The Impact of Nonlinearity on the Targeted Observations for Tropical Cyclone Prediction

2017 ◽  
pp. 675-692
Author(s):  
Feifan Zhou ◽  
He Zhang
Keyword(s):  
Tropical Cyclone ◽  
Targeted Observations ◽  
The Impact ◽  
Cyclone Prediction

scholarly journals The Effect of Atmosphere–Ocean Coupling on the Structure and Intensity of Tropical Cyclone Bejisa in the Southwest Indian Ocean

Atmosphere ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 688
Author(s):  
Soline Bielli ◽  
Christelle Barthe ◽  
Olivier Bousquet ◽  
Pierre Tulet ◽  
Joris Pianezze
Keyword(s):  
Tropical Cyclone ◽  
Mixed Layer ◽  
Heat Fluxes ◽  
Sea Surface ◽  
Ocean Mixed Layer ◽  
Surface Cooling ◽  
Southwest Indian Ocean ◽  
Left Hand ◽  
Sea Surface Cooling ◽  
The Impact

A set of numerical simulations is relied upon to evaluate the impact of air-sea interactions on the behaviour of tropical cyclone (TC) Bejisa (2014), using various configurations of the coupled ocean-atmosphere numerical system Meso-NH-NEMO. Uncoupled (SST constant) as well as 1D (use of a 1D ocean mixed layer) and 3D (full 3D ocean) coupled experiments are conducted to evaluate the impact of the oceanic response and dynamic processes, with emphasis on the simulated structure and intensity of TC Bejisa. Although the three experiments are shown to properly capture the track of the tropical cyclone, the intensity and the spatial distribution of the sea surface cooling show strong differences from one coupled experiment to another. In the 1D experiment, sea surface cooling (∼1 ∘C) is reduced by a factor 2 with respect to observations and appears restricted to the depth of the ocean mixed layer. Cooling is maximized along the right-hand side of the TC track, in apparent disagreement with satellite-derived sea surface temperature observations. In the 3D experiment, surface cooling of up to 2.5 ∘C is simulated along the left hand side of the TC track, which shows more consistency with observations both in terms of intensity and spatial structure. In-depth cooling is also shown to extend to a much deeper depth, with a secondary maximum of nearly 1.5 ∘C simulated near 250 m. With respect to the uncoupled experiment, heat fluxes are reduced from about 20% in both 1D and 3D coupling configurations. The tropical cyclone intensity in terms of occurrence of 10-m TC wind is globally reduced in both cases by about 10%. 3D-coupling tends to asymmetrize winds aloft with little impact on intensity but rather a modification of the secondary circulation, resulting in a slight change in structure.

Progress toward Cloud-Cleared Infrared radiance assimilation in a global modeling framework: Application to the 2017 Atlantic Tropical Cyclone Season.

2021 ◽  
Author(s):  
Niama Boukachaba ◽  
Oreste Reale ◽  
Erica L. McGrath-Spangler ◽  
Manisha Ganeshan ◽  
Will McCarty ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
Strong Impact ◽  
Modeling Framework ◽  
Atmospheric Infrared Sounder ◽  
Clear Sky ◽  
Earth Observing System ◽  
Infrared Radiance ◽  
Radiance Assimilation ◽  
The Impact ◽  
Tropical Cyclone Season

<p>Previous work by this team has demonstrated that assimilation of IR radiances in partially cloudy regions is beneficial to numerical weather predictions (NWPs), improving the representation of tropical cyclones (TCs) in global analyses and forecasts. The specific technique used by this team is based on the “cloud-clearing CC” methodology. Cloud-cleared hyperspectral IR radiances (CCRs), if thinned more aggressively than clear-sky radiances, have shown a strong impact on the analyzed representation and structure of TCs. However, the use of CCRs in an operational context is limited by 1) latency; and 2) external dependencies present in the original cloud-clearing algorithm. In this study, the Atmospheric InfraRed Sounder (AIRS) CC algorithm was (a) ported to NASA high end computing resources (HEC), (b) deprived of external dependencies, and (c) parallelized improving the processing by a factor of 70. The revised AIRS CC algorithm is now customizable, allowing user’s choice of channel selection, user’s model's fields as first guess, and could perform in real time. This study examines the benefits achieved when assimilating CCRs using the NASA’s Goddard Earth Observing System (GEOS) hybrid 4DEnVar system. The focus is on the 2017 Atlantic hurricane season with three infamous hurricanes (Harvey, Irma, and Maria) investigated in depth.  The impact of assimilating customized CCRs on the analyzed representation of tropical cyclone horizontal and vertical structure and on forecast skill is discussed.</p>

scholarly journals The Impact of Convective Momentum Transport on Tropical Cyclone Track Forecasts Using the Emanuel Cumulus Parameterization

