Wavelet transform applied to ERA5 global daily precipitation fields to assess changes in the rainfall patterns
Bibliographic record
Abstract
Wavelet transforms allow for the decomposition of precipitation fields over a broad range of spatial scales. The wavelet coefficients obtained from the decomposition of daily global precipitation fields can be used to study the spatial characteristics of the model simulated field over multiple spatial scales, in addition to diagnosing the effective model resolution. At a given spatial scale, the variability of the corresponding wavelet coefficients is proportional to the amount of “details” needed to pass from the representation of the precipitation field at that scale to the representation with respect to the next -finer- scale. The greater the variability, the greater the details required, and the greater the energy associated with that specific spatial scale. If we consider a sequence of daily precipitation fields over several years, we can construct the energy spectrum of global precipitation and analyse its evolution with climate change. We also propose utilising wavelets in future downscaling activity. We have applied a 2D Haar wavelet transform to the ERA5 global daily precipitation fields for the period from 1950 to 2020. Then, we have studied the variability of the daily wavelet coefficients for the different spatial scales. If we compare the two normal periods, 1961-1990 and 1991-2020, it can be seen that for the most recent period there is a shift of the maximum of the energy spectrum towards smaller scales. The shift is more pronounced in the tropics. If we consider the time series of the energies, there is an increase in the energies for most of the spatial scales after 1985. The growth rates among the scales are different, though, and precipitation at the Meso-beta scale and the lower part of the synoptic scale (up to around 440 km) has become more important in the total wavelet energy balance of daily precipitation. The wavelet analysis enables us to detect a change in the scale structure of the global daily precipitation patterns with climate change. The results presented here are part of the work described in detail in the scientific article: Benestad, R.E., Lussana, C., Lutz, J., Dobler, A., Landgren, O., Haugen, J.E., Mezghani, A., Casati, B. and Parding, K. M.: Global hydro-climatological indicators and changes in the global hydrological cycle and rainfall patterns, accepted for publication in PLOS Climate, 2022
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".