Dataset Overview
This dataset presents an innovative merged resource combining Convective Triggering Potential (CTP) and Humidity Index (HI) metrics from three leading reanalysis products: MERRA2, CFSR, and ERA5. Utilizing the Triple Collocation (TC) method, this dataset aims to provide a more reliable representation of atmospheric conditions by mitigating biases inherent in individual datasets.

Background and Methodology
The integration of CTP and HI from multiple sources addresses the challenges of relying on single-source reanalysis data. By merging these metrics, the dataset offers enhanced accuracy in characterizing atmospheric conditions which is crucial for weather prediction and climate research. Verification against IGRA2 in-situ measurements and AIRSv7 satellite observations ensures the reliability of the data for meteorological studies.

Theoretical Framework
The CTP-HI framework was developed to analyze the exchanges between the Earth's surface and the atmosphere, particularly focusing on the likelihood of convective precipitation. The approach, inspired by the work of Findell and Eltahir (2003).

Convective Triggering Potential (CTP): CTP assesses atmospheric stability by calculating the integrated area between the temperature profile and a moist adiabat from 100 to 300 hPa above the surface. A positive CTP indicates unstable conditions conducive to convection, while a negative value indicates stability.

Humidity Index (HI): HI quantifies the moisture content in the lower atmosphere by summing the dew point depressions at 50 and 150 hPa above the surface. Higher values suggest a drier atmosphere due to significant temperature and dew point differences, indicating low moisture content.

File Contents
Each file in this dataset, corresponding to a single year, contains the following data fields over 1x1 spatial resolution:

ctp: Convective Triggering Potential (measured in Kg/J)
hi: Humidity Index (measured in C)
lat, lon: Geographical coordinates (latitude and longitude)
msk: Mask field indicating valid data regions, with NaN values representing missing data in the Arctic
midx: Map index for grid points, aiding in data mapping
dd: Date vectors detailing year, month, and day

Usage and Applications
This dataset is particularly valuable for understanding and predicting weather patterns and climate regimes influenced by land-atmosphere (L-A) interactions. Researchers and meteorologists can utilize this robust tool for detailed analysis and forecasting tasks
The included readin.ipynb file provides Python code to read in and plot the dataset, facilitating easy access and visualization of the data.

Reference
Findell, K. L. and Eltahir, E. A. B.: Atmospheric Controls on Soil MoistureBoundary Layer Interactions. Part II: Feedbacks within the Continental United States, J. Hydrometeor, 4, 570583, https://doi.org/10.1175/1525-7541(2003)004<0570:ACOSML>2.0.CO;2 , 2003.