Weather Research Forecasting Model Research Articles

Mitigating Atmospheric Effects in InSAR Stacking Based on Ensemble Forecasting with a Numerical Weather Prediction Model

The interferometric synthetic aperture radar (InSAR) technique is widely utilized to measure ground-surface displacement. One of the main limitations of the measurements is the atmospheric phase delay effects. For satellites with shorter wavelengths, the atmospheric delay mainly consists of the tropospheric delay influenced by temperature, pressure, and water vapor. Tropospheric delay can be calculated using numerical weather prediction (NWP) model at the same moment as synthetic aperture radar (SAR) acquisition. Scientific researchers mainly use ensemble forecasting to produce better forecasts and analyze the uncertainties caused by physic parameterizations. In this study, we simulated the relevant meteorological parameters using the ensemble scheme of the stochastic physic perturbation tendency (SPPT) based on the weather research forecasting (WRF) model, which is one of the most broadly used NWP models. We selected an area in Foshan, Guangdong Province, in the southeast of China, and calculated the corresponding atmospheric delay. InSAR images were computed through data from the Sentinel-1A satellite and mitigated by the ensemble mean of the WRF-SPPT results. The WRF-SPPT method improves the mitigating effect more than WRF simulation without ensemble forecasting. The atmospherically corrected InSAR phases were used in the stacking process to estimate the linear deformation rate in the experimental area. The root mean square errors (RMSE) of the deformation rate without correction, with WRF-only correction, and with WRF-SPPT correction were calculated, indicating that ensemble forecasting can significantly reduce the atmospheric delay in stacking. In addition, the ensemble forecasting based on a combination of initial uncertainties and stochastic physic perturbation tendencies showed better correction performance compared with the ensemble forecasting generated by a set of perturbed initial conditions without considering the model’s uncertainties.

Evaluation of regional simulation of surface ozone over Southeast Asia using ground-based observation at two existing Global Atmospheric Watch stations

High level of ground level ozone concentrations was found in most of Southeast Asian (SEA) large cities and often exceeded the national ambient air quality standard. Ozone and PM10 are among of the critical air quality parameters that cause the unhealthy air quality index. Effort to mitigate ozone pollution is greatly complicated due to the photochemistry processes therefore photochemical smog modelling has been widely used. Surface ozone simulation in SEA was done using CHIMERE and weather research forecast (WRF) model. Emission inventory of ozone precursors was done for three countries in the domain, i.e. Indonesia, Thailand and Cambodia. Modelling performance evaluation for meteorological parameters and ozone at the SEA big cities was done in another study. This paper focused on the model evaluation conducted at the two remote sites represented by 2 (two) global atmospheric watch (GAW) remote stations of Bukit Kototabang (BKT) and Danum Valley (DNV). Evaluation result showed an overestimation of observed ozone in BKT while a contradictive result was seen in DNV station which was due to the ozone chemistry and inaccurate estimation of emissions (both anthropogenic and biogenic emission). The evaluation conducted at the remote sites was not even better than that conducted previously at the urban areas. Statistically, only mean normalized gross error and unpaired peak accuracy values that satisfy the criteria for surface ozone modelling suggesting major improvement required for ozone precursors emission inventory data.

Modelled changes in selected agroclimatic indices over the croplands of western Canada under the RCP8.5 scenario

AbstractTo assess the potential change in agroclimatic indices in western Canada, this study used a convection‐permitting Weather Research Forecasting (WRF) model to conduct simulations for the current climate (CTL, 2000–2015) and future climate under the RCP8.5 scenario based on a pseudo‐global‐warming (PGW) approach. Both CTL and PGW simulations were bias‐corrected to the GEM‐CaPA dataset using a multivariate quantile mapping method. An evaluation of the CTL simulation of daily maximum and minimum temperatures and precipitation during the growing season against the gridded observations has been performed, indicating good agreements in the spatial patterns of air temperature and precipitation in western Canada. The PGW − CTL differences in several selected agroclimatic indices were then examined. Due to rising temperatures, substantial increases in growing degree‐days (GDD) by 800–1,200° days and reductions in frost days by 10 to 20 days, favouring regional crop production, are found in southern Alberta and Saskatchewan. However, global warming also poses great risks to Canadian agriculture by modifying heat accumulations and water availability during the growing season. Plant heat stress will substantially increase by ∼50° days in southern Alberta and Saskatchewan, offsetting the positive effects caused by the reduction in frost days and increase in GDD. The southern Canadian Prairies will experience statistically significant increases in the number of dry days and precipitation deficit, suggesting an exacerbation of water stress on the Canadian Prairies by global warming.

