National Centers For Environmental Protection Research Articles

Estimation of OH in urban plumes using TROPOMI-inferred NO2 ∕ CO

Abstract. A new method is presented for estimating urban hydroxyl radical (OH) concentrations using the downwind decay of the ratio of nitrogen dioxide over carbon monoxide column-mixing ratios (XNO2/XCO) retrieved from the Tropospheric Monitoring Instrument (TROPOMI). The method makes use of plumes simulated by the Weather Research and Forecast model (WRF-Chem) using passive-tracer transport, instead of the encoded chemistry, in combination with auxiliary input variables such as Copernicus Atmospheric Monitoring Service (CAMS) OH, Emission Database for Global Atmospheric Research v4.3.2 (EDGAR) NOx and CO emissions, and National Center for Environmental Protection (NCEP)-based meteorological data. NO2 and CO mixing ratios from the CAMS reanalysis are used as initial and lateral boundary conditions. WRF overestimates NO2 plumes close to the center of the city by 15 % to 30 % in summer and 40 % to 50 % in winter compared to TROPOMI observations over Riyadh. WRF-simulated CO plumes differ by 10 % with TROPOMI in both seasons. The differences between WRF and TROPOMI are used to optimize the OH concentration, NOx, CO emissions and their backgrounds using an iterative least-squares method. To estimate OH, WRF is optimized using (a) TROPOMI XNO2/XCO and (b) TROPOMI-derived XNO2 only. For summer, both the NO2/CO ratio optimization and the XNO2 optimization increase the prior OH from CAMS by 32 ± 5.3 % and 28.3 ± 3.9 %, respectively. EDGAR NOx and CO emissions over Riyadh are increased by 42.1 ± 8.4 % and 101 ± 21 %, respectively, in summer. In winter, the optimization method doubles the CO emissions while increasing OH by ∼ 52 ± 14 % and reducing NOx emissions by 15.5 ± 4.1 %. TROPOMI-derived OH concentrations and the pre-existing exponentially modified Gaussian function fit (EMG) method differ by 10 % in summer and winter, confirming that urban OH concentrations can be reliably estimated using the TROPOMI-observed NO2/CO ratio. Additionally, our method can be applied to a single TROPOMI overpass, allowing one to analyze day-to-day variability in OH, NOx and CO emission.

Contribution to the Study of Forest Fires in Semi-Arid Regions with the Use of Canadian Fire Weather Index Application in Greece

Forest fires are of critical importance in the Mediterranean region. Fire weather indices are meteorological indices that produce information about the impact as well as the characteristics of a fire event in an ecosystem and have been developed for that reason. This study explores the spatiotemporal patterns of the FWI system within a study area defined by the boundaries of the Greek state. The FWI has been calculated and studied for current and future periods using data from the CFSR reanalysis model from the National Centers for Environmental Protection (NCEP) as well as data from NASA satellite programs and the European Commission for Medium-Range Weather Forecasts (ECWMF) in the form of netCDF files. The calculation and processing of the results were conducted in the Python programming language, and additional drought- and fire-related indices were calculated, such as the standardized precipitation index (SPI), number of consecutive 50-day dry periods (Dry50), the Fosberg fire weather index (FFWI), the days where the FWI exceeds values of 40 and 50 days (FWI > 40) and (days FWI > 50). Similar patterns can easily be noted for all indices that seem to have their higher values concentrated in the southeast of the country owing to the higher temperatures and more frequent drought events that affect the indices’ behavior in both the current and future periods.

Atmospheric analysis of dust storms in Sistan region

The Sistan Basin in Southwest Asia has been identified as one of the most active sources of dust and windiest terrestrial environments in the world. In this paper, atmospheric analysis of 7 severe dust storms in the Sistan and the Hamoun Lake Basin region during the past two decades was performed. The national center for environmental protection and national center for atmospheric research (NCEP-NCAR) data were used for synoptic analysis of dust events. The locations of low and high pressure systems which were responsible for occurring the dust events were identified and dust plumes routes were presented. The atmospheric analysis results demonstrated that the high-pressure systems over the Caspian Sea and surrounding areas and low-pressure systems over the Indian subcontinent and surrounding areas interactions result in northerly, westerly, and northwesterly winds over the Sistan region, and caused the dust events in this area and transport of dust plumes to east and southeast area. Hamoun Lake Basin in Iran, located in southern and southwestern parts of Afghanistan, and western, northwestern, and central Pakistan are more affected by dust events than other parts of Sistan. An investigation of the areas that were affected by dust plumes showed that southern and southwestern parts of Afghanistan and western Pakistan have the highest AODs and dust load at the surveyed events. The most intense dust activity in Sistan occurs during summertime and based on the results, the maximum amount of dust load reaches 5 gm−2 in the region in some cases.

