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During the Deepwater Horizon (DWH) blowout, photooxidation of surface oil led to the formation of persistent photooxidized compounds, still found in shoreline sediments a decade later. Studies demonstrated that photooxidation modified both biodegradation rates of the surface oil and the effectiveness of aerial dispersant applications. Despite the significant consequences of this weathering pathway, the lack of measurements prevented photooxidation to be accounted for in the DWH oil budget calculations and in most predictive models. Here we develop a Lagrangian photooxidation module that estimates the dose of solar radiation individual oil droplets receive while moving in the ocean, quantifies the likelihood of photooxidative changes, and continues to track the transport of these persistent photooxidized compounds. We estimate and track the likelihood of photooxidation of Lagrangian oil droplets in the upper layers of the water column for the DWH case by coupling the net shortwave radiation from NOGAPS to the oil application of the Connectivity Modeling System (oil-CMS). The dose of solar radiation upon a droplet is computed with the intensity of the incoming irradiance at the ocean’s surface, the light attenuation coefficient, and the depth of the oil droplets. Considering a range of DWH empirical irradiance thresholds, we find that photooxidation can happen at short time scales of hours to days, in agreement with the new paradigm of oil photooxidation. Furthermore, the oxidized compounds are likely to form in a 110 km radius around the response site, suggesting that the oil reaching the coastline was already photooxidized. This new dynamic coupling provides a powerful tool to test oil weathering hypotheses, refine the oil budget during the DWH, and ultimately inform rapid response in future oil spills.

This study has examined both: (i) the global structure and seasonal variability of the eastward- and westward-travelling quasi 6-day waves (Q6DWs) observed in the geopotential height (GPH) NOGAPS-ALPHA hourly forecast data during the time interval of 14 months (January 2009–February 2010), and (ii) the climatology and interannual variability of the Q6DWs observed in the satellite MLS/Aura GPH data for an extended period of 10 years (January 2005–December 2014). Both data sets are analyzed by using the same approach. The detailed analysis revealed that the westward-travelling Q6DWs have been identified mainly at mid-high latitudes with zonal wave numbers 1 and 2. Two different types of eastward-travelling waves have been found: (i) waves at middle and high latitudes with zonal wave numbers 1 and 2, which are observed in the local winters, and (ii) waves observed over the equator with zonal wave number 1, which maximize mainly between June and September with a secondary enhancement between January–March belonging to the fast Kelvin waves. Some signatures of the solar cycle and SSW impact on the interannual variability as well as ∼2–3-year variability have been distinguished in the different ∼6-day waves. This study draws attention to both the rarely studied winter-time eastward-travelling PWs and the impact of the SSW events, particularly the major ones, on the short- and long-term variability of the PW populations.

We investigate the benefit of high altitude nudging in simulations of the structure and short-term variability of the upper mesosphere and lower thermosphere (UMLT) dynamical meteorology during boreal winter, specifically around the time of the January 2009 sudden stratospheric warming. We compare simulations using the Specified Dynamics, Whole Atmosphere Community Climate Model, extended version, nudged using atmospheric specifications generated by the Navy Operational Global Atmospheric Prediction System, Advanced Level Physics High Altitude. Two sets of simulations are carried out: one uses nudging over a vertical domain from 0-90 km; the other uses nudging over a vertical domain from 0-50 km. The dynamical behavior is diagnosed from ensemble mean and standard deviation of winds, temperature, and zonal accelerations due to resolved and parameterized waves. We show that the dynamical behavior of the UMLT is quite different in the two experiments, with prominent differences in the structure and variability of constituent transport. We compare the results of our numerical experiments to observations of carbon monoxide by the Atmospheric Chemistry Experiment – Fourier Transform Spectrometer to show that the high-altitude nudging is capable of reproducing with high fidelity the observed variability, and traveling planetary waves are a crucial component of the dynamics. The results of this study indicate that to capture the key physical processes that affect short-term variability (defined as the atmospheric behavior within about 10 days of a stratospheric warming) in the UMLT, specification of the atmospheric state in the stratosphere alone is not sufficient, and upper atmospheric specifications are needed.

The main stages are considered of the process of Roshydromet forecast technologies modernization that started in the 1990s, especially those related to the use of supercomputers for operational numerical weather prediction (NWP) and to the development of supercomputer technologies for NWP with different lead times. Some outcomes of the modernization are presented.

Since October 2013 a convective-scale weather prediction model has been used operationally to provide short-term forecasts covering large parts of the Nordic region. The model is now operated by a bilateral cooperative effort [Meteorological Cooperation on Operational Numerical Weather Prediction (MetCoOp)] between the Norwegian Meteorological Institute and the Swedish Meteorological and Hydrological Institute. The core of the model is based on the convection-permitting Applications of Research to Operations at Mesoscale (AROME) model developed by Météo-France. In this paper the specific modifications and updates that have been made to suit advanced high-resolution weather forecasts over the Nordic regions are described. This includes modifications in the surface drag description, microphysics, snow assimilation, as well as an update of the ecosystem and surface parameter description. Novel observation types are introduced in the operational runs, including ground-based Global Navigation Satellite System (GNSS) observations and radar reflectivity data from the Norwegian and Swedish radar networks. After almost two years’ worth of experience with the AROME-MetCoOp model, the model’s sensitivities to the use of specific parameterization settings are characterized and the forecast skills demonstrating the benefit as compared with the global European Centre for Medium-Range Weather Forecasts’ Integrated Forecasting System (ECMWF-IFS) are evaluated. Furthermore, case studies are provided to demonstrate the ability of the model to capture extreme precipitation and wind events.

Forecast solar exposure fields produced by the Australian Bureau of Meteorology’s updated numerical weather prediction systems were validated against multiple sites for the 2012 calendar year. The updated systems are denoted as the Australian Community Climate and Earth-System Simulator (ACCESS) model and became operational in August 2010. The systems are based on the Met Office’s Unified Model and feature improved assimilation methods and radiation parameterizations that were expected to greatly improve forecasts of solar exposure several days in advance. In this study forecasts of global, direct, and diffuse exposure from the mesoscale model ACCESS-A were validated. Statistics were generated for all-sky and clear-sky conditions. Additionally, evaluation of the model’s forecast exposure through single-layer low clouds was conducted. Results show an improvement in global forecasts relative to the older operational model; however, forecasts of diffuse and direct exposure still suffer from large biases. These can be attributed to the choices of the asymmetry factor used in the two-stream approximation for incoming radiation, which determines scattering of the direct beam through clouds.

