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Magnitude of atmospheric turbulence, a key driver of several processes that contribute to aerosol (i.e., particle) deposition, is underrepresented in current models. Various formulations have been developed to model particle dry deposition; all these formulations typically rely on friction velocity and some use additional ad hoc factors to represent enhanced impacts of turbulence. However, none were formally linked with the three-dimensional (3-D) turbulence. Here, we propose a set of 3-D turbulence-dependent resistance formulations for particle dry deposition simulation and intercompare the performance of new resistance formulations with that obtained from using the existing formulations and measured dry deposition velocity. Turbulence parameters such as turbulence velocity scale, turbulence factor, intensity of turbulence, effective sedimentation velocity, and effective Stokes number are newly introduced into two different particle deposition schemes to improve turbulence strength representation. For an assumed particle size distribution, the newly proposed schemes predict stronger diurnal variation of particle dry deposition velocity and are comparable to corresponding measurements while existing formulations indicate large underpredictions. We also find that the incorporation of new turbulence parameters either introduced or added stronger diurnal variability to sedimentation velocity and collection efficiencies values, making the new schemes predict higher deposition values during daytime and nighttime when compared to existing schemes. The findings from this research may help improve the capability of dry deposition schemes in regional and global models.

Condensable particulate matter (CPM) emitted from stationary combustion and mobile sources exhibits high emissions and a large proportion of organic components. However, CPM is not generally measured when conducting emission surveys of PM in most countries, including China. Consequently, previous emission inventories have not included emission rates for CPM. Here, we construct an emission inventory of CPM in China with a focus on organic aerosols (OAs) based on collected CPM emission information. Results show that OA emissions are enhanced twofold after the inclusion of CPM in a new inventory for China for the years 2014 and 2017. Considering organic CPM emissions and model representations of secondary OA (SOA) formation from CPM, a series of sensitivity cases have been simulated here using the three-dimensional Community Multiscale Air Quality (CMAQ) model to estimate the contributions of CPM emissions to atmospheric OA and fine PM (PM2.5, particulate matter with aerodynamic diameter not exceeding 2.5 µm) concentrations in China. Compared with observations at a Beijing site during a haze episode from 14 October to 14 November 2014, estimates of the temporal average primary OA (POA) and SOA concentrations were greatly improved after including the CPM effects. These scenarios demonstrated the significant contributions of CPM emissions from stationary combustion and mobile sources to the POA (51 %–85 %​​​​​​​), SOA (42 %–58 %), and total OA concentrations (45 %–75 %). Furthermore, the contributions of CPM emissions to total OA concentrations were demonstrated over the 2 major cities and 26 other cities of the Beijing–Tianjin–Hebei region (hereafter referred to as the “BTH2 + 26 cities”) in December 2018, with average contributions of up to 49 %, 53 %, 54 %, and 50 % for Handan, Shijiazhuang, Xingtai, and Dezhou, respectively. Correspondingly, the inclusion of CPM emissions also narrowed the gap between simulated and observed PM2.5 concentrations over the BTH2 + 26 cities. These results improve the simulation performance of atmospheric OA and PM2.5 and may also provide important implications for the sources of OA.

Different functions are used to account for turbulence strength in the atmospheric boundary layer for different stability regimes. These functions are one of the sources for differences among different atmospheric models' predictions and associated biases. Also, turbulence strength is underrepresented in some of the resistance formulations. To address these issues with dry deposition, firstly we take advantage of three-dimensional (3-D) turbulence information in estimating resistances by proposing and validating a 3-D turbulence velocity scale that is relevant for different stability regimes of boundary layer. Secondly, we hypothesize and validate that friction velocity measured by 3-D sonic anemometer can be effectively replaced by the new turbulence velocity scale multiplied by the von Karman constant. Finally, we (1) present a set of resistance formulations for ozone (O

The cloud liquid water path (LWP), ice water path (IWP), and precipitation simulated with uniform- and variable-resolution numerical experiments using the Model for Prediction Across Scales (MPAS) are compared against Clouds and the Earth's Radiant Energy System (CERES) and Tropical Rainfall Measuring Mission data. Our comparison between monthly-mean model diagnostics and satellite data focuses on the convective activity regions of the tropical Pacific Ocean, extending from the Tropical Eastern Pacific Basin where trade wind boundary layer clouds develop to the Western Pacific Warm Pool characterized by deep convective updrafts capped with extended upper-tropospheric ice clouds. Using the scale-aware Grell–Freitas (GF) and Multi-scale Kain–Fritsch (MSKF) convection schemes in conjunction with the Thompson cloud microphysics, uniform-resolution experiments produce large biases between simulated and satellite-retrieved LWP, IWP, and precipitation. Differences in the treatment of shallow convection lead the LWP to be strongly overestimated when using GF, while being in relatively good agreement when using MSKF compared to CERES data. Over areas of deep convection, uniform- and variable-resolution experiments overestimate the IWP with both MSKF and GF, leading to strong biases in the top-of-the-atmosphere longwave and shortwave radiation relative to satellite-retrieved data. Mesh refinement over the Western Pacific Warm Pool does not lead to significant improvement in the LWP, IWP, and precipitation due to increased grid-scale condensation and upward vertical motions. Results underscore the importance of evaluating clouds, their optical properties, and the top-of-the-atmosphere radiation budget in addition to precipitation when performing mesh refinement global simulations.