Operational Wave Forecast Selection in the Atlantic Ocean Using Random Forests

The existence of multiple wave forecasts leads to the question of which one should be used in practical ocean engineering applications. Ensemble forecasts have emerged as an important complement to deterministic forecasts, with better performances at mid-to-long ranges; however, they add another option to the variety of wave predictions that are available nowadays. This study developed random forest (RF) postprocessing models to identify the best wave forecast between two National Centers for Environmental Protection (NCEP) products (deterministic and ensemble). The supervised learning classifier was trained using National Data Buoy Center (NDBC) buoy data and the RF model accuracies were analyzed as a function of the forecast time. A careful feature selection was performed by evaluating the impact of the wind and wave variables (inputs) on the RF accuracy. The results showed that the RF models were able to select the best forecast only in the very short range using input information regarding the significant wave height, wave direction and period, and ensemble spread. At forecast day 5 and beyond, the RF models could not determine the best wave forecast with high accuracy; the feature space presented no clear pattern to allow for successful classification. The challenges and limitations of such RF predictions for longer forecast ranges are discussed in order to support future studies in this area.

Pollution patterns and their meteorological analysis all over China

Fine particulate matter (PM2.5) and ozone (O3) are the two important pollutants because of their harmfulness. In addition to pollution emission level and location, meteorological factors affect the distribution of extreme pollution, so analysing the weather fields under extreme pollution is of great significance to predict extreme pollution. In this study, based on PM2.5 and O3 atmospheric composition monitoring data from China National Environmental Monitoring Center (CNEMC) Network and daily meteorological data from Version 2 of Climate Forecast System of National Centers for Environmental Protection (NCEP) Climate Forecast System, the distribution types of wintertime PM2.5 and summertime O3 pollution in China from 2015 to 2019 were classified with Empirical orthogonal function (EOF) method, and the types of weather characteristics were analyzed, respectively. The results showed that the distribution of extreme pollution and the distribution of meteorological fields had a good correspondence. High PM2.5 was accompanied with high relative humidity (RH), higher temperature (T), low pressures (P) and low wind speeds (W). The two maximum centers of correlation coefficients between PM2.5 and the atmospheric circulation field were located on Siberia and the east of Japan. High O3 was accompanied with dry environments and higher temperature. O3 contaminated areas were accompanied with low pressures, while the correlation with wind speeds were not so significant. Ocean cyclones seriously affected O3 pollution levels in the south and along the coast. The two maximum centers of correlation coefficients between O3 mass concentration and 500 hPa geopotential height were located on the east of Lake Baikal and southern China. The relevant indexes established in the key areas had all passed the significance test and had an indicative significance for forecasting the pollution level. We believe that the apparent correlations between PM2.5(O3) and RH, T, as well as W related to the external factors associated with the life cycle of midlatitude mesoscale weather system, rather than the underlying direct internal cause.

Barotropic boundary conditions and tide forcing in split-explicit high resolution coastal ocean models