A global deterministic wave prediction system (GDWPS) is used to improve regional forecasts of waves off the Canadian coastline and help support the practice of safe marine activities in Canadian waters. The wave model has a grid spacing of ¼° with spectral resolution of 36 frequency bins and 36 directional bins. The wave model is driven with hourly 10-m winds generated by the operational global atmospheric prediction system. Ice conditions are updated every three hours using the ice concentration forecasts generated by the Global Ice–Ocean Prediction System. Wave forecasts are evaluated over two periods from 15 August to 31 October 2014 and from 15 December 2014 to 28 February 2015, as well as over select cases during the fall of 2012. The global system is shown to improve wave forecast skill over regions where forecasts were previously produced using limited-area models only. The usefulness of a global expansion is demonstrated for large swell events affecting the northeast Pacific. The first validation of a Canadian operational wave forecast system in the Arctic is presented. Improvements in the representation of forecast wave fields associated with tropical cyclones are also demonstrated. Finally, the GDWPS is shown to result in gains of at least 12 h of lead time.

The refined Cloud Imaging and Particle-Size (CIPS) cloud wind tracking algorithm is elaborated and the wind product is assessed against the Navy Operational Global Atmospheric Prediction System - Advanced Level Physics and High Altitude (NOGAPS-ALPHA) winds and the horizontal wind model (HWM14) climatological winds. Multiple searching frame sizes are adopted to generate the preliminary wind sets which are then merged and further edited based on the clustering of the similar wind directions (±20°). The mean values of the clusters within the sampling grids of 1.5 ° × 1.5 ° or 4.5 ° × 4.5 ° are taken as the final wind product. At the coincidences the CIPS and NOGAPS winds show a moderate degree of deterministic consistency. We have further shown that on the orbit-to-orbit basis when the NOGAPS modeled ice and CIPS measured ice correlate better, the wind agreement is also better. The difference in the two wind sets is most likely attributed to the NOGAPS temperature being deviated from the true temperature that will affect the geostrophic component of the winds and also to the fact that the CIPS winds are often ageostrophic and are cascaded into smaller scales. The CIPS zonal (westward) winds are decreased and then reversed in early June and late August whereas in the core of the season they are stronger. This overall variation pattern is shared by both NOGAPS and HWM14 zonal winds. Both NOGAPS and HWM14 zonal winds exhibit ~8–10 m/s difference between cases using all local times (LTs) and the CIPS LT range 13–23 h due to the dominant diurnal migrating tides, and this may partially interpret the weaker CIPS zonal winds. The meridional (equatorward) winds do not follow any established intra-seasonal variation pattern but rather the variability is susceptible to the sampling longitudes/latitudes.

The Naval Oceanographic Office (NAVOCEANO) provides daily operational global, regional, and coastal ocean model forecasts and their associated prediction products. The models utilized include three-dimensional circulation, wave and ice forecasting systems that have been developed to meet Navy requirements; the models are forced by Navy atmospheric models and constrained by Navy-developed bottom topography. In addition, NAVOCEANO acquires, quality controls, and delivers real-time ocean observations from both in situ and remote-sensing resources for assimilation by the ocean models. These observations are also used to assess model skill and develop ocean climatologies. A major supercomputing capacity is needed to run this ocean modeling suite, as well as a uniquely skilled model operations team that keeps the systems running. Dedicated ocean forecasters interpret the predictions and apply the information to Navy operations. The path from requirements to development to operations demonstrates the close links between research and development production, and operational Navy applications. This path also provides innovative future ocean modeling plans directed to improve oceanographic support to the Navy.

Navy Operational Global Atmospheric Prediction System (NOGAPS) analysis and operational forecasts are evaluated against the Interim ECMWF Re-Analysis (ERA-Interim; ERAI) and satellite data, and compared with the Global Forecast System (GFS) analysis and forecasts, using both performance- and physics-based metrics. The NOGAPS analysis captures realistic Madden–Julian oscillation (MJO) signals in the dynamic fields and the low-level premoistening leading to active convection, but the MJO signals in the relative humidity (RH) and diabatic heating rate (Q1) fields are weaker than those in the ERAI or the GFS analysis. The NOGAPS forecasts, similar to the GFS forecasts, have relatively low prediction skill for the MJO when the MJO initiates over the Indian Ocean and when active convection is over the Maritime Continent. The NOGAPS short-term precipitation forecasts are broadly consistent with the Climate Prediction Center (CPC) morphing technique (CMORPH) precipitation results with regionally quantitative differences. Further evaluation of the precipitation and column water vapor (CWV) indicates that heavy precipitation develops too early in the NOGAPS forecasts in terms of the CWV, and the NOGAPS forecasts show a dry bias in the CWV increasing with forecast lead time. The NOGAPS underpredicts light and moderate-to-heavy precipitation but overpredicts extremely heavy rainfall. The vertical profiles of RH and Q1 reveal a dry bias within the marine boundary layer and a moist bias above. The shallow heating mode is found to be missing for CWV < 50 mm in the NOGAPS forecasts. The diabatic heating biases are associated with weaker trade winds, weaker Hadley and Walker circulations over the Pacific, and weaker cross-equatorial flow over the Indian Ocean in the NOGAPS forecasts.

In October 2010, typhoon Megi induced a profound cold wake of size 800 km by 500 km with sea surface temperature cooling of 8°C in the South China Sea (SCS). More interestingly, the cold wake shifted from the often rightward bias to both sides of the typhoon track and moved to left in a few days. Using satellite data, in situ measurements and numerical modeling based on the East Asian Seas Nowcast/Forecast System (EASNFS), we performed detailed investigations. To obtain realistic typhoon-strength atmospheric forcing, the EASNFS applied typhoon-resolving Weather Research and Forecasting (WRF) model wind field blended with global weather forecast winds from the U.S. Navy Operational Global Atmospheric Prediction System (NOGAPS). In addition to the already known impacts from the slow typhoon translation speed and shallow pre-exiting ocean thermocline, we found the importance of the unique geographical setting of the SCS and the NE monsoon. As the event happened in late October, NE monsoon already started and contributed to the southwestward ambient surface current. Together with the topographicβ effect, the cold wake shifted westward to the left of Megi's track. It was also found that Megi expelled waters away from the SCS and manifested as a gush of internal Kelvin wave exporting waters through the Luzon Strait. The consequential sea level depression lasted and presented a favorable condition for cold dome development. Fission of the north-south elongated cold dome resulted afterward and produced two cold eddies that dissipated slowly thereafter.

The spatial and temporal variations of wind field and the atmospheric turbulence characteristics are important factors that affect the migration and dispersion of local atmospheric pollutants. Since the numerical meteorological forecasting system with high resolution is the foundation of atmospheric dispersion simulation, more and more attention has been drawn to the application of numerical weather prediction systems in wind field prediction and atmospheric simulation. This paper reviewed the widespread application of numerical weather prediction systems in wind field prediction and atmospheric simulation with the focus on the mesoscale weather forecasting system with high resolution, for instance WRF and MM5, as well as the requirement for numerical weather prediction in the simulation research of wind field prediction and atmospheric dispersion.

