Improving the Four-Dimensional Incremental Analysis Update (4DIAU) with the HWRF 4DEnVar Data Assimilation System for Rapidly Evolving Hurricane Prediction

Short-term spin-up for strong storms is a known difficulty for the operational Hurricane Weather Research and Forecasting (HWRF) model after assimilating high-resolution inner-core observations. Our previous study associated this short-term intensity prediction issue with the incompatibility between the HWRF model and the data assimilation (DA) analysis. While improving physics and resolution of the model was found helpful, this study focuses on further improving the intensity predictions through the four-dimensional incremental analysis update (4DIAU).In the traditional 4DIAU, increments are pre-determined by subtracting background forecasts from analyses. Such pre-determined increments implicitly require linear evolution assumption during the update, which are hardly valid for rapid-evolving hurricanes. To confirm the hypothesis, a corresponding 4D analysis nudging (4DAN) method which uses online increments is first compared with the 4DIAU in an oscillation model. Then, variants of 4DIAU are proposed to improve its application for nonlinear systems. Next, 4DIAU, 4DAN and their proposed improvements are implemented into the HWRF 4DEnVar DA system and are investigated with hurricane Patricia (2015).Results from both oscillation model and HWRF model show that: 1. the pre-determined increments in 4DIAU can be detrimental when there are discrepancies between the updated and background forecasts during a nonlinear evolution. 2. 4DAN can improve the performance of incremental update upon 4DIAU, but its improvements are limited by the over-filtering. 3. Relocating initial background before the incremental update can improve the corresponding traditional methods. 4. the feature-relative 4DIAU method improves the incremental update the most and produces the best track and intensity predictions for Patricia among all experiments.

Improved simulation of 19th- and 20th-century North Atlantic hurricane frequency after correcting historical sea surface temperatures

Confidence in dynamical and statistical hurricane prediction is rooted in the skillful reproduction of hurricane frequency using sea surface temperature (SST) patterns, but an ensemble of high-resolution atmospheric simulation extending to the 1880s indicates model-data disagreements that exceed those expected from documented uncertainties. We apply recently developed corrections for biases in historical SSTs that lead to revisions in tropical to subtropical SST gradients by ±0.1°C. Revised atmospheric simulations have 20% adjustments in the decadal variations of hurricane frequency and become more consistent with observations. The improved simulation skill from revised SST estimates not only supports the utility of high-resolution atmospheric models for hurricane projections but also highlights the need for accurate estimates of past and future patterns of SST changes.

Estudio comparativo de técnicas de minería de datos para la predicción de rutas de huracanes

Los huracanes son las tormentas más grandes y violentas que pueden existir sobre la tierra. Su peligrosidad radica en la velocidad que pueden alcanzar sus vientos, llegando a superar los 250 kilómetros por hora y desatando 9 billones de litros de lluvia al día, en consecuencia, sus efectos son a gran escala y con frecuencia muy destructivos en pérdidas humanas y materiales. A sabiendas de la inexactitud en las trayectorias, muchos habitantes esperan hasta el último momento antes de abandonar su hogar y pertenencias con la esperanza de que el fenómeno meteorológico cambie su curso. Es por esto que surge la necesidad de determinar la mejor técnica para predecir rutas de huracanes. El estudio consistió en entrenar los algoritmos de las técnicas de predicción, regresión lineal, k vecinos más cercanos y perceptrón multicapa, para obtener los modelos que permitan la comparación de datos predictivos con las trayectorias reales de huracanes y así determinar la exactitud de la predicción. Se encontró que la técnica de regresión lineal obtuvo los mejores resultados. Hurricanes are the largest and most violent storms that exist on Earth. Their dangerousness lies in the speed that can reach their winds, reaching over 250 km per hour and unleashing 9 billion liters of rain a day, so their effects are large scale and very destructive. Due to the effects mentioned before, the number of human and material losses are high. This is because of the inaccuracy in trajectories and many inhabitants wait until the last moment to leave their home and belongings, in the hope that the weather phenomenon will change its course. In this way arises the need to find the best hurricane prediction technique. The study consisted in training the algorithms of prediction techniques, linear regression, k nearest neighbors and multilayer perceptron, to obtain the models that allow the comparison of predictive data with the actual hurricane trajectories and thus determine the accuracy of the prediction. It was found that the linear regression technique obtained the best results.

