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Using multi‐satellite observations, the cloud dynamic and microphysical characteristics were revealed during the rapid intensification (RI) of super‐typhoon Nanmadol (2022). As the storm intensifies, the eyewall contracts, the upper‐level divergence strengthens, and the cirrus cloud increases, leading to stronger upper‐level radial outflow and the vertical updraft. Meanwhile, it is found that there exists a dynamically attractive area in the outer rainbands, where particles grow effectively and form “a small amount of large particles” around 300 km from the eye. A theory of cloud dynamics‐microphysics interaction, called “tunnel theory,” is further proposed to explain the generation, accumulation, and concentrated downflow of large particles in the outer rainbands during RI. Results suggest the unique feature of particle distribution in the outer rainbands could be a potential indicator for RI. Plain Language Summary The rapid intensification (RI) of tropical cyclones (TCs) becomes more frequent in recent years, but the TC RI forecasts still remain challenging. Better understanding of the physical processes associated with RI of TCs would essentially improve its forecasting capability. The cloud dynamical and microphysical processes, especially their interactions that respond to RI are not well explored. In this study, the cloud macro and micro characteristics associated with RI of a super‐typhoon Nanmadol (2022) over the western Pacific are investigated using multiple satellites observations. The storm underwent RI during 15–16 September 2022, and it has wreaked havoc on Japan's most cities as it moved across the Japanese island afterward with a track length of about 1,120 km. It is found inside Nanmadol as well as other typhoons that a few large particles tend to occur in the outer rainbands during RI, due to the interaction of cloud dynamical and microphysical processes. Such unique feature of particle distribution in the outer rainbands could be a potential indicator for RI, and should also be paid attention to in model forecasting of typhoon precipitation. Key Points First satellite‐based observational study on the interaction of cloud dynamics and microphysics during typhoon rapid intensification (RI) The eyewall contracts, the upper‐level divergence strengthens, and the convection column increases, providing kinetic energy for typhoon RI A “tunnel theory” is proposed for the generation, accumulation, and downflow of large particles in the outer rainbands during typhoon RI

As satellite observation technology develops and the number of Earth observation (EO) satellites increases, satellite observations have become essential to developments in the understanding of the Earth and its environment. However, the current impacts to the remote sensing community of different EO satellite data and possible future trends of EO satellite data applications have not been systematically examined. In this paper, we review the impacts of and future trends in the use of EO satellite data based on an analysis of data from 15 EO satellites whose data are widely used. Articles that reference EO satellite missions included in the Web of Science core collection for 2020 were analyzed using scientometric analysis and meta-analysis. We found the following: (1) the number of publications and citations referencing EO satellites is increasing exponentially; however, the number of articles referencing AVHRR, SPOT, and TerraSAR is tending to decrease; (2) papers related to EO satellites are concentrated in a small number of journals: 43.79% of the articles that were reviewed were published in only 13 journals; and (3) remote sensing impact factor (RSIF), a new impact index, was constructed to measure the impacts of EO satellites and to predict future trends in applications of their data. Landsat, Sentinel, MODIS, Gaofen, and WorldView were found to be the most significant current EO satellite missions and MODIS data to have the widest range of applications. Over the next five years (2021–2025), it is expected that Sentinel will become the satellite mission with the greatest influence.

The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), is NASA’s latest reanalysis for the satellite era (1980 onward) using the Goddard Earth Observing System, version 5 (GEOS-5), Earth system model. MERRA-2 provides several improvements over its predecessor (MERRA-1), including aerosol assimilation for the entire period. MERRA-2 assimilates bias-corrected aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer and the Advanced Very High Resolution Radiometer instruments. Additionally, MERRA-2 assimilates (non bias corrected) AOD from the Multiangle Imaging SpectroRadiometer over bright surfaces and AOD from Aerosol Robotic Network sunphotometer stations. This paper, the second of a pair, summarizes the efforts to assess the quality of the MERRA-2 aerosol products. First, MERRA-2 aerosols are evaluated using independent observations. It is shown that the MERRA-2 absorption aerosol optical depth (AAOD) and ultraviolet aerosol index (AI) compare well with Ozone Monitoring Instrument observations. Next, aerosol vertical structure and surface fine particulate matter (PM2.5) are evaluated using available satellite, aircraft, and ground-based observations. While MERRA-2 generally compares well to these observations, the assimilation cannot correct for all deficiencies in the model (e.g., missing emissions). Such deficiencies can explain many of the biases with observations. Finally, a focus is placed on several major aerosol events to illustrate successes and weaknesses of the AOD assimilation: the Mount Pinatubo eruption, a Saharan dust transport episode, the California Rim Fire, and an extreme pollution event over China. The article concludes with a summary that points to best practices for using the MERRA-2 aerosol reanalysis in future studies.

