Atmospheric Infrared Sounder Data Research Articles

Monthly distribution of diurnal total column ozone based on the 2011 satellite data in Peninsular Malaysia

Ozone (O3) is a radiatively active trace gas, and naturally present in our atmosphere, that plays a prominent role in atmosphere heating rates due to its good capability to absorb the infrared radiation. O3 occurs both naturally in the Earth’s upper atmosphere and at the ground level. As we breathe the air on Earth, O3 causes damage to the lung tissue and plants as it is an injurious pollutant; it is a major constituent of smog. The atmospheric O3 observations can only be made on global and continental scales by remote sensing instruments situated in the space. The satellite-borne sensor, namely the Atmospheric Infrared Sounder (AIRS) included on the EOS Aqua satellite, was employed to investigate the spatial and temporal variations of diurnal total column Ozone burden over Peninsular Malaysia for the year 2011. The analysis of O3 above five dispersed stations in the study area shows the seasonal variation in the O3 fluctuated considerably observed between NEM and SWM seasons. The mean and the standard deviation of monthly O3 was 244.7±26.8DU for the entire period, and O3 values strongly correlated with weather conditions. The highest O3 values occurred over industrial and congested urban zones (271.5DU) on May at Johor. The lowest O3 values were observed during NEM in the pristine coastal environment on December at Kuantan (217DU; at 3.45°N, 103.20°E). The O3 has an inverse relationship with the rain and positive with temperature. The monthly O3 maps were obtained from the NASA-operated Giovanni portal (http://disc.sci.gsfc.nasa.gov/giovanni). The AIRS data and the satellite measurements are able to measure the increase of the atmosphere O3 concentrations over different areas.

Simultaneous observations of convective gravity waves from a ground‐based airglow imager and the AIRS satellite experiment

We report the first joint observations of convectively generated gravity waves (GWs) using an OH airglow imager in Colorado and the Atmospheric Infrared Sounder (AIRS) onboard the Aqua satellite. Convective GWs, appearing as concentric rings, are observed over the western Great Plain regions of North America in the evening of 3 June 2008 in the airglow images. Inspecting both weather radars and AIRS radiances at 8.1 µm, strong convective clouds are found near the center of the concentric rings. The AIRS data at 4.3 µm show semicircular GWs with horizontal wavelengths of 60–80 km at 0900 UT, whereas the airglow imager observed circular GWs with horizontal wavelengths of ~44 km and airglow emission perturbation of ~6% at the same geographic location at 0910 UT. Large‐scale GWs (horizontal wavelengths greater than 100 km) emanating northwestward can be seen in both AIRS data and airglow images at different times. The imager observed small‐scale ripples associated with unstable concentric GWs in the mesopause in the early evening. Given that the brightness temperature perturbation of the GWs in the AIRS data is about 0.16 K and assuming that the GWs propagate without dissipation from the stratosphere to the upper mesosphere, the expected airglow emission perturbation caused by the GWs would be 4%–17%. Ray tracing simulations are performed to demonstrate that the GWs seen in AIRS and in the imager were likely excited by the same convective system.

Retrieval of Ice Cloud Properties from AIRS and MODIS Observations Based on a Fast High-Spectral-Resolution Radiative Transfer Model

AbstractA computationally efficient high-spectral-resolution cloudy-sky radiative transfer model (HRTM) in the thermal infrared region (700–1300 cm−1, 0.1 cm−1 spectral resolution) is advanced for simulating the upwelling radiance at the top of atmosphere and for retrieving cloud properties. A precomputed transmittance database is generated for simulating the absorption contributed by up to seven major atmospheric absorptive gases (H2O, CO2, O3, O2, CH4, CO, and N2O) by using a rigorous line-by-line radiative transfer model (LBLRTM). Both the line absorption of individual gases and continuum absorption are included in the database. A high-spectral-resolution ice particle bulk scattering properties database is employed to simulate the radiation transfer within a vertically nonisothermal ice cloud layer. Inherent to HRTM are sensor spectral response functions that couple with high-spectral-resolution measurements in the thermal infrared regions from instruments such as the Atmospheric Infrared Sounder (AIRS) and Infrared Atmospheric Sounding Interferometer. When compared with the LBLRTM and the discrete ordinates radiative transfer model (DISORT), the root-mean-square error of HRTM-simulated single-layer cloud brightness temperatures in the thermal infrared window region is generally smaller than 0.2 K. An ice cloud optical property retrieval scheme is developed using collocated AIRS and Moderate Resolution Imaging Spectroradiometer (MODIS) data. A retrieval method is proposed to take advantage of the high-spectral-resolution instrument. On the basis of the forward model and retrieval method, a case study is presented for the simultaneous retrieval of ice cloud optical thickness τ and effective particle size Deff that includes a cloud-top-altitude self-adjustment approach to improve consistency with simulations.

