scholarly journals Toward the Probabilistic Simulation of Storm Surge and Inundation in a Limited-Resource Environment

2010 ◽  
Vol 138 (7) ◽  
pp. 2953-2974 ◽  
Author(s):  
Justin R. Davis ◽  
Vladimir A. Paramygin ◽  
David Forrest ◽  
Y. Peter Sheng
Keyword(s):  
Storm Surge ◽  
Forecast Error ◽  
Limited Resource ◽  
Forecast Errors ◽  
Research Association ◽  
Probabilistic Simulation ◽  
Hurricane Charley ◽  
Storm Surge Modeling ◽  
National Hurricane Center ◽  
Resource Environment

Abstract To create more useful storm surge and inundation forecast products, probabilistic elements are being incorporated. To achieve the highest levels of confidence in these products, it is essential that as many simulations as possible are performed during the limited amount of time available. This paper develops a framework by which probabilistic storm surge and inundation forecasts within the Curvilinear Hydrodynamics in 3D (CH3D) Storm Surge Modeling System and the Southeastern Universities Research Association Coastal Ocean Observing and Prediction Program’s forecasting systems are initiated with specific focus on the application of these methods in a limited-resource environment. Ensemble sets are created by dividing probability density functions (PDFs) of the National Hurricane Center model forecast error into bins, which are then grouped into priority levels (PLs) such that each subsequent level relies on results computed earlier and has an increasing confidence associated with it. The PDFs are then used to develop an ensemble of analytic wind and pressure fields for use by storm surge and inundation models. Using this approach applied with official National Hurricane Center (OFCL) forecast errors, an analysis of Hurricane Charley is performed. After first validating the simulation of storm surge, a series of ensemble simulations are performed representing the forecast errors for the 72-, 48-, 24-, and 12-h forecasts. Analysis of the aggregated products shows that PL4 (27 members) is sufficient to resolve 90% of the inundation within the domain and appears to represent the best balance between accuracy and timeliness of computed products for this case study. A 5-day forecast using the PL4 set is shown to complete in 83 min, while the intermediate PL2 and PL3 products, representing slightly less confidence, complete in 14 and 28 min, respectively.

scholarly journals Reducing Operational Hurricane Intensity Forecast Errors during Eyewall Replacement Cycles

2016 ◽  
Vol 31 (2) ◽  
pp. 601-608 ◽  
Author(s):  
James P. Kossin ◽  
Mark DeMaria
Keyword(s):  
Forecast Error ◽  
Intensity Change ◽  
Forecast Errors ◽  
Hurricane Intensity ◽  
Intensity Forecast ◽  
Prediction Scheme ◽  
Hurricane Prediction ◽  
Eyewall Replacement Cycles ◽  
National Hurricane Center ◽  
Intensity Evolution

Abstract Eyewall replacement cycles (ERCs) are fairly common events in tropical cyclones (TCs) of hurricane intensity or greater and typically cause large and sometimes rapid changes in the intensity evolution of the TC. Although the details of the intensity evolution associated with ERCs appear to have some dependence on the ambient environmental conditions that the TCs move through, these dependencies can also be quite different than those of TCs that are not undergoing an ERC. For example, the Statistical Hurricane Prediction Scheme (SHIPS), which is used in National Hurricane Center operations and provides intensity forecast skill that is, on average, equal to or greater than deterministic numerical model skill, typically identifies an environment that is not indicative of weakening during the onset and subsequent evolution of an ERC. Contrarily, a period of substantial weakening does typically begin near the onset of an ERC, and this disparity can cause large SHIPS intensity forecast errors. Here, a simple model based on a climatology of ERC intensity change is introduced and tested against SHIPS. It is found that the application of the model can reduce intensity forecast error substantially when applied at, or shortly after, the onset of ERC weakening.

scholarly journals Recent Progress in Tropical Cyclone Intensity Forecasting at the National Hurricane Center

2020 ◽  
Vol 35 (5) ◽  
pp. 1913-1922 ◽  
Author(s):  
John P. Cangialosi ◽  
Eric Blake ◽  
Mark DeMaria ◽  
Andrew Penny ◽  
Andrew Latto ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
Forecast Error ◽  
Forecast Errors ◽  
Atlantic Basin ◽  
Cyclone Intensity ◽  
The Past ◽  
Intensity Forecast ◽  
Past Half Century ◽  
National Hurricane Center ◽  
Past Half

AbstractIt has been well documented that the National Hurricane Center (NHC) has made significant improvements in Atlantic basin tropical cyclone (TC) track forecasting during the past half century. In contrast, NHC’s TC intensity forecast errors changed little from the 1970s to the early 2000s. Recently, however, there has been a notable decrease in TC intensity forecast error and an increase in intensity forecast skill. This study documents these trends and discusses the advancements in TC intensity guidance that have led to the improvements in NHC’s intensity forecasts in the Atlantic basin. We conclude with a brief projection of future capabilities.