A persisting problem in ocean modeling around coastal areas has been the adequate imposition of open boundary conditions, especially in the presence of complex bathymetric features commonly found in shelf seas. Ocean models with horizontal resolutions of O(1km) can generate submesoscale current variability not captured by coarser regional models which resolve mesoscale flows approximately in geostrophic and thermal-wind balance. In practice, higher-resolution systems will provide analyses with enhanced spatial detail but may be less skillful at predicting the evolution of mesoscale eddies. This study aims at assessing the role of open boundary conditions, tidal forcing and tide filtering techniques in a coastal ocean model using a split-explicit time marching scheme with horizontal resolutions of O(1km) and its effect in coastal sea level dynamics and ocean currents. Numerical experiments have been performed using the Regional Ocean Modeling System (ROMS) with different uniform horizontal resolution: 3.6km, 1.2km and 740m. Initial and lateral boundary conditions are derived from the U.S. Naval Oceanographic Office (NAVOCEANO) operational AmSeas model forecast, a 3.6-km resolution application of the regional Navy Coastal Ocean Model (NCOM) that encompasses the Gulf of Mexico and Caribbean Sea. Meteorological conditions are interpolated from the Navy’s Coastal Ocean-Atmosphere Mesoscale Prediction System (COAMPS) model with the exception of surface stresses, which are computed from a 2-km application of the Weather Research and Forecasting (WRF) model used by National Center for Environmental Protection’s (NCEP) National Digital Forecast Database (NDFD). Tidal variability is imposed in two ways: 1) by specifying the time evolution of sea level at the open boundaries and 2) spectrally by specifying the harmonic phases and amplitudes from the TPXO global inverse tide solutions at the boundaries. Sub-tidal low frequency conditions are imposed by filtering high frequencies out of the regional NCOM solution. A spectral analysis of the sea surface height and barotropic velocity is performed via Fourier’s transform, showing significant differences at higher frequencies. Tide signals are then reconstructed and removed from the open boundary condition’s (OBC’s) in 2 ways: 1) using Rich Pawlowicz’s t_tide package (i.e., classic harmonic analysis) (Pawlowicz, 2002 [1]) and 2) with traditional low-pass filters. The spectral analysis provides a comprehensive characterization of the response in coastal sea level dynamics, which is difficult to elucidate in the time domain. Modeled results of sea surface height and ocean currents are validated with NOAA tide gauges and Acoustic Doppler Current Profilers. The tide filtering approach with the low-pass filter yields significant improvement of water levels in coastal areas, and similar performance in the modeled currents. The grid sensitivity analysis shows significant reduction of error when increasing the resolution from 3.6km to 1.2km, but no further improvement is found at higher resolution. Power spectra shows that further increasing the resolution yields better agreement for higher frequencies but no significant reduction in error.

Development and validation of a coastal ocean forecasting system for Puerto Rico and the U.S. virgin islands

An ocean forecasting system has been developed for the coastal area of Puerto Rico and the U.S. Virgin Islands. This paper presents the development and validation of the Puerto Rico Operational Regional Ocean Modeling System (PRO-ROMS) developed by the Caribbean Coastal Ocean Observing System (CARICOOS), which aims at improving currently available short-term forecasts of ocean currents for this region, and to resolve sub-mesoscale variability absent from the currently operational regional systems. The coastal ocean forecasting system is based on the Regional Ocean Modeling System (ROMS), and provides a 3-day forecast of ocean currents, water levels, temperature and salinity. Initial and lateral boundary conditions are derived from the U.S. Naval Oceanographic Office (NAVOCEANO) operational AmSeas forecasting system, a 3.6-km resolution application of the regional Navy Coastal Ocean Model (NCOM) that encompasses the Gulf of Mexico and Caribbean Sea. Meteorological conditions are interpolated from the Navy’s Coupled Ocean-Atmosphere Mesoscale Prediction System (COAMPS) with the exception of surface stresses, which are computed from a 2-km application of the Weather Research and Forecasting (WRF) model used by National Centers for Environmental Protection’s (NCEP) National Digital Forecast Database (NDFD). Tidal variability is imposed using ROMS spectral forcing by specifying the harmonic phases and amplitudes from the TPXO global inverse tide solutions at the boundaries and sub-tidal conditions are imposed by filtering out high frequencies of barotropic variables from the lateral boundary conditions interpolated from AmSeas. Modeled results of sea surface height are validated with NOAA tide gauges, ocean currents are validated with Acoustic Doppler Current Profilers (ADCP) and High Frequency Radars (HFR). Computed statistics show improvement of the ocean current forecast in PRO-ROMS compared to AmSeas, especially at higher frequencies dominated by the tides and in small channels where the bathymetry is better represented by the higher resolution coastal ocean forecasting system.

