There is a growing interest in emerging opportunistic sensors for precipitation, motivated by the need to improve its quantitative estimates at the ground. The scope of this work is to present a preliminary assessment of the accuracy of commercial microwave link (CML) retrieved rainfall rates in Northern Italy. The CML product, obtained by the open-source RAINLINK software package, is evaluated on different scales (single link, 5 km×5 km grid, river basin) against the precipitation products operationally used at Arpae-SIMC, the regional weather service of Emilia-Romagna, in Northern Italy. The results of the 15 min single-link validation with nearby rain gauges show high variability, which can be caused by the complex physiography and precipitation patterns. Known sources of errors (e.g. the attenuation caused by the wetting of the antennas or random fluctuations in the baseline) are particularly hard to mitigate in these conditions without a specific calibration, which has not been implemented. However, hourly cumulated spatially interpolated CML rainfall maps, validated with respect to the established regional gauge-based reference, show similar performance (R² of 0.46 and coefficient of variation, CV, of 0.78) to adjusted radar-based precipitation gridded products and better performance than satellite-based ones. Performance improves when basin-scale total precipitation amounts are considered (R² of 0.83 and CV of 0.48). Avoiding regional-specific calibration therefore does not preclude the algorithm from working but has some limitations in probability of detection (POD) and accuracy. A widespread underestimation is evident at both the grid box scale (mean error of −0.26) and the basin scale (multiplicative bias of 0.7), while the number of false alarms is generally low and becomes even lower as link coverage increases. Also taking into account delays in the availability of the data (latency of 0.33 h for CML against 1 h for the adjusted radar and 24 h for the quality-controlled rain gauges), CML appears as a valuable data source in particular from a local operational framework perspective. Finally, results show complementary strengths for CMLs and radars, encouraging joint exploitation.

A series of typhoons and tropical storms have produced extreme precipitation events in Vietnam during the first part of the 2020 monsoon season: events of this magnitude pose significant challenges to remote sensing Quantitative Precipitation Estimation (QPE) techniques. The weather-monitoring needs of modern human activities require that these challenges be overcome. In order to address this issue, in this work, seven precipitation products were validated with high spatial and temporal detail against over 1200 rain gauges in Vietnam during six case studies tailored around the most intense events of 2020. The data sources included the Vietnamese weather radar network, IMERG Early run and Final run, the South Korean GEO-KOMPSAT-2A and Chinese FengYun-4A geostationary satellites, DPR on board the GPM-Core Observatory, and European ERA5-Land reanalysis. All products were resampled to a standardized 0.02° grid and compared at hourly scale with ground stations measurements. The results indicated that the radars product was the most capable of reproducing the information collected by the rain gauges during the selected extreme events, with a correlation coefficient of 0.70 and a coefficient of variation of 1.38. However, it exhibited some underestimation, approximately 30%, in both occurrence and intensity. Conversely, geostationary products tended to overestimate moderate rain rates (FY-4A) and areas with low precipitation (GK-2A). More complex products such as ERA5-Land and IMERG failed to capture the highest intensities typical of extreme events, while GPM-DPR showed promising results in detecting the highest rain rates, but its capability to observe isolated events was limited by its intermittent coverage.

Opportunistic sensors are increasingly used for rainfall measurement. However, their raw data are collected by a variety of systems that are often not primarily intended for rainfall monitoring, resulting in a plethora of different data formats and a lack of common standards. This hinders the sharing of opportunistic sensing (OS) data, their automated processing, and, at the end, their practical usage and integration into standard observation systems. This paper summarises the experiences of the more than 100 members of the OpenSense Cost Action involved in the OS of rainfall. We review the current practice of collecting and storing precipitation OS data and corresponding metadata, and propose new common guidelines describing the requirements on data and metadata collection, harmonising naming conventions, and defining human-readable and machine readable file formats for data and metadata storage. We focus on three sensors identified by the OpenSense community as prominent representatives of the OS of precipitation: Commercial microwave links (CML): fixed point-to-point radio links mainly used as backhauling connections in telecommunication networks Satellite microwave links (SML): radio links between geostationary Earth orbit (GEO) satellites and ground user terminals. Personal weather stations (PWS): non-professional meteorological sensors owned by citizens. The conventions presented in this paper are primarily designed for storing, handling, and sharing historical time series and do not consider specific requirements for using OS data in real time for operational purposes. The conventions are already now accepted by the ever growing OpenSense community and represent an important step towards automated processing of OS raw data and community development of joint OS software packages.

