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Development and validation of the CHIRTS-daily quasi-global high-resolution daily temperature data set
Andrew Verdin1, Chris Funk2,3, Pete Peterson4
1Minnesota Population Center, University of Minnesota Twin Cities, Minnesota, USA. averdin@umn.edu.
Scientific Data
|September 15, 2020
Summary
We created CHIRTS-daily, a high-resolution daily temperature dataset. This new climate record offers accurate temperature estimates, especially in data-sparse regions, improving climate analysis.
Area of Science:
- Climatology
- Remote Sensing
- Data Science
Background:
- Accurate daily temperature data is crucial for climate research and understanding extreme weather events.
- Existing climate records often have limitations in spatial resolution or data availability, particularly in remote areas.
Purpose of the Study:
- To introduce and validate CHIRTS-daily, a novel high-resolution daily temperature dataset.
- To demonstrate the utility of remotely sensed data for improving temperature estimations compared to traditional interpolation methods.
Main Methods:
- Merging monthly Climate Hazards center InfraRed Temperature with Stations (CHIRTS) data with daily European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA5) temperatures.
- Validating the CHIRTS-daily dataset against in situ station observations from multiple sources.
- Assessing the dataset's performance in representing temperature trends, including heat waves.
Main Results:
- CHIRTS-daily provides daily 2-meter air temperature estimates from 1983-2016 across a wide latitudinal range (60°S-70°N).
- Validation shows high accuracy, with correlation coefficients between CHIRTS-daily and station observations ranging from 0.7 to 0.9.
- The dataset demonstrates a very good representation of heat wave trends, outperforming interpolated methods in data-scarce regions.
Conclusions:
- CHIRTS-daily is a valuable, high-resolution dataset for daily temperature analysis, enhancing climate monitoring capabilities.
- Remotely sensed infrared temperature data offers a more accurate representation of true atmospheric conditions than interpolation alone.
- This dataset will aid researchers in studying climate variability and extreme events globally.
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