Since 2014, space agencies have launched advanced meteorological imagers into the geostationary (GEO) orbit encircling Earth's equator, known as the GEO-Ring. JMA, NOAA, and KMA launched imagers measuring 16 spectral bands with thermal resolutions of 2 km and full-disk coverage every 10 min. China Meteorological Administration's (CMA's) Fengyun-4A (FY-4A) series, launched in 2016, observes 14 bands with 4-km thermal resolution and 15-min full-disk scans. In 2022, EUMETSAT introduced the Meteosat Third Generation (MTG) imager, offering 16 channels, 2-km thermal resolution, and 10-min full-disk coverage. Together, these satellites provide near-global coverage with improved capabilities over earlier generations. The 10-12 common channels across the latest imagers enable retrieval of diverse atmospheric variables at high temporal resolution. These data represent a substantial advance beyond the early 1980s when the International Satellite Cloud Climatology Project (ISCCP) was first developed. The challenge facing any new GEO-Ring project, such as one being planned as part of a next generation of ISCCP (ISCCP-NG), is to define a new baseline from these measurements and processing methods to extract meaningful information for the scientific community in the coming decades. This paper outlines the design of a GEO-Ring radiance project to support a future ISCCP-NG and many other applications and emphasizes the benefits compared to the B1 and B3 data used in ISCCP.

Fundamental climate data records rely on consistent satellite observations, yet long-term geostationary (GEO) datasets are affected by various image anomalies. This work provides the most extensive overview to date of anomalies in the GEO-ring archive spanning five decades of data from; Meteorological Satellite (Meteosat), Himawari, Synchronous Meteorological Satellite (SMS), and Geostationary Operational Environmental Satellite (GOES) satellites. This work provides a common reference for the community working with long-term geostationary archives. In total, 18 generic anomaly types have been identified, recurring across sensors. Furthermore, 14 specific anomaly types have been identified, which are limited to individual satellites. Each anomaly is described and illustrated, including the affected channels and satellite platforms. This catalogue provides a common reference for the analysis and interpretation of long-term geostationary image archives and forms the basis for the ongoing development of the GEO-ring Image Anomaly Detection software, which will support the production of fundamental climate data records by automatically identifying and masking anomalous observations. Future work will use this framework to quantify the prevalence of the identified anomaly types across the complete GEO-ring archive.

Upper tropospheric water vapour plays a crucial role in the climate system by providing a strong positive feedback, particularly in the tropics. Upper Tropospheric Humidity (UTH) is strongly linked to large-scale atmospheric circulation, including the Hadley and Walker circulations, which undergo pronounced modulation during El Niño–Southern Oscillation (ENSO) events. In this study, we examine ENSO-related changes in the tropical distribution of UTH using long-term climatological datasets of UTH and sea surface temperature (SST). Significant upper-tropospheric drying (moistening) during El Niño (La Niña) years is observed over the Maritime Continent, the western Pacific, and the Indian subcontinent. These UTH anomalies are accompanied by corresponding negative (positive) anomalies in precipitation and upper-level cloud fractions, indicating a strong coupling between UTH and tropical convection. However, the statistical significance of these signals over the Indian subcontinent is limited, suggesting that ENSO influences UTH over India indirectly, likely mediated by regional circulation and monsoon dynamics. Overall, our results highlight ENSO as a key driver of tropical UTH variability through its impact on atmospheric circulation and convection.

ABSTRACTThe west coast of India has recently been experiencing torrential monsoon rains, a trend that studies indicate is likely to continue under future warming scenarios. This study investigates the link between moisture flux and extreme rainfall over the west coast, using observational and reanalysis datasets for the monsoon seasons (June to September) from 1990 to 2023. The analysis shows that, over the Indian subcontinent, rainfall along the west coast is primarily influenced by large‐scale moisture flux from the Arabian Sea. By decomposing the vertically integrated moisture flux into dynamic and thermodynamic components, this study observes that the thermodynamic component of moisture flux exhibits an increasing trend over the southwest coast, while this increasing trend is more prominent for the dynamic component over the northwest coast. Extreme rainfall over the southwest coast is increasing at a rate of 0.23 mm per season, attributed primarily to the increase in the thermodynamic component of moisture flux. It is observed that the rate of sea surface temperature (SST) increase over the Arabian Sea is faster than over the Bay of Bengal, with the average SST over the southeast Arabian Sea exceeding 28°C in recent years. Observations indicate that warming over the southeast Arabian Sea is strongly coupled with moisture accumulation observed over the southwest coast. This study provides strong evidence of a link between moisture transport, extreme rainfall and SST, identifying the southwest coast as a region vulnerable to climate change. Over the northwest coast, the incidence of extreme rainfall is associated with the strengthening of dynamic processes, and the mean monsoon rainfall in this region is increasing in alignment with the rising dynamic component of moisture flux.

