Towards a hierarchical parametrization to address prior uncertainty in ensemble-based data assimilation

Ensemble-based methods are becoming popular assisted history matching techniques with a growing number of field applications. These methods use an ensemble of model realizations, typically constructed by means of geostatistics, to represent the prior uncertainty. The performance of the history matching is very dependent on the quality of the initial ensemble. However, there is a significant level of uncertainty in the parameters used to define the geostatistical model. From a Bayesian viewpoint, the uncertainty in the geostatistical modeling can be represented by a hyper-prior in a hierarchical formulation. This paper presents the first steps towards a general parametrization to address the problem of uncertainty in the prior modeling. The proposed parametrization is inspired in Gaussian mixtures, where the uncertainty in the prior mean and prior covariance is accounted by defining weights for combining multiple Gaussian ensembles, which are estimated during the data assimilation. The parametrization was successfully tested in a simple reservoir problem where the orientation of the major anisotropic direction of the permeability field was unknown.     

On improving tropical cyclone track forecasts using a scale-selective data assimilation approach: a case study

A dynamical downscaling approach based on scale-selective data assimilation (SSDA) is applied to tropical cyclone (TC) track forecasts. The results from a case study of super Typhoon Megi (2010) show that the SSDA approach is very effective in improving the TC track forecasts by fitting the large-scale wind field from the regional model to that from the global forecast system (GFS) forecasts while allowing the small-scale circulation to develop freely in the regional model. A comparison to the conventional spectral-nudging four-dimensional data assimilation (FDDA) indicates that the SSDA approach outperforms the FDDA in TC track forecasts because the former allows the small-scale features in a regional model to develop more freely than the latter due to different techniques used. In addition, a number of numerical experiments are performed to investigate the sensitivity of SSDA’s effect in TC track forecasts to some parameters in SSDA, including the cutoff wave number, the vertical layers of the atmosphere being adjusted, and the interval of SSDA implementation. The results show that the improvements are sensitive in different extent to the above three parameters.     

Cyclone track prediction using INSAT data

INSAT visible and infrared imageries of three cyclones in the Bay of Bengal during the period 1984–1987 were analysed with a view to improve the cyclone track prediction in this region. It was observed that the rotation in the major structural cloud features (as seen from the cloud-top temperature maps) associated with these cyclones in the Bay of Bengal is followed with a change in direction of their movement. This method is seen to be particularly effective when the cyclone is severe and when the major cloud features persist for a reasonably longer time. In the present study, only the direction of movement is forecast assuming a uniform speed of the cyclone.     

Best track parameters of tropical cyclones over the North Indian Ocean: a review

India Meteorological Department has the responsibility of monitoring and prediction of cyclonic disturbances (CDs) including tropical cyclone (TC) and depression, collection, processing and archival of all data pertaining to CDs and preparation of best track data over the North Indian Ocean (NIO). The process of post-season analysis of CDs to determine the best estimate of a CD??s position and intensity along with other characteristics during its lifetime is described as ??best tracking??. The best tracking procedure has undergone several changes world-over including NIO due to change in definition and classification of TCs, monitoring and analysis tools and procedure and physical understanding of TCs. There have been a few attempts to document the temporal changes in the best track procedure including changes in observational network, monitoring technique, area of responsibility for monitoring, terminology and classification of the TCs over the NIO. Hence, a study has been undertaken to review the temporal variations in all the above aspects of best tracking procedure and its impact on quality of best track parameters over the NIO. The problems and prospective with the best track data over the (NIO) have been presented and discussed. Based on quality and availability, the whole period of best track information may be broadly classified into four phases, viz. (i) pre-1877, (ii) 1877?C1890, (iii) 1891?C1960 and (iv) 1961?C2010. The period of 1961?C2010 may be further classified into (a) 1961?C1973, (b) 1974?C1990 and (c) 1991?C2010. As optimum observational network including satellite leading to better estimation of location and intensity without missing of CDs was available since 1961, the climatology of genesis, location, intensity, movement (track) and landfall can be best represented based on the data set of 1961?C2010. The best track parameters need to be reanalysed since 1891, based on the present criteria/classification of CDs to develop a digital data set of every six hourly position, intensity and other characteristics throughout the life period of each recorded CD over the NIO to meet the world standard. At least attempt should be made from 1974 when all types of major data including satellite, radar, surface and upper air observations are available for best track analysis. The reanalysis of best track parameters can help in better understanding and prediction of CDs and address the issues related to climate change aspects over the NIO region.     

