Impact of Satellite Observations on the Tropical Cyclone Track Forecasts of the Navy Operational Global Atmospheric Prediction System

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.

The influence of convective momentum transport (CMT) on tropical cyclone (TC) track forecasts is examined in the Navy Operational Global Atmospheric Prediction System (NOGAPS) with the Emanuel cumulus parameterization. Data assimilation and medium-range forecast experiments show that for 35 tropical cyclones during August and September 2004 the inclusion of CMT in the cumulus parameterization significantly improves the TC track forecasts. The tests show that the track forecasts are very sensitive to the magnitude of the Emanuel parameterization’s convective momentum transport parameter, which controls the CMT tendency returned by the parameterization. While the overall effect of this formulation of CMT in NOGAPS data assimilation/medium-range forecasts results in the surface pressure of tropical cyclones being less intense (and more consistent with the analysis), the parameterization is not equivalent to a simple diffusion of winds in the presence of convection. This is demonstrated by two data assimilation/medium-range forecast tests in which a vertical diffusion algorithm replaces the CMT. Two additional data assimilation/medium-range forecast experiments were conducted to test whether the skill increase primarily comes from the CMT in the immediate vicinity of the tropical cyclones. The results show that the inclusion of the CMT calculation in the vicinity of the TC makes the largest contribution to the increase in forecast skill, but the general contribution of CMT away from the TC also plays an important role.

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.

All highly erroneous (>300 n mi or 555 km at 72 h) Navy Operational Global Atmospheric Prediction System (NOGAPS) and U.S. Navy version of the Geophysical Fluid Dynamics Laboratory model (GFDN) tropical cyclone track forecasts in the western North Pacific during 1997 are examined. Error mechanisms that are more related to midlatitude circulations are described in this paper and those errors that predominantly occur while the tropical cyclone (TC) is still in the Tropics are addressed in a companion paper. Responsible error mechanisms are described by conceptual models that are all related to known tropical cyclone motion processes that are being misrepresented in the dynamical models. As in the companion paper, characteristics and symptoms in the forecast tracks and model fields that accompany these frequently recurring error mechanisms are documented and illustrative case studies are presented. Whereas 21 GFDN forecasts were degraded by an improper prediction of a midlatitude system evolution, o...

The paper evaluates the meteorological quality and operational utility of the Navy Operational Global Atmospheric Prediction System (NOGAPS) in forecasting tropical cyclones. It is shown that the model can provide useful predictions of motion and formation on a real-time basis in the western North Pacific. The meterological characteristics of the NOGAPS tropical cyclone predictions are evaluated by examining the formation of low-level cyclone systems in the tropics and vortex structure in the NOGAPS analysis and verifying 72-h forecasts. The adjusted NOGAPS track forecasts showed equitable skill to the baseline aid and the dynamical model. NOGAPS successfully predicted unusual equatorward turns for several straight-running cyclones.

All highly erroneous (>300 n mi or 555 km at 72 h) Navy Operational Global Atmospheric Prediction System (NOGAPS) and U.S. Navy version of the Geophysical Fluid Dynamics Laboratory model (GFDN) tropical cyclone track forecasts in the western North Pacific during 1997 are examined. Responsible error mechanisms are described by conceptual models that are all related to known tropical cyclone motion processes that are being misrepresented in the dynamical models. Error mechanisms that predominantly occur while the tropical cyclone is still in the Tropics are described in this paper, and those errors that are more related to midlatitude circulations are addressed in a companion paper. Of the 69 NOGAPS large-error cases, 39 were attributed to excessive direct cyclone interaction (E-DCI), 12 cases of excessive ridge modification by the tropical cyclone (E-RMT), and 10 cases of excessive reverse trough formation (E-RTF). Of the 50 GFDN large-error cases, 31 were E-DCI, and only two E-RMT and two E-RTF c...

Experimental wind datasets were derived for two time periods (13–20 July and 24 August–10 September 1995) from GOES-8 observations processed at the University of Wisconsin Cooperative Institute for Meteorological Satellite Studies (UW CIMSS). The first dataset was focused on Tropical Storm Chantal, and the second dataset was focused on the multiple-storm environment that included Hurricanes Humberto, Iris, and Luis. Both datasets feature a processing and quality control strategy designed to optimize the quantity and content of geostationary satellite-derived winds in the vicinity of tropical cyclones. Specifically, the winds were extracted from high-density targets obtained from multispectral imagery, which included three water vapor bands (6.7, 7.0, and 7.3 μm), infrared, and visible. The Navy Operational Global Atmospheric Prediction System (NOGAPS) was used as the vehicle to determine the impact of these winds upon tropical cyclone track forecasts. During the 1995 Atlantic hurricane season the...