An observational and modeling study of a sea fog event over the yellow and east China seas on 17 March 2014

The Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery, weather charts, objectively reanalyzed data, the observational data and station sounding data were analyzed to investigate a sea fog event occurred over the Yellow and East China Seas on March 17, 2014. The sounding profiles, weather situations and the related meteorological factors during the development and dissipation stages of this sea fog event were documented. Weather Research Forecast (WRF) model was applied to simulate this sea fog case. The simulated horizontal atmospheric visibility, cloud water, humidity, and vertical wind profile during the different stages of this fog event were analyzed. During the development stage of this sea fog, a southerly lower-jet with 16–18 ms-1, an inversion layer and a cold center over the Yellow Sea were detected. The relative humidity in the fog area was above 95%. The specific humidity over the East China Sea was higher than that over the Yellow Sea. Southerly was dominated in fog area. However, during the dissipation stage of this sea fog, westerly replaced the southerly and at the lower level, southerly jet disappeared. A dry air area formed over the Shandong Peninsula and moved eastwards. Moreover, the WRF modeling result showed that the simulated atmospheric horizontal visibility and cloud water were approximately consistent with the MODIS satellite imagery. Most of cloud water concentrated below 200–400 m, and the cloud water in the southern part of fog area extended to a higher height than the northern part. While both of air temperature and dew-point temperature were close to sea surface temperature.

Flood Impacts on Critical Infrastructure in a Coastal Floodplain in Western Puerto Rico during Hurricane María

Flooding during extreme weather events damages critical infrastructure, property, and threatens lives. Hurricane María devastated Puerto Rico (PR) on 20 September 2017. Sixty-four deaths were directly attributable to the flooding. This paper describes the development of a hydrologic model using the Gridded Surface Subsurface Hydrologic Analysis (GSSHA), capable of simulating flood depth and extent for the Añasco coastal flood plain in Western PR. The purpose of the study was to develop a numerical model to simulate flooding from extreme weather events and to evaluate the impacts on critical infrastructure and communities; Hurricane María is used as a case study. GSSHA was calibrated for Irma, a Category 3 hurricane, which struck the northeastern corner of the island on 7 September 2017, two weeks before Hurricane María. The upper Añasco watershed was calibrated using United States Geological Survey (USGS) stream discharge data. The model was validated using a storm of similar magnitude on 11–13 December 2007. Owing to the damage sustained by PR’s WSR-88D weather radar during Hurricane María, rainfall was estimated in this study using the Weather Research Forecast (WRF) model. Flooding in the coastal floodplain during Hurricane María was simulated using three methods: (1) Use of observed discharge hydrograph from the upper watershed as an inflow boundary condition for the coastal floodplain area, along with the WRF rainfall in the coastal flood plain; (2) Use of WRF rainfall to simulate runoff in the upper watershed and coastal flood plain; and (3) Similar to approach (2), except the use of bias-corrected WRF rainfall. Flooding results were compared with forty-two values of flood depth obtained during face-to-face interviews with residents of the affected communities. Impacts on critical infrastructure (water, electric, and public schools) were evaluated, assuming any structure exposed to 20 cm or more of flooding would sustain damage. Calibration equations were also used to improve flood depth estimates. Our model included the influence of storm surge, which we found to have a minimal effect on flood depths within the study area. Water infrastructure was more severely impacted by flooding than electrical infrastructure. From these findings, we conclude that the model developed in this study can be used with sufficient accuracy to identify infrastructure affected by future flooding events.

Analysis of Outdoor Thermal Discomfort Over the Kingdom of Saudi Arabia.

