TITLE

Univariate and Multivariate Assimilation of AIRS Humidity Retrievals with the Local Ensemble Transform Kalman Filter

AUTHOR(S)
Junjie Liu; Hong Li; Kalnay, Eugenia; Kostelich, Eric J.; Szunyogh, Istvan
PUB. DATE
November 2009
SOURCE
Monthly Weather Review;Nov2009, Vol. 137 Issue 11, p3918
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
This study uses the local ensemble transform Kalman filter to assimilate Atmospheric Infrared Sounder (AIRS) specific humidity retrievals with pseudo relative humidity (pseudo-RH) as the observation variable. Three approaches are tested: (i) updating specific humidity with observations other than specific humidity (“passive q”), (ii) updating specific humidity only with humidity observations (“univariate q”), and (iii) assimilating the humidity and the other observations together (“multivariate q”). This is the first time that the performance of the univariate and multivariate assimilation of q is compared within an ensemble Kalman filter framework. The results show that updating the humidity analyses by either AIRS specific humidity retrievals or nonhumidity observations improves both the humidity and wind analyses. The improvement with the multivariate-q experiment is by far the largest for all dynamical variables at both analysis and forecast time, indicating that the interaction between the specific humidity and the other dynamical variables through the background error covariance during data assimilation process yields more balanced analysis fields. In the univariate assimilation of q, the humidity interacts with the other dynamical variables only through the forecast process. The univariate assimilation produces more accurate humidity analyses than those obtained when no humidity observations are assimilated, but it does not improve the accuracy of the zonal wind analyses. The 6-h total column precipitable water forecast also benefits from the improved humidity analyses, with the multivariate q experiment having the largest improvement.
ACCESSION #
45302016

 

Related Articles

  • Adaptive Sampling with the Ensemble Transform Kalman Filter. Part I: Theoretical Aspects. Bishop, Craig H.; Etherton, Brian J.; Majumdar, Sharanya J. // Monthly Weather Review;Mar2001, Vol. 129 Issue 3, p420 

    Presents a study which introduced a suboptimal Kalman filter called the ensemble transform Kalman filter (ET KF), which provides a framework for assimilating observations and for estimating the effect of observations on forecast error variance. Background of Kalman filters and how they can be...

  • A Note on Temperature and Relative Humidity Corrections for Humidity Sensors. Fleming, Rex J. // Journal of Atmospheric & Oceanic Technology;Dec98, Vol. 15 Issue 6, p1511 

    A widely used relative humidity (RH) sensor in atmospheric science is based upon a capacitive device that outputs voltage as a linear function of RH and then is corrected by an empirically determined polynomial expression, which is only a function of temperature. Based upon results of a dry...

  • A new methodology for the extension of the impact of data assimilation on ocean wave prediction. Galanis, George; Emmanouil, George; Chu, Peter C.; Kallos, George // Ocean Dynamics;Jun2009, Vol. 59 Issue 3, p523 

    It is a common fact that the majority of today's wave assimilation platforms have a limited, in time, ability of affecting the final wave prediction, especially that of long-period forecasting systems. This is mainly due to the fact that after “closing” the assimilation window, i.e.,...

  • Performance of the ensemble Kalman filter outside of existing wells for a channelized reservoir. Peters, Elisabeth // Computational Geosciences;Mar2011, Vol. 15 Issue 2, p345 

    The ensemble Kalman filter (EnKF) appears to give good results for matching production data at existing wells. However, the predictive power of these models outside of the existing wells is much more uncertain. In this paper, for a channelized reservoir for five different cases with different...

  • Are squall lines detected by NCEP-NCAR reanalyses? Sow, B.; Viltard, A.; de Félice, P.; Deme, A.; Adamou, G. // Meteorology & Atmospheric Physics;Oct2005, Vol. 90 Issue 3/4, p209 

    The aim of the paper is to show that NCEP-NCAR reanalyses do display modifications in the atmosphere at the occurrence of squall lines (SLs). 135 SLs were detected at Dakar (Senegal) during the summers of 1981–95. A compositing method was applied to temperature, specific humidity and...

  • Estimation and correction of surface wind-stress bias in the Tropical Pacific with the Ensemble Kalman Filter. LEEUWENBURGH, OLWIJN // Tellus: Series A;Aug2008, Vol. 60 Issue 4, p716 

    The assimilation of high-quality in situ data into ocean models is known to lead to imbalanced analyses and spurious circulations when the model dynamics or the forcing contain systematic errors. Use of a bias estimation and correction scheme has been shown to significantly improve the balance...

  • Characteristics of Initial Perturbations in the Ensemble Prediction System of the Korea Meteorological Administration. Kay, Jun Kyung; Kim, Hyun Mee // Weather & Forecasting;Jun2014, Vol. 29 Issue 3, p563 

    In this study, the initial ensemble perturbation characteristics of the new Korea Meteorological Administration (KMA) ensemble prediction system (EPS), a version of the Met Office Global and Regional Ensemble Prediction System, were analyzed over two periods: from 1 June to 31 August 2011, and...

  • Using Precipitation Observations in a Mesoscale Short-Range Ensemble Analysis and Forecasting System. Fujita, Tadashi; Stensrud, David J.; Dowell, David C. // Weather & Forecasting;Jun2008, Vol. 23 Issue 3, p357 

    A simple method to assimilate precipitation data from a synthesis of radar and gauge data is developed to operate alongside an ensemble Kalman filter that assimilates hourly surface observations. The mesoscale ensemble forecast system consists of 25 members with 30-km grid spacing and...

  • The Ensemble Kalman Filter: theoretical formulation and practical implementation. Evensen, Geir // Ocean Dynamics;Nov2003, Vol. 53 Issue 4, p343 

    The purpose of this paper is to provide a comprehensive presentation and interpretation of the Ensemble Kalman Filter (EnKF) and its numerical implementation. The EnKF has a large user group, and numerous publications have discussed applications and theoretical aspects of it. This paper reviews...

Share

Read the Article

Courtesy of THE LIBRARY OF VIRGINIA

Sorry, but this item is not currently available from your library.

Try another library?
Sign out of this library

Other Topics