Three-dimensional Data Assimilation Research Articles

Data Assimilation for Rainfall-Runoff Prediction Based on Coupled Atmospheric-Hydrologic Systems with Variable Complexity

The data assimilation technique is an effective method for reducing initial condition errors in numerical weather prediction (NWP) models. This paper evaluated the potential of the weather research and forecasting (WRF) model and its three-dimensional data assimilation (3DVar) module in improving the accuracy of rainfall-runoff prediction through coupled atmospheric-hydrologic systems. The WRF model with the assimilation of radar reflectivity and conventional surface and upper-air observations provided the improved initial and boundary conditions for the hydrological process; subsequently, three atmospheric-hydrological systems with variable complexity were established by coupling WRF with a lumped, a grid-based Hebei model, and the WRF-Hydro modeling system. Four storm events with different spatial and temporal rainfall distribution from mountainous catchments of northern China were chosen as the study objects. The assimilation results showed a general improvement in the accuracy of rainfall accumulation, with low root mean square error and high correlation coefficients compared to the results without assimilation. The coupled atmospheric-hydrologic systems also provide more accurate flood forecasts, which depend upon the complexity of the coupled hydrological models. The grid-based Hebei system provided the most stable forecasts regardless of whether homogeneous or inhomogeneous rainfall was considered. Flood peaks before assimilation were underestimated more in the lumped Hebei model relative to the other coupling systems considered, and the model seems more applicable for homogeneous temporal and spatial events. WRF-Hydro did not exhibit desirable predictions of rapid flood process recession. This may reflect increasing infiltration due to the interaction of atmospheric and land surface hydrology at each integration, resulting in mismatched solutions for local runoff generation and confluence.

Single-Doppler Velocity Retrieval of the Wind Field in a Tornadic Supercell Using Mobile, Phased-Array, Doppler Radar Data

AbstractA three-dimensional data assimilation (3DVar) least squares–type single-Doppler velocity retrieval (SDVR) algorithm is utilized to retrieve the wind field of a tornadic supercell using data collected by a mobile, phased-array, Doppler radar [Mobile Weather Radar (MWR) 05XP] with very high temporal resolution (6 s). It is found that the cyclonic circulation in the hook-echo region can be successfully recovered by the SDVR algorithm. The quality of the SDVR analyses is evaluated by dual-Doppler syntheses using data collected by two mobile Doppler radars [Doppler on Wheels 6 and 7 (DOW6 and DOW7, respectively)]. A comparison between the SDVR analyses and dual-Doppler syntheses confirms the conclusion reached by an earlier theoretical analysis that because of the temporally discrete nature of the radar data, the wind speed retrieved by single-Doppler radar is always underestimated, and this underestimate occurs more significantly for the azimuthal (crossbeam) wind component than for the radial (along beam) component. However, the underestimate can be mitigated by increasing the radar data temporal resolution. When the radar data are collected at a sufficiently high rate, the azimuthal wind component may be overestimated. Even with data from a rapid scan, phased-array, Doppler radar, our study indicates that it is still necessary to calculate the SDVR in an optimal moving frame of reference. Finally, the SDVR algorithm’s robustness is demonstrated. Even with a temporal resolution (2 min) much lower than that of the phased-array radar, the cyclonic flow structure in the hook-echo region can still be retrieved through SDVR using data observed by DOW6 or DOW7, although a difference in the retrieved fields does exist. A further analysis indicates that this difference is caused by the location of the radars.

Application of surface pressure measurements of O2-band differential absorption radar system in three-dimensional data assimilation on hurricane: Part II — A quasi-observational study

This is the second part on assessing the impacts of assimilating various distributions of sea-level pressure (SLP) on hurricane simulations, using the Weather and Research Forecast (WRF) three dimensional variational data assimilation system (3DVAR). One key purpose of this series of study is to explore the potential of using remotely sensed sea surface barometric data from O2-band differential absorption radar system currently under development for server weather including hurricane forecasts. In this part II we further validate the conclusions of observational system simulation experiments (OSSEs) in the part I using observed SLP for three hurricanes that passed over the Florida peninsula. Three SLP patterns are tested again, including all available data near the Florida peninsula, and a band of observations either through the center or tangent to the hurricane position. Before the assimilation, a vortex SLP reconstruction technique is employed for the use of observed SLP as discussed in the part I. In agreement with the results from OSSEs, the performance of assimilating SLP is enhanced for the two hurricanes with stronger initial minimum SLP, leading to a significant improvement in the track and position relative to the control where no data are assimilated. On the other hand, however, the improvement in the hurricane intensity is generally limited to the first 24–48h of integration, while a high resolution nested domain simulation, along with assimilation of SLP in the coarse domain, shows more profound improvement in the intensity. A diagnostic analysis of the potential vorticity suggests that the improved track forecasts are attributed to the combined effects of adjusting the steering wind fields in a consistent manner with having a deeper vortex, and the associated changes in the convective activity.