In this study, the variability and trends of the outdoor thermal discomfort index (DI) in the Kingdom of Saudi Arabia (KSA) were analyzed over the 39‐year period of 1980–2018. The hourly DI was estimated based on air temperature and relative humidity data obtained from the next‐generation global reanalysis from the European Center for Medium‐Range Weather Forecasts and in‐house high‐resolution regional reanalysis generated using an assimilative Weather Research Forecast (WRF) model. The DI exceeds 28°C, that is, the threshold for human discomfort, in all summer months (June to September) over most parts of the KSA due to a combination of consistently high temperatures and relative humidity. The DI is greater than 28°C for 8–16 h over the western parts of KSA and north of the central Red Sea. A DI of >28°C persistes for 7–9 h over the Red Sea and western KSA for 90% of summer days. The spatial extent and number of days with DI > 30°C, that is, the threshold for severe human discomfort, are significantly lower than those with DI > 28°C. Long‐term trends in the number of days with DI > 28°C indicate a reduced rate of increase or even a decrease over some parts of the southwestern KSA in recent decades (1999–2018). Areas with DI > 30°C, in particular the northwestern regions of the Arabian Gulf and its adjoining regions, also showed improved comfort levels during recent decades. Significant increases in population and urbanization have been reported throughout the KSA during the study period. Analysis of five‐years clinical data suggests a positive correlation between higher temperatures and humidity with heat‐related deaths during the Hajj pilgrimage. The information provided herein is expected to aid national authorities and policymakers in developing necessary strategies to mitigate the exposure of humans to high levels of thermal discomfort in the KSA.

Effect of the Assimilation Frequency of Radar Reflectivity on Rain Storm Prediction by Using WRF-3DVAR

An attempt was made to evaluate the impact of assimilating Doppler Weather Radar (DWR) reflectivity together with Global Telecommunication System (GTS) data in the three-dimensional variational data assimilation (3DVAR) system of the Weather Research Forecast (WRF) model on rain storm prediction in Daqinghe basin of northern China. The aim of this study was to explore the potential effects of data assimilation frequency and to evaluate the outputs from different domain resolutions in improving the meso-scale NWP rainfall products. In this study, four numerical experiments (no assimilation, 1 and 6 h assimilation time interval with DWR and GTS at 1 km horizontal resolution, 6 h assimilation time interval with radar reflectivity, and GTS data at 3 km horizontal resolution) are carried out to evaluate the impact of data assimilation on prediction of convective rain storms. The results show that the assimilation of radar reflectivity and GTS data collectively enhanced the performance of the WRF-3DVAR system over the Beijing-Tianjin-Hebei region of northern China. It is indicated by the experimental results that the rapid update assimilation has a positive impact on the prediction of the location, tendency, and development of rain storms associated with the study area. In order to explore the influence of data assimilation in the outer domain on the output of the inner domain, the rainfall outputs of 3 and 1 km resolution are compared. The results show that the data assimilation in the outer domain has a positive effect on the output of the inner domain. Since the 3DVAR system is able to analyze certain small-scale and convective-scale features through the incorporation of radar observations, hourly assimilation time interval does not always significantly improve precipitation forecasts because of the inaccurate radar reflectivity observations. Therefore, before data assimilation, the validity of assimilation data should be judged as far as possible in advance, which can not only improve the prediction accuracy, but also improve the assimilation efficiency.

Assessment of Emission Reduction and Meteorological Change in PM2.5 and Transport Flux in Typical Cities Cluster during 2013–2017

Under the Air Pollution Prevention and Control Action Plan (APPCAP) implemented, China has witnessed an air quality change during the past five years, yet the main influence factors remain relatively unexplored. Taking the Beijing-Tianjin-Hebei (BTH) and Yangtze River Delta (YRD) regions as typical cluster cities, the Weather Research Forecasting (WRF) and Comprehensive Air Quality Model with Extension (CAMx) were introduced to demonstrate the meteorological and emission contribution and PM2.5 flux distribution. The results showed that the PM2.5 concentration in BTH and YRD significantly declined with a descend ratio of −39.6% and −28.1%, respectively. For the meteorological contribution, those regions had a similar tendency with unfavorable conditions in 2013–2015 (contribution concentration 1.6–3.8 μg/m3 and 1.1–3.6 μg/m3) and favorable in 2016 (contribution concentration −1.5 μg/m3 and −0.2 μg/m3). Further, the absolute value of the net flux’s intensity was positively correlated with the degree of the favorable/unfavorable weather conditions. When it came to emission intensity, the total net inflow flux increased, and the outflow flux decreased significantly across the border with the emission increasing. In short: the aforementioned results confirmed the effectiveness of the regional joint emission control and provided scientific support for the proposed effective joint control measures.