A new data assimilation procedure to develop a debris flow run-out model

Parameter calibration is one of the most problematic phases of numerical modeling since the choice of parameters affects the model’s reliability as far as the physical problems being studied are concerned. In some cases, laboratory tests or physical models evaluating model parameters cannot be completed and other strategies must be adopted; numerical models reproducing debris flow propagation are one of these. Since scale problems affect the reproduction of real debris flows in the laboratory or specific tests used to determine rheological parameters, calibration is usually carried out by comparing in a subjective way only a few parameters, such as the heights of soil deposits calculated for some sections of the debris flows or the distance traveled by the debris flows using the values detected in situ after an event has occurred. Since no automatic or objective procedure has as yet been produced, this paper presents a numerical procedure based on the application of a statistical algorithm, which makes it possible to define, without ambiguities, the best parameter set. The procedure has been applied to a study case for which digital elevation models of both before and after an important event exist, implicating that a good database for applying the method was available. Its application has uncovered insights to better understand debris flows and related phenomena.     

Correction to: A novel methodological approach for land subsidence prediction through data assimilation techniques

Computational Geosciences - A Correction to this paper has been published: https://doi.org/10.1007/s10596-021-10079-6     

A novel methodological approach for land subsidence prediction through data assimilation techniques

Computational Geosciences - Anthropogenic land subsidence can be evaluated and predicted by numerical models, which are often built over deterministic analyses. However, uncertainties and...     

Impact of bogus vortex for track and intensity prediction of tropical cyclone

The initialization scheme designed to improve the representation of a tropical cyclone in the initial condition is tested during Orissa super cyclone (1999) over Bay of Bengal using the fifth-generation Pennsylvania State University — National Center for Atmospheric Research (Penn State — NCAR) Mesoscale Model (MM5). A series of numerical experiments are conducted to generate initial vortices by assimilating the bogus wind information into MM5. Wind speed and location of the tropical cyclone obtained from best track data are used to define maximum wind speed, and centre of the storm respectively, in the initial vortex. The initialization scheme produced an initial vortex that was well adapted to the forecast model and was much more realistic in size and intensity than the storm structure obtained from the NCEP analysis. Using this scheme, the 24-h, 48-h, and 72-h forecast errors for this case was 63, 58, and 46 km, respectively, compared with 120, 335, and 550 km for the non-vortex initialized case starting from the NCEP global analysis. When bogus vortices are introduced into initial conditions, the significant improvements in the storm intensity predictions are also seen. The impact of the vortex size on the structure of the initial vortex is also evaluated. We found that when the radius of maximum wind (RMW) of the specified vortex is smaller than that of which can be resolved by the model, the specified vortex is not well adapted by the model. In contrast, when the vortex is sufficiently large for it to be resolved on horizontal grid, but not so large to be unrealistic, more accurate storm structure is obtained.     

The sensitivity to the microphysical schemes on the skill of forecasting the track and intensity of tropical cyclones using WRF-ARW model

The Advanced Research WRF (ARW) model is used to simulate Very Severe Cyclonic Storms (VSCS) Hudhud (7–13 October, 2014), Phailin (8–14 October, 2013) and Lehar (24–29 November, 2013) to investigate the sensitivity to microphysical schemes on the skill of forecasting track and intensity of the tropical cyclones for high-resolution (9 and 3 km) 120-hr model integration. For cloud resolving grid scale (<5 km) cloud microphysics plays an important role. The performance of the Goddard, Thompson, LIN and NSSL schemes are evaluated and compared with observations and a CONTROL forecast. This study is aimed to investigate the sensitivity to microphysics on the track and intensity with explicitly resolved convection scheme. It shows that the Goddard one-moment bulk liquid-ice microphysical scheme provided the highest skill on the track whereas for intensity both Thompson and Goddard microphysical schemes perform better. The Thompson scheme indicates the highest skill in intensity at 48, 96 and 120 hr, whereas at 24 and 72 hr, the Goddard scheme provides the highest skill in intensity. It is known that higher resolution domain produces better intensity and structure of the cyclones and it is desirable to resolve the convection with sufficiently high resolution and with the use of explicit cloud physics. This study suggests that the Goddard cumulus ensemble microphysical scheme is suitable for high resolution ARW simulation for TC’s track and intensity over the BoB. Although the present study is based on only three cyclones, it could be useful for planning real-time predictions using ARW modelling system.     

Impact of horizontal resolution on prediction of tropical cyclones over Bay of Bengal using a regional weather prediction model

The present study is carried out to examine the performance of a regional atmospheric model in forecasting tropical cyclones over the Bay of Bengal and its sensitivity to horizontal resolution. Two cyclones, which formed over the Bay of Bengal during the years 1995 and 1997, are simulated using a regional weather prediction model with two horizontal resolutions of 165 km and 55 km. The model is found to perform reasonably well towards simulation of the storms. The structure, intensity and track of the cyclones are found to be better simulated by finer resolution of the model as compared to the coarse resolution. Rainfall amount and its distribution are also found to be sensitive to the model horizontal resolution. Other important fields, viz., vertical velocity, horizontal divergence and horizontal moisture flux are also found to be sensitive to model horizontal resolution and are better simulated by the model with finer horizontal grids.     

