Extension of 3DVAR to 4DVAR: Implementation of 4DVAR at the Meteorological Service of Canada

Abstract

On 15 March 2005, the Meteorological Service of Canada (MSC) proceeded to the implementation of a four-dimensional variational data assimilation (4DVAR) system, which led to significant improvements in the quality of global forecasts. This paper describes the different elements of MSC's 4DVAR assimilation system, discusses some issues encountered during the development, and reports on the overall results from the 4DVAR implementation tests. The 4DVAR system adopted an incremental approach with two outer iterations. The simplified model used in the minimization has a horizontal resolution of 170 km and its simplified physics includes vertical diffusion, surface drag, orographic blocking, stratiform condensation, and convection. One important element of the design is its modularity, which has permitted continued progress on the three-dimensional variational data assimilation (3DVAR) component (e.g., addition of new observation types) and the model (e.g., computational and numerical changes). This paper discusses some numerical problems that occur in the vicinity of the Poles where the semi-Lagrangian scheme becomes unstable when there is a simultaneous occurrence of converging meridians and strong wind gradients. These could be removed by filtering the winds in the zonal direction before they are used to estimate the upstream position in the semi-Lagrangian scheme. The results show improvements in all aspects of the forecasts over all regions. The impact is particularly significant in the Southern Hemisphere where 4DVAR is able to extract more information from satellite data. In the Northern Hemisphere, 4DVAR accepts more asynoptic data, in particular coming from profilers and aircrafts. The impact noted is also positive and the short-term forecasts are particularly improved over the west coast of North America. Finally, the dynamical consistency of the 4DVAR global analyses leads to a significant impact on regional forecasts. Experimentation has shown that regional forecasts initiated directly from a 4DVAR global analysis are improved with respect to the regional forecasts resulting from the regional 3DVAR analysis.

Impact of urban parameterization on high resolution air quality forecast with the GEM – AQ model

“…In the operational setup the GEM-AQ model starts every 24 h from analysed meteorological fields. Objective analysis is prepared on a global grid with resolution ∼35 km using the 4D-Var system (Gauthier et al, 2007). Thus, fine scale meteorological characteristics that result from the TEB parameterization are not carried between simulations.…”

“…The GEM-AQ model starts every 24 h from analysed meteorological fields prepared using the 4D-Var system (Gauthier et al, 2007). Model integration is done on a global variable resolution grid with a total of 200 by 182 grid points.…”

Use of Integrated Observations to Improve 0-36 h Flood Forecasting: Development and Application of a Coupled Atmosphere-Hydrology System in the Nanpan River Basin, China

“…5). Hence it is necessary to update the 3DVAR to 4DVAR (e.g., Gauthier et al 2007) to allow increased assimilated data volume (e.g., the GPS observations in other periods) for the operational NWPs in Yunnan province. The VAR-onlyRADIOSONDE assimilated the 10-site radiosonde observations, including the vertical profiles of air temperature, air pressure, relative humidity, wind direction and speed, and dew point temperature (hereafter Radio-V), while the VARonlyGROUND assimilated the 230-site surface observations, including air temperature, air pressure, relative humidity, wind direction and speed, and precipitation (hereafter Surface-V).…”

Comparative performance analysis of climate re-analysis approaches in Angola

“…The process advances by analysis cycles and, in each cycle, observations within a given assimilation window are combined with the model results to produce the "best" estimate of the state of the system, balancing the uncertainty in the observational data and in the model forecast. The 3D-Var analysis assumes a steady-state condition within the assimilation window, whereas 4D-Var analysis uses the forecast model to predict the change in time of the state of the system (Gauthier et al 2007). The NCEP-r1 was the first re-analysis project of its kind for NOAA and used a wide variety of weather observations from ships, planes, radiosonde observation (RAOBs), station data and satellite observations (Kalnay et al 1996), covering the period from January 1948 to the present.…”