Observation system simulation experiments using water vapor isotope information

Measurements of water vapor isotopes (δO and δD) have dramatically increased in recent years with the availability of new spectroscopic technology. To utilize these data more efficiently, this study first developed a new data assimilation system using a local transform ensemble Kalman filter (LETKF) and the Isotope-incorporated Global Spectral Model (IsoGSM). An observation system simulation experiment (OSSE) was then conducted. The OSSE used a synthetic data set of vapor isotope measurements, mimicking Tropospheric Emission Spectrometer (TES)-retrieved δD from the mid-troposphere, SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY)-retrieved δD from the water vapor column, and the virtual Global Network of Isotopes in Precipitation (GNIP)-like surface vapor isotope (both δD and δO) monitoring network. For TES and SCIAMACHY, we assumed a similar spatiotemporal coverage as that of the real data sets. For the virtual GNIP-like network, we assumed ~200 sites worldwide and 6-hourly measurements. An OSSE with 20 ensemble members was then conducted for January 2006. The results showed a significant improvement in not only the vapor isotopic field but also meteorological fields, such as wind speed, temperature, surface pressure, and humidity, when compared with a test with no observations. For surface air temperature, the global root mean square error has dropped by 10%, with 40–60% of the decrease occurring in the east-southeast Asia where the concentration of observations is relatively higher. When there is a conventional radiosonde network, the improvement gained by adding isotopic measurements was small but positive for all variables.