MM5 Data Research Articles

Applying Spatial Analysis Tools in Public Health: The Use of AERMOD in Modeling the Emission Dispersion of SO2 and NO2 to Identify Exposed Area to Health Risks

Background: The cement industry is one of the main contributors of pollutant gasses in the environment through stack emissions.Aim: This study aims to model the dispersion of SO2 and NO2 gasses and to determine the area of the dispersion by American Meteorological Society – Environmental Protection Agency Regulation Model or AERMOD has been utilized by PT. Semen Tonasa (Tonasa Cement, Ltd.).Methods: Meteorological data from AERMENT was collected from reanalysis of MM5 data. While topographical data was extracted from SRTM30 satellite data. The model was carried out for a year, to cover both the dry and rainy season.Results: The result of the modeling showed that the peak value of the concentration of SO2 and NO2 pollutants for one hour are 135 μg/m3 and 160 μg/m3 respectively (quality standards of SO2 and NO2 are 900 μg/Nm3 and 400 μg/Nm3). The area of dispersion tends to be in the eastern area, such as District Minasatene (Sub-district Bontoa, Kalabbirang, Minasatene dan Biraeng), District Bungoro (Sub-district Biringere, Sapanang, Mangilu, Bulu Tellue) and District Labakkang (Sub-district Taraweang). Key words: Spatial analysis, AERMOD, AERMOD, cement factory, exposed area, SO2 and NO2

Hydrologic implications of errors in bias-corrected regional reanalysis data for west central Florida

Summary This study investigated the limitations associated with using dynamically-downscaled, bias-corrected reanalysis data (i.e. regional reanalysis data) to predict hydrologic behavior of low-relief rainfall driven systems using an integrated surface/groundwater model. Four different sets of global reanalysis data (NCEP/NCAR-R1, NCEP-DOE-R2, ERA40, and 20CR) that were previously downscaled using two RCMs (MM5 and RSM) were obtained, bias-corrected on a daily basis using the CDF-mapping approach, and used to drive an integrated hydrologic model (INTB) that was previously calibrated and verified for the Tampa Bay region. All raw dynamically-downscaled reanalysis datasets accurately estimated the annual cycle of daily maximum and minimum temperature, except the NCEP/NCAR R1+MM5 data which consistently underestimated daily maximum temperature. All raw regional reanalysis precipitation data significantly overestimated precipitation, particularly for the dry season. Bias-correction using the CDF-mapping approach effectively removed biases in the temporal mean and standard deviation of both the daily precipitation and temperature predictions. Biases in the mean monthly and mean annual precipitation totals were removed by CDF-mapping on a daily basis, but the standard deviation of the monthly and annual precipitation totals were not accurately reproduced. Furthermore inaccuracies in actual daily precipitation time series aggregated into monthly and annual rainfall total time series that showed significant and temporally persistent errors. Precipitation timing errors produced by regional reanalysis data were propagated and enhanced by non-linear streamflow generation, groundwater flow and storage processes in the hydrologic model and produced significant errors in both actual and mean daily, monthly and annual streamflow and groundwater level predictions. These results show that improvement in large-scale reanalysis products and regional climate models may be required before dynamically downscaled bias-corrected reanalysis data can be used as a surrogate for observational data in hydrologic model applications for low-relief, rainfall driven systems.

Use of mesoscale model MM5 forecasts as proxies for surface meteorological and agroclimatic variables

D.O. Silva, F.J. Meza, and E. Varas. 2009. Use of mesoscale model MM5 forecasts as proxies for surface meteorological and agroclimatic variables. Cien. Inv. Agr. 36(3):369-380. There is increasing interest in meteorological information and its application to strategic planning at the farm as well as regional level. Although we have recently seen signifi cant improvements to strengthen and enlarge networks of weather observations, their density is still insuffi cient to cover large extents at the desired spatial and temporal resolution. Climate scientists have developed and used mesoscale models to understand and predict future atmospheric conditions. These models represent a major contribution to objective weather forecasts throughout numerical simulations. They use global circulation outputs as boundary conditions and can be run in a nested manner so as to increase their spatial resolution. Because of this, we can obtain information about weather variables in grid cells spaced 15 km apart covering important areas and providing information in places where analog or automatic stations are not available. The objective of this work is to evaluate the use of raw data from the MM5 mesoscale model as well as MOS-corrected information (a statistical post-processing of MM5 outputs) as a proxy for surface meteorological data. Temperature, wind speed, relative humidity, and daily solar radiation forecasts were evaluated for eleven stations in the Maipo river basin. In all cases, the MOS forecast produced better results than the raw MM5 data. Determination coeffi cients reached values near 0.9, and the RMSE was usually smaller for MOS-corrected data. The small variability of the MOS parameters allows their use as regional values to estimate meteorological data for the whole region, particularly at a weekly time step.

