Kiran Alapaty

Evaluating the performance of regional-scale photochemical modeling systems: Part I—meteorological predictions

In this study, the concept of scale analysis is applied to evaluate two state-of-science meteorological models, namely MM5 and RAMS3b, currently being used to drive regional-scale air quality models. To this end, seasonal time series of observations and predictions for temperature, water vapor, and wind speed were spectrally decomposed into fluctuations operating on the intra-day, diurnal, synoptic and longer-term time scales. Traditional model evaluation statistics are also presented to illustrate how the method of spectral decomposition can help provide additional insight into the models’ performance. The results indicate that both meteorological models under-represent the variance of fluctuations on the intra-day time scale. Correlations between model predictions and observations for temperature and wind speed are insignificant on the intra-day time scale, high for the diurnal component because of the inherent diurnal cycle but low for the amplitude of the diurnal component, and highest for the synoptic and longer-term components. This better model performance on longer time scales suggests that current regional-scale models are most skillful for characterizing average patterns over extended periods. The implications of these results to using meteorological models to drive photochemical models are discussed. r 2001 Elsevier Science Ltd. All rights reserved.

Simulation of Atmospheric Boundary Layer Processes Using Local- and Nonlocal-Closure Schemes

A soil‐vegetation‐atmospheric boundary layer model was developed to study the performance of two localclosure and two nonlocal-closure boundary layer mixing schemes for use in meteorological and air quality simulation models. Full interaction between the surface and atmosphere is achieved by representing surface characteristics and associated processes using a prognostic soil‐vegetation scheme and atmospheric boundary layer schemes. There are 30 layers in the lowest 3 km of the model with a high resolution near the surface. The four boundary layer schemes are tested by simulating atmospheric boundary layer structures over densely and sparsely vegetated regions using the observational data from the First ISLSCP (International Satellite Land Surface Climatology Project) Field Experiment (FIFE) and from Wangara. Simulation results indicate that the near-surface turbulent fluxes predicted by the four boundary layer schemes differ from each other, even though the formulation used to represent the surface-layer processes is the same. These differences arise from the differing ways of representing subgrid-scale vertical mixing processes. Results also indicate that the vertical profiles of predicted parameters (i.e., temperature, mixing ratio, and horizontal winds) from the four mixed-layer schemes differ from each other, particularly during the daytime growth of the mixed layer. During the evening hours, after the mixed layer has reached its maximum depth, the differences among these respective predicted variables are found to be insignificant. There were some general features that were associated with each of the schemes in all of the simulations. Compared with observations, in all of the cases the simulated maximum depths of the boundary layer for each scheme were consistently either lower or higher, superadiabatic lapse rates were consistently either stronger or weaker, and the intensity of the vertical mixing was either stronger or weaker. Also, throughout the simulation period in all case studies, most of the differences in the predicted parameters are present in the surface layer and near the top of the mixed layer.

Development and Evaluation of a Coupled Photosynthesis-Based Gas Exchange Evapotranspiration Model (GEM) for Mesoscale Weather Forecasting Applications

Current land surface schemes used for mesoscale weather forecast models use the Jarvis-type stomatal resistance formulations for representing the vegetation transpiration processes. The Jarvis scheme, however, despite its robustness, needs significant tuning of the hypothetical minimum-stomatal resistance term to simulate surface energy balances. In this study, the authors show that the Jarvis-type stomatal resistance/transpiration model can be efficiently replaced in a coupled land‐atmosphere model with a photosynthesis-based scheme and still achieve dynamically consistent results. To demonstrate this transformative potential, the authors developed and coupled a photosynthesis, gas exchange‐based surface evapotranspiration model (GEM) as a land surface scheme for mesoscale weather forecasting model applications. The GEM was dynamically coupled with a prognostic soil moisture‐soil temperature model and an atmospheric boundary layer (ABL) model. This coupled system was then validated over different natural surfaces including temperate C4 vegetation (prairie grass and corn field) and C3 vegetation (soybean, fallow, and hardwood forest) under contrasting surface conditions (such as different soil moisture and leaf area index). Results indicated that the coupled model was able to realistically simulate the surface fluxes and the boundary layer characteristics over different landscapes. The surface energy fluxes, particularly for latent heat, are typically within 10%‐20% of the observations without any tuning of the biophysical‐vegetation characteristics, and the response to the changes in the surface characteristics is consistent with observations and theory. This result shows that photosynthesis-based transpiration/stomatal resistance models such as GEM, despite various complexities, can be applied for mesoscale weather forecasting applications. Future efforts for understanding the different scaling parameterizations and for correcting errors for low soil moisture and/or wilting vegetation conditions are necessary to improve model performance. Results from this study suggest that the GEM approach using the photosynthesis-based soil vegetation atmosphere transfer (SVAT) scheme is thus superior to the Jarvis-based approaches. Currently GEM is being implemented within the Noah land surface model for the community Weather Research and Forecasting (WRF) Advanced Research Version Modeling System (ARW) and the NCAR high-resolution land data assimilation system (HRLDAS), and validation is under way.

