scholarly journals Impact of Satellite-Derived Rapid-Scan Wind Observations on Numerical Model Forecasts of Hurricane Katrina

2009 ◽  
Vol 137 (5) ◽  
pp. 1615-1622 ◽  
Author(s):  
Rolf H. Langland ◽  
Christopher Velden ◽  
Patricia M. Pauley ◽  
Howard Berger
Keyword(s):  
Numerical Model ◽  
Data Assimilation ◽  
Hurricane Katrina ◽  
Tropical Cyclones ◽  
Short Range ◽  
Geostationary Satellites ◽  
Geostationary Operational Environmental Satellite ◽  
Rapid Scan ◽  
Operational Forecasts

Abstract The impacts of special Geostationary Operational Environmental Satellite (GOES) rapid-scan (RS) wind observations on numerical model 24–120-h track forecasts of Hurricane Katrina are examined in a series of data assimilation and forecast experiments. The RS wind vectors are derived from geostationary satellites by tracking cloud motions through successive 5-min images. In these experiments, RS wind observations are added over the area 15°–60°N, 60°–110°W, and they supplement the observations used in operational forecasts. The inclusion of RS wind observations reduces errors in numerical forecasts of the Katrina landfall position at 1200 UTC 29 August 2005 by an average of 12% compared to control cases that include “targeted” dropsonde observations in the Katrina environment. The largest average improvements are made to the 84- to 120-h Katrina track forecasts, rather than to the short-range track forecasts. These results suggest that RS wind observations can potentially be used in future cases to improve track forecasts of tropical cyclones.

scholarly journals Evaluation of Operational Monsoon Moisture Surveillance and Severe Weather Prediction Utilizing COSMIC-2/FORMOSAT-7 Radio Occultation Observations

2021 ◽  
Vol 13 (15) ◽  
pp. 2979
Author(s):  
Yu-Chun Chen ◽  
Chih-Chien Tsai ◽  
Yi-Chao Wu ◽  
An-Hsiang Wang ◽  
Chieh-Ju Wang ◽  
...  
Keyword(s):  
Numerical Model ◽  
Data Assimilation ◽  
Water Resource Management ◽  
Radio Occultation ◽  
Positive Impact ◽  
Weather Prediction ◽  
Severe Weather ◽  
Model Experiments ◽  
Typhoon Structure ◽  
Numerical Model Experiments

Operational monsoon moisture surveillance and severe weather prediction is essential for timely water resource management and disaster risk reduction. For these purposes, this study suggests a moisture indicator using the COSMIC-2/FORMOSAT-7 radio occultation (RO) observations and evaluates numerical model experiments with RO data assimilation. The RO data quality is validated by a comparison between sampled RO profiles and nearby radiosonde profiles around Taiwan prior to the experiments. The suggested moisture indicator accurately monitors daily moisture variations in the South China Sea and the Bay of Bengal throughout the 2020 monsoon rainy season. For the numerical model experiments, the statistics of 152 moisture and rainfall forecasts for the 2020 Meiyu season in Taiwan show a neutral to slightly positive impact brought by RO data assimilation. A forecast sample with the most significant improvement reveals that both thermodynamic and dynamic fields are appropriately adjusted by model integration posterior to data assimilation. The statistics of 17 track forecasts for typhoon Hagupit (2020) also show the positive effect of RO data assimilation. A forecast sample reveals that the member with RO data assimilation simulates better typhoon structure and intensity than the member without, and the effect can be larger and faster via multi-cycle RO data assimilation.

Data assimilation with the ensemble Kalman filter in a numerical model of the North Sea

2016 ◽  
Vol 66 (8) ◽  
pp. 955-971 ◽  
Author(s):  
Stéphanie Ponsar ◽  
Patrick Luyten ◽  
Valérie Dulière
Keyword(s):  
Kalman Filter ◽  
Numerical Model ◽  
Data Assimilation ◽  
North Sea ◽  
Ensemble Kalman Filter ◽  
The North ◽  
The North Sea

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 Interpretation of Adaptive Observing Guidance for Atlantic Tropical Cyclones

2007 ◽  
Vol 135 (12) ◽  
pp. 4006-4029 ◽  
Author(s):  
C. A. Reynolds ◽  
M. S. Peng ◽  
S. J. Majumdar ◽  
S. D. Aberson ◽  
C. H. Bishop ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Total Energy ◽  
Tropical Cyclones ◽  
Error Variance ◽  
Naval Research ◽  
Error Statistics ◽  
Background Error ◽  
Analysis Error ◽  
The Impact ◽  
Perturbation Growth

