Weather Research Forecasting Model Research Articles

Fast domain-aware neural network emulation of a planetary boundary layer parameterization in a numerical weather forecast model

Abstract. Parameterizations for physical processes in weather and climate models are computationally expensive. We use model output from the Weather Research Forecast (WRF) climate model to train deep neural networks (DNNs) and evaluate whether trained DNNs can provide an accurate alternative to the physics-based parameterizations. Specifically, we develop an emulator using DNNs for a planetary boundary layer (PBL) parameterization in the WRF model. PBL parameterizations are used in atmospheric models to represent the diurnal variation in the formation and collapse of the atmospheric boundary layer – the lowest part of the atmosphere. The dynamics and turbulence, as well as velocity, temperature, and humidity profiles within the boundary layer are all critical for determining many of the physical processes in the atmosphere. PBL parameterizations are used to represent these processes that are usually unresolved in a typical climate model that operates at horizontal spatial scales in the tens of kilometers. We demonstrate that a domain-aware DNN, which takes account of underlying domain structure (e.g., nonlocal mixing), can successfully simulate the vertical profiles within the boundary layer of velocities, temperature, and water vapor over the entire diurnal cycle. Results also show that a single trained DNN from one location can produce predictions of wind speed, temperature, and water vapor profiles over nearby locations with similar terrain conditions with correlations higher than 0.9 when compared with the WRF simulations used as the training dataset.

Development of turbulent scheme in the FLEXPART-AROME v1.2.1 Lagrangian particle dispersion model

Abstract. The FLEXible PARTicle dispersion model FLEXPART, first released in 1998, is a Lagrangian particle dispersion model developed to simulate atmospheric transport over large and mesoscale distances. Due to FLEXPART's success and its open source nature, different limited area model versions of FLEXPART were released making it possible to run FLEXPART simulations by ingesting WRF (Weather Research Forecasting model), COSMO (Consortium for Small-scale Modeling) or MM5 (mesoscale community model maintained by Penn State university) meteorological fields on top of the ECMWF (European Centre for Medium-Range Weather Forecasts) and GFS (Global Forecast System) meteorological fields. Here, we present a new FLEXPART limited area model that is compatible with the AROME mesoscale meteorological forecast model (the Applications of Research to Operations at Mesoscale model).1 FLEXPART-AROME was originally developed to study mesoscale transport around La Réunion, a small volcanic island in the southwest Indian Ocean with a complex orographic structure, which is not well represented in current global operational models. We present new turbulent modes in FLEXPART-AROME. They differ from each other by dimensionality, mixing length parameterization, turbulent transport constraint interpretation and time step configuration. A novel time step was introduced in FLEXPART-AROME. Performances of new turbulent modes are compared to the ones in FLEXPART-WRF by testing the conservation of well-mixedness by turbulence, the dispersion of a point release at the surface and the marine boundary layer evolution around Réunion. The novel time step configuration proved necessary to conserve the well-mixedness in the new turbulent modes. An adaptive vertical turbulence time step was implemented, allowing the model to adapt on a finer timescale when significant changes in the local turbulent state of the atmosphere occur.

Variability of monsoon low‐level jet and associated rainfall over India

AbstractThe structure and climatology of the monsoon low‐level jet (MLLJ) is studied based on dynamically downscaled simulations over a 37‐year period (1980–2016) using the Weather Research Forecasting (WRF) model. The simulations are conducted by adopting a continuous initialization method with daily re‐initializations using ERA‐Interim data as initial and boundary conditions. Validation of the downscaled fields with radiosonde data shows that the model has reasonable skill in reproducing MLLJ characteristics. Analysis of the simulations suggests that the MLLJ exhibits systematic diurnal variation: maximum winds of the synoptically induced large‐scale monsoon jet occur during the daytime, and the orographic channelled winds through the mountains of East Africa, Hejaz, and Western Ghats in the night‐time. These diurnal changes in monsoon winds modulate the moisture convergence process and the associated evolution of rainfall over India. Seasonal and monthly climatology of monsoon winds shows that the model accurately reproduces the spatial pattern of winds and slightly overestimates (2–3 m/s) the mean monthly winds over the Bay of Bengal and Arabian Seas. Analysis of wind variability and the trends using 37 years simulations suggests that the MLLJ exhibits an increasing trend in wind speed on both seasonal and monthly scales, except for August which shows a decreasing trend. The weakening of the MLLJ in August has a profound influence on the number of monsoon depressions forming over the Bay of Bengal (which are decreasing), and on the number of break days (which are increasing) and associated precipitation reduction over the central Indian region.

