Donglian Sun

Contrasting the 2007 and 2005 hurricane seasons: Evidence of possible impacts of Saharan dry air and dust on tropical cyclone activity in the Atlantic basin

[1] In this study, we provide preliminary evidence of possible modulation by Saharan dust of hurricane genesis and intensification, by contrasting the 2007 and 2005 hurricane seasons. It is found that dust aerosol loadings over the Atlantic Ocean are much higher in 2007 than in 2005. The temperature difference between 2007 and 2005 shows warming in the low-middle troposphere (900–700 hPa) in the dusty region in the eastern North Atlantic, and cooling in the Main Development Region (MDR). The humidity (wind) differences between 2007 and 2005 indicate significant drying (subsidence) in the Western North Atlantic (WNA) in 2007. The drier air in the WNA in 2007 is found to be associated with the further westward transport of the Saharan air layer (SAL). To quantify wind pattern favorable for transport of SAL over the WNA, we define a zonal wind stretch index which shows significant long-term correlation with the mid-level humidity in the WNA. Analyses of the stretch index and related environmental controls suggest that the westward expansion of the Saharan dry air and dust layer can be an important factor in contributing to the difference between the relatively quiescent hurricane season in 2007 and the very active season of 2005. Citation: Sun, D., K. M. Lau, and M. Kafatos (2008), Contrasting the 2007 and 2005 hurricane seasons: Evidence of possible impacts of Saharan dry air and dust on tropical cyclone activity in the Atlantic basin, Geophys. Res. Lett., 35, L15405, doi:10.1029/2008GL034529.

Land surface temperature estimation from the next generation of Geostationary Operational Environmental satellites: GOES M-Q

Abstract The next generation of Geostationary Operational Environmental Satellites (GOES M–Q) will have only one thermal window channel instead of the current two split-window thermal channels. There is a need to evaluate the usefulness of this new configuration to retrieve parameters that presently are derived by utilizing the split-window characteristics. Two algorithms for deriving land surface temperatures (LSTs) from the GOES M–Q series have been developed and will be presented here. Both algorithms are based on radiative transfer theory; one uses ancillary total precipitable water (TPW) data, and the other is a two-channel (3.9 and 11.0 μm) algorithm that aims to improve atmospheric correction by utilizing the middle infrared (MIR) channel. The proposed algorithms are compared with a well-known generalized split-window algorithm. It is found that by adding TPW to the 11.0-μm channel, similar results to those from the generalized split-window algorithm are attained, and the combination of 3.9 and 11....

Numerical Simulations of the Impacts of the Saharan Air Layer on Atlantic Tropical Cyclone Development

Abstract In this study, the role of the Saharan air layer (SAL) is investigated in the development and intensification of tropical cyclones (TCs) via modifying environmental stability and moisture, using multisensor satellite data, long-term TC track and intensity records, dust data, and numerical simulations with a state-of-the-art Weather Research and Forecasting model (WRF). The long-term relationship between dust and Atlantic TC activity shows that dust aerosols are negatively associated with hurricane activity in the Atlantic basin, especially with the major hurricanes in the western Atlantic region. Numerical simulations with the WRF for specific cases during the NASA African Monsoon Multidisciplinary Analyses (NAMMA) experiment show that, when vertical temperature and humidity profiles from the Atmospheric Infrared Sounder (AIRS) were assimilated into the model, detailed features of the warm and dry SAL, including the entrainment of dry air wrapping around the developing vortex, are well simulated....

Seasonal Variations in Diurnal Temperature Range From Satellites and Surface Observations

The diurnal temperature range (DTR) is an important climate-change variable and, until recently, it was derived from station observations of surface air temperature (Ta). Station-based observations are sparse and unevenly distributed, making the use of satellites an attractive option for evaluating DTR. In this study, satellite-based estimates of DTR are evaluated against ground measurements of surface skin temperature (Ts) and compared with weather-station observations based on Ta. Geographical and seasonal differences were identified in both ground- and satellite-derived DTRs. Estimates of DTR from station-observed air temperature represent all-sky conditions while satellite estimates of DTR from surface skin temperature represent clear conditions only. For both station observations and satellite estimates, DTRs at rural locations tend to be larger than at urban sites. The DTRs based on Ts are larger than those derived from Ta under both all and clear-sky conditions. Clouds tend to reduce the magnitude of the DTR. The station-observed DTRs are found to be larger in summer than in winter over the entire U.S. The satellite-derived DTRs are larger in spring and fall than in winter and summer over the eastern U.S., while they are larger in spring and summer than in fall and winter over the western part. Evapotranspiration from land vegetation and the effects of water-vapor radiative forcing have a major effect on the detected spatial and seasonal variations in the DTR patterns

Case study of soil moisture effect on land surface temperature retrieval

Land surface temperature (LST) is an important element of the climate system. Remote sensing methods for estimating LST have been developed in the past and several of them have been implemented at large-scales. Geostationary satellites are of particular interest because they depict the diurnal cycle. Soil moisture has a strong effect on the magnitude of surface temperature via its influence on emissivity; yet, information on soil moisture at large scales is meager. It is of interest to estimate what effect soil moisture has on the retrieval accuracy of surface temperature by methods of remote sensing. In this study, newly developed algorithms to estimate land surface temperature (LST) from geostationary satellites will be applied to GOES-8 observations during the Southern Great Plains 1997 Hydrology Experiment (SGP-97) when surface observations of both soil moisture and surface temperature were made. The ground observations were used to first demonstrate the influence of soil moisture on the diurnal cycle of the surface temperature, its amplitude and the lag in LST maxima. Subsequently, it was established that errors in LST as derived from GOES-8 measurements have a negative correlation with soil moisture, namely, increasing with the decrease of soil moisture.

