Modeling and forecasting of rainfall reoccurrence changes using Markov Switching in Iran
Abstract This paper represents the recurrence (reoccurrence) changes in the rainfall series using Markov Switching models (MSM). The switching employs a dynamic pattern that allows a linear model to be combined with nonlinearity models a discrete structure. The result is the Markov Switching models...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2021-07-01
|
| Series: | SN Applied Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s42452-021-04728-9 |
| _version_ | 1818909208530124800 |
|---|---|
| author | Majid Javari |
| author_facet | Majid Javari |
| author_sort | Majid Javari |
| collection | DOAJ |
| description | Abstract This paper represents the recurrence (reoccurrence) changes in the rainfall series using Markov Switching models (MSM). The switching employs a dynamic pattern that allows a linear model to be combined with nonlinearity models a discrete structure. The result is the Markov Switching models (MSM) reoccurrence predicting technique. Markov Switching models (MSM) were employed to analyze rainfall reoccurrence with spatiotemporal regime probabilities. In this study, Markov Switching models (MSM) were used based on the simple exogenous probability frame by identifying a first-order Markov process for the regime probabilities. The Markov transition matrix and regime probabilities were used to analyze the rainfall reoccurrence in 167 synoptic and climatology stations. The analysis results show a low distribution from 0.0 to 0.2 (0–20%) per day spatially from selecting stations, probability mean of daily rainfall recurrence is 0.84, and a different distribution based on the second regime was found to be more remarkable to the rainfall variability. The rainfall reoccurrence in daily rainfall was estimated with relatively low variability and strong reoccurrence daily with ranged from 0.851 to 0.995 (85.1–99.5%) per day based on the spatial distribution. The variability analysis of rainfall in the intermediate and long variability and irregular variability patterns would be helpful for the rainfall variability for environmental planning. |
| first_indexed | 2024-12-19T22:23:16Z |
| format | Article |
| id | doaj.art-f0dd8f0cef894c8eb6e51f1455913538 |
| institution | Directory Open Access Journal |
| issn | 2523-3963 2523-3971 |
| language | English |
| last_indexed | 2024-12-19T22:23:16Z |
| publishDate | 2021-07-01 |
| publisher | Springer |
| record_format | Article |
| series | SN Applied Sciences |
| spelling | doaj.art-f0dd8f0cef894c8eb6e51f14559135382022-12-21T20:03:34ZengSpringerSN Applied Sciences2523-39632523-39712021-07-013811410.1007/s42452-021-04728-9Modeling and forecasting of rainfall reoccurrence changes using Markov Switching in IranMajid Javari0College of Social Science, Payame Noor UniversityAbstract This paper represents the recurrence (reoccurrence) changes in the rainfall series using Markov Switching models (MSM). The switching employs a dynamic pattern that allows a linear model to be combined with nonlinearity models a discrete structure. The result is the Markov Switching models (MSM) reoccurrence predicting technique. Markov Switching models (MSM) were employed to analyze rainfall reoccurrence with spatiotemporal regime probabilities. In this study, Markov Switching models (MSM) were used based on the simple exogenous probability frame by identifying a first-order Markov process for the regime probabilities. The Markov transition matrix and regime probabilities were used to analyze the rainfall reoccurrence in 167 synoptic and climatology stations. The analysis results show a low distribution from 0.0 to 0.2 (0–20%) per day spatially from selecting stations, probability mean of daily rainfall recurrence is 0.84, and a different distribution based on the second regime was found to be more remarkable to the rainfall variability. The rainfall reoccurrence in daily rainfall was estimated with relatively low variability and strong reoccurrence daily with ranged from 0.851 to 0.995 (85.1–99.5%) per day based on the spatial distribution. The variability analysis of rainfall in the intermediate and long variability and irregular variability patterns would be helpful for the rainfall variability for environmental planning.https://doi.org/10.1007/s42452-021-04728-9Markov SwitchingRegime probabilitySpatial distributionRecurrenceRainfall |
| spellingShingle | Majid Javari Modeling and forecasting of rainfall reoccurrence changes using Markov Switching in Iran SN Applied Sciences Markov Switching Regime probability Spatial distribution Recurrence Rainfall |
| title | Modeling and forecasting of rainfall reoccurrence changes using Markov Switching in Iran |
| title_full | Modeling and forecasting of rainfall reoccurrence changes using Markov Switching in Iran |
| title_fullStr | Modeling and forecasting of rainfall reoccurrence changes using Markov Switching in Iran |
| title_full_unstemmed | Modeling and forecasting of rainfall reoccurrence changes using Markov Switching in Iran |
| title_short | Modeling and forecasting of rainfall reoccurrence changes using Markov Switching in Iran |
| title_sort | modeling and forecasting of rainfall reoccurrence changes using markov switching in iran |
| topic | Markov Switching Regime probability Spatial distribution Recurrence Rainfall |
| url | https://doi.org/10.1007/s42452-021-04728-9 |
| work_keys_str_mv | AT majidjavari modelingandforecastingofrainfallreoccurrencechangesusingmarkovswitchinginiran |