A comprehensive and accurate global water vapor dataset is critical to the adequate understanding of water vapor's role in the earth's climate system. To begin to satisfy this need, the authors have produced a blended dataset made up of global, 5-yr (1988-92), l°x 1° spatial resolution, atmospheric water vapor (WV) and liquid water path products. These new products consist of both the daily total column-integrated composites and a multilayered WV product at three layers (1000-700, 700-500, 500-300 mb). The analyses combine WV retrievals from the Television and Infrared Operational Satellite (TIROS) Operational Vertical Sounder (TOVS), the Special Sensor Microwave/Imager, and radiosonde observations. The global, vertical-layered water vapor dataset was developed by slicing the blended total column water vapor using layer information from TOVS and radiosonde. Also produced was a companion, over oceans only, liquid water path dataset. Satellite observations of liquid water path are growing in importance since many of the global climate models are now either incorporating or contain liquid water as an explicit variable. The complete dataset (all three products) has been named NVAP, an acronym for National Aeronautics and Space Administration Water Vapor Project. This paper provides examples of the new dataset as well as scientific analysis of the observed annual cycle and the interannual variability of water vapor at global, hemispheric, and regional scales. A distinct global annual cycle is shown to be dominated by the Northern Hemisphere observations. Planetary-scale variations are found to relate well to recent independent estimates of tropospheric temperature variations. Maps of regional interannual variability in the 5-yr period show the effect of the 1992 ENSO and other features.

Spatial and temporal soil moisture dynamics are critically needed to improve the parameterization for hydrological and meteorological modeling processes. This study evaluates the statistical spatial structure of large-scale observed and simulated estimates of soil moisture under pre- and post-precipitation event conditions. This large scale variability is a crucial in calibration and validation of large-scale satellite based data assimilation systems. Spatial analysis using geostatistical approaches was used to validate modeled soil moisture by the Agriculture Meteorological (AGRMET) model using in situ measurements of soil moisture from a state-wide environmental monitoring network (Oklahoma Mesonet). The results show that AGRMET data produces larger spatial decorrelation compared to in situ based soil moisture data. The precipitation storms drive the soil moisture spatial structures at large scale, found smaller decorrelation length after precipitation. This study also evaluates the geostatistical approach for mitigation for quality control issues within in situ soil moisture network to estimates at soil moisture at unsampled stations.

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Milky seas are a rare form of marine bioluminescence where the nocturnal ocean surface produces a widespread, uniform and steady whitish glow. Mariners have compared their appearance to a daylit snowfield that extends to all horizons. Encountered most often in remote waters of the northwest Indian Ocean and the Maritime Continent, milky seas have eluded rigorous scientific inquiry, and thus little is known about their composition, formation mechanism, and role within the marine ecosystem. The Day/Night Band (DNB), a new-generation spaceborne low-light imager, holds potential to detect milky seas, but the capability has yet to be demonstrated. Here, we show initial examples of DNB-detected milky seas based on a multi-year (2012–2021) search. The massive bodies of glowing ocean, sometimes exceeding 100,000 km2 in size, persist for days to weeks, drift within doldrums amidst the prevailing sea surface currents, and align with narrow ranges of sea surface temperature and biomass in a way that suggests water mass isolation. These findings show how spaceborne assets can now help guide research vessels toward active milky seas to learn more about them.