Assimilation of Weather Radar Data Using WRF 3DVar Modelling for Rainfall Prediction

Data assimilation is one method of improving the initial condition of the atmosphere in numerical weather predictions. The weather-based radar data assimilation, which generates high-resolution data of about 250 meters with coverage of hundreds of kilometers, is expected to improve the quality of weather forecasts and analysis, especially in determining the precipitation forecasting or Quantitative Precipitation Forecasting (QPF). This study aims to investigate the role of assimilation of Doppler weather radar data in mesoscale models for the forecast of heavy rain events occurring in the Greater Jakarta area on 20 February 2017 and 13 June 2017. For this purpose, the reflectivity data and radial radar speeds obtained from weather radar Tangerang will be assimilated with the 3DVar system into the Weather Research Forecasting (WRF) numerical model version 3.9.1 and Tropical Physics Suite parameterized configuration recommended for the tropics. The output of assimilation model of radar data and without assimilation of the numerical model of WRF is verified by point and spatial with some observation data of Automatic Weather System (AWS) and GSMAP rainfall in Jabodetabek region. In general, the WRF model assimilation of radar data provides a better simulation of spatial rain events and better points compared to the WRF model without assimilation.

Modeled MABL Responses to the Winter Kuroshio SST Front in the East China Sea and Yellow Sea

AbstractBased on the Weather Research Forecasting (WRF) model, we designed a long‐term sea surface temperature (SST)‐forcing simulation to simulate the influences of SST front on the marine atmospheric boundary layer (MABL) over the East China Sea and the Yellow Sea during wintertime. The results revealed that SST front have significant relationship with MABL adjustment. The atmospheric modulations and the comparisons of the vector wind and the scalar wind indicate that the MABL responses to the SST front are quite different under different wind directions. When the prevailing wind blows parallel to the SST front (NE wind directions), the sea level pressure adjustment mechanism dominates the atmospheric adjustment. The variances of scalar wind and vector wind are similar. However, when prevailing wind crossing SST front from cold to warm (NW wind directions), both the sea level pressure adjustment mechanism and vertical mixing mechanism play an important role. The maximum variations of the scalar wind are just over the core of SST front, whereas maximum vector wind variations are over warm flank. When wind blowing from warm to cold (SE wind directions), the vertical mixing mechanism contributes more to the MABL responses. The maximum variations of the scalar wind are beyond the top of MABL, which occur over the core SST front. The further diagnosis analysis showed that the meridional transient eddies and mean flow interaction contribute to the scalar wind variations under cross‐front conditions.

Role of Sea Surface Temperature in Modulating Life Cycle of Tropical Cyclones over Bay of Bengal

Sea surface temperature (SST) varies significantly in the presence of tropical cyclones (TCs). Using fixed SST throughout the integration of high resolution TC models is general practice in research and operational endeavor over the North Indian Ocean. The present study is to assess the impact of updating realistic SST in TC lifetime on track, intensity and rainfall of TCs. The Hurricane Weather Research Forecast (HWRF) model of single domain with 9km resolution is used. A total of 31 forecast cases are considered from 6 TCs during 2007- 16 with unique track and intensity characteristics. A set of two numerical experiments are done without (CNTL) and with 6-hourly SST update (SST) in TC lifetime.Mean track and intensity errors show that there is an improvement of 3–41% in track during 12–120h forecast length for SST run. The SST runs improve landfall position and time prediction by 20% and 33% respectively. The cross track error of SST run is comparable (44km) with average errors available for this basin (34km); and along track errors are improved by 60% as compared to CNTL as well as average errors of the basin. The model is biased to overestimate a weaker TC and underestimate a stronger TC, however, the bias is reduced in SST run by 5–51%. The analyses of wind, enthalpy flux and warm core structures provide insight for realistic intensity prediction of SST run unlike CNTL. Rainfall intensity and radial distribution is also improved in SST run. Thus, this study highlights the significance of ocean coupling with TC models to advance forecast guidance.

