Advanced Research WRF Research Articles

Application of ATOVS Radiance with ARW WRF/GSI Data Assimilation System in the Prediction of Hurricane Katrina

In this study, we evaluated the impact of directly assimilating radiance on Hurricane Katrina forecasts over the Gulf of Mexico in the southeastern United States in August 2005. The ATOVS (i.e., The Advanced Television and Infrared Observation Satellite (TIROS)-N Operational Vertical Sounder) radiance data, the Gridpoint Statistical Interpolation (GSI) three-dimensional variational analysis (3DVAR) system, and the Advanced Research WRF (ARW WRF) model were employed. The results in a series of experiments show that after radiance data assimilation, the intensity and structure of initial fields including atmospheric flow, temperature and moisture have been modified somehow, especially with instruments using microwave bands such as AMSU-A/B. An anomalous southward pressure gradient has been added behind the hurricane center, which made the easterly flow go through the initial vortex center, accelerating westward movement of the hurricane. All data assimilation experiments obtain a similar forecast for the hurricane track before 36 h of model integration. After 36 h, the hurricane tracks in AMSU-A/B experiments are closer to the best track, but the tracks in HIRS3 and control experiments have a bigger error. However, we note that the improvement is limited, all assimilation experiments did not properly depict the deepening of the hurricane center around 1800 UTC 28 August.

Application of an Adiabatic WRF Adjoint to the Investigation of the May 2004 McMurdo, Antarctica, Severe Wind Event

The tangent linear and adjoint of an adiabatic version of the Weather Research and Forecasting (WRF) Model with its Advanced Research WRF (ARW) dynamic core have been developed. The source-to-source automatic differentiation tool [i.e., the Transformation of Algorithm (TAF) in FORTRAN] was used in the development. Tangent linear and adjoint checks of the developed adiabatic WRF adjoint modeling system (WAMS) were conducted, and all necessary correctness verification procedures were passed. As the first application, the adiabatic WAMS was used to study the adjoint sensitivity of a severe windstorm in Antarctica. Linearity tests indicated that an adjoint-based sensitivity study with the Antarctic Mesoscale Prediction System (AMPS) 90-km domain configuration for the windstorm is valid up to 24 h. The adjoint-based sensitivity calculation with adiabatic WAMS identified sensitive regions for the improvement of the 24-h forecast of the windstorm. It is indicated that the windstorm forecast largely relies on the model initial conditions in the area from the south part of the Trans-Antarctic Mountains to West Antarctica and between the Ross Ice Shelf and the South Pole. Based on the sensitivity analysis, the southerly or southeasterly wind at lower levels in the sensitivity region should be larger, the cyclone should be stronger, and the atmospheric stratification should be more stable over the north slope of the Trans-Antarctic Mountain to the Ross Ice Shelf, than the AMPS analyses. By constructing pseudo-observations in the sensitivity region using the gradient information of forecast windstorm intensity around McMurdo, the model initial conditions are revised with the WRF three-dimensional variational data assimilation, which leads to significant improvement in the prediction of the windstorm. An adjoint sensitivity study is an efficient way to identify sensitivity regions in order to collect more observations in the region for better forecasts in a specific aspect of interest.

Linking the advanced research WRF meteorological model with the CHIMERE chemistry-transport model

CHIMERE is a chemical transport model widely used within the air quality scientific community. The actual version of CHIMERE is coupled with the MM5 mesoscale meteorological model. To facilitate model development, MM5 is replaced here with the Weather Research and Forecasting (WRF) model. With the accuracy of air quality models being highly dependent on the quality of meteorological drivers, this coupling allows one to take advantage of relevant new developments in meteorological modelling.

