Information-theoretic analysis of commercial microwave link and environmental variables in rainfall estimation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21110%2F25%3A00388706" target="_blank" >RIV/68407700:21110/25:00388706 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.5194/amt-18-7445-2025" target="_blank" >https://doi.org/10.5194/amt-18-7445-2025</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5194/amt-18-7445-2025" target="_blank" >10.5194/amt-18-7445-2025</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Information-theoretic analysis of commercial microwave link and environmental variables in rainfall estimation

Popis výsledku v původním jazyce

Commercial microwave links (CMLs) are opportunistic rainfall sensors that provide indirect rainfall estimates from attenuation data. This is achieved by separating raindrop path attenuation from observed total loss and converting it to rainfall intensity using the k-R formula. Various methods have been proposed for CML rainfall retrieval using either attenuation data alone or additional environmental variables. However, most studies evaluate CML rainfall estimates deterministically and do not reveal how individual processing steps and variables affect rainfall estimation uncertainty. This study proposes to evaluate CML processing using an information-theoretic framework and demonstrates this probabilistic concept on two particular problems. The first analysis reveals the reduction of uncertainty in CML rainfall estimates by measuring the information content of individual variables and their combinations. Both quantitative and qualitative predictors are used, including sensor variables such as CML signal attenuation, and environmental variables such as temperature, or synoptic types. The rainfall intensity derived from the k-R formula and combined with synoptic type forms an informative combination of sensor and environmental variables for reducing uncertainty regarding reference rainfall intensity. The second analysis demonstrates the application of information theory for classifying wet and dry periods using signal attenuation data and other environmental variables. In a limited single-link evaluation, a non-parametric model indicated better performance than the reference approaches suggesting the potential of information theory in CML processing. The proposed framework enables the identification of informative sensor and environmental variables, the evaluation of the effects of different processing steps on the estimated rainfall intensity, or the development of a wet-dry classification model calibrated in a probabilistic manner ultimately facilitating the improvement of CML rainfall estimates.

Název v anglickém jazyce

Information-theoretic analysis of commercial microwave link and environmental variables in rainfall estimation

Popis výsledku anglicky

Commercial microwave links (CMLs) are opportunistic rainfall sensors that provide indirect rainfall estimates from attenuation data. This is achieved by separating raindrop path attenuation from observed total loss and converting it to rainfall intensity using the k-R formula. Various methods have been proposed for CML rainfall retrieval using either attenuation data alone or additional environmental variables. However, most studies evaluate CML rainfall estimates deterministically and do not reveal how individual processing steps and variables affect rainfall estimation uncertainty. This study proposes to evaluate CML processing using an information-theoretic framework and demonstrates this probabilistic concept on two particular problems. The first analysis reveals the reduction of uncertainty in CML rainfall estimates by measuring the information content of individual variables and their combinations. Both quantitative and qualitative predictors are used, including sensor variables such as CML signal attenuation, and environmental variables such as temperature, or synoptic types. The rainfall intensity derived from the k-R formula and combined with synoptic type forms an informative combination of sensor and environmental variables for reducing uncertainty regarding reference rainfall intensity. The second analysis demonstrates the application of information theory for classifying wet and dry periods using signal attenuation data and other environmental variables. In a limited single-link evaluation, a non-parametric model indicated better performance than the reference approaches suggesting the potential of information theory in CML processing. The proposed framework enables the identification of informative sensor and environmental variables, the evaluation of the effects of different processing steps on the estimated rainfall intensity, or the development of a wet-dry classification model calibrated in a probabilistic manner ultimately facilitating the improvement of CML rainfall estimates.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10509 - Meteorology and atmospheric sciences

Projekt

<a href="/cs/project/GF24-13677L" target="_blank" >GF24-13677L: Pokročilá kombinace dat z oportunistických senzorů a geostacionárních satelitů pro kvantitativní odhad srážek (MERGOSAT)</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Atmospheric Measurement Techniques

ISSN

1867-1381

e-ISSN

1867-8548

Svazek periodika

18

Číslo periodika v rámci svazku

23

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

19

Strana od-do

7445-7463

Kód UT WoS článku

001631792600001

EID výsledku v databázi Scopus

2-s2.0-105024357829