Atmospheric Motion Vectors (AMVs) are nearly continuous wind data estimated using satellites, derived from tracking cloud movements and water vapour gradients via sequential geostationary or polar satellite imagery. AMVs have been an integral part of Numerical Weather Prediction (NWP) since the early years, and hence, ensuring their quality is of utmost importance. This work utilizes the observations from the 205 MHz Stratosphere-Troposphere (ST) wind profiler radar placed at the Advanced Centre for Atmospheric Radar Research (ACARR) in Cochin (10.04$<^> \circ $ degrees N, 76.33$<^> \circ $ degrees E), India, to validate 3 years (2017-2019) of AMV data from the Indian Ocean Data Coverage (IODC) mission Climate Data Records (CDR) from Meteosat-8. The AMVs are classified into different atmospheric levels based on their pressure: lower, middle, and upper, and compared with collocated radar wind measurements. A detailed analysis was performed only on upper-level winds as filtering out low-quality AMVs significantly reduced the number of collocations in lower and middle levels. A strong agreement was observed between satellite and radar upper-level wind measurements with biases of $0.79 \pm 5.9$0.79 +/- 5.9 m ${{\rm{s}}<^>{ - {\rm{1}}}}$s-1 and 3.07$ \pm $+/- 35.6$<^> \circ $ degrees in wind speed and direction, respectively. Seasonal variability is seen in the wind speed discrepancies, such as more in winter and summer than in spring and autumn, and it can be attributed to the larger vertical shear during summer and winter. The maximum error in upper-level AMV height assignment is quantified to $ \pm 4$+/- 4 km. The observed differences may arise because satellite-derived AMV heights tend to be overestimated (underestimated) in low (high) wind speed conditions.

Earth's energy budget defines the balance between the incoming radiant solar energy reaching Earth and the energy returning to outer space. Clouds play a significant role in Earth's energy budget. Cloud Radiative Forcing (CRF) is the difference between the radiative fluxes at the top of the atmosphere in clear-sky and all-sky conditions. Clouds introduce two contrasting effects on the Earth's energy balance: the albedo effect and the longwave effect. Clouds reflect a large amount of incoming shortwave radiation and cool the Earth, known as the Albedo effect. The energy associated with the albedo effect is known as shortwave cloud radiative forcing (SWCRF). The longwave effect or longwave cloud radiative forcing (LWCRF) denotes the warming of Earth by the cloud-trapped longwave radiation that would otherwise escape to space. Understanding the variability in the amount and distribution of clouds in a warming climate is essential as they modulate the shortwave and longwave cloud radiative feedbacks (Harrison et al., 1990; Bony, S. et al., 2006) and, thereby, the Net CRF. Upper Tropospheric Humidity (UTH) is a vital climate variable that impacts the amount of outgoing longwave radiation. In the tropics, UTH is mainly driven by deep convection. The present study analyzes the influence of UTH on the longwave cloud radiative forcing in the tropics from 2000 to 2021. This study uses the satellite microwave (MW) and infrared (IR) UTH measurements. Clouds affect IR UTH measurements, while MW measurements provide UTH under all sky conditions. Clouds and the Earth's Radiant Energy System (CERES) satellite datasets are used to calculate cloud radiative forcing. This study quantifies the UTH-LWCRF relationship and shows that UTH can explain LWCRF variability in the tropics to a large extent. The joint distribution analysis shows that UTH has a significant impact on the variability of LWCRF over land, whereas over ocean regions, sea surface temperature plays a role in modulating the UTH-LWCRF relationship. Also, the UTH-LWCRF relationship is better represented with MW UTH than IR UTH, which can be attributed to the more comprehensive and accurate MW measurements even in cloudy conditions.

