IMPROVING THE HEAVY RAINFALL FORECAST BYASSIMILATING RETRIEVED MOISTURE AND RADAR DATA:PERFORMANCE AND VALIDATION WITH REAL CASES
Kao-Shen Chung2, Phuong-Nghi Do1, 2, Pay-Liam Lin2, Ching-Yin Ke2, Bo-An Tsai2

1Taiwan International Graduate (TIGP), Earth System Science, Academia Sinica, Taipei, Taiwan
2

Department of Atmospheric Sciences, National Central University, Taoyuan City, Taiwan
Scott M. Ellis3
3National Center for Atmospheric Research, Boulder, Colorado
Ya-Chien Feng44Pacific Northwest National Laboratory, Washington
ABSTRACT

Moisture information plays an important role in the convective initiation and heavy rain prediction.
This study examined the effect of assimilating S- and Ka-band dual‐wavelength-retrieved watervapor data with radial wind and reflectivity data. The S-PolKa vertical profile of humidity, which
provides environmental information before precipitation occurs, was obtained at low levels andthinned into averaged and four-quadrant profiles. The retrieved moisture information wasassimilated by two different strategies: (1) assimilation of water vapor data with radar data for theentire 2 h and (2) assimilation of water vapor data in the first hour, and radial velocity andreflectivity data in the second hour. By using the WRF local ensemble transform Kalman filter
data assimilation system, three real cases of the Dynamics of the Madden-Julian Oscillation
Experiment were examined through a series of experiments. The results revealed that assimilating
additional water vapor data more markedly improved the analysis at the convective scale than
assimilating radial wind and reflectivity data alone, leading to more significant improvements in
the rain forecast compared with assimilating radar data only. In addition, the strategy ofassimilating only retrieved water vapor data in the first hour and radial wind and reflectivity datain the second hour achieved the optimal analysis with which to launch the very short-term forecast,resulting in the most improvement of rain forecast compared with other experiments. Furthermore,assimilating moisture profiles into four quadrants achieved more accurate analysis and forecast.
Overall, our study demonstrated that the humidify information in nonprecipitation areas is criticalfor further improving the analysis and forecast of convective weather systems. Besides the SPolKa-retrieved water vapor, the impact of assimilating radar-retrieved refractivity that carries
moisture information near the surface and is available in both precipitation and nonprecipiation isinvestigated in our ongoing works. The preliminary results in two real cases of the Terraininfluenced Monsoon Rainfall Experiment/Southwest Monsoon Experiment reveal that with theadditional refractivity assimilation, more precise moisture corrections and stronger southwesterly
wind were obtained, leading to the better forecasting of both light and heavy rain during 6 h.
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Keywords: SPol-Ka-retrieved water vapor assimilation; radar-retrieved refractivity assimilation; radarassimilation, heavy rain.