Correction for GOES Imager Spectral Response Function Using GSICS. Part II: Applications

During the Geostationary Operational Environmental Satellite (GOES)-14 and -15 post-launch test (PLT) for science periods, an up to ~ 2 K mean brightness temperature (Tb) bias with respect to collocated Atmospheric Infrared Sounder (AIRS) and Infrared Atmospheric Sounding Interferometer (IASI) observations was observed in the absorptive IR channels of the GOES-14/15 Imagers. These large scene-dependent biases were believed to be caused mainly by spectral characterization errors. In this paper, we refined the spectral response function (SRF) shift algorithm which was developed during the GOES-13 PLT period to improve the GOES-14/15 Imager IR radiometric calibration accuracy by accurately calculating the impact of blackbody on the calibrated scene radiance. The uncertainty of the SRF shift algorithm was estimated and used to guide the final selection of the total amount of central wave-number shift. This refined algorithm was first verified with GOES-13 Imager Ch6 data and then used to evaluate and further revise the audited GOES-14/15 SRFs provided by the instrument vendor. Based on this algorithm, the optimal SRF shifts were -1.98 cm-1 for GOES-13 Ch6, -8.25 cm-1 for GOES-14 Ch3, -0.25 cm-1 for GOES-14 Ch6, -6.25 cm-1 for GOES-15 Ch3 and +0.50 cm-1 for GOES-15 Ch6. The newly shifted SRFs were operationally implemented into the GOES-14/15 Imager IR calibrations in the August of 2011 and successfully reduced the mean all-sky Tb bias with respect to the reference instrument to less than 0.15 K. The scene-dependent bias, which can be nonlinear at large erroneous SRF, was also greatly reduced. The same method was applied to correct the GOES-12 Imager Ch6 SRF which has a changing SRF error during its mission life. A strong linear relationship between the optimal SRF shifts and the mean Tb bias with respect to the AIRS data was observed at this channel. This strong linear relationship can be used to revise the GOES-12 Ch6 SRF for a better radiance simulation. The method described in this paper is particularly important to evaluate and revise the erroneous SRF, if it exists, after satellite launch yet before it becomes fully operational.

Coupling effect of ozone column and atmospheric infrared sounder data reveal evidence of earthquake precursor phenomena of Bam earthquake, Iran

Understanding the source mechanism of earthquakes may be the key to predict earthquakes. The testing of radioactive radiations and reactionary hypothesis of gases before and after quake events can help predict and monitor earthquake occurrence. In this study, the Atmospheric Infrared Sounder (AIRS) and the column ozone (O3) were applied to evaluate the December 26, 2003 earthquake of Bam city in western Iran. The results show that ozone concentration (column density) decreased about 30 DU and or 807 × 10E15/cm2 molecules. Using high-resolution AIRS data for the study area, we were able to discriminate gases that formed and changed before the main shock at least a day before the occurrence of the quake in Bam.

Estimation and validation of atmospheric water vapor content using a MODIS NIR band ratio technique based on AIRS water vapor products

Atmospheric water vapor validation needs simultaneous, well-defined, and independent information which are not easily available causing limitations in the development of remote sensing water vapor retrieval algorithms. This study is concerned with the retrieval of total atmospheric water vapor content and its validation. A band ratio method has been used to estimate the water vapor content based on Moderate Resolution Imaging Spectroradiometer (MODIS) Near InfraRed (NIR) data. The method uses MODIS bands 17, 18, and 19 as NIR bands and band 2 to remove the land cover reflectance. Furthermore, the Atmospheric Infrared Sounder (AIRS) has been used for both algorithm development and analysis of the results. The method has been modified to take into account the dry condition of the central parts of Iran. Using some various datasets, the method is implemented and evaluated quantitatively. The validation of the water vapor estimates has been undertaken by an analysis of AIRS data. The validation results shows error as low as 9 % for the estimated water vapor using the MODIS NIR band ratio method.

