Data · dataset · 2026
《Sounding Transformer Retrieval Method for Atmospheric Temperature and Humidity Profiles Based on Ground-Based Radiometer Simulation Data》Data
Listed in ScienceDB
This dataset is based on the inversion method of atmospheric temperature and humidity profiles using ground-based microwave radiometers to simulate brightness temperature.
Description
It contains 3713 valid paired samples and covers the period from January 1, 2020 to December 31, 2025. The observation station is Beijing Station (station number 54511, altitude about 32 meters).
The dataset mainly includes three types of data: (1) simulated microwave brightness temperature observed in the zenith direction of ground-based microwave radiometers, with a total of 46 channels, covering the K-band (21.46-31.54 GHz, 23 channels) and V-band (50.96-61.04 GHz, 23 channels); (2) True value of atmospheric temperature and humidity profile matched hourly with brightness temperature (from ground to approximately 10 km altitude, including temperature and absolute humidity stratification data); (3) Auxiliary feature data, including ground meteorological elements (2m temperature, relative humidity, weather phenomena, cloud cover, precipitation) as well as precipitation potential (PWV) and cloud liquid water content (CLW) inverted from ERA5 reanalysis data. This dataset is obtained based on the following method: taking the 12 hour sounding data from 54511 stations published by the University of Wyoming as atmospheric input, using the MonorTM v5.4 line by line radiative transfer model, and configuring zenith observation geometry according to the 46 microwave channels mentioned above for radiative transfer calculation, simulating the brightness temperature of ground-based microwave radiometers; At the same time, corresponding auxiliary features are obtained through ground meteorological observations and ERA5 reanalysis data.
Read the rest (2 more)
The data processing steps include: quality screening and format conversion of sounding data (CSV → NetCDF), generation of MonorTM input file (TAPE5) and batch calculation of brightness temperature, removal of missing and invalid samples, annotation and matching of sunny/rainy samples, spatiotemporal matching of auxiliary data, and final data cleaning and standardization. The output is a CSV format sample table and a matching sounding profile NetCDF file. This dataset can be used for the inversion of atmospheric temperature and humidity profiles based on ground-based microwave radiometer brightness temperature, especially for the training, validation, and comparative evaluation of machine learning inversion algorithms such as neural networks and transformers.
It can also serve as a benchmark dataset for microwave radiation transmission simulation and atmospheric profile remote sensing algorithm research. This dataset provides data support for the distribution of dataset samples, training process, inversion error, and comparison of typical case profiles in the paper.
Links
Where it is published
- DOI doi.org/10.57760/sciencedb.space.002z5 ↗
DOI / persistent id · from scidb cn
Catalogue records · 1
- OAI-PMH record scidb.cn/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=10.57760%2… ↗
metadata API · from scidb cn
Topics
- From keywords
- Earth & Environmental Science · Engineering · Humanities · Life Sciences · Social Science
- Inferred from text
- Geophysics 74% · Satellite remote sensing 65% · Simulation 75% · Tabular 65%
Provenance · 1 source records, 14 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| ScienceDB | 10.57760/sciencedb.space.002z5 | 9 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].anzsrc:group:3706 | enrichment · scidb cn | taxonomy-embedding@1.0.0 | title+keywords+description (74%) |
| concepts[field].local:field:earth-environmental | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:engineering | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:humanities | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:social-science | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[method].local:method:simulation | enrichment · scidb cn | keyword-concept-rules@1.0.0 | title+description (75%) |
| concepts[modality].local:modality:remote-sensing | enrichment · scidb cn | keyword-concept-rules@1.0.0 | title+description (65%) |
| concepts[modality].local:modality:tabular | enrichment · scidb cn | keyword-concept-rules@1.0.0 | title+description (65%) |
| description | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/description |
| license | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/rights |
| publication_date | source · scidb cn | connector:scidb_cn@1.0.0 | |
| title | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/title |