Development and Validation of a Long-Term, Global, Terrestrial Sensible Heat Flux Dataset

Sensible heat flux is a turbulent flux driving interactions between the Earth’s surface and the atmosphere, propelling local and regional climate. While turbulent fluxes are measured in situ, global scales require estimates at larger spatial scales, which can be made using remotely sensed satellite data. This study uses a first-order approximation to calculate the unconstrained hourly, terrestrial, 0.5°-resolution sensible heat flux using a land surface temperature consistent with the High Resolution Infrared Radiation Sounder (HIRS) retrievals, six reanalysis-based air temperature products, and a dataset of Zilitinkevich empirical constant Czilvalues. This sensible heat flux dataset is constrained using the daily Bowen ratio and available energy, to produce nine constrained, daily products. All resulting global, terrestrial averages are within the uncertainty range of ±6.3 W m−2from the 38.8 W m−2global annual average previously reported in the literature. The product constrained with the net radiation using the Moderate Resolution Infrared Spectroradiometer (MODIS) albedo and air temperature from the National Centers for Environmental Protection (NCEP) Climate Forecast System Reanalysis (CFSR) performs closest to the FLUXNET ground observations in the monthly analysis. These sensible heat flux estimates should be used for benchmarking global climate models at monthly or annual scales, and improvements should be made to the accuracy of input variables, particularly the temperature gradient, Czilestimates, and the roughness length.

Decadal evaluation of regional climate, air quality, and their interactions over the continental US and their interactions using WRF/Chem version 3.6.1

Abstract. The Weather Research and Forecasting model with Chemistry (WRF/Chem) v3.6.1 with the Carbon Bond 2005 (CB05) gas-phase mechanism is evaluated for its first decadal application during 2001–2010 using the Representative Concentration Pathway 8.5 (RCP 8.5) emissions to assess its capability and appropriateness for long-term climatological simulations. The initial and boundary conditions are downscaled from the modified Community Earth System Model/Community Atmosphere Model (CESM/CAM5) v1.2.2. The meteorological initial and boundary conditions are bias-corrected using the National Center for Environmental Protection's Final (FNL) Operational Global Analysis data. Climatological evaluations are carried out for meteorological, chemical, and aerosol–cloud–radiation variables against data from surface networks and satellite retrievals. The model performs very well for the 2 m temperature (T2) for the 10-year period, with only a small cold bias of −0.3 °C. Biases in other meteorological variables including relative humidity at 2 m, wind speed at 10 m, and precipitation tend to be site- and season-specific; however, with the exception of T2, consistent annual biases exist for most of the years from 2001 to 2010. Ozone mixing ratios are slightly overpredicted at both urban and rural locations with a normalized mean bias (NMB) of 9.7 % but underpredicted at rural locations with an NMB of −8.8 %. PM2.5 concentrations are moderately overpredicted with an NMB of 23.3 % at rural sites but slightly underpredicted with an NMB of −10.8 % at urban/suburban sites. In general, the model performs relatively well for chemical and meteorological variables, and not as well for aerosol–cloud–radiation variables. Cloud-aerosol variables including aerosol optical depth, cloud water path, cloud optical thickness, and cloud droplet number concentration are generally underpredicted on average across the continental US. Overpredictions of several cloud variables over the eastern US result in underpredictions of radiation variables (such as net shortwave radiation – GSW – with a mean bias – MB – of −5.7 W m−2) and overpredictions of shortwave and longwave cloud forcing (MBs of ∼ 7 to 8 W m−2), which are important climate variables. While the current performance is deemed to be acceptable, improvements to the bias-correction method for CESM downscaling and the model parameterizations of cloud dynamics and thermodynamics, as well as aerosol–cloud interactions, can potentially improve model performance for long-term climate simulations.

Numerical Weather Prediction for Himalayan Complex Terrain: Prospects of Variational Data Assimilation

<p>As part of the ongoing research activities at National Atmospheric Resource and Environmental Research Laboratory (NARERL) to realize high spatial and temporal resolution weather forecasts for Nepal, the Weather Research and Forecasting (WRF) modeling system performance with the National Center for Environmental Protection (NCEP) and National Center for Medium Range Weather Forecast (NCMRWF) initialization global meteorological data sets and the effect of surface observation data assimilation have been examined. The study shows that WRF modeling system reasonably well predicts the diurnal variation of upcoming weather events with both the data sets. The observation data assimilation from entire weather station distributed over the country may lead to the significant improvement in the accuracy and reliability of extended period of forecast. However, upper air observation data assimilation would be necessary to achieve desired precision and reliability of extended weather forecast.</p><p>Journal of Nepal Physical Society Vol.3(1) 2015: 67-72</p>