We have explored the sensitivity of the thermosphere and ionosphere to dynamical forcing from altitudes near the mesopause (~95 km). We performed five simulations, all for the year 2009, with the National Center for Atmospheric Research (NCAR)/Thermosphere Ionosphere Electrodynamics General Circulation Model (TIEGCM). Two simulations were driven with the NCAR Global Scale Wind Model, and three used output from the Advanced Level Physics High Altitude (ALPHA) version of the Navy's Operational Global Atmospheric Prediction System (NOGAPS). Use of NOGAPS-ALPHA allows for realistic meteorological variability from the lower atmosphere to propagate up into the TIEGCM, including a rich spectrum of nonmigrating tides. We find that the additional vertical transport from these tides causes a significant reduction in the calculated peak electron density of the ionospheric F2 layer (NmF2). The mechanism for this effect is the enhanced downward transport of atomic oxygen to the base of the thermosphere. In turn, this yields a greater relative abundance of N2 and hence enhanced recombination of ions and electrons. To get improved agreement with observed electron densities, we must reduce (Kzz) by a factor of 5. However, even with lower Kzz, our calculation still underestimates the NmF2 compared with radio occultation observations by the Constellation Observing System for Meteorology, Ionosphere and Climate satellite system. This underestimate of NmF2 may be linked to an overestimate of the nonmigrating tides in the coupled TIEGCM-NOGAPS calculations or to uncertainties in the bottom boundary for atomic oxygen in the TIEGCM.

Fuel conservation and carbon reduction are important issues in current naval operations. Optimal ship route (i.e. minimum fuel consumption) depends on specific ship platform characteristics and near real-time environment such as weather, ocean waves, and ocean currents. The environmental impact of shipping can be measured on different spatial and temporal scales. As a vital component of the smart voyage planning (SVP) decision aid, the US Navy’s meteorological and oceanographic (METOC) forecast systems play an important role in optimal ship routing, which enables fuel savings in addition to the aid of heavy weather avoidance. This study assesses the impact of METOC ensemble forecast systems on optimal ship route. Tests of the SVP decision aid tool are also conducted for operational fleet use and concept of operations for the USS Princeton guided missile cruiser (CG)-59 in a sea trial test following the 2012 Rim of the Pacific exercises.

Tropical cyclone (TC) forecasts rely heavily on output from global numerical models. While considerable research has investigated the skill of various models with respect to track and intensity, few studies have considered how well global models forecast TC genesis in the North Atlantic basin. This paper analyzes TC genesis forecasts from five global models [Environment Canada's Global Environment Multiscale Model (CMC), the European Centre for Medium-Range Weather Forecasts (ECMWF) global model, the Global Forecast System (GFS), the Navy Operational Global Atmospheric Prediction System (NOGAPS), and the Met Office global model (UKMET)] over several seasons in the North Atlantic basin. Identifying TCs in the model is based on a combination of methods used previously in the literature and newly defined objective criteria. All model-indicated TCs are classified as a hit, false alarm, early genesis, or late genesis event. Missed events also are considered. Results show that the models' ability to predict TC genesis varies in time and space. Conditional probabilities when a model predicts genesis and more traditional performance metrics (e.g., critical success index) are calculated. The models are ranked among each other, and results show that the best-performing model varies from year to year. A spatial analysis of each model identifies preferred regions for genesis, and a temporal analysis indicates that model performance expectedly decreases as forecast hour (lead time) increases. Consensus forecasts show that the probability of genesis noticeably increases when multiple models predict the same genesis event. Overall, this study provides a climatology of objectively identified TC genesis forecasts in global models. The resulting verification statistics can be used operationally to help refine deterministic and probabilistic TC genesis forecasts and potentially improve the models examined.

A multi-model archive of global deterministic forecasts and analyses from three operational systems is constructed to analyse recent Northern Hemisphere mid-latitude forecast skill from 2007 to 2012 and its relation to large-scale atmospheric flow anomalies defined by the Arctic Oscillation (AO) index. We find that the anomaly correlation coefficient (ACC) in 120-hr forecasts of 500 hPa geopotential height has similar variability on synoptic, monthly, and seasonal time scales in each of the three forecast systems examined here: the European Centre for Medium-Range Weather Forecasts, the National Centers for Environmental Prediction Global Forecast System, and the U.S. Navy Operational Global Atmospheric Prediction System. The results indicate that forecast skill as measured by the ACC is significantly correlated with the AO index and its transitions between negative and positive phase. Intervals of exceptionally high ACC skill during the 2009–2010 and 2010–2011 winters are associated with periods in which the AO remained in a persistent negative phase pattern. Episodes of low ACC, including so-called ‘forecast skill dropouts’ most frequently occur during transitions between negative and positive AO index and with positive AO index. The root mean square error (RMSE) of 120-hr forecast 500 hPa height is also modulated by the AO index, but to a lesser extent than the ACC. In two recent winters, the RMSE indicates lower 120-hr forecast accuracy during periods with negative AO index, which is opposite to ‘skill’ patterns provided by the ACC. These results suggest that the ACC is not in all situations an optimal metric with which to quantify model forecast skill, since the ACC can be higher when the large-scale atmospheric flow contains strong anomalies even if there is no actual improvement in model forecasts of that atmospheric state. Keywords: numerical weather prediction, anomaly correlation coefficient, forecast skill, Arctic Oscillation (Published: 14 November 2012) Citation: Tellus A 2012, 64 , 17531, http://dx.doi.org/10.3402/tellusa.v64i0.17531

The objective of this study is to i) investigate the effects of orography on the rainfall, wind, and cloud systems of the Typhoon Ketsana (2009) in Indochina, ii) determine rainfall distribution patterns and which parts of Indochina were most affected during Typhoon Ketsana, iii) identify trends in the cloud and rainfall distribution patterns and wind flow patterns in the synoptic scale on orographic effects during Typhoon Ketsana. Remote sensing techniques have been used to study the impacts of TCs. Using data from the remote sensing data such as Fengyun 2D (FY-2D) satellite, Global Digital Elevation Model (GDEM) from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite, wind information from the Navy Operational Global Atmospheric Prediction System (NOGAPS), and radiosonde data were applied in this study to determine the relationship of the typhoon with the orographic effect. This study provides examples of how the orographic effect is important to weather forecasters, as high mountain ranges were able to influence the distribution of the cloud, rainfall and even wind flow patterns during the typhoon season. This remote sensing technique allows tropical cyclones to be forecasted and their impacts to be defined, and it allows disaster zones to be determined.