scholarly journals A Deterministic Rapid Intensification Aid

2011 ◽  
Vol 26 (4) ◽  
pp. 579-585 ◽  
Author(s):  
Charles R. Sampson ◽  
John Kaplan ◽  
John A. Knaff ◽  
Mark DeMaria ◽  
Chris A. Sisko
Keyword(s):  
Real Time ◽  
Arithmetic Mean ◽  
Forecast Errors ◽  
Rapid Intensification ◽  
Intensity Forecast ◽  
Probability Threshold ◽  
Forecast Time ◽  
National Hurricane Center ◽  
Made In

Abstract Rapid intensification (RI) is difficult to forecast, but some progress has been made in developing probabilistic guidance for predicting these events. One such method is the RI index. The RI index is a probabilistic text product available to National Hurricane Center (NHC) forecasters in real time. The RI index gives the probabilities of three intensification rates [25, 30, and 35 kt (24 h)−1; or 12.9, 15.4, and 18.0 m s−1 (24 h)−1] for the 24-h period commencing at the initial forecast time. In this study the authors attempt to develop a deterministic intensity forecast aid from the RI index and, then, implement it as part of a consensus intensity forecast (arithmetic mean of several deterministic intensity forecasts used in operations) that has been shown to generally have lower mean forecast errors than any of its members. The RI aid is constructed using the highest available RI index intensification rate available for probabilities at or above a given probability (i.e., a probability threshold). Results indicate that the higher the probability threshold is, the better the RI aid performs. The RI aid appears to outperform the consensus aids at about the 50% probability threshold. The RI aid also improves forecast errors of operational consensus aids starting with a probability threshold of 30% and reduces negative biases in the forecasts. The authors suggest a 40% threshold for producing the RI aid initially. The 40% threshold is available for approximately 8% of all verifying forecasts, produces approximately 4% reduction in mean forecast errors for the intensity consensus aids, and corrects the negative biases by approximately 15%–20%. In operations, the threshold could be moved up to maximize gains in skill (reducing availability) or moved down to maximize availability (reducing gains in skill).

Hurricane Storm Surge Modeling for the Florida Southeast Coastline

2005 ◽  
Author(s):  
M. S. Gosselin ◽  
D. M. Sheppard ◽  
T. L. Glasser
Keyword(s):  
Storm Surge ◽  
Storm Surge Modeling ◽  
Hurricane Storm Surge

scholarly journals Southern High-Latitude Ensemble Data Assimilation in the Antarctic Mesoscale Prediction System

2005 ◽  
Vol 133 (12) ◽  
pp. 3431-3449 ◽  
Author(s):  
D. M. Barker
Keyword(s):  
Data Assimilation ◽  
Sampling Error ◽  
Forecast Error ◽  
Weather Prediction ◽  
Forecast Errors ◽  
Prediction System ◽  
Spatial Covariance ◽  
Ensemble Data Assimilation ◽  
Ensemble Data ◽  
The Antarctic

Abstract Ensemble data assimilation systems incorporate observations into numerical models via solution of the Kalman filter update equations, and estimates of forecast error covariances derived from ensembles of model integrations. In this paper, a particular algorithm, the ensemble square root filter (EnSRF), is tested in a limited-area, polar numerical weather prediction (NWP) model: the Antarctic Mesoscale Prediction System (AMPS). For application in the real-time AMPS, the number of model integrations that can be run to provide forecast error covariances is limited, resulting in an ensemble sampling error that degrades the analysis fit to observations. In this work, multivariate, climatologically plausible forecast error covariances are specified via averaged forecast difference statistics. Ensemble representations of the “true” forecast errors, created using randomized control variables of the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) three-dimensional variational (3DVAR) data assimilation system, are then used to assess the dependence of sampling error on ensemble size, data density, and localization of covariances using simulated observation networks. Results highlight the detrimental impact of ensemble sampling error on the analysis increment structure of correlated, but unobserved fields—an issue not addressed by the spatial covariance localization techniques used to date. A 12-hourly cycling EnSRF/AMPS assimilation/forecast system is tested for a two-week period in December 2002 using real, conventional (surface, rawinsonde, satellite retrieval) observations. The dependence of forecast scores on methods used to maintain ensemble spread and the inclusion of perturbations to lateral boundary conditions are studied.

