The　development　of　upper-air　meteorological　detection　is　contingent　upon　the　improvement　of　detection　instruments.　Air　pressure　sensors　play　a　key　role　in　high　altitude　meteorological　measurement,　but　they　can　be　frequently　affected　by　temperature　fluctutations,　resulting　in　less　accurate　measurement　data.　The　need　to　address　this　limitation　has　served　as　the　core　problem　for　meteorological　detection　and　drawn　great　attention　from　the　community.　In　this　paper,　we　propose　a　calibration　model　for　the　DF-RBF　air　pressure　sensor.　The　proposed　method　decomposes　the　detection　process　and　corrects　the　measurements　by　fitting　the　residuals　to　true　pressure　values.　In　particular,　we　first　calculate　the　error　(i.e.,　residual)　between　the　measured　pressure　value　and　the　true　pressure　values,　and　then　build　an　analytical　formula　to　represent　the　relationship　between　the　measurement　residual　and　the　temperature.　Then　we　decompose　the　function　and　fit　its　parameters　through　an　RBF　network.　Finally,　we　generate　the　calibrated　value　by　combining　the　measured　value　and　the　residual　estimated　by　the　analytical　formula.　In　our　experiments,　we　compare　against　a　baseline　method　which　predicts　air　pressure　directly　using　temperature　by　fitting　an　RBF　network.　We　observe　that　our　proposed　method,　which　combines　sensor　measurement　and　residual　modeling,　can　achieve　much　lower　measurement　error　(reduced　from　1.5　to　0.7,　over　53%　error).　This　shows　the　feasibility　and　potential　of　the　proposed　method　for　barometric　calibration.