Assessment of Upper Tropospheric Water Vapor Monthly Variation in Reanalyses With Near‐Global Homogenized 6.5‐μm Radiances From Geostationary Satellites

Abstract

The monthly variation of upper tropospheric water vapor (UTWV) simulated by six reanalysis data sets is evaluated with homogenized water vapor radiance observations from international geostationary (GEO) weather satellites by using a profile-to-radiance approach over 45°N to 45°S regions for the period 2015-2017. Results show that reanalysis data sets have an overall good agreement with observations. However, a widespread wet bias is found in all reanalyses and is more dominant in large-scale subsidence regions. JRA55 has the smallest wet bias while MERRA-2 exhibits the most humid upper troposphere. The temporal variation of brightness temperatures in response to the warm phase of El Niño-Southern Oscillation (ENSO) in 2015-2016 indicates that the UTWV field is regulated by both ascending and descending branches of the large-scale circulation. All six reanalyses roughly capture the temporal variation of UTWV in the developing and decay year of this ENSO event. However, they tend to overestimate the eastward propagation of high UTWV in the developing year, especially MERRA-2. The UTWV gradient over the tropical Pacific in the decay year is underestimated, with a dry bias over the convective western Pacific and a wet bias over the eastern Pacific in reanalysis data sets. These results may provide a useful tool for the climate modeling community for identifying and solving problems associated with UTWV simulation.

Tropical Free‐Tropospheric Humidity Differences and Their Effect on the Clear‐Sky Radiation Budget in Global Storm‐Resolving Models

“…Xue et al. (2020) found a wet bias with respect to satellite observations in the free troposphere, which is most pronounced in regions of large‐scale subsidence. Nevertheless, the data set provides a valuable constraint of the humidity distribution and can be used to estimate its natural variability.…”

Tropical free-tropospheric humidity differences and their effect on the clear-sky radiation budget in global strom-resolving models

“…It should be pointed out that potential biases with respect to observations exist in the ERA5 water vapor fields. Xue et al (2020) found a wet bias with respect to satellite observations in the free troposphere, which is most pronounced in regions of large-scale subsidence. Nevertheless, the data set provides a valuable constraint of the humidity distribution and can be used to estimate its natural variability.…”

Can Current Hyperspectral Infrared Sounders Capture the Small Scale Atmospheric Water Vapor Spatial Variations?

“…The spatial variations in radiances or BT measurements from AHI water vapor absorption bands can be interpreted as changes in moisture averaged from different atmospheric layers, which are nearly proportional to the natural logarithm of the humidity changes. This radiance-humidity relationship has been illustrated in many studies (Iacono et al, 2003;Soden & Bretherton, 1993;Xue et al, 2020), which found that this simple radiance-humidity relationship explains greater than 90% of the variances in BT for water vapor bands. For Band 8 (centered at 6.28 μm), a 2 K decrease in BT corresponds to a water vapor increase of ∼30% (∼40%) around 350 hPa for a summer (winter) profile.…”