A novel method for detecting deep convective clouds based on the height of inverted hyperspectral infrared spectra near 9.6 μm, i.e., H-index, is presented. Verification to their cloud top heights using Single Field-of-View Retrieval Products (SiFSAP) and CERES products demonstrates that this method provide an effective way to detect the deep convective clouds, with the DCCs detected using H-index >3.5 K being mostly above 250 hPa, and DCCi2 detected by H-index >11.5 K mostly above 150 hPa. Further analysis of the spatiotemporal distribution of these two categories of DCCs and DCCi2 over the Tibetan Plateau (TP) and surrounding Asian Monsoon regions using 13 years of CrIS data (2013–2025) reveals: (1) Frequent DCCs occur over the Western Himalaya, Pakistan, Northwest India during summer, and western Himalayan DCCs are significantly more likely to intensify into DCCi2 compared to those over the TP, confirming previous findings of extreme deep convection along steep Himalayan slopes; (2) The occurrence of DCCs over the TP exhibits a strong correlation with ENSO cycles, with less DCCS during El Niño years (2015, 2019) and more DCCs during La Niña year (2022), and DCCs over the TP shows a decreasing trend: and (3) nighttime (∼1:30 am) DCCs are more frequent than daytime (∼1:30 pm) over the TP. Notably, frequent DCCs occurrence is found near Ya’an, Sichuan (the “Rain City”), particularly during summer nights. Frequent DCCs are also evident over the Bay of Bengal and South China Sea. From the annual cycle of DCCs, the maximum DCCs occurrence over the Western Himalaya and TP lags behind ocean regions by approximately 1 month, reflecting monsoon propagation dynamics. These results demonstrate the value of hyperspectral infrared data for studying DCCs over the TP and Asian Monsoon regions. This new method can be easily applied to other hyperspectral infrared sounders, such as AIRS and IASI, enabling DCC studies in other regions and facilitating climatological analysis using over 20 years of accumulated hyperspectral sounder data from AIRS, IASI and CrIS.
Coastal zones along the Arabian Gulf have experienced extensive urban expansion and land–water transformation over the past two decades, yet comparable Gulf-wide evidence on how these changes vary across national, urban, and local scales remains limited. Building on a previously published regional assessment, this study extends the analysis through normalized comparisons across eight Gulf countries, quantitative assessment of 13 coastal urban centers, and examination of 33 selected 10 × 10 km Focus Zones. Landsat imagery from 2001 to 2021 and Random Forest classification were used to map urbanization, water-to-nonwater transitions, and nonwater-to-water transitions. These transitions are operationally termed reclamation and inundation, respectively, without independently attributing their causes. Regional results indicate approximately 6,827 km2 of nonurban-to-urban conversion, 1,011 km2 of water-to-nonwater conversion, and 1,810 km2 of nonwater-to-water conversion. The added comparisons reveal substantial differences in transformation patterns among countries, coastal urban centers, and selected local settings. Pooled overall classification accuracies were 92.00% for 2001 and 92.96% for 2021. Although mapped nonwater-to-water conversion exceeded the reverse transition regionally, this balance does not independently establish erosion, sea-level rise, permanent land loss, or flood risk. The analysis provides a consistent framework for comparing coastal transformation at regional, national, urban, and local scales, while highlighting the need to distinguish engineered development, natural coastal processes, and acquisition-related differences when interpreting mapped changes. Future work should integrate higher-frequency imagery, tidal normalization, elevation data, and field or very-high-resolution validation to better distinguish engineered, natural, and climate-related coastal processes. This study is therefore framed as a two-date, long-term change assessment based on endpoint comparisons between 2001 and 2021, rather than a continuous time-series analysis of the timing or sequence of change.
