Abstract

It is common practice to use Newtonian relaxation, or nudging, throughout meteorological model simulations to create “dynamic analyses” that provide the characterization of the meteorological conditions for retrospective air quality model simulations. Given the impact that meteorological conditions have on air quality simulations, it has been assumed that the resultant air quality simulations would be more skillful by using dynamic analyses rather than meteorological forecasts to characterize the meteorological conditions, and that the statistical trends in the meteorological model fields are also reflected in the air quality model. This article, which is the first of two parts, demonstrates the impact of nudging in the meteorological model on retrospective air quality model simulations. Here, meteorological simulations are generated by the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) using both the traditional dynamic analysis approach and using forecasts for a summertime period. The resultant fields are then used to characterize the meteorological conditions for emissions processing and air quality simulations using the Community Multiscale Air Quality (CMAQ) Modeling System. As expected, on average, the near-surface meteorological fields show a significant degradation over time in the forecasts (when nudging is not used), while the dynamic analyses maintain nearly constant statistical scores in time. The use of nudged MM5 fields in CMAQ generally results in better skill scores for daily maximum 1-h ozone mixing ratio simulations. On average, the skill of the daily maximum 1-h ozone simulation deteriorates significantly over time when nonnudged MM5 fields are used in CMAQ. The daily maximum 1-h ozone mixing ratio also degrades over time in the CMAQ simulation that uses MM5 dynamic analyses, although to a much lesser degree, despite no aggregate loss of skill over time in the dynamic analyses themselves. These results affirm the advantage of using nudging in MM5 to create the meteorological characterization for CMAQ for retrospective simulations, and it is shown that MM5-based dynamic analyses are robust at the surface throughout 5.5-day simulations.

Abstract

For air quality modeling, it is important that the meteorological fields that are derived from meteorological models reflect the best characterization of the atmosphere. It is well known that the accuracy and overall representation of the modeled meteorological fields can be improved for retrospective simulations by creating dynamic analyses in which Newtonian relaxation, or “nudging,” is used throughout the simulation period. This article, the second of two parts, provides additional insight into the value of using nudging-based data assimilation for dynamic analysis in the meteorological fields for air quality modeling. Meteorological simulations are generated by the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) using both the traditional dynamic analysis approach and forecasts for a summertime period. The resultant meteorological fields are then used for emissions processing and air quality simulations using the Community Multiscale Air Quality Modeling System (CMAQ). The predictions of surface and near-surface meteorological fields and ozone are compared with a small network of collocated meteorological and air quality observations. Comparisons of 2-m temperature, 10-m wind speed, and surface shortwave radiation show a significant degradation over time when nudging is not used, whereas the dynamic analyses maintain consistent statistical scores over time for those fields. Using nudging in MM5 to generate dynamic analyses, on average, leads to a CMAQ simulation of hourly ozone with smaller error. Domainwide error patterns in specific meteorological fields do not directly or systematically translate into error patterns in ozone prediction at these sites, regardless of whether nudging is used in MM5, but large broad-scale errors in shortwave radiation prediction by MM5 directly affect ozone prediction by CMAQ at specific sites.

Abstract

A challenging problem in numerical weather prediction is to optimize the use of meteorological observations in data assimilation. Even assimilation techniques considered “optimal” in the “least squares” sense usually involve a set of assumptions that prescribes the horizontal and vertical distributions of analysis increments used to update the background analysis. These assumptions may impose limitations on the use of the data that can adversely affect the data assimilation and any subsequent forecast.

Virtually all widely used operational analysis and dynamic-initialization techniques assume, at some level, that the errors are isotropic and so the data can be applied within circular regions of influence around measurement sites. Whether implied or used directly, circular isotropic regions of influence are indiscriminate toward thermal and wind gradients that may reflect changes of air mass. That is, the analytic process may ignore key flow-dependent information available about the physical error structures of an individual case. Although this simplification is widely recognized, many data assimilation schemes currently offer no practical remedy.

