CLEAR: a new discrete multiplicative random cascade model for disaggregating path-integrated rainfall estimates from commercial microwave links
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21110%2F25%3A00384956" target="_blank" >RIV/68407700:21110/25:00384956 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.5194/amt-18-4467-2025" target="_blank" >https://doi.org/10.5194/amt-18-4467-2025</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.5194/amt-18-4467-2025" target="_blank" >10.5194/amt-18-4467-2025</a>
Alternative languages
Result language
angličtina
Original language name
CLEAR: a new discrete multiplicative random cascade model for disaggregating path-integrated rainfall estimates from commercial microwave links
Original language description
A novel disaggregation algorithm for commercial microwave links (CMLs), named CLEAR (CML Segments with Equal Amounts of Rain), is proposed. CLEAR utilizes a multiplicative random cascade generator to control the splitting of link segments, with the generator's standard deviation dependent on the rain rate and segment length. Spatial consistency during the splitting process is maintained using rain rate information from neighboring CMLs. CLEAR is evaluated on a network of 77 CMLs in Prague. The performance is assessed first using simulated rainfall fields and second through a case study with real attenuation data from the network to demonstrate its applicability in real-world scenarios. Results from the virtual rainfall fields indicate good overall performance, including the generation of realistic spatial patterns. CLEAR effectively estimates maximal and minimal rain rates along CML paths and outperforms a commonly used benchmark algorithm. The stochastic nature of CLEAR allows it to represent uncertainty as an ensemble of rain rate distributions along CML paths. However, the generated ensembles significantly underestimate overall variability along the paths. Additionally, the case study on real data highlights challenges associated with uncertainties in CML quantitative precipitation estimates, which are common across all methods. In conclusion, CLEAR contributes to generating more representative rainfall distributions along CMLs, which is critical for spatial reconstruction of rainfall fields from path-integrated CML data. It also has the potential to reduce errors in CML quantitative precipitation estimates caused by assuming uniform rain rates along CML paths.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10501 - Hydrology
Result continuities
Project
<a href="/en/project/GF24-13677L" target="_blank" >GF24-13677L: Merging of rain rate estimates from opportunistic sensors and geostationary satellites (MERGOSAT)</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Atmospheric Measurement Techniques
ISSN
1867-1381
e-ISSN
1867-8548
Volume of the periodical
18
Issue of the periodical within the volume
17
Country of publishing house
DE - GERMANY
Number of pages
16
Pages from-to
4467-4482
UT code for WoS article
001568993300001
EID of the result in the Scopus database
2-s2.0-105022498452