Korrektur Mehrjahres Meereis Konzentration Schätzungen Mikrowelle Satelliten-Beobachtungen mit der Lufttemperatur , Meereis Drift und dynamische Verbindungspunkte
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
Arctic sea ice cover is a sensitive climate indicator. Due to the warming climate, it has decreased dramatically in the Arctic over the past three decades. Moreover, multiyear ice (MYI), ice which has survived at least one summer, is decreasing at a much higher rate. MYI concentration can be retrieved from microwave remote sensing data. However, the retrieval shows flaws under specific weather conditions. The current thesis is motivated by the need of better estimates of MYI distribution. It introduces three methods to improve/correct the MYI concentration estimates from microwave satellite observations. The first method builds upon the NASA Team algorithm and uses dynamic tie points to compensate the temporal variations of tie points (typical brightness temperatures of each surface type at all the channels). The MYI retrievals in winters (Oct-May) of the years 1989-2012 show that the method with dynamic tie points yields higher estimates than the original method in most years. Both methods show clear declining trends of the MYI area from 1989 to 2012, which is consistent with the sea ice extent minimum. The MYI concentration retrieval with the NASA Team algorithm is most sensitive to the tie points of MYI and FYI at 19 GHz vertical polarized channel. These tie points should be treated with more caution when dynamic tie points are used. The second and third methods are two correction schemes used to account for radiometric anomalies that trigger the erroneous MYI concentration retrievals from microwave satellite observations. The correction based on air temperature is introduced to restore the underestimated MYI concentration under warm conditions. It utilizes the fact that the warm spell in autumn lasts for a few days and replaces the erroneous MYI concentrations with interpolated ones. It is applied to MYI retrievals from the Environment Canada Ice Concentration Extractor (ECICE) using inputs from QuikSCAT and AMSR-E data, acquired over the Arctic in a series of autumn seasons (Sep-Dec) from 2003 to 2008. The correction works well by identifying and correcting the anomalous MYI concentrations. For September of the six years, it introduces over 1.0x105 km2 MYI area, except for 2005. The correction based on ice drift is designed to correct the overestimated MYI concentrations that are impacted by factors such ice deformation, snow wetness and metamorphism. It utilizes ice drift records to constrain the MYI changes within a predicted contour and uses two thresholds of passive microwave radiometric parameters to account for snow wetness and metamorphism. It is applied to the MYI concentration retrievals from ECICE in winters (Oct-May) from 2002 to 2009. Qualitative comparison with Radarsat-1 SAR images and quantitative comparison against results from previous studies show that the correction works well by removing the anomalous high MYI concentrations. On average, the correction reduces 5.2x105 km2 of the estimated MYI area in Arctic except for the April-May time frame, when the reduction is larger as the warmer weather prompts the condition of the anomalous snow radiometric signatures. Both corrections can be used as post-processings to all the microwave-based MYI concentration retrieval algorithms. Due to the regional effect of weather conditions, they could be important in the operational applications. In addition, both corrections take the spatial and temporal continuity of MYI into account, which gives a new insight that instantaneous observations alone of sea ice may lead to ambiguities in determination of partial ice concentrations. This approach may be applicable to the retrieval of other sea ice parameters as well.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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