Effect of Assimilating Himawari-8 Atmospheric Motion Vectors on Forecast Errors over East Asia

2018 ◽  
Vol 35 (9) ◽  
pp. 1737-1752 ◽  
Author(s):  
Dae-Hui Kim ◽  
Hyun Mee Kim
Keyword(s):  
East Asia ◽  
Geopotential Height ◽  
Forecast Error ◽  
Experimental Period ◽  
Energy Norm ◽  
Observation System ◽  
Forecast Errors ◽  
Motion Vectors ◽  
Atmospheric Motion ◽  
Atmospheric Motion Vectors

AbstractIn this study, the effect of assimilating Himawari-8 (HIMA-8) atmospheric motion vectors (AMVs) on forecast errors in East Asia is evaluated using observation system experiments based on the Weather Research and Forecasting Model and three-dimensional variational data assimilation system. The experimental period is from 1 August to 30 September 2015, during which both HIMA-8 and Multifunctional Transport Satellite-2 (MTSAT-2) AMVs exist. The energy-norm forecast error based on the analysis of each experiment as reference was reduced more by replacing MTSAT-2 AMVs with HIMA-8 AMVs than by adding HIMA-8 AMVs to the MTSAT-2 AMVs. When the HIMA-8 AMVs replaced or were added to MTSAT-2 AMVs, the observation impact was reduced, which implies the analysis–forecast system was improved by assimilating HIMA-8 AMVs. The root-mean-square error (RMSE) of the 500-hPa geopotential height forecasts based on the analysis of each experiment decreases more effectively when the region lacking in upper-air wind observations is reduced by assimilating both MTSAT-2 and HIMA-8 AMVs. When the upper-air radiosonde (SOUND) observations are used as reference, assimilating more HIMA-8 AMVs decreases the forecast error. Based on various measures, the assimilation of HIMA-8 AMVs has a positive effect on the reduction of forecast errors. The effects on the energy-norm forecast error and the RMSE based on SOUND observations are greater when HIMA-8 AMVs replaced MTSAT-2 AMVs. However, the effects on the RMSE of the 500-hPa geopotential height forecasts are greater when both HIMA-8 and MTSAT-2 AMVs were assimilated, which implies potential benefits of assimilating AMVs from several satellites for forecasts over East Asia depending on the choice of measurement.

scholarly journals Identifying mesoscale high-wind features within extratropical cyclones

2021 ◽  
Author(s):  
Lea Eisenstein ◽  
Peter Knippertz ◽  
Joaquim G. Pinto
Keyword(s):  
Forecast Error ◽  
Grid Point ◽  
Potential Temperature ◽  
Conveyor Belt ◽  
Extratropical Cyclones ◽  
Forecast Errors ◽  
Wind Gust ◽  
High Wind ◽  
Exclusion Criteria ◽  
Surface Observations

<p>Extratropical cyclones cause strong winds and heavy precipitation events and are therefore one of the most dangerous natural hazards in Europe. The strongest winds within these cyclones are mostly connected to four mesoscale dynamical features: the warm (conveyor belt) jet (WJ), the cold (conveyor belt) jet (CJ), cold-frontal convective features (CFC) and the sting jet (SJ). While all four have high wind gust speeds in common, the timing, location and some further characteristics typically differ and hence likely also the forecast errors occurring in association with them.</p><p>Here we present an objective identification approach for the four features named above based on their most important characteristics in wind, rainfall, pressure and temperature evolution. The main motivations for this are to generate a climatology for Central Europe, to analyse forecast error specific to individual features, and to ultimately improve forecasts of high wind events through feature-dependent statistical post-processing. To achieve, we ideally want to be able to identify the features in surface observations and in forecasts in a consistent way.</p><p>Based on a dataset of hourly observations over Europe and nine windstorm cases during the winter seasons 2017/18, 2018/19 and 2019/20, it became apparent that mean sea-level pressure tendency, potential temperature tendency, change in wind direction and precipitation (all one-hourly) are most important for the distinction between the WJ and CFC. Further adding the time (relative to storm evolution) and location (relative to the storm centre) of occurrence helps to identify the CJ. Ultimately, the identification of each feature is based on a score on a scale from 0 to 10 that reflects the various criteria for a station or grid point. Additionally, exclusion criteria for each feature are defined to rule out locations that meet some criteria (and thus have a positive score) but strongly violate others. Finally, smooth contours are drawn around each feature to define their spatial extent.</p><p>While the distinction between WJ and CFC seems to work reliably, the identification of CJ remains ambiguous and needs further parameters and exclusion criteria to avoid too large areas and overlap with other features. Furthermore, SJ and CJ are very difficult to distinguish based on surface observations alone and are therefore taken together for this preliminary analysis. Once the definition of criteria is finalised, a climatology will be compiled based on observations and the German COSMO model and forecast errors analysed for said model.</p>

scholarly journals Use of remote-sensing reflectance to constrain a data assimilating marine biogeochemical model of the Great Barrier Reef