Geostationary Extended Observations, or GeoXO, is NOAA’s future geostationary satellite constellation, set to launch in the early 2030s and operate into the 2050s. Given changes to the Earth system, improvements in technology, and expanding needs of satellite data users, GeoXO will extend NOAA’s current observation suite by adding three new instruments and one new spacecraft. Improved versions of the imager and lightning mapper will again be placed on East and West satellites, where they will monitor severe storms, tropical cyclones, fires, and other hazards. They will be joined by an ocean color instrument designed for detection of harmful algal blooms, phytoplankton, chlorophyll- a , and other constituents. The third geostationary spacecraft will be placed in the center of the United States and will carry a hyperspectral infrared sounder, an atmospheric composition instrument, and potentially a partner payload. Radiances from the sounder will be assimilated into numerical weather prediction models to improve forecasts, and sounder-derived retrievals of vertical profiles of temperature and water vapor will allow forecasters to detect and track areas of enhanced instability. Retrievals of pollutants such as nitrogen dioxide and ozone from the new atmospheric composition instrument along with trace gas measurements from the sounder will be used to improve air quality monitoring, forecasts, and warnings in addition to climate monitoring. Once complete, the GeoXO constellation will contribute to an international “geo ring” of satellites that will be used for worldwide weather, oceans, climate, and air quality monitoring. This revolutionary new geostationary satellite constellation will provide critical observations for a changing Earth system.

A high-resolution regional reanalysis of the Indian Monsoon Data Assimilation and Analysis (IMDAA) project is made available to researchers for deeper understanding of the Indian monsoon and its variability. This 12-km resolution reanalysis covering the satellite era from 1979 to 2018 using a 4D-Var data assimilation method and the U.K. Met Office Unified Model is presently the highest resolution atmospheric reanalysis carried out for the Indian monsoon region. Conventional and satellite observations from different sources are used, including Indian surface and upper air observations, of which some had not been used in any previous reanalyses. Various aspects of this reanalysis, including quality control and bias correction of observations, data assimilation system, land surface analysis, and verification of reanalysis products, are presented in this paper. Representation of important weather phenomena of each season over India in the IMDAA reanalysis verifies reasonably well against India Meteorological Department (IMD) observations and compares closely with ERA5. Salient features of the Indian summer monsoon are found to be well represented in the IMDAA reanalysis. Characteristics of major semipermanent summer monsoon features (e.g., low-level jet and tropical easterly jet) in IMDAA reanalysis are consistent with ERA5. The IMDAA reanalysis has captured the mean, interannual, and intraseasonal variability of summer monsoon rainfall fairly well. IMDAA produces a slightly cooler winter and a hotter summer than the observations; the reverse is true for ERA5. IMDAA captured the fine-scale features associated with a notable heavy rainfall episode over complex terrain. In this study, the fine grid spacing nature of IMDAA is compromised due to the lack of comparable resolution observations for verification.

This study presents a new global baseline of mangrove extent for 2010 and has been released as the first output of the Global Mangrove Watch (GMW) initiative. This is the first study to apply a globally consistent and automated method for mapping mangroves, identifying a global extent of 137,600 km 2 . The overall accuracy for mangrove extent was 94.0% with a 99% likelihood that the true value is between 93.6–94.5%, using 53,878 accuracy points across 20 sites distributed globally. Using the geographic regions of the Ramsar Convention on Wetlands, Asia has the highest proportion of mangroves with 38.7% of the global total, while Latin America and the Caribbean have 20.3%, Africa has 20.0%, Oceania has 11.9%, North America has 8.4% and the European Overseas Territories have 0.7%. The methodology developed is primarily based on the classification of ALOS PALSAR and Landsat sensor data, where a habitat mask was first generated, within which the classification of mangrove was undertaken using the Extremely Randomized Trees classifier. This new globally consistent baseline will also form the basis of a mangrove monitoring system using JAXA JERS-1 SAR, ALOS PALSAR and ALOS-2 PALSAR-2 radar data to assess mangrove change from 1996 to the present. However, when using the product, users should note that a minimum mapping unit of 1 ha is recommended and that the error increases in regions of disturbance and where narrow strips or smaller fragmented areas of mangroves are present. Artefacts due to cloud cover and the Landsat-7 SLC-off error are also present in some areas, particularly regions of West Africa due to the lack of Landsat-5 data and persistence cloud cover. In the future, consideration will be given to the production of a new global baseline based on 10 m Sentinel-2 composites.

