Data · dataset · 2026
Sources of error in dual-wavelength radar remote sensing of cloud liquid water content
Listed in National Center for Atmospheric Research
Dual-wavelength ratio (DWR) techniques offer the prospect of producing high-resolution mapping of cloud microphysical properties, including retrievals of cloud liquid water content (LWC) from reflectivity measured by millimeter-wavelength radars.
Description
Unfortunately, noise and artifacts in the DWR require smoothing to obtain physically realistic values of LWC with a concomitant loss of resolution. Factors that cause inaccuracy in the retrieved LWC include uncertainty in gas and liquid water attenuation coefficients, Mie scattering due to large water droplets or ice particles, corruption of the radar reflectivities by noise and nonatmospheric returns, and artifacts due to mismatched radar illumination volumes.
The error analysis presented here consists of both analytic and heuristic arguments; it is illustrated using data from the Mount Washington Icing Sensors Project (MWISP) and from an idealized simulation. In addition to offering insight into design considerations for a DWR system, some results suggest methods that may mitigate some of these sources of error for existing systems and datasets.
Links
Get the data
- Publisher page n2t.org/ark:/85065/d7j38stf ↗
documentation · download · from data ucar edu
Where it is published
- data.ucar.edu /dataset/sources-of-error-in-dual-wavelength-radar-remote-sensi… ↗
National Center for Atmospheric Research dataset page
landing page · from data ucar edu
Catalogue records · 1
- CKAN API data.ucar.edu/api/3/action/package_show?id=9cff9b6f-73c3-4ec0-ae24-2517d8984… ↗
metadata API · from data ucar edu
Topics
- From keywords
- Earth & Environmental Science · Ocean & Atmospheric Science
- Inferred from text
- Satellite remote sensing 65% · Simulation 75%
Provenance · 1 source records, 9 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| National Center for Atmospheric Research | 9cff9b6f-73c3-4ec0-ae24-2517d89840c6 | 10 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| concepts[field].local:field:earth-environmental | mapping · data ucar edu | connector:data_ucar_edu@1.0.0 | |
| concepts[field].local:field:ocean-atmospheric | mapping · data ucar edu | connector:data_ucar_edu@1.0.0 | |
| concepts[method].local:method:simulation | enrichment · data ucar edu | keyword-concept-rules@1.0.0 | title+description (75%) |
| concepts[modality].local:modality:remote-sensing | enrichment · data ucar edu | keyword-concept-rules@1.0.0 | title+description (65%) |
| created_date | source · data ucar edu | connector:data_ucar_edu@1.0.0 | |
| description | source · data ucar edu | connector:data_ucar_edu@1.0.0 | /notes |
| publication_date | source · data ucar edu | connector:data_ucar_edu@1.0.0 | |
| title | source · data ucar edu | connector:data_ucar_edu@1.0.0 | /title |
| updated_date | source · data ucar edu | connector:data_ucar_edu@1.0.0 |