This analysis examines the predictability of several key forecasting parameters using the ECMWF Variable Ensemble Prediction System (VarEPS) for tropical cyclones (TCs) in the North Indian Ocean (NIO) including tropical cyclone genesis, pregenesis and postgenesis track and intensity projections, and regional outlooks of tropical cyclone activity for the Arabian Sea and the Bay of Bengal. Based on the evaluation period from 2007 to 2010, the VarEPS TC genesis forecasts demonstrate low false-alarm rates and moderate to high probabilities of detection for lead times of 1–7 days. In addition, VarEPS pregenesis track forecasts on average perform better than VarEPS postgenesis forecasts through 120 h and feature a total track error growth of 41 n mi day−1. VarEPS provides superior postgenesis track forecasts for lead times greater than 12 h compared to other models, including the Met Office global model (UKMET), the Navy Operational Global Atmospheric Prediction System (NOGAPS), and the Global Forecasting System (GFS), and slightly lower track errors than the Joint Typhoon Warning Center. This paper concludes with a discussion of how VarEPS can provide much of this extended predictability within a probabilistic framework for the region.

This article presents the adjoint-data assimilation system (adjoint-DAS) approach to evaluate the forecast sensitivity with respect to the specification of the observation-error covariance (R-sensitivity) and background-error covariance (B-sensitivity) in a four-dimensional variational (4D-Var) DAS with a single outer-loop iteration. Computationally efficient estimates to the forecast impact of adjustments in the error covariance models are obtained by exploiting the mathematical properties of the R- and B-sensitivity matrices and their relationship with the observation sensitivity vector. An additional contribution of this work is that it establishes a synergistic link between various methodologies to analyze the DAS performance: observation sensitivity and impact assessment, error covariance sensitivity, and a posteriori diagnosis. The practical ability to obtain sensitivity information with respect to R- and B-parameters is presented with the adjoint versions of the Naval Research Laboratory Atmospheric Variational Data Assimilation System–Accelerated Representer (NAVDAS-AR) and the Navy Operational Global Atmospheric Prediction System (NOGAPS). The adjoint approach is used to provide guidance on the forecast impact of weighting the radiance data in the DAS according to observation-error variance estimates derived from an a posteriori diagnosis. The results indicate that information extracted from both error covariance diagnosis and sensitivity analysis is necessary to design parameter tuning procedures that are effective in reducing the forecast errors. Copyright © 2012 Royal Meteorological Society

Global daily reanalysis fields from the Navy Operational Global Atmospheric Prediction System (NOGAPS) are used to analyze Northern Hemisphere summertime (June–September) developing and nondeveloping disturbances for tropical cyclone (TC) formation from 2003 to 2008. This is Part II of the study focusing on the western North Pacific (WNP), following Part I for the North Atlantic (NATL) basin. Tropical cyclone genesis in the WNP shows different characteristics from that in the NATL in both large-scale environmental conditions and prestorm disturbances. A box difference index (BDI) is used to identify parameters in differentiating between the developing and nondeveloping disturbances. In order of importance, they are 1) 800-hPa maximum relative vorticity, 2) rain rate, 3) vertically averaged horizontal shear, 4) vertically averaged divergence, 5) 925–400-hPa water vapor content, 6) SST, and 7) translational speed. The study indicates that dynamic variables are more important in TC genesis in the WNP, while in Part I of the study the thermodynamic variables are identified as more important in the NATL. The characteristic differences between the WNP and the NATL are compared.

This study investigates the characteristic differences of tropical disturbances that eventually develop into tropical cyclones (TCs) versus those that did not, using global daily analysis fields of the Navy Operational Global Atmospheric Prediction System (NOGAPS) from the years 2003 to 2008. Time filtering is applied to the data to extract tropical waves with different frequencies. Waves with a 3–8-day period represent the synoptic-scale disturbances that are representatives as precursors of TCs, and waves with periods greater than 20 days represent the large-scale background environmental flow. Composites are made for the developing and nondeveloping synoptic-scale disturbances in a Lagrangian frame following the disturbances. Similarities and differences between them are analyzed to understand the dynamics and thermodynamics of TC genesis. Part I of this study focuses on events in the North Atlantic, while Part II focuses on the western North Pacific. A box difference index (BDI), accounting for both the mean and variability of the individual sample, is introduced to subjectively and quantitatively identify controlling parameters measuring the differences between developing and nondeveloping disturbances. Larger amplitude of the BDI implies a greater possibility to differentiate the difference between two groups. Based on their BDI values, the following parameters are identified as the best predictors for cyclogenesis in the North Atlantic, in the order of importance: 1) water vapor content within 925 and 400 hPa, 2) rain rate, 3) sea surface temperature (SST), 4) 700-hPa maximum relative vorticity, 5) 1000–600-hPa vertical shear, 6) translational speed, and 7) vertically averaged horizontal shear. This list identifies thermodynamic variables as more important controlling parameters than dynamic variables for TC genesis in the North Atlantic. When the east and west (separated by 40°W) Atlantic are examined separately, the 925–400-hPa water vapor content remains as the most important parameter for both regions. The SST and maximum vorticity at 700 hPa have higher importance in the east Atlantic, while SST becomes less important and the vertically averaged horizontal shear and horizontal divergence become more important in the west Atlantic.

A high-order accurate radiative transfer (RT) model developed by Fu and Liou has been implemented into the Navy Operational Global Atmospheric Prediction System (NOGAPS) to improve the energy budget and forecast skill. The Fu–Liou RT model is a four-stream algorithm (with a two-stream option) integrating over 6 shortwave bands and 12 longwave bands. The experimental 10-day forecasts and analyses from data assimilation cycles are compared with the operational output, which uses a two-stream RT model of three shortwave and five longwave bands, for both winter and summer periods. The verifications against observations of radiosonde and surface data show that the new RT model increases temperature accuracy in both forecasts and analyses by reducing mean bias and root-mean-square errors globally. In addition, the forecast errors also grow more slowly in time than those of the operational NOGAPS because of accumulated effects of more accurate cloud–radiation interactions. The impact of parameterized cloud effective radius in estimating liquid and ice water optical properties is also investigated through a sensitivity test by comparing with the cases using constant cloud effective radius to examine the temperature changes in response to cloud scattering and absorption. The parameterization approach is demonstrated to outperform that of constant radius by showing smaller errors and better matches to observations. This suggests the superiority of the new RT model relative to its operational counterpart, which does not use cloud effective radius. An effort has also been made to improve the computational efficiency of the new RT model for operational applications.