Influence of El Niño on Midtropospheric CO2 from Atmospheric Infrared Sounder and Model

Abstract The authors investigate the influence of El Niño on midtropospheric CO2 from the Atmospheric Infrared Sounder (AIRS) and the Model for Ozone and Related Chemical Tracers, version 2 (MOZART-2). AIRS midtropospheric CO2 data are used to study the temporal and spatial variability of CO2 in response to El Niño. CO2 differences between the central and western Pacific Ocean correlate well with the Southern Oscillation index. To reveal the temporal and spatial variability of the El Niño signal in the AIRS midtropospheric CO2, a multiple regression method is applied to the CO2 data from September 2002 to February 2011. There is more (less) midtropospheric CO2 in the central Pacific and less (more) midtropospheric CO2 in the western Pacific during El Niño (La Niña) events. Similar results are seen in the MOZART-2 convolved midtropospheric CO2, although the El Niño signal in the MOZART-2 is weaker than that in the AIRS data.

Using AIRS retrievals in the WRF-LETKF system to improve regional numerical weather prediction

ABSTRACTIn addition to conventional observations, atmospheric temperature and humidity profile data from the Atmospheric Infrared Sounder (AIRS) Version 5 retrieval products are assimilated into the Weather Research and Forecasting (WRF) model, using the local ensemble transform Kalman filter (LETKF). Although a naive assimilation of all available quality-controlled AIRS retrieval data yields an inferior analysis, the additional enhancements of adaptive inflation and horizontal data thinning result in a general improvement of numerical weather prediction skill due to AIRS data. In particular, the adaptive inflation method is enhanced so that it no longer assumes temporal homogeneity of the observing network and allows for a better treatment of the temporally inhomogeneous AIRS data. Results indicate that the improvements due to AIRS data are more significant in longer-lead forecasts. Forecasts of Typhoons Sinlaku and Jangmi in September 2008 show improvements due to AIRS data.

Preliminary applications of a land surface temperature retrieval method to IASI and AIRS data

Land surface temperature (LST) is one of the key state variables for many applications. This article aims to apply our previously developed LST retrieval method to infrared atmospheric sounding interferometer (IASI) and atmospheric infrared sounder (AIRS) data. On the basis of the opposite characteristics of the atmospheric spectral absorption and surface spectral emissivity, a ‘downwelling radiance residual index’ (DRRI) has been recalled and improved to obtain LST and emissivity. To construct an efficient DRRI, an automatic channel selection procedure has been proposed, and 11 groups of channels have been selected within the range 800–1000 cm−1. The DRRI has been tested with IASI and AIRS data. For the IASI data, the radiosonde data have been used to correct for atmospheric effects and to retrieve LST, while the atmospheric profiles retrieved from AIRS data have been used to perform the atmospheric corrections and subsequently to estimate LST from AIRS data. The differences between IASI- and Moderate Resolution Imaging Spectroradiometer (MODIS)-derived LSTs are no more than 2 K, while the differences between AIRS- and MODIS-derived LSTs are less than 5 K. Even though an exceptionally problematic value occurred (–12.89 K), the overall differences between AIRS-estimated LST and the AIRS L2 LST product are no more than 5 K. Although the IASI-derived LST is more accurate than the AIRS-derived one, the convenient retrieval of AIRS atmospheric profile made this method more applicable. Limitations and uncertainties in retrieving LST using the DRRI method are also discussed.

An investigation of statistical link between inversion strength and carbon monoxide over Scandinavia in winter using AIRS data

Temperature inversions influence the local air quality at smaller scales and the pollution transport at larger spatio-temporal scales and are one of the most commonly observed meteorological phenomena over Scandinavia (54°N–70°N, 0–30°E) during winter. Here, apart from presenting key statistics on temperature inversions, a large-scale co-variation of inversion strength and carbon monoxide (CO), an ideal pollution tracer, is further quantified at six vertical levels in the free troposphere during three distinct meteorological regimes that are identified based on inversion strength. Collocated temperature and CO profiles from Atmospheric Infrared Sounder (AIRS) are used for this purpose. Higher values of CO (up to 15%) are observed over Scandinavia during weakly stable regimes at all vertical levels studied, whereas lower CO values (up to 10%) are observed when inversions become stronger and elevated. The observed systematic co-variation between CO and inversion strength in three meteorological regimes is most likely explained by the efficacy of long-range transport to influence tropospheric composition over Scandinavia. We argue that this large-scale co-variation of temperature inversions and CO would be a robust metric to test coupling of large-scale meteorology and chemistry in transport models.