Attribution of the United States "warming hole": aerosol indirect effect and precipitable water vapor.

Aerosols can influence the climate indirectly by acting as cloud condensation nuclei and/or ice nuclei, thereby modifying cloud optical properties. In contrast to the widespread global warming, the central and south central United States display a noteworthy overall cooling trend during the 20th century, with an especially striking cooling trend in summertime daily maximum temperature (Tmax) (termed the U.S. “warming hole”). Here we used observations of temperature, shortwave cloud forcing (SWCF), longwave cloud forcing (LWCF), aerosol optical depth and precipitable water vapor as well as global coupled climate models to explore the attribution of the “warming hole”. We find that the observed cooling trend in summer Tmax can be attributed mainly to SWCF due to aerosols with offset from the greenhouse effect of precipitable water vapor. A global coupled climate model reveals that the observed “warming hole” can be produced only when the aerosol fields are simulated with a reasonable degree of accuracy as this is necessary for accurate simulation of SWCF over the region. These results provide compelling evidence of the role of the aerosol indirect effect in cooling regional climate on the Earth. Our results reaffirm that LWCF can warm both winter Tmax and Tmin.

UNCERTAINTY IN THE SPECIFICATION OF SURFACE CHARACTERISTICS: A STUDY OF PREDICTION ERRORS IN THE BOUNDARY LAYER

The effects of uncertainty in the specification of surface characteristics on simulated atmospheric boundary layer (ABL) processes and structure were investigated using a one-dimensional soil-vegetation-boundary layer model. Observational data from the First International Satellite Land Surface Climatology Project Field Experiment were selected to quantify prediction errors in simulated boundary-layer parameters. Several numerical 12-hour simulations were performed to simulate the convective boundary-layer structure, starting at 0700 LT 6 June 1987.In the control simulation, measured surface parameters and atmospheric data were used to simulate observed boundary-layer processes. In the remaining simulations, five surface parameters – soil texture, initial soil moisture, minimum stomatal resistance, leaf area index, and vegetation cover – were varied systematically to study how uncertainty in the specification of these surface parameters affects simulated boundary-layer processes.The simulated uncertainty in the specification of these five surface parameters resulted in a wide range of errors in the prediction of turbulent fluxes, mean thermodynamic structure, and the depth of the ABL. Under certain conditions uncertainty in the specifications of soil texture and minimum stomatal resistance had the greatest influence on the boundary-layer structure. A lesser but still moderately strong effect on the simulated ABL resulted from (1) a small decrease (4%) in the observed initial soil moisture (although a large increase [40%] had only a marginal effect), and (2) a large reduction (66%) in the observed vegetation cover. High uncertainty in the specification of leaf area index had only a marginal impact on the simulated ABL. It was also found that the variations in these five surface parameters had a negligible effect on the simulated horizontal wind fields. On the other hand, these variations had a significant effect on the vertical distribution of turbulent heat fluxes, and on the predicted maximum boundary-layer depth, which varied from about 1400–2300 m across the 11 simulations. Thus, uncertainties in the specification of surface parameters can significantly affect the simulated boundary-layer structure in terms of meteorological and air quality model predictions.

An Observation-Based Investigation of Nudging in WRF for Downscaling Surface Climate Information to 12-km Grid Spacing

AbstractPrevious 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 appl...

Assimilating Surface Data to Improve the Accuracy of Atmospheric Boundary Layer Simulations

Large errors in atmospheric boundary layer (ABL) simulations can be caused by inaccuracies in the specification of surface characteristics in addition to assumptions and simplifications made in boundary layer formulations or other model deficiencies. For certain applications, such as air quality studies, these errors can have significant effects. To reduce such errors, a continuous surface data assimilation technique is developed. In this technique, surface-layer temperature and water vapor mixing ratio are directly assimilated by using the analyzed surface data. Then, the difference between the observations and model results is used to calculate adjustments to the surface fluxes of sensible and latent heat. These adjustments are then used to calculate a new estimate of the ground temperature, thereby affecting the simulated surface fluxes on the subsequent time step. This indirect data assimilation is applied simultaneously with the direct assimilation of surface data in the model’s lowest layer, thereby maintaining greater consistency between the ground temperature and the surface-layer mass-field variables. A one-dimensional model was used to study the improvements that result from applying this technique for ABL simulations in two cases. It was found that application of the new technique led to significant reductions in ABL modeling errors.

