Four-dimensional variational data assimilation experiments for a heavy rain case during the 2002 IOP in China (original) (raw)
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Four-Dimensional Variational Data Assimilation for the Blizzard of 2000
Monthly Weather Review, 2002
The Weather Research and Forecasting (WRF) model-based variational data assimilation system (WRF-Var) has been extended from three-to four-dimensional variational data assimilation (WRF 4D-Var) to meet the increasing demand for improving initial model states in multiscale numerical simulations and forecasts. The initial goals of this development include operational applications and support to the research community. The formulation of WRF 4D-Var is described in this paper. WRF 4D-Var uses the WRF model as a constraint to impose a dynamic balance on the assimilation. It is shown to implicitly evolve the background error covariance and to produce the flow-dependent nature of the analysis increments. Preliminary results from real-data 4D-Var experiments in a quasi-operational setting are presented and the potential of WRF 4D-Var in research and operational applications are demonstrated. A wider distribution of the system to the research community will further develop its capabilities and to encourage testing under different weather conditions and model configurations.
Four-Dimensional Variational Assimilation of Precipitation Data
Monthly Weather Review, 1995
This paper studies the impact of assimilating rain-derived information in the European Centre for Medium-Range Weather Forecasts (ECMWF) four-dimensional variational (4DVAR) system. The approach is based on a one-dimensional variational (1DVAR) method. First, model temperature and humidity profiles are adjusted by assimilating observed surface rain rates in 1DVAR. Second, 1DVAR total column water vapor (TCWV) estimates are assimilated in 4DVAR. Observations used are Tropical Rainfall Measuring Mission (TRMM) surface rainrate estimates from the TRMM Microwave Imager. Two assimilation experiments making use of 1DVAR TCWV were run for a 15-day period. The ''Rain-1'' experiment only assimilates 1DVAR retrievals where the observed rain rate is nonzero while the ''Rain-2'' experiment assimilates all 1DVAR TCWV estimates. The period selected includes Hurricane Bonnie, which was well sampled by TRMM (late August 1998). Results show a positive impact on the humidity analysis of assimilating 1DVAR TCWV in 4DVAR. The model rain rates at the analysis time are closer to the TRMM observations showing a posteriori the consistency of the two-step approach chosen to assimilate rain-rate information in 4DVAR. The modification of the humidity analysis induces changes in the wind and pressure analysis. In particular the analysis of the track of Hurricane Bonnie is noticeably improved for the early stage of the storm development for both the Rain-1 and Rain-2 experiments. When Bonnie is in a mature stage the influence of the 1DVAR TCWV assimilation is to intensify the hurricane. Comparison with Clouds and the Earth's Radiant Energy System (CERES) measurements also show a neutral impact on the radiative fluxes at the top-of-the atmosphere when using 1DVAR TCWV estimates. The impact on the forecasts is a slight reduction of the model precipitation spindown over tropical oceans. Objective scores for the Tropics are improved, particularly for wind and for upper-tropospheric temperature. Analysis and forecast results are generally better for the Rain-2 experiment compared to Rain-1, implying that the 1DVAR TCWV estimates retrieved where no rain is observed provide useful information to 4DVAR.
Four-dimensional data assimilation and numerical weather prediction
2003
All forecast models, whether they represent the state of the weather, the spread of a disease, or levels of economic activity, contain unknown parameters. These parameters may be the model's initial conditions, its boundary conditions, or other tunable parameters which have to be determined. Four dimensional variational data assimilation (4D-Var) is a method of estimating this set of parameters by optimizing the fit between the solution of the model and a set of observations which the model is meant to predict. The four dimensional nature of 4D-Var reflects the fact that the observation set spans not only three dimensional space, but also a time domain. Although the method of 4D-Var described in this report is not restricted to any particular system, the application described here has a Numerical Weather Prediction (NWP) model at its core, and the parameters to be determined are the initial conditions of the model. The purpose of this report is to give a survey covering assimilation of Doppler radar wind data into a high-resolution NWP model. Some associated problems, such as sensitivity to small variations in the initial conditions or due to small changes in the background variables, and biases due to nonlinearity are also studied.
Experiments on 4D-Var assimilation of rainfall data using an incremental formulation
Quarterly Journal of the Royal Meteorological Society, 2003
Assimilations of isolated surface rainfall observations are performed using two methodologies compatible with the incremental formulation of the European Centre for Medium-Range Weather Forecasts operational 4D-Var system. In a rst method (de ned as '1D-Var C 4D-Var') 1D-Var retrievals of total column water vapour in rainy areas are assimilated in 4D-Var, while a second approach assimilates rainfall rates in 4D-Var directly. In addition, the behaviour of two contrasted convection schemes (mass-ux and adjustment) is examined. The minimization performs generally better for the adjustment scheme and for the '1D-Var C 4D-Var' method (i.e. larger decrease of the cost function). The usefulness of the direct 4D-Var is limited by various inconsistencies present in the current incremental formulation and by the extreme sensitivity of the convection schemes to small changes in humidity. The two methods produce similar and small modi cations to the dynamical elds, that are partly explained by the fact that the observation is assimilated early in the time window of 4D-Var (3 hours). This study demonstrates the need for convection schemes with smooth Jacobians in the variational context. It also con rms the robustness of the '1D-Var C 4D-Var' approach than can be used for operational rainfall-rate assimilation as long as identi ed problems with the incremental 4D-Var are not solved.