This study is Part II of the effort to improve the forecasting of sudden stratospheric warming (SSW) events by using a version of the Navy Operational Global Atmospheric Prediction System (NOGAPS) that covers the full stratosphere. In Part I, extended-range (3 week) hindcast experiments (without data assimilation) for the January 2009 Arctic major SSW were performed using NOGAPS with a unified orographic drag parameterization that consists of the schemes employed by Webster et al., as well as Kim and Arakawa and Kim and Doyle. Part I demonstrated that the model with upgraded middle-atmospheric orographic drag physics better forecasts the magnitude and evolution of the SSW and better simulates the trend of the Arctic Oscillation (AO) index. In this study (Part II), a series of 5-day hindcast experiments is performed with cycling data assimilation using the Naval Research Laboratory Atmospheric Variational Data Assimilation System-Accelerated Representer (NAVDAS-AR), a four-dimensional variational data assimilation (4DVAR) system. Further efforts are made to improve the hindcasting of SSW by improving the satellite radiance bias correction process that strongly affects the data assimilation. The innovation (observation minus background) limit is optimally determined to reduce the rejection of useful radiance data. It is found that when the innovation limit is properly set, both the analysis and forecast of the SSW event can be improved, and that the orographic drag helps improve the SSW forecast.

A set of about 2500 Doppler wind lidar (DWL) profiles was measured by the DLR Falcon aircraft during the life cycle of Typhoon Sinlaku in the western North Pacific as part of the THORPEX Pacific Asian Regional Campaign (T-PARC) 2008. These DWL profiles were assimilated in the global models of the European Centre for Medium-Range Weather Forecasts (ECMWF) and the Naval Research Laboratory (NRL). The beneficial impact of DWL observations is demonstrated with data denial experiments of both models and differences of the observation impact on analyses and forecasts are analysed. Additionally, the impact is quantified using the adjoint observation impact calculation. These calculations confirm the beneficial impact of DWL observations. The total relative contribution of DWL observations is about twice as high in the NRL system compared to the ECMWF system, which may be due to the lower number of satellite observations assimilated in the NRL system. In the NRL system, the DWL impact per observation is higher than that of other wind observations, whereas in the ECMWF system the DWL impact per observations is similar to other aircraft observations and lower than that of radiosondes. The results confirm preceding studies assimilating airborne DWL observations in numerical weather prediction models and underline the high expectations for future space-borne DWL instruments. Copyright c � 2011 Royal Meteorological Society

Two versions of the Navy Operational Global Atmospheric Prediction System (NOGAPS) global ensemble, with and without a stochastic convection scheme, are compared regarding their performance in predicting the development and evolution of tropical cyclones. Forecasts of four typhoons, one tropical storm, and two selected nondeveloping tropical systems from The Observing System Research and Predictability Experiment (THORPEX) Pacific Asian Regional Campaign and Tropical Cyclone Structure 2008 (T-PARC/TCS-08) field program during August and September 2008 are evaluated. It is found that stochastic convection substantially increases the spread in ensemble storm tracks and in the vorticity and height fields in the vicinity of the storm. Stochastic convection also has an impact on the number of ensemble members predicting genesis. One day prior to the system being declared a tropical depression, on average, 31% of the ensemble members predict storm development when the ensemble includes initial perturbations only. When stochastic convection is included, this percentage increases to 50%, but the number of “false alarms” for two nondeveloping systems also increases. However, the increase in false alarms is smaller than the increase in correct development predictions, indicating that stochastic convection may have the potential for improving tropical cyclone forecasting.

This study presents the assessment and seasonal variation of wave energy along the southern coast of Java, Bali, and Nusa Tenggara Waters. For this purpose, the WAVEWATCH-III numerical model was used to simulate the Significant Wave Height in the study region over 25 years covering the time interval of 1991–2015. Wind field data input for the model was a combined dataset of Cross-Calibrated Multi-Platform, Navy Global Environmental Model, and Navy Operational Global Atmospheric Prediction System. In general, the simulation results showed that there was a good potential of wave energy along the southern coast of Java and Bali with the value of higher than 20 kW/m. Furthermore, it was found that there were 12 points which were considered to be the most promising high wave energy potential spots along the near-shore coastal waters of the Java and Bali southern coast. There was a noticeable seasonal variation of wave energy along the study area associated with tropical monsoon climates, which led to the highest value in the period of June to August for about 40 kW/m and the lowest value in the period of December to February for about 20 kW/m. All the selected points for wave energy exhibited dominant wave propagation between northward and northeastward. Moreover, there was an evident increasing trend of wave energy with the peak value appearing in 2005 for all stations.

The hurricanes can be detected by many remote sensors, but synthetic aperture radar (SAR) can yield high-resolution (sub-kilometer) and low-level wind information that cannot be seen below the cloud by other sensors. In this paper, an assessment of SAR capability of monitoring high-resolution hurricane was conducted. A case study was carried out to retrieve ocean surface wind field from C-band RADARSAT-1 SAR image which captured the structure of hurricane Helene over the Atlantic Ocean on 20 September, 2006. With wind direction from the outputs of U.S. Navy Operational Global Atmospheric Prediction System (NOGAPS) model, C-band geophysical model functions (GMFs) which describe the normalized radar cross section (NRCS) dependence on the wind speed and the geometry of radar observations (i.e., incidence angle and azimuth angle with respect to wind direction) such as CMOD5 and newly developed CIWRAP models have been tested to extract wind speed from SAR data. The SAR retrieved ocean surface winds were compared to the aircraft wind speed observations from stepped frequency microwave radiometer (SFMR). The results show the capability of hurricane wind monitoring by SAR.

A very strong Arctic major sudden stratospheric warming (SSW) event occurred in late January 2009. The stratospheric temperature climbed abruptly and the zonal winds reversed direction, completely splitting the polar stratospheric vortex. A hindcast of this event is attempted by using the Navy Operational Global Atmospheric Prediction System (NOGAPS), which includes the full stratosphere with its top at around 65 km. As Part I of this study, extended-range (3 week) forecast experiments are performed using NOGAPS without the aid of data assimilation. A unified parameterization of orographic drag is designed by combining two parameterization schemes; one by Webster et al., and the other by Kim and Arakawa and Kim and Doyle. With the new unified orographic drag scheme implemented, NOGAPS is able to reproduce the salient features of this Arctic SSW event owing to enhanced planetary wave activity induced by more comprehensive subgrid-scale orographic drag processes. The impact of the SSW on the tropospheric circulation is also investigated in view of the Arctic Oscillation (AO) index, which calculated using 1000-hPa geopotential height. The NOGAPS with upgraded orographic drag physics better simulates the trend of the AO index as verified by the Met Office analysis, demonstrating its improved stratosphere–troposphere coupling. It is argued that the new model is more suitable for forecasting SSW events in the future and can serve as a tool for studying various stratospheric phenomena.