Improving High-Resolution Weather Forecasts Using the Weather Research and Forecasting (WRF) Model with an Updated Kain–Fritsch Scheme

AbstractEfforts to improve the prediction accuracy of high-resolution (1–10 km) surface precipitation distribution and variability are of vital importance to local aspects of air pollution, wet deposition, and regional climate. However, precipitation biases and errors can occur at these spatial scales due to uncertainties in initial meteorological conditions and/or grid-scale cloud microphysics schemes. In particular, it is still unclear to what extent a subgrid-scale convection scheme could be modified to bring in scale awareness for improving high-resolution short-term precipitation forecasts in the WRF Model. To address these issues, the authors introduced scale-aware parameterized cloud dynamics for high-resolution forecasts by making several changes to the Kain–Fritsch (KF) convective parameterization scheme in the WRF Model. These changes include subgrid-scale cloud–radiation interactions, a dynamic adjustment time scale, impacts of cloud updraft mass fluxes on grid-scale vertical velocity, and lift...

Increasing the credibility of regional climate simulations by introducing subgrid‐scale cloud‐radiation interactions

The radiation schemes in the Weather Research and Forecasting (WRF) model have previously not accounted for the presence of subgrid-scale cumulus clouds, thereby resulting in unattenuated shortwave radiation, which can lead to overly energetic convection and overpredicted surface precipitation. This deficiency can become problematic when applying WRF as a regional climate model (RCM). Therefore, modifications were made to the WRF model to allow the Kain–Fritsch (KF) convective parameterization to provide subgrid-scale cloud fraction and condensate feedback to the rapid radiative transfer model–global (RRTMG) shortwave and longwave radiation schemes. The effects of these changes are analyzed via 3 year simulations using the standard and modified versions of WRF, comparing the modeled results with the North American Regional Reanalysis (NARR) and Climate Forecast System Reanalysis data, as well as with available data from the Surface Radiation Network and Clouds and Earths Radiant Energy System. During the summer period, including subgrid cloudiness estimated by KF in the RRTMG reduces the surface shortwave radiation, leading to less buoyant energy, which is reflected in a smaller diabatic convective available potential energy, thereby alleviating the overly energetic convection. Overall, these changes have reduced the overprediction of monthly, regionally averaged precipitation during summer for this RCM application, e.g., by as much as 49 mm for the southeastern U.S., to within 0.7% of the NARR value of 221 mm. These code modifications have been incorporated as an option available in the latest version of WRF (v3.6).

The Impacts of Indirect Soil Moisture Assimilation and Direct Surface Temperature and Humidity Assimilation on a Mesoscale Model Simulation of an Indian Monsoon Depression

This study investigates the impact of the Flux-Adjusting Surface Data Assimilation System (FASDAS) and the four-dimensional data assimilation (FDDA) using analysis nudging on the simulation of a monsoon depression that formed over India during the 1999 Bay of Bengal Monsoon Experiment (BOBMEX) field campaign. FASDAS allows for the indirect assimilation/adjustment of soil moisture and soil temperature together with continuous direct surface data assimilation of surface temperature and surface humidity. Two additional numerical experiments [control (CTRL) and FDDA] were conducted to assess the relative improvements to the simulation by FASDAS. To improve the initial analysis for the FDDA and the surface data assimilation (SDA) runs, the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) simulation utilized the humidity and temperature profiles from the NOAA Television Infrared Observation Satellite (TIROS) Operational Vertical Sounder (TOVS), surface winds from the Quick Scatterometer (QuikSCAT), and the conventional meteorological upper-air (radiosonde/rawinsonde, pilot balloon) and surface data. The results from the three simulations are compared with each other as well as with NCEP–NCAR reanalysis, the Tropical Rainfall Measuring Mission (TRMM), and the special buoy, ship, and radiosonde observations available during BOBMEX. As compared with the CTRL, the FASDAS and the FDDA runs resulted in (i) a relatively better-developed cyclonic circulation and (ii) a larger spatial area as well as increased rainfall amounts over the coastal regions after landfall. The FASDAS run showed a consistently improved model simulation performance in terms of reduced rms errors of surface humidity and surface temperature as compared with the CTRL and the FDDA runs.