Retrieval of global in-cloud conversion efficiency estimates based on satellite-measured H2O–HDO pairs

In-cloud conversion efficiency, defined as the fraction of water vapor converted into precipitation during ascent, is a key but weakly constrained variable of the atmospheric water cycle. It summarizes the combination of microphysical and dynamical processes that control precipitation formation. We present a methodology to retrieve observation-derived estimates of the conversion efficiency globally using paired H₂O–HDO measurements from the Infrared Atmospheric Sounding Interferometer (IASI) aboard the MetOp satellites for the period 2014–2020.
IASI δD retrievals from mid-tropospheric clear-sky regions are combined with 15-day backward Lagrangian trajectories calculated using three-dimensional wind data from the ERA5 reanalysis to identify last saturation events along air-parcel histories. These events are diagnosed using specific hydrometeor content thresholds, while precipitation-contaminated and humidity-non-conserving cases are excluded. To link isotope signals to conversion efficiency, a simple Rayleigh condensation box model is applied along the diagnosed ascent pathways. For convective ascent, the model follows pseudo-adiabatic vertical motion from cloud base to the diagnosed last saturation locations associated with the IASI observations; for slantwise ascent, the box model is applied along 48-hour Lagrangian trajectories. Modeled δD profiles are then combined with IASI observations to derive in-cloud conversion efficiencies constrained by the observed water isotope signals, within the uncertainty range of the remote sensing observations and the trajectory calculation.
The resulting dataset will provide the first global satellite-derived estimates of in-cloud conversion efficiency for both convective and slantwise ascents. Case studies ranging from mesoscale convective systems in the tropics to warm conveyor belts in the midlatitudes demonstrate the methodology and illustrate distinct efficiency regimes, offering a new observational constraint on moist process representations in the atmosphere.