In regional studies, reanalysis datasets can extend precipitation time series with insufficient observations. In the present study, the ERA5 precipitation dataset was compared to observational datasets from meteorological stations in nine different precipitation zones of Iran (0.125° × 0.125° grid box) for the period 2000–2018, and measurement criteria and skill detection criteria were applied to analyze the datasets. The results of the daily analysis revealed that the correlation between ERA5 and observed precipitation were larger than 0.5 at 90% of stations. Also, The daily standard relative bias indicated that precipitation was overestimated in zone 6. As detection criteria, the frequency bias index (FBI) and proportion correct (PC) showed that the ERA5 data could capture daily precipitation events. Correlation confidence comparisons between the ERA5 and observational time series at daily, monthly, and seasonal scales revealed that the correlation confidence was higher at monthly and seasonal scales. The standard relative bias results at monthly and seasonal scales followed the daily relative bias results, and most of the ERA5 underestimations during the summer belonged to zone 1 in the coastal area of the Caspian Sea with convective precipitation. In addition, some complex mountainous regions were associated with overestimated precipitation, especially in northwest Iran (zone 6) in different time scales.

The numerical weather forecast model Weather Research and Forecasting (WRF) has a range of applications because it offers multiple physical options, enabling the users to optimizing WRF for specific scales, geographical locations and applications. Summer rainfall cannot be predicted well in North West of Iran (NWI). Most of them are convective. Sometimes rainfall is heavy, so that it causes flash flood. In this research, some configurations of WRF were tested with four summer rainfall events in NWI to find the best configuration. Five cumulus, four planetary boundary layers (PBL) and two microphysical schemes were combined. Twenty-six different configurations (models) were implemented at two resolutions of 5 and 15 km for duration of 48 h. Four events, with over 20 mm convective daily rainfall total, were selected at NWI during summer season between 2010 and 2015. These events were tested by developing 26 unique models. Results were verified using several methods. The aim was to find the best results during the first 24 h. Although no single configuration can be introduced for all times, thresholds, and atmospheric system to provide reliable and accurate forecast, the best configuration for WRF can be identified. Kain-Fritsch (new Eta), Betts-Miller-Janjic, Modified Kain-Fritsch, Multi-scale Kain-Fritsch and newer Tiedtke cumulus schemes and Mellor-Yamada-Janjic, Shin-Hong ‘scale-aware’, Medium Range Forecast (MRF) and Yonsei University (YSU) Planetary Boundary Layer schemes and Kessler, WRF Single Moment 3 class simple ice (WSM3) microphysics schemes were selected. The result show that Cumulus schemes are the most sensitive and Microphysics schemes are the less sensitive. The comparison of 15 km and 5 km resolution simulations do not show obvious advantages in downscaling the results. Configuration with newer Tiedtke cumulus, Mellor-Yamada-Janjic PBL, WSM3 and Kessler microphysics schemes give the best results for the 5 and 15 km resolutions. The output image of models and statistical methods verification indexes show that WRF could not accurately simulate convective rainfall in the NWI in summer.

Extreme weather, by definition, is any unexpected, unusual, unpredictable, severe or unseasonal weather condition. A rainfall event that is considered normal in one region may be considered a torrent in a dry region and may cause flash flooding. Therefore, appropriate weather warnings need to be issued with respect to areas with different climates. Additionally, these alerts should be easy to understand—by clear classification—in order to apply reinforcements. Early warning levels not only depend on the intensity and duration of rainfall events, but also on the initial water stress conditions, land cover situations and degree of urbanization. This research has focused on defining different warning levels in northwest Iran using long-term precipitation data from 87 weather stations well distributed across the study area. Here, in order to determine alert levels, TOPSIS (The Order of Preference by Similarity to Ideal Solution), as one of the most common methods in multi-criteria decision making, has been used. Results show that five main levels of alerts can be derived, leading to the provision of spatial maps. Further, it can be deduced that these levels are highly associated to the location of a region at different times: months/seasons. It has been observed that the issuance of a warning for precipitation should correspond with the location and time. At one location during different seasons, different alert levels would be raised corresponding to the rainfall. It was also concluded that using of fixed alert levels and extending them to larger areas without considering the seasons could be grossly misleading.

This study examines the spatiotemporal characteristics of extreme precipitation indices in Iran. It analyzes data from 38 synoptic stations across the country, covering the period from 1990 to 2020, focusing on the 11 most common extreme precipitation indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI). The analysis employs the Mann–Kendall (M–K) trend test. The findings indicate that the indices PRCPTOT (annual total precipitation), R20 mm (very heavy precipitation days), R10 mm (heavy precipitation days), R25 mm (number of wet days), Rx1 day (maximum 1-day precipitation), Rx5 day (maximum 5-day precipitation), SDII (simple daily intensity index), R95p (very wet day precipitation), R99p (extremely wet day precipitation), and CWDs (consecutive wet days) showed the highest values in the northern and western regions of the country, particularly at stations like Ramsar, Hamedan, Ilam, Kermanshah, and Yasouj. Conversely, the eastern and southeastern parts of the country showed the lowest values for these indices. The Consecutive Dry Day (CDD) index exhibited the highest values at Zabol station (228 days) and Abadan station (193 days) in the southern region of the country. Generally, precipitation extremes in the western, northwestern, and Caspian Sea coasts showed an increasing trend, while the eastern, southeastern, and central parts of the country demonstrated a decreasing trend. The trend test results indicate significant mutations in all precipitation indices, except for SDII, with mutation points primarily occurring during the decade from 2000 to 2010. The magnitude of mutation for each index post-mutation is generally greater than before. This study provides valuable information for decision-makers in agriculture, food security, water, and the environment. It also serves as a resource for natural disaster prevention and mitigation.

The living conditions in the Urmia Basin (northwestern Iran) face significant challenges due to dust events. This study investigates the spatial and temporal characteristics of dust phenomena in the Urmia Basin using MERRA-2 data and observational data from Tabriz, Urmia, Sarab, and Mahabad over a 30-year period (1990–2019). The findings reveal that despite several fluctuations, the annual number of dusty days increased from the 1990s to the 2010s in the Urmia Basin. The maximum number of dusty days was found to predominantly occur in May (spring) and October (autumn), driven by two distinct mechanisms. In early autumn, developing synoptic systems associated with increased wind speeds can cause dust emission from dry land sources. Consequently, an increase in dust wet deposition, precipitation, dust surface concentration, and the number of dusty days occurs in October. In contrast, a sharp decrease in precipitation from April to May leads to drying soil and dust emission in May. Among the studied cities, Tabriz experienced the highest number of dusty days (728) due to the combined effects of cross-border and local dust sources. The highest dust column density and dust dry deposition in the south and east of Urmia Lake indicate the impact of declining water levels, which resulted in a dry lakebed as the primary local dust source. The MERRA-2 spatial distribution reveals that dust surface concentration, and the number of dusty days decrease similarly from the southwest to the northeast of the Urmia Basin as the distance from cross-border dust sources increases. A positive correlation is observed between the number of dusty days and MEERA-2 data, including dust surface concentration, dust dry deposition, column mass dust, and total aerosol extinction, with coefficients of 0.74, 0.71, 0.69, and 0.68, respectively.