GPS Precipitable Water Vapor Estimations over Costa Rica: A Comparison against Atmospheric Sounding and Moderate Resolution Imaging Spectrometer (MODIS)

• Published in: Climate (Basel) 2019-05, Vol.7 (5), p.63
• Main Authors: Campos-Arias, Polleth, Esquivel-Hernández, Germain, Valverde-Calderón, José Francisco, Rodríguez-Rosales, Stephanie, Moya-Zamora, Jorge, Sánchez-Murillo, Ricardo, Boll, Jan
• Format: Article
• Language: English
• Subjects: Atmosphere; Atmospheric sounding; Atmospheric temperature; Climate change; Climate change influences; Climatic conditions; Clouds; Convergence zones; Distribution; Dry season; Estimates; Global positioning systems; GPS; Imaging spectrometers; Imaging techniques; Intertropical convergence zone; MODIS; Positioning systems; Precipitable water; Precipitation; Receivers & amplifiers; Regression models; Remote sensing; Resolution; Satellites; Seasonal distribution; Statistical analysis; Trade winds; Tropical climate; Tropical environment; Tropical environments; Water vapor; Water vapor distribution; Water vapour; Weather
• ISSN: 2225-1154; 2225-1154
• DOI: 10.3390/cli7050063

The quantification of water vapor in tropical regions like Central America is necessary to estimate the influence of climate change on its distribution and the formation of precipitation. This work reports daily estimations of precipitable water vapor (PWV) using Global Positioning System (GPS) delay data over the Pacific region of Costa Rica during 2017. The GPS PWV measurements were compared against atmospheric sounding and Moderate Resolution Imaging Spectrometer (MODIS) data. When GPS PWV was calculated, relatively small biases between the mean atmospheric temperatures (Tm) from atmospheric sounding and the Bevis equation were found. The seasonal PWV fluctuations were controlled by two of the main circulation processes in Central America: the northeast trade winds and the latitudinal migration of the Intertropical Convergence Zone (ITCZ). No significant statistical differences were found for MODIS Terra during the dry season with respect GPS-based calculations (p > 0.05). A multiple linear regression model constructed based on surface meteorological variables can predict the GPS-based measurements with an average relative bias of −0.02 ± 0.19 mm/day (R2 = 0.597). These first results are promising for incorporating GPS-based meteorological applications in Central America where the prevailing climatic conditions offer a unique scenario to study the influence of maritime moisture inputs on the seasonal water vapor distribution.