热带气旋沉积动力与沉积记录

热带气旋是全球最具破坏性的天气系统之一。随着全球气候变暖,热带气旋的异常活动受到了各国政府、民众和学术界广泛的关注。本文分析了热带气旋对海洋沉积物"从源到汇"过程中的作用及其机制,表明热带气旋是陆源沉积物、有机碳和污染物通过河流向海洋输送的重要控制因素;热带气旋产生的波浪、强流和内波可造成河口和陆架沉积物再悬浮、侵蚀、液化甚至剪切破坏,对陆架沉积物向深海输送起重要作用。虽然热带气旋有随全球变暖而增强的趋势,但热带气旋发生频率与全球变暖之间的关系尚不明确,对未来热带气旋发生频率的预测结果也存在较大分歧,对该问题的研究需要长期、高分辨率的热带气旋记录,使用热带气旋沉积记录研究热带气旋活动规律具有必要性和可行性。     

A new cluster algorithm for orientation data

An algorithm to classify data points on the sphere in distinct cluster groups is defined. The characteristics of the cluster groups and the rule for assigning data to the groups are related to a continuous differentiable density estimation. The modes of the estimated density are assumed to be representative of the groups; data points are then assigned to the mode reached by the steepest ascent. The major advantage of this procedure is its sensitivity in detecting cluster groups independently of their geometry and configuration. As a consequence, the procedure is capable of handling orientation data that may be arranged in girdles.     

A parametric Markov renewal model for predicting tropical cyclones in Bangladesh

In this paper, we consider a Markov renewal process (MRP) to model tropical cyclones occurred in Bangladesh during 1877–2009. The model takes into account both the occurrence history and some physical constraints to capture the main physical characteristics of the storm surge process. We assume that the sequence of cyclones constitutes a Markov chain, and sojourn times follow a Weibull distribution. The parameters of the Weibull MRP jointly with transition probabilities are estimated using the maximum likelihood method. The model shows a good fit with the real events, and probabilities of occurrence of different types of cyclones are calculated for various lengths of time interval using the model. Stationary probabilities and mean recurrence times are also calculated. A brief comparison with a Poisson model and a marked Poisson model has also been demonstrated.     

Sensitivity of inland decay of North Atlantic tropical cyclones to soil parameters

Using the HURDAT best track analysis of track and intensity of tropical cyclones that made landfall over the continental United States during the satellite era (1980?C2005), we analyze the role of land surface variables on the cyclone decay process. The land surface variables considered in the present study included soil parameters (soil heat capacity and its surrogate soil bulk density), roughness, topography and local gradients of topography. The sensitivity analysis was carried out using a data-adaptive genetic algorithm approach that automatically selects the most suitable variables by fitting optimum empirical functions that estimates cyclone intensity decay in terms of given observed variables. Analysis indicates that soil bulk density (soil heat capacity) has a dominant influence on cyclone decay process. The decayed inland cyclone intensities were found to be positively correlated with the cube of the soil bulk density (heat capacity). The impact of the changes in soil bulk density (heat capacity) on the decayed cyclone intensity is higher for higher intensity cyclones. Since soil bulk density is closely related to the soil heat capacity and inversely proportional to the thermal diffusivity, the observed relationship can also be viewed as the influence of cooling rate of the land surface, as well as the transfer of heat and moisture underneath a land-falling storm. The optimized prediction function obtained by statistical model processes in the present study that predicts inland intensity changes during 6-h interval showed high fitness index and small errors. The performance of the prediction function was tested on inland tracks of eighteen hurricanes and tropical storms that made landfall over the United States between 2001 and 2010. The mean error of intensity prediction for these cyclones varied from 1.3 to 15.8 knots (0.67?C8.12?m?s^?1). Results from the data-driven analysis thus indicate that soil heat flux feedback should be an important consideration for the inland decay of tropical cyclones. Experiments were also undertaken using Weather Research Forecasting (WRF) Advanced Research Version (ARW ver 3.3) to assess the sensitivity of the soil parameters (roughness, heat capacity and bulk density) on the post-landfall structure of select storms. The model was run with 1-km grid spacing, limited area single domain with boundary conditions from the North American Regional Reanalysis. Of different experiments, only the surface roughness change and soil bulk density (heat capacity) change experiments showed some sensitivity to the intensity change. The WRF results thus have a low sensitivity to the land parameters (with only the roughness length showing some impact). This calls for reassessing the land surface response on post-landfall characteristics with more detailed land surface representation within the mesoscale and hurricane modeling systems.     