The Weather Research and Forecasting Model with Aerosol–Cloud Interactions (WRF-ACI) configuration is used to investigate the scale dependency of aerosol–cloud interactions (ACI) across the “gray zone” scales for grid-scale and subgrid-scale clouds. The impacts of ACI on weather are examined across regions in the eastern and western United States at 36, 12, 4, and 1 km grid spacing for short-term periods during the summer of 2006. ACI impacts are determined by comparing simulations with current climatological aerosol levels to simulations with aerosol levels reduced by 90%. The aerosol–cloud lifetime effect is found to be the dominant process leading to suppressed precipitation in regions of the eastern United States, while regions in the western United States experience offsetting impacts on precipitation from the cloud lifetime effect and other effects that enhance precipitation. Generally, the cloud lifetime effect weakens with decreasing grid spacing due to a decrease in relative importance of autoconversion compared to accretion. Subgrid-scale ACI are dominant at 36 km, while grid-scale ACI are dominant at 4 and 1 km. At 12 km grid spacing, grid-scale and subgrid-scale ACI processes are comparable in magnitude and spatial coverage, but random perturbations in grid-scale ACI impacts make the overall grid-scale ACI impact appear muted. This competing behavior of grid- and subgrid-scale clouds complicate the understanding of ACI at 12 km within the current WRF modeling framework. The work implies including subgrid-scale cloud microphysics and ice/mixed-phase-cloud ACI processes may be necessary in weather and climate models to study ACI effectively.

The Weather Research and Forecasting Model with Aerosol–Cloud Interactions (WRF-ACI) is developed for studying aerosol effects on gridscale and subgrid-scale clouds using common aerosol activation and ice nucleation formulations and double-moment cloud microphysics in a scale-aware subgrid-scale parameterization scheme. Comparisons of both the standard WRF and WRF-ACI models’ results for a summer season against satellite and reanalysis estimates show that the WRF-ACI system improves the simulation of cloud liquid and ice water paths. Correlation coefficients for nearly all evaluated parameters are improved, while other variables show slight degradation. Results indicate a strong cloud lifetime effect from current climatological aerosols increasing domain average cloud liquid water path and reducing domain average precipitation as compared to a simulation with aerosols reduced by 90%. Increased cloud-top heights indicate a thermodynamic invigoration effect, but the impact of thermodynamic invigoration on precipitation is overwhelmed by the cloud lifetime effect. A combination of cloud lifetime and cloud albedo effects increases domain average shortwave cloud forcing by ~3.0 W m−2. Subgrid-scale clouds experience a stronger response to aerosol levels, while gridscale clouds are subject to thermodynamic feedbacks because of the design of the WRF modeling framework. The magnitude of aerosol indirect effects is shown to be sensitive to the choice of autoconversion parameterization used in both the gridscale and subgrid-scale cloud microphysics, but spatial patterns remain qualitatively similar. These results indicate that the WRF-ACI model provides the community with a computationally efficient tool for exploring aerosol–cloud interactions.

International audience; Climate change is predicted to induce more extreme events such as storms, heat waves, drought and floods. Dust storms are frequently occurring in northern China. Those storms degrade air quality by decreasing visibility and inducing cardiovascular and respiratory diseases. To control dust storms, the Chinese government has launched a large-scale afforestation program by planting trees in arid areas, but the effectiveness of this program is still uncertain because the trajectories and altitudes of dust transport are poorly known. In particular, afforestation would be effective only if dust transport occurs at low altitudes. To test this hypothesis, we analyzed the extreme dust storm from May 2 to 7, 2017, which resulted in record-breaking dust loads over northern China. For that, we used dust RGB-composite data from the Himawari-8 satellite and the cloud-aerosol lidar, moderate-resolution imaging spectroradiometer data, and surface monitoring data. The source regions of the dust storms were identified using the hybrid single-particle Lagrangian integrated trajectory model and infrared pathfinder satellite observation. Contrary to our hypothesis, results show that dust is transported at high altitude of 1.0-6.5 km over long distances from northwestern China. This finding explains why the afforestation has not been effective to prevent this storm. Results also disclose the highest particulate matter (PM) concentrations of 447.3 μg/m 3 for PM 2.5 and 1842.0 μg/m 3 for PM 10 during the dust storm. Those levels highly exceed Chinese ambient air quality standards of 75 μg/m 3 for PM 2.5 and 150 μg/m 3 for PM 10 .

Efforts 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 lifting condensation level–based entrainment methodology that includes scale dependency. A series of 48-h retrospective forecasts using a combination of three treatments of convection (KF, updated KF, and the use of no cumulus parameterization), two cloud microphysics schemes, and two types of initial condition datasets were performed over the U.S. southern Great Plains on 9- and 3-km grid spacings during the summers of 2002 and 2010. Results indicate that 1) the source of initial conditions plays a key role in high-resolution precipitation forecasting, and 2) the authors’ updated KF scheme greatly alleviates the excessive precipitation at 9-km grid spacing and improves results at 3-km grid spacing as well. Overall, the study found that the updated KF scheme incorporated into a high-resolution model does provide better forecasts for precipitation location and intensity.

