Search

Satellite infrared detectors cannot penetrate clouds, especially precipitating clouds. Improving precipitation estimation accuracy based on infrared brightness temperature has always been important but challenging. In this paper, based on the infrared brightness temperature of the Advanced Geosynchronous Radiation Imager (AGRI) onboard China's Feng‐Yun 4A satellite, we develop and evaluate a new precipitation estimation method. First, using static data, physical characteristics of clouds, cloud image texture features, temporal motion features, and AGRI infrared channel brightness temperature, we construct features for a machine learning model. Then, we develop precipitation estimation methods. Precipitation is estimated in two steps: classification and regression. We employ a random forest classification model to identify whether there is precipitation in a given field of view. If there is precipitation, a multi‐model ensemble regression learning method is used to estimate the areas with this precipitation. The ensemble learning method uses convex optimization to integrate prediction results based on the optimization of hyperparameters of five basic models (i.e., those of random forest, XGBoost, LightGBM, decision tree, and extra tree models). Furthermore, two regression stacking ensemble models—the Least Absolute Shrinkage and Selection Operator (herein referred to as Stacking1‐LASSO) and K‐nearest neighbor (herein referred to as Stacking2‐KNN)—are used to predict the results of the aforementioned basic models. The results of basic models are used as inputs of these two stacking models. Finally, based on the Integrated Multi‐satellitE Retrievals for GPM (IMERG) precipitation product and rain gauge precipitation data, we conduct precipitation estimation experiments and evaluate our methods. The results show that ensemble learning models have greater accuracy in estimating precipitation than the basic models. When using IMERG precipitation as the target precipitation, ensemble learning models can estimate the central area of heavy precipitation during typhoons Ampil and Maria. The ensemble learning estimation effect is better than that of Stacking2‐KNN. Moreover, when rain gauge data is used as the target precipitation, ensemble learning can also estimate the center of heavy precipitation and with good consistency with recorded satellite brightness temperature data. Key Points: A convex optimization weighted and stacked ensemble learning method for satellite data estimation of precipitation is developedNear real‐time precipitation estimates can be obtained throughout the day using only geographic information and Advanced Geosynchronous Radiation Imager infrared brightness temperature dataThe proposed ensemble learning method can estimate the central areas of typhoons and short‐term heavy rainfall [ABSTRACT FROM AUTHOR]

Microphysical observations of precipitating particles are critical data sources for numerical weather prediction models and remote sensing retrieval algorithms. However, obtaining coherent data sets of particle microphysics is challenging as they are often unindexed, distributed across disparate institutions, and have not undergone a uniform quality control process. This work introduces a unified, comprehensive Northern Hemisphere particle microphysical data set from the National Aeronautics and Space Administration precipitation imaging package (PIP), accessible in a standardized data format and stored in a centralized, public repository. Data is collected from 10 measurement sites spanning 34° latitude (37°N–71°N) over 10 years (2014–2023), which comprise a set of 1,070,000 precipitating minutes. The provided data set includes measurements of a suite of microphysical attributes for both rain and snow, including distributions of particle size, vertical velocity, and effective density, along with higher‐order products including an approximation of volume‐weighted equivalent particle densities, liquid equivalent snowfall, and rainfall rate estimates. The data underwent a rigorous standardization and quality assurance process to filter out erroneous observations to produce a self‐describing, scalable, and achievable data set. Case study analyses demonstrate the capabilities of the data set in identifying physical processes like precipitation phase‐changes at high temporal resolution. Bulk precipitation characteristics from a multi‐site intercomparison also highlight distinct microphysical properties unique to each location. This curated PIP data set is a robust database of high‐quality particle microphysical observations for constraining future precipitation retrieval algorithms, and offers new insights toward better understanding regional and seasonal differences in bulk precipitation characteristics. Plain Language Summary: This work introduces a new particle microphysics data set that is useful for improving weather prediction models and in enhancing precipitation estimation techniques. The data set, produced from National Aeronautics and Space Administration's precipitation imaging package, is comprehensive, well documented, and easy to access. It includes observations from 10 locations across the Northern Hemisphere over 10 years, providing information on both rain and snow. This information includes details like particle size, speed, and density, as well as estimates of rainfall and snowfall rates. The data has been standardized and checked for quality, making it reliable and easy to use. This product is a valuable resource for refining methods to measure precipitation, and offers new insights into regional and seasonal precipitation patterns. Key Points: This data set contains high temporal resolution, disdrometer‐derived precipitation microphysics observations from 10 sites over 10 yearsRigorous quality control practices yield a scalable, self‐describing data set packaged into a common, standardized NetCDF formatThe data's diverse geographic and environmental coverage offers new insights into regional and seasonal precipitation processes and patterns [ABSTRACT FROM AUTHOR]