Estimation of vegetative mercury emissions in China

Vegetative mercury emissions were estimated within the framework of Biogenic Emission Inventory System (BEIS3 V3.11). In this estimation, the 19 categories of U.S. Geological Survey landcover data were incorporated to generate the vegetation-specific mercury emissions in a 81-km Lambert Conformal model grid covering the total Chinese continent. The surface temperature and cloud-corrected solar radiation from a Mesoscale Meteorological model (MM5) were retrieved and used for calculating the diurnal variation. The implemented emission factors were either evaluated from the measured mercury flux data for forest, agriculture and water, or assumed for other land fields without available flux data. Annual simulations using the MM5 data were performed to investigate the seasonal emission variation. From the sensitivity analysis using two sets of emission factors, the vegetative mercury emissions in China domain were estimated to range from a lower limit of 79 x 10(3) kg/year to an upper limit of 177 x 10(3) kg/year. The modeled vegetative emissions were mainly generated from the eastern and southern China. Using the estimated data, it is shown that mercury emissions from vegetation are comparable to that from anthropogenic sources during summer. However, the vegetative emissions decrease greatly during winter, leaving anthropogenic sources as the major sources of emission.

Simulation of satellite water vapour lidar measurements: Performance assessment under real atmospheric conditions

A lidar simulator has been applied to assess the performances of a satellite water vapour differential absorption lidar (DIAL) system. Measurements performed by the airborne Deutsches Zentrum für Luft-und Raumfahrt (DLR) water vapour DIAL on 15 May 2002 during ESA's Water Vapour Lidar Experiment (WALEX), in combination with PSU/NCAR Mesoscale Model (MM5) output, were used to obtain backscatter and water vapour fields with high resolution and accuracy. These data and model output serve as input for the simulator, allowing for the performance of satellite DIAL under highly-inhomogeneous atmospheric conditions including clouds to be assessed. The airborne measurements show an intrusion of stratospheric air into the troposphere, and MM5 data used above the DLR Falcon airplane flight altitude are characterized by very high upper tropospheric humidity levels, comparable to those associated with strong mid-latitude transport events from the troposphere to the lowermost stratosphere. Results of the simulator reveal that the maximum systematic error does not exceed 5% up to 16 km, except in the presence of thick cirrus and mid level clouds with an optical thickness up to 2 and, occasionally, inside the dry stratospheric intrusion, while the random error is less than 20% up to 16 km when spatial measurement resolutions are applied that follow the World Meteorological Organization (WMO) threshold observational requirements for numerical weather prediction (NWP). The bias is even smaller if a drier upper troposphere/lower stratosphere (UTLS) region from a reference atmosphere is considered. The results confirm the capability of satellite water vapour DIAL systems to retrieve thin structures of the tropospheric water vapour and particle backscatter fields, as well as its capability to provide low bias and random error measurements even in the presence of clouds.

The Use of Global and Mesoscale Meteorological Model Data to Predict the Transport and Dispersion of Tracer Plumes over Washington, D.C.

Abstract The data from a yearlong tracer dispersion experiment over Washington, D.C., in 1984 were used to evaluate Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) dispersion model calculations using coarse global meteorological reanalysis data [NCEP–NCAR and 40-Yr ECMWF Re-Analysis (ERA-40)] and calculations using meteorological data fields created by running a high-resolution meteorological model [fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5)]. None of the meteorological models were optimized for urban environments. The dispersion calculation using the ERA-40 data showed better performance than those using the NCEP–NCAR data and comparable performance to those using MM5 data fields. Calculations with MM5 data that used shorter-period forecasts were superior to calculations that used forecast data that extended beyond 24 h. Daytime dispersion model calculations using the MM5 data showed an underprediction bias not evident in calculations using the ERA-40 data or for nighttime calculations using either meteorological dataset. It was found that small changes in the wind direction for all meteorological model data resulted in dramatic improvements in dispersion model performance. All meteorological data modeled plume directions were biased 10°–20° clockwise to the measured plume direction. This bias was greatest when using the global meteorological data. A detailed analysis of the wind observations during the November intensive, which had the greatest difference between the model and measured plume directions, showed that only the very lowest level of observed winds could account for the transport direction of the measured plume. In the Northern Hemisphere, winds tend to turn clockwise with height resulting in positive directional transport bias if the lowest-level winds are not represented in sufficient detail by the meteorological model.

Development of a processor in BEIS3 for estimating vegetative mercury emission in the continental United States

We have developed a regression-based processor for estimating vegetative mercury emission within the framework of Biogenic Emission Inventory System Version 3.11 (BEIS3 V3.11). In this development, we incorporated the 230 categories of USGS landcover data to generate the vegetation-specific mercury emission in a 36-km Lambert Conformal model grid covering the continental United States (CONUS). The surface temperature and cloud-corrected solar radiation from a Mesoscale Meteorological model (MM5) were retrieved and used for calculating the diurnal variation. The implemented emission factors were either evaluated from the measured mercury flux data for selected tree species, wetland and water, or assumed for the tree species without available flux data. Annual simulations using the 2001 USEPA MM5 data were performed to investigate the seasonal emission variation. From our sensitivity analysis using three sets of emission factors, we estimated that the vegetative mercury emission in the CONUS domain ranges from a lower limit of 31 ton yr −1 to an upper limit of 140 ton yr −1, with the best estimate at 44 ton yr −1. The modeled vegetative emission was mainly contributed from southeast US. Using the best estimate data, it is shown that mercury emission from vegetation is comparable to that from anthropogenic sources in summer (nearly half of the total emission). However, the vegetative emission decreases greatly in winter, leaving anthropogenic sources as the major emission source (>90% in winter months). Modeling assessment indicates that including vegetative emission (44 ton yr −1) can force an increase of ambient mercury concentration of up to 0.2 ng m −3 in summer midday, but has little impact on dry deposition of mercury. Additional emission factors can be implemented in the model once further mercury flux data become available.