This paper explores the hypothesis that a tropical cyclone (TC) may produce baroclinic waves through the divergent circulation that arises from its low-level inflow and upper-level outflow. The model setting is a quasigeostrophic (QG) two-layer fluid in which the effect of the tropical cyclone is parameterized through a source term on the QG potential vorticity equation. Equations predicting the spectral subset of baroclinic waves that are excited through linear resonance are derived. The near-TC pattern of the baroclinic waves in the streamfunction field typically takes the form of a ridge–trough couplet whose phase with respect to the TC varies with the speed and direction of the TCs motion vector. The predictions from the linearized theory are verified in two ways: 1) fully nonlinear simulations are shown and 2) comparison is made to the observed upper-level ridge–trough couplets produced by recurving TCs in the Navy’s Operational Global Prediction System (NOGAPS). The implications of this work for the predictability of downstream impacts from recurving TCs are briefly described.

The results from a∼1 km resolution HYbrid Coordinate Ocean Model (HYCOM), forced by 1/2° Navy Operational Global Atmospheric Prediction System (NOGAPS) atmospheric data, were used in order to study the dynamic response of the Persian Gulf to wintertime shamal forcing. Shamal winds are strong northwesterly winds that occur in the Persian Gulf area behind southeast moving cold fronts. The period from 20 November to 5 December 2004 included a well defined shamal event that lasted 4–5 days. In addition to strong winds (16 m s −1 ) the winter shamal also brought cold dry air ( T a =20 °C, q a =10 g kg −1 ) which led to a net heat loss in excess of 1000 W m −2 by increasing the latent heat flux. This resulted in SST cooling of up to 10 °C most notably in the northern and shallower shelf regions. A sensitivity experiment with a constant specific humidity of q a =15 g kg −1 confirmed that about 38% of net heat loss was due to the air–sea humidity differences. The time integral of SST cooling closely followed the air–sea heat loss, indicating an approximate one-dimensional vertical heat balance. It was found that the shamal induced convective vertical mixing provided a direct mechanism for the erosion of stratification and deepening of the mixed layer by 30 m. The strong wind not only strengthened the circulation in the entire Persian Gulf but also established a northwestward flowing Iranian Coastal Current (ICC, 25–30 cm s −1 ) from the Strait of Hormuz to about 52°E, where it veered offshore. The strongest negative sea level of 25–40 cm was generated in the northernmost portion of the Gulf while the wind setup against the coast of the United Arab Emirates established a positive sea level of 15–30 cm. The transport through the Strait of Hormuz at 56.2°E indicated an enhanced outflow of 0.25 Sv (Sv≡10 6 m 3 s −1 ) during 24 November followed by an equivalent inflow on the next day.

Indonesia is passed by an atmospheric phenomenon, called the Madden-Julian Oscillation (MJO), which has an impact on the wave height in the Indonesian Seas. The significant wave height is simulated using WAVEWATCH-III (WW3) numerical model in Indonesian region (90 °E-150 °E, 20 °N-20 °S) forced by surface winds from Cross-Calibrated Multi-Platform (CCMP), Navy Global Environmental Model (NAVGEM), and Navy Operational Global Atmospheric Prediction System (NOGAPS). This simulation is concentrated on MJO phase 3, 4, and 5 which passed through Indonesia and its adjacent waters that occurred in particular time between 1990-2015. In this study, the impact of MJO was analyzed during every monsoon season. In addition, wind speed analysis was carried out to further enrich the analysis of the MJO impact. The simulation result shows that MJO exerts the highest impact during phase 5 and DJF, which contributes to the increase of wind speed (WS) and significant wave height (SWH) in Indonesian inner seas by 6 m/s and 30 cm, respectively, and in southern Lesser Sunda Island by 8 m/s and 1.2 m, respectively. MJO can also contribute to decreasing of the WS and SWH, when it occurred during DJF and MAM phase 3, and JJA phase 4. There is no noticabe change of WS and SWH during SON.

Problem statement: A two-dimensional wave prediction model along the best track of Typhoon Linda 1997 was interested to study the impact of typhoon wind-wave characteristics. The dynamical wave model with deep water condition was used to predict the wave height (Hs) of Typhoon Linda before and after entering into the Gulf of Thailand (GoT). Approach: The standard one-way nested grid for a regional scale of the third generation WAve Model Cycle 4 (WAMC4) is scrutinized in the present study. This model is enabled to solve the spectral energy balance equation on a coarse resolution grid in order to produce boundary conditions for a small area by the nested grid technique along the best track of typhoon. The model takes full advantage of the fine resolution wind fields in space and time produced by the available US Navy Operational Global Atmospheric Prediction System (NOGAPS) model with 1° resolution. The nested grid application was developed in order to gradually increase the resolution from the open ocean towards the South China Sea (SCS) and the Gulf of Thailand (GoT) respectively. Results: The model results were predicted at five stations which were before and during the typhoon entering into the GoT. The wind speeds of the stations 1-5 were in ranges of 5.14-29.81, 4.11-28.27, 0.51-24.67, 0.51-31.35 and 0.51-33.41 m sec-1, respectively. While the Hs of these stations were found in ranges of 0.54-2.99, 0.68-2.85, 0.11-1.57, 0.12-2.92 and 0.09-2.76 m, respectively. The model results were compared with buoy observations at Ko-Chang and Rayong locations in the GoT which were obtained from the Seawatch project. The comparison of those results at Ko-Chang and Rayong showed the percentage errors of 11.20 and 15.12% respectively. Conclusion: The model results presented the relationship of typhoon wind-induced ocean wave at five stations along the best track. The tendency of the Hs from the model in the spherical coordinate propagation with deep water condition in the fine grid domain was in good agreement with the Hs from the observations.

A widely used observation space covariance localization method is shown to adversely affect satellite radiance assimilation in ensemble Kalman filters (EnKFs) when compared to model space covariance localization. The two principal problems are that distance and location are not well defined for integrated measurements, and that neighboring satellite channels typically have broad, overlapping weighting functions, which produce true, nonzero correlations that localization in radiance space can incorrectly eliminate. The limitations of the method are illustrated in a 1D conceptual model, consisting of three vertical levels and a two-channel satellite instrument. A more realistic 1D model is subsequently tested, using the 30 vertical levels from the Navy Operational Global Atmospheric Prediction System (NOGAPS), the Advanced Microwave Sounding Unit A (AMSU-A) weighting functions for channels 6–11, and the observation error variance and forecast error covariance from the NRL Atmospheric Variational Data Assimilation System (NAVDAS). Analyses from EnKFs using radiance space localization are compared with analyses from raw EnKFs, EnKFs using model space localization, and the optimal analyses using the NAVDAS forecast error covariance as a proxy for the true forecast error covariance. As measured by mean analysis error variance reduction, radiance space localization is inferior to model space localization for every ensemble size and meaningful observation error variance tested. Furthermore, given as many satellite channels as vertical levels, radiance space localization cannot recover the true temperature state with perfect observations, whereas model space localization can.

