Yong-Run Guo

A Three-Dimensional Variational Data Assimilation System for MM5: Implementation and Initial Results

Abstract A limited-area three-dimensional variational data assimilation (3DVAR) system applicable to both synoptic and mesoscale numerical weather prediction is described. The system is designed for use in time-critical real- time applications and is freely available to the data assimilation community for general research. The unique features of this implementation of 3DVAR include (a) an analysis space represented by recursive filters and truncated eigenmodes of the background error covariance matrix, (b) the inclusion of a cyclostrophic term in 3DVARs explicit mass–wind balance equation, and (c) the use of the software architecture of the Weather Research and Forecast (WRF) model to permit efficient performance on distributed-memory platforms. The 3DVAR system is applied to a multiresolution, nested-domain forecast system. Resolution and seasonal- dependent background error statistics are presented. A typhoon bogusing case study is performed to illustrate the 3DVAR response to a single surface pressure...

Four-Dimensional Variational Data Assimilation for WRF: Formulation and Preliminary Results

Abstract 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 encoura...

The Weather Research and Forecasting Model's Community Variational/Ensemble Data Assimilation System: WRFDA

Data assimilation is the process by which observations are combined with short-range NWP model output to produce an analysis of the state of the atmosphere at a specified time. Since its inception in the late 1990s, the multiagency Weather Research and Forecasting (WRF) model effort has had a strong data assimilation component, dedicating two working groups to the subject. This article documents the history of the WRF data assimilation effort, and discusses the challenges associated with balancing academic, research, and operational data assimilation requirements in the context of the WRF effort to date. The WRF Models Community Variational/Ensemble Data Assimilation System (WRFDA) has evolved over the past 10 years, and has resulted in over 30 refereed publications to date, as well as implementation in a wide range of real-time and operational NWP systems. This paper provides an overview of the scientific capabilities of WRFDA, and together with results from sample operation implementations at the U.S. ...

Assimilation of Doppler Radar Observations with a Regional 3DVAR System: Impact of Doppler Velocities on Forecasts of a Heavy Rainfall Case

Abstract In this paper, the impact of Doppler radar radial velocity on the prediction of a heavy rainfall event is examined. The three-dimensional variational data assimilation (3DVAR) system for use with the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) is further developed to enable the assimilation of radial velocity observations. Doppler velocities from the Korean Jindo radar are assimilated into MM5 using the 3DVAR system for a heavy rainfall case that occurred on 10 June 2002. The results show that the assimilation of Doppler velocities has a positive impact on the short-range prediction of heavy rainfall. The dynamic balance between atmospheric wind and thermodynamic fields, based on the Richardson equation, is introduced to the 3DVAR system. Vertical velocity (w) increments are included in the 3DVAR system to enable the assimilation of the vertical velocity component of the Doppler radial velocity observation. The forecast of the hydrometeor variables of cloud water (qc...

Assimilation of Precipitable Water Measurements into a Mesoscale Numerical Model

Abstract Significant progress has been made over the past decade in the development of remote-sensing instruments to profile wind and temperature. However, the current technology of profiling water vapor remotely is still far from perfect. Although some promising optical research systems, such as the Raman lidar, can provide high vertical resolution profiles of water vapor, it may be years before they are generally available. Currently, there are several systems that can measure the vertically integrated water vapor (i.e., precipitable water) with a high degree of accuracy. In this paper we use a simple method to assimilate precipitable water measurements (possibly from a network of dual-channel ground-based microwave radiometers or a satellite-based system) into a mesoscale model. The basic idea is to relax the predicted precipitable water toward the observed value, while retaining the vertical structure of the model humidity field. We test this method with the special 3-h soundings available from the Se...

Assimilation of Atmospheric Radio Refractivity Using a Nonhydrostatic Adjoint Model

Abstract Recently, a new approach to remote sensing of water vapor based on the Global Positioning System (GPS) has been proposed. Specifically, the bending of radio signals propagating from GPS satellites to a receiver on a low earth-orbiting satellite can be used to derive vertical profiles of atmospheric refractivity. Vertical profiles of temperature and water vapor can then be retrieved from the refractivity measurements. This is potentially a valuable data source for the meteorological community. However, before such measurements are used for operational numerical weather prediction, we need to assess the accuracy of the retrieved temperature and moisture fields and properly assimilate these observations into a numerical model. A 4D data assimilation system based on the adiabatic version of the Penn State-NCAR Mesoscale Model and its adjoint was developed. A series of observing system simulation experiments was then conducted to assess the impact of GPS-derived atmospheric refractivity data. Specific...