Monthly Weather Review, 2000
The formulation of the National Centers for Environmental Prediction four-dimensional variational dataassimilation (4D-Var) system is described. Results of applying 4D-Var over a one-week assimilation period, with a full set of physical parametrizations, are presented and compared with those of 3D-Var. The linearization has been performed without simplifications and, therefore, the tangent-linear and adjoint codes are consistent with the nonlinear physical parametrizations. The 4D-Var assimilation is similar in formulation to the 3D-Var analysis, except that observations are used at the appropriate time in 4D-Var. Compared with the 3D-Var runs, the 4D-Var results showed good convergences, smaller analysis increments, and a comparable fit of analyses and short-range forecasts to observations. A consistent improvement with the 4D-Var system is observed in short-range (six-hour) forecasts of all model variables except the specific humidity. The temperature analyses from 4D-Var were found to be better in most of the areas where the analysis errors from 3D-Var were largest, although the globally averaged root-mean-square difference in the 4D-Var temperature analysis was larger due to a very small degradation in some parts of the globe that include data-rich areas. The globally averaged root-mean-square difference in the 4D-Var specific-humidity analysis, compared with that of 3D-Var, was larger and was found to result from slightly increased analysis-error maxima in the 4D-Var results over data-sparse tropical regions. The 3 4 day forecasts from 4D-Var analyses compared more favourably than forecasts from the 3D-Var analyses with the targeted mid-Pacific dropwindsonde observations available from the 1998 North Pacific Experiment. Compared with conventional observations, a consistent improvement in the 1-5 day forecasts of wind and temperature was shown in the tropics and the southern hemisphere.
CIRA/CSU Four-Dimensional Variational Data Assimilation System
Monthly Weather Review, 2005
A new four-dimensional variational data assimilation (4DVAR) system is developed at the Cooperative Institute for Research in the Atmosphere (CIRA)/Colorado State University (CSU). The system is also called the Regional Atmospheric Modeling Data Assimilation System (RAMDAS). In its present form, the 4DVAR system is employing the CSU/Regional Atmospheric Modeling System (RAMS) nonhydrostatic primitive equation model. The Weather Research and Forecasting (WRF) observation operator is used to access the observations, adopted from the WRF three-dimensional variational data assimilation (3DVAR) algorithm. In addition to the initial conditions adjustment, the RAMDAS includes the adjustment of model error (bias) and lateral boundary conditions through an augmented control variable definition. Also, the control variable is defined in terms of the velocity potential and streamfunction instead of the horizontal winds. The RAMDAS is developed after the National Centers for Environmental Predic...
Journal of the Meteorological Society of Japan, 2007
The Meteorological Research Institute of the Japan Meteorological Agency has developed a cloudresolving nonhydrostatic 4-dimensional variational assimilation system (NHM-4DVAR), based on the Japan Meteorological Agency Nonhydrostatic Model (JMA-NHM), in order to investigate the mechanism of heavy rainfall events induced by mesoscale convective systems (MCSs). A horizontal resolution of the NHM-4DVAR is set to 2 km to resolve MCSs, and the length of the assimilation window is 1-hour. The control variables of the NHM-4DVAR are horizontal wind, vertical wind, nonhydrostatic pressure, potential temperature, surface pressure and pseudo relative humidity. Perturbations to the dynamical processes, and the advection of water vapor are considered, but these to the other physical processes are not taken into account. The NHM-4DVAR is applied to the heavy rainfall event observed at Nerima, central part of Tokyo metropolitan area, on 21 July 1999. Doppler radar's radial wind data, Global Positioning System's precipitable water vapor data, and surface temperature and wind data are assimilated as high temporal and spatial resolution data. The Nerima heavy rainfall is well reproduced in the assimilation and subse
Monthly Weather Review, 2016
The momentum variables of streamfunction and velocity potential are used as control variables in a number of operational variational data assimilation systems. However, in this study it is shown that, for limited-area high-resolution data assimilation, the momentum control variables ψ and χ (ψχ) pose potential difficulties in background error modeling and, hence, may result in degraded analysis and forecast when compared with the direct use of x and y components of wind (UV). In this study, the characteristics of the modeled background error statistics, derived from an ensemble generated from Weather Research and Forecasting (WRF) Model real-time forecasts of two summer months, are first compared between the two control variable options. Assimilation and forecast experiments are then conducted with both options for seven convective events in a domain that encompasses the Rocky Mountain Front Range using the three-dimensional variational data assimilation (3DVar) system of the WRF Mo...