Application of surface pressure measurements from O2-band differential absorption radar system in three-dimensional data assimilation on hurricane: Part I – An observing system simulation experiments study

Sea level pressure (SLP) is an important variable in regulating hurricane motion. However, SLP generally cannot be measured in open oceans due to limited buoys. Because of the potential availability of an O2-band differential absorption radar for sea surface barometry, we investigate the value of assimilating various patterns of SLP from such a system on hurricane prediction using the Weather Research and Forecasting (WRF) three-dimensional variational data assimilation system (3DVAR) based on Observing System Simulation Experiments (OSSEs). An important objective of this series of study is to explore the potential to use space and airborne sea surface air pressure measurements from an O2-band differential absorption radar currently under development for server weather including hurricane forecasts. The surface pressure patterns include an area of SLP, and a band of SLP either through the center or tangent to the hurricane position; the latter two distributions are similar to what could be obtained from the differential absorption radar system, which could be installed on spaceborne satellites and/or mounted on reconnaissance aircraft. In the banded pressure cases, we propose a vortex reconstruction technique based on surface pressure field.Assimilating observations from the reconstructed surface pressure leads to a better representation of initial SLP and vertical cross-section of wind, relative to the control where no data is assimilated and to the assimilation without vortex reconstruction. In eight of the nine OSSEs simulations on three hurricanes with three leading times of integration, which cover a wide range of initial minimum SLP from 951 to 1011hPa, substantial improvements are found not only in the hurricane track and position, but also in the hurricane intensity, in terms of the SLP and maximum surface wind. The only case without significant improvement is resulted from the very weak initial condition (SLP 1011hPa), which had no clear indication of tropical disturbance at the stage for initialization. The improvements of assimilation are generally enhanced for the stronger hurricanes whose differences in initial minimum SLP between nature run and control are larger.

Obstacles and benefits of the implementation of a reduced-rank smoother with a high resolution model of the tropical Atlantic Ocean

Abstract. Most of oceanographic operational centers use three-dimensional data assimilation schemes to produce reanalyses. We investigate here the benefits of a smoother, i.e. a four-dimensional formulation of statistical assimilation. A square-root sequential smoother is implemented with a tropical Atlantic Ocean circulation model. A simple twin experiment is performed to investigate its benefits, compared to its corresponding filter. Despite model's non-linearities and the various approximations used for its implementation, the smoother leads to a better estimation of the ocean state, both on statistical (i.e. mean error level) and dynamical points of view, as expected from linear theory. Smoothed states are more in phase with the dynamics of the reference state, an aspect that is nicely illustrated with the chaotic dynamics of the North Brazil Current rings. We also show that the smoother efficiency is strongly related to the filter configuration. One of the main obstacles to implement the smoother is then to accurately estimate the error covariances of the filter. Considering this, benefits of the smoother are also investigated with a configuration close to situations that can be managed by operational center systems, where covariances matrices are fixed (optimal interpolation). We define here a simplified smoother scheme, called half-fixed basis smoother, that could be implemented with current reanalysis schemes. Its main assumption is to neglect the propagation of the error covariances matrix, what leads to strongly reduce the cost of assimilation. Results illustrate the ability of this smoother to provide a solution more consistent with the dynamics, compared to the filter. The smoother is also able to produce analyses independently of the observation frequency, so the smoothed solution appears more continuous in time, especially in case of a low frenquency observation network.

TAMDAR Observation Assimilation in WRF 3D-Var and Its Impact on Hurricane Ike (2008) Forecast

AbstractThis study evaluates the impact of atmospheric observations from the Tropospheric Airborne Meteorological Data Reporting (TAMDAR) observing system on numerical weather prediction of hurricane Ike (2008) using three-dimensional data assimilation system for the Weather Research and Forecast (WRF) model (WRF 3D-Var). The TAMDAR data assimilation capability is added to WRF 3D-Var by incorporating the TAMDAR observation operator and corresponding observation processing procedure. Two 6-h cycling data assimilation and forecast experiments are conducted. Track and intensity forecasts are verified against the best track data from the National Hurricane Center.The results show that, on average, assimilating TAMDAR observations has a positive impact on the forecasts of hurricane Ike. The TAMDAR data assimilation reduces the track errors by about 30 km for 72-h forecasts. Improvements in intensity forecasts are also seen after four 6-h data assimilation cycles. Diagnostics show that assimilation of TAMDAR da...

