Abstract
Abstract. This study analyzes the characteristics of GPS tropospheric estimates (zenith wet delays – ZWDs, gradients, and post-fit phase residuals) during the passage of mesoscale convective systems (MCSs) and evaluates their sensitivity to the research-level GPS data processing strategy implemented. Here, we focus on MCS events observed during the monsoon season of West Africa. This region is particularly well suited for the study of these events due to the high frequency of MCS occurrences in the contrasting climatic environments between the Guinean coast and the Sahel. This contrast is well sampled with data generated by six African Monsoon Multidisciplinary Analysis (AMMA) GPS stations. Tropospheric estimates for a 3-year period (2006–2008), processed with both the GAMIT and GIPSY-OASIS software packages, were analyzed and intercompared. First, the case of a MCS that passed over Niamey, Niger, on 11 August 2006 demonstrates a strong impact of the MCS on GPS estimates and post-fit residuals when the GPS signals propagate through the convective cells as detected on reflectivity maps from the MIT C-band Doppler radar. The estimates are also capable of detecting changes in the structure and dynamics of the MCS. However, the sensitivity is different depending on the tropospheric modeling approach adopted in the software. With GIPSY-OASIS, the high temporal sampling (5 min) of ZWDs and gradients is well suited for detecting the small-scale, short-lived, convective cells, while the post-fit residuals remain quite small. With GAMIT, the lower temporal sampling of the estimated parameters (hourly for ZWDs and daily for gradients) is not sufficient to capture the rapid delay variations associated with the passage of the MCS, but the post-fit phase residuals clearly reflect the presence of a strong refractivity anomaly. The results are generalized with a composite analysis of 414 MCS events observed over the 3-year period at the six GPS stations with the GIPSY-OASIS estimates. A systematic peak is found in the ZWDs coincident with the cold pool crossing time associated with the MCSs. The tropospheric gradients reflect the path of the MCS propagation (generally from east to west). This study concludes that ZWDs, gradients, and post-fit phase residuals provide relevant and complementary information on MCSs passing over or in the vicinity of a GPS station.
Highlights
The American Meteorological Society (2015) defines a mesoscale convective system (MCS) as “a cloud system that occurs in connection with an ensemble of thunderstorms and produces a contiguous precipitation area on the order of 100 km or more in horizontal scale in at least one direction
The GPS tropospheric estimates retrieved from the GIPSYOASIS software allows for the MCS to be documented with a 5 min sampling rate (Fig. 8)
The first uses the double-difference observations from a regional network of GPS stations centered on the study area (West Africa) with the GPS Analysis at Massachusetts Institute of Technology (GAMIT) software
Summary
The American Meteorological Society (2015) defines a mesoscale convective system (MCS) as “a cloud system that occurs in connection with an ensemble of thunderstorms and produces a contiguous precipitation area on the order of 100 km or more in horizontal scale in at least one direction. We address the question of the accuracy of AMMA GPS observations and the informational content of tropospheric delay estimates during the passage of MCSs over West Africa, comparing the two main researchlevel GPS data processing approaches: network processing, in which differentiated or undifferentiated observations from several stations are processed simultaneously, and precise point positioning (PPP), in which each station is processed independently but requires precise satellite orbits and clocks as calculated by the International GNSS Service (IGS) or one of its analysis centers (http://www.igs.org, last access: 25 July 2019) In both approaches, many processing options can be used, depending on the functionality of the software, such as least squares adjustment or Kalman filter, the choice of the elevation cutoff angle, the modeling of the troposphere, the rejection procedure of the erroneous observations (screening), among others.
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