A regional Observing System Simulation Experiment (OSSE) framework was developed at NOAA/AOML and University of Miami that features a high-resolution regional nature run (27-km regional domain with 9/3/1 km storm-following nests with WRF-ARW; HNR1; Nolan et al., 2013) embedded within a lower-resolution (T511) ECMWF global nature run (JONR; Andersson and Masutani, 2010). Simulated observations are generated by sampling from HNR1 and are provided to a data assimilation scheme, which produces analyses for a high-resolution regional forecast model, the 2014 operational Hurricane-WRF model (WRF-NMM). For data assimilation, NOAA's Gridpoint Statistical Interpolation (GSI) and EnKF (Whitaker and Hamill, 2002) systems are used. Analyses are performed on the parent domain at 9-km resolution. The forecast model uses a single storm-following 3-km resolution nest. Synthetic CYGNSS wind speed data have also been created from HNR1, and the impacts of the assimilation of these synthetic wind speed data on the forecasts of tropical cyclone track and intensity will be discussed.
In addition to the choice of assimilation scheme, we have also examined a number of other factors/parameters that effect the impact of simulated CYGNSS observations, including frequency of data assimilation cycling (e.g., hourly, 3-hourly and 6-hourly) and the assimilation of scalar versus vector synthetic CYGNSS winds. Vector winds were derived from retrieved scalar CYGNSS winds by a variational analysis of surface wind (2-dimensional). The Variational Analysis Method (VAM) is a technique to blend surface wind observations with an a priori, or background, gridded surface wind field. The resulting analysis is optimal from the Baysian estimation point of view and also includes fluid dynamical constraints that insure the solution is smooth and physically reasonable.
We have found sensitivity to all of the factors tested and will summarize the methods used for testing as well as results. Generally, we have found that the optimal cycling frequency depends on assimilation scheme; derived CYGNSS vector winds have more impact than scalar winds; and flow-dependent background error covariances (e.g., EnKF) are better than static or climatological assumptions about the background error covariance.
Andersson, E., and M. Masutani (2010), Collaboration on observing system simulation experiments (Joint OSSE), ECMWF Newsl., No. 123, pp. 14–16, ECMWF, Reading, U. K. [Available at http://www.emc.ncep.noaa.gov/research/JointOSSEs/publications/JOSSE-Publication-files/Andersson_JOSSE_ECMWF_News_No123.pdf, accessed 18 May 2013.].
Nolan, D. S., R. Atlas, K. T. Bhatia, and L. R. Bucci (2013), Development and validation of a hurricane nature run using the joint OSSE nature run and the WRF model, J. Adv. Model. Earth Syst., 5, 382–405, doi:10.1002/jame.20031.
Whitaker, J. and T. M. Hamill, 2002: Ensemble data assimilation without perturbed observations. Mon. Wea. Rev., 130, 1913–1924.