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)

The Naval Research Laboratory’s Coupled Ocean/Atmosphere Mesoscale Prediction System – Tropical Cyclone ensemble (COAMPS-TC ensemble)

The 11-member Coupled Ocean/Atmosphere Mesoscale Prediction System for Tropical Cyclones (COAMPS-TC) ensemble has been developed by the Naval Research Laboratory (NRL) to produce probabilistic forecasts of tropical cyclone (TC) track, intensity and structure. All members run with a storm-following inner grid at convection-permitting 4-km horizontal resolution. The COAMPS-TC ensemble is constructed via a combination of perturbations to initial and boundary conditions, the initial vortex, and model physics to account for a variety of different sources of uncertainty that affect track and intensity forecasts. Unlike global model ensembles, which do a reasonable job capturing track uncertainty but not intensity, mesoscale ensembles such as the COAMPS-TC ensemble are necessary to provide a realistic intensity forecast spectrum.The initial and boundary condition perturbations are responsible for generating the majority of track spread at all lead times, as well as the intensity spread from 36-120 h. The vortex and physics perturbations are necessary to produce meaningful spread in the intensity prediction from 0-36 h. In a large sample of forecasts from 2014-2017, the ensemble-mean track and intensity forecast is superior to the unperturbed control forecast at all lead times, demonstrating a clear advantage to running an ensemble versus a deterministic forecast. The spread-skill relationship of the ensemble is also examined, and is found to be very well calibrated for track, but is under-dispersive for intensity. Using a mixture of lateral boundary conditions derived from different global models is found to improve upon the spread-skill score for intensity, but it is hypothesized that additional physics perturbations will be necessary to achieve realistic ensemble spread.

Track Dependence of Tropical Cyclone Intensity Forecast Errors in the COAMPS-TC Model

This study examines the dependence of tropical cyclone (TC) intensity forecast errors on track forecast errors in the Coupled Ocean/Atmosphere Mesoscale Prediction System for Tropical Cyclones (COAMPS-TC) model. Using real-time forecasts and retrospective experiments during 2015-2018, verification of TC intensity errors conditioned on different 5-day track error thresholds shows that reducing the 5-day track errors by 50-70% can help reduce the absolute intensity errors by 18-20% in the 2018 version of the COAMPS-TC model. Such impacts of track errors on the TC intensity errors are most persistent at 4-5 day lead times in all three major ocean basins, indicating a significant control of global models on the forecast skill of the COAMPS-TC model. It is of interest to find, however, that lowering the 5-day track errors below 80 nm does not reduce TC absolute intensity errors further. Instead, the 4-5 day intensity errors appear to be saturated at around 10-12 kt for cases with small track errors, thus suggesting the existence of some inherent intensity errors in regional models.Additional idealized simulations under a perfect model scenario reveal that the COAMPS-TC model possesses an intrinsic intensity variation at the TC mature stage in the range of 4-5 kt, regardless of the large-scale environment. Such intrinsic intensity variability in the COAMPS-TC model highlights the importance of potential chaotic TC dynamics, rather than model deficiencies, in determining TC intensity errors at 4-5 day lead times. These results indicate a fundamental limit in the improvement of TC intensity forecasts by numerical models that one should consider in future model development and evaluation.

Understanding Error Distributions of Hurricane Intensity Forecasts during Rapid Intensity Changes

The characteristics of official National Hurricane Center (NHC) intensity forecast errors are examined for the North Atlantic and east Pacific basins from 1989 to 2018. It is shown how rapid intensification (RI) and rapid weakening (RW) influence yearly NHC forecast errors for forecasts between 12 and 48 h in length. In addition to being the tail of the intensity change distribution, RI and RW are at the tails of the forecast error distribution. Yearly mean absolute forecast errors are positively correlated with the yearly number of RI/RW occurrences and explain roughly 20% of the variance in the Atlantic and 30% in the east Pacific. The higher occurrence of RI events in the east Pacific contributes to larger intensity forecast errors overall but also a better probability of detection and success ratio. Statistically significant improvements to 24-h RI forecast biases have been made in the east Pacific and to 24-h RW biases in the Atlantic. Over-ocean 24-h RW events cause larger mean errors in the east Pacific that have not improved with time. Environmental predictors from the Statistical Hurricane Intensity Prediction Scheme (SHIPS) are used to diagnose what conditions lead to the largest RI and RW forecast errors on average. The forecast error distributions widen for both RI and RW when tropical systems experience low vertical wind shear, warm sea surface temperature, and moderate low-level relative humidity. Consistent with existing literature, the forecast error distributions suggest that improvements to our observational capabilities, understanding, and prediction of inner-core processes is paramount to both RI and RW prediction.

