Over the past four decades, remote sensing of the atmosphere from space has undergone a steady progression from qualitative vertically-integrated measurements to quantitative vertically-resolved information - providing high-density observations. Satellite observations are especially useful in data sparse ocean areas where TC's occur.
Of particular recent interest is the issue of the effect of dust aerosols on TC's. Here, remote sensing (RS) data become even more crucial, particularly vertical measurements. As such, the importance of African dust has been increasingly recognized for its potential influence on climate and TC's worldwide (Arimoto, 2001; Goudie and Middleton, 2002; Kaufman et al., 2002). Radiative forcing (e.g., Carlson and Benjamin, 1980; d'Almeida, 1987; Li et al., 2003; Miller and Tegen, 1998; Weaver et al., 2002; Christopher and Jones, 2006) is recognized as the most important aspect of African dust. Such outbreaks are associated with an elevated layer of hot, dry air outbreaks (e.g., Prospero and Carlson, 1981). Dry air affects tropical clouds and precipitation directly through thermal structure and indirectly through dry air entrainment (e.g., Mapes and Zuidema, 1996; Yoneyma and Parsons, 1999; Zhang and Chou, 1999; Tompkins, 2001).
Tropical cyclones/hurricanes in the Atlantic basin often develop from mesoscale convective systems embedded within the African Easterly Waves (AEWs) that develop over West Africa. Previous studies show that the Atlantic basin major hurricane activity is associated with western Sahelian monsoon rainfall, and negative Sahelian rainfall anomalies or droughts are associated with suppressed Atlantic basin TC activity (Goldenberg and Shapiro, 1996). They explained that drought induced the increase of vertical wind shear and therefore inhibited TC development.
Since rainfall in the Sahel is found to be highly anti-correlated with African dust (Prospero and Lamb, 2003), the Atlantic basin TC activity is also found to be anti-correlated with African dust outbreaks (Evan et al., 2006). Recently, Wu et al., 2006 analyzed the effects of the Saharan air layer (SAL) on Hurricane Isabel by incorporating the Atmospheric Infrared Sounder (AIRS on the Aqua satellite) measurements into MM5 model runs. They found that the SAL may have delayed the formation of Hurricane Isabel and inhibited the development of another tropical disturbance following behind it. Dunion and Velden (2004) suggest the dry SAL can suppress Atlantic tropical cyclone activity by increasing the vertical wind shear and stabilizing the environment at low levels.
We also believe the radiative forcing effects of dust aerosols on sea surface temperature (SST) may be an important factor to influence TC activity as shown in the study of Lau and Kim (2007). The reason is because SST plays a fundamental role in the inter-annual variability of tropical storm frequency and intensity (Vitart et al., 1999), and a direct role in providing moist enthalpy (i.e., latent and sensible heat flux) to intensify tropical cyclones.
In the study of TC development and intensification, as well as the possible aerosol effects on TC formation, several data sets have become of interest, which can be coupled to numerical runs (cf. Boybeyi et al., 2007; Kafatos et al., 2006; Sun et al. 2007). Some of these data sets include;
• MODIS from the NASA GSFC DAAC;
o Daytime aerosol optical depth (AOD) at 0.55 mm, o Daytime SST.
• AIRS daytime vertical temperature and moisture profiles from the NASA GSFC DAAC.
• CALIPSO vertical data from the NASA GSFC DAAC.
• Tropical cyclone data from the National Hurricane Center (NHC) Hurricane Best Track Files (HURDAT), available at http://www.nhc.noaa.gov/pastall. shtml.
• NOAA Extended Reconstructed SST V2 products from 1854 to 2003 (http://lwf. ncdc.noaa.gov/oa/climate/research/sst/sst.html#ersst).
• Monthly mean wind and humidity profile data from the NCEP-NCAR reanalysis (NNR) from 1948 to present.
• Dust concentration data at the Barbados sampling station (13o 10'N, 50o30'W), which provide the most extensive long-term records with aerosol measurements that start in 1965 (Prospero and Lamb, 2003).
• Aerosol Index (AI) data with 1o X 1.25o resolution are from 1980 to 2006 (1980 to 1993 from the Nimbus and 1996-2000 from the total ozone mapping spectrometer, TOMS, and 2004-2006 from the Ozone Mapping Instrument, OMI, from the NASA GSFC) with missing data from 1993 to 1995 (http://toms.gsfc. nasa.gov/aerosols/aerosols_v8.html). The AI data from 2001-2003 are not used due to the unreliability in the TOMS data.
The primary factors that will determine the use of any observational data in NWP models are the quality and spatial and temporal resolution of the data. Since the current in-situ atmospheric measurement system is inadequate to determine the current state of the atmosphere particularly over water areas where TCs develop, remote sensing observations provide a viable option with better geographical coverage. This aspect of remote sensing data is important for NWP models. As to the quality of remote sensing data, over past four decades, quality of the data has undergone a steady improvement. For example, the standard error for surface temperature is now less than 0.8 K, the SST data have accuracies to +/—0.4 degrees Celsius, temperature profiles have accuracies to 1°K in 1 km layer, and water vapor profiles have accuracies of 15% in 2-km layer in the troposphere. In short, the regular and opportunity driven observations constitute the backbone of the global observing network and will likely do so for the foreseeable future. Satellite derived atmospheric properties have the accuracy, coverage, and resolution to dominate our understanding of the dynamics of the atmosphere. The utilization of this data in NWP models will improve the forecast accuracy of such models.
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