Current global historical reanalyzes prevent to adequately examine the role of the near‐core surface wind structural properties on tropical cyclones climate trends. Here we provide theoretical and observational evidences that they are crucial for the monitoring of integrated kinetic energy. The kinetic energy balance is reduced to a simple rule involving two parameters characterizing the surface wind structure and directly suggested by the governing equations. The theory is uniquely verified with a database of high‐resolution ocean surface winds estimated from all‐weather spaceborne synthetic aperture radar. Such measurements provide indirect estimates of a multiplicative constant modulating the kinetic energy balance and associated with the system thermodynamics. Consequently, accumulated high‐resolution acquisitions of the ocean surface shall allow to better monitor the integrated kinetic energy and provide new means to tackle climatological studies of tropical cyclones destructiveness. Plain Language Summary Studying the long‐term climate trends of tropical cyclones is challenging because the historical data is not always reliable. One particular issue concerns the accurate reporting of surface wind properties near the core of these storms in past and present records. This study uses both theory and high‐resolution surface wind observations from satellite radar to highlight the importance of investigating these properties, specifically for monitoring the total energy, which is a measure of a storm destructive potential. Two spatial scales describing the tropical cyclone wind structure are identified and may be efficiently measured thanks to the high‐resolution sensor. The storm energy equilibrium is shown to be controlled by these two spatial scales, in both theory and observations. This equilibrium is also influenced by the temperature characteristics of a storm, which are themselves modulated by environmental and climatological conditions. Consequently, future high‐resolution observations from the satellite radar should help better understanding the dependence of integrated kinetic energy with space and time. Key Points High‐resolution spaceborne synthetic aperture radar measurements inform on the tropical cyclone kinetic energy balance The tropical cyclone integrated kinetic energy balance is controlled by the surface wind decay and thermodynamical characteristics Accumulating high‐resolution surface wind measurements shall allow to better assess trends in the tropical cyclone destructive potential

Electrostatic electron cyclotron harmonic (ECH) waves have been analyzed using different satellite data. However, single‐mission studies prevent a systematic understanding of the emissions in Earth's magnetosphere. We perform a comprehensive survey of ECH waves using observations from Van Allen Probes, Arase, and MMS satellites spanning over 2012–2023. Our results indicate that these waves cover a broad spatial region of L = 3–15, |MLAT| < ∼40°, and nearly all magnetic local time sectors, showing a pronounced regional dependence. In the inner magnetosphere (L < ∼6), ECH wave power peaks in the premidnight‐to‐noon sectors, while the waves in outer regions (L > 6) exhibit dayside‐maximized occurrence rates. Wave amplitudes are strongest on the nightside but display a secondary pre‐noon peak at L > 8. In addition, ECH waves are predominantly confined near the equator (|MLAT| < 5°) at L < ∼8, in contrast to the much broader latitudinal distribution (|MLAT| < 35°) at higher L.

The Joint Center for Satellite Data Assimilation (JCSDA) Community Radiative Transfer Model (CRTM) is a fast, 1D radiative transfer model used in numerical weather prediction, calibration/validation, etc., across multiple federal agencies and universities. The key benefit of the CRTM is that it is a satellite simulator. It provides a highly accurate representation of satellite radiances by using the specific sensor response functions convolved with a Line-by-Line Radiative Transfer Model (LBLRTM). CRTM covers the spectral ranges consistent with all present operational and most research satellites, from visible to microwave. The capability to simulate ultraviolet radiances and support space-based radar sensors is being added over the next 2 years in CRTM version 3.0. In addition to simulated radiances, the CRTM also provides Jacobian outputs needed to interpret satellite observations for numerical weather prediction. The Jacobian estimates how changes in geophysical parameters affect simulated measurements from satellite sensors. Using the Jacobian in modeling and weather prediction improves the accuracy and efficiency of data analysis, leading to better weather predictions. The CRTM model’s success and growth depend on community contributions and evaluation. To facilitate this, we have made the CRTM highly accessible through modular programming, clear documentation and tutorials, public domain licensing, unfettered public access via GitHub, and a clear path to operational implementation for innovative research. We encourage and welcome contributions from the community to help us continue to improve the CRTM.

Wildfires are the largest terrestrial source of atmospheric ammonia (NH3), yet their impacts on NH3 concentrations and ammonium (NH4+) deposition remain poorly quantified. In this study, we evaluate the effects of the record‐breaking 2023 Canadian wildfire season on NH3 concentration and NH4+ deposition across the Upper Midwest. This study integrates satellite observations, ground‐based data, and in situ aircraft measurements. In May–June 2023, NH3 concentrations increased at 83% of ground sites, and NH4+ deposition flux rose at 100% of ground sites in the Upper Midwest. Satellite data showed significantly higher column‐averaged NH3 in 47% of grid cells in the Upper Midwest. On 1 August, a smoke plume over the Midwest corresponded with an AEROMMA flight observing enhanced NH3, NH4+, carbon monoxide, and acetonitrile. These findings highlight the substantial impact of wildfire smoke on NH3 and NH4+ at regional scales, with implications for nitrogen cycling, air quality, and atmospheric modeling.