Nudging (data assimilation) is used in many regional integrated meteorology-air quality models to reduce biases in simulated climatology. However, in such modeling systems, temperature changes due to nudging could compete with temperature changes induced by radiatively active and hygroscopic short-lived tracers leading to two interesting dilemmas: when nudging is continuously applied, what are the relative sizes of these two radiative forces at regional and local scales? How do these two forces present in the free atmosphere differ from those present at the surface? This work studies these two issues by converting temperature changes due to nudging into pseudo radiative effects (PRE) at the surface (PRE_sfc), in troposphere (PRE_atm), and at the top of atmosphere (PRE_toa), and comparing PRE with the reported aerosol radiative effects (ARE). Results show that the domain-averaged PRE_sfc is smaller than ARE_sfc estimated in previous studies and this work, but could be significantly larger than ARE_sfc at local scales. PRE_atm is also much smaller than ARE_atm. These results indicate that appropriate nudging methodology could be applied to the integrated models to study aerosol radiative effects at continental/regional scales, but it should be treated with caution for local scale applications.

It has been proposed that the summer “South Flood–North Drought” (SFND) pattern observed in China over recent decades is caused by the relative impacts of global warming, aerosol loading, and natural variability on regional rainfall (1⇓–3). This conclusion is supported by a recent study by Day et al. (4) in which the SFND is attributed to the changes in the frequency of frontal rain events. Using a technique called the Frontal Rain Event Detection Algorithm for the observations during 1951–2007, decadal changes in the amount and distribution of rainfall in eastern China were found to be overwhelmingly due to changes in frontal rainfall (4). Day et al. conclude that frontal rainfall was envisioned as the product of large-scale frontal convergence and the nonfrontal rainfall was because of local convection, orographic rainfall, and typhoon rainfall. The authors further imply that the shifts in frequency and latitude of frontal rainfall over the recent decades in eastern China reflect changes in large-scale atmospheric circulation.

Severe and persistent haze pollution involving fine particulate matter (PM2.5) concentrations reaching unprecedentedly high levels across many cities in China poses a serious threat to human health. Although mandatory temporary cessation of most urban and surrounding emission sources is an effective, but costly, short-term measure to abate air pollution, development of long-term crisis response measures remains a challenge, especially for curbing severe urban haze events on a regular basis. Here we introduce and evaluate a novel precision air pollution control approach (PAPCA) to mitigate severe urban haze events. The approach involves combining predictions of high PM2.5 concentrations, with a hybrid trajectory-receptor model and a comprehensive 3-D atmospheric model, to pinpoint the origins of emissions leading to such events and to optimize emission controls. Results of the PAPCA application to five severe haze episodes in major urban areas in China suggest that this strategy has the potential to significantly mitigate severe urban haze by decreasing PM2.5 peak concentrations by more than 60% from above 300 μg m−3 to below 100 μg m−3, while requiring ~30% to 70% less emission controls as compared to complete emission reductions. The PAPCA strategy has the potential to tackle effectively severe urban haze pollution events with economic efficiency.

Many regions in China experience air pollution episodes because of the rapid urbanization and industrialization over the past decades. Here we analyzed the effect of emission controls implemented during the G-20 2016 Hangzhou summit on air quality. Emission controls included a forced closure of highly polluting industries, and limiting traffic and construction emissions in the cities and surroundings. Particles with aerodynamic diameter lower than 2.5 μm (PM_(2.5)) and ozone (O_3) were measured. We also simulated air quality using a forecast system consisting of the two-way coupled Weather Research and Forecast and Community Multi-scale Air Quality (WRF-CMAQ) model. Results show PM_(2.5) and ozone levels in Hangzhou during the G-20 Summit were considerably lower than previous to the G-20 Summit. The predicted concentrations of ozone were reduced by 25.4%, whereas the predicted concentrations of PM_(2.5) were reduced by 56%.

Many convective parameterization schemes define a convective adjustment time scale τ as the time allowed for dissipation of convective available potential energy (CAPE). The Kain–Fritsch scheme defines τ based on an estimate of the advective time period for deep convective clouds within a grid cell, with limits of 1800 and 3600 s, based on practical cloud-lifetime considerations. In simulations from the Weather Research and Forecasting (WRF) Model using 12-km grid spacing, the value of τ often defaults to the lower limit, resulting in relatively rapid thermodynamics adjustments and high precipitation rates. Herein, a new computation for τ in the Kain–Fritsch scheme is implemented based on the depth of the buoyant layer and the convective velocity scale. This new τ formulation is applied using 12- and 36-km model grid spacing in conjunction with a previous modification that takes into account the radiation effects of parameterized convective clouds. The dynamically computed convective adjustment time scale is shown to reduce the precipitation bias by approximately 15% while also providing improved simulations of inland rainfall from tropical storms.

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 Earth's 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).

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.

