GFS Data Research Articles

Validation of WRF Rainfall Model during the November 4th, 2021 Flash Flood Event in Batu City – Indonesia

Flash floods in Batu City, Indonesia on November 4th, 2021, caused damage to property, agricultural land, settlements, death of livestock, and loss of human life. One of the important factors triggering this flash flood is heavy rainfall. This study aims to analyse the spatial and temporal dynamics of rainfall as a trigger for the event. Rainfall modelling was conducted using the WRF model with 2 microphysical schemes and 3 cumulus schemes. The data used is GFS data on October 30th, 2021, with a resolution of 0.25 x0.25 degrees which is used to predict rain events until November 4, 2021. Contingency table verification, RMSE value verification, and verification using tolerance limits are used to verify the results of modelling data based on BMKG observation data. The results of this study show that this model can predict rain and non-rain events very well. However, this model is not good enough to predict the thickness of rain until November 4, 2021. The best scheme in modelling rainfall is scheme 5 (Lin and Betts Miller Janjic). Scheme 5 has the smallest RMSE value of 77, the data fit is appropriate and the medium error rate in scheme 5 is 84%. Scheme 5 with GFS data input started from 02 November 2021 can record rain events on 04 November 2021 with extreme rain categories. This extreme rain is influenced by convective clouds. The temperature of the cloud tops changes significantly from 13.00 WIB to 14.00 local time, namely from −4 degrees, and decreases to −76 degrees Celsius.

Time Series Forecasting of Significant Wave Height using GRU, CNN-GRU, and LSTM

Predicting wave height is essential to reduce significant risks for shipping or activities carried out at sea. Waves inherit a stochastic nature, mainly generated by wind and propagated through the ocean, making them challenging to forecast. In this paper, we design time series wave forecasting using a deep learning model, which is a hybrid Convolutional Neural Network (CNN)-Gated Recurrent Unit (GRU) or CNN-GRU. We use two time series of wave data sets, i.e., reanalysis data from ERA5 by ECMWF and GFS from NOAA. As a study area, we choose Pelabuhan Ratu, located in the south of West Java which is connected to the open Indian Ocean. Moreover, we also compare the results by using other deep learning models, i.e., the Long Short-Term Memory (LSTM) and GRU. We evaluated these models to forecast 7, 14, and 30 days. Models' performance is assessed using RMSE, MAPE, and Correlation Coefficient (CC). For predicting 30 days, using the ERA5 data, the CNN-GRU model produces relatively accurate results with an RMSE value of 1.8844 and CC of 0.9938, whereas for the GFS data, results in RMSE value of 1.8852 and CC of 0.9915.

A new approach for modeling pulse height spectra of gamma-ray detectors from passing radioactive cloud in a case of NPP accident

A comprehensive approach for modeling the pulse height spectra of gamma-ray detectors from passing radioactive cloud in a case of accident at NPP has been developed. It involves modeling the transport of radionuclides in the atmosphere using Lagrangian stochastic model, WRF meteorological processor with an ARW core and GFS data to obtain spatial distribution of radionuclides in the air at a given moment of time. Applying representation of the cloud as superposition of elementary sources of gamma radiation the pulse height spectra are calculated based on data on flux density from point isotropic sources and detector response function. The proposed approach allows us to obtain time-dependent spectra for any complex radionuclide composition of the release. The results of modeling the pulse height spectra of the scintillator detector NaI(Tl) Ø63x63 mm for a hypothetical severe accident at a NPP are presented.

