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Record W2298956148

The US MOPEX data set.

2006· article· en· W2298956148 on OpenAlexaboutno aff
John C. Schaake, S. Cong, Qingyun Duan

Bibliographic record

VenueUniversity of North Texas Digital Library (University of North Texas) · 2006
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicPrecipitation Measurement and Analysis
Canadian institutionsnot available
FundersNational Oceanic and Atmospheric AdministrationLawrence Livermore National LaboratoryU.S. Department of Energy
KeywordsHydrometeorologyEnvironmental scienceMeteorologyHydrological modellingClimatologyDrainage basinStructural basinEstimationSurface runoffClimate modelRange (aeronautics)Water resourcesHydrology (agriculture)PrecipitationClimate changeGeographyGeologyEngineeringCartography
DOInot available

Abstract

fetched live from OpenAlex

A key step in applying land surface parameterization schemes is to estimate model parameters that vary spatially and are unique to each computational element. Improved methods for parameter estimation (especially for parameters important to runoff response) are needed and require data from a wide range of climate regimes throughout the world. Accordingly, the GEWEX Hydrometeorology Panel (GHP) endorsed the concept of an international Model Parameter Estimation Project (MOPEX) at its Toronto meeting, August 1996. Phase I of MOPEX was funded by NOAA in FY 1997, Phase II in FY 2000 and Phase III in FY 2003. MOPEX was adopted as projects of the IAHS/WMO Committee on GEWEX and of the WMO Commission on Hydrology (CHy) and now is a contributor to the Combine Enhanced Observing Period (CEOP) of the World Climate Research Program (WCRP). In 2004 MOPEX became a Working Group of the IAHS Prediction for Ungaged Basins (PUB) Initiative. MOPEX also is expected to contribute to the work of the Hydrologic Ensemble Prediction Experiment (HEPEX) (Franz et al, 2005). The primary goal of MOPEX is to develop techniques for the a priori estimation of the parameters used in land surface parameterization schemes of atmospheric models and in hydrologic models. A major early effort of MOPEX has been to assemble a large number of high quality historical hydrometeorological and river basin characteristics data sets for a wide range of river basins (500-10,000 km{sup 2}) throughout the world. MOPEX data sets are available via the Internet (ftp://hydrology.nws.noaa.gov). This paper documents the development of data sets for U.S. river basins. Several highly successful parameter estimation workshops have been organized by MOPEX. The first was held as part of the IAHS meeting in Birmingham, England in July, 1999. The second workshop was hosted April, 2002 in Tucson, AZ by SAHRA/University of Arizona. The third MOPEX workshop was held as part of the IAHS meeting in Sapporo, July, 2003. The fourth workshop, Paris, July,2005 was organized by the Cemagref in collaboration with the ENGREF, Meteo France, National Weather Service and the SAHRA/University of Arizona. The fifth workshop was held as part of the IAHS meeting, February, 2005, Foz do Iguacu, Brazil. The purpose of the future phases of the project is to: (1) continue collect additional international data sets; update data from the U.S. by adding recent years, including data for elevation zones in mountainous areas and refining energy forcing; (2) continue to conduct international MOPEX workshops; (3) provide leadership to develop a better scientific understanding of how to improve procedures for a priori parameter estimation, (4) make a significant hydrological contribution to CEOP and PUBS, and (5) demonstrate transferability of MOPEX results. The basic data collection strategy being used in MOPEX is to seek most readily available and highest quality data first. During the next 3 years analyses of the available MOPEX data sets by the international scientific community will be emphasized.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.099
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.004
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.019
GPT teacher head0.166
Teacher spread0.147 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2006
Admission routes1
Has abstractyes

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