Multiple-Radar Data Assimilation and Short-Range Quantitative Precipitation Forecasting of a Squall Line Observed during IHOP_2002

Abstract The impact of multiple–Doppler radar data assimilation on quantitative precipitation forecasting (QPF) is examined in this study. The newly developed Weather Research and Forecasting (WRF) model Advanced Research WRF (ARW) and its three-dimensional variational data assimilation system (WRF 3DVAR) are used. In this study, multiple–Doppler radar data assimilation is applied in WRF 3DVAR cycling mode to initialize a squall-line convective system on 13 June 2002 during the International H2O Project (IHOP_2002) and the ARW QPF skills are evaluated for the case. Numerical experiments demonstrate that WRF 3DVAR can successfully assimilate Doppler radial velocity and reflectivity from multiple radar sites and extract useful information from the radar data to initiate the squall-line convective system. Assimilation of both radial velocity and reflectivity results in sound analyses that show adjustments in both the dynamical and thermodynamical fields that are consistent with the WRF 3DVAR balance constraint and background error correlation. The cycling of the Doppler radar data from the 12 radar sites at 2100 UTC 12 June and 0000 UTC 13 June produces a more detailed mesoscale structure of the squall-line convection in the model initial conditions at 0000 UTC 13 June. Evaluations of the ARW QPF skills with initialization via Doppler radar data assimilation demonstrate that the more radar data in the temporal and spatial dimensions are assimilated, the more positive is the impact on the QPF skill. Assimilation of both radial velocity and reflectivity has more positive impact on the QPF skill than does assimilation of either radial velocity or reflectivity only. The improvement of the QPF skill with multiple-radar data assimilation is more clearly observed in heavy rainfall than in light rainfall. In addition to the improvement of the QPF skill, the simulated structure of the squall line is also enhanced by the multiple–Doppler radar data assimilation in the WRF 3DVAR cycling experiment. The vertical airflow pattern shows typical characteristics of squall-line convection. The cold pool and its related squall-line convection triggering process are better initiated in the WRF 3DVAR analysis and simulated in the ARW forecast when multiple–Doppler radar data are assimilated.

Numerical Prediction of an Antarctic Severe Wind Event with the Weather Research and Forecasting (WRF) Model

Abstract This study initiates the application of the maturing Weather Research and Forecasting (WRF) model to the polar regions in the context of the real-time Antarctic Mesoscale Prediction System (AMPS). The behavior of the Advanced Research WRF (ARW) in a high-latitude setting and its ability to capture a significant Antarctic weather event are investigated. Also, in a suite of sensitivity tests, the impacts of the assimilation of Moderate Resolution Imaging Spectroradiometer (MODIS) atmospheric motion vectors on ARW Antarctic forecasts are explored. The simulation results are analyzed and the statistical significance of error differences is assessed. It is found that with the proper consideration of MODIS data the ARW can accurately simulate a major Antarctic event, the May 2004 McMurdo windstorm. The ARW simulations illuminate an episode of high-momentum flow responding to the complex orography of the vital Ross Island region. While the model captures the synoptic setting and basic trajectory of the cyclone driving the event, there are differences on the mesoscale in the evolution of the low pressure system that significantly affect the forecast results. In general, both the ARW and AMPS’s fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) tend to underforecast the wind magnitudes, reflecting their stalling and filling of the system near Ross Island. It is seen, however, that both targeted data assimilation and grid resolution enhancement can yield improvement in the forecast of the key parameter of wind speed. It is found that the assimilation of MODIS observations can significantly improve the forecast for a high-impact Antarctic weather event. However, the application to the retrievals of a filter accounting for instrument channel, observation height, and surface type is necessary. The results indicate benefits to initial conditions and high-resolution, polar, mesoscale forecasts from the careful assimilation of nontraditional satellite observations over Antarctica and the Southern Ocean.

Summer Rainfall Forecast Spread in an Ensemble Initialized with Different Soil Moisture Analyses

