The Efficacy of the WRF-ARW Model in the Genesis and Intensity Forecast of Tropical Cyclone Fani over the Bay of Bengal

An attempt has been carried out to assess the efficacy of the Weather Research and Forecasting (WRF) model in predicting the genesis and intensification events of Very Severe Cyclonic Storm (VSCS) Fani (26 April – 04 May 2019) over the Bay of Bengal (BoB). WRF model has been conducted on a single domain of 10 km horizontal resolution using the Global Data Assimilation System (GDAS) FNL (final) data (0.250 × 0.250). According to the model simulated outcome analysis, the model is capable of predicting the Minimum Sea Level Pressure (MSLP) and Maximum Sustainable Wind Speed (MSWS) pattern reasonably well, despite some deviations. The model has forecasted the Lowest Central Pressure (LCP) of 919 hPa and the MSWS of 70 ms-1 based on 0000 UTC of 26 April. Except for the model run based on 0000 UTC of 26 April, the simulated values of LCP are relatively higher than the observations. According to the statistical analysis, MSLP and MSWS at 850 hPa level demonstrate a significantly greater influence on Tropical Cyclone (TC) formation and intensification process than any other parameters. The model can predict the intensity features well enough, despite some uncertainty regarding the proper lead time of the model run. Reduced lead time model run, particularly 24 to 48 hr, can be chosen to forecast the genesis and intensification events of TC with minimum uncertainty. Journal of Engineering Science 12(3), 2021, 85-100

Impact study of integrated precipitable water estimated from Indian GPS measurements

The Global Positioning System – Integrated Precipitable Water (IPW) data from Indian stations namely Chennai, Guwahati, Kolkata, Mumbai and New Delhi have been assimilated in the National Centre for Medium Range Weather Forecasting’s (NCMRWF) Global Data Assimilation System (GDAS). Gridpoint Statistical Interpolation (GSI) Scheme of GDAS analysis is experimented with the global model T254L64. The analyses and forecasts are carried out at triangular truncation of wave number 254 and with 64 levels in vertical. Global analyses are carried four times (0000 UTC, 0600 UTC, 1200 UTC and 1800 UTC) daily with intermittent time scheme. Model integrations are carried up to 168 hours. The present study examines the impact that integrated precipitable water has over various meteorological parameters. The study reveals that the assimilation of IPW data influences the analyses and corresponding forecasts of the weather model T254L64. This is an attempt of assimilation of IPW data of the aforesaid five Indian stations in the global model and examination of corresponding impact on various meteorological parameters over Indian region. It is seen that for the layers above 750 hPa the zonal and meridional wind components for IPW analyses have less biases. Forecasts from IPW simulations are found to have consistently by lower 850 hPa wind vector root mean square error (RMSE) where as at 250 hPa, improvement in IPW runs are seen only for day-1 and day-4 forecasts. For temperature at 850 hPa, IPW forecasts valid for day-4 & day-5 are better. At 250 hPa, temperature RMSE for IPW runs is lower for day-1 forecasts. Mean error of IPW forecasts at 250 hPa is lower for all the days of forecasts. Also, geo-potential RMSE for the IPW runs at 250 hPa is lower for all the days of forecasts. Forecasts vs analyses study shows positive impact of IPW assimilation on the anomaly and pattern correlations.

Prediction of Thunderstorms based on Atmospheric Instability Indices over Bangladesh using WRF-ARW Model

In this study an attempt has been made to inspect the forecasting of thunderstorms based on two cases (1st case: 17th May, 2019 and 2nd case: 31st March, 2019) over Dhaka using WRF Model. The model is run for 72 hours with 03 nested domain of 09 km, 03 km and 01 km horizontal resolutions using 0.25º X 0.25º six hourly global data assimilation system. For model simulation, Milbrandt-Yau Double-Moment 7-class scheme (9) has been used as microphysics scheme in this study. The model performance is evaluated by calculating hourly instability indices (VTI, TTI, KI, CTI, MCAPE, MCIN, BRN, LI, SI, SWI) value and have been compared with the threshold value of indices. Different meteorological parameters such as MSLP, temperature, winds at upper (300 hPa) and lower (925 hPa) level, relative humidity along with vertical cross section are also studied by the model and compared with the favorable conditions for forming of thunderstorms. Area rage rainfall (hourly) value has been also calculated and compared with indices value to comprehend the nature of thunderstorms. Observing the indices value it is seen that all indices value increase sharply 5-6 hours before of thunderstorm occurring and MCAPE is giving more reliable result. Moreover, this study shows that inner two domains (3 and 1 km resolution) are giving better results than outer one and which indices are more probable in forecasting of thunderstorm for our country as well as giving less Root Mean square Error. From the simulated and validated results, it can be concluded that the model performance of instability indices can be used as forecasting of thunderstorms over Bangladesh.

