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David Halpern, Dimitris Menemenlis, and Xiaochun Wang

Abstract

The impact of data assimilation on the transports of eastward-flowing Equatorial Undercurrent (EUC) and North Equatorial Countercurrent (NECC) in the Pacific Ocean from 145°E to 95°W during 2004–05 and 2009–11 was assessed. Two Estimating the Circulation and Climate of the Ocean, Phase II (ECCO2), solutions were analyzed: one with data assimilation and one without. Assimilated data included satellite observations of sea surface temperature and ocean surface topography, in which the sampling patterns were approximately uniform over the 5 years, and in situ measurements of subsurface salinity and temperature profiles, in which the sampling patterns varied considerably in space and time throughout the 5 years. Velocity measurements were not assimilated. The impact of data assimilation was considered significant when the difference between the transports computed with and without data assimilation was greater than 5.5 × 106 m3 s−1 (or 5.5 Sv; 1 Sv ≡ 106 m3 s−1) for the EUC and greater than 5.0 Sv for the NECC. In addition, the difference of annual-mean transports computed from 3-day-averaged data was statistically significant at the 95% level. The impact of data assimilation ranged from no impact to very substantial impact when data assimilation increased the EUC transport and decreased the NECC transport. The study’s EUC results had some correspondence with other studies and no simple agreement or disagreement pattern emerged among all studies of the impact of data assimilation. No comparable study of the impact of data assimilation on the NECC has been made.

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Takuya Kawabata, Hironori Iwai, Hiromu Seko, Yoshinori Shoji, Kazuo Saito, Shoken Ishii, and Kohei Mizutani

Abstract

The authors evaluated the effects of assimilating three-dimensional Doppler wind lidar (DWL) data on the forecast of the heavy rainfall event of 5 July 2010 in Japan, produced by an isolated mesoscale convective system (MCS) at a meso-gamma scale in a system consisting of only warm rain clouds. Several impact experiments using the nonhydrostatic four-dimensional variational data assimilation system (NHM-4DVAR) and the Japan Meteorological Agency nonhydrostatic model with a 2-km horizontal grid spacing were conducted in which 1) no observations were assimilated (NODA), 2) radar reflectivity and radial velocity determined by Doppler radar and precipitable water vapor determined by GPS satellite observations were assimilated (CTL), and 3) radial velocity determined by DWL were added to the CTL experiment (LDR) and five data denial and two observational error sensitivity experiments. Although both NODA and CTL simulated an MCS, only LDR captured the intensity, location, and horizontal scale of the observed MCS. Assimilating DWL data improved the wind direction and speed of low-level airflows, thus improving the accuracy of the simulated water vapor flux. The examination of the impacts of specific assimilations and assigned observation errors showed that assimilation of all data types is important for forecasting intense MCSs. The investigation of the MCS structure showed that large amounts of water vapor were supplied to the rainfall event by southerly flow. A midlevel inversion layer led to the production of exclusively liquid water particles in the MCS, and in combination with the humid airflow into the MCS, this inversion layer may be another important factor in its development.

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Yicun Zhen and Fuqing Zhang

Abstract

This study proposes a variational approach to adaptively determine the optimum radius of influence for ensemble covariance localization when uncorrelated observations are assimilated sequentially. The covariance localization is commonly used by various ensemble Kalman filters to limit the impact of covariance sampling errors when the ensemble size is small relative to the dimension of the state. The probabilistic approach is based on the premise of finding an optimum localization radius that minimizes the distance between the Kalman update using the localized sampling covariance versus using the true covariance, when the sequential ensemble Kalman square root filter method is used. The authors first examine the effectiveness of the proposed method for the cases when the true covariance is known or can be approximated by a sufficiently large ensemble size. Not surprisingly, it is found that the smaller the true covariance distance or the smaller the ensemble, the smaller the localization radius that is needed. The authors further generalize the method to the more usual scenario that the true covariance is unknown but can be represented or estimated probabilistically based on the ensemble sampling covariance. The mathematical formula for this probabilistic and adaptive approach with the use of the Jeffreys prior is derived. Promising results and limitations of this new method are discussed through experiments using the Lorenz-96 system.

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Hailing Zhang and Zhaoxia Pu

Abstract

A series of numerical experiments are conducted to examine the impact of surface observations on the prediction of landfalls of Hurricane Katrina (2005), one of the deadliest disasters in U.S. history. A specific initial time (0000 UTC 25 August 2005), which led to poor prediction of Hurricane Katrina in several previous studies, is selected to begin data assimilation experiments. Quick Scatterometer (QuikSCAT) ocean surface wind vectors and surface mesonet observations are assimilated with the minimum central sea level pressure and conventional observations from NCEP into an Advanced Research version of the Weather Research and Forecasting Model (WRF) using an ensemble Kalman filter method. Impacts of data assimilation on the analyses and forecasts of Katrina’s track, landfalling time and location, intensity, structure, and rainfall are evaluated. It is found that the assimilation of QuikSCAT and mesonet surface observations can improve prediction of the hurricane track and structure through modifying low-level thermal and dynamical fields such as wind, humidity, and temperature and enhancing low-level convergence and vorticity. However, assimilation of single-level surface observations alone does not ensure reasonable intensity forecasts because of the lack of constraint on the mid- to upper troposphere. When surface observations are assimilated with other conventional data, obvious enhancements are found in the forecasts of track and intensity, realistic convection, and surface wind structures. More importantly, surface data assimilation results in significant improvements in quantitative precipitation forecasts (QPFs) during landfalls.

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James A. Cummings and Ole Martin Smedstad

Abstract

The impact of the assimilation of ocean observations on reducing global Hybrid Coordinate Ocean Model (HYCOM) 48-h forecast errors is presented. The assessment uses an adjoint-based data impact procedure that characterizes the forecast impact of every observation assimilated, and it allows the observation impacts to be partitioned by data type, geographic region, and vertical level. The impact cost function is the difference between HYCOM 48- and 72-h forecast errors computed for temperature and salinity at all model levels and grid points. It is shown that routine assimilation of large numbers of observations consistently reduces global HYCOM 48-h forecast errors for both temperature and salinity. The largest error reduction is due to the assimilation of temperature and salinity profiles from the tropical fixed mooring arrays, followed by Argo, expendable bathythermograph (XBT), and animal sensor data. On a per-observation basis, the most important global observing system is Argo. The beneficial impact of assimilating Argo temperature and salinity profiles extends to all depths sampled, with salinity impacts maximum at the surface and temperature impacts showing a subsurface maximum in the 100–200-m-depth range. The reduced impact of near-surface Argo temperature profile levels is due to the vertical covariances in the assimilation that extend the influence of the large number of sea surface temperature (SST) observations to the base of the mixed layer. Application of the adjoint-based data impact system to identify a data quality problem in a geostationary satellite SST observing system is also provided.

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