Ingesting microwave sounder radiances for improvement in track forecast of cyclone Vardah
14 May 2018-Journal of Applied Remote Sensing (SPIE-Intl Soc Optical Eng)-Vol. 12, Iss: 02, pp 026015
TL;DR: In this article, a local ensemble transform Kalman filter assimilation algorithm is adopted to ingest microwave sounder radiances directly into the ARW-WRF model, and the effect of assimilation is observed to improve the minimum sea-level pressure values, whereas the improvements in the maximum sustainable wind speed are not significant.
Abstract: Ingesting microwave sounder radiances from SAPHIR of Megha–Tropiques has been attempted. A local ensemble transform Kalman filter assimilation algorithm is adopted to ingest radiances directly into the ARW–WRF model. The forward radiative transfer calculations were surrogated with an artificial neural network (ANN) based on the fast radiative transfer model. Raining pixels from the observations were removed using a threshold test on the observed brightness temperatures. Following this, corrections of both scan and air mass biases were accomplished using a predictor-based approach. The bias characteristics of each channel were calculated from ab initio clear-sky profiles from European reanalysis Interim reanalysis data. The vertical localization functions required for the radiance observations were chosen to be similar to the weighting function of the respective channel. The overall performance of the SAPHIR radiance assimilation in terms of the average error over the forecast period showed a positive impact on the cyclone track prediction when compared with the control run and the best track data from the Indian Meteorological Department. The effect of assimilation is observed to improve the minimum sea-level pressure values, whereas the improvements in the maximum sustainable wind speed are not significant. An assimilation experiment was set up to ingest channel-wise radiances independently, and it was concluded that the assimilation of channel 5 radiances results in the least error in the track forecast. The effect of using ensembles generated by initial perturbations in (i) temperature and (ii) both temperature and humidity was studied. The ensembles generated from perturbations in both humidity and temperature resulted in a better 72-h track compared with perturbation of only one of them. The overall performance of the assimilation of all the six channels for both 48- and 72-h forecast lead times showed a considerable improvement against the control run without any assimilation. Furthermore, the results show a degradation of the forecast of cyclone track in the first 24 h. The sensitivity toward channel-wise radiances showed a positive impact on the precipitation forecast when compared with global precipitation mission rainfall estimates. Threat and bias scores were used for quantitative assessment of precipitation, which indicated improvements in skill after assimilating all six channel radiances from SAPHIR. Finally, a sequential assimilation experiment was set up, and the improvements in the analysis fields were computed.
Citations
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01 Jan 2020
TL;DR: The Megha-Tropiques mission has been operating since 12 October 2011 and serves research and operational objectives related to the tropical water and energy cycle as mentioned in this paper, where the satellite is on a low inclination orbit that enhances the sampling over the intertropical belt.
Abstract: The Megha-Tropiques mission is operating since 12 October, 2011 and serves research and operational objectives related to the tropical water and energy cycle. The satellite is on a low inclination orbit that enhances the sampling over the intertropical belt. The original payloads were dedicated to the estimation of the radiation budget at the top of the atmosphere, the water vapor profiles and the instantaneous precipitation rate. The original suite of geophysical products that was developed permitted to demonstrate the proof of concept of the mission in the early part of its operation. Following an unfortunately expedited exploitation of the conically scanning multispectral radiometer (16 months), efforts have been geared to mitigate the loss by extending the use of the 183 GHz sounder towards the precipitation objectives. This induced some delays in the setting of the current set of products that are now being used for research investigations. Despites not being an operational meteorological satellite, the real time capability of the mission has shown its usefulness with a large and growing set of Numerical Weather Prediction centers assimilating the Megha-Tropiques data, in clear and total skies. After 7 years in space, the satellite and operating instruments are in excellent shape and sustain their very good initial performances. The mission has acquired a large and unique set of observations of the tropical water and energy cycle which is only at the beginning of its exploitation.
3 citations
TL;DR: In this paper , the authors examined the impact of three-dimensional variational data assimilation utilizing the multivariate background error covariance (BEC) estimates, in combination with the model calibration, for the simulations of seven tropical cyclones over the Bay of Bengal region.