ATLAS-1.0: Atmospheric Lagrangian dispersion model for tephra transport and deposition

ATLAS-1.0 (ATmospheric LAgrangian diSpersion) is a new atmospheric dispersion and sedimentation Lagrangian model tailored to volcanic tephra/ash. The model solves the Advection–Diffusion–Sedimentation equation across multiple scales (from regional to global) and can be driven off-line by different numerical weather prediction models in combination. For example, meteorological data from the mesoscale Weather Research Forecast (WRF) model can be combined with data from the Global Forecast System (GFS) so that ATLAS automatically selects the highest resolution data available in any part of the computational domain. ATLAS can be used in forward mode to forecast ash dispersal from a volcano (or from extended sources) or in backward mode to integrate trajectories backwards in time and constrain unknown source term characteristics. Multiple source terms can be defined, e.g. to simulate several eruption phases with different granulometric characteristics on a single model execution. We validate the implementation of the model using the 2011 Cordón Caulle and the 2015 Calbuco eruptions and compare the results with previous simulations performed with the FALL3D model. The code has been designed from scratch to facilitate future parallelization, inclusion of ash resuspension schemes, and ensemble-based probabilistic forecast assimilating data from satellite retrievals.

Uncertainty Quantification of a Coupled Model for Wind Prediction at a Wind Farm in Japan

Reliable and accurate short-term prediction of wind speed at hub height is very important to optimize the integration of wind energy into existing electrical systems. To this end, a coupled model based on the Weather Research Forecasting (WRF) model and Open Source Field Operation and Manipulation (OpenFOAM) Computational Fluid Dynamics (CFD) model is proposed to improve the forecast of the wind fields over complex terrain regions. The proposed model has been validated with the quality-controlled observations of 15 turbine sites in a target wind farm in Japan. The numerical results show that the coupled model provides more precise forecasts compared to the WRF alone forecasts, with the overall improvements of 26%, 22% and 4% in mean error (ME), root mean square error (RMSE) and correlation coefficient (CC), respectively. As the first step to explore further improvement of the coupled system, the polynomial chaos expansion (PCE) approach is adopted to quantitatively evaluate the effects of several parameters in the coupled model. The statistics from the uncertainty quantification results show that the uncertainty in the inflow boundary conditions to the CFD model affects more dominantly the hub-height wind prediction in comparison with other parameters in the turbulence model, which suggests an effective approach to parameterize and assimilate the coupling interface of the model.

Modeling of the Effects of Wintertime Aerosols on Boundary Layer Properties Over the Indo Gangetic Plain

AbstractWintertime fog and severe aerosol loading in the boundary layer over South Asia, especially Indo Gangetic Plain (IGP), causes disruptions in the day‐to‐day life of millions of people living in the region, and these heavy pollution episodes result in relentless effects on human health. Weather Research Forecasting model coupled with aerosol chemistry is used to investigate the radiative effects and feedbacks of aerosols on the evolution and structure of the planetary boundary layer over the region. In general, aerosols lead to cooling at the surface, which decreases the nonradiative fluxes (latent and sensible heat fluxes), weakens the turbulent diffusion, and inhibits/delays the growth of convective boundary layer. The impact was maximum over IGP where aerosol‐induced solar dimming (60–80 W/m2) led to a cooling of −0.6 to −2 ° C at the surface, which decreases the noontime boundary layer height by ∼200 m. This effect overwhelms the diabatic heating due to absorbing aerosols in the atmosphere. The significant decrease in sensible (∼11–23 W/m2) and latent heat flux (5–14 W/m2) was observed over IGP and western India. Aerosol boundary layer interaction increases the aerosol loading near the surface (PM2.5) by 3 to 30 μg/m3, which further deteriorate the air quality and visibility over the region. The aerosol forcing increases the relative humidity in the lower boundary layer and amplifies the aerosol forcing. The fog events over the Indian regions identified using INSAT‐3D observations coincide with the regions where aerosol‐boundary layer interactions are very significant. The aerosol‐boundary layer interaction has significance in forecasting fog events and planning mitigation strategies for improving the air quality over the region.