Variational Assimilation of Precipitable Water Using a Nonhydrostatic Mesoscale Adjoint Model. Part I: Moisture Retrieval and Sensitivity Experiments

Abstract Recently it has been proposed that the phase delay associated with the radio signals propagating from GPS satellites to a ground-based GPS receiving station can be used to infer the vertically integrated water vapor (precipitable water—PW) with a high degree of accuracy. Since a ground-based GPS receiving station is relatively inexpensive, a specially designed, dense GPS network can provide PW measurements with unprecedented coverage. Such a data set can potentially have a significant impact on operational numerical weather prediction. In this paper, a series of numerical experiments were conducted using a variational (4DVAR) data assimilation system based on The Pennsylvania State University –National Center for Atmospheric Research mesoscale model MM5 and its adjoint. The special soundings collected in SESAME (Severe Environmental Storms and Mesoscale Experiment) 1979 wore used in two sets of experiments. In the first set, a 1-h assimilation window and an analysis of the observed PW data were u...

Four-dimensional variational data assimilation of heterogeneous mesoscale observations for a strong convective case

Abstract On 19 September 1996, a squall line stretching from Nebraska to Texas with intense embedded convection moved eastward across the Kansas–Oklahoma area, where special observations were taken as part of a Water Vapor Intensive Observing Period sponsored by the Atmospheric Radiation Measurement program. This provided a unique opportunity to test mesoscale data assimilation strategies for a strong convective event. In this study, a series of real-data assimilation experiments is performed using the MM5 four-dimensional variational data assimilation (4DVAR) system with a full physics adjoint. With a grid size of 20 km and 15 vertical layers, the MM5-4DVAR system successfully assimilated wind profiler, hourly rainfall, surface dewpoint, and ground-based GPS precipitable water vapor data. The MM5-4DVAR system was able to reproduce the observed rainfall in terms of precipitation pattern and amount, and substantially reduced the model errors when verified against independent observations. Additional data a...

Application of WRF 3DVAR to Operational Typhoon Prediction in Taiwan: Impact of Outer Loop and Partial Cycling Approaches

AbstractIn this paper, the impact of outer loop and partial cycling with the Weather Research and Forecasting Model’s (WRF) three-dimensional variational data assimilation system (3DVAR) is evaluated by analyzing 78 forecasts for three typhoons during 2008 for which Taiwan’s Central Weather Bureau (CWB) issued typhoon warnings, including Sinlaku, Hagupit, and Jangmi. The use of both the outer loop and the partial cycling approaches in WRF 3DVAR are found to reduce typhoon track forecast errors by more than 30%, averaged over a 72-h period. The improvement due to the outer loop approach, which can be more than 42%, was particularly significant in the early phase of the forecast. The use of the outer loop allows more observations to be assimilated and produces more accurate analyses. The assimilation of additional nonlinear GPS radio occultation (RO) observations over the western North Pacific Ocean, where traditional observational data are lacking, is particularly useful. With the lack of observations over...

A Vortex Relocation Scheme for Tropical Cyclone Initialization in Advanced Research WRF

Abstract This paper introduces a relocation scheme for tropical cyclone (TC) initialization in the Advanced Research Weather Research and Forecasting (ARW-WRF) model and demonstrates its application to 70 forecasts of Typhoons Sinlaku (2008), Jangmi (2008), and Linfa (2009) for which Taiwan’s Central Weather Bureau (CWB) issued typhoon warnings. An efficient and dynamically consistent TC vortex relocation scheme for the WRF terrain-following mass coordinate has been developed to improve the first guess of the TC analysis, and hence improves the tropical cyclone initialization. The vortex relocation scheme separates the first-guess atmospheric flow into a TC circulation and environmental flow, relocates the TC circulation to its observed location, and adds the relocated TC circulation back to the environmental flow to obtain the updated first guess with a correct TC position. Analysis of these typhoon cases indicates that the relocation procedure moves the typhoon circulation to the observed typhoon positi...