Three-dimensional variational assimilation with a multivariate background error covariance for the Model for Prediction Across Scales – Atmosphere with the Joint Effort for Data assimilation Integration (JEDI-MPAS 2.0.0-beta)

This paper describes the three-dimensional variational (3D-Var) data assimilation (DA) system for the Model for Prediction Across Scales – Atmosphere with the Joint Effort for Data assimilation Integration (JEDI-MPAS). Its core element is a multivariate background error covariance implemented through multiple linear variable changes, including a wind variable change from stream function and velocity potential to zonal- and meridional-wind components, a vertical linear regression representing wind–mass balance, and multiplication by a diagonal matrix of error standard deviations. The univariate spatial correlations for the “unbalanced” variables utilize the Background error on Unstructured Mesh Package (BUMP), which is one of the generic components in the JEDI framework. The variable changes and univariate correlations are modeled directly on the native MPAS unstructured mesh. BUMP provides utilities to diagnose parameters of the covariance model, such as correlation lengths, from an ensemble of forecast differences, though some manual adjustment of the parameters is necessary because of mismatches between the univariate correlation function assumed by BUMP and the correlation structure in the sample of forecast differences. The resulting multivariate covariances, as revealed by single-observation tests, are qualitatively similar to those found in previous global 3D-Var systems. Month-long cycling DA experiments using a global quasi-uniform 60 km mesh demonstrate that 3D-Var, as expected, performs somewhat worse than a pure ensemble-based covariance, while a hybrid covariance, which combines that used in 3D-Var with the ensemble covariance, significantly outperforms both 3D-Var and the pure ensemble covariance. Due to its simple workflow and minimal computational requirements, the JEDI-MPAS 3D-Var system can be useful for the research community.</p

Precipitation data assimilation in WRFDA 4D-Var: implementation and application to convection-permitting forecasts over United States

Precipitation data assimilation has been developed in the Weather Research and Forecasting model data assimilation system (WRFDA) using four-dimensional variational (4D-Var) approach. Unlike other conventional data, precipitation is an integral quantity and it is not included as a control variable in WRFDA. A simplified Kessler scheme is used in tangent linear and adjoint model. Precipitation data are directly assimilated in WRFDA 4D-Var, and the assimilation of precipitation will have feedback to all the control variables via the constraint of the linearized physics package. Firstly, we present single observation experiments to exhibit how dynamic, thermodynamic and moisture fields are adjusted by assimilating rainfall information. Then, the National Centers for Environmental Prediction Stage IV precipitation data are assimilated for a heavy rainfall case on 9 June 2010 at a convection-permitting model setting (4-km). Finally, we conducted one-week experiments to further validate the robustness of the results for precipitation assimilation. Results show that precipitation assimilation has a positive impact on model fields, particularly on the low-level humidity. For the impact on precipitation forecasts, it indicates that precipitation assimilation reduces spin-up time efficiently, removes false alarms and produces model forecast precipitation closer to the observations through the changes in temperature, moisture and wind imposed in the analyses. The impact from precipitation assimilation persists up to three hours on average

The intraseasonal variability of winter semester surface air temperature in Antarctica

This study investigates systematically the intraseasonal variability of surface air temperature over Antarctica by applying empirical orthogonal function (EOF) analysis to the National Centers for Environmental Prediction, US Department of Energy, Reanalysis 2 data set for the period of 1979 through 2007. The results reveal the existence of two major intraseasonal oscillations of surface temperature with periods of 26 - 30 days and 14 days during the Antarctic winter season in the region south of 60°S. The first EOF mode shows a nearly uniform spatial pattern in Antarctica and the Southern Ocean associated with the Antarctic Oscillation. The mode-1 intraseasonal variability of the surface temperature leads that of upper atmosphere by one day with the largest correlation at 300-hPa level geopotential heights. The intraseasonal variability of the mode-1 EOF is closely related to the variations of surface net longwave radiation the total cloud cover over Antarctica. The other major EOF modes reveal the existence of eastward propagating phases over the Southern Ocean and marginal region in Antarctica. The leading two propagating modes respond to Pacific-South American modes. Meridional winds induced by the wave train from the tropics have a direct influence on the surface air temperature over the Southern Ocean and the marginal region of the Antarctic continent. Key words: Antarctic climate, surface air temperature, intraseasonal variability, Antarctic Oscillation (Published: 22 February 2011) Citation: Polar Research 2011, 30, 6039, DOI: 10.3402/polar.v30i0.603