It has been proposed that the current and future global warming caused by increased greenhouse gases can produce a northward shift of the Earth’s rain belt (1). This conclusion is supported by a recent study in PNAS by Yang et al. (2) in which the carbon isotope composition of 21 loess-soil sections from the Chinese Loess Plateau for the past 20 ka have been systematically investigated. The authors concluded that at least a 300 km northwestward migration of the monsoon rain belt had occurred from the cold Last Glacial Maximum (LGM) to the warm Holocene in China on the basis of the spatiotemporal patterns of C4 plant biomass. The authors further imply that the observed southern drift of the summer monsoon rain belt in China during the past few decades will reverse itself and migrate north with the continuation of global warming. However, Yang et al.’s (2) conclusion fails to consider climate effects of anthropogenic aerosols, a critically important factor for the recent southward migration of the summer monsoon rain belt in China (3⇓⇓–6). The climate model experiments reveal that the observed widespread decrease of summer monsoon rain over South Asia can be attributed mainly to … [↵][1]1To whom correspondence may be addressed. Email: shaocaiyu{at}zju.edu.cn or alapaty.kiran{at}epa.gov. [1]: #xref-corresp-1-1

A low pressure system that formed on 21 September 2006 over eastern India/Bay of Bengal intensified into a monsoon depression resulting in copious rainfall over north-eastern and central parts of India. Four numerical experiments are performed to examine the performance of assimilation schemes in simulating this monsoon depression using the Fifth Generation Mesoscale Model (MM5). Forecasts from a base simulation (with no data assimilation), a four-dimensional data assimilation (FDDA) system, a simple surface data assimilation (SDA) system coupled with FDDA, and a flux-adjusting SDA system (FASDAS) coupled with FDDA are compared with each other and with observations. The model is initialized with Global Forecast System (GFS) forecast fields starting from 19 September 2006, with assimilation being done for the first 24 hours using conventional observations, sounding and surface data of temperature and moisture from Advanced TIROS Operational Vertical Sounder satellite and surface wind data over the ocean from QuikSCAT. Forecasts are then made from these assimilated states. In general, results indicate that the FASDAS forecast provides more realistic prognostic fields as compared to the other three forecasts. When compared with other forecasts, results indicate that the FASDAS forecast yielded lower root-mean-square (r.m.s.) errors for the pressure field and improved simulations of surface/near-surface temperature, moisture, sensible and latent heat fluxes, and potential vorticity. Heat and moisture budget analyses to assess the simulation of convection revealed that the two forecasts with the surface data assimilation (SDA and FASDAS) are superior to the base and FDDA forecasts. An important conclusion is that, even though monsoon depressions are large synoptic systems, mesoscale features including rainfall are affected by surface processes. Enhanced representation of land-surface processes provides a significant improvement in the model performance even under active monsoon conditions where the synoptic forcings are expected to be dominant.

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.

The flux-adjusting surface data assimilation system (FASDAS) is developed to provide continuous adjustments for initial soil moisture and temperature and for surface air temperature and water vapor mixing ratio for mesoscale models. In the FASDAS approach, surface air temperature and water vapor mixing ratio are directly assimilated by using the analyzed surface observations. Then, the difference between the analyzed surface observations and model predictions of surface layer temperature and water vapor mixing ratio are converted into respective heat fluxes, referred to as adjustment heat fluxes of sensible and latent heat. These adjustment heat fluxes are then used in the prognostic equations for soil temperature and moisture via indirect assimilation in the form of several new adjustment evaporative fluxes. Thus, simulated surface fluxes for the subsequent model time step are affected such that the predicted surface air temperature and water vapor mixing ratio conform more closely to observations. The simultaneous application of indirect and direct data assimilation maintains greater consistency between the soil temperature–moisture and the surface layer mass-field variables. The FASDAS is coupled to a land surface submodel in a three-dimensional mesoscale model and tests are performed for a 10-day period with three one-way nested domains. The FASDAS is applied in the analysis nudging mode for two coarse-resolution nested domains and in the observational nudging mode for a fine-resolution nested domain. Further, the effects of FASDAS on two different initial specifications of a three-dimensional soil moisture field are also studied. Results indicate that the FASDAS consistently improved the accuracy of the model simulations.

Asymmetrical convective non-local scheme (CON) with varying upward mixing rates is developed for simulation of vertical turbulent mixing in the convective boundary layer in air quality and chemical transport models.The upward mixing rate form the surface layer is parameterized using the sensible heat flux and the friction and convective velocities. Upward mixing rates varying with height are scaled with an amount of turbulent kinetic energy in layer, while the downward mixing rates are derived from mass conservation.This scheme provides a less rapid mass transport out of surface layer into other layers than other asymmetrical convective mixing schemes.In this paper, we studied the performance of a nonlocal convective mixing scheme with varying upward mixing in the atmospheric boundary layer and its impact on the concentration of pollutants calculated with chemical and air-quality models. This scheme was additionally compared versus a local eddy-diffusivity scheme (KSC). Simulated concentrations of NO(2) and the nitrate wet deposition by the CON scheme are closer to the observations when compared to those obtained from using the KSC scheme.Concentrations calculated with the CON scheme are in general higher and closer to the observations than those obtained by the KSC scheme (of the order of 15-20%). Nitrate wet deposition calculated with the CON scheme are in general higher and closer to the observations than those obtained by the KSC scheme.To examine the performance of the scheme, simulated and measured concentrations of a pollutant (NO(2)) and nitrate wet deposition was compared for the year 2002. The comparison was made for the whole domain used in simulations performed by the chemical European Monitoring and Evaluation Programme Unified model (version UNI-ACID, rv2.0) where schemes were incorporated.

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.