Spatial Characteristics of Deep-Developed Boundary Layers and Numerical Simulation Applicability over Arid and Semi-Arid Regions in Northwest China

The spatial distribution and long-time variation of the deep-developed boundary layer are not well understood in arid and semi-arid regions of northwest China. ERA-Interim (ECMWF Reanalysis data, ECMWF: European Centre for Medium-Range Weather Forecasts) were used to study the deep-developed boundary layer in the five representative areas in summer and then the Weather Research Forecast (WRF) model was applied to simulate and verify its applicability. The results show that the boundary layer heights in the five representative areas are higher in late spring and summer (the highest is 2485~3502 m in June) and lower in autumn, winter and early spring (the lowest is 758~907 m in December). The seasonal variations of the boundary layer height are smaller at 02:00 BJT and 08:00 BJT, while the variations are relatively larger at 14:00 BJT and 20:00 BJT. The atmospheric boundary layer, with heights over 4000 m, generally exists in late spring and summer. The boundary layer heights are higher in the arid region than in the semi-arid region and the deep-developed boundary layer lasts longer in the arid region. The boundary layer heights present reductions from the northwest to the southeast, except for Minqin in the middle north. The numerical simulation results show that there is a significant difference between different combinations of parameterization schemes to simulate the deep-developed boundary layer in these areas. The combination Goddard+SLAB+ACM2 performs better in the extreme arid area, Dunhuang, and the arid areas, Jiuquan and Minqin, whereas the simulation effect of the combination Dudhia+Noah+ACM2 is better in the semi-arid areas, Yuzhong and Lanzhou. The difference between the schemes is related to the determination of the boundary layer height.

Ensemble flood forecasting based on a coupled atmospheric-hydrological modeling system with data assimilation

In order to prolong the lead time of the flood forecasts, numerical weather prediction (NWP) models are more and more widely used to provide high-resolution rainfall forecasts as the feed of the rainfall-runoff model for flood forecasts. In this study, a coupled atmospheric-hydrological modeling system is constructed by integrating the weather research forecasting (WRF) model and the Hebei rainfall-runoff model together with the WRF three-dimensional variational (3DVar) data assimilation module. Four storm events from two mountainous catchments in Jin-Jing-Ji Region of Northern China are selected to evaluate the performance of the coupled system for real-time flood forecasting. Five different combinations of physical parameterizations are adopted with the WRF model to provide a forecast rainfall ensemble. Conventional observations and radar reflectivity data are assimilated by the 3DVar data assimilation module to update the initial and lateral boundary conditions of the WRF model in order to provide more reliable rainfall forecasts. The results show that the atmospheric-hydrological modeling system can provide satisfactory flood forecasts, especially for the storm events with relatively even rainfall distribution in the spatial dimension. Appropriate data assimilation show positive effect on improving the accumulation process of the rainfall which therefore helps effectively increase the forecast accuracy of the coupled modeling system.

Understanding Hurricane Storm Surge Generation and Propagation Using a Forecasting Model, Forecast Advisories and Best Track in a Wind Model, and Observed Data—Case Study Hurricane Rita

Meteorological forcing is the primary driving force and primary source of errors for storm surge forecasting. The objective of this study was to learn how forecasted meteorological forcing influences storm surge generation and propagation during a hurricane so that storm surge models can be reliably used to forecast actual events. Hindcasts and forecasts of Hurricane Rita (2005) storm surge was used as a case study. Meteorological forcing or surface wind/pressure fields for Hurricane Rita were generated using both the Weather Research and Forecasting (WRF) full-scale forecasting model along with archived hurricane advisories ingested into a sophisticated parametric wind model, namely Generalized Asymmetric Holland Model (GAHM). These wind fields were used to forecast Rita storm surges. Observation based wind fields from the OceanWeather Inc. (OWI) Interactive Objective Kinematic Analysis (IOKA) model, and Best track wind data ingested into the GAHM model were used to generate wind fields for comparison purposes. These wind fields were all used to hindcast Rita storm surges with the ADvanced CIRCulation (ADCIRC) model coupled with the Simulating Waves Nearshore (SWAN) model in a tightly coupled storm surge-wave model referred to as ADCIRC+SWAN. The surge results were compared against a quality-controlled database of observed data to assess the performance of these wind fields on storm surge generation and propagation. The surge hindcast produced by the OWI wind field performed the best, although some high water mark (HWM) locations were overpredicted. Although somewhat underpredicted, the WRF wind fields forecasted wider surge extent and wetted most HWM locations. The hindcast using the Best track parameters in the GAHM and the forecast using forecast/advisories from the National Hurricane Center (NHC) in the GAHM produced strong and narrow wind fields causing localized high surges, which resulted in overprediction near landfall while many HWM locations away from wind bands remained dry.