On the understanding of very severe cyclone storm Ockhi with the WRF-ARW model

Understanding the dynamics of tropical cyclones (TCs) in terms of intensity, and their trajectory is essential for an adequate early warning and mitigation. The present study attempts to understand the synoptic features of the recent TC Ockhi through a simulation-based approach with the Weather Research and Forecasting (WRF, version 3.8.1) model. Ockhi is considered as a unique TC that originated from depression in the Bay of Bengal on 29 November 2017, recurved towards the Arabian Sea, where it intensified into a very severe cyclone storm and weakened on 5 December 2017. WRF model forced with initial condition from (global forecasting system) GFS data and sea surface temperature (SST) from Group for High-Resolution SST (GHRSST) product for different lead times to test the potential sensitivity of the model. One with an extended period from 20 November to 20 December 2017 (WRF1) and another initiated from 27 November to 6 December 2017 (WRF2). Comparison of the simulated track with the best track estimates from the Indian Meteorological Department indicated an overall track deviation greater than 100 km for both the simulations. The analysis with the extended lead time simulation indicates that the WRF simulated sea level pressure and wind intensity are close to that observed by Arabian sea buoys; CB02, AD08, AD10, and AD07. Daily averaged wind estimate comparison of WRF1 with Scatsat-1 and ERA-5 indicates that the model is slightly overestimating, whereas comparison of peak wind intensity with the time instantaneous swath product of Scatsat-1 leads to underestimation. Analysis of various simulated synoptic features of the cyclone, as discussed in this paper, indicates that the model is skillful in capturing the various stages of cyclone Ockhi.

Importance of Weather Conditions in a Flight Corridor

Current research initiatives, such as the Single European Sky Air Traffic Management Research Program, call for an air traffic system with improved safety and efficiency records and environmental compatibility. The resulting multi-criteria system optimization and individual flight trajectories require, in particular, reliable three-dimensional meteorological information. The Global (Weather) Forecast System only provides data at a resolution of around 100 km. We postulate a reliable interpolation at high resolution to compute these trajectories accurately and in due time to comply with operational requirements. We investigate different interpolation methods for aerodynamic crucial weather variables such as temperature, wind speed, and wind direction. These methods, including Ordinary Kriging, the radial basis function method, neural networks, and decision trees, are compared concerning cross-validation interpolation errors. We show that using the interpolated data in a flight performance model emphasizes the effect of weather data accuracy on trajectory optimization. Considering a trajectory from Prague to Tunis, a Monte Carlo simulation is applied to examine the effect of errors on input (GFS data) and output (i.e., Ordinary Kriging) on the optimized trajectory.

Impacts of Chemical Initial Conditions in the WRF-CMAQ Model on the Ozone Forecasts in Eastern China

Ozone (O3) has become the major factor for exceeding air pollution standards in many Chinese cities, especially in the more economically developed and densely populated regions, such as eastern China. In this study, we applied the Weather Research and Forecasting/Community Multiscale Air Quality (WRF/CMAQ) model to predict the air quality, and evaluated the influences of different chemical initial conditions on the O3 forecasts with observations in Tai’an and other 13 cities in eastern China in June 2021. The influences of different chemical initial conditions on the O3 forecasts are presented by using two sets of meteorological data (NCEP Final Operational Global Analysis [FNL] and Global Forecast System [GFS]) as initial conditions (IC) and boundary conditions (BC) to drive the WRF/CMAQ model. It was found that the O3 concentrations forecasted by FNL-GFS, in which the chemical IC derived from the CMAQ simulation results by using the FNL data as IC and BC, were closer to observations in all cities than GFS-GFS, in which the chemical IC derived from the CMAQ simulation results by using the GFS data as IC and BC. The normalized mean bias (NMB) values of FNL-GFS for O3 met the benchmark (±15%), while the NMB values of GFS-GFS in Hangzhou and Shijiazhuang did not meet the benchmark. The model performances in Tai’an city were similar to those in 13 cities with better results for FNL-GFS than GFS-GFS. The comparisons of contributions of source regions to O3 in the receptor Tai’an city indicate that different episodes had different relative contributions of source regions and that the simulations of FNL-GFS were more similar to the retrospective simulations than GFS-GFS. The comparisons of contributions of different source sectors to O3 in Tai’an city show that industry emissions are the largest contributor, followed by transportation, power plants and residential emissions.