The objective of this study is to investigate the impact of a surface data assimilation (SDA) technique, together with the traditional four-dimensional data assimilation (FDDA), on the simulation of a monsoon depression that formed over India during the field phase of the 1999 Bay of Bengal Monsoon Experiment (BOBMEX). The SDA uses the analyzed surface data to continuously assimilate the surface layer temperature as well as the water vapor mixing ratio in the mesoscale model. The depression for the greater part of this study was offshore and since successful application of the SDA would require surface information, a method of estimating surface temperature and surface humidity using NOAA-TOVS satellites was used. Three sets of numerical experiments were performed using a coupled mesoscale model. The first set, called CONTROL, uses the NCEP (National Center for Environmental Prediction) reanalysis for the initial and lateral boundary conditions in the MM5 simulation. The second and the third sets implemented the SDA of temperature and moisture together with the traditional FDDA scheme available in the MM5 model. The second set of MM5 simulation implemented the SDA scheme only over the land areas, and the third set extended the SDA technique over land as well as sea. Both the second and third sets of the MM5 simulation used the NOAA-TOVS and QuikSCAT satellite and conventional upper air and surface meteorological data to provide an improved analysis. The results of the three sets of MM5 simulations are compared with one another and with the analysis and the BOBMEX 1999 buoy, ship, and radiosonde observations. The predicted sea level pressure of both the model runs with assimilation resembles the analysis closely and also captures the large-scale structure of the monsoon depression well. The central sea level pressures of the depression for both the model runs with assimilation were 2–4 hPa lower than the CONTROL. The results of both the model runs with assimilation indicate a larger spatial area as well as increased rainfall amounts over the coastal regions after landfall compared with the CONTROL. The impact of FDDA and SDA, the latter over land, resulted in reduced errors of the following: 1.45 K in temperature, 0.39 m s−1 in wind speed, and 14° in wind direction compared with the BOBMEX buoy observation, and 1.43 m s−1 in wind speed, 43° in wind direction, and 0.75% in relative humidity compared with the CONTROL. The impact of SDA over land and sea compared with SDA over land only showed a further marginal reduction of errors: 0.23 K in air temperature (BOBMEX buoy) and 1.33 m s−1 in wind speed simulations.

Two land-surface parameterization schemes (ISBA (Interactions Soil-Biosphere-Atmosphere) and LAPS (Land-Air-Parameterization-Scheme)) of differing complexity were implemented into a 1-D atmospheric boundary layer model and evaluated utilizing the special observational data collected during the summer of 1987 from the FIFE measurements. Results from two case studies were analyzed during which boundary layer processes dominated the lower atmospheric processes. In the first case study (6 June 1987), we found that differences in the estimation of stomatal resistance in the two land-surface models (LSMs) triggered many of the differences in results obtained using the two LSMs. In the second case study (11 July 1987), only minor differences existed between respective net radiation and stomatal resistance estimations, leading to similar boundary layer structures. Diagnostic results indicate that in both case studies growth rates of and depths of atmospheric boundary layers and vertical profiles of virtual potential temperatures, water vapor mixing ratio, and horizontal winds are very similar in LAPS and ISBA. Our future studies are directed at evaluating these two schemes using a regional climate version of the WRF model. Copyright © 2006 Royal Meteorological Society.

The Flux-Adjusting Surface Data Assimilation System (FASDAS) uses the surface observational analysis to directly assimilate surface layer temperature and water vapor mixing ratio and to indirectly assimilate soil moisture and soil temperature in numerical model predictions. Both soil moisture and soil temperature are important variables in the development of deep convection. In this study, FASDAS coupled within the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) was used to study convective initiation over the International H2O Project (IHOP_2002) region, utilizing the analyzed surface observations collected during IHOP_2002. Two 72-h numerical simulations were performed. A control simulation was run that assimilated all available IHOP_2002 measurements into the standard MM5 four-dimensional data assimilation. An experimental simulation was also performed that assimilated all available IHOP_2002 measurements into the FASDAS version of the MM5, where surface observations were used for the FASDAS coupling. Results from this case study suggest that the use of FASDAS in the experimental simulation led to the generation of greater amounts of precipitation over a more widespread area as compared to the standard MM5 FDDA used in the control simulation. This improved performance is attributed to better simulation of surface heat fluxes and their gradients.

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 20(th) 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.

A soil–vegetation–atmosphere transfer scheme based on a one-dimensional boundary layer model has been developed to study the sensitivity of boundary layer growth on the surface temperature fields and land-use types. Full interaction between the surface and atmosphere is achieved by representing the surface and turbulent mixing processes by using a Land Air Parameterization Scheme (LAPS) and the Blackadar's local closure PBL scheme. Sensitivity tests are performed by simulating planetary boundary layer structures over different underlying surfaces, soil textures and soil moisture contents using the meteorological data for July 17, 1999 in Philadelphia, PA. Also, a simpler surface parameterization used in the MM5 model results in a significantly higher surface temperature as well as the PBL depth over the urban area than those obtained by the LAPS parameterization in which a new method, including a combination of aggregated fluxes and parameters in calculating the surface temperature, is applied.

