Research

My whole research is focused on data assimilation for the Earth System, including:

  • Coupled Data Assimilation (CDA)
  • Satellite Data Assimilation

I. Coupled Data Assimilation (CDA)

Q1: How DA methods and coupled DA strategies affect the accuracy of the coupled analyses?

To answer this question, I have developed a CDA testbed MAOOAM-CDAS for the quasi-geostrophic coupled atmosphere-ocean model MAOOAM, which itself exhbits coupled low-frequency variablity. MAOOAM-CDAS includes incremental 3D/4D-Var, Ensemble Transform Kalman Filter (ETKF) with coupled, weakly coupled (WC), stronlgy coupled (SC) DA configurations, and includes a system like ECMWF CERA, which utilizes 4D-Var for the atmosphere and 3D-FGAT for the ocean using the outer-loop coupling.

CDA comparison

With the MAOOAM-CDAS, I found that:

  • While the WC & SC atmosphere analyses show comparable accuracies, the SC ocean analysis is more accurate than the WC ocean analysis for both ensemble and variational DA methods.
  • With full atmospheric- and oceanic-observation network, the SC 4D-Var shows comparable performance as the SC ETKF, better than SC 3D-Var. CERA’s outer-loop coupling approach is comparable to SC 4D-Var/ETKF.
  • With only atmospheric observation network, all variational-based methods (3D/4D-Var, CERA) experience difficulty stablizing the ocean analysis.

More details can be found in this paper [Penny et al., JAMES, 2019].


Q2: How much do the ocean surface analysis methods affect the coupled-state analyses?


II. Satellite Data Assimilation

Q1: How to localize hyperspectral radiance observations and further improve the EnKF analysis?


Q2: How to assimilate effectively precipitation retrievals with non-Gaussian errors?


Q3: How to quantify the bias of clear-sky radiance from the infrared imagers onboard geostaionary satellites ?