Passive Microwave Imagers (PMWIs) aboard meteorological satellites have been instrumental in advancing the understanding of Earth’s atmospheric and surface processes, providing invaluable data for weather forecasting, climate monitoring, and environmental research. This review examines the relevance, applications, and benefits of PMWI data, focusing on their practical use and benefits to society rather than the specific techniques or algorithms involved in data processing. Specifically, it assesses the impact of PMWI data on Tropical Cyclone (TC) intensity and structure, global precipitation and extreme events, flood prediction, the effectiveness of tropical storm and hurricane watches, fire severity and carbon emissions, weather forecasting, and drought mitigation. Additionally, it highlights the importance of PMWIs in hydrometeorological and real-time applications, emphasizing their current usage and potential for improvement. Key recommendations from users include expanding satellite networks for more frequent global coverage, reducing data latency, and enhancing resolution to improve forecasting accuracy. Despite the notable benefits, challenges remain, such as a lack of direct research linking PMWI data to broader societal outcomes, the time-intensive process of correlating PMWI use with measurable societal impacts, and the indirect links between PMWI and improved weather forecasting and disaster management. This study provides insights into the effectiveness and limitations of PMWI data, stressing the importance of continued research and development to maximize their contribution to disaster preparedness, climate resilience, and global weather forecasting.

This study aimed to compare and evaluate three satellite-based precipitation estimation products: Integrated Multi-satellitE Retrievals for Global Precipitation Measurement Early Run (IMERG-Early Run), Climate Prediction Center MORPHing technique Real Time (CMORPH-RT), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Dynamic Infrared Rain rate Now (PDIR-Now) to identify the optimal integration strategies to improve the extreme rainfall estimation during Super Typhoon Yagi (September, 2024) in Hanoi, Vietnam, using validation data from 25 ground stations. In-depth analysis of three extreme rainfall series during Typhoon Yagi (6–9 September 2024), examining 93 extreme rainfall events at the 95th percentile precipitation threshold (R95p = 21.78 mm/h), combined with statistics at lower percentile thresholds (R1p, R5p, R10p, and R90p) and upper percentile threshold (R99p), revealed IMERG-Early best captured the peak rainfall, CMORPH-RT achieved highest total rainfall accuracy, while PDIR-Now offered the best spatial analysis. However, limitations included time lags, inability to detect rainfall events above R99p (41.69 mm/hour), and low detection rates (8–12%) in areas first impacted by the typhoon. This study identified that integration strategies combining different satellite products based on their strengths at specific time scales showed potential for improved rainfall estimation: PDIR-Now with IMERG-Early (1–3 h) and IMERG-Early with CMORPH-RT (6–12 h). These integration approaches accounted for each product’s unique capabilities in capturing different aspects of extreme rainfall during super typhoon events.

In this paper, artificial neural network (ANN) was used for downscaling the outputs of general circulation models (GCMs) to evaluate changes in precipitation and mean temperature for a future period in Urmia at the north-west of Iran. MIROC-ESM-CHEM from IPCC AR5 was selected as an acceptable model based on correlation coefficient (CC) values, which is calculated between precipitation of GCM models and precipitation data prepared by Urmia Meteorological Organization for 1951-2000. As a first step, the most important parameters of the MIROC-ESM-CHEM were selected before the downscaling process by ANN in the base period (1951-2000). Afterward, the future projections of precipitation and mean temperature during 2020-2060 were applied using ANN-based simulation according to the CC method. By comparing the results, the MIROC-ESM-CHEM showed a 2.01% increase under RCP4.5 and a 0.16% decrease under RCP8.5 in annual precipitation. Also, the temperature projection outputs showed the annual mean temperature would increase in the future period in this area, and it is likely to get warmer.

This research develops a Deep Learning (DL) - based algorithm to fill gaps in satellite data by leveraging multi-source input combinations, assessing temporal information impact, and implementing a hybrid loss function that combines Kullback–Leibler (KL) divergence with Mean Squared Error (MSE) computed in logarithmic space and Mean Absolute Error (MAE) to improve the model’s ability to capture heavy rainfall events. The model uses a Convolutional Neural Network U-Net (CNN-UNet) architecture to fuse Infrared Brightness Temperature (IR Tb) from NOAA’s geostationary satellites with merged microwave precipitation (MWprecipitation) from NASA’s Integrated Multi-satellitE Retrievals for Global Precipitation Measure (GPM)–Early (IMERG–E), delivering a temporally and spatially complete trajectory of rainfall. The model is trained on June–August (2014–2017) data and tested on June–August (2018) data. The NASA Ground Validation Multi-Radar/Multi-Sensor System (GV-MRMS) serves as the reference dataset, while the NASA uncalibrated IMERG–E (precipitationUncal) precipitation is used as the baseline. Different CNN-UNet configurations are explored using varying input combinations and loss functions. Results show that CNN-UNet effectively reconstructs missing MWprecipitation data by exploiting spatial and temporal patterns in IR Tb. Models incorporating both data sources, temporal context, and a balanced hybrid loss function outperform simpler configurations. The best-performing CNN-UNet configuration shows good agreement with MRMS observations, better captures rainfall frequency distributions, and shows improvements in continuous and categorical metrics for summertime precipitation relative to near-real-time IMERG-E over the study area. Furthermore, comparing CNN-UNet and IMERG–E with MRMS across several heavy rainfall events shows promising results. These findings highlight the potential of CNN-UNet for near-real-time satellite rainfall estimation.