Abstract: The main objective of the present study is to examine the impact of three-dimensional variational data assimilation utilizing the multivariate background error covariance (BEC) estimates, in combination with the model calibration, for the simulations of seven tropical cyclones over the Bay of Bengal region. The study indicates that the utilization of multivariate BEC in assimilation influences the model forecasts in terms of wind speed at 10 m height, precipitation, cyclone tracks and cyclone intensity. The assimilation experiments conducted with a previously calibrated model combined with the control variable option 6 (cv6) of BEC have reduced the overall root mean square error (RMSE) of 10 m wind speed by 17.02%, precipitation by 11.14%, cyclone track by 41.93% and the intensity by 25.5% when compared to the default model simulations without assimilation. The best experimental setup is then used for the operational forecast of a recent cyclone Gulab. The results show an RMSE reduction of 18.61% in the cyclone track and 28.99% in intensity forecasts. These results also confirm that the utilization of cv6 BEC in the assimilation of conventional and radiance observations on a calibrated model improves the forecast of tropical cyclones over the Bay of Bengal region.
2 citations
References
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TL;DR: In this article, a new sequential data assimilation method is proposed based on Monte Carlo methods, a better alternative than solving the traditional and computationally extremely demanding approximate error covariance equation used in the extended Kalman filter.
Abstract: A new sequential data assimilation method is discussed. It is based on forecasting the error statistics using Monte Carlo methods, a better alternative than solving the traditional and computationally extremely demanding approximate error covariance equation used in the extended Kalman filter. The unbounded error growth found in the extended Kalman filter, which is caused by an overly simplified closure in the error covariance equation, is completely eliminated. Open boundaries can be handled as long as the ocean model is well posed. Well-known numerical instabilities associated with the error covariance equation are avoided because storage and evolution of the error covariance matrix itself are not needed. The results are also better than what is provided by the extended Kalman filter since there is no closure problem and the quality of the forecast error statistics therefore improves. The method should be feasible also for more sophisticated primitive equation models. The computational load for reasonable accuracy is only a fraction of what is required for the extended Kalman filter and is given by the storage of, say, 100 model states for an ensemble size of 100 and thus CPU requirements of the order of the cost of 100 model integrations. The proposed method can therefore be used with realistic nonlinear ocean models on large domains on existing computers, and it is also well suited for parallel computers and clusters of workstations where each processor integrates a few members of the ensemble.
4,816 citations
DOI•
01 Jun 2005
TL;DR: The Weather Research and Forecasting (WRF) model as mentioned in this paper was developed as a collaborative effort among the NCAR Mesoscale and Microscale Meteorology (MMM) Division, the National Oceanic and Atmospheric Administration's (NOAA) National Centers for Environmental Prediction (NCEP) and Forecast System Laboratory (FSL), the Department of Defense's Air Force Weather Agency (AFWA) and Naval Research Laboratory (NRL), the Center for Analysis and Prediction of Storms (CAPS) at the University of Oklahoma, and the Federal Aviation Administration (F
Abstract: : The development of the Weather Research and Forecasting (WRF) modeling system is a multiagency effort intended to provide a next-generation mesoscale forecast model and data assimilation system that will advance both the understanding and prediction of mesoscale weather and accelerate the transfer of research advances into operations. The model is being developed as a collaborative effort ort among the NCAR Mesoscale and Microscale Meteorology (MMM) Division, the National Oceanic and Atmospheric Administration's (NOAA) National Centers for Environmental Prediction (NCEP) and Forecast System Laboratory (FSL), the Department of Defense's Air Force Weather Agency (AFWA) and Naval Research Laboratory (NRL), the Center for Analysis and Prediction of Storms (CAPS) at the University of Oklahoma, and the Federal Aviation Administration (FAA), along with the participation of a number of university scientists. The WRF model is designed to be a flexible, state-of-the-art, portable code that is an efficient in a massively parallel computing environment. A modular single-source code is maintained that can be configured for both research and operations. It offers numerous physics options, thus tapping into the experience of the broad modeling community. Advanced data assimilation systems are being developed and tested in tandem with the model. WRF is maintained and supported as a community model to facilitate wide use, particularly for research and teaching, in the university community. It is suitable for use in a broad spectrum of applications across scales ranging from meters to thousands of kilometers. Such applications include research and operational numerical weather prediction (NWP), data assimilation and parameterized-physics research, downscaling climate simulations, driving air quality models, atmosphere-ocean coupling, and idealized simulations (e.g boundary-layer eddies, convection, baroclinic waves).
2,567 citations
TL;DR: In this article, the authors proposed an ensemble Kalman filter for data assimilation using the flow-dependent statistics calculated from an ensemble of short-range forecasts (a technique referred to as Ensemble Kalman filtering) in an idealized environment.