A method for estimating profiles of turbulent transfer coefficients inside a vegetation canopy and their use in calculating the air temperature inside tall grass canopies in land surface schemes for environmental modeling is presented. The proposed method, based on K theory, is assessed using data measured in a maize canopy. The air temperature inside the canopy is determined diagnostically by a method based on detailed consideration of 1) calculations of turbulent fluxes, 2) the shape of the wind and turbulent transfer coefficient profiles, and 3) calculation of the aerodynamic resistances inside tall grass canopies. An expression for calculating the turbulent transfer coefficient inside sparse tall grass canopies is also suggested, including modification of the corresponding equation for the wind profile inside the canopy. The proposed calculations of K-theory parameters are tested using the Land–Air Parameterization Scheme (LAPS). Model outputs of air temperature inside the canopy for 8–17 July 2002 are compared with micrometeorological measurements inside a sunflower field at the Rimski Sancevi experimental site (Serbia). To demonstrate how changes in the specification of canopy density affect the simulation of air temperature inside tall grass canopies and, thus, alter the growth of PBL height, numerical experiments are performed with LAPS coupled with a one-dimensional PBL model over a sunflower field. To examine how the turbulent transfer coefficient inside tall grass canopies over a large domain represents the influence of the underlying surface on the air layer above, sensitivity tests are performed using a coupled system consisting of the NCEP Nonhydrostatic Mesoscale Model and LAPS. Financed by Serbian Ministry for Science and Technology under Contracts BTR.S.02.0401.B and BTR.S.02.0427.B

We present a dry deposition modeling approach that includes vegetation-atmosphere interactions through photosynthesis/carbon assimilation relationships. Gas deposition velocity (Vd) is calculated using an electrical resistance-analog approach in a coupled soil-vegetation-atmosphere transfer (SVAT) model. For this, a photosynthesis-based surface evapotranspiration and gas exchange model is dynamically coupled to an atmospheric model with prognostic soil hydrology and surface energy balance. The effective surface resistance (composed of aerodynamic, boundary layer, and canopy-based resistances) is calculated for a realistic and fully interactive estimation of gaseous deposition velocity over natural surfaces. Based on this coupled framework, the photosynthesis- based gas deposition approach is evaluated using observed deposition velocity estimates for ozone over a soybean field (C3 photosynthesis pathway) and a corn field (C4 photosynthesis pathway). Overall, observed Vd and modeled Vd show good qualitative and quantitative agreement. Results suggest that photosynthesis-based physiological approaches can be adopted to efficiently develop deposition velocity estimates over natural surfaces. Such a physiological approach can also be used for generalizing results from field measurements and for investigating the controlling relationships among various atmospheric and surface variables in estimating deposition velocity.

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.

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.

The Multiscale Air Quality Simulation Platform (MAQSIP) is used to simulate transport of carbon monoxide (CO) as a passive tracer over North America, Europe, and the North Atlantic during the April 1994 Measurement of Air Pollution from Satellites (MAPS) mission. MAQSIP is driven by meteorological fields generated by the Pennsylvania State University/National Center for Atmospheric Research fifth-generation mesoscale model. Model CO surface emissions from biomass burning, fossil fuel combustion, nonmethane hydrocarbon oxidation, oceans, and soils are based on inventories from the Belgian Institute for Space Aeronomy and the Global Emissions Inventory Activity. Predicted CO mixing ratios are vertically weighted for comparison with MAPS observations. The spread in the mission-averaged vertically weighted simulated CO mixing ratios (∼38 ppbv, compared to 60 ppbv in the MAPS data) suggests that CO surface emissions significantly affect MAPS observations on a weekly timescale. Good qualitative agreement is found between MAPS observations and model predictions on several temporal and spatial scales. Possible reasons for discrepancies are examined. A simulation without cumulus convection increases CO mixing ratios in the lower model layers and depletes CO above, resulting in a complex pattern of increases and decreases upon vertical weighted integration. Another simulation, which included a diurnal emissions variation, produced significant changes in instantaneous local CO mixing ratios, but had a minimal effect on the mission-averaged MAPS comparisons.

The influence of soil moisture and vegetation variation on simulation of monsoon circulation and rainfall is investigated. For this purpose a simple land surface parameterization scheme is incorporated in a three-dimensional regional high resolution nested grid atmospheric model. Based on the land surface parameterization scheme, latent heat and sensible heat fluxes are explicitly estimated over the entire domain of the model. Two sensitivity studies are conducted; one with bare dry soil conditions (no latent heat flux from land surface) and the other with realistic representation of the land surface parameters such as soil moisture, vegetation cover and landuse patterns in the numerical simulation. The sensitivity of main monsoon features such as Somali jet, monsoon trough and tropical easterly jet to land surface processes are discussed. Results suggest the necessity of including a detailed land surface parameterization in the realistic short-range weather numerical predictions. An enhanced short-range prediction of hydrological cycle including precipitation was produced by the model, with land surface processes parameterized. This parameterization appears to simulate all the main circulation features associated with the summer monsoon in a realistic manner.

Stomatal resistance (Rs) forms a pivotal component of the surface energy budget and of the terrestrial biosphere‐atmosphere interactions. Using a statistical‐graphical technique, the Rs-related interactions between different atmospheric and physiological variables are resolved explicitly from observations made during the First ISLSCP (International Satellite Land Surface Climatology Project) Field Experiment (FIFE). A similar analysis was undertaken for the Rs parameterization schemes, as used in the present models. Three physiological schemes (the Ball‐Woodrow‐Berry, Kim and Verma, and Jacobs) and one operational Jarvis-type scheme were evaluated in terms of their ability to replicate the terrestrial biosphere‐atmosphere interactions. It was found that all of the Rs parameterization schemes have similar qualitative behavior for routine meteorological applications (without carbon assimilation). Compared to the observations, there was no significant difference found in employing either the relative humidity or the vapor pressure deficit as the humidity descriptor in the analysis. Overall, the relative humidity‐based interactions were more linear than the vapor pressure deficit and hence could be considered more convenient in the scaling exercises. It was found that with high photosynthesis rates, all of the schemes had similar behavior. It was found with low assimilation rates, however, that the discrepancies and nonlinearity in the interactions, as well as the uncertainties, were exaggerated.

