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Diagnosing Non-gaussianity of Forecast and Analysis Errors in a Convective Scale Model : Volume 2, Issue 4 (18/07/2015)

By Legrand, R.

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Book Id: WPLBN0004020120
Format Type: PDF Article :
File Size: Pages 30
Reproduction Date: 2015

Title: Diagnosing Non-gaussianity of Forecast and Analysis Errors in a Convective Scale Model : Volume 2, Issue 4 (18/07/2015)  
Author: Legrand, R.
Volume: Vol. 2, Issue 4
Language: English
Subject: Science, Nonlinear, Processes
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Historic
Publication Date:
2015
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

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Montmerle, T., Michel, Y., & Legrand, R. (2015). Diagnosing Non-gaussianity of Forecast and Analysis Errors in a Convective Scale Model : Volume 2, Issue 4 (18/07/2015). Retrieved from http://www.netlibrary.net/


Description
Description: Centre National de Recherches Météorologiques (CNRM), Toulouse, France. In numerical weather prediction, the problem of estimating initial conditions is usually based on a Bayesian framework. Two common derivations respectively lead to the Kalman filter and to variational approaches. They rely on either assumptions of linearity or assumptions of Gaussianity of the probability density functions of both observation and background errors. In practice, linearity and Gaussianity of errors are tied to one another, in the sense that a nonlinear model will yield non-Gaussian probability density functions, and that standard methods may perform poorly in the context of non-Gaussian probability density functions.

This study aims to describe some aspects of non-Gaussianity of forecast and analysis errors in a convective scale model using a Monte-Carlo approach based on an ensemble of data assimilations. For this purpose, an ensemble of 90 members of cycled perturbed assimilations has been run over a highly precipitating case of interest. Non-Gaussianity is measured using the K2-statistics from the D'Agostino test, which is related to the sum of the squares of univariate skewness and kurtosis.

Results confirm that specific humidity is the least Gaussian variable according to that measure, and also that non-Gaussianity is generally more pronounced in the boundary layer and in cloudy areas. The mass control variables used in our data assimilation, namely vorticity and divergence, also show distinct non-Gaussian behavior. It is shown that while non-Gaussianity increases with forecast lead time, it is efficiently reduced by the data assimilation step especially in areas well covered by observations. Our findings may have implication for the choice of the control variables.


Summary
Diagnosing non-Gaussianity of forecast and analysis errors in a convective scale model

Excerpt
Anderson, E. and Jarvinen, H.: Variational quality control, Q. J. Roy. Meteorol. Soc., 125, 697–722, doi:10.1002/qj.49712555416, 1999.; Anderson, T. W. and Darling, D. A.: A test of goodness of fit, J. Am. Stat. Assoc., 49, 765–769, 1954.; Auligné, T., Lorenc, A., Michel, Y., Montmerle, T., Jones, A., Hu, M., and Dudhia, J.: Toward a new cloud analysis and prediction system, B. Am. Meteorol. Soc., 92, 207–210, 2011.; Berre, L.: Estimation of synoptic and mesoscale forecast error covariances in a limited-area model, Mon. Weather Rev., 128, 644–667, 2000.; Berre, L. and Desroziers, G.: Filtering of background error variances and correlations by local spatial averaging: a review, Mon. Weather Rev., 138, 3693–3720, 2010.; Berre, L., Ecaterina Ştefănescu, S., and Belo Pereira, M.: The representation of the analysis effect in three error simulation techniques, Tellus A, 58, 196–209, 2006.; Bocquet, M., Pires, C. A., and Wu, L.: Beyond Gaussian statistical modeling in geophysical data assimilation, Mon. Weather Rev., 138, 2997–3023, 2010.; Brousseau, P., Berre, L., Bouttier, F., and Desroziers, G.: Background-error covariances for a convective-scale data-assimilation system: AROME–France 3D-Var, Q. J. Roy. Meteorol. Soc., 137, 409–422, 2011.; Courtier, P., Thépaut, J.-N., and Hollingsworth, A.: A strategy for operational implementation of 4D-Var, using an incremental approach, Q. J. Roy. Meteorol. Soc., 120, 1367–1387, 1994.; D'Agostino, R. B.: Transformation to normality of the null distribution of G1, Biometrika, 57, 679–681, 1970.; Dee, D. P. and da Silva, A. M.: The choice of variable for atmospheric moisture analysis, Mon. Weather Rev., 131, 155–171, 2003.; Fisher, M.: Background error covariance modelling, in: Seminar on Recent Development in Data Assimilation for Atmosphere and Ocean, Seminar on Recent Development in Data Assimilation for Atmosphere and Ocean, ECMWF, 45–63, 2003.; Gustafsson, N., Thorsteinsson, S., Stengel, M., and Holm, E.: Use of a nonlinear pseudo-relative humidity variable in a multivariate formulation of moisture analysis, Q. J. Roy. Meteorol. Soc., 137, 1004–1018, doi:10.1002/qj.813, 2011.; Hodyss, D.: Accounting for skewness in ensemble data assimilation, Mon. Weather Rev., 140, 2346–2358, 2012.; Ducrocq, V., Belamari, S., Boudevillain, B., Bousquet, O., Cocquerez, P., Doerenbecher, A., Drobinski, P., Flamant, C., Labatut, L., Lambert, D., Nuret, M., Richard, E., Roussot, O., Testor, P., Arbogast, P., Ayral, P.-A., Van Baelen, J., Basdevant, C., Boichard, J.-L., Bourras, D., Bouvier, C., Bouin, M.-N., Bock, O., Braud,I., Champollion, C., Coppola, L., Coquillat, S., Defer, E., Delanoë, J., Delrieu, G., Didon-Lescot, J.-F., Durand, P., Estournel, C., Fourrié, N., Garrouste, O., Giordani, H., Le Coz, J., Michel, Y., Nuissier, O., Roberts, G., Saïd, F., Schwarzenboeck, A., Sellegri, K., Taupier-Letage, I., Vandervaere J.-P.: HyMeX, les campagnes de mesures: focus sur les événements extrêmes en Méditerranée, Société météorologique de France, Paris, France, La Météorologie, 80, 37–47, 2013.; Ducrocq, V., Braud, I., Davolio, S., Ferretti, R., Flamant, C., Jansá, A., Kalthoff, N., Richard, E., Taupier-Letage, I., Ayral, P.-A., Belamari, S., Berne, A., Borga, M., Boudevillain, B., Bock, O., Boichard, J.-L., Bouin, M.-N., Bousquet, O., Bouvier, C., Chiggiato, J., Cimini, D., Corsmeier, U., Coppola, L., Cocquerez, P., Defer, E., Delanoë, J., Di Girolamo, P., Doerenbecher, A., Drobinski, P., Dufournet, Y., Fourrié, N., Gourley, J. J., Labatut, L., Lambert, D., Le Coz, J., Marzano, F. S., Molinié, G., Montani, A., Nord, G., Nuret, M., Ramage, K., Rison, B., Roussot, O., Said, F., Schwarzenboeck, A., Testor, P., Van-Baelen, J., Vincendon, B., Aran, M., and Tamayo, J.: HyMeX-SOP1, the field campaign dedicated to heavy precipitation and flash flooding in the northwestern Mediterranean, B. Am. Meteo

 

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