DOI: 10.5281/zenodo.17462417
title: Daily 2-meter air temperature dataset for the Yangtze and Yellow River source regions at 4-kilometer resolution, 1960–1979

description: 1. This dataset addresses the need for high-precision meteorological drivers in the complex terrain regions of the Yangtze and Yellow River headwaters on the Qinghai-Tibet Plateau. It provides daily 2-meter near-surface air temperature data in NC format at the regional scale for the Yangtze and Yellow River headwaters, with a spatial resolution of 4.0 km (approximately 0.03333°). The temporal range covers January 1, 1960, to December 31, 1979. The development process is as follows: First, a two-year (1960 and 1961) WRF simulation with a spatial resolution of 1/30° was conducted. Second, a downscaling model based on a convolutional neural network (CNN) was trained at the daily scale using the WRF simulation results. This downscaling model comprises four convolutional layers (for feature extraction) and one subpixel convolution layer (for constructing high-resolution data). Model inputs include coarse-resolution air temperature data, coarse-resolution terrain data (i.e., grid-based elevation and elevation standard deviation), and high-resolution terrain data. The output is high-resolution meteorological data. The trained model is then applied to downscale long-term ERA5 reanalysis data, generating high-resolution (1/30°≈4 km) gridded air temperature data (ERA5_CNN). Cumulative distribution function (CDF) bias correction is implemented using source region station observations, and the final regional product is cropped to the boundaries of the Yangtze-Yellow River source area. Data strictly adheres to CF-1.8 / ACDD-1.3 metadata specifications, provided in NetCDF-4 format for seamless integration with Python scientific computing and direct loading in ArcGIS Pro.

2. Data Content and Elements:

Variable: T_2m(time, lat, lon) — 2 m daily mean air temperature (°C), standard_name=air_temperature; associated scalar coordinate height=2.0 m; grid_mapping=crs(WGS84).

3. Spatial-Temporal Extent: Longitude 90.547953–103.413333°E, Latitude 32.148290–36.114560°N; Time 1960-01-01 to 1979-12-31 (including leap years).

4. Resolution: Spatial 0.03333° × 0.03333° (approx. 4 km), Temporal Daily.

5. Naming convention: Daily file ERA5_CNN_t2m_4km_daily_YYYYMMDD.nc (time=1).

6. Coordinates: lat/degN, lon/degE in ascending order; CRS: EPSG:4326.

7. Production Background and Method Overview: The Qinghai-Tibet Plateau features significant topographic variations and distinct surface conditions, making it challenging for conventional 0.25° reanalysis or simple interpolation methods to capture local thermodynamic-topographic effects. This dataset employs WRF short-term high-resolution simulations to learn the “high-resolution to low-resolution” mapping relationship, using CNN to capture nonlinear spatial features for statistical downscaling of ERA5 across all time periods. Subsequently, CDF correction is applied using source region station observations (site-scale mapping constructed and IDW spatially interpolated), significantly reducing high-altitude cold biases and enhancing station consistency.

8. Advantages and Features:

① Higher spatial resolution with terrain detail preservation (≈4 km vs 0.25°);

② Combination of physical prior (dynamic downscaling) + deep learning (statistical downscaling) outperforms interpolation-only spatial sharpening;

③ Improved station consistency after bias correction, yielding more reliable extreme events and interannual variability;

 

9. Applications: Permafrost/active layer thermal status assessment, frost-thaw index and N-factor calculation, watershed hydrological and ecological modeling, regional climate change detection, surface process simulation, and disaster risk assessment.

 

 
description: Dataset Description:1. Reanalysis Data (Background Fields)ERA5 2 m Temperature (ECMWF/Copernicus): Features extensive spatiotemporal coverage and advanced assimilation schemes, providing large-scale daily/hourly near-surface meteorological fields. Original resolution is approximately 0.25° at hourly intervals; aggregated to daily averages in this work as one of the CNN input features.2. Short-Term High-Resolution Atmospheric Simulation (High-Resolution Features)WRF (ARW) short-term simulations cover the full year of 1960 and the critical season of June–September 1961. They provide high-resolution structural information on near-surface temperature and topography, used to construct target-scale (kilometer-level) temperature “training” samples. These are employed to train the CNN to learn the Low-High resolution mapping relationship and spatial features under complex terrain.3. Station Observations (Correction and Validation)Used for independent validation and uncertainty assessment (daily conventional meteorological observations from the China Meteorological Administration (CMA)), sourced from 29 stations within and adjacent to the source region (15 stations with data starting from 1960). These are employed for:a) Training/validation set partitioning and cross-validation;b) Statistical mapping construction for CDF bias correction;c) Independent evaluation (RMSE/bias/correlation coefficients, etc.)4. Other Auxiliary Data4 km DEM (terrain standard deviation, etc., usable as static feature input for CNN models);
description: 1. Preprocessing: Clipping of ERA5 data at 0.25° resolution for the background field to extract a rectangular area encompassing the Yangtze and Yellow River source regions; unification to WGS84 grid; outlier removal and basic quality control.2. Dynamo-statistical downscaling: Conducted high-resolution simulations using WRF during representative periods. Combined with static factors like topography, trained a CNN model to map ERA5 low-resolution temperatures to kilometer-scale resolution.3. Bias correction: Applied CDF quantile mapping to correct downscaled results for bias across independent years and seasons, ensuring temporal consistency.4. Data writing and metadata: Generate NetCDF4 files compliant with CF-1.8/ACDD-1.3 standards, organized in both daily and annual formats.This dataset is produced through the workflow: “reanalysis base fields + short-term high-resolution simulation + CNN statistical downscaling + CDF bias correction”:(1) Preprocessing: Standardize ERA5 to daily averages; harmonize coordinates, time zones, and leap years;(2) Short-term high-resolution simulation: Acquire high-resolution fields over complex terrain using WRF short-term high-resolution simulations (full year 1960 + critical season June–September 1961) as target features for CNN training;(3) CNN Downscaling: Construct a CNN model with ERA5 and static terrain/WRF short-term low-resolution simulations as inputs, targeting high-resolution air temperature. Post-training, perform daily inferences for the entire 1960–1979 period to obtain 0.03333° resolution air temperature fields;(4) CDF Bias Correction: Construct station-specific CDF maps (“observation–downscaled”) using station observations to correct grid cells (station → inverse distance weighted spatial interpolation);(5) Quality control and clipping: Numerical plausibility check ([-80, 60] °C), temporal continuity verification, CF/ACDD/CRS metadata refinement, clipping to source region boundaries, and output in NetCDF-4 format.