Hurricane Track Forecasts Research Articles

An Objective Quality Control of Surface Contamination Observations for ABI Water Vapor Radiance Assimilation

AbstractA quality control (QC) process which handles surface impacts is an important step toward successful assimilation of the Advanced Baseline Imager (ABI) water vapor (WV) band radiances. If the QC is too relaxed, many surface contaminated radiances get assimilated. If the QC is too stringent, useful radiances are rejected. Either way can result in reduced or even compromised observation impacts. A new machine learning‐based QC scheme for the three ABI WV bands is developed and optimized to help understand the importance and effectiveness of the scheme. Unlike previous schemes which are dependent on the background, this scheme extracts and blends the surface information from 7 ABI bands (bands 8–10, 13–16) to determine if a WV radiance is affected by the surface. Simulation studies show that the new QC scheme is effective in retaining radiances that are either unaffected by the surface or have very small surface contamination. It is highly effective in rejecting radiances with large surface contamination. Numerical experiments from a single case study of Hurricane Harvey (2017) were carried out to optimize the QC and to understand the potential impacts on forecasts. The use of the new QC scheme shows that radiances from each WV band have substantially added value. Combining them has a positive impact on hurricane track forecasts compared with existing QC schemes. Hence, it is critical that an optimized QC scheme is used for infrared WV radiance assimilation. It provides a balance between positive impacts from useful radiances and negative impacts from surface contaminated radiances.

Combined Assimilation of Doppler Wind Lidar and Tail Doppler Radar Data over a Hurricane Inner Core for Improved Hurricane Prediction with the NCEP Regional HWRF System

Accurate specification of hurricane inner-core structure is critical to predicting the evolution of a hurricane. However, observations over hurricane inner cores are generally lacking. Previous studies have emphasized Tail Doppler radar (TDR) data assimilation to improve hurricane inner-core representation. Recently, Doppler wind lidar (DWL) has been used as an observing system to sample hurricane inner-core and environmental conditions. The NOAA P3 Hurricane Hunter aircraft has DWL installed and can obtain wind data over a hurricane’s inner core when the aircraft passes through the hurricane. In this study, we examine the impact of assimilating DWL winds and TDR radial winds on the prediction of Hurricane Earl (2016) with the NCEP operational Hurricane Weather Research and Forecasting (HWRF) system. A series of data assimilation experiments are conducted with the Gridpoint Statistical Interpolation (GSI)-based ensemble-3DVAR hybrid system to identify the best way to assimilate TDR and DWL data into the HWRF forecast system. The results show a positive impact of DWL data on hurricane analysis and prediction. Compared with the assimilation of u and v components, assimilation of DWL wind speed provides better hurricane track and intensity forecasts. Proper choices of data thinning distances (e.g., 5 km horizontal thinning and 70 hPa vertical thinning for DWL) can help achieve better analysis in terms of hurricane vortex representation and forecasts. In the analysis and forecast cycles, the combined TDR and DWL assimilation (DWL wind speed and TDR radial wind, along with other conventional data, e.g., NCEP Automated Data Processing (ADP) data) offsets the downgrade analysis from the absence of DWL observations in an analysis cycle and outperforms assimilation of a single type of data (either TDR or DWL) and leads to improved forecasts of hurricane track, intensity, and structure. Overall, assimilation of DWL observations has been beneficial for analysis and forecasts in most cases. The outcomes from this study demonstrate the great potential of including DWL wind profiles in the operational HWRF system for hurricane forecast improvement.

