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Water vapor is the most dominant greenhouse gas and plays a critical role in the climate by regulating the Earth’s radiation budget and hydrological cycle. A comprehensive dataset is required to describe the temporal and spatial distribution of water vapor, evaluate the performance of climate and weather prediction models in terms of simulating tropospheric humidity, and understand the role of water vapor and its feedback in the climate system. Satellite microwave and radiosonde measurements are the two main sources of tropospheric humidity. However, both datasets are subject to errors and uncertainties. This talk focuses on our knowledge of these errors and uncertainties as well as some applications of such observations, as follows:

The quality of operational radiosonde data was investigated for different sensor types. It was found that the use of a variety of sensors over the globe introduces temporal and spatial errors in the data. Furthermore, it was shown that the daytime radiation dry bias, which is one of the most important errors in radiosonde data, depends on both sensor type and radiosonde launch time.

Radiometric errors in satellite data were investigated using both intercomparisons of coincident observations as well as validation versus high-quality radiosonde and Global Positioning System Radio Occultation (GPS-RO) data. Overall, the absolute accuracy of the microwave satellite data cannot still be validated due to the lack of reference measurements.

In addition, a novel technique for correcting geolocation errors in microwave satellite data was developed based on the difference between ascending and descending observations along the coastlines. Using this method, several important errors including timing errors and sensor mounting errors were found in some of the microwave instruments.

Finally, since satellite data are indirect measurements, a method was developed to transform satellite radiances from different water vapor channels to layer averaged humidity. The technique is very fast because radiative transfer calculations are only required to determine the empirical coefficients. This technique was then used to evaluate the diurnal variation of tropospheric humidity in the tropical region.
This research was funded by NOAA Environmental Data Record and JPSS Programs.

Short Bio:

Dr. Isaac Moradi received his MSc. in Meteorology from University of Tehran first Ph.D. is Climatology and Environmental Planning from Kwarizmi University of Tehran and his second Ph.D. in Radio and Space Science from Chalmers University of Technology, Sweden. Before joining the University of Maryland, he worked at the University of Tehran, Ministry of Energy, and Lulea University of Technology, Sweden. Since joining ESSIC, the University of Maryland in 2010, he has been working on satellite data calibration, radiative transfer modeling, product retrieval, data assimilation, and OSSEs. He has been working at NASA GMAO since 2015 where his work is focused on satellite data assimilation, OSSE, and radiative transfer modeling. Before joining GMAO, he worked at NOAA Joint Center for Satellite Data Assimilation, as well as NOAA STAR. Dr. Moradi is currently representing ESSIC in the UMD Senate, and also is an editor of Atmospheric Measurement Techniques.

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