Logistic Model as a Statistical Downscaling Approach for Forecasting a Wet or Dry Day in the Bagmati River Basin

A binary logistic model is developed for probabilistic prediction of a wet or dry day based upon daily rainfall data from 1981 to 2008 taken from 25 stations of Bagmati River basin. The predictor variables included in the model are daily relative humidity, air surface temperature, sea level pressure, v-wind which are expressed as principal components of 9 grids of the National Centers for Environmental Protection (NCEP)/National Center for Atmospheric Research (NCAR) Reanalysis data with resolution of 2.50×2.50. Principal component analysis is used to reduce the dimension of the predictors in the presence of spatial correlations between grids and thus reduce their multicollinearity effect. The result depicts that the model has 86.4 percent predictive capability in the analysis period (1981-2000) and 86.1 in the validation period (2001-2008) along with support of receiver operating characteristic (ROC) analysis. The results demonstrate that the first two principal components of relative humidity are the key predictor variables with respective odds ratios (ORs) of 4.18 and 3.61, respectively. The other statistically significant predictors are the second principal component of v-wind with OR 1.43, the second and first principal components of air surface temperature with ORs 1.38 and 0.76, respectively and the first principal component of sea level pressure with OR 0.44. Goodness-of-fit test, ROC analysis and other main diagnostic tests showed that the fitted logistic model is characterized by good fits for analysis as well as validation period.

Satellite-derived solar resource maps for Myanmar

Solar resource maps for use in solar energy applications have been produced for Myanmar. A satellite-based solar radiation model, originally developed for the tropics, was improved and applied for the region. A 13-year period (1998–2010) of imagery data from GMS 5, GOES 9 and MTSAT-1R satellites was used as the main input in the model. The absorption and scattering of solar radiation by various atmospheric constituents was also taken into account. The absorption of solar radiation due to water vapour was estimated from precipitable water database obtained from the National Center for Environmental Protection (NCEP), USA. The total column ozone obtained from TOMS/EP and OMI/AURA satellites were used to calculate solar radiation absorption by ozone. The visibility data observed at meteorological stations in Myanmar and neighbouring countries were employed to estimate solar radiation depletion due to aerosols. In order to validate the model, five pyranometer stations were established in different regions of Myanmar and a two-year period of data from these stations were used for the model validation. Additionally, global solar radiation measured at 10 stations in a neighbouring country was also employed for the validation. It was found that monthly average global radiation obtained from the measurements and that estimated from the model was in good agreement, with a root mean square difference of 9.6% at monthly scale. After the validation, the model was used to estimate monthly average global radiation over Myanmar and the results were presented as solar resource maps. The maps revealed that geographical distribution of solar radiation was strongly influenced by the topography of the country and the tropical monsoons.

Reply to comment by Keith J. Beven and Hannah L. Cloke on “Hyperresolution global land surface modeling: Meeting a grand challenge for monitoring Earth's terrestrial water”