Impacts of Observation Errors on Hurricane Forecasts When Assimilating Hyperspectral Infrared Sounder Radiances in Partially Cloudy Skies

AbstractHyperspectral infrared (IR) sounders provide high vertical resolution atmospheric sounding information that can improve the forecast accuracy of numerical weather prediction (NWP) models. Due to the challenges of assimilating cloudy radiances, NWP centers usually assimilate only radiances that are not affected by clouds. An imager based cloud‐clearing technique provides an alternative and effective way to remove the cloud effects from a partially cloudy field‐of‐view and derive the equivalent clear sky radiances or the cloud‐cleared radiances (CCRs) for assimilation in NWP. Since the observation error is amplified in the cloud‐clearing, or noise amplification process, it is necessary to inflate the observation errors appropriately in order to achieve the optimal value‐added impact from assimilating CCRs. The estimation of observation error inflation is established and discussed. Hurricane Harvey (2017) and Hurricane Maria (2017) are used to simulate and understand the impacts of observation error inflation on the assimilation of Cross‐track Infrared Sounder CCRs for hurricane forecast improvement. Both the precipitation location and intensity forecasts are improved when assimilating CCRs with an inflated observation error for Hurricane Harvey (2017). Assimilating CCRs with an inflated observation error adjusts the temperature and geopotential height fields and further affects the hurricane structures to improve the hurricane track forecasts, thereby demonstrating the importance of using hyperspectral IR measurements in partially cloudy skies for simulating the hurricane structure and improving its forecast. This method can be applied to other imager/sounder combined observations for improving sounder radiance assimilation in cloudy skies and has potential for operational applications.

The Impact of Cross‐track Infrared Sounder (CrIS) Cloud‐Cleared Radiances on Hurricane Joaquin (2015) and Matthew (2016) Forecasts

AbstractHyperspectral infrared (IR) sounders provide high vertical resolution atmospheric sounding information that can improve the forecast skill in numerical weather prediction. Commonly, only clear radiances are assimilated, because IR sounder observations are highly affected by clouds. A cloud‐clearing (CC) technique, which removes the cloud effects from an IR cloudy field of view (FOV) and derives the cloud‐cleared radiances (CCRs) or clear‐sky equivalent radiances, can be an alternative yet effective way to take advantage of the thermodynamic information from cloudy skies in data assimilation. This study develops a Visible Infrared Imaging Radiometer Suite (VIIRS)‐based CC method for deriving Cross‐track Infrared Sounder (CrIS) CCRs under partially cloudy conditions. Due to the lack of absorption bands on VIIRS, two important quality control steps are implemented in the CC process. Validation using VIIRS clear radiances indicates that the CC method can effectively obtain the CrIS CCRs for FOVs with partial cloud cover. To compare the impacts from assimilation of CrIS original radiances and CCRs, three experiments are carried out on two storm cases, Hurricane Joaquin (2015) and Hurricane Matthew (2016), using Gridpoint Statistical Interpolation assimilation system and Weather Research and Forecasting‐Advanced Research Version models. At the analysis time, more CrIS observations are assimilated when using CrIS CCRs than with CrIS original radiances. Comparing temperature, specific humidity, and U/V winds with radiosondes indicates that the data impacts are growing larger with longer time forecasts (beyond 72 h forecast). Hurricane track forecasts also show improvements from the assimilation of CrIS CCRs due to better weather system forecasts. The impacts of CCRs on intensity are basically neutral with mixed positive and negative results.

The Practical Predictability of Storm Tide from Tropical Cyclones in the Gulf of Mexico

The potential for storm surge to cause extensive property damage and loss of life has increased urgency to more accurately predict coastal flooding associated with landfalling tropical cyclones. This work investigates the sensitivity of coastal inundation from storm tide (surge + tide) to four hurricane parameters—track, intensity, size, and translation speed—and the sensitivity of inundation forecasts to errors in forecasts of those parameters. An ensemble of storm tide simulations is generated for three storms in the Gulf of Mexico, by driving a storm surge model with best track data and systematically generated perturbations of storm parameters from the best track. The spread of the storm perturbations is compared to average errors in recent operational hurricane forecasts, allowing sensitivity results to be interpreted in terms of practical predictability of coastal inundation at different lead times. Two types of inundation metrics are evaluated: point-based statistics and spatially integrated volumes. The practical predictability of surge inundation is found to be limited foremost by current errors in hurricane track forecasts, followed by intensity errors, then speed errors. Errors in storm size can also play an important role in limiting surge predictability at short lead times, due to observational uncertainty. Results show that given current mean errors in hurricane forecasts, location-specific surge inundation is predictable for as little as 12–24 h prior to landfall, less for small-sized storms. The results also indicate potential for increased surge predictability beyond 24 h for large storms by considering a storm-following, volume-integrated metric of inundation.

