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Triple Collocation based Merged Dataset for Convective Triggering Potential (CTP) and Humidity Index (HI)
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| Type: | Resource | |
| Storage: | The size of this resource is 1.2 GB | |
| Created: | Apr 08, 2024 at 5:45 p.m. | |
| Last updated: | Apr 17, 2024 at 12:25 p.m. | |
| DOI: | 10.4211/hs.90bf9b575b684c849e617f620c2d63fb | |
| Citation: | See how to cite this resource | |
| Content types: | Multidimensional Content |
| Sharing Status: | Published |
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| Views: | 71 |
| Downloads: | 4 |
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Abstract
This resource introduces a merged dataset, integrating Convective Triggering Potential (CTP) and Humidity Index (HI) from three established reanalysis products: the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA2), Climate Forecast System Reanalysis (CFSR), and the European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5). This innovative dataset, crafted using the Triple Collocation (TC) method, addresses the challenges posed using single-source reanalysis data and offers a more reliable representation of atmospheric conditions. It mitigates biases associated with individual datasets and compensates for satellite-derived estimates' shortcomings, such as missing observations and lower vertical resolution. This merged CTP-HI product offers a robust alternative to single-source datasets, enhancing accuracy in characterizing atmospheric conditions and addressing the limitations of satellite-derived data. Verification against the Integrated Global Radiosonde Archive version 2 (IGRA2) in-situ measurements and Atmospheric Infrared Sounder version 7 (AIRSv7) satellite observations ensure reliability for meteorological research. The dataset provides a valuable tool for analyzing atmospheric stability and humidity, with potential implications for weather prediction and climate research.
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Content
readme.txt
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.
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Credits
Funding Agencies
This resource was created using funding from the following sources:
| Agency Name | Award Title | Award Number |
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| National Aeronautics and Space Administration | Soil Moisture Active-Passive Mission Science Team Program | NNH19ZDA001N-SMAP |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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