WATER RESOURCES RESEARCH, VOL. 48, W01802, doi:10.1029/2011WR011202, 2012 Reply to comment by Keith J. Beven and Hannah L. Cloke on ‘‘Hyperresolution global land surface modeling: Meeting a grand challenge for monitoring Earth’s terrestrial water’’ Eric F. Wood, 1 Joshua K. Roundy, 1 Tara J. Troy, 1 Rens van Beek, 2 Marc Bierkens, 2 Eleanor Blyth, 3 Ad de Roo, 4 Petra Doll, 5 Mike Ek, 6 James Famiglietti, 7 David Gochis, 8 Nick van de Giesen, 9 Paul Houser, 10 Peter Jaffe, 1 Stefan Kollet, 11 Bernhard Lehner, 12 Dennis P. Lettenmaier, 13 Christa D. Peters-Lidard, 14 Murugesu Sivapalan, 15 Justin Sheffield, 1 Andrew J. Wade, 16 and Paul Whitehead 17 Received 25 July 2011; revised 9 November 2011; accepted 3 December 2011; published 21 January 2012. Citation: Wood, E. F., et al. (2012), Reply to comment by Keith J. Beven and Hannah L. Cloke on ‘‘Hyperresolution global land surface modeling: Meeting a grand challenge for monitoring Earth’s terrestrial water’’ Water Resour. Res., 48, W01802, doi:10.1029/ 2011WR011202. Introduction [ 1 ] The authors of Wood et al. [2011, hereafter W2011] would like to thank Beven and Cloke [2012, hereafter BC2012] for furthering the discussion about the pathway to- ward a global-scale hyper-resolution water-energy-biogeo- chemistry land surface modeling capability: its need, feasibility and development. Their comment brings focus to the discussion and shows that the proposed challenge to our community is one element in a long history of hydrology Department of Civil and Environmental Engineering, Princeton Uni- versity, Princeton, New Jersey, USA. Department of Physical Geography, University of Utrecht, Utrecht, Netherlands. Centre for Ecology and Hydrology, Wallingford, UK. Institute for Environment and Sustainability, European Commission Joint Research Center, Ispra, Italy. Institute of Physical Geography, J. W. Goethe University, Frankfurt am Main, Germany. Environmental Modeling Center, National Centers for Environmental Protection, Suitland, Maryland, USA. UC Center for Hydrologic Modeling, University of California-Irvine, Irvine, California, USA. Research Applications Laboratory, National Center for Atmospheric Research, Boulder, Colorado, USA. Department of Water Management, Delft University of Technology, Delft, Netherlands. Department of Geography and GeoInformation Science, George Mason University, Fairfax, Virginia, USA. Meteorological Institute, University of Bonn, Bonn, Germany. Department of Geography, McGill University, Montreal, Quebec, Canada. Department of Civil and Environmental Engineering, University of Washington, Seattle, Washington, USA. Hydrological Sciences Laboratory, NASA Goddard Space Flight Cen- ter, Greenbelt, Maryland, USA. Department of Civil and Environmental Engineering and Department of Geography, University of Illinois Urbana-Champaign, Urbana, Illinois, USA. School of Human and Environmental Science, University of Reading, Reading, UK. School of Geography and the Environment, Oxford University, Oxford, UK. Copyright 2012 by the American Geophysical Union 0043-1397/12/2011WR011202 model developments with the goal to improving hydrologic predictions and understanding. [ 2 ] What is laid out in W2011 is, first and foremost, a grand challenge because (1) there is a grand need, (2) there are great new opportunities, and (3) if the hydrologic com- munity does not do it someone else will do it, albeit poorly. The reader is directed to W2011 for a discussion of the growing need for continental-scale land surface models that consider improved, scale-appropriate parameteriza- tions of the water, energy and biogeochemical cycles at resolutions on the order of 10 2 to 10 3 m grid resolutions. Some examples are presented, which were not meant be to comprehensive in their scope of detail, that include surface-subsurface interactions, land-atmospheric interac- tions and coupling, water quality that includes nonpoint pollution, and human impacts that include water manage- ment, land cover change and the effects of climate change. [ 3 ] The commentary by BC2012 focuses on just one chal- lenge or building block described in W2011: the issue of parameterization of subgrid heterogeneity and the resulting uncertainty—what they refer to as ‘‘epistemic uncertainty.’’ BC2012 interprets the Grand Challenge in W2011 as ‘‘simply moving to finer resolutions.’’ This is not what W2011 says or proposes. There are many new building blocks available for the research into hyper-resolution modeling: (1) new data sources and measurement techniques for precipitation, topog- raphy, vegetation cover, soils, but also soil moisture, evapo- transpiration, water storages (rivers, lakes, groundwater storages, soil moisture); (2) new physics—new sets of gov- erning equations, including new approaches to developing closure relations; (3) new approaches to handling known and unknown uncertainties in model structure, variables and numerics, including characterizing subgrid heterogeneity (including new ways to capture their effects) based on new insights into ecohydrology and hydropedology and approaches that utilize the coevolution of climate, soils, vege- tation and topography; (4) new approaches that can better include nonlinear feedbacks between various subsystems, and local, regional and global cycles and teleconnections; (5) new regionalization efforts aimed at learning from compara- tive analysis across climatic, geologic and human-impact W01802 1 of 3

A new methodology development for the regulatory forecasting of PM10. Application in the Greater Athens Area, Greece