Abstract: The possibility of performing data assimilation using the flow-dependent statistics calculated from an ensemble of short-range forecasts (a technique referred to as ensemble Kalman filtering) is examined in an idealized environment. Using a three-level, quasigeostrophic, T21 model and simulated observations, experiments are performed in a perfect-model context. By using forward interpolation operators from the model state to the observations, the ensemble Kalman filter is able to utilize nonconventional observations. In order to maintain a representative spread between the ensemble members and avoid a problem of inbreeding, a pair of ensemble Kalman filters is configured so that the assimilation of data using one ensemble of shortrange forecasts as background fields employs the weights calculated from the other ensemble of short-range forecasts. This configuration is found to work well: the spread between the ensemble members resembles the difference between the ensemble mean and the true state, except in the case of the smallest ensembles. A series of 30-day data assimilation cycles is performed using ensembles of different sizes. The results indicate that (i) as the size of the ensembles increases, correlations are estimated more accurately and the root-meansquare analysis error decreases, as expected, and (ii) ensembles having on the order of 100 members are sufficient to accurately describe local anisotropic, baroclinic correlation structures. Due to the difficulty of accurately estimating the small correlations associated with remote observations, a cutoff radius beyond which observations are not used, is implemented. It is found that (a) for a given ensemble size there is an optimal value of this cutoff radius, and (b) the optimal cutoff radius increases as the ensemble size increases.
1,827 citations
TL;DR: In this paper, an ensemble adjustment Kalman filter is proposed to estimate the probability distribution of the state of a model given a set of observations using Monte Carlo approximations to the nonlinear filter.
Abstract: A theory for estimating the probability distribution of the state of a model given a set of observations exists. This nonlinear filtering theory unifies the data assimilation and ensemble generation problem that have been key foci of prediction and predictability research for numerical weather and ocean prediction applications. A new algorithm, referred to as an ensemble adjustment Kalman filter, and the more traditional implementation of the ensemble Kalman filter in which “perturbed observations” are used, are derived as Monte Carlo approximations to the nonlinear filter. Both ensemble Kalman filter methods produce assimilations with small ensemble mean errors while providing reasonable measures of uncertainty in the assimilated variables. The ensemble methods can assimilate observations with a nonlinear relation to model state variables and can also use observations to estimate the value of imprecisely known model parameters. These ensemble filter methods are shown to have significant advantag...
1,660 citations
TL;DR: In this paper, the EnSRF algorithm is proposed, which uses the traditional Kalman gain for updating the ensemble mean but uses a reduced loss to update deviations from the EnKF mean.
Abstract: The ensemble Kalman filter (EnKF) is a data assimilation scheme based on the traditional Kalman filter update equation. An ensemble of forecasts are used to estimate the background-error covariances needed to compute the Kalman gain. It is known that if the same observations and the same gain are used to update each member of the ensemble, the ensemble will systematically underestimate analysis-error covariances. This will cause a degradation of subsequent analyses and may lead to filter divergence. For large ensembles, it is known that this problem can be alleviated by treating the observations as random variables, adding random perturbations to them with the correct statistics. Two important consequences of sampling error in the estimate of analysis-error covariances in the EnKF are discussed here. The first results from the analysis-error covariance being a nonlinear function of the backgrounderror covariance in the Kalman filter. Due to this nonlinearity, analysis-error covariance estimates may be negatively biased, even if the ensemble background-error covariance estimates are unbiased. This problem must be dealt with in any Kalman filter‐based ensemble data assimilation scheme. A second consequence of sampling error is particular to schemes like the EnKF that use perturbed observations. While this procedure gives asymptotically correct analysis-error covariance estimates for large ensembles, the addition of perturbed observations adds an additional source of sampling error related to the estimation of the observation-error covariances. In addition to reducing the accuracy of the analysis-error covariance estimate, this extra source of sampling error increases the probability that the analysis-error covariance will be underestimated. Because of this, ensemble data assimilation methods that use perturbed observations are expected to be less accurate than those which do not. Several ensemble filter formulations have recently been proposed that do not require perturbed observations. This study examines a particularly simple implementation called the ensemble square root filter, or EnSRF. The EnSRF uses the traditional Kalman gain for updating the ensemble mean but uses a ‘‘reduced’’ Kalman gain to update deviations from the ensemble mean. There is no additional computational cost incurred by the EnSRF relative to the EnKF when the observations have independent errors and are processed one at a time. Using a hierarchy of perfect model assimilation experiments, it is demonstrated that the elimination of the sampling error associated with the perturbed observations makes the EnSRF more accurate than the EnKF for the same ensemble size.
1,477 citations