A one-dimensional chemistry-boundary layer model was used to study the effects of differing representations of atmospheric boundary layer (ABL) processes on simulated concentrations of passive and chemically reactive tracers. Two local- and two nonlocal-closure ABL schemes were used to perform numerical simulations during convective conditions in the ABL. Observational data from the First International Statellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE) were used to provide initial meteorological conditions while representative chemical concentrations and surface and elevated emission rates were used to provide initial chemical conditions and chemical sources to the one-dimensional model. Two sets of numerical simulations were performed using the four ABL schemes. The first set simulated bottom-to-top mixing characteristics, and the second set simulated top-to-bottom mixing characteristics. Model simulations were performed for 12h starting from 0700LT 11 July 1987. Our analysis indicate that the simulated concentrations of both passive and reactive chemical species were sensitive to the type of ABL scheme used to represent turbulent mixing processes. Characteristic features associated with each scheme (e.g., growth and intensity of mixing in the ABL) contributed to the differences among the simulated species concentrations. For some of the chemical species these differences were large, particularly in the surface layer and in the interfacial layers of the ABL. In turn, differences caused by the differing mixing representations resulted in different chemical production/destruction rates. As a consequence, the simulated species concentrations differed among the simulations. We also found that chemical species concentrations were more sensitive to the type of ABL scheme in the bottom-to-top mixing simulations than in the top-to-bottom simulations.

Two geometrical and two advection-equivalent spatial interpolation schemes were tested in providing lateral boundary conditions to a nested grid domain. Geometric interpolation schemes used in this study are a zeroth- order and a quadratic scheme, while the two advection-equivalent interpolation schemes were based on upwind and Bott’s advection schemes. The test problem involves an initially cone-shaped distribution of a scalar advected from a coarse to a fine grid. Simulation results were compared to the exact solution to study magnitude and phase characteristics of each scheme. Results indicated that Bott’s advection-equivalent interpolation scheme provided better interface conditions and, consequently, a more accurate transition of the signal from a coarse to a fine grid.

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.

Interactions among atmospheric parameters exist at different scales. The pristine approach for observational or model data analysis involves changing the input parameters one at a time (OAT) and studying the effect on the system. Limitations of this approach for atmospheric applications are discussed. A fractional factorial (FF) based study is evolved and a methodology is outlined involving dynamic graphical analysis. Observational data from the FIFE and HAPEX‐MOBILHY experiments are utilized with a vegetation and soil moisture scheme dynamically coupled in a planetary boundary layer model to demonstrate the robustness of this approach. Both low‐resolution and high‐resolution designs are considered. Various aspects of the vegetation‐atmosphere interactions are delineated. Results obtained from the interaction‐based FF approach differ considerably from the earlier OAT‐type studies.

This study implemented first, second and glaciation aerosol indirect effects (AIE) on resolved clouds in the two-way coupled Weather Research and Forecasting Community Multiscale Air Quality (WRF–CMAQ) modeling system by including parameterizations for both cloud drop and ice number concentrations on the basis of CMAQ-predicted aerosol distributions and WRF meteorological conditions. The performance of the newly developed WRF–CMAQ model, with alternate Community Atmospheric Model (CAM) and Rapid Radiative Transfer Model for GCMs (RRTMG) radiation schemes, was evaluated with observations from the Clouds and the See http://ceres.larc.nasa.gov/. Earth's Radiant Energy System (CERES) satellite and surface monitoring networks (AQS, IMPROVE, CASTNET, STN, and PRISM) over the continental US (CONUS) (12 km resolution) and eastern Texas (4 km resolution) during August and September of 2006. The results at the Air Quality System (AQS) surface sites show that in August, the normalized mean bias (NMB) values for PM2.5 over the eastern US (EUS) and the western US (WUS) are 5.3% (−0.1%) and 0.4% (−5.2%) for WRF–CMAQ/CAM (WRF–CMAQ/RRTMG), respectively. The evaluation of PM2.5 chemical composition reveals that in August, WRF–CMAQ/CAM (WRF–CMAQ/RRTMG) consistently underestimated the observed SO42- by −23.0% (−27.7%), −12.5% (−18.9%) and −7.9% (−14.8%) over the EUS at the Clean Air Status Trends Network (CASTNET), Interagency Monitoring of Protected Visual Environments (IMPROVE) and Speciated Trends Network (STN) sites, respectively. Both configurations (WRF–CMAQ/CAM, WRF–CMAQ/RRTMG) overestimated the observed mean organic carbon (OC), elemental carbon (EC) and and total carbon (TC) concentrations over the EUS in August at the IMPROVE sites. Both configurations generally underestimated the cloud field (shortwave cloud forcing, SWCF) over the CONUS in August due to the fact that the AIE on the subgrid convective clouds was not considered when the model simulations were run at the 12 km resolution. This is in agreement with the fact that both configurations captured SWCF and longwave cloud forcing (LWCF) very well for the 4 km simulation over eastern Texas, when all clouds were resolved by the finer resolution domain. The simulations of WRF–CMAQ/CAM and WRF–CMAQ/RRTMG show dramatic improvements for SWCF, LWCF, cloud optical depth (COD), cloud fractions and precipitation over the ocean relative to those of WRF default cases in August. The model performance in September is similar to that in August, except for a greater overestimation of PM2.5 due to the overestimations of SO42-, NH4+, NO3-, and TC over the EUS, less underestimation of clouds (SWCF) over the land areas due to the lower SWCF values, and fewer convective clouds in September. This work shows that inclusion of indirect aerosol effect treatments in WRF–CMAQ represents a significant advancement and milestone in air quality modeling and the development of integrated emissions control strategies for air quality management and climate change mitigation.