Climate extremes increasingly disrupt the supply of agricultural commodities grown in concentrated geographic regions. Cocoa (Theobroma cacao) plants are particularly vulnerable, since Côte d’Ivoire and Ghana grow about 60% of the world’s supply. Climate precursors of agricultural price shocks are typically reported at country scales, while recent Earth observation (EO) products provide high-resolution cocoa classification maps at plantation scales. The latter provide an opportunity to sample hydro-meteorological variables over the cocoa growing area to investigate potential association with future market changes. During 2024, cocoa futures rose above US$12,000 per metric ton after 2 decades below US$3,800. Here we analyze weekly records of satellite and reanalysis precipitation, soil moisture and 2 m temperature and cocoa futures prices from 2000 to 2025. We identify change points with the Pettitt test and a block bootstrap that allows for autocorrelation, and we monitor an energy index from a lag-embedded principal component analysis fitted only on 2000 to 2017. The mapped footprint contracted by 3,757 km2 (2.5%) between 2020 and 2024. Soil moisture shifted downward in December 2023, and temperature upward in July 2023, although the temperature shift loses significance once the warming trend is removed. The price entered a high-price regime in March 2024, and both climate shifts preceded it in 87% of bootstrap replicates. The energy index entered its most intense alert episode on 19 November 2023, 16 weeks before the high-price regime. It had raised three earlier episodes without a comparable price rise, and removing the warming trend of the calibration period shortened the lead to 5 weeks. Beyond the cocoa-masked overland area, the soil moisture shift was 26% smaller and the index alerted only after the high-price regime began. The findings indicate that climate records sampled over plantation-scale cocoa maps can provide physically grounded indicators of excessive heat and soil drying that may precede supply shocks. The methodology is reproducible and transferable to other geographically concentrated crop types and EO products.
The accuracy of climate reanalyses is limited by observation availability, especially satellite soundings prior to 1979, when operational polar-orbiting satellites become more prevalent. This paper reports on successful efforts to locate the data record collected by the pioneering instrument Special Sensor H (SSH) mounted on four Defense Meteorological Satellite Program (DMSP) block-5D1 satellites (F-1 to F-4) from 1976 to 1980. This data record was unavailable in recent decades, until the present investigations. The SSH instrument features 8 Thermal InfraRed (TIR) channels, one sensitive to ozone and the others primarily sensitive to atmospheric temperature and the surface, and 8 Far InfraRed (FIR) water-vapor-sensitive channels. The FIR region of the electromagnetic spectrum, which has not been much monitored since SSH, is under renewed interest with the recent PREFIRE and upcoming ESA FORUM missions. The SSH data record that could be rescued, from March 1977 to February 1980, covers an important time period in the global climate record, coinciding with the acceleration of ozone layer depletion. The paper presents the methodology used to recover the observations from the original tapes, and the derivation of a fast radiative transfer model capable of supporting data assimilation. The observations are assessed using radiance simulations and comparisons to the contemporary instrument High-resolution Infrared Radiation Sounder on NOAA satellites. An Observing System Experiment is conducted, using an early and lower-resolution version of the upcoming ERA6 reanalysis data assimilation system. Several configuration trials are made, to separate the impact of FIR channels from other channels. The results indicate that the assimilation of all available channels sensitive to temperature, ozone, and water vapor, including those in the FIR, yields the largest impact to improve the analyses and forecasts. Radiance simulations of the hyperspectral sounder SI-1 mounted on Soviet satellite Meteor-29 in 1979 demonstrate that DMSP SSH observations’ assimilation leads to a better representation of the SI-1 measurements, confirming the positive impact of SSH to improve reanalysis quality.
IntroductionMethane on Mars is of interest both for understanding Martian weather and climate and as a possible signature of anaerobic subsurface life. We present a concept for measuring column CH4 mole fractions on Mars from space using a 3.3 μm lidar.MethodsThe lidar transmits online and offline wavelengths sequentially on the strongest CH4 absorption line in the 3.3 μm band. Combining this measurement with surface pressure from a 2 μm pressure-measurement lidar gives the column CH4 mole fraction.ResultsThe concept could observe CH4 in the 1–50 ppb range. The lower end of this range is the detection limit under ideal conditions. Reaching it requires new fiber laser technology that can transmit sufficiently strong pulses.DiscussionWith this laser development, the concept could be used to study Martian weather and climate and to search for potential signs of anaerobic underground life.
