A Comparative Study of the Atmospheric Boundary Layer Type in the Local Data Assimilation and Prediction System using the Data of Boseong Standard Weather Observatory

2021 ◽  
Vol 42 (5) ◽  
pp. 504-513
Author(s):  
Sung Eun Hwang ◽  
Byeong-Taek Kim ◽  
Young Tae Lee ◽  
Seung Sook Shin ◽  
Ki Hoon Kim
Keyword(s):  
Boundary Layer ◽  
Data Assimilation ◽  
Comparative Study ◽  
Atmospheric Boundary Layer ◽  
Prediction System ◽  
Local Data

scholarly journals Ensemble-based data assimilation of atmospheric boundary layer observations improves the soil moisture analysis

2021 ◽  
Author(s):  
Tobias Sebastian Finn ◽  
Gernot Geppert ◽  
Felix Ament
Keyword(s):  
Boundary Layer ◽  
Soil Moisture ◽  
Kalman Filter ◽  
Data Assimilation ◽  
Atmospheric Boundary Layer ◽  
Ensemble Kalman Filter ◽  
Land Surface ◽  
Model Systems ◽  
Surface Component ◽  
Moisture Analysis

Abstract. We revise the potential of assimilating atmospheric boundary layer observations into the soil moisture. Previous studies often stated a negative assimilation impact of boundary layer observations on the soil moisture analysis, but recent developments in physically-consistent hydrological model systems and ensemble-based data assimilation lead to an emerging potential of boundary layer observations for land surface data assimilation. To explore this potential, we perform idealized twin experiments for a seven-day period in Summer 2015 with a coupled atmosphere-land modelling platform. We use TerrSysMP for these limited-area simulations with a horizontal resolution 1.0 km in the land surface component. We assimilate sparse synthetic 2-metre-temperature observations into the land surface component and update the soil moisture with a localized Ensemble Kalman filter. We show a positive assimilation impact of these observations on the soil moisture analysis during day-time and a neutral impact during night. Furthermore, we find that hourly-filtering with a three-dimensional Ensemble Kalman filter results in smaller errors than daily-smoothing with a one-dimensional Simplified Extended Kalman filter, whereas the Ensemble Kalman filter additionally allows us to directly assimilate boundary layer observations without an intermediate optimal interpolation step. We increase the physical consistency in the analysis for the land surface and boundary by updating the atmospheric temperature together with the soil moisture, which as a consequence further reduces errors in the soil moisture analysis. Based on these results, we conclude that we can merge the decoupled data assimilation cycles for the land surface and the atmosphere into one single cycle with hourly-like update steps.

Characteristics of the Night and Day Time Atmospheric Boundary Layer at Dome C, Antarctica

2007 ◽  
Vol 25 ◽  
pp. 49-55 ◽  
Author(s):  
S. Argentini ◽  
I. Pietroni ◽  
G. Mastrantonio ◽  
A. Viola ◽  
S. Zilitinchevich
Keyword(s):  
Boundary Layer ◽  
Atmospheric Boundary Layer ◽  
Night And Day
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