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.

Using accurate inputs of wind speed is crucial in wind resource assessment, as predicted power is proportional to the wind speed cubed. This study outlines a methodology for combining multiple ocean satellite winds and winds from WRF simulations in order to acquire the accurate reconstructed offshore winds which can be used for offshore wind resource assessment. First, wind speeds retrieved from Synthetic Aperture Radar (SAR) and Scatterometer ASCAT images were validated against in situ measurements from seven coastal meteorological stations in South China Sea (SCS). The wind roses from the Navy Operational Global Atmospheric Prediction System (NOGAPS) and ASCAT agree well with these observations from the corresponding in situ measurements. The statistical results comparing in situ wind speed and SAR-based (ASCAT-based) wind speed for the whole co-located samples show a standard deviation (SD) of 2.09 m/s (1.83 m/s) and correlation coefficient of R 0.75 (0.80). When the offshore winds (i.e., winds directed from land to sea) are excluded, the comparison results for wind speeds show an improvement of SD and R, indicating that the satellite data are more credible over the open ocean. Meanwhile, the validation of satellite winds against the same co-located mast observations shows a satisfactory level of accuracy which was similar for SAR and ASCAT winds. These satellite winds are then assimilated into the Weather Research and Forecasting (WRF) Model by WRF Data Assimilation (WRFDA) system. Finally, the wind resource statistics at 100 m height based on the reconstructed winds have been achieved over the study area, which fully combines the offshore wind information from multiple satellite data and numerical model. The findings presented here may be useful in future wind resource assessment based on satellite data.

Abstract 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.

Abstract 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.

Abstract 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.

Abstract The effect on weather forecast performance of incorporating ensemble covariances into the initial covariance model of the four-dimensional variational data assimilation (4D-Var) Naval Research Laboratory Atmospheric Variational Data Assimilation System-Accelerated Representer (NAVDAS-AR) is investigated. This NAVDAS-AR-hybrid scheme linearly combines the static NAVDAS-AR initial background error covariance with a covariance derived from an 80-member flow-dependent ensemble. The ensemble members are generated using the ensemble transform technique with a (three-dimensional variational data assimilation) 3D-Var-based estimate of analysis error variance. The ensemble covariances are localized using an efficient algorithm enabled via a separable formulation of the localization matrix. The authors describe the development and testing of this scheme, which allows for assimilation experiments using differing linear combinations of the static and flow-dependent background error covariances. The tests are performed for two months of summer and two months of winter using operational model resolution and the operational observational dataset, which is dominated by satellite observations. Results show that the hybrid mode data assimilation scheme significantly reduces the forecast error across a wide range of variables and regions. The improvements were particularly pronounced for tropical winds. The verification against radiosondes showed a greater than 0.5% reduction in vector wind RMS differences in areas of statistical significance. The verification against self-analysis showed a greater than 1% reduction from verifying against analyses between 2- and 5-day lead time at all eight vertical levels examined in areas of statistical significance. Using the Navy's summary of verification results, the Navy Operational Global Atmospheric Prediction System (NOGAPS) scorecard, the improvements resulted in a score (+1) that justifies a major system upgrade.

Abstract An algorithm to generate wave fields consistent with forecasts from the official U.S. tropical cyclone forecast centers has been made available in near–real time to forecasters since summer 2007. The algorithm removes the tropical cyclone from numerical weather prediction model surface wind field forecasts, replaces the removed winds with interpolated values from surrounding grid points, and then adds a surface wind field generated from the official forecast into the background. The modified wind fields are then used as input into the WAVEWATCH III model to provide seas consistent with the official tropical cyclone forecasts. Although this product is appealing to forecasters because of its consistency and its superior tropical cyclone track forecast, there has been only anecdotal evaluation of resulting wave fields to date. This study evaluates this new algorithm for two years’ worth of Atlantic tropical cyclones and compares results with those of WAVEWATCH III run with U.S. Navy Operational Global Atmospheric Prediction System (NOGAPS) surface winds alone. Results show that the new algorithm has generally improved forecasts of maximum significant wave heights and 12-ft seas’ radii in proximity to tropical cyclones when compared with forecasts produced using only the NOGAPS surface winds.

