Quick Scatterometer Data Research Articles

Antarctic snow melt detection based on the synergy of SSM/I and QuikSCAT

AbstractMicrowave radiometer SSM/I (Special Sensor Microwave Imager) data and scatterometer QuikSCAT (Quick Scatterometer) data have been widely used for near-surface snow melt detection based on their sensitivity to liquid water present in snow. The SSM/I data have high reliability and the QuikSCAT data have high spatial resolution. In order to improve the accuracy of Antarctic near-surface snow melt detection, we propose a new method based on the synergy of SSM/I and QuikSCAT data, i.e. the snow melt physical model incorporates the complementary advantages of both datasets. Based on comparisons with temperature data from three automatic weather stations, the proposed algorithm improved the accuracy of snow melt detection. The algorithm could also be applied to other regions, which would provide further evidence to support its use and additional data to document changes in the Antarctic due to global climate change.

Research on the interannual variability of the great whirl and the related mechanisms

Based on AVISO (archiving, validation and interpretation of satellite data in oceanography) data from 1993 to 2010, QuikSCAT (Quick Scatterometer) data from 2000 to 2008, and Argo data from 2003 to 2008, the interannual variability of the Great Whirl (GW) and related mechanisms are studied. It shows that the origin and termination times of the GW, as well as its location and intensity, have significant interannual variability. The GW appeared earliest (latest) in 2004 (2008) and vanished earliest (latest) in 2006 (2001), with the shortest (longest) duration in 2008 (2001). Its center was most southward (northward) in 2007 (1995), while the minimum (maximum) amplitude and area occurred in 2003 and 2002 (1997 and 2007), respectively. The GW was weaker and disappeared earlier with its location tending to be in the southwest in 2003, while in 2005 it was stronger, vanished later and tended to be in northeast. The abnormal years were often not the same among different characters of the GW, and were not all coincident with ENSO (El Nino-Southern Oscillation) or IOD (Indian Ocean Dipole) events, indicating the very complex nature of GW variations. Mechanism investigations shows that the interannual variability of intraseasonal wind stress curl in GW region results in that of the GW. The generation of the GW is coincident with the arrival of Rossby waves at the Somali coast in spring; the intensity of the GW is also influenced by Rossby waves. The termination of the GW corresponds well to the second one of the top two peaks in the baroclinic energy conversion rate in GW region, and the intensity and the position of the GW are also closely related to the top two baroclinic energy conversion rates.

Assimilating QuikSCAT Ocean Surface Winds with the Weather Research and Forecasting Model for Surface Wind-Field Simulation over the Chukchi/Beaufort Seas

To achieve a high-quality simulation of the surface wind field in the Chukchi/Beaufort Sea region, quick scatterometer (QuikSCAT) ocean surface winds were assimilated into the mesoscale Weather Research and Forecasting model by using its three-dimensional variational data assimilation system. The SeaWinds instrument on board the polar-orbiting QuikSCAT satellite is a specialized radar that measures ice-free ocean surface wind speed and direction at a horizontal resolution of 12.5 km. A total of eight assimilation case studies over two five-day periods, 1–5 October 2002 and 20–24 September 2004, were performed. The simulation results with and without the assimilation of QuikSCAT winds were then compared with QuikSCAT data available during the subsequent free-forecast period, coastal station observations, and North American Regional Reanalysis data. It was found that QuikSCAT winds are a potentially valuable resource for improving the simulation of ocean near-surface winds in the Chukchi/Beaufort Seas region. Specifically, the assimilation of QuikSCAT winds improved, (1) offshore surface winds as compared to unassimilated QuikSCAT winds, (2) sea-level pressure, planetary boundary-layer height, as well as surface heat fluxes, and (3) low-level wind fields and geopotential height. Verification against QuikSCAT data also demonstrated the temporal consistency and good quality of QuikSCAT observations.