2016 ◽  
Author(s):  
Emlyn M. Jones ◽  
Mark E. Baird ◽  
Mathieu Mongin ◽  
John Parslow ◽  
Jenny Skerratt ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Great Barrier Reef ◽  
Forecast Error ◽  
Weather Prediction ◽  
Biogeochemical Model ◽  
Forecast Errors ◽  
Barrier Reef ◽  
Chl A ◽  
Remote Sensing Reflectance ◽  
Assimilation System

Abstract. Skilful marine biogeochemical (BGC) models are required to understand a range of coastal and global phenomena such as changes in nitrogen and carbon cycles. The refinement of BGC models through the assimilation of variables calculated from observed in-water inherent optical properties (IOPs), such as phytoplankton absorption, is problematic. Empirically-derived relationships between IOPs and variables such as Chlorophyll-a concentration (Chl-a), Total Suspended Solids (TSS) and Color Dissolved Organic Matter (CDOM) have been shown to have errors that can exceed 100 % of the observed quantity. These errors are greatest in shallow coastal regions, such as the Great Barrier Reef (GBR), due the additional signal from bottom reflectance. Rather than assimilate quantities calculated using error-prone IOP algorithms, this study demonstrates the advantages of assimilating quantities calculated directly from the less error-prone satellite remote-sensing reflectance. The assimilation of a directly-observed quantity, in this case remote-sensing reflectance, is analogous to the assimilation of temperature brightness in Numerical Weather Prediction (NWP), or along-track sea-surface height in hydrodynamic models. To assimilate the observed reflectance, we use an in-water optical model to produce an equivalent simulated remote-sensing reflectance, and calculate the mis-match between the observed and simulated quantities to constrain the BGC model with a Deterministic Ensemble Kalman Filter (DEnKF). Using the assumption that simulated surface Chl-a is equivalent to remotely-sensed OC3M estimate of Chl-a resulted in a forecast error of approximately 75 %. Alternatively, assimilation of remote-sensing reflectance resulted in a forecast error of less than 40 %. Thus, in the coastal waters of the GBR, assimilating remote-sensing reflectance halved the forecast errors. When the analysis and forecast fields from the assimilation system are compared with the non-assimilating model, an independent comparison to in-situ observations of Chl-a, TSS, and dissolved inorganic nutrients (NO3, NH4 and DIP) show that errors are reduced by up to 90 %. In all cases, the assimilation system improves the result compared to the non-assimilating model. This approach allows for the incorporation of vast quantities of remote-sensing observations that have in the past been discarded due to shallow water and/or artefacts introduced by terrestrially-derived TSS and CDOM, or the lack of a calibrated regional IOP algorithm.

scholarly journals ROAD TEMPERATURE MODELLING WITHOUT IN-SITU SENSORS

2017 ◽  
Vol 12 (4) ◽  
pp. 241-247 ◽  
Author(s):  
Karol Opara ◽  
Jan Zieliński
Keyword(s):  
Energy Balance ◽  
Urban Areas ◽  
Initial Conditions ◽  
Forecast Error ◽  
Weather Data ◽  
Forecast Errors ◽  
The Road ◽  
In Situ Sensors ◽  
Pavement Temperature

Modelling of the pavement temperature facilitates winter road maintenance. It is used for predicting the glaze formation and for scheduling the spraying of the de-icing brine. The road weather is commonly forecasted by solving the energy balance equations. It requires setting the initial vertical profile of the pavement temperature, which is often obtained from the Road Weather Information Stations. The paper proposes the use of average air temperature from seven preceding days as a pseudo-observation of the subsurface temperature. Next, the road weather model is run with a few days offset. It first uses the recent, historical weather data and then the available forecasts. This approach exploits the fact that the energy balance models tend to “forget” their initial conditions and converge to the baseline solution. The experimental verification was conducted using the Model of the Environment and Temperature of Roads and the data from a road weather station in Warsaw over a period of two years. The additional forecast error introduced by the proposed pseudo-observational initialization averages 1.2 °C in the first prediction hour and then decreases in time. The paper also discusses the use of Digital Surface Models to take into account the shading effects, which are an essential source of forecast errors in urban areas. Limiting the use of in-situ sensors opens a perspective for an economical, largescale implementation of road meteorological models.

scholarly journals Storm Surge Modeling in Puget Sound

2019 ◽  
Author(s):  
Zhaoqing Yang ◽  
Taiping Wang ◽  
Luca Castrucci
Keyword(s):  
Storm Surge ◽  
Puget Sound ◽  
Storm Surge Modeling
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