Tracking Hurricane Dorian in GDELT and Twitter

GDELT is a machine coded database of events that uses both foreign and domestic news feeds and contains over a quarter of a billion worldwide event records categorized into three hundred categories. This paper compares the spatial footprint of GDELT event mentions with those of event related geo-tagged tweets for Hurricane Dorian in the South-Eastern United States. Besides examining event related GDELT and Twitter data abundance, the study relates areas of elevated GDELT news and tweeting activities to the locations of the hurricane track over a six-day period, and statistically analyzes distances between daily GDELT event mentions and tweets, and the hurricane center on different days. It assesses the potential role of the geographic coverage of the cone in hurricane prediction maps on the level of event related news and tweeting activities. The study also discusses pros and cons of both data sources for event tracking with regards to data abundance, spatial and temporal resolution, and thematic accuracy.

The impact of ocean waves on spray stress and surface drag coefficient

Purpose The behaviors of sea surface drag coefficient should be well understood for an accurate hurricane prediction. The speed of wind has been applied to characterize the spray production; however, this could result in inaccurate spray productions compared to the experimental or field data if the influence of wave state is not considered. This paper aims to integrate a new sea spray generation function, described by windsea Reynolds number, into the spray momentum flux formula to calculate the spray momentum. Design/methodology/approach On the basis of this spray momentum, this study proposes the new formulas of spray stress and drag coefficient when the wind speed is high. Findings Results of the revised formulas show that wave status had significant effects on the spray stress and sea surface drag coefficient. Also, wave age was found to be an important parameter that affects the drag coefficient. The drag coefficient decreased with the increasing wave age. Comparison between this study’s theoretical and observation values of drag coefficient shows that the study results are close to the measured values. Research limitations/implications The research findings can enhance the understanding of the behaviors of sea surface drag for an accurate hurricane prediction. Originality/value A new sea spray generation function, described by windsea Reynolds number, is integrated into the spray momentum flux formula to calculate the spray momentum. On the basis of this spray momentum, this study proposes the new formulas of spray stress and drag coefficient when the wind speed is high.

The Unexpected Rapid Intensification of Tropical Cyclones in Moderate Vertical Wind Shear. Part I: Overview and Observations

A satellite-based investigation is performed of a class of tropical cyclones (TCs) that unexpectedly undergo rapid intensification (RI) in moderate vertical wind shear between 5 and 10 m s−1 calculated as 200–850-hPa shear. This study makes use of both infrared (IR; 11 μm) and water vapor (WV; 6.5 μm) geostationary satellite data, the Statistical Hurricane Prediction Intensity System (SHIPS), and model reanalyses to highlight commonalities of the six TCs. The commonalities serve as predictive guides for forecasters and common features that can be used to constrain and verify idealized modeling studies. Each of the TCs exhibits a convective cloud structure that is identified as a tilt-modulated convective asymmetry (TCA). These TCAs share similar shapes, upshear-relative positions, and IR cloud-top temperatures (below −70°C). They pulse over the core of the TC with a periodicity of between 4 and 8 h. Using WV satellite imagery, two additional features identified are asymmetric warming/drying upshear of the TC relative to downshear, as well as radially thin arc-shaped clouds on the upshear side. The WV brightness temperatures of these arcs are between −40° and −60°C. All of the TCs are sheared by upper-level anticyclones, which limits the strongest environmental winds to near the tropopause.

Supplementary video to The Wrath of Zemi: Arawak Hurricane Prediction in the Caribbean

Supplementary video to The Wrath of Zemi: Arawak Hurricane Prediction in the Caribbean