WRF-model sensitivity test and assimilation studies of Cempaka tropical cyclone

The WRF model was used to forecast the most intensive stage of Cempaka Tropical Cyclone (TC) on 27 - 29 November 2017. This study evaluates the combination of cumulus and microphysics parameterization and the efficiency of assimilation method to predict pressure values at the center of the cyclone, maximum wind speed, and cyclone track. This study tested 18 combinations of cumulus and microphysics parameterization schemes to obtain the best combination of both parameterization schemes which later on called as control model (CTL). Afterward, assimilation schemes using 3DVAR cycles of 1, 3, 6 hours, and 4DVAR, namely RUC01, RUC03, RUC06, and 4DV, were evaluated for two domains with grid size of each 30 and 10 km. GFS data of 0.25-degree and the Yogyakarta Doppler Radar data were used as the initial data and assimilation data input, respectively. The result of the parameterization test shows that there is no combination of parameterization schemes that constantly outperform all variables. However, the combination of Kain-Fritsch and Thompson can produce the best prediction of tropical cyclone track compared to other combinations. While, the RUC03 assimilation scheme was noted as the most efficient method based on the accuracy of track prediction and duration of model time integration.

Impact of Boundary Layer Physics on Tropical Cyclone Simulations in the Bay of Bengal Using the WRF Model

In this work, the sensitivity of tropical cyclone (TC) simulations over the Bay of Bengal to planetary boundary layer (PBL) physics in the WRF model is investigated. Numerical simulations are performed with WRF-ARW model using NCEP GFS data for five very severe cyclonic storms (Vardha, Hudhud, Phailin, Lehar and Thane). Five conceptually different PBL schemes (YSU, MYJ, QNSE, MYNN and BouLac) are evaluated. Results of 25 sensitivity experiments showed that PBL physics mainly affected the intensity while producing small variations in track prediction. The QNSE, followed by MYJ and BouLac, produced highly intensified storms, and MYNN produced weakly intensified storms. The YSU scheme showed better comparisons with IMD best track estimates. From the analysis of five cyclones, it is found that the YSU produced minimum errors for central pressure (−5.4, −0.8, −2.6, −5.25 hPa), maximum wind (19, 7.6, −0.96, −0.77 m/s) and track (66, 146, 182, 217 km) at 24-, 48-, 72- and 96-h forecast intervals. Analysis of various thermodynamical and dynamical parameters clearly showed that the PBL physics impacts the predictions by variation of (1) surface energy fluxes, (2) convergence, (3) inflow/outflow, (4) tangential winds, (5) vertical motion and (6) strength of the warm core and associated storm structure. A detailed analysis conducted in the case of Hudhud indicated that the PBL schemes influenced the intensity predictions through a WISHE type of feedback by the variation of convergence, radial inflow, vertical motion, and surface fluxes. While the YSU and MYNN schemes produced moderate values of radial inflow, the QNSE, MYJ and BouLac schemes produced stronger inflow. The stronger inflow, spin-up and stronger wind-induced transport of energy fluxes in the QNSE, MYJ and BouLac schemes led to a stronger convection and a higher intensification of TCs in these simulations.

태풍사전방재모델의 예측 정확도 향상을 위한 연구 Part II: GDAPS, RDAPS, GFS 자료별 WRF 민감도 분석

태풍사전방재모델의 예측 정확도 향상을 위한 연구 Part II: GDAPS, RDAPS, GFS 자료별 WRF 민감도 분석

Comparative analysis of data on air temperature based on current weather data sets for 2007-2019

Modern tasks in the field of agriculture require meteorological information of high spatial and temporal resolution. In this study, air temperature was compared using the CRU_TS, ERA5-Land, and GFS datasets in the study area for the period from 2007 to 2019. The information obtained showed a high level of correlation (0.9) of the considered data sets for the study period. However, a more detailed analysis revealed that there may be months when the air temperature values of different data sets can vary significantly for several consecutive years, and the magnitude of these deviations relative to the compared data sets can vary depending on the time of year. In addition, features of the underlying surface, for example, the presence of extensive mountain ranges, can affect the final data and the results of their comparisons. It is shown that modern ERA5-Land and GFS data sets are not inferior to CRU_TS data, and their advantages in the form of high spatial and temporal resolution can provide better results for solving problems.

