Extreme Rainfall Indices Prediction with Atmospheric Parameters and Ocean–Atmospheric Teleconnections Using a Random Forest Model

Globally, extreme rainfall has intense impacts on ecosystems and human livelihoods. However, no effort has yet been made to forecast the extreme rainfall indices through machine learning techniques. In this paper, a new extreme rainfall indices forecasting model is proposed using a random forest (RF...

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Vydáno v:Journal of applied meteorology and climatology Ročník 61; číslo 6; s. 651 - 667
Hlavní autoři: Uddin, Md. Jalal, Li, Yubin, Tamim, Md. Yahya, Miah, Md. Babul, Ahmed, S. M. Shahriar
Médium: Journal Article
Jazyk:angličtina
Vydáno: Boston American Meteorological Society 01.06.2022
Témata:
Air temperature
Atmospheric forcing
Convective available potential energy
Dipoles
El Nino
El Nino phenomena
El Nino-Southern Oscillation event
Environmental impact
Extreme weather
Forecasting
Heavy rainfall
Lead time
Livelihoods
Machine learning
Mathematical models
Modelling
Monthly rainfall
North Atlantic Oscillation
Ocean-atmosphere system
Oceans
Parameters
Potential energy
Rain
Rainfall
Rainfall forecasting
Relative humidity
Southern Oscillation
Statistical analysis
Teleconnections
ISSN:1558-8424, 1558-8432
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Popis
Shrnutí:Globally, extreme rainfall has intense impacts on ecosystems and human livelihoods. However, no effort has yet been made to forecast the extreme rainfall indices through machine learning techniques. In this paper, a new extreme rainfall indices forecasting model is proposed using a random forest (RF) model to provide effective forecasts of monthly extreme rainfall indices. In addition, RF feature importance is proposed in this study to identify the most and least important features for the proposed model. This study forecasts only statistically significant extreme rainfall indices over Bangladesh including consecutive dry days (CDD), the number of heavy rain days (R10mm; rainfall ≥ 10 mm), and the number of heavy rain days (R20mm; rainfall ≥ 20 mm) within 1–3 months of lead time. The proposed model uses monthly antecedent CDD, R10mm, and R20mm including atmospheric parameters and ocean–atmospheric teleconnections, namely, convective available potential energy (CAPE), relative humidity (RH), air temperature (TEM), El Niño–Southern Oscillation (ENSO), Indian Ocean dipole (IOD), and North Atlantic Oscillation (NAO), as the inputs to the model. Results show that the proposed model yields the best performance to forecast CDD, R10mm, and R20mm with only the antecedent of these indices as input. Ocean–atmospheric teleconnections (IOD, ENSO, and NAO) are useful for CDD forecasting, and local atmospheric parameters (CAPE, RH, and TEM) are useful for R10mm and R20mm forecasting. The results suggest that adding atmospheric parameters and ocean–atmospheric teleconnections is useful to forecast extreme rainfall indices.
Bibliografie:ObjectType-Article-1
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ISSN:1558-8424
1558-8432
DOI:10.1175/JAMC-D-21-0170.1