2007 ◽  
Vol 135 (4) ◽  
pp. 1195-1207 ◽  
Author(s):  
Timothy F. Hogan ◽  
Randal L. Pauley
Keyword(s):  
Tropical Cyclone ◽  
Data Assimilation ◽  
Tropical Cyclones ◽  
Momentum Transport ◽  
Cumulus Parameterization ◽  
Transport Parameter ◽  
Medium Range Forecast ◽  
Medium Range ◽  
Convective Momentum Transport ◽  
The Impact

Abstract The influence of convective momentum transport (CMT) on tropical cyclone (TC) track forecasts is examined in the Navy Operational Global Atmospheric Prediction System (NOGAPS) with the Emanuel cumulus parameterization. Data assimilation and medium-range forecast experiments show that for 35 tropical cyclones during August and September 2004 the inclusion of CMT in the cumulus parameterization significantly improves the TC track forecasts. The tests show that the track forecasts are very sensitive to the magnitude of the Emanuel parameterization’s convective momentum transport parameter, which controls the CMT tendency returned by the parameterization. While the overall effect of this formulation of CMT in NOGAPS data assimilation/medium-range forecasts results in the surface pressure of tropical cyclones being less intense (and more consistent with the analysis), the parameterization is not equivalent to a simple diffusion of winds in the presence of convection. This is demonstrated by two data assimilation/medium-range forecast tests in which a vertical diffusion algorithm replaces the CMT. Two additional data assimilation/medium-range forecast experiments were conducted to test whether the skill increase primarily comes from the CMT in the immediate vicinity of the tropical cyclones. The results show that the inclusion of the CMT calculation in the vicinity of the TC makes the largest contribution to the increase in forecast skill, but the general contribution of CMT away from the TC also plays an important role.

scholarly journals The impact of climate model sea surface temperature biases on tropical cyclone simulations

2018 ◽  
Vol 53 (1-2) ◽  
pp. 173-192 ◽  
Author(s):  
Wei-Ching Hsu ◽  
Christina M. Patricola ◽  
Ping Chang
Keyword(s):  
Tropical Cyclone ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Climate Model ◽  
Sea Surface ◽  
The Impact

scholarly journals Impact of Four-Dimensional Variational Data Assimilation of Atmospheric Motion Vectors on Tropical Cyclone Track Forecasts

2006 ◽  
Vol 21 (4) ◽  
pp. 663-669 ◽  
Author(s):  
Dongliang Wang ◽  
Xudong Liang ◽  
Yihong Duan ◽  
Johnny C. L. Chan
Keyword(s):  
Tropical Cyclone ◽  
Data Assimilation ◽  
Mesoscale Model ◽  
State University ◽  
Motion Vectors ◽  
Geostationary Meteorological Satellite ◽  
Atmospheric Motion ◽  
Atmospheric Motion Vectors ◽  
Data Assimilation Technique ◽  
The Impact

Abstract The fifth-generation Pennsylvania State University–National Center for Atmospheric Research nonhydrostatic Mesoscale Model is employed to evaluate the impact of the Geostationary Meteorological Satellite-5 water vapor and infrared atmospheric motion vectors (AMVs), incorporated with the four-dimensional variational (4DVAR) data assimilation technique, on tropical cyclone (TC) track predictions. Twenty-two cases from eight different TCs over the western North Pacific in 2002 have been examined. The 4DVAR assimilation of these satellite-derived wind observations leads to appreciable improvements in the track forecasts, with average reductions in track error of ∼5% at 12 h, 12% at 24 h, 10% at 36 h, and 7% at 48 h. Preliminary results suggest that the improvement depends on the quantity of the AMV data available for assimilation.

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