Impact Study of FY-3B MWRI Data Assimilation in WRFDA

In the first attempt to configure the Fengyun-3B satellite’s Microwave Radiation Imager (MWRI) radiance data in the Weather Research Forecast (WRF) model’s Data Assimilation system (WRFDA), the impact of MWRI data assimilation on the analysis and forecast of Typhoon Son-Tinh in 2012 was evaluated with WRFDA’s three-dimensional variational (3DVAR) data-assimilation scheme. Compared to a benchmark experiment with no MWRI data, assimilating MWRI radiances improved the analyses of typhoon central sea level pressure (CSLP), warm core structure, and wind speed. Moreover, verified with European Center for Medium-Range Weather Forecasts (ECMWF) analysis data, significant improvements in model variable forecast, such as geopotential height and specific humidity, were produced. Substantial error reductions in track, CSLP, and maximum-wind-speed forecasts with MWRI assimilation was also obtained from analysis time to 48 h forecast.

Health Benefits of Electrifying Chicago's Municipal Vehicle Fleet

BackgroundApproximately 100 000 American citizens die prematurely each year from air pollution exposure. To lower ambient pollution and co-emitted greenhouse gases, some cities have enacted vehicle electrification policies. In this study, we aimed to simulate changes in atmospheric composition due to the electrification of Chicago's municipal vehicle fleet and quantify the corresponding health and climate benefits of this policy. MethodsWe simulated air quality at the neighbourhood scale (1·3 × 1·3 km) over the Chicago metropolitan area using the coupled Weather Research Forecast and Community Multiscale Air Quality Modelling System chemistry-climate model. We modified the 2014–16 National Emissions Inventory to create input emissions for baseline and electric vehicle adoption scenarios in Summer (August 2018) and Winter (January 2019) months. In the electric vehicle adoption scenario, we removed combustion products of the municipal vehicle fleet (school buses, transit buses, and refuse trucks) from the emissions data. To quantify the health effects of electric vehicle adoption, we calculated the difference in health response between the baseline and electrification scenarios by use of a suite of health response functions. FindingsWe found that fleet electrification reduced CO2 emissions (–1·4%) and NO2 concentrations (–1·0%) while modestly increasing O3 concentrations (+0·3%). Monthly average NO2 reductions were found in high-density areas and along interstate highways, whereas O3 increases were more prominent in the Chicagoland suburbs. Particulate matter changes were modest and spatially heterogenous. The pollutant changes resulted in both positive and negative health outcomes that were largely off setting. InterpretationThe inverse relationship between O3 and NO2 is known as the “weekend effect”, in which the removal of NO2 results in an increase in O3 due to titration in volatile organic compound-limited environments. This relationship is particularly evident over the highways, where excess NO2 reduces O3 concentrations. The decision to electrify vehicles should consider air quality changes in addition to CO2 due to complicating chemical interactions within urban environments. FundingUbben Program for Carbon and Climate Science at Northwestern University, US National Science Foundation.

Effect of adoption of electric vehicles on public health and air pollution in China: a modelling study

