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Record W1500240012 · doi:10.2151/jmsj.2015-020

Estimation of Raindrop Size Distribution and Rainfall Rate from Polarimetric Radar Measurements at Attenuating Frequency Based on the Self-Consistency Principle

2015· article· en· W1500240012 on OpenAlexfundno aff
Ahoro Adachi, Takahisa Kobayashi, Hiroshi Yamauchi

Bibliographic record

VenueJournal of the Meteorological Society of Japan Ser II · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicPrecipitation Measurement and Analysis
Canadian institutionsnot available
FundersNational Oceanic and Atmospheric AdministrationJapan Society for the Promotion of ScienceMinistry of Education, Culture, Sports, Science and TechnologyMcGill UniversityUniversity of ReadingColorado State University
KeywordsDisdrometerPolarimetryRadarDifferential phaseAttenuationConsistency (knowledge bases)PrecipitationRemote sensingRain gaugeMeteorologyEnvironmental scienceMathematicsPhase (matter)PhysicsComputer scienceOpticsGeologyScatteringGeometry

Abstract

fetched live from OpenAlex

A method for estimating three parameters of a gamma raindrop size distribution (DSD) model and the rainfall rate from polarimetric radar at attenuating frequency was developed. The algorithm was developed based on the self-consistency principle but was expanded to consider the attenuation effect by describing the interrelation between polarimetric measurements along the range profile. The proposed method does not require any assumptions of relation among DSD parameters or simplifications of equations that describe the relation between the axis ratio and diameter of raindrops, which have been used in previous studies. Moreover, the proposed algorithm needs no external reference data such as two-dimensional video disdrometer measurements for attenuation corrections because it retrieves the co-polar and differential specific attenuation from the interrelation among the polarimetric measurements. The performance of this algorithm was evaluated by comparison with optical disdrometers and a weighing precipitation gauge. The evaluation of the algorithm showed that the retrieved three DSD parameters of raindrops, reflectivity, and differential reflectivity from actual C-band polarimetric radar data have fairly good agreement with those obtained by surface measurements. Moreover, rainfall rates retrieved using this algorithm have comparable precision with those estimated from the specific differential phase, and outperform those estimated through the so-called Z-R relation, particularly during heavy rainfall. Furthermore, the effects of raindrop temperature and shape parameter on the retrieval of the rainfall rate were examined. The results show that for radar operating at C-band, a raindrop temperature error of 10°C may be negligible in rainfall rate estimations, whereas a shape parameter error of 2 may increase the error of the rainfall rate estimation by 10 %.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.052
GPT teacher head0.244
Teacher spread0.192 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations11
Published2015
Admission routes1
Has abstractyes

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Same venueJournal of the Meteorological Society of Japan Ser IISame topicPrecipitation Measurement and AnalysisFrench-language works237,207