Ensemble Sensitivity Analysis-Based Ensemble Transform with 3D Rescaling Initialization Method for Storm-Scale Ensemble Forecast

Feng, Yuxuan; Min, Jinzhong; Zhuang, Xiaoran; Wang, Shiqi

doi:10.3390/atmos10010024

Cited by 8 publications

(7 citation statements)

References 40 publications

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“…To evaluate the impact of each perturbed variable (parameter) on the spatial distribution of precipitation, we use 24 h precipitation data of 379 active weather stations located within the simulation area D2 to obtain the Equitable Threat Score (ETS) of the precipitation forecast [25]. The ETS is calculated as follows:…”

Section: Equitable Threat Score (Ets)mentioning

confidence: 99%

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Ensemble Forecasting Experiments Using the Breeding of Growing Modes with Perturbed Land Variables

Zeng

Liang

Wang³

et al. 2021

Atmosphere

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Although land surface influences atmospheric processes significantly, insufficient studies have been conducted on the ensemble forecasts using the breeding of growing modes (BGM) with perturbed land surface variables. To investigate the practicability of perturbed land variables for ensemble forecasting, we used the ARWv3 mesoscale model to generate ensembles for an event of 24 h heavy rainfall with perturbed atmospheric and land variables by the BGM method. Results show that both atmospheric and land variables can generate initial perturbations with BGM, except that they differ in time and saturation characteristics, e.g., saturation is generally achieved in approximately 30 h with a growth rate of ~1.30 for atmospheric variables versus 102 h and growth rate of 1.02 for land variables. With the increase in precipitation, the importance of the perturbations of land variables also increases as compared to those of atmospheric variables. Moreover, the influence of the perturbations of land variables on simulated precipitation is still relatively large, although smaller than that of atmospheric variables, e.g., the spreads of perturbed atmospheric and land subsets were 7.3 and 3.8 mm, respectively. The benefits of perturbed initialisation can also be observed in terms of probability forecast. All findings indicate that the BGM method with perturbed land variables has the potential to ensemble forecasts for precipitation.

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Section: Equitable Threat Score (Ets)mentioning

confidence: 99%

“…As land surface can also play an important role in affecting the atmospheric processes [23][24][25], the suitability of the land variables for generating perturbed initial conditions using the BGM method should be considered.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Forecasting Experiments Using the Breeding of Growing Modes with Perturbed Land Variables

Zeng

Liang

Wang³

et al. 2021

Atmosphere

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“…Um modelo que é utilizado para a previsão de tempo no sul de Minas Gerais em modo operacional no Centro de Estudos de Previsão de Tempo e Clima de Minas Gerais (CEPreMG; Campos et al, 2016), na Universidade Federal de Itajubá, é o Weather Research and Forecast (WRF; Skamarock et al, 2019). Esse modelo é utilizado para simulações de diversos eventos climáticos (Feng et al, 2019), inclusive eventos de mesoescala onde resolve equações tridimensionais de Euler. Além disso é possível caracterizar diversas condições físicas e numéricas para um determinado conjunto de dados (Gonzalez Alonso- de-Linaje et al, 2019;Skamarock et al, 2008).…”

Section: Introductionunclassified

Avaliação do modelo Weather Research and Forecasting (WRF) na simulação operacional de um evento de frente fria no sudeste do Brasil

Araújo

Garcia

Monteiro

et al. 2023

Rev. Bras. Geog. Fis.

