Use of Incremental Analysis Updates in 4D-Var Data Assimilation

The four-dimensional variational （4D-Var） data assimilation systems used in most operational and research centers use initial condition increments as control variables and adjust initial increments to find optimal analysis solutions. This approach may sometimes create discontinuities in analysis fields and produce undesirable spin ups and spin downs. This study explores using incremental analysis updates （IAU） in 4D-Var to reduce the analysis discontinuities. IAU-based 4D-Var has almost the same mathematical formula as conventional 4D-Var if the initial condition increments are replaced with time-integrated increments as control variables. The IAU technique was implemented in the NASA/GSFC 4D-Var prototype and compared against a control run without IAU. The results showed that the initial precipitation spikes were removed and that other discontinuities were also reduced, especially for the analysis of surface temperature.

Estimation and Correction of Model Bias in the NASA/GMAO GEOS5 Data Assimilation System:Sequential Implementation

This study presents a simplified multivariate bias correction scheme that is sequentially implemented in the GEOS5 data assimilation system and compared against a control experiment without model bias correction. The results show considerable improvement in terms of the mean biases of rawinsonde observation-minus-background （OmB） residuals for observed water vapor, wind and temperature variables. The time series spectral analysis shows whitening of bias-corrected OmB residuals, and mean biases for rawinsonde observation-minus-analysis （OmA） are also improved. Some wind and temperature biases in the control experiment near the equatorial tropopause nearly vanish from the bias-corrected experiment. Despite the analysis improvement, the bias correction scheme has only a moderate impact on forecast skill. Significant interaction is also found among quality-control, satellite observation bias correction, and background bias correction, and the latter positively impacts satellite bias correction.

Satellite Data Assimilation of Upper-Level Sounding Channels in HWRF with Two Different Model Tops

The Advanced Microwave Sounding Unit-A（AMSU-A） onboard the NOAA satellites NOAA-18 and NOAA-19 and the European Organization for the Exploitation of Meteorological Satellites（EUMETSAT）Met Op-A, the hyperspectral Atmospheric Infrared Sounder（AIRS） onboard Aqua, the High resolution Infra Red Sounder（HIRS） onboard NOAA-19 and Met Op-A, and the Advanced Technology Microwave Sounder（ATMS） onboard Suomi National Polar-orbiting Partnership（NPP） satellite provide upper-level sounding channels in tropical cyclone environments. Assimilation of these upper-level sounding channels data in the Hurricane Weather Research and Forecasting（HWRF） system with two different model tops is investigated for the tropical storms Debby and Beryl and hurricanes Sandy and Isaac that occurred in 2012. It is shown that the HWRF system with a higher model top allows more upper-level microwave and infrared sounding channels data to be assimilated into HWRF due to a more accurate upper-level background profile. The track and intensity forecasts produced by the HWRF data assimilation and forecast system with a higher model top are more accurate than those with a lower model top.

A DEVELOPMENTAL FRAMEWORK FOR IMPROVING HURRICANE MODEL PHYSICAL PARAMETERIZATIONS USING AIRCRAFT OBSERVATIONS

As part of NOAA’s Hurricane Forecast Improvement Program(HFIP),this paper addresses the important role of aircraft observations in hurricane model physics validation and improvement.A model developmental framework for improving the physical parameterizations using quality-controlled and post-processed aircraft observations is presented,with steps that include model diagnostics,physics development,physics implementation and further evaluation.Model deficiencies are first identified through model diagnostics by comparing the simulated axisymmetric multi-scale structures to observational composites.New physical parameterizations are developed in parallel based on in-situ observational data from specially designed hurricane field programs.The new physics package is then implemented in the model,which is followed by further evaluation.The developmental framework presented here is found to be successful in improving the surface layer and boundary layer parameterization schemes in the operational Hurricane Weather Research and Forecast(HWRF) model.Observations for improving physics packages other than boundary layer scheme are also discussed.