BackgroundAdoption of electric vehicles has the potential to reduce air pollutant and greenhouse gas emissions, hence China has implemented policies incentivising use of electric vehicles. However, much is unknown about the potential air quality and public health benefits of electric vehicles, including optimal vehicle type prioritisation and the vehicles' ability to reduce acute health impacts due to extreme air quality events. We aimed to assess the potential climate and acute public health benefits of use of electric vehicles during an extreme winter air pollution event. MethodsIn this modelling study, we used the Weather Research Forecast and Community Multiscale Air Quality Modeling System air quality model to simulate the interplay between weather and atmospheric chemistry. We used this model to examine potential co-benefits of electric vehicle adoption during an extreme pollution episode in China. We simulated heavy-duty and light-duty electric vehicle adoption scenarios, in which 40% of the population adopted a heavy-duty or light-duty passenger electric vehicle, re-mapped battery power needs to energy generation facilities, and characterised differences in public health outcomes using China-specific concentration-response functions. FindingsWe found that widespread adoption of heavy-duty electric vehicles would reduces nitric oxide and fine particulate matter resulting in 562 (95% CI 410–723) fewer premature acute deaths than the non-electrified baseline scenario. However, widespread adoption of heavy-duty electric vehicles does not reduce carbon dioxide emissions without the addition of emission-free electricity generation. By contrast, widespread adoption of light-duty electric vehicles robustly reduces greenhouse gas emissions, but results in lesser air quality improvements and fewer premature deaths avoided (145 [95% CI 38–333]) than the heavy-duty scenario. Economic effects of human health endpoints and carbon dioxide reductions for adoption of light-duty electric vehicles are nearly double those of a heavy-duty electric vehicle scenario (US$155 million vs $87 million). InterpretationAmelioration of severe winter pollution events through adoption of electric vehicles could provide a moderate public health benefit, but continued emission reductions in the power generation sector will have greater human health and economic benefits in China. FundingUbben Program for Carbon and Climate Science at Northwestern University.

First steps towards an all-sky assimilation framework for tropical cyclone event over Bay of Bengal region: Evaluation and assessment of GMI radiances

The present study designs a data assimilation framework for all-sky GMI radiances in the Weather Research Forecast (WRF) model for the prediction of tropical cyclone events over the Bay of Bengal (BOB) region. Experiments are performed using the RTTOV-SCATT radiative transfer model for three cyclone events (Hudhud (2014), Vardah (2016), and Kyant (2016)) for 19 V, 23 V, 37 V, 89 V, 166 V, 183±3, and 183±7 V channels. Results show that observed and simulated brightness temperatures (Tb) agree well for low-frequency channels (<89 GHz). However, significant discrepancies do exist when simulating low Tb at high frequency channels. Therefore, a set of 26 simulations is considered using snow particle shapes from single scattering property databases. A new methodology is devised in order to compare the 26 simulations with observations; taking into account the uncertainties on hydrometeor radiative properties. Results reveal that model simulations are characterized by an underestimation of occurrences within several Tb ranges (~200–300 K). The probability distribution function (PDF) of normalized observed-minus first guess is close to a Gaussian shape after applying a dedicated model of observation errors. The potential to integrate the GMI sensor data within a WRF data assimilation system is thus demonstrated.

Analysis of seasonal snowmelt contribution using a distributed energy balance model for a river basin in the Altai Mountains of northwestern China

AbstractSnowmelt water is a vital freshwater resource in the Altai Mountains of northwestern China. Yet its seasonal hydrological cycle characteristics could change under a warming climate and more rapid spring snowmelt. Here, we simulated snowmelt runoff dynamics in the Kayiertesi River catchment, from 2000 to 2016, by using an improved hydrological distribution model that relied on high‐resolution meteorological data acquired from the National Centers for Environmental Prediction (Fnl‐NCEP) that were downscaled using the Weather Research Forecasting model. Its predictions were compared to observed runoff data, which confirmed the simulations' reliability. Our results show the model performed well, in general, given its daily validation Nash–Sutcliffe efficiency (NSE) of 0.62 (from 2013 to 2015) and a monthly NSE score of 0.68 (from 2000 to 2010) for the studied river basin of the Altai Mountains. In this river basin catchment, snowfall accounted for 64.1% of its precipitation and snow evaporation for 49.8% of its total evaporation, while snowmelt runoff constituted 29.3% of the annual runoff volume. Snowmelt's contribution to runoff in the Altai Mountains can extend into non‐snow days because of the snowmelt water retained in soils. From 2000 to 2016, the snow‐to‐rain ratio decreased rapidly, however, the snowmelt contribution remained relatively stable in the study region. Our findings provide a sound basis for making snowmelt runoff predictions, which could be used prevent snowmelt‐induced flooding, as well as a generalizable approach applicable to other remote, high‐elevation locations where high‐density, long‐term observational data are currently lacking. How snowmelt contributes to water dynamics and resources in cold regions is garnering greater attention. Our proposed model is thus timely perhaps, enabling more comprehensive assessments of snowmelt contributions to hydrological processes in those alpine regions characterized by seasonal snow cover.