Altimeter data assimilation in the tropical Indian Ocean using water property conserving scheme

Altimeter data have been assimilated in an ocean general circulation model using the water property conserving scheme. Two runs of the model have been conducted for the year 2004. In one of the runs, altimeter data have been assimilated sequentially, while in another run, assimilation has been suppressed. Assimilation has been restricted to the tropical Indian Ocean. An assessment of the strength of the scheme has been carried out by comparing the sea surface temperature (SST), simulated in the two runs, with in situ derived as well as remotely sensed observations of the same quantity. It has been found that the assimilation exhibits a significant positive impact on the simulation of SST. The subsurface effect of the assimilation could be judged by comparing the model simulated depth of the 20°C isotherm (hereafter referred to as D20), as a proxy of the thermocline depth, with the same quantity estimated from ARGO observations. In this case also, the impact is noteworthy. Effect on the dynamics has been judged by comparison of simulated surface current with observed current at a moored buoy location, and finally the impact on model sea level forecast in a free run after assimilation has been quantified in a representative example.     

Simulation of cyclones using synthetic data

A number of sensitivity experiments have been conducted to investigate the influence of using synthetic data on cyclone forecasts by a global spectral model. Some well known vortices have been used and the generated wind and pressure profiles are compared. It is found that the Rankine vortex and Holland’s vortex show the best representation of cyclonic circulation. Hence these two vortices are used in the sensitivity studies to simulate two cyclones, one of May 1979 and the other of August 1979. For this purpose the FGGE level-III b data set, produced at ECM WF, UK is used. Synthetic temperature and humidity data are also introduced to make the cyclones more realistic. With the use of Holland’s vortex the system is found to move faster than with the Rankine vortex. Also, the tracks of the cyclones simulated with Rankine vortex are found to be on the left side of the observed track while that of Holland’s vortex is on the right side of the observed track. However, substantial filling up of the systems are noticed with introduction of diabatic initialization of the mass and velocity fields and the forecasts of both the vortices behave differently. It is suggested that proper selection of synthetic vortex, initialization scheme and resolution of the model are very important for better forecast of cyclones.     

Mitigating the effects of storm surges generated by tropical cyclones: A proposal

One of the regions of the globe that is frequently and very significantly affected by storm surges is Bangladesh. These high amplitude water-level oscillations are generated by the meteorological forcing fields due to tropical cyclones in the Bay of Bengal. The tide also plays a significant role in determining the time history of the total water level. Due to the greenhouse warming associated with the increasing levels of carbon dioxide in the atmosphere, it is expected that the frequency and intensity of tropical cyclones in the Bay of Bengal will increase substantially within the next 50 to 100 years. This new breed of tropical cyclones, referred to as hypercanes, will generate storm surges on the coast of Bangladesh which could attain amplitudes of up to 15 m, much greater than the present-day amplitudes of up to 6 m. Various mitigation procedures are discussed and compared.     

Swarm intelligence and neural nets in forecasting the maximum sustained wind speed along the track of tropical cyclones over Bay of Bengal

Tropical cyclones are well-known extreme weather and the cause of considerable damages, injuries and loss of life. The assessment of the maximum sustained wind speed along the track of the tropical cyclones is very important for estimating the strength of the cyclones. The swarm intelligence in the form of ant colony optimization (ACO) technique is introduced in this study to compute the pheromone deposition along the track of tropical cyclones followed by neural nets to forecast the maximum sustained wind speed of the cyclones occurring over the Bay of Bengal of North Indian Ocean. The ACO is a nonlinear problem-based meta-heuristic optimization method for finding approximate solutions to discrete optimization problems and simulates the decision-making processes of ant colony similar to other adaptive learning techniques. The method has shown its application potential in various fields including the prediction of monsoon rainfall. In this study, the amount of pheromone deposition during the successive stages of the cyclones has been estimated. A range of minimum central pressure (MCP), central pressure drop (PD), maximum sustained wind speed (MSWS) and intensity (T-No) associated with the cyclones of Bay of Bengal are utilized to form the input matrix of the neural nets. The neural nets are trained to forecast the maximum sustained wind speed along the track of the tropical cyclones over Bay of Bengal. The result reveals that the errors in forecasting the MSWS along the track of tropical cyclones with 6, 12, 18 and 24 h lead time are 2.6, 2.9, 3.1 and 4.8, respectively. The result is compared with the existing dynamical, statistical and adaptive models to evaluate the skill of the present model. The result is well validated with observation.