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Vertical profiles of nitric oxide (NO) concentration are derived between 120 and 250 km using updated NO emission rates measured by Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) instrument on the NASA Thermosphere Ionosphere Mesosphere Energetics and Dynamics satellite. The Naval Research Laboratory Mass Spectrometer Incoherent Scatter Radar (MSIS) 2.1 model is used to provide the required parameters of temperature, atomic oxygen number density, and molecule oxygen number density needed to derive the NO concentrations using a non‐local thermodynamic equilibrium (non‐LTE) model. The SABER NO concentration shows a significant correlation with solar activity with larger peak NO concentrations and higher altitude extent during solar maximum years compared to those during the solar minimum years. The SABER NO agrees well with the MSIS 2.1 NO at altitudes above 120 km for all latitudes, while the pronounced SABER‐MSIS NO discrepancy below 120 km is likely due to the temperature underestimation by MSIS 2.1. A detailed error analysis is presented and considers systematic and random errors in all the terms in the non‐LTE model used to derive the NO concentration. Random error in MSIS 2.1 temperature and atomic oxygen dominates the uncertainty in single NO profiles above 120 km. We estimated a systematic error up to ∼36% between 120 and 250 km during solar maximum years. Plain Language Summary: Nitric oxide (NO) is a minor constituent of the Earth's thermosphere and regulates the energy budget by emitting infrared radiation that cools the region. Understanding the distribution of the NO concentration is important in understanding the energy redistribution in the Earth's upper atmosphere. In this paper, a new NO concentration data set between 120 and 250 km from January 2002 to the present day is derived from the latest Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) NO emission measurements and Mass Spectrometer Incoherent Scatter Radar (MSIS) 2.1 empirical model of temperature and O, and O2 number density. The derived SABER NO concentration varies with solar activity. The SABER NO agrees well with the empirical MSIS 2.1 NO between 120 and 250 km. Below 120 km, the temperature may be warmer than predicted by MSIS 2.1, leading to a substantial difference between SABER NO and MSIS 2.1 NO. A comprehensive error analysis considering systematic and random errors in all the terms in the model used to derive the NO concentration from SABER data shows that the systematic uncertainty of the SABER NO density is up to 36% above 120 km. Key Points: The nitric oxide (NO) 5.3 μm infrared emission rates derived from Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) measurements have been updatedVertical profiles of NO concentration, spanning 20+ years, are derived from the SABER NO emission rates, and Mass Spectrometer Incoherent Scatter Radar (MSIS) 2.1 T, n(O), and n(O2)The SABER NO concentrations exhibit good agreement (±30%) with those in the MSIS 2.1 empirical model between 120 and 250 km [ABSTRACT FROM AUTHOR]

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]