Impact of Cumulus Parameterization on Model Convergence of Tropical Cyclone Destructive Potential Simulation at Grey-Zone Resolutions: A Numerical Investigation

The Weather Research Forecast model (WRF) is used to examine the destructive potential of tropical cyclone (TC) Shanshan (2006) at various horizontal resolutions (7.5 km–1 km) with different cumulus parameterization (CP) schemes. It is found that the calculated Power Dissipation Index (PDI) increases while the size-dependent destructive potential (PDS) decreases as the grid spacing decreases for all CP-scheme simulations, which indicates a weak model convergence in both PDI and PDS calculations. Moreover, it is change of the storm intensity and inner-core size that lead to the non-convergence of PDI and PDS respectively. At a higher resolution, convection becomes more explicitly resolved, which leads to larger diabatic heating. As a result, the radial pressure gradient force (PGF) increases, and the radius of maximum wind (RMW) decreases. The area of strong diabatic heating subsequently becomes closer to the TC center, which further increases the TC intensity and the PGF near the eyewall. With such a positive feedback loop, the PGF increases and the RMW decreases as the resolution increases. Note that a perfect model should converge well in the simulation of both TC intensity and size, and thus converge in the PDS. For some CP experiments, the calculated PDS convergence is relatively strong, but it is a result of offset between the non-convergent simulations of TC intensity and size. In contrast, the Grell–Freitas scheme exhibits a stronger convergence in the simulations of TC intensity and size, although the convergence in PDS is relatively weak, but is closer to the truth.

Comparing Statistical and Semi-Distributed Rainfall–Runoff Models for a Large Subtropical Watershed: A Case Study of Jiulong River Catchment, China

In this contribution, the authors present their preliminary investigations into modeling the rainfall–runoff generation relation in a large subtropical catchment (Jiulong River catchment) on the southeast coast of China. Previous studies have mostly focused on modeling the streamflow and water quality of its small rural subcatchments. However, daily runoff on the scale of the whole catchment has not been modeled before, and hourly runoff data are desirable for some oceanographic applications. Three methods are proposed for modeling streamflow using rainfall outputted by the Weather Research Forecast (WRF) model, calculated potential evaporation (PET), and land cover type: (i) a ridge regression model; (ii) NPRED-KNN: a nonparametric k-nearest neighbor model (KNN) employing a parameter selection method (NPRED) based on partial information coefficient; (iii) the Hydrological Simulation Program-Fortran (HSPF) model with an hourly time step. Results show that the NPRED-KNN approach is the most unsuitable method of those tested. The HSPF model was manually calibrated, and ridge regression performs no worse than HSPF based on daily verification, whilst HSPF can produce realist daily flow time series, of which ridge regression is incapable. The HSPF model seems less prone to systematic underprediction when replicating monthly-annual water balance, and it successfully replicates the baseflow index (the flow intensity) of the Jiulong River catchment system.

Future projection of heat waves over China under global warming within the CORDEX-EA-II project

Driven by four global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 5 (CMIP5) (i.e., CNRM-CM5, EC-EARTH, GFDL-ESM2M and MPI-ESM-LR) under the Representative Concentration Pathway 8.5 (RCP8.5) scenario, projections for future changes in heat waves over China are performed by Weather Research Forecasting (WRF) model simulations for future (FTR, 2031–2055) and present (1981–2005) periods. Six heat wave indices are applied to characterize heat waves based on their frequency, duration, magnitude, intensity, accumulated occurrence days and severity. Analyses show that notable increases in heat wave indices cover all of China. More areas will endure more frequent, longer lasting and more severe heat waves in the coming decades. The increasing tendencies of heat wave indices in the FTR period are more significant than those at present, indicating that heat waves will intensify more rapidly in the future. The impacts of climate changes on the accumulated properties of heat waves are more substantial than those on the individual aspects of heat waves. It is also projected that stronger heat waves with prolonged durations and more severe magnitudes will occur more often in the FTR period, whereas relatively weaker heat waves would occur less often. Hence, the occurrence of extreme heat waves shows a more remarkable increase than the occurrence of moderate heat waves. The changes in heat waves can be largely explained by the changes in the dominating high-pressure systems.