The local ensemble transform (ET) analysis perturbation scheme is adapted to generate perturbations to both atmospheric variables and sea‐surface temperature (SST). The adapted local ET scheme is used in conjunction with a prognostic model of SST diurnal variation and the Navy Operational Global Atmospheric Prediction System (NOGAPS) global spectral model to generate a medium‐range forecast ensemble. When compared to a control ensemble, the new forecast ensemble with SST variation exhibits notable differences in various physical properties including the spatial patterns of surface fluxes, outgoing longwave radiation (OLR), cloud radiative forcing, near‐surface air temperature and wind speed, and 24‐h accumulated precipitation. The structure of the daily cycle of precipitation also is substantially changed, generally exhibiting a more realistic midday peak of precipitation. Diagnostics of ensemble performance indicate that the inclusion of SST variation is very favorable to forecasts in the Tropics. The forecast ensemble with SST variation outscores the control ensemble in the Tropics across a broad set of metrics and variables. The SST variation has much less impact in the Midlatitudes. Further comparison shows that SST diurnal variation and the SST analysis perturbations are each individually beneficial to the forecast from an overall standpoint. The SST analysis perturbations have broader benefit in the Tropics than the SST diurnal variation, and inclusion of the SST analysis perturbations together with the SST diurnal variation is essential to realize the greatest gains in forecast performance.

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.

Abstract 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.

Abstract 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

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.

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.

Abstract 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.

Abstract 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.

In this paper, we perform a comparison of wind speed measurements from the ENVISAT Advanced Synthetic Aperture Radar (ASAR), the MetOp-A Advanced Scatterometer (ASCAT), the U.S. National Data Buoy Center's moored buoys, and the U.S. Navy Operational Global Atmospheric Prediction System (NOGAPS) model. These comparisons were made in near U.S. coast regions over a 17-month period from March 2009 to July 2010. The ASAR wind speed retrieval agreed well with the scatterometer and model estimates, with mean differences ranging from -0.69 to 0.85 m/s and standard deviations between 1.16 and 1.77 m/s, depending upon the ASAR beam mode type. The results indicate that ASAR-derived ocean surface wind speeds are as accurate as the ASCAT and NOGAPS wind products. Comparisons between ASCAT winds and synthetic aperture radar (SAR) winds averaged at different spatial resolutions show very little change. This demonstrates that it is suitable that the scatterometer wind retrieval geophysical model function, i.e., CMOD5, is used for SAR wind retrieval. The impact of C-band VV polarization SAR calibration error on wind retrieval is also discussed.

Abstract Enhanced atmospheric motion vectors (AMVs) produced from the geostationary Multifunctional Transport Satellite (MTSAT) are assimilated into the U.S. Navy Operational Global Atmospheric Prediction System (NOGAPS) to evaluate the impact of these observations on tropical cyclone track forecasts during the simultaneous western North Pacific Ocean Observing System Research and Predictability Experiment (THORPEX) Pacific Asian Regional Campaign (TPARC) and the Tropical Cyclone Structure—2008 (TCS-08) field experiments. Four-dimensional data assimilation is employed to take advantage of experimental high-resolution (space and time) AMVs produced for the field campaigns by the Cooperative Institute for Meteorological Satellite Studies. Two enhanced AMV datasets are considered: 1) extended periods produced at hourly intervals over a large western North Pacific domain using routinely available MTSAT imagery and 2) limited periods over a smaller storm-centered domain produced using special MTSAT rapid-scan imagery. Most of the locally impacted forecast cases involve Typhoons Sinlaku and Hagupit, although other storms are also examined. On average, the continuous assimilation of the hourly AMVs reduces the NOGAPS tropical cyclone track forecast errors—in particular, for forecasts longer than 72 h. It is shown that the AMVs can improve the environmental flow analyses that may be influencing the tropical cyclone tracks. Adding rapid-scan AMV observations further reduces the NOGAPS forecast errors. In addition to their benefit in traditional data assimilation, the enhanced AMVs show promise as a potential resource for advanced objective data-targeting methods.

Abstract 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.

Ocean Surface Wind Speed of Hurricane Helene Observed by SAR

Abstract 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.