Comparison of Regional Atmospheric Model Forecasts under Different Initial and Boundary Conditions

The results of forecasts of the WRF-ARW numerical mesoscale model with two sets of initial and boundary conditions are presented. The first set comprises the forecasts of the GFS global model (USA), and the second set, the forecasts of the SL-AV global model (Russia). The quality of the WRF-ARW forecasts is assessed by their comparison with the data of surface and upper-air meteorological observations for the European part of Russia in winter and summer. It is shown that the 72-hour forecast results are close to the results based on the GFS data if the Russian global model data are used as initial and boundary conditions.

On the opportunities of diagnosis and forecast of deep convection during operational activity

Introduction. The increase of cyclonic storms intensity is one of the manifestations of widespread climate change. This determines emergence of extreme weather phenomena resulting in human and economic losses. Convective phenomena causing economic damage which increased significantly in recent years present one of the causes of abnormal weather conditions.
 The purpose. The purpose of this article is to substantiate and to evaluate the technique of identification and forecasting the meteorological conditions “warning” of a high convective phenomena potential that leads to destructive consequences.
 RAOB software along with GFS data were used when conducting the study.
 Results. The technique of forecasting the phenomena connected with deep convection within the territory of Ukraine was tested in the article. It establishes the extent to which the existing criterial values of quantitative characteristics of convection allow forecasting the occurrence of weather phenomena related to it, their evolution and movement. Given the current spatio-temporal constraints to use the data of atmospheric radio sounding, the authors apply the combination of those with GFS forecasting data. It expands opportunities for forecasting such phenomena as thunderstorms and tornadoes. In particular, the use of forecasting data of wind profile, temperature advection intensity and humidity makes it possible to determine the trend to change of factors affecting convection intensity.
 Conclusion. Combining the radio sounding data with the forecasting numerical modelling data makes it possible to increase the efficiency of convective phenomena forecasts due to the opportunity of recording the dynamics of changes of stratification of atmosphere and moisture accumulation in its lower levels.
 Directions for future research. Adaptation of existing criterial values of convection parameters for the territory of Ukraine and determination of their forecasting significance will play an important role in future studies. However, absence of a systemized database associated with occurrence and evolution of convective phenomena make the task more complicated. That is why creation thereof is required.

The Satellite Atmospheric Sounder IKFS-2: 2. Validation of the Temperature Sounding of the Atmosphere

The validation of measurements of vertical temperature profiles by the IKFS-2 instrument (the Meteor-M no. 2 satellite) in cloudless conditions was based on the comparison with radiosonde measurements and data from NCEP GFS analysis. Vertical resolution matching is shown to decrease the root-mean-square (RMS) difference between the results provided by the two measurement methods by 0.2–1 K depending on the altitude in the atmosphere. When the vertical resolution is matched the absolute values of the mean differences between the satellite and the radiosonde measurements do not exceed 1 K and the mean square differences vary from 1.2 to 1.8 K, except for the surface layer, where they reach 2 K and 4 K, respectively. The RMS differences between the satellite temperature measurements and NCEP GFS data show worse agreement over the ground (up to 2 K against 1.2–1.5 K for sounders). In the 300–600 hPa pressure interval, the RMS difference between satellite and NCEP GFS data is less than 1 K over water surface but increases to 1.5–2 K over land. In the cloudless cases, the accuracy of sounding over water surface by IKFS-2 (when compared with the analysis results) approaches the accuracy displayed by IASI, which is functionally similar to IKFS-2.

Convective and microphysics parameterization impact on simulating heavy rainfall in Semarang (case study on February 12th, 2015)

The meteorological model WRF-ARW version 3.8.1 is used for simulating the heavy rainfall in Semarang that occurred on February 12th, 2015. Two different convective schemes and two different microphysics scheme in a nested configuration were chosen. The sensitivity of those schemes in capturing the extreme weather event has been tested. GFS data were used for the initial and boundary condition. Verification on the twenty-four hours accumulated rainfall using GSMaPsatellite data shows that Kain-Fritsch convective scheme and Lin microphysics scheme is the best combination scheme among the others. The combination also gives the highest success ratio value in placing high intensity rainfall area. Based on the ROC diagram, KF-Lin shows the best performance in detecting high intensity rainfall. However, the combination still has high bias value.