Abstract Adaptive observing guidance products for Atlantic tropical cyclones are compared using composite techniques that allow one to quantitatively examine differences in the spatial structures of the guidance maps and relate these differences to the constraints and approximations of the respective techniques. The guidance maps are produced using the ensemble transform Kalman filter (ETKF) based on ensembles from the National Centers for Environmental Prediction and the European Centre for Medium-Range Weather Forecasts (ECMWF), and total-energy singular vectors (TESVs) produced by ECMWF and the Naval Research Laboratory. Systematic structural differences in the guidance products are linked to the fact that TESVs consider the dynamics of perturbation growth only, while the ETKF combines information on perturbation evolution with error statistics from an ensemble-based data assimilation scheme. The impact of constraining the SVs using different estimates of analysis error variance instead of a total-energy norm, in effect bringing the two methods closer together, is also assessed. When the targets are close to the storm, the TESV products are a maximum in an annulus around the storm, whereas the ETKF products are a maximum at the storm location itself. When the targets are remote from the storm, the TESVs almost always indicate targets northwest of the storm, whereas the ETKF targets are more scattered relative to the storm location and often occur over the northern North Atlantic. The ETKF guidance often coincides with locations in which the ensemble-based analysis error variance is large. As the TESV method is not designed to consider spatial differences in the likely analysis errors, it will produce targets over well-observed regions, such as the continental United States. Constraining the SV calculation using analysis error variance values from an operational 3D variational data assimilation system (with stationary, quasi-isotropic background error statistics) results in a modest modulation of the target areas away from the well-observed regions, and a modest reduction of perturbation growth. Constraining the SVs using the ETKF estimate of analysis error variance produces SV targets similar to ETKF targets and results in a significant reduction in perturbation growth, due to the highly localized nature of the analysis error variance estimates. These results illustrate the strong sensitivity of SVs to the norm (and to the analysis error variance estimate used to define it) and confirm that discrepancies between target areas computed using different methods reflect the mathematical and physical differences between the methods themselves.

scholarly journals Data assimilation of dust aerosol observations for CUACE/Dust forecasting system

2007 ◽  
Vol 7 (3) ◽  
pp. 8309-8332 ◽  
Author(s):  
T. Niu ◽  
S. L. Gong ◽  
G. F. Zhu ◽  
H. L. Liu ◽  
X. Q. Hu ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Atmospheric Chemistry ◽  
Three Dimensional ◽  
System A ◽  
Source Regions ◽  
Operational Forecasts ◽  
Forecasting System ◽  
Dust Loading ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. A data assimilation system (DAS) was developed for the Chinese Unified Atmospheric Chemistry Environment – Dust (CUACE/Dust) forecast system and applied in the operational forecasts of sand and dust storm (SDS) in spring 2006. The system is based on a three dimensional variational method (3D-Var) and uses extensively the measurements of surface visibility and dust loading retrieval from the Chinese geostationary satellite FY-2C. The results show that a major improvement to the capability of CUACE/Dust in forecasting the short-term variability in the spatial distribution and intensity of dust concentrations has been achieved, especially in those areas far from the source regions. The seasonal mean Threat Score (TS) over the East Asia in spring 2006 increased from 0.22 to 0.31 by using the data assimilation system, a 41% enhancement. The assimilation results usually agree with the dust loading retrieved from FY-2C and visibility distribution from surface meteorological stations, which indicates that the 3D-Var method is very powerful for the unification of observation and numerical modeling results.

The risk of large-scale trade bottlenecks due to simultaneous port disruptions

2021 ◽  
Author(s):  
Jasper Verschuur ◽  
Elco Koks ◽  
Jim Hall
Keyword(s):  
Supply Chain ◽  
Natural Disasters ◽  
Hurricane Katrina ◽  
Tropical Cyclones ◽  
Large Scale ◽  
Global Analysis ◽  
Stress Test ◽  
National Level ◽  
Trade Network ◽  
Port Infrastructure

<p>Reliable port infrastructure is essential for the facilitation of international trade flows. Disruptions to port infrastructure can result in trade bottlenecks, in particular if multiple key ports are affected simultaneously due to natural disasters with large spatial footprints such as earthquakes and tropical cyclones (Verschuur et al. 2019). For instance, Hurricane Katrina (2005) disrupted port operations in multiple ports in New Orleans, which transport around 45% of the country’s food and farm products, resulting in more than USD800 million export losses and price spikes of food products (Trepte and Rice, 2014). In order to improve the resilience of the transport and supply-chain network, the risk of large-scale trade bottlenecks need to be quantified on global scale. However, to date, the risk of single and multiple port failures due to large-scale natural disasters, and the resulting consequences, has not yet been explored.</p><p> </p><p>Here, we present a global analysis of the risk of simultaneous port disruptions due to tropical cyclones and the associated risk of bottlenecks in the national and global maritime trade network. To do this, we have combined a new global dataset on the port-to-port trade network with 10,000 years of synthetic tropical cyclone tracks (Bloemendaal et al., 2020) and an impact-module that estimates the duration of the port disruption as a function of cyclone wind speed. We show how certain countries and specific economic sectors within countries are at risk of large-scale trade bottlenecks, mainly due to the concentration of trade in a few key ports that are geographically clustered.</p><p> </p><p>These results can be used to stress test the global maritime transport network and inform strategies to improve supply-chain resilience (e.g. diversification of transport and import). Moreover, it can support port planning on a national level to make strategic investments to reduce the risk of trade bottlenecks or to design post-disaster emergency response strategies (e.g. rerouting strategies to alternative ports).</p>

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