Effects of ensemble and summary displays on interpretations of geospatial uncertainty data

Ensemble and summary displays are two widely used methods to represent visual-spatial uncertainty; however, there is disagreement about which is the most effective technique to communicate uncertainty to the general public. Visualization scientists create ensemble displays by plotting multiple data points on the same Cartesian coordinate plane. Despite their use in scientific practice, it is more common in public presentations to use visualizations of summary displays, which scientists create by plotting statistical parameters of the ensemble members. While prior work has demonstrated that viewers make different decisions when viewing summary and ensemble displays, it is unclear what components of the displays lead to diverging judgments. This study aims to compare the salience of visual features – or visual elements that attract bottom-up attention – as one possible source of diverging judgments made with ensemble and summary displays in the context of hurricane track forecasts. We report that salient visual features of both ensemble and summary displays influence participant judgment. Specifically, we find that salient features of summary displays of geospatial uncertainty can be misunderstood as displaying size information. Further, salient features of ensemble displays evoke judgments that are indicative of accurate interpretations of the underlying probability distribution of the ensemble data. However, when participants use ensemble displays to make point-based judgments, they may overweight individual ensemble members in their decision-making process. We propose that ensemble displays are a promising alternative to summary displays in a geospatial context but that decisions about visualization methods should be informed by the viewer’s task.

Optimistic bias and the consistency of hurricane track forecasts

Forecast graphics depicting a hurricane track and uncertainty cone have become pervasive in the communication of hurricane risk. This study examined whether the effect of hurricane tracks on risk perception is influenced by the consistency and optimistic bias. Specifically, it focused on the differences between forecasts that remain consistent compared to those that veer away for a forecast period. To answer the research question, this study conducted online surveys in which respondents from two coastal universities were asked risk perception questions based on a series of forecast graphics. Other variables measured included dispositional optimism, general hurricane risk perception, and hurricane information use. Optimistic bias was calculated from two of the risk perception questions. Results did not indicate strong support for an influence of optimistic bias or changing forecast track on risk perception. There was limited evidence that a veering track scenario may lead to differences in risk judgments about another location, but most measures of personal risk estimation were not influenced by the track. Dispositional optimism was not related to optimistic bias or many of the risk perception variables tested, including general hurricane risk perception. There did appear to be an interaction between track scenario and optimistic bias with more relationships being significant among those who received the consistent track scenario.

Impact of CYGNSS Ocean Surface Wind Speeds on Numerical Simulations of a Hurricane in Observing System Simulation Experiments

AbstractThe NASA Cyclone Global Navigation Satellite System (CYGNSS) was launched in late 2016. It will make available frequent ocean surface wind speed observations throughout the life cycle of tropical storms and hurricanes. In this study, the impact of CYGNSS ocean surface winds on numerical simulations of a hurricane case is assessed with a research version of the Hurricane Weather Research and Forecasting Model and a Gridpoint Statistical Interpolation analysis system in a regional observing system simulation experiment framework. Two different methods for reducing the CYGNSS data volume were tested: one in which the winds were thinned and one in which the winds were superobbed.The results suggest that assimilation of the CYGNSS winds has great potential to improve hurricane track and intensity simulations through improved representations of the surface wind fields, hurricane inner-core structures, and surface fluxes. The assimilation of the superobbed CYGNSS data seems to be more effective in improving hurricane track forecasts than thinning the data.