To explore the potential value of case-adaptive, noncircular weighting in a computationally efficient manner, an approach for structure-dependent weighting of observations (SWOBS) is investigated in a continuous data assimilation scheme. In this study, SWOBS is used to dynamically initialize the PSU–NCAR Mesoscale Model using temperature and wind data in a series of observing-system simulation experiments. Results of this heuristic study suggest that improvements in analysis and forecast skill are possible with case-specific, flow-dependent, anisotropic weighting of observations.

Abstract

This study evaluates interior nudging techniques using the Weather Research and Forecasting (WRF) model for regional climate modeling over the conterminous United States (CONUS) using a two-way nested configuration. NCEP–Department of Energy Atmospheric Model Intercomparison Project (AMIP-II) Reanalysis (R-2) data are downscaled to 36 km × 36 km by nudging only at the lateral boundaries, using gridpoint (i.e., analysis) nudging and using spectral nudging. Seven annual simulations are conducted and evaluated for 1988 by comparing 2-m temperature, precipitation, 500-hPa geopotential height, and 850-hPa meridional wind to the 32-km North American Regional Reanalysis (NARR). Using interior nudging reduces the mean biases for those fields throughout the CONUS compared to the simulation without interior nudging. The predictions of 2-m temperature and fields aloft behave similarly when either analysis or spectral nudging is used. For precipitation, however, analysis nudging generates monthly precipitation totals, and intensity and frequency of precipitation that are closer to observed fields than spectral nudging. The spectrum of 250-hPa zonal winds simulated by the WRF model is also compared to that of the R-2 and NARR. The spatial variability in the WRF model is reduced by using either form of interior nudging, and analysis nudging suppresses that variability more strongly than spectral nudging. Reducing the nudging strengths on the inner domain increases the variability but generates larger biases. The results support the use of interior nudging on both domains of a two-way nest to reduce error when the inner nest is not otherwise dominated by the lateral boundary forcing. Nevertheless, additional research is required to optimize the balance between accuracy and variability in choosing a nudging strategy.

Abstract

An important question in regional climate downscaling is whether to constrain (nudge) the interior of the limited-area domain toward the larger-scale driving fields. Prior research has demonstrated that interior nudging can increase the skill of regional climate predictions originating from historical data. However, there is concern that nudging may also inhibit the regional model’s ability to properly develop and simulate mesoscale features, which may reduce the value added from downscaling by altering the representation of local climate extremes. Extreme climate events can result in large economic losses and human casualties, and regional climate downscaling is one method for projecting how climate change scenarios will affect extreme events locally. In this study, the effects of interior nudging are explored on the downscaled simulation of temperature and precipitation extremes. Multidecadal, continuous Weather Research and Forecasting model simulations of the contiguous United States are performed using coarse reanalysis fields as proxies for global climate model fields. The results demonstrate that applying interior nudging improves the accuracy of simulated monthly means, variability, and extremes over the multidecadal period. The results in this case indicate that interior nudging does not inappropriately squelch the prediction of temperature and precipitation extremes and is essential for simulating extreme events that are faithful in space and time to the driving large-scale fields.

Abstract

An urban canopy parameterization (UCP) is implemented into the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) to improve meteorological fields in the urban boundary layer for finescale (∼1-km horizontal grid spacing) simulations. The UCP uses the drag-force approach for dynamics and a simple treatment of the urban thermodynamics to account for the effects of the urban environment. The UCP is evaluated using a real-data application for Philadelphia, Pennsylvania. The simulations show that the UCP produces profiles of wind speed, friction velocity, turbulent kinetic energy, and potential temperature that are more consistent with the observations taken in urban areas and data from idealized wind tunnel studies of urban areas than do simulations that use the roughness approach. In addition, comparisons with meteorological measurements show that the UCP simulations are superior to those that use the roughness approach. This improvement of the treatment of the urban areas in the meteorological model could have implications for simulating air chemistry processes at this scale.