A three-component decomposition is applied to global analysis data to show the existence of a beta gyre, which causes Tropical Cyclone (TC) to drift from a large-scale environmental steering current. Analyses from the Global Data Assimilation and Prediction System (GDAPS) of the Korea Meteorological Administration (KMA), the Global Forecast System (GFS) of NCEP, and the Navy Operational Global Atmospheric Prediction System (NOGAPS) are used in this study.

Singular vectors (SVs) are used to study the sensitivity of 2-day forecasts of recurving tropical cyclones (TCs) in the western North Pacific to changes in the initial state. The SVs are calculated using the tangent and adjoint models of the Navy Operational Global Atmospheric Prediction System (NOGAPS) for 72 forecasts for 18 TCs in the western North Pacific during 2006. In addition to the linear SV calculation, nonlinear perturbation experiments are also performed in order to examine 1) the similarity between nonlinear and linear perturbation growth and 2) the downstream impacts over the North Pacific and North America that result from changes to the 2-day TC forecast. Both nonrecurving and recurving 2-day storm forecasts are sensitive to changes in the initial state in the near-storm environment (in an annulus approximately 500 km from the storm center). During recurvature, sensitivity develops to the northwest of the storm, usually associated with a trough moving in from the west. These upstream sensitivities can occur as far as 4000 km to the northwest of the storm, over the Asian mainland, which has implications for adaptive observations. Nonlinear perturbation experiments indicate that the linear calculations reflect case-to-case variability in actual nonlinear perturbation growth fairly well, especially when the growth is large. The nonlinear perturbations show that for recurving tropical cyclones, small initial perturbations optimized to change the 2-day TC forecast can grow and propagate downstream quickly, reaching North America in 5 days. The fastest 5-day perturbation growth is associated with recurving storm forecasts that occur when the baroclinic instability over the North Pacific is relatively large. These results suggest that nonlinear forecasts perturbed using TC SVs may have utility for predicting the downstream impact of TC forecast errors over the North Pacific and North America.

The tropical cyclone (TC) track forecasts of the Navy Operational Global Atmospheric Prediction System (NOGAPS) were evaluated for a number of data assimilation experiments conducted using observational data from two periods: 4 July–31 October 2005 and 1 August–30 September 2006. The experiments were designed to illustrate the impact of different types of satellite observations on the NOGAPS TC track forecasts. The satellite observations assimilated in these experiments consisted of feature-track winds from geostationary and polar-orbiting satellites, Special Sensor Microwave Imager (SSM/I) total column precipitable water and wind speeds, Advanced Microwave Sounding Unit-A (AMSU-A) radiances, and Quick Scatterometer (QuikSCAT) and European Remote Sensing Satellite-2 (ERS-2) scatterometer winds. There were some differences between the results from basin to basin and from year to year, but the combined results for the 2005 and 2006 test periods for the North Pacific and Atlantic Ocean basins indicated that the assimilation of the feature-track winds from the geostationary satellites had the most impact, ranging from 7% to 24% improvement in NOGAPS TC track forecasts. This impact was statistically significant at all forecast lengths. The impact of the assimilation of SSM/I precipitable water was consistently positive and statistically significant at all forecast lengths. The improvements resulting from the assimilation of AMSU-A radiances were also consistently positive and significant at most forecast lengths. There were no significant improvements/degradations from the assimilation of the other satellite observation types [e.g., Moderate Resolution Imaging Spectroradiometer (MODIS) winds, SSM/I wind speeds, and scatterometer winds]. The assimilation of all satellite observations resulted in a gain in skill of roughly 12 h for the NOGAPS 48- and 72-h TC track forecasts and a gain in skill of roughly 24 h for the 96- and 120-h forecasts. The percent improvement in these forecasts ranged from almost 20% at 24 h to over 40% at 120 h.

The forecast model and three-dimensional variational data assimilation components of the Navy Operational Global Atmospheric Prediction System (NOGAPS) have each been extended into the upper stratosphere and mesosphere to form an Advanced Level Physics High Altitude (ALPHA) version of NOGAPS extending to ~100 km. This NOGAPS-ALPHA NWP prototype is used to assimilate stratospheric and mesospheric temperature data from the Microwave Limb Sounder (MLS) and the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) instruments. A 60-day analysis period in January and February 2006, was chosen that includes a well documented stratospheric sudden warming. SABER and MLS temperatures indicate that the SSW caused the polar winter stratopause at ~40 km to disappear, then reform at ~80 km altitude and slowly descend during February. The NOGAPS-ALPHA analysis reproduces this observed stratospheric and mesospheric temperature structure, as well as realistic evolution of zonal winds, residual velocities, and Eliassen-Palm fluxes that aid interpretation of the vertically deep circulation and eddy flux anomalies that developed in response to this wave-breaking event. The observation minus forecast (O-F) standard deviations for MLS and SABER are ~2 K in the mid-stratosphere and increase monotonically to about 6 K in the upper mesosphere. Increasing O-F standard deviations in the mesosphere are expected due to increasing instrument error and increasing geophysical variance at small spatial scales in the forecast model. In the mid/high latitude winter regions, 10-day forecast skill is improved throughout the upper stratosphere and mesosphere when the model is initialized using the high-altitude analysis based on assimilation of both SABER and MLS data.

The ensemble transform (ET) scheme changes forecast perturbations into analysis perturbations whose amplitudes and directions are consistent with a user-provided estimate of analysis error covariance. A practical demonstration of the ET scheme was undertaken using Naval Research Laboratory (NRL) Atmospheric Variational Data Assimilation System (NAVDAS) analysis error variance estimates and the Navy Operational Global Atmospheric Prediction System (NOGAPS) numerical weather prediction (NWP) model. It was found that the ET scheme produced forecast ensembles that were comparable to or better in a variety of measures than those produced by the Fleet Numerical and Oceanography Center (FNMOC) bred-growing modes (BGM) scheme. Also, the demonstration showed that the introduction of stochastic perturbations into the ET forecast ensembles led to a substantial improvement in the agreement between the ET and NAVDAS analysis error variances. This finding is strong evidence that even a small-sized ET ensemble is capable of obtaining good agreement between the ET and NAVDAS analysis error variances, provided that NWP model deficiencies are accounted for. Last, since the NAVDAS analysis error covariance estimate is diagonal and hence ignores multivariate correlations, it was of interest to examine the ET analysis perturbations’ spatial correlation. Tests showed that the ET analysis perturbations exhibited statistically significant, realistic multivariate correlations.