Increasing vertical resolution in US models to improve track forecasts of Hurricane Joaquin with HWRF as an example

The atmosphere-ocean coupled Hurricane Weather Research and Forecast model (HWRF) developed at the National Centers for Environmental Prediction (NCEP) is used as an example to illustrate the impact of model vertical resolution on track forecasts of tropical cyclones. A number of HWRF forecasting experiments were carried out at different vertical resolutions for Hurricane Joaquin, which occurred from September 27 to October 8, 2015, in the Atlantic Basin. The results show that the track prediction for Hurricane Joaquin is much more accurate with higher vertical resolution. The positive impacts of higher vertical resolution on hurricane track forecasts suggest that National Oceanic and Atmospheric Administration/NCEP should upgrade both HWRF and the Global Forecast System to have more vertical levels.

Observing System Simulation Experiments to Assess the Potential Impact of New Observing Systems on Hurricane Forecasting

AbstractObserving System Simulation Experiments (OSSEs) are an important tool for evaluating the potential impact of new or proposed observing systems, as well as for evaluating trade-offs in observing system design, and in developing and assessing improved methodology for assimilating new observations. Extensive OSSEs have been conducted at the National Aeronautical and Space Administration (NASA) Goddard Space Flight Center (GSFC) and the National Oceanic and Atmospheric Administration (NOAA) Atlantic Oceanographic and Meteorological Laboratory (AOML) over the last three decades. These OSSEs determined correctly the quantitative potential for several proposed satellite observing systems to improve weather analysis and prediction prior to their launch; evaluated trade-offs in orbits, coverage, and accuracy for space-based wind lidars; and were used in the development of the methodology that led to the first beneficial impacts of satellite surface winds on numerical weather prediction. This paper summarizes early applications of global OSSEs to hurricane track forecasting and new experiments using both global and regional models. These latter experiments are aimed at assessing potential impact on hurricane track and intensity prediction over the oceans and at landfall.

Observing System Simulation Experiments (OSSEs) to Evaluate the Potential Impact of an Optical Autocovariance Wind Lidar (OAWL) on Numerical Weather Prediction

AbstractThe potential impact of Doppler wind lidar (DWL) observations from a proposed optical autocovariance wind lidar (OAWL) instrument is quantified in observing system simulation experiments (OSSEs). The OAWL design would provide profiles of useful wind vectors along a ground track to the left of the International Space Station (ISS), which is in a 51.6° inclination low-Earth orbit (LEO). These observations are simulated realistically, accounting for cloud and aerosol distributions inferred from the OSSE nature runs (NRs), and measurement and sampling error sources. The impact of the simulated observations is determined in both global and regional OSSE frameworks. The global OSSE uses the ECMWF T511 NR and the NCEP operational Global Data Assimilation System at T382 resolution. The regional OSSE uses an embedded hurricane NR and the NCEP operational HWRF data assimilation system with outer and inner domains of 9- and 3-km resolution, respectively.The global OSSE results show improved analyses and forecasts of tropical winds and extratropical geopotential heights. The tropical wind RMSEs are significantly reduced in the analyses and in short-term forecasts. The tropical wind improvement decays as the forecasts lengthen. The regional OSSEs are limited but show some improvements in hurricane track and intensity forecasts.

Stenomaps: Shorthand for shapes.

We address some of the challenges in representing spatial data with a novel form of geometric abstraction-the stenomap. The stenomap comprises a series of smoothly curving linear glyphs that each represent both the boundary and the area of a polygon. We present an efficient algorithm to automatically generate these open, C1-continuous splines from a set of input polygons. Feature points of the input polygons are detected using the medial axis to maintain important shape properties. We use dynamic programming to compute a planar non-intersecting spline representing each polygon's base shape. The results are stylised glyphs whose appearance may be parameterised and that offer new possibilities in the 'cartographic design space'. We compare our glyphs with existing forms of geometric schematisation and discuss their relative merits and shortcomings. We describe several use cases including the depiction of uncertain model data in the form of hurricane track forecasting; minimal ink thematic mapping; and the depiction of continuous statistical data.

