Optimizing Boundary Conditions in GNSS Tomography: A Continuous 7‐Month Case Study in the Amazon

Different estimates of the regional water vapor scale height, taken from ERA5 reanalysis, in situ observations and the direct optimization of retrieved water vapor profiles in GNSS tomography, are found to have major impact in the performance of tomographic inversions, with the better results displaying mean errors comparable to radiosondes. The analysis uses 7 months of GNSS (Global Navigation Satellite Systems) observations in the Amazon Dense GNSS Network near Manaus, Brazil, in 2011–2012, to compute a time series of water vapor profiles, with a tomographic technique capable of producing quasi‐instantaneous inversions with minimal external data or constraints. Results compare very well with 12‐hourly in situ radiosondes, especially in the lower troposphere above 2 km, and its daily‐to‐seasonal variability compares well with WRF (Weather Research and Forecasting) convective‐permitting simulations driven by ERA5 boundary conditions, suggesting that GNSS tomography may be an important source of atmospheric water vapor data for different applications. Plain Language Summary Global Navigation Satellite Systems data from GPS, retrieved in the Amazon rainforest near Manaus during a 7‐month period, are used to compute time‐continuous vertical profile of water vapor above the rainforest by tomographic inversion, providing a unique view of the variability of the water vapor field in this crucial sector of the South American climate system. Variability from subdaily to seasonal time scales is clearly present, with a strong signature of the transition into the wet season of the South American monsoon. Key Points Water vapor tomography in the central Amazon captures diurnal‐to‐seasonal variability and the transition to the wet season Tomography outperforms atmospheric reanalysis in the lower troposphere and may constitute a source of high temporal resolution data An estimate of the regional water vapor scale height is found to be crucial to the accuracy of the tomographic inversion