Abstract

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 reanalysis show significant inconsistencies. Here, we theoretically assess with an Observation System Simulation Experiment (OSSE) the potential of the MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water (MUSICA) Infrared Atmospheric Sounding interferometer (IASI) mid-tropospheric water isotopologue data for constraining uncertainties in meteorological analysis fields. For this purpose, we use the Isotope-incorporated General Spectral Model (IsoGSM) together with a Local Ensemble Transform Kalman Filter (LETKF) and assimilate synthetic MUSICA IASI isotopologue observations. We perform two experiments consisting each of two ensemble simulation runs, one ensemble simulation where we assimilate conventional observations (temperature, humidity and wind profiles obtained from radiosonde and satellite data) and a second one where we assimilate additionally to the conventional observations the synthetic IASI isotopologue data. In the second experiment, we perform one ensemble simulation where only synthetic IASI isotopologue data are assimilated and another one where no observational data at all are assimilated. The first experiment serves to assess the impact of the IASI isotopologue data additional to the conventional observations and the second one to assess the direct impact of the IASI isotopologue data on the meteorological variables, especially on the heating rates and vertical velocity. The assessment is performed for the tropics in the latitude range from 10° S to 10° N. When the synthetic isotopologue data are additionally assimilated, we derive in both experiments lower Root-Mean Square Deviations (RMSDs) and improved skills with respect to meteorological variables (improvement by about 8–13 %). However, heating rates and vertical motion can only be improved throughout the troposphere when additionally to IASI δD conventional observations are assimilated. When only IASI δD is assimilated the improvement in vertical velocity and heating rate is minor (up to a few percent) and restricted to the mid-troposphere. Nevertheless, these assimilation experiments indicate that IASI isotopologue observations have the potential to reduce the uncertainties of diabatic heating rates and meteorological variables in the tropics and in consequence offer potential for improving meteorological analysis, weather forecasts and climatepredictions in the tropical regions.

Highlights

  • In the past 40 years, medium-range weather forecasts have undergone significant improvements (Bauer et al, 2015)

  • In the following this experiment is called PREPBUFR and the assimilation run with the conventional observations is called DA_prepbufr and the one with the additional assimilation of Infrared Atmospheric Sounding interferometer (IASI) δD data is called DA_prepbufr_IASI

  • The assimilation experiments described in the previous sections show that the assimilation of δD has the potential to improve the meteorological analysis in the tropics, both alone and together with conventional observations (PREPBUFR). 370 heating rates and vertical motion can only be improved throughout the troposphere when to IASI δD conventional observations are considered

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Summary

Introduction

In the past 40 years, medium-range weather forecasts have undergone significant improvements (Bauer et al, 2015). Diabatic heating rates obtained from current meteorological reanalysis show significant inconsistencies (e.g. Chan and Nigam, 2009; Ling and Zhang, 2013; Wright and Fueglistaler, 2013) This jeopardises the accuracy of both climate predictions and numerical weather prediction. Yoshimura et al (2014) developed a new data assimilation system using a Local Ensemble Transform Kalman Filter (LETKF) and the Isotope-incorporated Global Spectral Model (IsoGSM) They applied this assimilation system to an Observation System Simulation Experiment (OSSE) using a synthetic data set that mimicked water vapour isotope measurements from the Tropospheric Emission Spectrometer (TES), the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) and the Global Network of Isotopes in Precipitation (GNIP). In the study by Toride et al (2021) the same OSSE was used, but synthetic isotope data from the Infrared Atmospheric

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