Multirule Based Diagnostic Approach for the Fog Predictions Using WRF Modelling Tool

Abstract

The prediction of fog onset remains difficult despite the progress in numerical weather prediction. It is a complex process and requires adequate representation of the local perturbations in weather prediction models. It mainly depends upon microphysical and mesoscale processes that act within the boundary layer. This study utilizes a multirule based diagnostic (MRD) approach using postprocessing of the model simulations for fog predictions. The empiricism involved in this approach is mainly to bridge the gap between mesoscale and microscale variables, which are related to mechanism of the fog formation. Fog occurrence is a common phenomenon during winter season over Delhi, India, with the passage of the western disturbances across northwestern part of the country accompanied with significant amount of moisture. This study implements the above cited approach for the prediction of occurrences of fog and its onset time over Delhi. For this purpose, a high resolution weather research and forecasting (WRF) model is used for fog simulations. The study involves depiction of model validation and postprocessing of the model simulations for MRD approach and its subsequent application to fog predictions. Through this approach model identified foggy and nonfoggy days successfully 94% of the time. Further, the onset of fog events is well captured within an accuracy of 30–90 minutes. This study demonstrates that the multirule based postprocessing approach is a useful and highly promising tool in improving the fog predictions.

An operational fog prediction system for Delhi using the 330 m Unified Model

“…Although a large number of observational studies regarding fog in Delhi based on ground‐based instrumentation exist (e.g., Ghude et al, ; Mohan & Payra, ), modelling attempts in simulating fog formation processes are limited. Payra and Mohan () present some three‐dimensional (3D) simulations using the Weather Research and Forecasting (WRF) model, demonstrating that the model can produce reasonable forecasts at the mesoscale, but post‐processing techniques can significantly improve the micro‐scale skill. Visibility and fog predictions by the National Centre for Medium Range Weather Forecasting's (NCMRWF) Unified Model (NCUM) global configuration were validated against satellite and ground‐based visibility and fog measurements by Aditi, George, Gupta, Rajagopal, and Basu ().…”

Forecasting radiation fog at climatologically contrasting sites: evaluation of statistical methods and WRF

“…Moreover, Steeneveld et al () found how the onset of the modelled fog was more sensitive to the planetary boundary layer (PBL) scheme used whereas the dissipation depended more on the microphysics parametrization. On the other hand, Payra and Mohan () and Ryerson and Hacker (2014) obtained positive temperature and negative relative humidity biases during fog periods and stated that LWC is not a useful direct model output for fog forecasting. Van der Velde et al () showed how the WRF model was able to provide correct values of temperature and mixing ratio, but had problems producing LWC.…”

Previsão de Nevoeiro Utilizando Multicritérios Baseados em Simulações do Modelo WRF para o Aeroporto Internacional Afonso Pena

“…Skamarock et al, (2019) apresentam uma descrição abrangente sobre o modelo WRF utilizada como ferramenta de simulação. Diversos autores têm estudado diferentes configurações do modelo WRF para simulação de processos físicos dentro da camada limite (ex: nevoeiro), vide Goswami & Tyagi (2007) b) Utiliza-se uma discretização vertical com 33 níveis (conforme Payra & Mohan, 2014), dos quais 10 níveis encontram-se abaixo de 2.500 m; e c) Selecionaram-se as parametrizações de processos físicos: Kessler Scheme (Kessler, 1969) para microfísica, Kain-Fritsch Scheme (Kain, 2004) para nuvens do tipo cumulus; Rapid Radiative Transfer Model-RRTMG (Iacono et al, 2008) para radiação de ondas longa e curta; Mellor-Yamada-Janjic Scheme (MYJ) (Janjic, 1994) para camada limite planetária; e Noah-MP Land Surface Model (Niu et al, 2011;Yang et al, 2011) para processos de superfície; Eta Similarity Scheme (Janjic, 1994) para camada de superfície.…”

“…França et al (2018) investigaram dois eventos de nevoeiro no Aeroporto Internacional de Guarulhos, São Paulo -Brasil, usando dados coletados em alta frequência (15 minutos) de estações de superfície automáticas e do perfil vertical de vento da baixa troposfera extraídos pelo Sound Detection And Ranging (SODAR) e a radiossondagem, a cada 12h, do Aeroporto Campo de Marte, São Paulo -Brasil, mostrando que os processos associados a fenômenos de baixa visibilidade (início, duração e fim) estão correlacionados com o comportamento da camada limite (ex: altura da camada, energia cinética turbulenta, intensidade do vento, entre outros). Payra & Mohan (2014), por sua vez, desenvolveram um procedimento que apresenta resultados de previsão de nevoeiro bastante assertivos − com 24 horas de antecedência − baseado no ajuste de uma árvore de decisão (multicritério), considerando dados meteorológicos de superfície de Nova Delhi, na Índia, para caracterização de eventos de nevoeiro, e dados prognosticados gerados pelo modelo atmosférico Weather Research and Forecasting (WRF, Skamarock et al, 2019). Os resultados de ocorrência de nevoeiro e nãonevoeiro apontam uma taxa de acerto de cerca de 94% e com acurácia do início dos eventos variando de 30 a 90 minutos.…”