AIMERG: a new Asian precipitation dataset (0.1°/half-hourly, 2000–2015) by calibrating GPM IMERG at daily scale using APHRODITE
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract. Precipitation estimates with finer quality and spatio-temporal resolutions play significant roles in understanding the global and regional cycles of water, carbon and energy. Satellite-based precipitation products are capable of detecting spatial patterns and temporal variations of precipitation at finer resolutions, which is particularly useful over poorly gauged regions. However, satellite-based precipitation product are the indirect estimates of precipitation, inherently containing regional and seasonal systematic biases and random errors. In this study, focusing on the potential drawbacks in generating Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) and its recently updated retrospective IMERG in Tropical Rainfall Measuring Mission (TRMM) era (finished in July, 2019), which were only calibrated at monthly scale using ground observations, Global Precipitation Climatology Centre (GPCC, 1.0°/Monthly), we aimed to propose a new calibration algorithm for IMERG at daily scale, and to provide a new AIMERG precipitation dataset (0.1°/half-hourly, 2000–2015, Asia) with better quality, calibrated by Asian Precipitation Highly Resolved Observational Data Integration (APHRODITE, 0.25°/Daily) at daily scale for the Asian applications. And the main conclusions included but not limited to: (1) the proposed daily calibration algorithm (Daily Spatio-Temporal Disaggregation Calibration Algorithm, DSTDCA) was effective in considering the advantages from both satellite-based precipitation estimates and the ground observations; (2) AIMERG performed better than IMERG at different spatio-temporal scales, in terms of both systematic biases and random errors, over the China Main land; and (3) APHRODITE demonstrated significant advantages than GPCC in calibrating the IMERG, especially over the mountainous regions with complex terrain, e.g., the Tibetan Plateau. Additionally, Results of this study suggests that it is a promising and applicable daily calibration algorithm for GPM in generating the future IMERG in either operational scheme or retrospective manner. The AIMERG data record (0.1°/half-hourly, 2000–2015, Asia) is freely available at http://argi-basic.hihanlin.com:8000/d/d925fecf60/. Additionally, the AIMERG data is also freely accessible at https://doi.org/10.5281/zenodo.3609352 (for the period from 2000 to 2008) and https://doi.org/10.5281/zenodo.3609507 (for the period from 2009 to 2015) (Ma et al., 2020).
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it