[1] Convective systems and associated cloudiness directly influence regional and local atmospheric radiation budgets, as well as dynamics and thermodynamics, through feedbacks. However, most subgrid-scale convective parameterizations in regional weather and climate models do not consider cumulus cloud feedbacks to radiation, resulting in biases in several meteorological parameters. We have incorporated this key feedback process into a convective parameterization and a radiation scheme in the Weather Research and Forecasting model, and evaluated the impacts of including this process in short-term weather and multiyear climate simulations. Introducing subgrid-scale convective cloud-radiation feedbacks leads to a more realistic simulation of attenuation of downward surface shortwave radiation. Reduced surface shortwave radiation moderates the surface forcing for convection and results in a notable reduction in precipitation biases. Our research reveals a need for more in-depth consideration of the effects of subgrid-scale clouds in regional meteorology/climate and air quality models on radiation, photolysis, cloud mixing, and aerosol indirect effects.

Objective analysis and diagnostic methods are used to provide hourly meteorological fields to many air quality simulation models. The viability of using predictions from the Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model version 4 (MM4) together with four-dimensional data assimilation, technique to provide meteorological information to the U.S. EPA Regional Oxidant Model (ROM) was studied. Two numerical simulations were performed for eight days using the ROM for a domain covering the eastern United States. In the first case, diagnostically analyzed data were used to provide meteorological conditions, while in the second case the MM4's prognostic data were used. Comparisons of processed diagnostic and prognostic meteorological data indicated differences in dynamical, thermodynamical, and other derived variables. Uncertainties and forecast errors present in the predicted vertical temperature profiles led to estimation of lower mixed-layer heights (∼ 30%–50%) ...

Sensitivity of a regional scale model to different sea surface temperatures (SSTs) in the context of short-range prediction of monsoon rainfall is studied using a three dimensional regional scale model. For the month of July, over certain regions of Arabian Sea, Bay of Bengal and the Indian Ocean, observed SSTs are about 1 to 2°C warmer than the climatological SSTs. Two numerical experiments are performed using observed and climatological SSTs for an active monsoon period. It is found that the evaporation increases over these surrounding oceans when the observed SSTs are used. As expected, warmer SSTs caused the surface pressure to decrease by 2 to 3 hPa leading to local accelerations of winds. As a result, stronger circulation patterns are induced. Area-averaged evaporation is about 20% higher and the rainfall 10% higher when observed SSTs are used. Effect of an uniform increase in SST by 2°C over climatological values is also investigated. Comparison of simulations with the climatic and the uniformly increased SSTs indicated that the rainfall predictions are qualitatively similar to those obtained with the observed SSTs. Area-averaged evaporation is about 40% higher and the rainfall 15% higher than those obtained with the climatic SSTs. Results from these three numerical experiments indicate that the short-range prediction of monsoon weather is sensitive to the sea surface temperature distribution. This is mainly because of variations in mesoscale circulations caused by the local gradients in the SSTs.

Exchange Model (Noah-GEM) were only 0.2 cm s � 1 and 0.6 cm s � 1 , respectively. The observations also showed considerable PAN deposition at night with typical Vd values of 0.2–0.6 cm s � 1 , while the estimated values from both models were less than 0.1 cm s � 1 . Noah-GEM modeled more realistic stomatal resistance (Rs) than WDDM, as compared with observations of water vapor exchange fluxes. The poor performance of WDDM for stomatal uptake is mainly due to its lack of dependence on leaf area index. Thermal decomposition was found to be relatively unimportant for measured PAN fluxes as shown by the lack of a relationship between measured total surface conductance and temperature. Thus, a large part of the underprediction in Vd from both models should be caused by the underestimation of nonstomatal uptake, in particular, the cuticle uptake. Sensitivity tests on both stomatal and nonstomatal resistances terms were conducted and some recommendations were provided.

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A nested grid regional model with a high vertical resolution in the atmospheric boundary layer is used to simulate various atmospheric processes during an active monsoon period. A turbulence kinetic energy closure scheme is used to predict the boundary-layer structure. Model predictions indicate different structures of the boundary layer over land and oceans, as observed. Significant diurnal variation in boundary-layer structure and associated processes is predicted over land and negligible variations over oceans. The Somali jet over the Arabian Sea is well predicted. Location of the predicted monsoon depression and the associated rainfall are in good agreement with the observations. Also, predicted rainfall and its spatial distribution along the west coast of India are in good agreement with the observations.