A Multi-Platform Hydrometeorological Analysis of the Flash Flood Event of 15 November 2017 in Attica, Greece

Urban areas often experience high precipitation rates and heights associated with flash flood events. Atmospheric and hydrological models in combination with remote-sensing and surface observations are used to analyze these phenomena. This study aims to conduct a hydrometeorological analysis of a flash flood event that took place in the sub-urban area of Mandra, western Attica, Greece, using remote-sensing observations and the Chemical Hydrological Atmospheric Ocean Wave System (CHAOS) modeling system that includes the Advanced Weather Research Forecasting (WRF-ARW) model and the hydrological model (WRF-Hydro). The flash flood was caused by a severe storm during the morning of 15 November 2017 around Mandra area resulting in extensive damages and 24 fatalities. The X-band dual-polarization (XPOL) weather radar of the National Observatory of Athens (NOA) observed precipitation rates reaching 140 mm/h in the core of the storm. CHAOS simulation unveils the persistent orographic convergence of humid southeasterly airflow over Pateras mountain as the dominant parameter for the evolution of the storm. WRF-Hydro simulated the flood using three different precipitation estimations as forcing data, obtained from the CHAOS simulation (CHAOS-hydro), the XPOL weather radar (XPOL-hydro) and the Global Precipitation Measurement (GMP)/Integrated Multi-satellitE Retrievals for GPM (IMERG) satellite dataset (GPM/IMERG-hydro). The findings indicate that GPM/IMERG-hydro underestimated the flood magnitude. On the other hand, XPOL-hydro simulation resulted to discharge about 115 m3/s and water level exceeding 3 m in Soures and Agia Aikaterini streams, which finally inundated. CHAOS-hydro estimated approximately the half water level and even lower discharge compared to XPOL-hydro simulation. Comparing site-detailed post-surveys of flood extent, XPOL-hydro is characterized by overestimation while CHAOS-hydro and GPM/IMERG-hydro present underestimation. However, CHAOS-hydro shows enough skill to simulate the flooded areas despite the forecast inaccuracies of numerical weather prediction. Overall, the simulation results demonstrate the potential benefit of using high-resolution observations from a X-band dual-polarization radar as an additional forcing component in model precipitation simulations.

Analysis of a dense haze event over North-eastern Pakistan using WRF-Chem model and remote sensing

Urban areas in Pakistan are experiencing escalation in haze episodes. Due to increase in economy, emissions of gaseous and aerosol components from automobiles, road constructions and industry is growing in Pakistan which has resulted in worsening air quality during winter and post-monsoon season. North Eastern (NE) Pakistan (71–74.5°E, 28–34°N) is experiencing haze and fog episodes because of the increase in aerosol pollution levels. In addition to anthropogenic emissions, winter pollution over NE Pakistan is associated with unfavourable meteorological conditions. Lahore, a metropolitan city in NE Pakistan experienced a dense haze event during the first week of November 2016. In this article, we examine the pollution levels before, during and after this heavily polluted episode in NE Pakistan based on model simulations and remote sensing data. In particular, the Weather Research Forecasting model coupled with chemistry (WRF-Chem) and pertinent satellite data from MODIS are utilized towards validation. Another goal of this study is to characterize sources and causes of this haze event especially over Lahore. Particulate matter concentrations, AOD, and concentration of gaseous components increased many folds than the usual levels. Additionally, Vertical Feature Mask (VFM) results are presented from measurements by the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite revealing vertical layers of aerosol of thickness ranging from 2 to 5 km in the study region and period.