GSI-Based Four-Dimensional Ensemble–Variational (4DEnsVar) Data Assimilation: Formulation and Single-Resolution Experiments with Real Data for NCEP Global Forecast System

AbstractA four-dimensional (4D) ensemble–variational data assimilation (DA) system (4DEnsVar) was developed, building upon the infrastructure of the gridpoint statistical interpolation (GSI)-based hybrid DA system. 4DEnsVar used ensemble perturbations valid at multiple time periods throughout the DA window to estimate 4D error covariances during the variational minimization, avoiding the tangent linear and adjoint of the forecast model. The formulation of its implementation in GSI was described. The performance of the system was investigated by evaluating the global forecasts and hurricane track forecasts produced by the NCEP Global Forecast System (GFS) during the 5-week summer period assimilating operational conventional and satellite data. The newly developed system was used to address a few questions regarding 4DEnsVar. 4DEnsVar in general improved upon its 3D counterpart, 3DEnsVar. At short lead times, the improvement over the Northern Hemisphere extratropics was similar to that over the Southern Hemisphere extratropics. At longer lead times, 4DEnsVar showed more improvement in the Southern Hemisphere than in the Northern Hemisphere. The 4DEnsVar showed less impact over the tropics. The track forecasts of 16 tropical cyclones initialized by 4DEnsVar were more accurate than 3DEnsVar after 1-day forecast lead times. The analysis generated by 4DEnsVar was more balanced than 3DEnsVar. Case studies showed that increments from 4DEnsVar using more frequent ensemble perturbations approximated the increments from direct, nonlinear model propagation better than using less frequent ensemble perturbations. Consistently, the performance of 4DEnsVar including both the forecast accuracy and the balances of analyses was in general degraded when less frequent ensemble perturbations were used. The tangent linear normal mode constraint had positive impact for global forecast but negative impact for TC track forecasts.

Assimilating Airborne Doppler Radar Observations with an Ensemble Kalman Filter for Convection-Permitting Hurricane Initialization and Prediction: Katrina (2005)

Abstract Through a Weather Research and Forecasting model (WRF)-based ensemble Kalman filter (EnKF) data assimilation system, the impact of assimilating airborne radar observations for the convection-permitting analysis and prediction of Hurricane Katrina (2005) is examined in this study. A forecast initialized from EnKF analyses of airborne radar observations had substantially smaller hurricane track forecast errors than NOAA’s operational forecasts and a control forecast initialized from NCEP analysis data for lead times up to 120 h. Verifications against independent in situ and remotely sensed observations show that EnKF analyses successfully depict the inner-core structure of the hurricane vortex in terms of both dynamic (wind) and thermodynamic (temperature and moisture) fields. In addition to the improved analyses and deterministic forecast, an ensemble of forecasts initiated from the EnKF analyses also provided forecast uncertainty estimates for the hurricane track and intensity. Also documented here are the details of a series of data thinning and quality control procedures that were developed to generate superobservations from large volumes of airborne radial velocity measurements. These procedures have since been implemented operationally on the NOAA hurricane reconnaissance aircraft that allows for more efficient real-time transmission of airborne radar observations to the ground.

Application of the WRF Hybrid ETKF–3DVAR Data Assimilation System for Hurricane Track Forecasts

Abstract A hybrid ensemble transform Kalman filter (ETKF)–three-dimensional variational data assimilation (3DVAR) system developed for the Weather Research and Forecasting Model (WRF) was studied for the forecasts of the tracks of two major hurricanes, Ike and Gustav, in 2008 over the Gulf of Mexico. The impacts of the flow-dependent ensemble covariance generated by the ETKF were revealed by comparing the forecasts, analyses, and analysis increments generated by the hybrid data assimilation method with those generated by the 3DVAR that used the static background covariance. The root-mean-square errors of the track forecasts by the hybrid data assimilation (DA) method were smaller than those by the 3DVAR for both Ike and Gustav. Experiments showed that such improvements were due to the use of the flow-dependent covariance provided by the ETKF ensemble in the hybrid DA system. Detailed diagnostics further revealed that the increments produced by the hybrid and the 3DVAR were different for both the analyses of the hurricane itself and its environment. In particular, it was found that the hybrid, using the flow-dependent covariance that gave the hurricane-specific error covariance estimates, was able to systematically adjust the position of the hurricane during the assimilation whereas the 3DVAR was not. The study served as a pilot study to explore and understand the potential of the hybrid method for hurricane data assimilation and forecasts. Caution needs to be taken to extrapolate the results to operational forecast settings.

