collected from a rainfed wheat field in southwestern France. Surface soil moisture is retrieved based on SNR phases estimated by the Least Square Estimation method, assuming the relative antenna height is constant. It is found that vegetation growth breaks up the constant relative antenna height assumption. A vegetation-height retrieval algorithm is proposed using the SNR-dominant period (the peak period in the average power spectrum derived from a wavelet analysis of SNR). Soil moisture and vegetation height are retrieved at different time periods (before and after vegetation's significant growth in March). The retrievals are compared with two independent reference data sets: in situ observations of soil moisture and vegetation height, and numerical simulations of soil moisture, vegetation height and above-ground dry biomass from the ISBA (interactions between soil, biosphere and atmosphere) land surface model. Results show that changes in soil moisture mainly affect the multipath phase of the SNR data (assuming the relative antenna height is constant) with little change in the dominant period of the SNR data, whereas changes in vegetation height are more likely to modulate the SNR-dominant period. Surface volumetric soil moisture can be estimated (R² = 0.74, RMSE = 0.009 m³ m⁻³) when the wheat is smaller than one wavelength (∼ 19 cm). The quality of the estimates markedly decreases when the vegetation height increases. This is because the reflected GNSS signal is less affected by the soil. When vegetation replaces soil as the dominant reflecting surface, a wavelet analysis provides an accurate estimation of the wheat crop height (R² = 0.98, RMSE = 6.2 cm). The latter correlates with modeled above-ground dry biomass of the wheat from stem elongation to ripening. It is found that the vegetation height retrievals are sensitive to changes in plant height of at least one wavelength. A simple smoothing of the retrieved plant height allows an excellent matching to in situ observations, and to modeled above-ground dry biomass.

The benefit of assimilating high spatial and temporal resolution Global Positioning System (GPS) zenith tropospheric delay (ZTD) observations within a high‐resolution numerical weather prediction (NWP) system is evaluated during IOP 9 of the Convective and Orographically‐induced Precipitation Study (COPS) campaign. In the framework of the international COPS field experiment, which took place from June to August 2007, a dense GPS network was deployed in the Vosges mountain region in eastern France and in the Black Forest region in southwestern Germany.GPS ZTD observations collected during the COPS field campaign, in addition to the European operational GPS data, are assimilated into the AROME/3D‐Var system. We investigate the impact of assimilating these GPS ZTD observations on the forecast of convective systems that travelled over eastern France during the three days of COPS IOP 9 (18–20 July 2007). The results show a clear positive impact on short‐range quantitative precipitation forecasts when GPS ZTD observations are assimilated. The impact is most important on 19 July 2007 (IOP 9b). The present study benefits from additional validation measurements collected during the COPS campaign. In particular we find that the assimilation of ZTD data induces changes in the low‐to‐middle tropospheric water vapour vertical structure that are consistent with water vapour measurements from radiosonde and airborne lidar.As most of the improvement is gained by assimilating GPS ZTD observations from the European operational GPS network, this study opens up encouraging horizons for the assimilation of GPS ZTD in new high‐resolution operational NWP systems. Copyright © 2009 Royal Meteorological Society

High spatial and temporal variability of mesoscale moisture fields is still a challenge for quantitative precipitation forecast within numerical weather prediction (NWP) models especially over ocean regions where observations are lacking. This study presents the comparison between integrated water vapor over the Mediterranean Sea determined from shipborne GPS, the NWP ALADIN/Météo‐France model and MODIS retrieval during a 4‐month campaign (autumn 2008).While moisture prediction of the NWP remains accurate during most of the observation period, there are significant periods with offsets between GPS data and model predictions. We analyze such events and discuss the associated meteorological situation. Copyright © 2012 Royal Meteorological Society