Can the assimilation of IASI water isotopologue observations improve the quality of meteorological analyses fields?

The strong coupling between atmospheric circulation, moisture pathways and atmospheric diabatic heating is responsible for most climate feedback mechanisms and controls the evolution of severe weather events. However, diabatic heating rates obtained from current meteorological reanalyses show significant inconsistencies. Water isotopologue observations (e.g. H₂O and HDO) assimilated into meteorological reanalyses can make an invaluable contribution since the isotopologue composition depends on the history of phase transition. Therefore, isotopologue observations can provide information that is closely linked to latent heating processes. Using the retrieval recipe of MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water), the free tropospheric water vapour isotopologue composition can be retrieved from IASI spectra measured for cloud free conditions.

Here, we theoretically assess with an Observation Simulation Experiment (OSSE) the potential of the MUSICA IASI isotopologue data for constraining uncertainties in analyses fields. For this purpose, we use the isotopes-incorporated General Spectral Model (IsoGSM) and mock MUSICA IASI isotopologue observations. We use the Local Transform Ensemble Kalman Filter (LETKF) data assimilation method and perform two different experiments. In a first experiment we assimilate temperature, humidity and wind profiles obtained from radiosonde and satellite data. In a second experiment we assimilate additionally the mocked IASI isotopologue data. When mocked isotopologue data are additionally assimilated, we find reduced ensemble spreads with respect to meteorological variables and rain rates. This indicates that IASI isotopologue observations can indeed reduce the uncertainties of latent heating rates and meteorological analysis fields and in consequence offer potential for improving weather forecasts.