Observation bias correction with an ensemble Kalman filter

被引：48

作者：

Fertig, Elana J. ^{[1
]}

Baek, Seung-Jong

Hunt, Brian R. ^{[2
,3
,4
]}

Ott, Edward ^{[5
]}

Szunyogh, Istvan ^{[3
,6
]}

Aravequia, Jose A. ^{[3
,6
,7
]}

Kalnay, Eugenia ^{[3
,6
]}

Li, Hong ^{[8
]}

Liu, Junjie ^{[9
]}

机构：

[1] Johns Hopkins Univ, Baltimore, MD 21205 USA

[2] Univ Maryland, Inst Res Elect & Appl Phys, Dept Elect & Comp Engn, College Pk, MD 20742 USA

[3] Univ Maryland, Inst Phys Sci & Technol, College Pk, MD 20742 USA

[4] Univ Maryland, Dept Math, College Pk, MD 20742 USA

[5] Univ Maryland, Dept Phys, College Pk, MD 20742 USA

[6] Univ Maryland, Dept Atmospher & Ocean Sci, College Pk, MD 20742 USA

[7] Brazilian Inst Space Res, Ctr Weather Forecast & Climat Studies, BR-12630 Cahoeira Paulista, SP, Brazil

[8] Shanghai Typhoon Inst, Shanghai, Peoples R China

[9] Univ Calif Berkeley, Dept Earth & Planetary Sci, Berkeley, CA 94720 USA

来源：

TELLUS SERIES A-DYNAMIC METEOROLOGY AND OCEANOGRAPHY | 2009年 / 61卷 / 02期

基金：

美国国家科学基金会;

关键词：

ATMOSPHERIC DATA ASSIMILATION; NUMERICAL WEATHER PREDICTION; PERFECT MODEL EXPERIMENTS; GLOBAL-MODEL; SYSTEM; RADIANCES; DYNAMICS; GCM;

D O I：

10.1111/j.1600-0870.2008.00378.x

中图分类号：

P4 [大气科学（气象学）];

学科分类号：

0706 ; 070601 ;

摘要：

This paper considers the use of an ensemble Kalman filter to correct satellite radiance observations for state dependent biases. Our approach is to use state-space augmentation to estimate satellite biases as part of the ensemble data assimilation procedure. We illustrate our approach by applying it to a particular ensemble scheme-the local ensemble transform Kalman filter (LETKF)-to assimilate simulated biased atmospheric infrared sounder brightness temperature observations from 15 channels on the simplified parameterizations, primitive-equation dynamics (SPEEDY) model. The scheme we present successfully reduces both the observation bias and analysis error in perfect-model simulations.

引用

页码：210 / 226

页数：17

共 48 条

[31]

Miyoshi T., 2005, Ensemble Kalman Filter Experiments with a Primitive-equation Global Model

[32] Atmospheric simulations using a GCM with simplified physical parametrizations. I: model climatology and variability in multi-decadal experiments [J].