The essential climate variables (ECVs) defined by the Global Climate Observing System (GCOS) of the World Meteorological Organization (WMO) are the baseline for establishing and maintaining a global observing system that critically contributes to characterizing Earth's climate. The ECVs describe parts of the physical, chemical, and biological Earth system. Originally, scientists developed and used individual ECV products for the analysis of the climate system and its changes. In recent years, the focus has shifted to using combined (i.e., multiple) and in-filled (i.e., without gap) ECV products, e.g., to analyze the energy, water, and carbon cycles. Requirements for ECV products have been established by the GCOS. Although the requirements include spatial and temporal resolution and product, they often lack a detailed breakdown per application beyond a few broad areas. This paper presents a universal and statistically rigorous procedure to determine-for any ECV product at any location on Earth-the observational requirements of different climate applications while considering variability due to natural and anthropogenic sources and due to observational limitations. We provide a categorization of known types of climate applications. Furthermore, we discuss the sources of natural and anthropogenic variability of ECVs and of uncertainties in observational ECV products, such as carbon dioxide mole fraction, sea surface temperature, and accumulated precipitation, and show how to quantify these. Finally, the presented procedure is applied to illustrate how observational requirements of a sea surface temperature product can be determined for a regional and global climate application. The procedure, however, may apply to observational ECV products from any source including from in situ measurements. SIGNIFICANCE STATEMENT: The paper addresses the request of the Global Climate Observing System (GCOS) for transparent and quantitative information on the quality of its essential climate variable (ECV) products and verifying their suitability for different climate applications. Beyond describing its international embedding, this position paper proposes a universal and statistically rigorous procedure to assess the feasibility of an ECV product for any climate application at any location on Earth. The procedure-which is based on metrological principles and assumes Gaussian distributed anomalies over time-considers both natural and anthropogenic variability of the ECV as well as observational limitations of the ECV product. Although the procedure is demonstrated for satellite-based ECV products, it can also be applied to other measurement datasets.

This study investigates and quantifies the characteristics of Mesoscale Convective Systems (MCSs) associated with extreme rainfall events that occurred in 2018, 2019, and 2024, and compares them to heavy rainfall events that took place between 2020 and 2021, with a focus on the southwest coast of India, which has been experiencing devastating torrential rains since 2018, resulting in significant loss of life and property. The MCSs are tracked for these events using high temporal (15-min) and spatial resolution (3 km) Meteosat SEVIRI geostationary satellite observations. The MCS characteristics for the years marked by extreme rain events 2018, 2019, and 2024 stand out as unique, exhibiting an expansive area of approximately 1010 to 1011 m2. The extreme event that occurred on 29 July 2024 was catastrophic, triggering an enormous landslide in Wayanad, a northern district of Kerala, early on 30 July 2024, claiming over 300 lives, with many others still unaccounted for. Compared to 2019, the extreme rainfall event of 2024 is particularly prominent, with a strong clustering of MCS proximal to the coast. The observations indicate that the precipitation associated with 2024 is more severe than it was in 2019. The study highlights that the transition from heavy to extreme heavy rainfall over the southwest coast is facilitated by the aggregation of massive MCS over the southwest coast. The clustering of MCS is a proxy for strong moisture convergence over the southwest coast, which can aid in robust ascending motions and ultimately, extreme rainfall. Through this study, we emphasize the importance of real-time monitoring of MCS over the southwest coast in the current scenario of recurring extreme events over this region.

In the context of recent anomalous behavior of the Indian summer monsoon, this study investigates the long-term changes in deep convective clouds and extreme rainfall during the summer monsoon periods from 2000 to 2020. Through the analysis of a long-term, climate-quality satellite data set, we provide observational evidence of significant increases in the cloud top height of deep convective clouds over the Indian subcontinent and adjacent oceanic regions. These regions also exhibit substantial increases in the frequency of occurrence of deep convective clouds. The observations indicate that the deep convective cloud top temperature decreases at approximately 4 K per decade. Our analysis reveals that these changes have accelerated markedly in the recent decade, especially post-2010. It is observed that there has been a significant increase in extreme monsoon rainfall, across the Indian subcontinent during the years 2000-2020. This emphasizes the connection between the intensification of deep convective clouds and monsoon extremes.

The work performed in this study evaluated the application of generalized pretrained object detection models for the identification and classification of tropical storm (TS) systems through transfer learning. While the majority of literature focuses on developing bespoke models for this application, these typically require significantly more training data, compute resources, and time to train the models due to the large number of parameters the model has to tune to achieve similar results. These models also required additional preprocessing steps, such as extracting the storm from the image, and used a limited number of classes to describe the intensity of the storms. The approach presented here used considerably less data than the majority of other work (2–10x fewer input images) and a larger number of classes. The accuracies of the produced models trained on four different experimental datasets (varying the amount of data and number of classes) through this approach were 75%, 82%, 69%, and 89%. Overall, the models produced promising results, performing approximately equal to the bespoke models with scope to improve the performance of the model.