Assessment of Wind Pattern Accuracy from the QuikSCAT Satellite and the WRF Model along the Galician Coast (Northwest Iberian Peninsula)

Abstract Surface wind along the Galician coast is a key factor allowing the analysis of important oceanographic features that are related to the great primary production in this area, as upwelling events. A comparative analysis between surface winds obtained from the Quick Scatterometer (QuikSCAT), the Weather Research and Forecasting (WRF) Model, and in situ observations from buoys along the Galician coast is carried out from November 2008 to October 2009. This comparison evaluates the accuracy of satellite and modeled data. The results show that the wind speeds derived from QuikSCAT and the WRF Model are similar along the coast, with errors ranging from 1.5 to 2 m s−1. However, QuikSCAT tends to overestimate wind speeds when compared to the buoys measurements. Regarding the wind direction, the RMSE values are about 35° for the stations under analysis. The bias presents a similar pattern between satellite and modeled data, with positive values at the western coast and negative values at the middle and northern coasts, the satellite data always being lower in absolute value than the modeled data. A spatial comparison between QuikSCAT and WRF data is also performed over the whole Galician coast to evaluate the differences between the two datasets. This comparison shows that the modeled wind speed tends to be lower than satellite winds over the entire domain, with the highest RMSE and bias values found for the wind speed and direction observed near the shoreline.

Influence of a southern shift of the ITCZ from Quick Scatterometer data on the Pacific North Equatorial Countercurrent

By analyzing the climatologically averaged wind stress during 2000–2007, it is found that the easterly wind stress in the northern tropical Pacific Ocean from Quick Scatterometer (QSCAT) data was stronger than those from Tropical Atmosphere Ocean (TAO) data and from National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis I. As a result, the Intertropical Convergence Zone (ITCZ) in the Pacific Ocean is more southward in the QSCAT data than in the NCEP/NCAR data. Relative to the NCEP wind, the southern shift of the ITCZ in the QSCAT data led to negative anomaly of wind stress curl north of a latitude of 6°N. The negative anomaly results in downward Ekman pumping in the central Pacific. The excessive local strong easterly wind also contributes to the downward Ekman pumping. This downward Ekman pumping suppresses the thermocline ridge, reduces the meridional thermocline slope and weakens the North Equatorial Countercurrent (NECC). These effects were confirmed by numerical experiments using two independent ocean general circulation models (OGCMs). Furthermore, the excessive equatorial easterly wind stress was also found to contribute to the weaker NECC in the OGCMs. A comparison between the simulations and observation data indicates that the stronger zonal wind stress and its southern shift of QSCAT data in the ITCZ region yield the maximum strength of the simulated NECC only 33% of the magnitude derived from observation data and even led to a “missing” NECC in the western Pacific.

Size and Strength of Tropical Cyclones as Inferred from QuikSCAT Data

A comprehensive statistical climatology of the size and strength of the tropical cyclones (TCs) occurring over the western North Pacific (WNP; including the South China Sea) and the North Atlantic (NA; including the Gulf of Mexico and the Caribbean Sea) between 1999 and 2009 is constructed based on Quick Scatterometer (QuikSCAT) data. The size and strength of a TC are defined, respectively, as the azimuthally averaged radius of 17 m s−1 of ocean-surface winds (R17) and the azimuthally averaged tangential wind within 1°–2.5°-latitude radius from the TC center (outer-core wind strength, OCS). The mean TC size and strength are found to be 2.13° latitude and 19.6 m s−1, respectively, in the WNP, and 1.83° latitude and 18.7 m s−1 in the NA. While the correlation between size and strength is strong (r ≈ 0.9), that between intensity and either size or strength is weak. Seasonally, midsummer (July) and late-season (October) TCs are significantly larger in the WNP, while the mean size is largest in September in the NA. The percentage frequency of TCs having large size or high strength is also found to vary spatially and seasonally. In addition, the interannual variation of TC size and strength in the WNP correlate significantly with the TC lifetimes and the effect of El Niño over the WNP. TC lifetime and seasonal subtropical ridge activities are shown to be potential factors that affect TC size and strength.