The Weber barotropic model (WBAR) was originally developed using predefined 850–200-hPa analyses and forecasts from the NCEP Global Forecasting System. The WBAR tropical cyclone (TC) track forecast performance was found to be competitive with that of more complex numerical weather prediction models in the North Atlantic. As a result, WBAR was revised to incorporate the Navy Operational Global Atmospheric Prediction System (NOGAPS) analyses and forecasts for use at the Joint Typhoon Warning Center (JTWC). The model was also modified to analyze its own storm-dependent deep-layer mean fields from standard NOGAPS pressure levels. Since its operational installation at the JTWC in May 2003, WBAR TC track forecast performance has been competitive with the performance of other more complex NWP models in the western North Pacific. Its TC track forecast performance combined with its high availability rate (93%–95%) has warranted its inclusion in the JTWC operational consensus. The impact of WBAR on consensus TC track forecast performance has been positive and WBAR has added to the consensus forecast availability (i.e., having at least two models to provide a consensus forecast).

In June 1990, the assimilation of synthetic tropical cyclone observations into the Navy Operational Global Atmospheric Prediction System (NOGAPS) was initiated at Fleet Numerical Oceanography Center (FNOC). These observations are derived directly from the information contained in the tropical cyclone warnings issued by the Joint Typhoon Warning Center (JTWC) and the National Hurricane Center. This paper describes these synthetic observations, the evolution of their use at FNOC, and the details of their assimilation into NOGAPS. The results of a comprehensive evaluation of the 1991 NOGAPS tropical cyclone forecast performance in the western North Pacific are presented. NOGAPS analysis and forecast position errors were determined for all tropical circulations of tropical storm strength or greater. It was found that, after the assimilation of synthetic observations, the NOGAPS spectral forecast model consistently maintained the tropical circulations as evidenced by detection percentages of 96%, 90% ...

An adjoint-based method is used to calculate the impact of observation data on a measure of short-range forecast error in the Navy Operational Global Atmospheric Prediction System (NOGAPS) during November and December 2003. The evaluated observations include all regular satellite and in situ data assimilated in the Naval Research Laboratory (NRL) Atmospheric Variational Data Assimilation System (NAVDAS) at 1800 UTC, and also targeted dropsonde profiles provided by the North Atlantic Observing-System Research and Predictability Experiment (THORPEX) Regional Campaign (NA-TReC) field program. Commerical aircraft observations account for 46% of the total forecast error reduction by observations in the NA-TReC domain, which includes the North Atlantic and adjacent regions of North America and Europe. Targeted dropsonde data have high impact per observation, but the impact of all dropsonde data is less than 2% of the total during the 2-month study period. Eight of 12 targeted dropsonde cases reduce forecast error. The percent of total impact for other observations assimilated at 1800 UTC in the NA-TReC domain is as follows: Advanced Microwave Sounding Unit-A (AMSU-A) radiances (16%), satellite winds (14%), land surface data (9%), radiosondes (8%), and ship-surface data (5%). If observations over the entire global domain are evaluated, the largest impact of data provided at 1800 UTC during November and December 2003 is provided by AMSU-A radiance data (48% of total).

The convective parameterization of Emanuel has been employed in the forecast model of the Navy Operational Global Atmospheric Prediction System (NOGAPS) since 2000, when it replaced a version of the relaxed Arakawa–Schubert scheme. Although in long-period data assimilation forecast tests the Emanuel scheme has been found to perform quite well in NOGAPS, particularly for tropical cyclones, some weaknesses have also become apparent. These weaknesses include underprediction of heavy-precipitation events, too much light precipitation, and unrealistic heating at upper levels. Recent research efforts have resulted in modifications of the scheme that are designed to reduce such problems. One change described here involves the partitioning of the cloud-base mass flux into mixing cloud mass flux at individual levels. The new treatment significantly reduces a heating anomaly near the tropopause that is associated with a large amount of mixing cloud mass flux ascribed to that region in the original Emanuel ...

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.

The Navy Operational Global Atmospheric Prediction System (NOGAPS) has proven itself to be competitive with any of the large forecast models run by the large operational forecast centers around the world. The navy depends on NOGAPS for an astonishingly wide range of applications, from ballistic winds in the stratosphere to air-sea fluxes to drive ocean general circulation models. Users of these applications will benefit from a better understanding of how a system such as NOGAPS is developed, what physical assumptions and compromises have been made, and what they can reasonably expect in the future as the system continues to evolve. The discussions will be equally relevant for users of products from other large forecast centers, e.g., National Meteorological Center, European Centre for Medium-Range Weather Forecasts. There is little difference in the scientific basis of the models and the development methodologies used for their development. However, the operational priorities of each center and t...

A set of criteria is developed to identify tropical cyclone (TC) formations in the Navy Operational Global Atmospheric Prediction System (NOGAPS) analyses and forecast fields. Then the NOGAPS forecasts of TC formations from 1997 to 1999 are verified relative to a formation time defined to be the first warning issued by the Joint Typhoon Warning Center. During these three years, the spatial distributions of TC formations were strongly affected by an El Nino–Southern Oscillation event. The successful NOGAPS predictions of formation within a maximum separation threshold of 4° latitude are about 70%–80% for 24-h forecasts, and drop to about 20%–30% for 120-h forecasts. The success rate is higher for formations in the South China Sea and between 160°E and 180° but is generally lower between 120° and 160°E. The composite 850-hPa large-scale flow for the formations between 120° and 160°E is similar to a monsoon confluence region with marked cross-equatorial flow. Therefore, it is concluded that the skil...

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.