TCOM-H2O: TOMCAT CTM and Occultation Measurements based daily zonal stratospheric H2O profile dataset [1991-2021] constructed using machine-learning.
收藏NIAID Data Ecosystem2026-05-02 收录
下载链接:
https://zenodo.org/record/7912903
下载链接
链接失效反馈官方服务:
资源简介:
Methodology: TOMCAT simulation is performed at T64L32 resolution that is similar to the one used in Dhomse et al., (2021, 2022) for 1991-2021 time period. Simulations are performed using ERA-5 reanalysis data. Collocated H2O profiles are divided in five latitude bins: SH polar (90S-50S), SH mid-lat (70S-20S), tropics (40S-40N), NH mid-lat (20N-70N) and NH polar (50N-90N). Initially, differences between TOMCAT and satellite measurements are calculated for each zonal bins for 46 height levels (15km to 60km). A separate XGBoost regression models are trained for the H2O differences at each level for a given latitude bin. These trained XGBoost models are used to estimate H2O bias correction for all day/night-time (2 X11323 days) TOMCAT grids sampled at 1.30 am and 1.30 pm local time at the equator. This way we get bias corrections for a given model grid that are added to the original TOMCAT day and night time profiles. Height resolved H2O profile data are then interpolated on 28-pressure levels (300 - 0.1hPa), using pressure levels at TOMCAT grids. For overlapping latitude bins, we use averages and then calculate daily zonal mean values, to get somewhat smoother fields near the boundaries. For more details see attached presentation.
Dataset also includes two files containing daily mean zonal mean H2O profiles on height (15-60 km) and pressure (300-0.1 hPa) levels:
zmh2o_TCOM_hlev_T2Dz_1991_2021.nc – height level data (15 to 60 km)
zmh2o_TCOM_plev_T2Dz_1991_2021.nc – pressure level data (300 to 0.1 hPa)
We are aware of the dry biases in original TOMCAT H2O profiles as TTL entry mixing ratios are fixed for all the model time step. Therefore, H2O enhancement due to tropical convective clouds is absent in our model.
A methodology description paper (for TCOM-CH4 and TCOM-N2O data) is under review in ESSD
Dhomse, S. S. and Chipperfield, M. P.: Using machine-learning to construct TOMCAT model and occultation measurement-based stratospheric methane (TCOM-CH4) and nitrous oxide (TCOM-N2O) profile data sets, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2023-47, in review, 2023
创建时间:
2024-07-12