Application of QuikSCAT Backscatter to SMAP Validation Planning: Freeze/Thaw State Over ALECTRA Sites in Alaska From 2000 to 2007

The mapping of the predominant freeze/thaw state of the landscape is one of the main objectives of the National Aeronautics and Space Administration's proposed Soil Moisture Active Passive (SMAP) mission. This study applies Alaska Ecological Transect (ALECTRA) biophysical network temperature measurements and satellite radar scatterometer data from the Quick Scatterometer (QuikSCAT) to evaluate some of the validation issues regarding the planned SMAP freeze/thaw measurements. Although the QuikSCAT data are acquired at Ku-band frequency, rather than at the L-band frequency of the proposed SMAP instrument, QuikSCAT data do provide a high temporal fidelity over the ALECTRA sites, similar to SMAP. The results of this study show that multiple temperature measurements representative of individual landscape components (soil, snow cover, vegetation, and atmosphere) covering different types of terrain within the satellite field of view are important for understanding the freeze/thaw process and the aggregate radar backscatter response to that process. The backscatter temporal dynamics and relative contribution of the freeze/thaw state of these landscape elements to radar signal vary with land cover, seasonal weather, and climate conditions.

A Statistical Analysis of Near-Core Surface Wind Asymmetries in Typhoons Obtained from QuikSCAT Data

The relationships between near-core surface wind asymmetries in typhoons and both the environmental vertical wind shear and typhoon movement are examined using the historical QuikSCAT (NASA Quick Scatterometer) wind data, putting stress on the azimuthal location of the tangential wind maximum. The results clearly show that the highest winds occur preferentially to the left of shear as well as to the right of storm motion when their effects on the wind fields are separately evaluated using a composite approach. A further composite analysis reveals that the magnitude of left to right asymmetry with respect to storm motion or shear depends largely on the directional difference between shear and storm motion with the largest asymmetry being generated when their directions are opposite to each other. In cases where shear and storm motion vectors point toward a similar direction under relatively strong shear conditions, the left to right asymmetry is very small and a significant percentage of the cases exhibit a left-of-motion maximum. These results are highly consistent with previous ones which were obtained from the JMA (Japan Meteorological Agency) operational mesoscale analysis wind data. In the present study, the effect of inner-core rainfall asymmetry on the azimuthal location of the tangential wind maximum is also examined. The results show that the relation between shear and surface wind asymmetry can be described in terms of rainfall asymmetry in place of shear, as expected from the increasingly well-documented strong connection between shear and rainfall asymmetry, suggesting that the asymmetric features of over-ocean wind fields in typhoons could be estimated to some degree using information on typhoon motion and rainfall asymmetry.

Structure change of Typhoon Nari (2007) in the weakening stage

The structure of Typhoon Nari (11th 2007) was investigated based on the field observations and the numerical simulation with the Weather Research and Forecasting (WRF) model. The observations were carried out using the wind profiler and dual Doppler radar at Haenam Weather Observatory and Jeju Island, which were analyzed along with the Quick Scatterometer (QuikSCAT) dataset. The simulation data were obtained from the 60 hours run of the WRF model to get the detailed structure, intensity, and track of the typhoon during the period from 0000 UTC 14 September 2007 to 1200 UTC 16 September. The TC initialization for the numerical simulation was performed with the use of the 3D balanced bogus vortex and spherical high-order filter proposed by Kwon and Cheong (2010) to provide accurate forecasts of the track and intensity. The horizontal wind fields at the surface, the tangential and radial winds, were analyzed using the QuikSCAT data and the model results. The vertical wind structure was investigated using the dual-Doppler and wind profiler data in combination with the model simulation results. The structure change of Typhoon Nari during the weakening stage was analyzed based on the simulations. Compared to the mature stage, in the weakening stage the Typhoon Nari was found to have stronger asymmetric component, and it exhibited stronger vertical tilt but with reduced vertical extent. It was shown that the strong uppertropospheric outflow loses its strength gradually in the weakening stage, but the height of maximum outflow layer was remained unchanged due to the environmental forcing by the upper-troposphere jet. Comparison of the observation and model simulation revealed that a weak outflow was formed in the mid-troposphere in addition to the primary outflow in the upper troposphere.

