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Cyclone Surface Pressure Fields and Frontogenesis from NASA Scatterometer (NSCAT) Winds David F

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Cyclone Surface Pressure Fields and Frontogenesis from NASA Scatterometer (NSCAT) Winds David F JOURNAL OF GEOPHYSICAL~EARCH, VOL. 105,NO. C10,PAGES 23,967-23,981, OcroBER 15, 200) Cyclone surface pressure fields and frontogenesis from NASA scatterometer (NSCAT) winds David F. Zierden, Mark A. Bourassa,and JamesJ. O'Brien Center for Ocean-Atmospheric Prediction Studies, Florida State University, Tallahassee,32306-284{) Abstract. Two extratropical marine cyclones and their associatedfrontal features are examined by computing surface pressure fields from NASA scatterometer (NSCAT) winds. A variational method solves for a new surface pressure field by blending high- resolution (25 kIn) relative vorticity computed along the satellite track with an initial geostrophic vorticity field. Employing this method with each successivepass of the satellite over the study area allows this surface pressure field to evolve as dictated by the relative vorticity patterns computed from NSCA T winds. The result is a high-resolution surface pressure field that captures features such as fronts and low-pressure centers in more detail than National Centers for Environmental Prediction (NCEP) reanalyses.While using the actual relative vorticity to adjust the geostrophic vorticity ignores the ageostrophy of surface winds, which can be significant in the vicinity of fronts and jet streaks, it is a necessaryapproximation given that the technique uses only surface data. The NSCAT surface pressure fields prove to be nearly as accurate as NCEP reanalyseswhen compared to ship and buoy observations,which is an encouraging result given that NCEP reanalyses incorporate a myriad of data sources and the NSCA T fields rely primarily on one source. In addition, the high-resolution relative vorticity fields computed from NSCAT winds reveal the location of surface fronts in great detail. These fronts are verified using NCEP analyses,in situ data, and satellite imagery. 1. Introduction the assimilation of ERS-1 winds into the European Centre for Medium-Range Weather Prediction (ECMWF) model im- The lack of conventional data over the oceanshas long been pacted the forecasts only marginally [Hoffman, 1993].Andrews a limiting factor in the accuracyof weather forecasting [Atlas et and Ben [1998] demonstrated marked improvements in the ai., 1985]. Often, the only data available are surface observa- United Kingdom Meteorological Office forecasts by assimilat- tions from ships and buoys, which are sparseoutside shipping ing ERS-1 winds, particularly over the Southern Ocean where lanes and the Tropical Ocean-Global Atmosphere experiment conventional data are sparse. More recently, Atlas and Hoff- (TOGA)-Tropical Atmosphere-Ocean (TAD) buoy anay. man [2000] found that the greatest positive impacts of NSCAT Conventional data are now supplemented with satellite data, winds on NWP forecastsresulted from the vertical extensionof and the challenge lies in finding methods to utilize these new surface winds and the modification of surface pressure fields. data sources best. One such source is surface wind vector Some studies have employed scatterometer winds in diag- measurementsform spaceborne scatterometers,which can be nostic studies of midlatitude and tropical cyclones.In many of used to derive surface pressure fields. these studies, scatterometers were only one of many data NASA scatterometer (NSCAT) and other scatterometers sources implemented in improving NWP analysesof the fea- provided wind measurements over the ocean with much ture [Antheset aI., 1983; Tomossiniet aI., 1998;Liu et aI., 1998]. greater resolution and coveragethan were previously available. In contrast, Harlan and O'Brien [1986] assimilated only Sea- Recent research looked to find ways to utilize this high-quality sat-A scatterometer data with National Centers for Environ- data source. A common approach was to form gridded prod- mental Prediction (NCEP, formerly NMC) surface pressure ucts [Liu et al., 1998; Bourassa et al., 1999; Verschellet al., fields to obtain an improved estimate of the central pressurein 1999].These gridded products were used to drive ocean circu- the QE-II storm of 1978. All of these studies showed how lation models, to improve surface fluxes for general circulation scatterometer winds improved estimates of the central surface models, and to study the evolution of regional winds. pressuresand predicted intensities of the systems. The assimilation of scatterometer winds has also had a p0s- Brown and Zeng [1994] developed a method for computing itive impact on numerical weather prediction (NWP). Early surface pressure fields in midlatitude cyclones using ERS-1 impact studies [Baker et aI., 1984, Duffy et ai., 1984] using winds from a single swath and a boundary layer model. Surface Seasat-A winds improved surface analyses significantly, but gradient winds were found using ERS-1 wind data as input to had limited effects on higher levels and forecasts. Duffy and the boundary layer model. Surface pressures were then com- Atlas [1986] first demonstrated improved forecasts with the puted from the gradient winds, and a reference pressure was vertical extension of Seasat-A surface winds, which adjusted located within the field. The computed surface pressure fields mass at higher levels of the model, not just the surface. Later, distinguished fronts and located the centers of cyclones accu- Copyright2(XX) by the AmericanGeophysical Union. rately while giving improved estimates of central pressureover Papernumber 200)J~2 NCEP analyses.Hsu and Wwtele [1997] employed this method 0148-0227/OOf2{XX}J~2$09.00 with Sea.