dorsal/arxiv
View SchemaComparison between Local Ensemble Transform Kalman Filter and PSAS in the NASA finite volume GCM: perfect model experiments
| Authors | Junjie Liu, Elana Judith Fertig, Hong Li, Eugenia Kalnay, Brian R. Hunt, Eric J. Kostelich, Istvan Szunyogh, Ricardo Todling |
|---|---|
| Categories | |
| ArXiv ID | physics/0703066 |
| URL | https://arxiv.org/abs/physics/0703066 |
| DOI | 10.5194/npg-15-645-2008 |
Abstract
This paper explores the potential of Local Ensemble Transform Kalman Filter (LETKF) by comparing the performance of LETKF with an operational 3D-Var assimilation system, Physical-Space Statistical Analysis System (PSAS), under a perfect model scenario. The comparison is carried out on the finite volume Global Circulation Model (fvGCM) with 72 grid points zonally, 46 grid points meridionally and 55 vertical levels. With only forty ensemble members, LETKF obtains an analysis and forecasts with lower RMS errors than those from PSAS. The performance of LETKF is further improved, especially over the oceans, by assimilating simulated temperature observations from rawinsondes and conventional surface pressure observations instead of geopotential heights. An initial decrease of the forecast errors in the NH observed in PSAS but not in LETKF suggests that the PSAS analysis is less balanced. The observed advantage of LETKF over PSAS is due to the ability of the forty-member ensemble from LETKF to capture flow-dependent errors and thus create a good estimate of the true background uncertainty. Furthermore, localization makes LETKF highly parallel and efficient, requiring only 5 minutes per analysis in a cluster of 20 PCs with forty ensemble members.
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"abstract": "This paper explores the potential of Local Ensemble Transform Kalman Filter\n(LETKF) by comparing the performance of LETKF with an operational 3D-Var\nassimilation system, Physical-Space Statistical Analysis System (PSAS), under a\nperfect model scenario. The comparison is carried out on the finite volume\nGlobal Circulation Model (fvGCM) with 72 grid points zonally, 46 grid points\nmeridionally and 55 vertical levels. With only forty ensemble members, LETKF\nobtains an analysis and forecasts with lower RMS errors than those from PSAS.\nThe performance of LETKF is further improved, especially over the oceans, by\nassimilating simulated temperature observations from rawinsondes and\nconventional surface pressure observations instead of geopotential heights. An\ninitial decrease of the forecast errors in the NH observed in PSAS but not in\nLETKF suggests that the PSAS analysis is less balanced. The observed advantage\nof LETKF over PSAS is due to the ability of the forty-member ensemble from\nLETKF to capture flow-dependent errors and thus create a good estimate of the\ntrue background uncertainty. Furthermore, localization makes LETKF highly\nparallel and efficient, requiring only 5 minutes per analysis in a cluster of\n20 PCs with forty ensemble members.",
"arxiv_id": "physics/0703066",
"authors": [
"Junjie Liu",
"Elana Judith Fertig",
"Hong Li",
"Eugenia Kalnay",
"Brian R. Hunt",
"Eric J. Kostelich",
"Istvan Szunyogh",
"Ricardo Todling"
],
"categories": [
"physics.ao-ph"
],
"doi": "10.5194/npg-15-645-2008",
"title": "Comparison between Local Ensemble Transform Kalman Filter and PSAS in the NASA finite volume GCM: perfect model experiments",
"url": "https://arxiv.org/abs/physics/0703066"
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