The paper introduces a new methodology for the prediction of daily PM10 concentrations, in line with the regulatory framework introduced through the EU Directive 2008/50/EC. The proposed approach is based on the efficient utilisation of the data collected over short time intervals (hourly) rather than the daily values used to derive the daily regulatory threshold. It is sufficiently simple and easily applicable in operational forecasting systems with the ability to accept as inputs both historical data and exogenous paraeters, such as meteorological variables. The application of the proposed methodology is demonstrated using data from five monitoring stations of air pollutants located in Athens, over a five year period (2000–2004) as well as compatible meteorological data from the NCEP (National Centers for Environmental Protection). A set of different models have been tested at the same time to reveal the effectiveness of the proposed approach, both univariate and multivariate, and linear and non-linear models. The analysis of all examined datasets has shown conclusive evidence that the introduction of the newly developed procedure which utilises data collected over a shorter horizon can significantly increase the forecasting ability of any developed model using daily historic PM10 data, under all examined metrics.

Change in ozone air pollution over Chicago associated with global climate change

This study uses statistical downscaling to estimate the impact of future climate change on air quality. We employ historical observations of surface ozone (O3) over the Chicago area, large‐scale climate variables from the National Center for Environmental Protection (NCEP) reanalysis data, and climate projections from three GCMs (GFDL, PCM, and HadCM3), driven by two SRES emission scenarios (A1FI and B1 for GFDL and PCM; A2 and B1 for HadCM3). This approach calculates historic relationships between meteorology and O3, and considers how future meteorology would affect ground‐level O3 if these relationships remain constant. Ozone mixing ratios over Chicago are found to be most sensitive to surface temperature, horizontal surface winds, surface relative humidity, incoming solar radiation, and cloud cover. Considering the change in O3 due to global climate change alone, summertime (June, July, and August) mean mixing ratios over Chicago are projected to increase by 6–17 ppb by the end of the century, depending on assumptions about global economic growth and choice of GCM. Changes are greater under higher climate emissions scenarios and more sensitive climate models (e.g. 24 ppb for GFDL A1FI as compared to 2 ppb for PCM B1). However, this approach does not take into account changes in O3‐precursor emissions nor changes in local and lake‐effect meteorology, which could combine with climate change to either enhance or diminish the projected change in local mixing ratios. Statistical downscaling is performed with the Statistical DownScaling Model (SDSM v. 4.1, a publicly available scientific analysis and decision‐support tool.

Annual cycle of the South Indian Ocean (Seychelles‐Chagos) thermocline ridge in a regional ocean model

The presence of an upwelling “dome”‐like feature in the thermocline depth at 55°E–65°E, 5°S–12°S in the southwest tropical Indian Ocean (SWTIO) has been suggested in earlier work. However, the position, shape, and forcing mechanisms behind this upwelling region are not well understood. In this study, a regional ocean model is applied to the tropical South Indian Ocean. Experiments with monthly climatological winds from both Quick Scatterometer (QuikSCAT) and the National Centers for Environmental Protection (NCEP) reanalyses are performed. An annual and semiannual signal is present in the depth of the model thermocline. The model results suggest that SWTIO upwelling is focused in the west during austral spring and summer and forms a zonally elongated ridge during austral autumn and winter, termed here the Seychelles‐Chagos thermocline ridge. Although the large‐scale wind stress curl plays a major role in maintaining this upwelling ridge, local divergence between the southeasterly trade winds and the monsoon westerlies are shown to impact this region, as well as remote forcing through the arrival of both upwelling and downwelling annual Rossby waves from the eastern Indian Ocean.

Soil moisture estimation using an artificial neural network: a feasibility study

An artificial neural network (ANN) based algorithm is implemented and tested for soil moisture estimation. The ANN model is calibrated (trained) and validated (tested) with data including National Centers for Environmental Protection (NCEP) daily precipitation; normalized difference vegetation index (NDVI) data processed by the US Geological Survey Earth Resources Observations Systems (USGS EROS) data center; Geostationary Operational Environmental Satellite (GOES) based, cloud-masked infrared (IR) skin temperature produced by the University of Maryland; and soil moisture profiles measured over the Oklahoma (OK) Mesonet. The performance of the ANN model is evaluated by direct comparison between the soil moisture estimated by the ANN model and the Mesonet measurements and by examining the correlation between them. Strong correlation is demonstrated between the ANN estimates and Mesonet measurements for spatially averaged data. This work suggests that the ANN model is a promising alternative to soil moisture estimation. The advantage of the ANN approach to soil moisture estimation is that it can provide estimates having resolution commenmmensurate with remotely sensed IR data and has the potential for worldwide coverage.