AVALIAÇÃO DE DIFERENTES ESQUEMAS DE CAMADA LIMITE PARA SIMULAÇÃO E PREVISÃO DE EVENTOS DE NEVOEIRO NO RIO GRANDE DO SUL

In this work we used two boundary layer schemes to reproduce numerically a persistent fog event in Santa Maria, Rio Grande do Sul. The model employed was WRF using, as initial and boundary conditions, GFS data. The boundary layer employed schemes were the Mellor-Yamada-Janic (MYJ) and Yonsei University (YSU). Generally, boundary layer overestimate of wind speed values schemes 10m and represent relatively well temperature. However, the relative humidity does not represent the fog condition, not reaching saturation in some cases. When saturation is achieved the same if not properly maintained during the occurrence of the event.

Cause and predictability for the severe haze pollution in downtown Beijing in November–December 2015

Based on the hourly PM2.5 concentrations, meteorological variable records and ERA-Interim reanalysis data, a series of diagnostic analyses were conducted to explore the possible meteorological causes for the severe haze pollution that occurred in Beijing in November–December 2015. Using the online-coupled WRF-Chem model and GFS data, the predictability of hourly and daily PM2.5 concentrations was evaluated. The results showed that, in the context of pollutant emission, the severe haze pollution in downtown Beijing in November–December 2015 was primarily attributed to anomalous local meteorological conditions, which were caused and strengthened by anomalous large-scale atmospheric circulations. The abnormal changes in the upper troposphere appeared to trigger the anomalies in the middle-lower troposphere and the local conditions. The numerical simulations can capture the spatial distribution patterns of the PM2.5 concentrations for predictions of 1 to 10days in advance. The PM2.5 concentration trends in downtown Beijing were generally consistent with the predictions on both daily and hourly time-scales, although the predictability decreased gradually as the lead times prolonged. The predictability of the daily mean PM2.5 concentration was slightly higher than that of the hourly concentration. The statistical indices suggested that the predictions of daily and hourly mean PM2.5 concentrations were generally skillful and reliable for maximum lead times of 8 and 5days, respectively.

Analyzing the Grell–Freitas Convection Scheme from Hydrostatic to Nonhydrostatic Scales within a Global Model

Abstract The authors implemented the Grell–Freitas (GF) parameterization of convection in which the cloud-base mass flux varies quadratically as a function of the convective updraft fraction in the global nonhydrostatic Model for Prediction Across Scales (MPAS). They evaluated the performance of GF using quasi-uniform meshes and a variable-resolution mesh centered over South America, the resolution of which varied between hydrostatic (50 km) and nonhydrostatic (3 km) scales. Four-day forecasts using a 50-km and a 15-km quasi-uniform mesh, initialized with GFS data for 0000 UTC 10 January 2014, reveal that MPAS overestimates precipitation in the tropics relative to the Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis data. Results of 4-day forecasts using the variable-resolution mesh reveal that over the refined region of the mesh, GF performs as a precipitating shallow convective scheme, whereas over the coarse region of the mesh, GF acts as a conventional deep convective scheme. As horizontal resolution increases and subgrid-scale motions become increasingly resolved, the contribution of convective and grid-scale precipitation to the total precipitation decreases and increases, respectively. Probability density distributions of precipitation highlight a smooth transition in the partitioning between convective and grid-scale precipitation, including at gray-zone scales across the transition region between the coarsest and finest regions of the global mesh. Variable-resolution meshes spanning between hydrostatic and nonhydrostatic scales are shown to be ideal tools to evaluate the horizontal scale dependence of parameterized convective and grid-scale moist processes.