Revision of Convection and Vertical Diffusion Schemes in the NCEP Global Forecast System

Abstract A new physics package containing revised convection and planetary boundary layer (PBL) schemes in the National Centers for Environmental Prediction’s Global Forecast System is described. The shallow convection (SC) scheme in the revision employs a mass flux parameterization replacing the old turbulent diffusion-based approach. For deep convection, the scheme is revised to make cumulus convection stronger and deeper to deplete more instability in the atmospheric column and result in the suppression of the excessive grid-scale precipitation. The PBL model was revised to enhance turbulence diffusion in stratocumulus regions. A remarkable difference between the new and old SC schemes is seen in the heating or cooling behavior in lower-atmospheric layers above the PBL. While the old SC scheme using the diffusion approach produces a pair of layers in the lower atmosphere with cooling above and heating below, the new SC scheme using the mass-flux approach produces heating throughout the convection layers. In particular, the new SC scheme does not destroy stratocumulus clouds off the west coasts of South America and Africa as the old scheme does. On the other hand, the revised deep convection scheme, having a larger cloud-base mass flux and higher cloud tops, appears to effectively eliminate the remaining instability in the atmospheric column that is responsible for the excessive grid-scale precipitation in the old scheme. The revised PBL scheme, having an enhanced turbulence mixing in stratocumulus regions, helps prevent too much low cloud from forming. An overall improvement was found in the forecasts of the global 500-hPa height, vector wind, and continental U.S. precipitation with the revised model. Consistent with the improvement in vector wind forecast errors, hurricane track forecasts are also improved with the revised model for both Atlantic and eastern Pacific hurricanes in 2008.

Revision of Convection and Vertical Diffusion Schemes in the NCEP Global Forecast System

A new physics package containing revised convection and planetary boundary layer (PBL) schemes in the National Centers for Environmental Prediction’s Global Forecast System is described. The shallow convection (SC) scheme in the revision employs a mass flux parameterization replacing the old turbulent diffusion-based approach. For deep convection, the scheme is revised to make cumulus convection stronger and deeper to deplete more instability in the atmospheric column and result in the suppression of the excessive grid-scale precipitation. The PBL model was revised to enhance turbulence diffusion in stratocumulus regions. A remarkable difference between the new and old SC schemes is seen in the heating or cooling behavior in lower-atmospheric layers above the PBL. While the old SC scheme using the diffusion approach produces a pair of layers in the lower atmosphere with cooling above and heating below, the new SC scheme using the mass-flux approach produces heating throughout the convection layers. In particular, the new SC scheme does not destroy stratocumulus clouds off the west coasts of South America and Africa as the old scheme does. On the other hand, the revised deep convection scheme, having a larger cloud-base mass flux and higher cloud tops, appears to effectively eliminate the remaining instability in the atmospheric column that is responsible for the excessive grid-scale precipitation in the old scheme. The revised PBL scheme, having an enhanced turbulence mixing in stratocumulus regions, helps prevent too much low cloud from forming. An overall improvement was found in the forecasts of the global 500-hPa height, vector wind, and continental U.S. precipitation with the revised model. Consistent with the improvement in vector wind forecast errors, hurricane track forecasts are also improved with the revised model for both Atlantic and eastern Pacific hurricanes in 2008.