Acidic loadings in South Korean ecosystems by long-range transport and local emissions

Exceedances of sulfur and nitrogen critical loads in South Korean ecosystems caused by long-range transport and local emissions of sulfur and nitrogen have been estimated using the maximum critical load of sulfur and the critical load of nutrient nitrogen. The long-term-averaged deposition of sulfur and nitrogen is estimated with a simplified chemical model and the K -mean clustering technique. The three consecutive days of gridded daily mean National Center for Environmental Protection (NCEP) reanalyzed 850 hPa geopotential height fields with and without precipitation on the last day over South Korea are used for clustering of synoptic patterns for the period of 1994–1998. Two emission conditions are simulated for each cluster to estimate long-term averaged depositions of sulfur and nitrogen by long-range transport and local emissions over South Korea. One condition takes all emissions within the simulated domain into account as a base case and the other condition excludes all South Korean emissions but includes all of the other emissions, as a control case. The results of the present study indicate that the contribution of long-range transport to the annual total deposition over South Korea is found to be about 40% ( 530 eq ha - 1 yr - 1 ) for sulfur and 49% ( 650 eq ha - 1 yr - 1 ) for nitrogen, of which 55% for sulfur and 58% for nitrogen are contributed by wet deposition. This suggests the importance of wet deposition through the transformed acidic precursors for long-range transport to South Korea's total deposition of sulfur and nitrogen. The estimated exceedance for South Korean ecosystems indicates that the current estimate of total sulfur deposition affects about 42% of the South Korean ecosystems adversely, of which 14% is attributed to South Korean source only and the rest 28% is attributed to long-range transport together with South Korean source. Long-range transport of sulfur itself does not exceed the maximum critical load of sulfur. On the other hand, the current estimate of total nitrogen deposition is found to affect all South Korean ecosystems adversely. About 65% and 15% of the Korean ecosystems are, respectively, found to be affected adversely by South Korean nitrogen emissions and by long-range transport, suggesting that the reduction of nitrogen deposition from both South Korean sources and long-range transport is a prerequisite to ensure South Korean ecosystem sustainability.

Long-range transport contribution to dry deposition of acid pollutants in South Korea

Abstract Long-range transport for dry acidic deposition over South Korea has been estimated using a simplified chemical model and the K-mean clustering technique. The three consecutive daily mean National Center for Environmental Protection reanalyzed 850 hPa geopotential height fields without precipitation on the last day over South Korea are used for clustering of synoptic patterns for the period of 1994–1998. Two emission conditions are simulated for each cluster to estimate long-term impact by long-range transport for acidic dry deposition over South Korea. One condition takes all emissions within the simulated domain into account as a base case and the other condition excludes South Korea's emission and all oxidizers and considering all of the other emissions as a control case. The results of the present study indicate that the contribution of long-range transport to the annual total South Korea's dry deposition of sulfur is found to be about 30% (78 eq ha −1 yr −1 ) , of which 65% and 35% are contributed by SO2 and SO42−, respectively, whereas that of total nitrogen deposition is found to be 49% (240 eq ha −1 yr −1 ) , of which 80% and 20% are contributed by the transformed nitrogen species including HNO3, NH4+ and NO3−, and the primary pollutants of NOx and NH3, respectively, suggesting the importance of secondary pollutants of nitrogen species for long-range transport to South Korea's total dry deposition of nitrogen.

Estimation of nitrogen dry deposition in South Korea

Dry deposition of nitrogen over South Korea has been estimated using a simplified chemical model and the K-mean clustering technique. The three consecutive daily mean National Center for Environmental Protection (NCEP) reanalyzed 850 hPa geopotential height fields without precipitation on the last day over South Korea are used for clusterings of synoptic patterns for the period of 1994–1998. The estimated long-term dry deposition of nitrogen by aggregating each cluster over South Korea is found to be about 530 eq ha −1 yr −1, of which 30%, 23%, 19%, 18% and 9% are contributed by HNO 3, NH 4 +, NO 2, NH 3 and NO 3 − nitrogen species, suggesting the importance of secondary pollutants of nitrogen species for dry deposition of nitrogen.