Improvement of Mesoscale Forecasts of Monsoon Depressions Through Assimilation of QuikSCAT Wind Data: Two Case Studies Over India

Monsoon depressions form during the Southwest Indian Monsoon over the Bay of Bengal and provide copious rainfall over the eastern and central parts of the country. Since these depressions form over sea, a region of data scarcity, satellite data provides only source of information of the meteorological system. Furthermore, for short-range prediction, it is extremely important to have accurate initial conditions for better model performance. In this study, effects of three dimensional variational (3DVAR) assimilation of the Quick Scatterometer (QuikSCAT) data is used in the simulation of two monsoon depressions (MDs) that formed during 2-5 September and 27-30 September 2006 using the Weather Research and Forecast (WRF) modeling system. The National Center for Environmental Prediction - Global Forecast (NCEP-GFS) fields were used for the initial and lateral boundary conditions. Two model runs were employed in this study; first a control (CTRL) or a base run without any data assimilation and another a 3DVAR run in which QuikSCAT data was assimilated using the 3DVAR assimilation. The model results from both runs were compared with one another as well as with Tropical Rainfall Measurement Mission (TRMM) observations and Global Analysis (GFS-ANL) fields. The results of the time and area averaged vertical profile of relative vorticity over monsoon depressions indicate that the 3DVAR run is in closer agreement with GFS-ANL as compared to the CTRL run. Furthermore, the well-known temperature structure of a monsoon depression (cold core at low levels and warm core at upper levels) is better simulated by the 3DVAR run. While there is a clear and marked positive impact of ingesting the QuikSCAT data in terms of simulated precipitation for the depression that formed during 27-30 September 2006, improvement in the simulated rainfall due to QuikSCAT assimilation is slight for the other depression that formed during 2-5 September 2006. Consistent with the above observations, there is a clear improvement in the quantitative measures of the skill scores with lower bias, lower false alarms and higher probability of detection for almost all rainfall thresholds for the model runs which have assimilated the QuikSCAT observations.

Uncertainty in Scatterometer-Derived Vorticity

Abstract A more versatile and robust technique is developed for determining area-averaged surface vorticity based on vector winds from swaths of remotely sensed wind vectors. This technique could also be applied to determine the curl of stress, and it could be applied to any gridded dataset of winds or stresses. The technique is discussed in detail and compared to two previous studies that focused on early development of tropical systems. Error characteristics of the technique are examined in detail. Specifically, three independent sources of error are explored: random observational error, truncation error, and representation error. Observational errors are due to random errors in the wind observations and determined as a worst-case estimate as a function of averaging spatial scale. The observational uncertainty in the Quick Scatterometer (QuikSCAT)-derived vorticity averaged for a roughly circular shape with a 100-km diameter, expressed as one standard deviation, is approximately 0.5 × 10−5 s−1 for the methodology described herein. Truncation error is associated with the assumption of linear changes between wind vectors. Uncertainty related to truncation has more spatial organization in QuikSCAT data than observational uncertainty. On 25- and 50-km scales, the truncation errors are very large. The third type of error, representation error, is due to the size of the area being averaged compared to values with 25-km length scales. This type of error is analogous to oversmoothing. Tropical and subtropical low pressure systems from three months of QuikSCAT observations are used to examine truncation and representation errors. Representation error results in a bias of approximately −1.5 × 10−5 s−1 for area-averaged vorticity calculated on a 100-km scale compared to vorticity calculated on a 25-km scale. The discussion of these errors will benefit future projects of this nature as well as future satellite missions.

Tropical Cyclone Initialization with Dynamical Retrieval from a Modified UWPBL Model

A new scheme, termed Vortex Initialization with the Assimilation of Retrieved Variables (VIRV), is presented to improve the initialization of regional numerical model for Tropical Cyclone (TC) prediction. In this scheme, the horizontal winds in Planetary Boundary Layer (PBL) and the sea level pressure (SLP), retrieved from Quick Scatterometer (QuikSCAT) data obtained using a modified University of Washington Planetary Boundary Layer (UWPBL) model, are assimilated with a cycled three-dimensional variational (3DVAR) technique to produce the initialized analysis. The procedures of retrieval are implemented under the joint dynamical constraints of the gradient wind, secondary circulation, and thermal stratification. Moreover, in order to improve the analysis of TC intensity, the roughness parameterization in the UWPBL model was modified for the case of strong surface wind. The sensitivities of the structure, intensity, and track of TC to the VIRV are then examined by two numerical experiments for TC Bilis (2006) and TC Fung-wong (2008). The maximum Wind Speed (MWS) and minimum Sea Level Pressure (MSLP) retrieved from the QuikSCAT data obtained using the modified UWPBL model show more agreement with the observations relative to those derived from the analysis of the National Center for Environmental Prediction (NCEP global model). The analysis of TC intensity cfm enhanced using VIRV by modifying the low-level (upper-level) convergence (divergence), vertical shear of horizontal wind, transportation of moisture. Significant improvement on 48-h TC simulation is identified in the MWS, with 22.8% error reduction. In particular, the Modification of Roughness Parameterization (MRP) enhanced the simulation of MWS by 6.9%. Finally, the VIRV also reduces the simulation error in the �

Impacts of Satellite-Observed Winds and Total Precipitable Water on WRF Short-Range Forecasts over the Indian Region during the 2006 Summer Monsoon

Abstract Assimilation experiments have been performed with the Weather Research and Forecasting (WRF) model’s three-dimensional variational data assimilation (3DVAR) scheme to assess the impacts of NASA’s Quick Scatterometer (QuikSCAT) near-surface winds, and Special Sensor Microwave Imager (SSM/I) wind speed and total precipitable water (TPW) on the analysis and on short-range forecasts over the Indian region. The control (without satellite data) as well as WRF 3DVAR sensitivity runs (which assimilated satellite data) were made for 48 h starting daily at 0000 UTC during July 2006. The impacts of assimilating the different satellite dataset were measured in comparison to the control run, which does not assimilate any satellite data. The spatial distribution of the forecast impacts (FIs) for wind, temperature, and humidity from 1-month assimilation experiments for July 2006 demonstrated that on an average, for 24- and 48-h forecasts, the satellite data provided useful information. Among the experiments, WRF wind speed prediction was improved by QuikSCAT surface wind and SSM/I TPW assimilation, while temperature and humidity prediction was improved due to the assimilation of SSM/I TPW. The rainfall prediction has also been improved significantly due to the assimilation of SSM/I TPW, with the largest improvement seen over the west coast of India. Through an improvement of the surface wind field, the QuikSCAT data also yielded a positive impact on the precipitation, particularly for day 1 forecasts. In contrast, the assimilation of SSM/I wind speed degraded the humidity and rainfall predictions.

Evaluation of surface and near‐surface melt characteristics on the Greenland ice sheet using MODIS and QuikSCAT data

The Greenland ice sheet has been the focus of much attention recently because of increasing melt in response to regional climate warming and can be studied using Moderate Resolution Imaging Spectroradiometer (MODIS) and Quick Scatterometer (QuikSCAT) data. To improve our ability to measure surface melt, we use remote sensing data products to study surface and near‐surface melt characteristics of the Greenland ice sheet for the 2007 melt season when record melt extent and runoff occurred. MODIS daily land surface temperature (LST), MODIS daily snow albedo, and a special diurnal melt product derived from QuikSCAT (QS) scatterometer data, are all effective in measuring the evolution of melt on the ice sheet. These daily products, produced from different parts of the electromagnetic spectrum, are sensitive to different geophysical features, though QS‐ and MODIS‐derived melt generally show excellent correspondence when surface melt is present. Values derived from the MODIS daily snow albedo product drop in response to melt and change with apparent grain size changes. For the 2007 melt season, the MODIS LST and QS products detect 766,184 km2 ± 8% and 862,769 km2 ± 3% of melt, respectively. The QS product detects about 11% greater melt extent than is detected by the MODIS LST product probably because QS is more sensitive to surface melt and can also see subsurface melt. The consistency of the response of the different products demonstrates unequivocally that physically meaningful melt/freeze boundaries are detected. We have demonstrated that when these products are used together we can improve the precision in mapping surface and near‐surface melt extent on the Greenland ice sheet.

The Operational Use of QuikSCAT Ocean Surface Vector Winds at the National Hurricane Center