In the tropics, deep convection, which is often organized into convective systems, plays a crucial role in the water and energy cycles by significantly contributing to surface precipitation and forming upper-level ice clouds. The arrangement of these deep convective systems, as well as their individual properties, has recently been recognized as a key feature of the tropical climate. Using data from Africa and the tropical Atlantic Ocean as a case study, recent shifts in convective organization have been analyzed through a well-curated, unique record of METEOSAT observations spanning four decades. The findings indicate a significant shift in the occurrence of deep convective systems, characterized by a decrease in large, short-lived systems and an increase in smaller, longer-lived ones. This shift, combined with a nearly constant deep cloud fraction over the same period, highlights a notable change in convective organization. These new observational insights are valuable for refining emerging kilometer-scale climate models that accurately represent individual convective systems but struggle to realistically simulate their overall arrangement.

Abstract Most coupled model simulations substantially overestimate tropical tropospheric warming trends over the satellite era, undermining the reliability of model-projected future climate change. Here we show that the model-observation discrepancy over the satellite era has arisen in large part from multi-decadal climate variability and residual biases in the satellite record. Analyses indicate that although the discrepancy is closely linked to multi-decadal variability in the tropical Pacific sea surface temperatures, the overestimation remains over the satellite era in model simulations forced by observed time-varying sea surface temperatures with a La Niña-like pattern. Regarding moist thermodynamic processes governing tropical tropospheric warming, however, we find a broad model-observation consistency over a post-war period, suggesting that residual biases in the satellite record may contribute to model-observation discrepancy. These results underscore the importance of sustaining an accurate long-term observing system as well as constraining the model representation of tropical Pacific sea surface temperature change and variability.

This study focuses on the changes in the upper tropospheric humidity (UTH) associated with two different extreme precipitation conditions for the period 2000–2019 over the Indian summer monsoon region. The analysis embodies UTH datasets derived from microwave sounders on‐board NOAA and MetOp‐A polar‐orbiting satellites. The circulation characteristics in the upper troposphere are studied using the high‐resolution ERA5 reanalysis data. The analysis of UTH variability over the Indian region shows a unique positive (negative) UTH anomaly patch extending from northwestern regions of India to the northern Arabian Sea for the enhanced (deficient) rainfall days over central India during the southwest monsoon period. The investigation reveals that deep convection alone does not impact the UTH variability. Rather the circulation in the upper troposphere also plays a crucial role in UTH distribution. The dynamics in the upper troposphere cause large‐scale dispersal of both wet and dry air in the upper troposphere, which is linked to the strengthening/weakening of Asian monsoon anticyclone. The study indicates that monsoon extremes exhibit a distinct moisture distribution pattern in the upper troposphere, influenced by upper‐level dynamics, which are associated with the intensity of the Asian monsoon anticyclone.

This dataset is published in support of a tentative journal publication in a peer-reviewed journal. The full data record is scheduled for release under DOI:10.15770/EUM_SEC_CLM_0086

Abstract Climate services are largely supported by climate reanalyses and by satellite Fundamental (Climate) Data Records (F(C)DRs). This paper demonstrates how the development and the uptake of F(C)DR benefit from radiance simulations, using reanalyses and radiative transfer models. We identify three classes of applications, with examples for each application class. The first application is to validate assumptions during F(C)DR development. Hereto we show the value of applying advanced quality controls to geostationary European (Meteosat) images. We also show the value of a cloud mask to study the spatio‐temporal coherence of the impact of the Mount Pinatubo volcanic eruption between Advanced Very High Resolution Radiometer (AVHRR) and the High‐resolution Infrared Radiation Sounder (HIRS) data. The second application is to assess the coherence between reanalyses and observations. Hereto we show the capability of reanalyses to reconstruct spectra observed by the Spektrometer Interferometer (SI‐1) flown on a Soviet satellite in 1979. We also present a first attempt to estimate the random uncertainties from this instrument. Finally, we investigate how advanced bias correction can help to improve the coherence between reanalysis and Nimbus‐3 Medium‐Resolution Infrared Radiometer (MRIR) in 1969. The third application is to inform F(C)DR users about particular quality aspects. We show how simulations can help to make a better‐informed use of the corresponding F(C)DR, taking as examples the Nimbus‐7 Scanning Multichannel Microwave Radiometer (SMMR), the Meteosat Second Generation (MSG) imager, and the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave Water Vapor Profiler (SSM/T‐2).

