scholarly journals Impact of four-dimensional data assimilation (FDDA) on urban climate analysis

2015 ◽  
Vol 7 (4) ◽  
pp. 1997-2011 ◽  
Author(s):  
Linlin Pan ◽  
Yubao Liu ◽  
Yuewei Liu ◽  
Lei Li ◽  
Yin Jiang ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Urban Climate ◽  
Climate Analysis ◽  
Four Dimensional Data Assimilation

CONSTRUCTION OF LAND USE DATABASE BEFORE URBANIZATION FOR GLOBAL URBAN CLIMATE ANALYSIS

2019 ◽  
Vol 75 (2) ◽  
pp. I_1039-I_1044
Author(s):  
Yuuri NARITA ◽  
Alvin C. G. VARQUEZ ◽  
Makoto NAKAYOSHI ◽  
Manabu KANDA
Keyword(s):  
Land Use ◽  
Urban Climate ◽  
Climate Analysis

On four-dimensional data-assimilation

Tellus ◽  
1973 ◽  
Vol 25 (6) ◽  
pp. 595-595
Author(s):  
INGEMAR HOLMSTRÖM
Keyword(s):  
Data Assimilation ◽  
Four Dimensional Data Assimilation

scholarly journals Impact of an observational time window on coupled data assimilation: simulation with a simple climate model

2017 ◽  
Vol 24 (4) ◽  
pp. 681-694 ◽  
Author(s):  
Yuxin Zhao ◽  
Xiong Deng ◽  
Shaoqing Zhang ◽  
Zhengyu Liu ◽  
Chang Liu ◽  
...  
Keyword(s):  
Data Assimilation ◽  
General Circulation ◽  
Climate Model ◽  
Time Window ◽  
Coupled Model ◽  
Circulation Model ◽  
Scale Interactions ◽  
Climate Signals ◽  
Coupled Data Assimilation ◽  
Climate Analysis

Abstract. Climate signals are the results of interactions of multiple timescale media such as the atmosphere and ocean in the coupled earth system. Coupled data assimilation (CDA) pursues balanced and coherent climate analysis and prediction initialization by incorporating observations from multiple media into a coupled model. In practice, an observational time window (OTW) is usually used to collect measured data for an assimilation cycle to increase observational samples that are sequentially assimilated with their original error scales. Given different timescales of characteristic variability in different media, what are the optimal OTWs for the coupled media so that climate signals can be most accurately recovered by CDA? With a simple coupled model that simulates typical scale interactions in the climate system and twin CDA experiments, we address this issue here. Results show that in each coupled medium, an optimal OTW can provide maximal observational information that best fits the characteristic variability of the medium during the data blending process. Maintaining correct scale interactions, the resulting CDA improves the analysis of climate signals greatly. These simple model results provide a guideline for when the real observations are assimilated into a coupled general circulation model for improving climate analysis and prediction initialization by accurately recovering important characteristic variability such as sub-diurnal in the atmosphere and diurnal in the ocean.

Four-dimensional data assimilation with a wide range of scales

1995 ◽  
Vol 47 (5) ◽  
pp. 974-997 ◽  
Author(s):  
Monique Tanguay ◽  
Peter Bartello ◽  
Pierre Gauthier
Keyword(s):  
Data Assimilation ◽  
Wide Range ◽  
Four Dimensional Data Assimilation

Four-dimensional data assimilation with a wide range of scales

1995 ◽  
Vol 47 (5) ◽  
pp. 974-997 ◽  
Author(s):  
MONIQUE TANGUAY ◽  
PETER BARTELLO ◽  
PIERRE GAUTHIER
Keyword(s):  
Data Assimilation ◽  
Wide Range ◽  
Four Dimensional Data Assimilation

On four-dimensional data-assimilation

Tellus ◽  
1973 ◽  
Vol 25 (6) ◽  
pp. 595-595
Author(s):  
Ingemar Holmström
Keyword(s):  
Data Assimilation ◽  
Four Dimensional Data Assimilation
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