Examination of all‐sky infrared radiance simulation of Hiwamari‐8 for global data assimilation and model verification

Abstract. The ability of a data assimilation system to deal effectively with nonlinearities arising from the prognostic model or the relationship between the control variables and the available observations has received a lot of attention in theoretical studies based on very simplified test models. Less work has been done to quantify the importance of nonlinearities in operational, state-of-the-art global data assimilation systems. In this paper we analyse the nonlinear effects present in ECMWF 4D-Var and evaluate the ability of the incremental formulation to solve the nonlinear assimilation problem in a realistic NWP environment. We find that nonlinearities have increased over the years due to a combination of increased model resolution and the ever-growing importance of observations that are nonlinearly related to the state. Incremental 4D-Var is well suited for dealing with these nonlinear effects, but at the cost of increasing the number of outer loop relinearisations. We then discuss strategies for accommodating the increasing number of sequential outer loops in the tight schedules of operational global NWP.

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A Two-Season Impact Study of the WindSat Surface Wind Retrievals in the NCEP Global Data Assimilation System

Weather and Forecasting ◽

10.1175/2010waf2222377.1 ◽

2010 ◽

Vol 25 (3) ◽

pp. 931-949 ◽

Cited By ~ 2

Author(s):

Li Bi ◽

James A. Jung ◽

Michael C. Morgan ◽

John F. Le Marshall

Keyword(s):

Data Assimilation ◽

Surface Wind ◽

Temperature Fields ◽

Experimental Simulation ◽

Naval Research ◽

Surface Winds ◽

Control Simulation ◽

Wind Retrievals ◽

Global Data ◽

Assimilation System

Abstract A two-season observing system experiment (OSE) was used to quantify the impacts of assimilating the WindSat surface winds product developed by the Naval Research Laboratory (NRL). The impacts of assimilating these surface winds were assessed by comparing the forecast results through 168 h for the months of October 2006 and March 2007. The National Centers for Environmental Prediction’s (NCEP) Global Data Assimilation/Global Forecast System (GDAS/GFS) was used, at a resolution of T382-64 layers, as the assimilation system and forecast model for these experiments. A control simulation utilizing all the data types assimilated in the operational GDAS was compared to an experimental simulation that added the WindSat surface winds. Quality control procedures required to assimilate the surface winds are discussed. Anomaly correlations (ACs) of geopotential heights at 1000 and 500 hPa were evaluated for the control and experiment during both seasons. The geographical distribution of the forecast impacts (FIs) on the wind field and temperature fields at 10-m height and 500 hPa is also discussed. The results of this study show that assimilating the surface wind retrievals from the WindSat satellite improve the NCEP GFS wind and temperature forecasts. A positive FI, which suggests that the error growth of the experiment is slower than the control, has been realized in the NCEP GDAS/GFS wind and temperature forecasts through 24 h. The WindSat experiment AC scores are similar to the control simulation AC scores until the day 6 forecasts, when the improvements in the WindSat experiment become greater for both seasons and in most of the cases.

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