Implementation of All-Sky Assimilation of Microwave Humidity Sounding Channels in Environment Canada’s Global Deterministic Weather Prediction System
Bibliographic record
Abstract
Abstract Cloud-affected microwave humidity sounding radiances were excluded from assimilation in the hybrid four-dimensional ensemble–variational (4D-EnVar) system of the Global Deterministic Prediction System (GDPS) at Environment and Climate Change Canada (ECCC). This was due to the inability of the current radiative transfer model to consider the scattering effect from frozen hydrometeors at these frequencies. In addition to upgrading the observation operator to RTTOV-SCATT, quality control, bias correction, and 4D-EnVar assimilation components are modified to perform all-sky assimilation of Microwave Humidity Sounder (MHS) channel 2–5 observations over ocean in the GDPS. The input profiles to RTTOV-SCATT are extended to include liquid cloud, ice cloud, and cloud fraction profiles for the simulation and assimilation of MHS observations over water. There is a maximum (35%) increase in the number of channel 2 assimilated MHS observations with smaller increases for channels 3–5 in the all-sky experiment compared to the clear-sky experiment, mostly because of newly assimilated cloud-affected observations. The standard deviation (stddev) of difference between the observed global positioning system radio occultation (GPSRO) refractivity observations and the corresponding simulated values using the background state was reduced in the lower troposphere below 9 km in the all-sky experiment. Verifications of forecasts against the radiosonde observations show statistically significant reductions of 1% in the stddev of error for geopotential height, temperature, and horizontal wind for the all-sky experiment between 72- and 120-h forecast ranges in the troposphere in the Northern Hemisphere domain. Verifications of forecasts against ECMWF analyses also show small improvements in the zonal mean of stddev of error for temperature and horizontal wind for the all-sky experiment between 72- and 120-h forecast ranges. This work was planned for operational implementation in the GDPS in fall 2023.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".