Track and Landfall Characteristics of Very Severe Cyclonic Storm Fani over the Bay of Bengal using WRF Model

An attempt has been made to assess the capability of the Weather Research and Forecasting (WRF) model in simulating the track and landfall characteristics of Tropical Cyclone (TC) Fani (25th April – 05th May 2019) over the Bay of Bengal (BoB). WRF model has conducted on a single domain of 10 km horizontal resolution using Global Data Assimilation System (GDAS) data (0.250×0.250). The model predicted outcomes show auspicious agreement with the observed datasets of the Bangladesh Meteorological Department (BMD) and India Meteorological Department (IMD). It is found that the diminished lead time of the model run plays a crucial role in delivering good consistency with the minimum forecast uncertainty. A strong correlation between the track and intensity forecast deviations has also been determined. According to the results, the model simulation which captures the minimum deviation in the intensity forecast also ensures better track prediction of the system. The feasibility of the track and landfall forecast by the model even up to 27 hr advance is reasonably well. Finally, it can be decided that the model is capable to predict the cyclonic storm Fani precisely and it can be chosen confidently for future events over the BoB. Dhaka Univ. J. Sci. 69(2): 101-108, 2021 (July)

Impact of Global Data Assimilation System atmospheric models on astroparticle showers

We present a methodology to simulate the impact of the atmospheric models in the background particle flux on ground detectors using the Global Data Assimilation System. The methodology was within the ARTI simulation framework developed by the Latin American Giant Observatory Collaboration. The ground level secondary flux simulations were performed with a tropical climate at the city of Bucaramanga, Colombia. To validate our methodology, we built monthly profiles over Malargüe between 2006 and 2011, comparing the maximum atmospheric depth, X_max, with those calculated with the Auger atmospheric option in CORSIKA. The results show significant differences between the predefined CORSIKA atmospheres and their corresponding Global Data Assimilation System atmospheric profiles.

Spatio-Temporal Variations of Precipitable Water Vapor and Horizontal Tropospheric Gradients from GPS during Typhoon Lekima

GPS data during Typhoon Lekima at 700 stations in China were processed by the Precise Point Positioning (PPP) method. A refined regional Tm model was used to derive the precipitable water vapor (PWV) at these GPS stations. Spatio-temporal variations of PWV with the typhoon process were analyzed. As the typhoon approached, PWV at stations near the typhoon center increased sharply from about 50 mm to nearly 80 mm and then dropped back to about 40–50 mm as the typhoon left. Comparisons of GPS, radiosonde, the Global Data Assimilation System (GDAS) Global Forecast System (GFS) analysis products and ERA5 reanalysis products at four matched GPS-RS stations show overall overestimations of PWV from radiosonde, GFS and ERA5 compared with GPS in a statistical perspective. An empirical orthogonal functions (EOF) analysis of the PWV during the typhoon event revealed some different patterns of variability, with both the first EOF (~36.1% of variance) and second EOF (~30.3% of variance) showing distinctively large anomalies over the typhoon landing locations. The typhoon caused a large horizontal tropospheric gradient (HTG) with the magnitude reaching 5 mm and the direction pointing to the typhoon center when it made a landfall on mainland China. The magnitude and the consistency of the HTG direction decreased overall as the typhoon weakened.