Recent Progress in Tropical Cyclone Intensity Forecasting at the National Hurricane Center

It has been well documented that the National Hurricane Center (NHC) has made significant improvements in Atlantic basin tropical cyclone (TC) track forecasting during the past half century. In contrast, NHC’s TC intensity forecast errors changed little from the 1970s to the early 2000s. Recently, however, there has been a notable decrease in TC intensity forecast error and an increase in intensity forecast skill. This study documents these trends and discusses the advancements in TC intensity guidance that have led to the improvements in NHC’s intensity forecasts in the Atlantic basin. We conclude with a brief projection of future capabilities.

Sensitivity on tendency perturbations of tropical cyclone short-range intensity forecasts generated by WRF

<p>The present study uses the nonlinear singular vector (NFSV) approach to identify the optimally-growing tendency perturbations of the Weather Research and Forecasting (WRF) model for tropical cyclone (TC) intensity forecasts. For nine selected TC cases, the NFSV-tendency perturbations of the WRF model, including components of potential temperature and/or moisture, are calculated when TC intensities are forecasted with a 24-hour lead time, and their respective potential temperature components are demonstrated to have more impact on the TC intensity forecasts. The perturbations coherently show barotropic structure around the central location of the TCs at the 24-hour lead time, and their dominant energies concentrate in the middle layers of the atmosphere. Moreover, such structures do not depend on TC intensities and subsequent development of the TC. The NFSV-tendency perturbations may indicate that the model uncertainty that is represented by tendency perturbations but associated with the inner-core of TCs, makes larger contributions to the TC intensity forecast uncertainty. Further analysis shows that the TC intensity forecast skill could be greatly improved as preferentially superimposing an appropriate tendency perturbation associated with the sensitivity of NFSVs to correct the model, even if using a WRF with coarse resolution.</p><div> <div> </div> </div>

Description of the Determination Processes for the Typical Research and Development Intensity Normative Indicators

The article is devoted to description of the determination processes for the typical research and development (R&D) intensity normative indicators. In the theoretical part, the authors consider the standards system formation and labor costs norms for R&D. The main composit element (CE) hierarchy of the R&D technology is given. The scheme of the development algorithm for the R&D labor costs standards is drawn. The labor costs norming technique for research works is considered. The procedure for determining the labor costs normative volume for a standardized object is determined. In the research part, the article’s authors examined the automated system components used to determine labor intensity forecast indicators in the product life cycle information support. The process of determining the normative labor costs volume based on eight consecutive stages is presented. The database composition necessary for the product life cycle information support is described. Modules for projects’ planning and monitoring in the automated system framework are considered structurally. The modules’ composition used for the analysis of production systems and forecasting production economic indicators is determined. The regulatory requirements for the production’s modules for technological support and technical regulation are given as part of the automated system work for determining labor intensity forecast indicators in the product life cycle information support. The article concludes with an algorithm for estimating the R&D work clusters’ cost and the aircraft’s distributed systems creation and development.

Short-Term Tropical Cyclone Intensity Forecasting from Satellite Imagery Based on the Deviation Angle Variance Technique

The deviation angle variance (DAV) is a parameter that characterizes the level of organization of a cloud cluster compared with a perfectly axisymmetric tropical cyclone (TC) using satellite infrared (IR) imagery, and can be used to estimate the intensity of the TC. In this study, the DAV technique is further used to analyze the relationship between satellite imagery and TC future intensity over the North Atlantic basin. The results show that the DAV of the TC changes ahead of the TC intensity change, and this can be used to predict short-term TC intensity. The DAV-IR 24-h forecast is close to the National Hurricane Center (NHC) 24-h forecast, and the bias is lower than NHC and other methods during weakening periods. Furthermore, an improved TC intensity forecast is obtained by incorporating all four satellite bands. Using SST and TC latitude as the other two predictors in a linear regression model, the RMSE and MAE of the DAV 24-h forecast are 13.7 and 10.9 kt (1 kt ≈ 0.51 m s−1), respectively, and the skill space of the DAV is about 5.5% relative to the Statistical Hurricane Intensity Forecast model with inland decay (Decay-SHIFOR) during TC weakening periods. Considering the DAV is an independent intensity technique, it could potentially add value as a member of the suite of operational intensity forecast techniques, especially during TC weakening periods.