~t-A winds in a similar study. The strength of the 23.967 23.968 ZIERDEN ET AL.: SURFACE PRESSURE FIELDS AND FRONTS FROM NSCAT WINDS 3000 2. Data 2.1. NSCAT 1!11J!11~j~li!li~i!!!i!. !;!i,,!!!!!!!li6m!!:j The primary data used in this study are the NSCAT2level n winds with a resolution of 25 kIn along the satellite's path. 2000 These winds are an updated version processedby the ,JetPro- pulsion Laboratory from measured backscatter using an im- e proved model function. NSCAT operated aboard Japan's .!4 ADEOS for 9 months from late September 1996through June 1997. NSCAT was the first of a new generation of scatterom- 1000 eters; it used many technological advances to improve the quality, coverage, and resolution of near-surface winds. NSCAT's radar operated in Ku band (13.995GHz) rather than the C band as was done by ERS-I. This frequency led to greater accuracy at low wind speeds «4 m S-I), although 0 sensitivity to attenuation by liquid water was increased. Engi- 1000 0 1000 neering advancements in the sensors increased the signal to kID noise ratio of the backscatter measurements,greatly improving Figure 1. Data coverageand resolution along the path of the ambiguity selection. In addition, each wind cell was viewed ADEOS. Dots mark the relative location of each wind sample. from three different angles. The NSCA T radar was dual- polarized from one antenna, providing additional measure- ments to aid in the ambiguity selection. NSCAT was equipped to measure backscatter on both sides of the satellite track, boundary layer approach was twofold: (1) the surface pressure doubling the coverage of ERS-l, which viewed only on one field was derived almost exclusively from scatterometer data, side. and (2) swath data were used directly, without averaging in A digital Doppler filter grouped overlapping backscatter spaceor time. The drawback was that pressurescould only be measurementsfrom the different viewing anglesinto 25 km by computed within the swath of wind data. A discussion of the 25 km cells. The wind speed and direction were computed for each cell using the observed backscatters and a lookup table. accuracy of scatterometer surface pressure fields is given by Calibration/validation of the NSCAT model function was more Zeng and Brown [1998]. These surface pressure fields showed accurate than previous scatterometersbecause of comparisons greatest improvement over NWP analysesover the Southern with high-quality in situ surface observations from research Hemisphere, where the lack of conventional observations can vessels [Bourassa et aI., 1997], National Data Buoy Center cause entire systems to be misplaced or missed all together (NDBC) buoys [Freilich and Dunbar, 1999], and the TOGA- [Brown and Levy, 1986;Levy and Brown, 1991]. TAO array (K. Kelley and S. Dickenson, personal communi- This study makes use of the high-quality NSCAT wind data cation, 1998). In particular, these in situ data included many by deducing surface pressure fields through the use of a vari- observations at low and high wind speeds, enabling accurate ational method. The primary goals are (1) to use NSCAT calI'bration/validation and remoVing the low wind speed biases winds to detemline surface pressure fields, (2) to follow the found in other scatterometers. evolution of surface features descn'bed mostly with NSCAT Attenuation by liquid in the atmosphere, particularly heavy data, (3) to locate and identify surface fronts, and (4) to pro- precipitation, is a disadvantage of the Ku band frequency. vide a surface pressure field that could be used to improve Contamination from precipitation droplets can significantly NWP over the oceans. degrade the quality of scatterometer-computed wind vectors. Section 2 descn'bes the data sets, including specifics of Ideally, inclusion of a passive microwave radiometer on the NSCAT and its near-surface wind observations. Section 3 de- satellite platform could have identified contaminated cells and tails the variational method used to detemline surface pres- flagged them appropriately. Unfortunately, mission specifica- sures.The variational method involves the assimilation of rel- tions and funding did not allow for such an instrument to be ative vorticity computed from the NSCA T wind vectors. included with NSCAT, so it is difficult to identify contaminated Surface fronts are located and identified in the relative vortic- cells. Studies are ongoing to determine the effects of precipi- ity field as localized bands of high relative vorticity (section tation on the overall accuracy of the NSCAT winds. 3.1.1). These features are verified as fronts using in situ obser- The ADEOS was a low-altitude, Sun synchronous, near- polar orbiter.
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