Gridded daily weather data for North America with comprehensive uncertainty quantification

Area of Science:

• Environmental Science
• Climate Science
• Data Science

Background:

• Daily high-resolution gridded weather data are crucial for ecological, hydrological, and Earth system modeling.
• Existing datasets often lack long-term coverage or sufficient accuracy for detailed studies.

Purpose of the Study:

• To introduce Daymet V4, a 40-year daily meteorological dataset for North America, Hawaii, and Puerto Rico.
• To document methodological improvements enhancing data accuracy and uncertainty quantification.

Main Methods:

• Development of a 1 km gridded daily meteorological dataset using direct observations.
• Implementation of updated algorithms including timing bias reduction, improved 3D regression, and novel handling of high-elevation temperature biases.
• Objective uncertainty quantification via cross-validation analysis for temperature and precipitation.

Main Results:

• Daymet V4 offers daily temperature, precipitation, radiation, vapor pressure, snow water equivalent, and day length for North America, Hawaii, and Puerto Rico.
• Cross-validation demonstrates technical validity and improved accuracy of the new dataset generation methods.
• Quantified uncertainty provides objective measures for temperature and precipitation data.

Conclusions:

• Daymet V4 represents a significant advancement in daily gridded weather data availability and quality.
• The dataset's improvements support more robust scientific research and modeling applications.
• Enhanced data accuracy and uncertainty metrics facilitate reliable use in diverse environmental studies.