Indirect Assimilation of Radar Reflectivity with WRF 3D-Var and Its Impact on Prediction of Four Summertime Convective Events

AbstractAn indirect radar reflectivity assimilation scheme has been developed within the Weather Research and Forecasting model three-dimensional data assimilation system (WRF 3D-Var). This scheme, instead of assimilating radar reflectivity directly, assimilates retrieved rainwater and estimated in-cloud water vapor. An analysis is provided to show that the assimilation of the retrieved rainwater avoids the linearization error of the Z–qr (reflectivity–rainwater) equation. A new observation operator is introduced to assimilate the estimated in-cloud water vapor. The performance of the scheme is demonstrated by assimilating reflectivity observations into the Rapid Update Cycle data assimilation and forecast system operating at Beijing Meteorology Bureau. Four heavy-rain-producing convective cases that occurred during summer 2009 in Beijing, China, are studied using the newly developed system. Results show that on average the assimilation of reflectivity significantly improves the short-term precipitation forecast skill up to 7 h. A diagnosis of the analysis fields of one case shows that the assimilation of reflectivity increases humidity, rainwater, and convective available potential energy in the convective region. As a result, the analysis successfully promotes the developments of the convective system and thus improves the subsequent prediction of the location and intensity of precipitation for this case.

On the relative contribution of wind and mass observations in numerical weather forecasting

The impacts of wind and mass observations on analyses and forecasts are studied. Idealized perfect and complete observations are examined. A realistic three-dimensional data assimilation technique is considered. Spatial structures of analysis errors resulting from assimilation of wind or mass observations are theoretically investigated. Forecast impacts are assessed using the geostrophic adjustment theory and spatial probabilistic error models. Numerical predictability experiments are conducted to verify the theoretic conclusions. The main result is that, on average, wind observation are more important than mass observations, especially in the tropics and in the upper troposphere and stratosphere. In the mid and high latitude lower troposphere, the wind and mass observational data appear to be of comparable utility.

Local-Domain Mesoscale Analysis and Forecast Model Support for the 1996 Centennial Olympic Games

Abstract The National Weather Service (NWS) developed the Olympic Weather Support System (OWSS) to provide specialized operational weather support for the 1996 Centennial Olympic Games in Atlanta. Operational implementation of the National Oceanic and Atmospheric Administration Forecast Systems Laboratory’s Local Analysis and Prediction System (LAPS) was a key element of the OWSS. LAPS is a complete, three-dimensional data assimilation system that produced subhourly atmospheric analyses on an 8-km grid covering all the Olympic venues. The LAPS analyses also provided initial conditions to the Regional Atmospheric Modeling System (RAMS) mesoscale forecast model. RAMS forecasts were generated at least every 3 h using 8- or 2-km grids. For the first time, a comprehensive operational analysis and forecast system operated in a local NWS forecast office to support meso-β-scale forecasts and warnings. Numerous benefits of LAPS–RAMS to the local forecast office were demonstrated. The OWSS, with LAPS–RAMS included,...

Continuous Assimilation of Geosat Altimeter Data into a Three-Dimensional Primitive Equation Gulf Stream Model

Abstract A three-dimensional data assimilation scheme is described and tested, using the Geosat altimeter data and a high-resolution, primitive equation, numerical ocean model of the Gulf Stream region. The assimilation scheme is based on an optimal interpolation approach in which data along satellite tracks are continuously interpolated horizontally and vertically into the model grid and assimilated with the model prognostic fields. Preprocessed correlations between surface elevation anomalies and subsurface temperature and salinity anomalies are used to project surface information into the deep ocean; model and data error estimates are used to optimize the assimilation. Analysis fields derived from the Navy's Optimum Thermal Interpolation System are used to initialize the model and to provide some estimate of errors. To evaluate the effectiveness of the assimilation scheme, the errors of model oceanic fields (surface elevation, Gulf Stream axis, temperature) with data assimilation are compared with erro...