&lt;p&gt;The utilisation of observations of past, present, and future geostationary satellites for climate monitoring is a challenge. Since the late 1970s, space agencies operated up to 50 geostationary satellite missions with a variety of instrumentation. Merging these observations in a quasi-global geostationary &#039;ring&#039; data record is essential for the provision of satellite-based data records of Essential Climate Variables (ECVs). EUMETSAT is engaged in data rescue, uncertainty characterisation, recalibration, and harmonisation of these observations and aims at the provision of the data to users on its joint EUMETSAT-ECMWF cloud infrastructure the so called European Weather Cloud and the EUMETSAT Data Store. The process of preparing satellite data for climate monitoring and analysis - such as undertaken by WCRP&amp;#8217;s project GEWEX - is tedious and only recently being recognised as fundamental first step in preparing records ECVs from these data.&amp;#160;&lt;/p&gt;&lt;p&gt;Past and present geostationary data come with the possibility of unforeseen radiometric, geometric, and metadata anomalies. These anomalies may be related to the instrument or the data processing. EUMETSAT developed a system that performs an automatic anomaly analysis to the observations of past and present Meteosat and JMA satellites. The system is able to detect the most common types of anomalies with a high probability of detection and low false alarm rate. The anomalies are stored in a data base so as to inform downstream processing. As the anomalies are flagged on a pixel-by-pixel basis the loss of data is kept to a minimum.&lt;/p&gt;&lt;p&gt;EUMETSAT recalibrated its anomaly screened infrared channel observations from MVIRI on Meteosat First Generation (MFG) and SEVIRI on Meteosat Second Generation (MSG) measurements against IASI, AIRS, and HIRS measurements. The recalibration improved the radiometric accuracy of MVIRI and SEVIR to less than 0.5 K. Such improvements allow the seamless use of these observations for the retrievals of ECVs data records from geostationary orbit covering more than 40 years. Similarly, EUMETSAT applied its recalibration approach to the instruments operated on JMA&amp;#8217;s geostationary satellites, resulting in similar improvements as made for the Meteosat satellites. Regarding satellite data quality, first steps have been made to provide recalibrated data with quantitative uncertainty estimates, as developed in the framework of the EU-H2020 FIDUCEO project. Such estimates add another dimension of quality information that is essential to make a data record a true climate data record. &amp;#160;With the aim to close the geostationary &#039;ring&#039;, EUMETSAT and NOAA now started applying the methods presented above to the US geostationary sensor data as well.&lt;/p&gt;&lt;p&gt;Once available, the individual time-series of recalibrated geostationary satellite data of the three collaborating organisations (EUMETSAT, JMA, and NOAA) will be quality controlled, cross-calibrated and merged into a single geostationary &amp;#8216;ring&amp;#8217; product. Hereto the methods developed by the ISCPP-NG will be used. The collaborating organisations plan to use the cloud computing infrastructure to work on the data that are distributed over three continents.&lt;/p&gt;

The spectral long-wave feedback parameter represents how Earth’s outgoing long-wave radiation adjusts to temperature changes and directly impacts Earth’s climate sensitivity. Most research so far has focused on the spectral integral of the feedback parameter. Spectrally resolving the feedback parameter permits inferring information about the vertical distribution of long-wave feedbacks, thus gaining a better understanding of the underlying processes. However, investigations of the spectral long-wave feedback parameter have so far been limited mostly to model studies. Here we show that it is possible to directly observe the global mean all-sky spectral long-wave feedback parameter using satellite observations of seasonal and interannual variability. We find that spectral bands subject to strong water-vapour absorption exhibit a substantial stabilizing net feedback. We demonstrate that part of this stabilizing feedback is caused by the change of relative humidity with warming, the radiative fingerprints of which can be directly observed. Therefore, our findings emphasize the importance of better understanding processes affecting the present distribution and future trends in relative humidity. This observational constraint on the spectral long-wave feedback parameter can be used to evaluate the representation of long-wave feedbacks in global climate models and to better constrain Earth’s climate sensitivity.

Tropical cyclones (TCs) are the most destructive weather systems that form over the tropical oceans, with about 90 storms forming globally every year. The timely detection and tracking of TCs are important for advanced warning to the affected regions. As these storms form over the open oceans far from the continents, remote sensing plays a crucial role in detecting them. Here we present an automated TC detection from satellite images based on a novel deep learning technique. In this study, we propose a multistaged deep learning framework for the detection of TCs, including, 1) a detector—Mask region-convolutional neural network (R-CNN); 2) a wind speed filter; and 3) a classifier—convolutional neural network (CNN). The hyperparameters of the entire pipeline are optimized to showcase the best performance using Bayesian optimization. Results indicate that the proposed approach yields high precision (97.10%), specificity (97.59%), and accuracy (86.55%) for test images.