Features of formation and evolution of a polar cyclone in the Barents and Kara seas in December 2020

Based on the GDAS (Global Data Assimilation System) information, the conditions for the formation and evolution of a polar cyclone in the Barents Sea in December 2020 are studied. The cyclone in interesting because after entering the Kara Sea, the cyclone did not weaken as usual but continued to intensify. The formation of the cyclone occurred near the ice edge under conditions of the cold Arctic air outbreak. After entering the Kara Sea, the cyclone continued to deepen, which was facilitated by latent and sensible heat fluxes in the ice-free western part of the sea. According to weather station data, when the cyclone moved further over land, the wind direction changed from mainly southern to northern, and wind speed increased significantly. There also was a sharp decrease in air temperature by 7–10 °С for three hours. Keywords: polar cyclone, global data assimilation system, wind speed, latent and sensible heat fluxes

Remote Sensing of Atmospheric CO and O3 Anomalies before and after Two Yutian MS7.3 Earthquakes

Satellite remote sensing data were used to extract concentrations and volume mixing ratios (VMR) of CO and O3 and Global Data Assimilation System (GDAS) data associated with Yutian MS7.3 earthquakes on March 21, 2008, and February 12, 2014. Difference value and anomaly index methods and the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model were used to simulate gas backward trajectories and analyze the relations between spatial and temporal variations in total columns of CO and O3 (TotCO and TotO3) and earthquakes. Then, the causes of abnormal changes were examined. Maximum anomalies in TotCO and TotO3 occurred one month before the 2008 earthquake and one month after the 2014 earthquake. Anomalies in TotCO and TotO3 were distributed along or were consistent with the fault zone. Furthermore, during the abnormal period, the coefficient of correlation between CO and O3 was 0.672 in 2008 and 0.638 in 2014, with both values significant at p < 0.05 . The correlation between TotCO and TotO3 was also significant. The abnormal phenomena of TotCO and TotO3 associated with the two earthquakes were attributed to underground gas escape, atmospheric chemical reactions, and atmospheric transportation caused by in situ stress in the generation of earthquakes.

Comparative Analysis of Three Methods for HYSPLIT Atmospheric Trajectories Clustering

Using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model to obtain backward trajectories and then conduct clustering analysis is a common method to analyze potential sources and transmission paths of atmospheric particulate pollutants. Taking Qingdao (N36 E120) as an example, the global data assimilation system (GDAS 1°) of days from 2015 to 2018 provided by National Centers for Environmental Prediction (NCEP) is used to process the backward 72 h trajectory data of 3 arrival heights (10 m, 100 m, 500 m) through the HYSPLIT model with a data interval of 6 h (UTC 0:00, 6:00, 12:00, and 18:00 per day). Three common clustering methods of trajectory data, i.e., K-means, Hierarchical clustering (Hier), and Self-organizing maps (SOM), are used to conduct clustering analysis of trajectory data, and the results are compared with those of the HYSPLIT model released by National Oceanic and Atmospheric Administration (NOAA). Principal Component Analysis (PCA) is used to analyze the original trajectory data. The internal evaluation indexes of Davies–Bouldin Index (DBI), Silhouette Coefficient (SC), Calinski Harabasz Index (CH), and I index are used to quantitatively evaluate the three clustering algorithms. The results show that there is little information in the height data, and thus only two-dimensional plane data are used for clustering. From the results of clustering indexes, the clustering results of SOM and K-means are better than the Hier and HYSPLIT model. In addition, it is found that DBI and I index can help to select the number of clusters, of which DBI is preferred for cluster analysis.

The Impact of Aerosols on Satellite Radiance Data Assimilation Using NCEP Global Data Assimilation System

Aerosol radiative effects have been studied extensively by climate and weather research communities. However, aerosol impacts on radiance in the context of data assimilation (DA) have received little research attention. In this study, we investigated the aerosol impacts on the assimilation of satellite radiances by incorporating time-varying three-dimensional aerosol distributions into the radiance observation operator. A series of DA experiments was conducted for August 2017. We assessed the aerosol impacts on the simulated brightness temperatures (BTs), bias correction and quality control (QC) algorithms for the assimilated infrared sensors, and analyzed temperature fields. We found that taking the aerosols into account reduces simulated BT in thermal window channels (8 to 13μm) by up to 4 K over dust-dominant regions. The cooler simulated BTs result in more positive first-guess departures, produce more negative biases, and alter the QC checks about 20%/40% of total/assimilated observations at the wavelength of 10.39μm. As a result, assimilating aerosol-affected BTs produces a warmer analyzed lower atmosphere and sea surface temperature which have better agreement with measurements over the trans-Atlantic region.