Part I of this two-part paper examined the satellite-derived rainfall accumulation and rain potential history of Hurricanes Isidore and Lili (2002). This paper (Part II) uses analyses from the Navy Operational Global Atmospheric Prediction System (NOGAPS) to examine the water budget and environmental parameters and their relationship to the precipitation for these two storms. Factors other than storm size are found to account for large volumetric differences in storm total rainfall between Lili and Isidore. It is found that the horizontal moisture convergence was crucial to the initiation and maintenance of Isidore’s intense rainfall before and during its landfall. When the storm was over the ocean, the ocean moisture flux (evaporation) was the second dominant term among the moisture sources that contribute to precipitation. During Isidore’s life history, the strong horizontal moisture flux convergence corresponded to the large storm total precipitable water. The large difference in budget-derived stored cloud ice and liquid water between Isidore and Lili is corroborated from Tropical Rainfall Measuring Mission (TRMM) measurements. During Isidore’s landfall, the decrease in environmental water vapor contributed to rainfall in a very small amount. These results indicate the importance of the environmental precipitable water and moisture convergence and ocean surface moisture flux in generating Isidore’s large rainfall volume and inland flooding as compared with Lili’s water budget history. Both the moisture convergence and ocean flux were small for Lili.

An analysis of the accuracy, and trends in the accuracy, of medium-range weather forecasts for Melbourne, Australia, is presented. The analysis shows that skill is evident in forecasts of temperature, rainfall, and qualitative descriptions of expected weather up to 7 days in advance. The analysis also demonstrates the existence of a long-term trend in the accuracy of the forecasts. For example, Day-3 forecasts of minimum temperature in recent years (average error ∼1.6 °C) are as skilful as Day-1 forecasts of minimum temperature in the 1960s and 1970s, whilst Day-4 forecasts of maximum temperature in recent years (average error ∼2.0 °C) are more skilful than Day-1 forecasts of maximum temperature in the 1960s and 1970s. It is suggested that this trend may be largely attributed to: a combination of (1) enhancements in the description of the atmosphere's initial state provided by remote sensing and other observational technologies; (2) advances in broad-scale numerical weather prediction (NWP); and (3) improvements in the forecast process that are supported by good organizational management, including developing and implementing new prediction techniques, and careful succession planning. Copyright © 2008 Royal Meteorological Society

Numerical weather prediction systems generate very large amounts of output data. This chapter considers two different use cases for which cloud computing can be of use: • the routine dissemination of model output data in a timely fashion; • the provision of very large research data sets. This is done in the context of the data produced by the European Centre for Medium-Range Weather Forecasts (ECMWF), an intergovernmental organization that is both a 24/7 operational service and a research institute.

Interannual and climatological variations of wind stress drag coefficient (CD) are examined over the global ocean from 1998 to 2004. Here CD is calculated using high temporal resolution (3- and 6-hourly) surface atmospheric variables from two datasets: 1) the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) and 2) the Navy Operational Global Atmospheric Prediction System (NOGAPS). The stability-dependent CD algorithm applied to both datasets gives almost identical values over most of the global ocean, confirming the validity of results. Overall, major findings of this paper are as follows: 1) the CD value can change significantly (e.g., >50%) on 12-hourly time scales around the Kuroshio and Gulf Stream current systems; 2) there is strong seasonal variability in CD, but there is not much interannual change in the spatial variability for a given month; 3) a global mean CD ≈ 1.25 × 10−3 is found in all months, while CD ≥ 1.5 × 10−3 is prevalent over the North Pacific and North Atlantic Oceans and in southern high-latitude regions as well, and CD ≤ 1.0 × 10−3 is typical in the eastern equatorial Pacific cold tongue; and 4) including the effects of air–sea stability on CD generally causes an increase of >20% in comparison to the one calculated based on neutral conditions in the tropical regions. Finally, spatially and temporally varying CD fields are therefore needed for a variety of climate and air–sea interaction studies.

Starting from 2003, a new typhoon surveillance program, Dropwindsonde Observations for Typhoon Surveillance near the Taiwan Region (DOTSTAR), was launched. During 2004, 10 missions for eight typhoons were conducted successfully with 155 dropwindsondes deployed. In this study, the impact of these dropwindsonde data on tropical cyclone track forecasts has been evaluated with five models (four operational and one research models). All models, except the Geophysical Fluid Dynamics Laboratory (GFDL) hurricane model, show the positive impact that the dropwindsonde data have on tropical cyclone track forecasts. During the first 72 h, the mean track error reductions in the National Centers for Environmental Prediction’s (NCEP) Global Forecast System (GFS), the Navy Operational Global Atmospheric Prediction System (NOGAPS) of the Fleet Numerical Meteorology and Oceanography Center (FNMOC), and the Japanese Meteorological Agency (JMA) Global Spectral Model (GSM) are 14%, 14%, and 19%, respectively. The track error reduction in the Weather Research and Forecasting (WRF) model, in which the initial conditions are directly interpolated from the operational GFS forecast, is 16%. However, the mean track improvement in the GFDL model is a statistically insignificant 3%. The 72-h-average track error reduction from the ensemble mean of the above three global models is 22%, which is consistent with the track forecast improvement in Atlantic tropical cyclones from surveillance missions. In all, despite the fact that the impact of the dropwindsonde data is not statistically significant due to the limited number of DOTSTAR cases in 2004, the overall added value of the dropwindsonde data in improving typhoon track forecasts over the western North Pacific is encouraging. Further progress in the targeted observations of the dropwindsonde surveillances and satellite data, and in the modeling and data assimilation system, is expected to lead to even greater improvement in tropical cyclone track forecasts.

Because the Joint Typhoon Warning Center (JTWC) has only four dynamical models for guidance in making 96- and 120-h track forecasts, an opportunity exists for improving the consensus forecast by the proper removal of a likely erroneous forecast to form a selective consensus (SCON). Forecast fields from all four models [the U.S. Navy Operational Global Atmospheric Prediction System (NOGAPS), the U.S. Navy version of the Geophysical Fluid Dynamics Laboratory model (GFDN), the Met Office (UKMO) model, and the Global Forecast System (GFS)] were available during the 2005 western North Pacific season to evaluate for the first time the error mechanisms leading to large track errors. As shown previously for the NOGAPS and GFDN models during the 2004 season, error sources related to the midlatitude circulations accounted for about 90% of all large 120-h track errors by all four models during the 2005 season. This dominance of midlatitude-related error source is a major shift from the 72-h errors, which include more errors related to tropical circulations. In the GFS model, 95% of the large errors occurred because of an incorrect depiction of the vertical structure of the tropical cyclone. A systematic error in the GFDN model was identified in which a false anticyclogenesis was predicted downstream of the Tibetan Plateau, which accounted for over 50% of the large GFDN track errors. The consensus spread versus consensus error relationship is examined to isolate those 20%–25% of cases with large spreads and large errors that are candidates for forming an SCON. If the model tracks that contributed to the large errors are eliminated, the average improvement of the SCON forecasts relative to the nonselective consensus is 222 (239) n mi during 2005 (2004), and the corresponding average improvement relative to the JTWC forecasts is 382 (203) n mi. This application of SCON is considered the potential “forecastability” in that it represents the optimum use of the present numerical guidance for consensus forecasting.