Impact of 4D-Var Data Assimilation on Performance of the Coupled Land–Atmosphere Model WRF–TOPX: A Case Study of a Flood Event in the Wangjiaba Watershed, China

AbstractTo assess the impact of four-dimensional variational (4D-Var) data assimilation on the performance of a land–atmosphere coupled model, the satellite precipitation of the Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) was assimilated into the Weather Research Forecast (WRF) Model, and the WRF was coupled to the hydrological model TOPX. Precipitation and evaporation were both investigated as connecting elements in the coupled model WRF–TOPX. Differing in whether the 4D-Var data assimilation and evaporation were applied, one control experiment and four experiments were performed to simulate a historical flood event that happened in the Wangjiaba watershed in eastern China. The key hydrological variables of precipitation, potential evaporation, soil moisture, and discharge in the studied flood process were evaluated. The results showed that 1) the 4D-Var data assimilation with the IMERG could reduce both the overestimations of the WRF-predicted precipitation and potential evaporation; 2) the applied 4D-Var data assimilation could improve considerably the accuracy of the soil moisture and discharges from the coupled model WRF–TOPX; and 3) evaporation was also an important factor to influence the net precipitation to affect the performance of the coupled land–atmosphere model. With the two connecting elements of precipitation and evaporation, the 4D-Var assimilation based on IMERG could improve the Nash–Sutcliffe coefficient of the coupled model WRF–TOPX from 0.483 to 0.521 at the hourly scale. These investigations can provide important implications for the land–atmosphere coupling with both the precipitation and evaporation and using the 4D-Var data assimilation with IMERG for flood simulation at a large scale.

Trend analysis of reduced nitrogen components over the Netherlands with the EMEP4NL and OPS model

Declining ammonia emissions are not always reflected in ammonia concentrations due to physicochemical processes and meteorology. Here we present a trend analysis of reduced nitrogen components over the Netherlands using two different types of atmospheric transport models: the Eulerian grid model EMEP/MSC-W and the plume model OPS.We employ calculations with the Eulerian grid model EMEP/MSC-W for the Netherlands in its EMEP4NL configuration. Using the Weather Research Forecast (WRF) model as meteorological driver plus detailed emission data over the Netherlands as input into the EMEP4NL model, we present simulation results over the Netherlands of reduced nitrogen components from this model at a horizontal resolution of 1.3 × 2.1 km.Using this configuration of the EMEP/MSC-W model (EMEP4NL), a trend analysis is performed over the period 2006–2015 for concentration and deposition of reduced nitrogen components over the Netherlands. The same analysis is performed with the OPS-model, a plume model with a Lagrangian trajectory model for long range transport. Both models use the same MACC III emission distribution for countries outside of the Netherlands, and spatially more detailed emissions for the Netherlands itself. Emission totals per SNAP (Supporting National Action and Planning) sector per country are used over the period 2006–2015, according to the latest CEIP (Centre on Emissions Inventories and Projections) expert estimates. The OPS-model is driven with yearly specific meteorological fields provided by the Royal Netherlands Meteorological Institute (KNMI). Furthermore, the OPS-model parameterizes its chemistry, whereas EMEP4NL uses a state-of-the-art chemistry scheme.Results from ammonia concentrations, ammonium concentrations and wet deposition as calculated with both models, are first compared with observations from the National Air Quality Monitoring Network in the Netherland (LML). Calculations of ammonia and wet deposition both agree well with the measurements in the OPS and the EMEP4NL model. Ammonium is better represented by calculations with the EMEP4NL model than by the OPS-model. Measurements of dry deposition of reduced nitrogen are very limited, therefore only a comparison is made between the model results of EMEP4NL and OPS.Subsequently, a trend analysis is performed over the period 2006–2015 for the reduced nitrogen components for both model calculation results and the measurements. The trends of all components are calculated over the mean values of monitoring stations over the Netherlands. The reported decline in the emissions of ammonia is not reflected in the ammonia concentrations and wet deposition of reduced nitrogen as measured and calculated with the OPS and the EMEP4NL model. Ammonium concentrations on the other hand are declining (also) due to the decrease of the SOx emissions over the period 2006–2015. Finally, both models show a slight decline in the dry deposition values, despite the fact that both models (and observations) do not show a decrease in the ammonia concentrations. A more detailed analysis of the comparison of dry deposition of reduced nitrogen between both models, and the influence of the physicochemical processes and meteorology is in preparation. Overall, it is found that the calculated trends for the different reduced nitrogen components with a grid model like EMEP4NL and a plume model like OPS are roughly in line with each other.