Abstract The performance of an ensemble forecasting system initialized using varied soil moisture alone has been evaluated for rainfall forecasts of six warm season convective cases. Ten different soil moisture analyses were used as initial conditions in the ensemble, which used the Weather Research and Forecasting (WRF) Advanced Research WRF (ARW) model at 4-km horizontal grid spacing with explicit rainfall. Soil moisture analyses from the suite of National Weather Service operational models—the Rapid Update Cycle, the North American Model (formerly known as the Eta Model), and the Global Forecasting System—were used to design the 10-member ensemble. For added insight, two other runs with extremely low and high soil moistures were included in this study. Although the sensitivity of simulated 24-h rainfall to soil moisture was occasionally substantial in both weakly forced and strongly forced cases, a U-shaped rank histogram indicated insufficient spread in the 10-member ensemble. This result suggests that ensemble forecast systems using soil moisture perturbations alone might not add enough variability to rainfall forecasts. Perturbations to both atmospheric initial conditions and land surface initial conditions as well as perturbations to other aspects of model physics may increase forecast spread. Correspondence ratio values for the 0.01- and 0.5-in. rainfall thresholds imply some spread in the soil moisture ensemble, but mainly in the weakly forced cases. Relative operating characteristic curves for the 10-member ensemble and for various rainfall thresholds indicate modest skill for all thresholds with the most skill associated with the lowest rainfall threshold, a result typical of warm season events.

A time-split nonhydrostatic atmospheric model for weather research and forecasting applications

The sub-grid-scale parameterization of clouds is one of the weakest aspects of weather and climate modeling today, and the explicit simulation of clouds will be one of the next major achievements in numerical weather prediction. Research cloud models have been in development over the last 45 years and they continue to be an important tool for investigating clouds, cloud-systems, and other small-scale atmospheric dynamics. The latest generation are now being used for weather prediction. The Advanced Research WRF (ARW) model, representative of this generation and of a class of models using explicit time-splitting integration techniques to efficiently integrate the Euler equations, is described in this paper. It is the first fully compressible conservative-form nonhydrostatic atmospheric model suitable for both research and weather prediction applications. Results are presented demonstrating its ability to resolve strongly nonlinear small-scale phenomena, clouds, and cloud systems. Kinetic energy spectra and other statistics show that the model is simulating small scales in numerical weather prediction applications, while necessarily removing energy at the gridscale but minimizing artificial dissipation at the resolved scales. Filtering requirements for atmospheric models and filters used in the ARW model are discussed.

Comparison of Impacts of WRF Dynamic Core, Physics Package, and Initial Conditions on Warm Season Rainfall Forecasts

Abstract A series of simulations for 15 events occurring during August 2002 were performed using the Weather Research and Forecasting (WRF) model over a domain encompassing most of the central United States to compare the sensitivity of warm season rainfall forecasts with changes in model physics, dynamics, and initial conditions. Most simulations were run with 8-km grid spacing. The Advanced Research WRF (ARW) and the nonhydrostatic mesoscale model (NMM) dynamic cores were used. One physics package (denoted NCEP) used the Betts–Miller–Janjic convective scheme with the Mellor–Yamada–Janjic planetary boundary layer (PBL) scheme and GFDL radiation package; the other package (denoted NCAR) used the Kain–Fritsch convective scheme with the Yonsei University PBL scheme and the Dudhia rapid radiative transfer model radiation. Other physical schemes were the same (e.g., the Noah land surface model, Ferrier et al. microphysics) in all runs. Simulations suggest that the sensitivity of the model to changes in physics is a function of which the dynamic core is used, and the sensitivity to the dynamic core is a function of the physics used. The greatest sensitivity in general is associated with a change in physics packages when the NMM core is used. Sensitivity to a change in physics when the ARW core is used is noticeably less. For light rainfall, the spread in the rainfall forecasts when physics are changed under the ARW core is actually less at most times than when the dynamic core is changed while NCAR physics are used. For light rainfall, the WRF model using NCAR physics is much more sensitive to a change in dynamic core than the WRF model using NCEP physics. For heavier rainfall, the opposite is true with a greater sensitivity occurring when NCEP physics is used. Sensitivity to initial conditions (Eta versus the Rapid Update Cycle with an accompanying small change in grid spacing) is generally less substantial than the sensitivity to changes in dynamic core or physics, except in the first 6–12 h of the forecast when it is comparable. As might be expected for warm season rainfall, the finescale structure of rainfall forecasts is more affected by the physics used than the dynamic core used. Surprisingly, however, the overall areal coverage and rain volume within the domain may be more influenced by the dynamic core choice than the physics used.