Assimilation and Initialization of Data for Tropical Weather Prediction

1997 ◽  
Author(s):  
T. N. Krishnamurti
Keyword(s):  
Weather Prediction ◽  
Tropical Weather

scholarly journals The Development and Evaluation of a Statistical–Dynamical Tropical Cyclone Genesis Guidance Tool

2016 ◽  
Vol 32 (1) ◽  
pp. 27-46 ◽  
Author(s):  
Daniel J. Halperin ◽  
Robert E. Hart ◽  
Henry E. Fuelberg ◽  
Joshua H. Cossuth
Keyword(s):  
Tropical Cyclone ◽  
Prediction Models ◽  
Weather Prediction ◽  
Multiscale Model ◽  
Global Model ◽  
Tropical Cyclone Genesis ◽  
Regression Equations ◽  
Global Models ◽  
Tropical Weather ◽  
Cyclone Genesis

Abstract The National Hurricane Center (NHC) has stated that guidance on tropical cyclone (TC) genesis is an operational forecast improvement need, particularly since numerical weather prediction models produce TC-like features and operationally required forecast lead times recently have increased. Using previously defined criteria for TC genesis in global models, this study bias corrects TC genesis forecasts from global models using multiple logistic regression. The derived regression equations provide 48- and 120-h probabilistic genesis forecasts for each TC genesis event that occurs in the Environment Canada Global Environmental Multiscale Model (CMC), the NCEP Global Forecast System (GFS), and the Met Office's global model (UKMET). Results show select global model output variables are good discriminators between successful and unsuccessful TC genesis forecasts. Independent verification of the regression-based probabilistic genesis forecasts during 2014 and 2015 are presented. Brier scores and reliability diagrams indicate that the forecasts generally are well calibrated and can be used as guidance for NHC’s Tropical Weather Outlook product. The regression-based TC genesis forecasts are available in real time online.

Propagation and growth mechanisms for convectively coupled equatorial Kelvin waves

2021 ◽  
Author(s):  
Adrian Matthews
Keyword(s):  
Weather Prediction ◽  
Relative Vorticity ◽  
Reanalysis Data ◽  
Kelvin Waves ◽  
Horizontal Wind ◽  
Source Terms ◽  
Kelvin Wave ◽  
Vorticity Budget ◽  
High Impact Weather ◽  
Tropical Weather

<p>Convectively coupled equatorial Kelvin waves (CCKWs) are tropical weather systems that bring high impact weather and flooding, particularly in the Maritime Continent. They are a key component of the tropical climate system through scale interactions with other phenomena such as the Madden--Julian oscillation (MJO). CCKWs share many key features with theoretical, dry, linear equatorial Kelvin waves, such as a predominantly zonal component of their horizontal wind anomalies, and eastward propagation. Here, a vorticity budget for CCKWs is constructed using reanalysis data, to identify the basic mechanisms of eastward propagation and the observed growth. The budget is closed, with a small residual. Vortex stretching, from the divergence of the Kelvin wave acting on planetary vorticity (the -f D term), is the sole mechanism by which the vorticity structure of a theoretical Kelvin wave propagates eastward. This term is also the key mechanism for the eastward propagation of CCKWs, but its different phasing also leads to growth of the CCKW. However, unlike in the theoretical wave, other vorticity source terms also play a role in the propagation and growth of CCKWs. In particular, vortex stretching from the divergence of the CCKW acting on its own relative vorticity (the -ζ D term) is actually the largest source term, and this contributes mainly to the growth of the CCKW, as well as to eastward propagation. Horizontal vorticity advection (and to a lesser extent, vertical advection), counters the vortex stretching, and acts to retard the growth of the CCKW. The tilting of horizontal vorticity into the vertical also plays a role. However, the meridional advection of planetary vorticity (the -β v term, the main mechanism for westward propagation of Rossby waves), is negligible. The sum of the source terms in this complex vorticity budget leads to eastward propagation and growth of the CCKWs. The implications for numerical weather prediction, forecasting and climate simulations are discussed.</p>

Neural Network Techniques for Time Series Prediction: A Review

2019 ◽  
Vol 3 (3) ◽  
Author(s):  
Muhammad Faheem Mushtaq ◽  
Urooj Akram ◽  
Muhammad Aamir ◽  
Haseeb Ali ◽  
Muhammad Zulqarnain
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Time Series Data ◽  
Weather Prediction ◽  
Time Series Prediction ◽  
Series Data ◽  
Prediction Problem ◽  
Neural Network Models ◽  
Physical Time

It is important to predict a time series because many problems that are related to prediction such as health prediction problem, climate change prediction problem and weather prediction problem include a time component. To solve the time series prediction problem various techniques have been developed over many years to enhance the accuracy of forecasting. This paper presents a review of the prediction of physical time series applications using the neural network models. Neural Networks (NN) have appeared as an effective tool for forecasting of time series.  Moreover, to resolve the problems related to time series data, there is a need of network with single layer trainable weights that is Higher Order Neural Network (HONN) which can perform nonlinearity mapping of input-output. So, the developers are focusing on HONN that has been recently considered to develop the input representation spaces broadly. The HONN model has the ability of functional mapping which determined through some time series problems and it shows the more benefits as compared to conventional Artificial Neural Networks (ANN). The goal of this research is to present the reader awareness about HONN for physical time series prediction, to highlight some benefits and challenges using HONN.

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