Abstract The utility and shortcomings of near-real-time ocean surface vector wind retrievals from the NASA Quick Scatterometer (QuikSCAT) in operational forecast and analysis activities at the National Hurricane Center (NHC) are described. The use of QuikSCAT data in tropical cyclone (TC) analysis and forecasting for center location/identification, intensity (maximum sustained wind) estimation, and analysis of outer wind radii is presented, along with shortcomings of the data due to the effects of rain contamination and wind direction uncertainties. Automated QuikSCAT solutions in TCs often fail to show a closed circulation, and those that do are often biased to the southwest of the NHC best-track position. QuikSCAT winds show the greatest skill in TC intensity estimation in moderate to strong tropical storms. In tropical depressions, a positive bias in QuikSCAT winds is seen due to enhanced backscatter by rain, while in major hurricanes rain attenuation, resolution, and signal saturation result in a large negative bias in QuikSCAT intensity estimates. QuikSCAT wind data help overcome the large surface data void in the analysis and forecast area of NHC’s Tropical Analysis and Forecast Branch (TAFB). These data have resulted in improved analyses of surface features, better definition of high wind areas, and improved forecasts of high-wind events. The development of a climatology of gap wind events in the Gulf of Tehuantepec has been possible due to QuikSCAT wind data in a largely data-void region. The shortcomings of ocean surface vector winds from QuikSCAT in the operational environment at NHC are described, along with requirements for future ocean surface vector wind missions. These include improvements in the timeliness and quality of the data, increasing the wind speed range over which the data are reliable, and decreasing the impact of rain to allow for accurate retrievals in all-weather conditions.

QuikSCAT Impacts on Coastal Forecasts and Warnings: Operational Utility of Satellite Ocean Surface Vector Wind Data

Abstract This study reports on the operational utility of ocean surface vector wind (SVW) data from Quick Scatterometer (QuikSCAT) observations in the National Oceanic and Atmospheric Administration (NOAA) National Weather Service (NWS) Weather Forecast Offices (WFOs) covering the coastal United States, including island states and territories. Thirty-three U.S. coastal WFOs were surveyed, and 16 WFO site visits were conducted, from late summer 2005 to the 2005/06 winter season, in order to quantify the impact of QuikSCAT SVW data on forecasts and warnings, with a particular focus on operations affecting marine users. Details of the survey design and site visit strategies are described. Survey results are quantified and site visit impressions are discussed. Key findings include (i) QuikSCAT data supplement primary datasets and numerical weather prediction fields, in the manual production of local public (weather) and marine forecasts and warnings; (ii) operational utility of satellite SVW data would be enhanced by SVW retrievals of finer temporal resolution, closer to the coasts; and (iii) rain flags in the SVW data have little impact on utility for WFO operations.

The Operational Impact of QuikSCAT Winds in Perth, Australia: Examples and Limitations

Abstract The preparation of accurate operational weather forecasts and the timely issuance of severe marine weather and ocean warnings and advisories for major oceanic weather systems impacting both coastal areas and the open ocean are major forecasting problems facing the Australian Bureau of Meteorology’s Regional Forecast Centre (RFC) and its collocated Tropical Cyclone Warning Centre (TCWC) in Perth, Western Australia. The region of responsibility for the Perth RFC is vast, covering a large portion of the southeast Indian and Southern Oceans, both of which are extremely data sparse, especially for near-surface marine wind data. Given that these coastline and open-ocean areas are subject to some of the world’s most intense tropical cyclones, rapidly intensifying midlatitude cyclones, and powerful cold fronts, there is now a heavy reliance upon NASA Quick Scatterometer (QuikSCAT) data for both routine and severe weather warning forecasts. The focus of this note is on the role of QuikSCAT data in the Perth RFC for the accurate and early detection of maritime severe weather systems, both tropical and extratropical. First, the role of QuikSCAT data is described, and then three cases are presented in which the QuikSCAT data were pivotal in providing forecast guidance. The cases are a severe tropical cyclone in its development phase off the northwest coast of Australia, a strong southeast Indian Ocean cold front, and an explosively developing midlatitude Southern Ocean cyclone. In each case, the Perth RFC would have been unable to provide early and high-quality operational forecast and warning guidance without the timely availability of the QuikSCAT surface wind data.