Utilization of WRF 3D Meteorological Data to Calculate Slant Total Delay for InSAR Atmospheric Correction

The atmosphere introduces excess delays into the synthetic aperture radar (SAR) signal trajectory, especially in the troposphere. InSAR atmospheric correction methods include the use of SAR data and external water vapor products. The latter is more effective. However, since the removal of atmospheric effects should use atmospheric delay products in the direction of the line of sight (LOS), it is necessary to convert the zenith total delay to slant delay in the LOS direction. Conventionally, the zenith delay is divided by the cosine of the average incident angles to obtain slant phase delays. But this method could cause large errors because it ignores the atmospheric horizontal gradient change and the small-scale vertical structure. These problems can be solved by using three-dimensional atmospheric data simulated by numerical models, especially in the case of intense weather changes or complex terrain. However, few scholars paid attention to the application into InSAR atmospheric correction, because of the computation complexity and low efficiency. As the requirement for higher accuracy and the introduction of large errors caused by increasing incidence angles, it is significantly imperative to make the utmost of this method. Weather Research Forecast (WRF) model can provide the precipitate water vapor (PWV) and refraction index at different levels in the three dimensions, and then the slant total delay can be obtained for removing the atmospheric effect on the InSAR process. The results demonstrate that using 3D data can obtain more accurate slant total delay and improve the accuracy of surface deformation from InSAR technology.

Short term wind energy resource prediction using WRF model for a location in western part of Turkey

Wind energy is a rapidly growing industry in Turkey. Wind power potential studies revealed that the most promising region for electricity generation is the western part of Turkey. Wind speed forecasting is necessary for power systems because of the intermittent nature of wind. Thus, accurate forecasting of wind power is recognized as a major contribution to reliable wind power integration. This paper assesses the performance of the weather research forecasting (WRF) model for wind speed and wind direction predictions up to 72 h ahead. The wind speeds and wind directions are evaluated based on the mean absolute error (MAE). Evaluations were also performed seasonally. Moreover, in order to improve the WRF simulations, a multi-input–single output artificial neural network (ANN) approach is applied to both wind speeds of the WRF model and wind power estimates, which are estimated from the wind speeds of the WRF model by using a power curve for the Soma wind power plant. Traditional error metrics were used for validations using wind tower mast data installed nearby the wind farm. The results from up to 72 h forecast horizon show that the WRF model slightly overpredicts the wind speeds. Wind speed predictions by the WRF model are found highly depending on the season, location, and wind direction. The model is also able to reproduce wind directions except for low wind speeds. Large MAEs are found for the winds less than 5 m/s. The performance of the WRF model for wind power prediction decreases with the increasing runtime. Root mean square error and normalized root mean square error (nRMSE) in wind powers range in between 123–261 kW and 13%–32% without performing the ANN approach, respectively. The improvement of the ANN depends on the forecast horizon, season, and location of turbine groups, as well as its application on either the wind speed outputs of the WRF model or wind power estimations. The ANN significantly improves the WRF at large forecast horizons for wind power estimations, for which it gives better results in the summer and reaches 29% improvement for summer on average for nRMSE. On the other hand, ANN adjusts the wind speed outputs of the model better than that of wind power estimations. For instance, the nRMSE is approximately 13% for 24 h winter wind speed simulations of the WRF for the turbine groups G1 and G4, after ANN adjustment. The ANN improves the results better for turbine group 1, because of less complexity of this group in the direction of prevailing wind. The evaluation of the ANN suggests that the approach can be used for improving the performance of the wind power forecast for this power plant.