Abstract

Previous research has demonstrated the ability to use the Weather Research and Forecasting model (WRF) and contemporary dynamical downscaling methods to refine global climate modeling results to a horizontal grid spacing of 36 km. Environmental managers and urban planners have expressed the need for even finer resolution in projections of surface-level weather to take into account local geophysical and urbanization patterns. In this study, WRF as previously applied at 36-km grid spacing is used with 12-km grid spacing with one-way nesting to simulate the year 2006 over the central and eastern United States. The results at both resolutions are compared with hourly observations of surface air temperature, humidity, and wind speed. The 12- and 36-km simulations are also compared with precipitation data from three separate observation and analysis systems. The results show some additional accuracy with the refinement to 12-km horizontal grid spacing, but only when some form of interior nudging is applied. A positive bias in precipitation found previously in the 36-km results becomes worse in the 12-km simulation, especially without the application of interior nudging. Model sensitivity testing shows that 12-km grid spacing can further improve accuracy for certain meteorological variables when alternate physics options are employed. However, the strong positive bias found for both surface-level water vapor and precipitation suggests that WRF as configured here may have an unbalanced hydrologic cycle that is returning moisture from land and/or water bodies to the atmosphere too quickly.

The National Air Quality Forecast Capability (NAQFC) currently provides next-day forecasts of ozone concentrations over the contiguous United States. It was developed collaboratively by NOAA and Environmental Protection Agency (EPA) in order to provide state and local agencies, as well as the general public, air quality forecast guidance. As part of the development process, the NAQFC has been evaluated utilizing strict monitor-to-gridcell matching criteria, and discrete-type statistics of forecast concentrations. While such an evaluation is important to the developers, it is equally, if not more important, to evaluate the performance using the same protocol as the model's intended application. Accordingly, the purpose of this article is to demonstrate the efficacy of the NAQFC from the perspective of a local forecaster, thereby promoting its use. Such an approach has required the development of a new evaluation protocol: one that examines the ability of the NAQFC to forecast values of the EPA's Air Quality Index (AQI) rather than ambient air concentrations; focuses on the use of categorical-type statistics related to exceedances and nonexceedances; and, most challenging, examines performance, not based on matched grid cells and monitors, but rather over a “local forecast region,” such as an air shed or metropolitan statistical area (MSA). Results from this approach, which is demonstrated for the Charlotte, North Carolina, MSA and subsequently applied to four additional MSAs during the summer of 2007, reveal that the quality of the NAQFC forecasts is generally comparable to forecasts from local agencies. Such findings will hopefully persuade forecasters, whether they are experienced with numerous tools at their disposal or inexperienced with limited resources, to utilize the NAQFC as forecast guidance.

Abstract

NOAA and the U.S. Environmental Protection Agency (EPA) have developed a national air quality forecasting (AQF) system that is based on numerical models for meteorology, emissions, and chemistry. The AQF system generates gridded model forecasts of ground-level ozone (O₃) that can help air quality forecasters to predict and alert the public of the onset, severity, and duration of poor air quality conditions. Although AQF efforts have existed in metropolitan centers for many years, this AQF system provides a national numerical guidance product and the first-ever air quality forecasts for many (predominantly rural) areas of the United States. The AQF system is currently based on NCEP’s Eta Model and the EPA’s Community Multiscale Air Quality (CMAQ) modeling system. The AQF system, which was implemented into operations at the National Weather Service in September of 2004, currently generates twice-daily forecasts of O₃ for the northeastern United States at 12-km horizontal grid spacing. Preoperational testing to support the 2003 and 2004 O₃ forecast seasons showed that the AQF system provided valuable guidance that could be used in the air quality forecast process. The AQF system will be expanded over the next several years to include a nationwide domain, a capability for forecasting fine particle pollution, and a longer forecast period. State and local agencies will now issue air quality forecasts that are based, in part, on guidance from the AQF system. This note describes the process and software components used to link the Eta Model and CMAQ for the national AQF system, discusses several technical and logistical issues that were considered, and provides examples of O₃ forecasts from the AQF system.