Model output from climate projections often requires bias‐adjustment to compensate for systematic model errors. A bias‐adjustment method for extreme precipitation intensity is proposed that preserves the scaling equation for different accumulation levels from hourly to daily, using intensity‐duration‐frequency (IDF) modeling. A validation is performed within a pseudo‐reality setting, based on hourly precipitation from 28 regional climate model projections of the EURO‐CORDEX ensemble over Belgium. The scaling‐based adjustment methods improve upon previous methods, an optimal method is identified, and, analytical quantile mapping methods must be avoided due to three identified problems. The ensemble mean of the adjusted extreme precipitation intensity obeys the above‐mentioned scale‐invariance property, which is consistent with observed extreme intensities. We thus show that IDF modeling provides added value in the context of bias‐adjustment, and, that the particular IDF model proposed balances well between accuracy and the preservation of desired properties such as scale invariance and consistency among rainfall durations. Plain Language Summary: Heavy precipitation is known to have major impacts on society, including crop damage, soil erosion, landslides, and increased flood risk. In recent years, there is a growing body of evidence that human‐induced climate change has intensified extreme precipitation on the global scale. Both regional and global climate models predict that this will continue as the climate warms. Nevertheless, trend analysis or climate‐change attribution of local rainfall extremes remains particularly difficult for many reasons including high variability in space and time. Therefore it is essential to use statistical methods that make use of all available information. Climate model simulations are known to exhibit systematic biases. Here, for the first time, we use a well‐established technique in hydrology (intensity‐duration‐frequency modeling) to adjust extreme precipitation intensity as a function of the rainfall duration and event probability. Validation results with a large multi‐model ensemble illustrate that the new method improves upon existing approaches. As an illustration, we present a bias‐adjusted multi‐model climate ensemble for future projections of heavy rainfall statistics over Belgium. Key Points: New bias‐adjustment methods for subdaily extreme precipitation consistent across different precipitation durationsPseudo‐reality cross‐validation shows higher skill than existing bias‐adjustment methods for subdaily extremesApplication to regional climate model projections over Belgium shows, despite large uncertainties, consistent future increase in subdaily precipitation extremes [ABSTRACT FROM AUTHOR]

Rise in mean temperature put a great deal of uncertainty about how weather and climate extremes may play out, particularly in India's varied climatic zones. Consequently, it is important to understand the possible changes in both magnitude and direction of weather and climate extremes like rainfall for different warming levels of 1.5 and 2°C scenarios concerning preindustrial and present levels. Hence in the present study, the precipitation behavior of seven North Indian states that is, Haryana, Himachal Pradesh, J&K, Punjab, Rajasthan, Uttar Pradesh, and Uttarakhand carefully studied using CMIP5 models. Future projections of precipitation has been done for the Paris Agreement global warming level of 1.5 and 2°C scenarios. Along with model validation and future projections of precipitation, the return period of extreme rainfall is also discussed to understand the behavior of the occurrence of extreme precipitation. Statistical analysis shows that the ensemble means have the least error as compared to the other six CMIP5 models. Therefore, future analysis has been done with the ensemble mean. Our findings show that the precipitation is likely to decrease in the 1.5°C scenarios, while it is likely to increase in the 2°C scenarios. The occurrence and intensity of extreme rainfall events are likely to be more frequent in all the models. The return period of the extreme rainfall events is likely to increase in all the states in both the warming scenarios. A three‐fold rise is likely to increase extreme rainfall events in the 2°C scenario. Key Points: Study performs a detailed analysis of rainfall behavior of the northern states India during preindustrial, present, 1.5 and 2° scenariosSix climate models were selected out of 26 CMIP5 climate models based on the capability of representing the monsoon rainfall over IndiaIn the 2°C scenario, the recurrence period for intense rainfall events will likely to increase by a factor of 3 [ABSTRACT FROM AUTHOR]