Measuring Storm Surge with an Airborne Wide-Swath Radar Altimeter

Abstract Over the years, hurricane track forecasts and storm surge models, as well the digital terrain and bathymetry data they depend on, have improved significantly. Strides have also been made in the knowledge of the detailed variation of the surface wind field driving the surge. The area of least improvement has been in obtaining data on the temporal/spatial evolution of the mound of water that the hurricane wind and waves push against the shore to evaluate the performance of the numerical models. Tide gauges in the vicinity of the landfall are frequently destroyed by the surge. Survey crews dispatched after the event provide no temporal information and only indirect indications of the maximum water level over land. The landfall of Hurricane Bonnie on 26 August 1998, with a surge less than 2 m, provided an excellent opportunity to demonstrate the potential benefits of direct airborne measurement of the temporal/spatial evolution of the water level over a large area. Despite a 160-m variation in aircraft altitude, an 11.5-m variation in the elevation of the mean sea surface relative to the ellipsoid over the flight track, and the tidal variation over the 5-h data acquisition interval, a survey-quality global positioning system (GPS) aircraft trajectory allowed the NASA scanning radar altimeter carried by a NOAA hurricane research aircraft to demonstrate that an airborne wide-swath radar altimeter could produce targeted measurements of storm surge that would provide an absolute standard for assessing the accuracy of numerical storm surge models.

A Two-Season Impact Study of Four Satellite Data Types and Rawinsonde Data in the NCEP Global Data Assimilation System

Abstract Extended-length observing system experiments (OSEs) during two seasons are used to quantify the contributions made to forecast quality by conventional rawinsonde data and four types of remotely sensed satellite data. The impact is measured by comparing the analysis and forecast results from an assimilation–forecast system using all data types with those excluding a particular observing system. The impact of the particular observing system is assessed by comparing the forecast results over extended periods. For these observing system experiments, forecast results are compared through 168 h for periods covering more than a month during both the summer and winter seasons of each hemisphere. The assimilation–forecast system used for these experiments is the National Centers for Environmental Prediction (NCEP) Global Data Assimilation System (GDAS) and the Global Forecast System (GFS). The case studies chosen consist of periods during January–February 2003 and August–September 2003. During these periods, a T254L64 layer version of NCEP’s global spectral model was used. The control run utilized all data types routinely assimilated in the GDAS. The experimental runs individually denied data from the Advanced Microwave Sounding Unit (AMSU), the High-Resolution Infrared Radiation Sounder (HIRS), geostationary satellite atmospheric motion vectors (GEO winds), in situ rawinsondes (raobs), and surface winds derived from the Quick Scatterometer (QuikSCAT). Differences between the control and denial experiment forecasts are accumulated over the two 45-day periods and are analyzed to demonstrate the impact of these data types. Anomaly correlations (ACs), forecast impacts (FIs), and hurricane track forecasts are evaluated for all experimental runs during both seasons. The anomaly correlations used the standard NCEP software suite and are partitioned into subsections covering the polar caps (60°–90°) and midlatitudes (20°–80°) of each hemisphere and the tropical region (20°N–20°S). Anomaly correlations of geopotential heights are shown at several pressure levels in the polar regions and midlatitudes. The root-mean-square error (RMSE) for 850- and 200-hPa wind vector differences are shown for the tropical region. The geographical distributions of forecast impacts on geopotential heights are also examined. The influence these data types have on tropical cyclone track forecasts are shown for both the Atlantic and Pacific basins and again are computed using standard algorithms developed and maintained at NCEP. The results demonstrate a positive impact from all data types with AMSU and rawinsonde data providing the largest anomaly correlation improvements in all zonal regions examined. Smaller forecast improvements are noticed from each of the other data types. In the Atlantic basin, each of the four satellite data types provides nearly equal improvement to the tropical cyclone track forecasts; however, GEO winds provide the largest improvement to track forecasts in the Pacific basin.

Impact of cloud microphysics on hurricane track forecasts

Simulations of Hurricane Rita (2005) at operational resolutions (30 and 12 km) reveal significant track sensitivity to cloud microphysical details, rivaling variation seen in the National Hurricane Center's multi‐model consensus forecast. Microphysics appears to directly or indirectly modulate vortex characteristics including size and winds at large radius and possibly other factors involved in hurricane motion. Idealized simulations made at higher (3 km) resolution help isolate the microphysical influence.