With the launch of the National Aeronautics and Space Administration (NASA) Indian Space Research Organisation synthetic aperture radar (NISAR), available open access Earth observation data reach a new milestone with an estimated data rate of ∼70 TB of NISAR science data produced daily. These additional data create many opportunities within the science and user community for new and more in-depth analyses, yet at the same time, most organizations lack the budget, resources, and skills to analyze all this data on-premise. This article introduces concepts on how synthetic aperture radar (SAR) processing software from commercial and open source providers can be coupled with highly scalable cloud processing techniques to digest SAR data at various input levels (from raw to value-added products). Included in this article are examples of the global-scale processing of Sentinel-1 data for InSAR coherence estimation, the input to global biomass modeling, and the routine operational processing of SAR time-series data for national and international near real-time flood mapping. The cloud-scaling Software for Earth big data Processing, Prediction Modeling, and Organization (SEPPO), which is used to meet the processing challenges utilizing Amazon Web Services (AWS) for large volume and operational SAR data processing in complex workflows is introduced.

Accurate precipitation measurement is critical for managing flood and drought risks. Traditional meteorological tools, such as rain gauges and remote sensors, have limitations in resolution, coverage, and cost-effectiveness. Recently, the opportunistic use of microwave communication signals has been explored to improve precipitation estimation. While there is growing interest in using satellite-to-earth microwave links (SMLs) for machine learning-based precipitation estimation, direct rainfall estimation from raw signal-to-noise ratio (SNR) data via deep learning remains underexplored. This study investigates a range of machine learning (ML) approaches, including deep learning (DL) models and traditional methods like gradient boosting machine (GBM), for estimating rainfall rates from SNR data collected by interactive satellite receivers. We develop real-time models for rainfall detection and estimation using downlink SNR signals from satellites to user terminals. By leveraging a year-long dataset from multiple locations—including SNR measurements paired with disdrometer and rain-gauge data—we explore and evaluate various ML models. Our final models include ensemble approaches for both rainfall detection and cumulative rainfall estimation. The proposed models provide a reliable solution for estimating precipitation using Earth–satellite microwave links, potentially improving precipitation monitoring. Compared to the state-of-the-art power-law-based models applied to similar datasets reported in the literature, our ML models achieve a $46\%$ reduction in the root mean squared error (RMSE) for event-based cumulative precipitation predictions.

Opportunistic sensors are increasingly used for rainfall measurement. However, their raw data are collected by a variety of systems that are often not primarily intended for rainfall monitoring, resulting in a plethora of different data formats and a lack of common standards. This hinders the sharing of opportunistic sensing (OS) data, their automated processing, and, at the end, their practical usage and integration into standard observation systems. This paper summarises the experiences of the more than 100 members of the OpenSense Cost Action involved in the OS of rainfall. We review the current practice of collecting and storing precipitation OS data and corresponding metadata, and propose new common guidelines describing the requirements on data and metadata collection, harmonising naming conventions, and defining human-readable and machine readable file formats for data and metadata storage. We focus on three sensors identified by the OpenSense community as prominent representatives of the OS of precipitation: Commercial microwave links (CML): fixed point-to-point radio links mainly used as backhauling connections in telecommunication networks Satellite microwave links (SML): radio links between geostationary Earth orbit (GEO) satellites and ground user terminals. Personal weather stations (PWS): non-professional meteorological sensors owned by citizens. The conventions presented in this paper are primarily designed for storing, handling, and sharing historical time series and do not consider specific requirements for using OS data in real time for operational purposes. The conventions are already now accepted by the ever growing OpenSense community and represent an important step towards automated processing of OS raw data and community development of joint OS software packages.