Vorticity-Based Detection of Tropical Cyclogenesis

Abstract Ocean wind vectors from the SeaWinds scatterometer aboard the Quick Scatterometer (QuikSCAT) satellite and Geostationary Operational Environmental Satellite (GOES) imagery are used to develop an objective technique that can detect and monitor tropical disturbances associated with the early stages of tropical cyclogenesis in the Atlantic basin. The technique is based on identification of surface vorticity and wind speed signatures that exceed certain threshold magnitudes, with vorticity averaged over an appropriate spatial scale. The threshold values applied herein are determined from the precursors of 15 tropical cyclones during the 1999–2004 Atlantic Ocean hurricane seasons using research-quality QuikSCAT data. The choice of these thresholds is complicated by the lack of suitable validation data. The combination of GOES and QuikSCAT data is used to track the tropical disturbances that are precursors to the 15 tropical cyclones. This combination of data can be used to test detection but is not as easily used to examine false alarms. Tropical disturbances are found for these cases within a range of 19–101 h before classification as tropical cyclones by the National Hurricane Center. The 15 cases are further subdivided based upon their origination source (i.e., easterly wave, upper-level cutoff low, stagnant frontal zone, etc.). The primary focus centers on the cases associated with tropical waves, because these waves account for the majority of all Atlantic tropical cyclones. The detection technique illustrates the ability to track these tropical disturbances from near the coast of Africa. Analysis of the pretropical cyclone (pre-TC) tracks for these cases depicts stages, related to wind speed and precipitation, in the evolution of a tropical disturbance within an easterly wave to a tropical cyclone.

The Impact of Assimilating SSM/I and QuikSCAT Satellite Winds on Hurricane Isidore Simulations

AbstractThree observational datasets of Hurricane Isidore (in 2002) were analyzed and compared: the Special Sensor Microwave Imager (SSM/I), the Quick Scatterometer (QuikSCAT) winds, and dropsonde winds. SSM/I and QuikSCAT winds were on average about 1.9 and 0.3 m s−1 stronger, respectively, than dropsonde winds. With more than 20 000 points of data, SSM/I wind speed was about 2.2 m s−1 stronger than QuikSCAT. Comparison of the wind direction observed by QuikSCAT with those from the dropsondes showed that the quality of QuikSCAT data is good. The effect of assimilating SSM/I wind speeds and/or QuikSCAT wind vectors for the analysis of Hurricane Isidore was assessed using the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) and its three-dimensional variational data assimilation system. For the Hurricane Isidore case study, it was found that the assimilation of either satellite winds strengthened the cyclonic circulation in the analysis. However, the increment of the QuikSCAT wind analysis is more complicated than that from the SSM/I analysis due to the correction of the storm location, a positive result from the assimilation of wind vectors. The increase in low-level wind speeds enhanced the air–sea interaction processes and improved the simulated intensity for Isidore. In addition, the storm structure was better simulated. Assimilation of QuikSCAT wind vectors clearly improved simulation of the storm track, in particular during the later period of the simulation, but lack of information about the wind direction from SSM/I data prevented it from having much of an effect. Assessing the assimilation of QuikSCAT wind speed versus wind vector data confirmed this hypothesis. The track improvement partially resulted from the relocation of the storm’s initial position after assimilation of the wind vectors. For this case study, it was found that the assimilation of SSM/I or QuikSCAT data had the greatest impact on the Hurricane Isidore simulation during the first 2 days.

On the Use of QuikSCAT Scatterometer Measurements of Surface Winds for Marine Weather Prediction

Abstract The value of Quick Scatterometer (QuikSCAT) measurements of 10-m ocean vector winds for marine weather prediction is investigated from two Northern Hemisphere case studies. The first of these focuses on an intense cyclone with hurricane-force winds that occurred over the extratropical western North Pacific on 10 January 2005. The second is a 17 February 2005 example that is typical of sea surface temperature influence on low-level winds in moderate wind conditions in the vicinity of the Gulf Stream in the western North Atlantic. In both cases, the analyses of 10-m winds from the NCEP and ECMWF global numerical weather prediction models considerably underestimated the spatial variability of the wind field on scales smaller than 1000 km compared with the structure determined from QuikSCAT observations. The NCEP and ECMWF models both assimilate QuikSCAT observations. While the accuracies of the 10-m wind analyses from these models measurably improved after implementation of the QuikSCAT data assimilation, the information content in the QuikSCAT data is underutilized by the numerical models. QuikSCAT data are available in near–real time in the NOAA/NCEP Advanced Weather Interactive Processing System (N-AWIPS) and are used extensively in manual analyses of surface winds. The high resolution of the QuikSCAT data is routinely utilized by forecasters at the NOAA/NCEP Ocean Prediction Center, Tropical Prediction Center, and other NOAA weather forecast offices to improve the accuracies of wind warnings in marine forecasts.