A New Short-Wave Infrared (SWIR) Method for Quantitative Water Fraction Derivation and Evaluation With EOS/MODIS and Landsat/TM Data

A quantitative method is developed for deriving water fraction from coarse- to medium-resolution satellite data with visible to short-wave infrared (SWIR) channels based on the linear mixture theory. The method uses a SWIR channel (1.64 μm) by assuming that the water-surface-leaving radiance in this channel is insignificant and is thus less affected by water types and water depth than near-infrared (NIR) channels for inland water bodies. For a mixed water pixel, a dynamic nearest neighbor searching (DNNS) method is used to find the nearby land pixels to determine the average land reflectance. The nearby pure water pixels with a similar water type to the subpixel water portion of the mixed water pixel are found dynamically to derive the average water reflectance. The average reflectance in the SWIR channel from both pure land pixels and water pixels is used to calculate the water fraction from a linear mixture model. The developed method is applied to Moderate Resolution Imaging Spectroradiometer (MODIS) data and shows promising results. High-resolution satellite data from the Thematic Mapper (TM) are used to evaluate the water fraction derived from MODIS. During pixel-to-pixel water fraction evaluation, TM data are spatially aggregated to MODIS resolution. When evaluated against the high-resolution TM observations, water fractions derived from MODIS using the DNNS method with the SWIR channel show a bias of -0.021 with a standard deviation of 0.0338. Comparing lake areas between TM and MODIS data also shows consistent results with the pixel-to-pixel water fraction comparison. The DNNS method is also compared to the traditional histogram method both with SWIR channel and NIR channel. The results show that the DNNS method is more accurate than the histogram method and that the SWIR channel is better than the NIR channel to derive highly accurate water fraction from coarse- to medium-resolution satellite data.

Comment on “Satellite altimetry and the intensification of Hurricane Katrina”

In a recent Eos article, Scharroo et al. [2005] reported that the dynamic sea topography anomalies along the track of Hurricane Katrina were the most prominent factors causing the intensification of Katrina as it passed over these anomalous regions in the Gulf of Mexico. They show that the sea surface temperature (SST) in the entire Gulf of Mexico was uniformly ∼30°C and was not associated with the rapid intensification of Katrina. We partly agree with their findings based on the results of dynamic topography associated with Katrinas intensification; however, we do not concur with their idea that SST was not linked with the rapid intensification of Katrina. Here, we show the significant impact of high SST anomaly in the Gulf on Katrinas rapid intensification and the role of anomalous SST in governing the air-sea interactions during its intensification.

Towards Operational Automatic Flood Detection Using EOS/MODIS Data

This study investigates how to derive water fraction and flood map from the Moderate-Resolution Imaging Spectroradiometer (MODIS) using a Regression Tree (RT) approach, which can integrate all predictors. The New Orleans, Louisiana floods in August 2005 were selected as a case study. MODIS surface reflectance with matched water fraction data were used for training. The tree-based regression models were obtained automatically through learning process. The tree structure reveals that near-infrared reflectance is more important than the difference and ratio between near-infrared and visible channels for water fraction estimate. Flood distributions were generated using the differences in water fraction values between after and before the flooding. The derived water fractions were evaluated against 30 m Thematic Mapper (TM) data from Landsat observations. Water fractions derived from the MODIS and TM data agree well (R 2 = 0.94, bias = 0.38 percent, and RMSE = 4.35 percent). The results show that the RT approach in dynamic monitoring of floods is acceptable.

Evaluation of 10 year AQUA/MODIS land surface temperature with SURFRAD observations

As the 10 year Moderate Resolution Imaging Spectroradiometer Land Surface Temperature MODIS LST becomes available, it is significant to perform a comprehensive evaluation on the long-term product before downstream users use it for climate studies and atmospheric models. In this study, a validation is carried out using observations from the US Surface Radiation budget (SURFRAD) network. Strict quality control removes cloud-contaminated samples from MODIS LST collection and decreases noise information from SURFRAD measurements, thereby making the validation more persuasive. With analysis on 19,735 valid samples, Aqua/MODIS LST from a split-window algorithm shows retrieval errors from –14 K to 17 K with a bias of –0.93 K, an RMSE of 2.65 K, and a standard deviation of 2.48 K. The errors also show strong seasonal signals. With correlation tests between LST errors and several other factors, it is disclosed that LST retrieval errors mainly come from atmospheric effects and surface emissivity uncertainties, which are closely related to relative air humidity, absolute air humidity, sensor zenith angle, wind speed, normalized difference vegetation index (NDVI), and soil moisture. In addition, the impacts from these factors may not be independent. These impact factors suggest a deficiency of the split-window algorithm in dealing with atmospheric and surface complexity and variety.

Association Rule Data Mining Applications for Atlantic Tropical Cyclone Intensity Changes

AbstractThis study applies a data mining technique called association rule mining to the analysis of intensity changes of North Atlantic tropical cyclones (TCs). The “best track” data from the National Hurricane Center and the Statistical Hurricane Intensity Prediction Scheme databases were stratified into tropical depressions, tropical storms, and category 1–5 hurricanes based on the Saffir–Simpson hurricane scale. After stratification, the seven resulting groups of TCs plus two additional aggregation groups were further separated into intensifying, weakening, and stable TCs. The analysis of the stratified data for preprocessing revealed that faster northward storm motion (the meridional component of storm motion) favors tropical storm intensification but does not favor the intensification of hurricanes. Intensifying tropical storms are more strongly associated with a higher convergence in the upper atmosphere (200-hPa relative eddy momentum flux convergence) than weakening tropical storms, while intensi...