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A região sudeste do Brasil é afetada o ano todo por sistemas transientes como as frentes frias. A incursão de sistemas frontais, além de afetar o dia a dia da população, pode causar danos e prejuízos a diversos setores da sociedade. Um modelo que é utilizado operacionalmente para a previsão de tempo no sul de Minas Gerais desde 2014 no Centro de Estudos de Previsão de Tempo e Clima de Minas Gerais (CEPreMG) da Universidade Federal de Itajubá é o Weather Research and Forecast (WRF). Em 2019, em decorrência da disponibilidade de dados do Global Forecast System (GFS) com maior resolução horizontal para a geração das condições iniciais e de fronteira necessárias como entrada ao modelo e do aumento da capacidade computacional disponível para as simulações, foram implementadas alterações no sistema em operação no CEPreMG. Assim, este estudo objetiva avaliar o desempenho do WRF na simulação de um sistema frontal que atuou no sudeste do Brasil em agosto de 2021. De forma geral, o modelo WRF simulou corretamente a posição e tempo de atuação do sistema, mas com algumas diferenças na intensidade. Isso não representa propriamente um problema uma vez que a resolução do WRF e dos dados usados na validação são diferentes. Evaluation of the Weather Research and Forecasting (WRF) model in the operational simulation of a cold weather event in southeastern Brazil A B S T R A C TThe southeastern region of Brazil is affected throughout the year by transient systems such as cold fronts. The incursion of frontal systems, in addition to affecting the daily life of the population, can cause damage and losses to various sectors of the society. A model that has been operationally used for weather forecasting in the southern region of Minas Gerais since 2014 at the Minas Gerais Weather and Climate Forecast Studies Center (CEPreMG) at the Federal University of Itajubá is the Weather Research and Forecast (WRF). In 2019, due to the availability of higher horizontal resolution data from the Global Forecast System (GFS) to generate the initial and boundary conditions necessary as inputs to the model and due to the increase in the computational capacity available, changes were implemented in the operational forecast system at CEPreMG. Thus, this study aims to evaluate the performance of the WRF in the simulation of a frontal system that was registered in the southeastern of Brazil in August 2021. In general, WRF model correctly simulated the position and evolution of the system, but with some differences regarding the intensity. This is not really a problem since the resolution of the WRF and the data used in the validation are different.

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“…Magnusson et al (2009) defined the rescaling factor as the ratio of the square root of the vertically integrated analysis error variance measured in the total energy to the square root of the vertically integrated analysis perturbation total energy. The two-dimensional mask was unable to represent the vertical structure of the analysis uncertainties, so Feng et al (2019) constructed the ensemble transform with 3D rescaling (ET_ 3DR) scheme. The total energy norm derived from the ensemble analysis from the NCEP hybrid 3DVar/EnKF system (Wang et al 2013) is used in the mask calculation.…”

Section: Introductionmentioning

confidence: 99%

Initial Perturbations Based on Ensemble Transform Kalman Filter with Rescaling Method for Ensemble Forecasting

Wang

Chen

Han-bin³

et al. 2021

Weather and Forecasting

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Ensemble forecast is a method to faithfully describe initial and model uncertainties in a weather forecasting system. Initial uncertainties are much more important than model uncertainties in the short-range numerical prediction. Currently, initial uncertainties are described by Ensemble Transform Kalman Filter (ETKF) initial perturbation method in Global and Regional Assimilation and Prediction Enhanced System-Regional Ensemble Prediction System (GRAPES-REPS). However, an initial perturbation distribution similar to the analysis error cannot be yielded in the ETKF method of the GRAPES-REPS. To improve the method, we introduce a regional rescaling factor into the ETKF method (we call it ETKF_R). We also compare the results between the ETKF and ETKF_R methods and further demonstrate how rescaling can affect the initial perturbation characteristics as well as the ensemble forecast skills. The characteristics of the initial ensemble perturbation improve after applying the ETKF_R method. For example, the initial perturbation structures become more reasonable, the perturbations are better able to explain the forecast errors at short lead times, and the lower kinetic energy spectrum as well as perturbation energy at the initial forecast times can lead to a higher growth rate of themselves. Additionally, the ensemble forecast verification results suggest that the ETKF_R method has a better spread-skill relationship, a faster ensemble spread growth rate and a more reasonable rank histogram distribution than ETKF. Furthermore, the rescaling has only a minor impact on the assessment of the sharpness of probabilistic forecasts. The above results all suggest that ETKF_R can be effectively applied to the operational GRAPES-REPS.

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Ensemble Sensitivity Analysis-Based Ensemble Transform with 3D Rescaling Initialization Method for Storm-Scale Ensemble Forecast

Cited by 8 publications

References 40 publications

Ensemble Forecasting Experiments Using the Breeding of Growing Modes with Perturbed Land Variables

Ensemble Forecasting Experiments Using the Breeding of Growing Modes with Perturbed Land Variables

Avaliação do modelo Weather Research and Forecasting (WRF) na simulação operacional de um evento de frente fria no sudeste do Brasil

Initial Perturbations Based on Ensemble Transform Kalman Filter with Rescaling Method for Ensemble Forecasting

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