Investigation of air pollution dispersion from kiln stacks based on seasonal using multi-model integration (WRF/CALPUFF)

Air pollution is one of the environmental problems that have a bad impact on human life. Air pollution sources come from various sources such as industry, mining, transportation activities, etc. This study aims to identify the seasonal dispersion of Sulfur Dioxide produced by kiln stacks in PT Semen Padang, Tbk. The Dispersion model used in this study is CALPUFF (For air pollution model) integrated with WRF (Weather Research Forecasting) (For meteorological model). CALPUFF requires the wind field generated by the CALMET model and the WRF model. From the simulation, the direction of SO2 dispersion on the wet season and the dry season is influenced by meteorological phenomena such as sea breeze and land breeze. The dispersion of Sulfur Dioxide in the dry season passes the urban area more than in the wet season, with the highest concentration is 122 µg/m3. On the other hand, in the wet season, the highest concentration is 120 µg/m3. These results are below of national quality standard in Indonesia (365 µg/m3). The statistic also shows that the correlation and error (RMSE) between the model and observed data are 0.63 and 5.49 in the wet season, whereas the correlation and error (RMSE) in the dry season are 0.74 and 2.89.

Sensitivity of aerosol optical properties to the aerosol size distribution over central Europe and the Mediterranean Basin using the WRF-Chem v.3.9.1.1 coupled model

Abstract. The size distribution of atmospheric aerosols plays a key role for understanding and quantifying the uncertainties related to aerosol–radiation and aerosol–cloud interactions. These interactions ultimately depend on the size distribution through optical properties (such as aerosol optical depth, AOD) or cloud microphysical properties. Hence, the main objective of this contribution is to disentangle the impact of the representation of aerosol size distribution on aerosol optical properties over central Europe, particularly over the Mediterranean Basin, during a summertime aerosol episode. To fulfill this objective, a sensitivity test has been conducted using the coupled chemistry–meteorology model WRF-Chem (Weather Research Forecast model coupled with Chemistry). The test modified the parameters defining a lognormal size distribution (geometric diameter and standard deviation) by 10 %, 20 %, and 50 %. Results reveal that the reduction in the standard deviation of the accumulation mode leads to the largest impacts on AOD due to a transfer of particles from the accumulation mode to the coarse mode. A reduction in the geometric diameter of the accumulation mode also has an influence on AOD representation since particles in this mode are assumed to be smaller. In addition, an increase in the geometric diameter of the coarse mode produces a redistribution through the total size distribution by relocating particles from the finer modes to the coarse.

Performance Evaluation of Sub-Grid Orographic Parameterization in the WRF Model over Complex Terrain in Central Asia

The terrain of Central Asia is complex and rugged over mountains. Consequently, wind speed is overestimated over mountains and plains when using the Weather Research Forecast (WRF) model in winter. To solve this problem, three different simulations (named as control simulation (CRTL), gravity waves (GWD), and flow-blocking drag (FBD), respectively) were designed to investigate the impact of sub-grid orography (gravity waves and flow-blocking drag) on wind forecasts. The results illustrated that near-surface wind-speed overestimations were alleviated when sub-grid orographic drag was used in GWD, though the upper-level wind fields at 500 hPa were excessively reduced compared to CRTL. Thus, we propose eliminating the gravity wave breaking at the upper level to improve upper-level wind underestimations and surface wind speeds at the same time. The sub-grid orographic drag stress of the vertical profile over mountains was reduced when only the flow-blocking drag was retained in FBD. This alleviated underestimations of the upper-level wind speed and near-surface wind, which both have the same positive effects as the gravity wave and flow-blocking total. The mean bias and root mean squared error reduced by 32.76% and 9.39%, respectively, compared to CRTL. Moreover, the temperature and specific humidity in the lower troposphere were indirectly improved. The results of the study demonstrate that it is better to remove sub-grid orographic gravity wave drag when using the gravity wave drag scheme of the WRF model.