Impact of Enhanced Satellite-Derived Atmospheric Motion Vector Observations on Numerical Tropical Cyclone Track Forecasts in the Western North Pacific during TPARC/TCS-08

Berger, Howard; Langland, Rolf H.; Velden, Christopher S.; Reynolds, C. A.; Pauley, Patricia M.

doi:10.1175/jamc-d-11-019.1

Cited by 51 publications

(29 citation statements)

References 14 publications

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“…This is done because assimilating many high-resolution observations, which represent small-scale variability the model cannot resolve, can introduce noise into the model and degrade the solution. Furthermore, the assimilation scheme assumes observation errors to be uncorrelated, and the super-obbing process reduces any such correlations that might exist, as well as reducing random error (Berger et al, 2011;Purser et al, 2000).…”

Section: Quality Controlmentioning

confidence: 99%

Assimilating GlobColour ocean colour data into a pre-operational physical-biogeochemical model

et al. 2012

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Abstract. As part of the GlobColour project, daily chlorophyll a observations, derived using remotely sensed ocean colour data from the MERIS, MODIS and SeaWiFS sensors, are produced. The ability of these products to be assimilated into a pre-operational global coupled physicalbiogeochemical model has been tested, on both a hindcast and near-real-time basis, and the impact on the system assessed. The assimilation was found to immediately and considerably improve the bias, root mean square error and correlation of modelled surface chlorophyll concentration compared to the GlobColour observations, an improvement which was sustained throughout the year and in every ocean basin. Errors against independent in situ chlorophyll observations were also reduced, both at and beneath the ocean surface. However, the model fit to in situ observations was not consistently better than that of climatology, due to errors in the underlying model. The assimilation scheme used is multivariate, updating all biogeochemical model state variables at all depths. The other variables were not degraded by the assimilation, with annual mean surface fields of nutrients, alkalinity and carbon variables remaining of similar quality compared to climatology. There was evidence of improved representation of zooplankton concentration, and reduced errors were seen against in situ observations of nitrate and pCO 2 , but too few observations were available to conclude about global model skill. The near-real-time GlobColour products were found to be sufficiently reliable for operational purposes, and of benefit to both operational-style systems and reanalyses.

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Section: Quality Controlmentioning

confidence: 99%

Assimilating GlobColour ocean colour data into a pre-operational physical-biogeochemical model

et al. 2012

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“…Techniques for assimilation of geostationary satellite data with high temporal resolution have been improving in recent years (Munro et al 2004;Peubey and McNally 2009;Berger et al 2011;Okamoto 2013;Wu et al 2014). Yamashita (2010) reported that track forecast errors for typhoon SINLAKU were reduced by 9 %-12 % on average by the assimilation of RS-AMVs in the experiments conducted during the THORPEX Pacific Asian Regional Campaign 2008 (Nakazawa et al 2008).…”

Section: Introductionmentioning

confidence: 99%

Assimilation Experiments of MTSAT Rapid Scan Atmospheric Motion Vectors on a Heavy Rainfall Event

Otsuka

Kunii

Seko

et al. 2015

Journal of the Meteorological Society of Japan

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Atmospheric motion vectors (AMVs) derived from 5-min rapid scan (RS) imagery of the Multi-functional Transport Satellite are expected to capture small-scale distributions of airflows better than typical AMVs derived from 30-min imagery because the observation interval of RS-AMV is shorter. The impact of these high-frequency data on the numerical forecasting of a heavy rainfall near a stationary front was investigated by conducting data assimilation experiments. As a part of preparation for the assimilation, RS-AMVs were compared with the firstguess field obtained from the Japan Meteorological Agency (JMA) nonhydrostatic model (NHM). The comparison result indicated that the RS-AMVs were of good quality and could be used in the JMA's operational NHM with 4D variational data assimilation (JNoVA). Assimilation experiments investigating a heavy rainfall event were conducted using different lengths of assimilation time slot and time intervals of spatial thinning for the assimilation of the RS-AMV data. The assimilation of RS-AMVs caused the initial wind fields to enhance the upper-level divergence and low-level convergence around the front. Consequently, the forecast of the rainfall amount was increased near the front, and the verification scores were slightly improved over the control experiment in the early forecast hours.

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“…Aberson also noted that storms not targeted with special observations could be impacted by dropwindsondes in other systems, as the influence of the dropwindsonde data propagates globally through the update cycle, and due to the spectral nature of the system. Berger et al (2011) examined the impact of enhanced satellite-derived atmospheric motion vectors (AMVs), as well as dropwindsonde data, on T-PARC TC forecasts in the Navy forecast system. [For a description of satellite products made available during T-PARC/TCS-08, please see Hawkins and Velden (2011).]…”

Section: Introductionmentioning

confidence: 99%

Tropical Cyclone Data Impact Studies: Influence of Model Bias and Synthetic Observations

Reynolds¹,

Langland²,

Pauley³

et al. 2013

Monthly Weather Review

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The impacts of assimilating dropwindsonde data and enhanced atmospheric motion vectors (AMVs) on tropical cyclone track forecasts are examined using the Navy global data assimilation and forecasting systems. Enhanced AMVs have the largest impact on eastern Pacific storms, while dropwindsonde data have the largest impact on Atlantic storms. Results in the western Pacific are mixed. Two western Pacific storms, Nuri and Jangmi, are examined in detail. For Nuri, dropwindsonde data and enhanced AMVs are at least as likely to degrade as to improve forecasts. For Jangmi, additional data improve track forecasts in most cases. An erroneous weakening of the forecasted subtropical high appears to contribute to the track errors for Nuri and Jangmi. Assimilation of enhanced AMVs systematically increases the analyzed heights in this region, counteracting this model bias. However, the impact of enhanced AMVs decreases rapidly as the model biases saturate at similar levels for experiments with and without the enhanced AMVs after the first few forecast days. Experiments are also conducted in which the errors assigned to synthetic tropical cyclone observations are increased. Moderate increases in the assigned errors improve track forecasts on average, but larger increases in the assigned errors produce mixed results. Both experiments allow for reductions in innovations and residuals when compared to dropwindsonde observations. These experiments suggest that a reformulation of the synthetic tropical cyclone observation scheme may lead to improved forecasts as more in situ and remote observations become available.

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Impact of Enhanced Satellite-Derived Atmospheric Motion Vector Observations on Numerical Tropical Cyclone Track Forecasts in the Western North Pacific during TPARC/TCS-08

Cited by 51 publications

References 14 publications

Assimilating GlobColour ocean colour data into a pre-operational physical-biogeochemical model

Assimilating GlobColour ocean colour data into a pre-operational physical-biogeochemical model

Assimilation Experiments of MTSAT Rapid Scan Atmospheric Motion Vectors on a Heavy Rainfall Event

Tropical Cyclone Data Impact Studies: Influence of Model Bias and Synthetic Observations

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