A Study of Near-Surface Boundary Layer Characteristics During the 2015 Chennai Flood in the Context of Urban-Induced Land Use Changes

Chennai metropolitan region along with the northern part of Tamil Nadu and southern part of Andhra Pradesh witnessed extreme rainfall events leading to urban flooding during November–December 2015. In order to understand the near-surface and boundary layer (BL) characteristics during the event in the context of land use and land cover (LULC) change, three decades of satellite images are analysed. In addition, the Weather Research Forecasting (WRF) model is used to perform finer-scale simulations, considering different land use (LU) data sets as input. For this purpose, LU datasets from the United Sates Geological Survey (USGS), Moderate Resolution Imaging Spectro-radiometer (MODIS), and Indian Space Research Organization (ISRO) are considered along with the Noah and Noah multi-physics (NMP) land surface model (LSM). Impact of Noah-based LSMs on the near-surface and BL characteristics over Chennai are examined besides the change in LULC during the flood event. Significant improvement of 1–2 °C is obtained in case of near-surface temperature in the simulation considering recent LU and the NMP LSM. Some WRF-simulated variables like near-surface temperature, relative humidity (RH) and convective available potential energy (CAPE) are compared with available observations for qualitative and quantitative analysis. The distorted variations of the near-surface and boundary layer parameters including temperature, BL height, sensible heat flux and CAPE, are mostly observed during phases with prevalent low-pressure systems due to the presence of large-scale forcing. In other phases, (where low-pressure systems are absent), with dominance of localised effects, noticeably higher values of the variables viz. near-surface air temperature, wind speed, RH and moisture flux, CAPE and BL height are attributed to the increased impervious layers inside the city boundary due to urbanization and its growth.

EVALUATION OF HIGH RESOLUTION URBAN LULC FOR SEASONAL FORECASTS OF URBAN CLIMATE USING WRF MODEL

Abstract. In all mesoscale models with urban parameterizations, urban area represented as a single entity to represent the influence of urban morphology. In the last few years, many Urban Canopy Models (UCM) have been developed by many researchers to model the urban energy fluxes, but their spatial resolution is too coarse. These models proves to be a hindrance in obtaining improved results for urban climatic studies due to their coarser resolution. So downscaling of climatic variables in an urban area is primary significance for urban climatic studies. Weather Research Forecasting Model (WRF) is the one of the models that has been used widely for downscaling the climatic variables at urban scale and it has been also integrated with UCM along with a number of urban sub physics options. In this study, modified high resolution Land Use Land Cover (LULC) representing three urban classes for the city of Chandigarh has been ingested into the model to examine and validate the model output with respect to ground observations. The model has been configured with two domains with a resolution of 3KM and 1KM and simulations were carried out for three days of the of four seasons of India, winter, summer, monsoon and post-monsoon for the analysis of seasonal variation. Improved values of Root Mean Square Error (RMSE) for surface temperature, relative humidity and wind speed was observed with modified high resolution LULC with BEM option as compared to single urban built up class. In terms of temperature, summer season showed very less RMSE than other seasons, i.e, 0.76 °C and . In terms of relative humidity, monsoon season showed very less RMSE than other seasons, i.e., 2.63% and in terms of wind speed, post monsoon season is giving less RMSE i.e., 1.01 m/s.

The effects of simulating volcanic aerosol radiative feedbacks with WRF-Chem during the Eyjafjallajökull eruption, April and May 2010

Explosive volcanic eruptions can inject large amounts of ash and gases into the atmosphere. Such volcanic aerosols can have a significant impact on the surrounding environment, and there is the need to closely investigate their effects on meteorology on local, regional, and even continental scale. This work presents a study of the 2010 Eyjafjallajökull volcanic eruption the resulting ash dispersion and its radiative feedback effects on the meteorological conditions with the Weather Research Forecasting model with on-line Chemistry (WRF-Chem). Two model runs, one meteorology-only simulation (without chemistry) and one that considers gas- and aerosol chemistry as well as direct- and semidirect aerosol feedbacks were performed and compared. Results for daily values show that aerosol radiative feedback effects can cool the atmosphere close to the surface on average by 1 °C with maximum cooling exceeding even 2 °C for the considered episode. Near-surface atmospheric wind speed changed on average by 0.5 m/s with maximum values above 2 m/s. Furthermore, the presence of ash aerosols affected the vertical shape of the profiles of wind speed and temperature and resulted in a better agreement with radiosonde measurements when radiative feedback effects were considered. Although the modeling of the dispersion of volcanic ash clouds is subject to large uncertainties, we have demonstrated that the WRF-Chem model can reproduce observations at surface levels and vertical profiles more realistically when radiative feedback effects are considered in the simulations.