Effect of physical parameterization schemes on track and intensity of cyclone LAILA using WRF model

The objective of the present study is to investigate in detail the sensitivity of cumulus parameterization (CP), planetary boundary layer (PBL) parameterization, microphysics parameterization (MP) on the numerical simulation of severe cyclone LAILA over Bay of Bengal using Weather Research & Forecasting (WRF) model. The initial and boundary conditions are supplied from GFS data of 1° × 1° resolution and the model is integrated in three ‘twoway’ interactive nested domains at resolutions of 60 km, 20 km and 6.6 km. Total three sets of experiments are performed. First set of experiments include sensitivity of Cumulus Parameterization (CP) schemes, while second and third set of experiments is carried out to check the sensitivity of different PBL and Microphysics Parameterization (MP) schemes. The fourth set contains initial condition sensitivity experiments. For first three sets of experiments, 0000 UTC 17 May 2010 is used as initial condition. In CP sensitivity experiments, the track and intensity is well simulated by Betts-Miller-Janjic (BMJ) schemes. The track and intensity of LAILA is very sensitive to the representation of large scale environmental flow in CP scheme as well as to the initial vertical wind shear values. The intensity of the cyclone is well simulated by YSU scheme and it depends upon the mixing treatment in and above PBL. Concentration of frozen hydrometeors, such as graupel in WSM6 MP scheme and latent heat released during auto conversion of hydrometeors may be responsible for storm intensity. An additional set of experiments with different initial vortex intensity shows that, small differences in the initial wind fields have profound impact on both track and intensity of the cyclone. The representation of the mid-tropospheric heating in WSM6 is mainly controlled by amount of graupel hydrometeor and thus might be one of the possible causes in modulating the storm’s intensity.

Simulation of European air quality by WRF–CMAQ models using AQMEII-2 infrastructure

The air quality modeling system WRF–CMAQ was applied to the European domain for the year 2010 in the frame of the Air Quality Model Evaluation International Initiative (AQMEII), Phase 2. The model system was set up for a domain of 5000×5000 km2 size with horizontal resolution of 25km. The emissions at European level were available through AQMEII and further processed in a way to feed the chemistry transport model CMAQ. The meso-meteorological model WRF was driven by NCEP GFS data with 1°×1° resolution. The chemical boundary conditions were extracted from MACC global simulation data. Model performance was investigated by means of AQMEII-2 web based evaluation platform and the monitoring data gathered for this activity. A preliminary model evaluation for ozone, nitrogen dioxide and particulate matter was conducted. The statistical analysis was based on comparison between simulated and observed concentrations at different type of surface stations in the EU wide domain (rural, urban, suburban), as well as for selected four cities. Model performance was characterized by overestimation for ozone and underestimation for the other pollutants. The relative statistical indicators were discussed also in view of recently published performance criteria.

Tracking source area of Shangdianzi station using Lagrangian particle dispersion model of FLEXPART

The FLEXPART model is a Lagrangian particle dispersion model developed by the Norwegian Institute for Air Research. In this paper, first the model simulation driven by GFS and ECWMF meteorological data is compared and evaluated with the observations of Shangdianzi station, then, taking CO as the tracer and adopting the method of screening background and non-background concentration, the FLEXPART model is used in tracing clean and polluted source areas of observation station. The comparisons of the simulations using GFS and ECWMF with the observations show that the two results using GFS and ECWMF are alike, and both can demonstrate the trend of CO concentration and better reflect the daily and monthly variation trend of CO. The correlation co-efficients of simulation value and actual measurement value of CO concentration are 0.67 and 0.71 for GFS and ECWMF respectively. Both simulations are slightly lower than the observations. Also the simulation discovers that the north-westnorthwest-northwest sector is the most important background sector of CO while the westsouthwest-south sector is the most important non-background sector for Shangdianzi station. The model can capture the distribution characteristics of background and pollution source areas of Shangdianzi station and better reflect the distribution of CO source areas in Beijing and the surrounding area. Copyright © 2013 Royal Meteorological Society