On the Surface Temperature Sensitivity of the Reflected Shortwave, Outgoing Longwave, and Net Incident Radiation

Abstract The global-mean top-of-atmosphere incident solar radiation (ISR) minus the outgoing longwave radiation (OLR) and the reflected shortwave radiation (RSW) is the net incident radiation (NET). This study analyzes the global-mean NET sensitivity to a change in the global-mean surface temperature by applying the interannual anomaly correlation technique to 9 yr of Atmospheric Infrared Sounder (AIRS) global measurements of RSW and OLR under cloudy and clear conditions. The study finds the observed sensitivity of NET that includes the effects of clouds to be −1.5 ± 0.25 (1σ) W m−2 K−1 and the clear NET sensitivity to be −2.0 ± 0.2 (1σ) W m−2 K−1, consistent with previous work using Earth Radiation Budget Experiment and Clouds and the Earth’s Radiant Energy System data. The cloud effect, +0.5 ± 0.2 (1σ) W m−2 K−1, is a positive component of the NET sensitivity. The similarity of the NET sensitivities derived from forced and unforced models invites a comparison between the observed sensitivities and the effective sensitivities calculated for the Fourth Assessment Report models, although this requires some caution: The effective model sensitivities with clouds range from −0.88 to −1.64 W m−2 K−1, the clear NET sensitivity in the models ranges from −2.32 to −1.73 W m−2 K−1, and the cloud forcing sensitivities range from +0.14 to +1.18 W m−2 K−1. The effective NET and clear NET sensitivities derived from the models are statistically consistent with those derived from the AIRS data, considering the observational and model derivation uncertainties.

Massive Dataset Analysis for NASA’s Atmospheric Infrared Sounder

The National Aeronautics and Space Administration’s (NASA) Atmospheric Infrared Sounder (AIRS) has been collecting large quantities of remote sensing data about the vertical structure of temperature, water vapor, and clouds in the Earth’s atmosphere since its launch aboard the Aqua spacecraft in mid-2002. These data are both global and high resolution, so they are uniquely able to provide distributional information about second- and higher-order interactions that are at the heart of understanding climate processes. However, these data are so large and complex that the structures of interest are not directly accessible without some form of data reduction, and data reduction is particularly problematic because of the way the data are staged and stored. Thus, AIRS data pose a classic problem in the analysis of modern massive datasets: how to quantify and understand global distributional relationships in datasets that are impossible to work with except in small pieces? Our approach is to hierarchically reduce the data in a way that preserves distributional characteristics across subsets formed by stratifying on intuitively meaningful variables. By exploring how distributions change as functions of the stratification variables, we gain insight into processes generating the data. In this article, we describe our implementation of methodology first proposed in two earlier papers. Here, we have operationalized the algorithm and demonstrated that it is both practical within the NASA data processing pipeline, and leads to important scientific insights that are only possible when massive datasets are reduced in a way that respects the structure of multivariate distributions.

A global climatology of upper-tropospheric ice supersaturation occurrence inferred from the Atmospheric Infrared Sounder calibrated by MOZAIC

Abstract. Ice supersaturation in the upper troposphere is a complex and important issue for the understanding of cirrus cloud formation. On one hand, infrared sounders have the ability to provide cloud properties and atmospheric profiles of temperature and humidity. On the other hand, they suffer from coarse vertical resolution, especially in the upper troposphere and therefore are unable to detect shallow ice supersaturated layers. We have used data from the Measurements of OZone and water vapour by AIrbus in-service airCraft experiment (MOZAIC) in combination with Atmospheric InfraRed Sounder (AIRS) relative humidity measurements and cloud properties to develop a calibration method for an estimation of occurrence frequencies of ice supersaturation. This method first determines the occurrence probability of ice supersaturation, detected by MOZAIC, as a function of the relative humidity determined by AIRS. The occurrence probability function is then applied to AIRS data, independently of the MOZAIC data, to provide a global climatology of upper-tropospheric ice supersaturation occurrence. Our climatology is then compared to ice supersaturation occurrence statistics from MOZAIC alone and related to high cloud occurrence from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). As an example of application it is compared to model climatologies of ice supersaturation from the Integrated Forecast System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF) and from the European Centre HAmburg Model (ECHAM4). This study highlights the benefits of multi-instrumental synergies for the investigation of upper tropospheric ice supersaturation.

Methane Interannual Distribution over Peninsular Malaysia from Atmospheric Infrared Sounder Data: 2003-2009

Methane (CH4) is a significant greenhouse gas (GHG's) with a relatively short atmospheric lifetime of about 12 years, and is released to the atmosphere by biological processes occurring in anaerobic environments. The CH4 is second in importance only to CO2 with regard to its environmental effects, and its relative global warming ability is 23 times that of CO2 over a time horizon of 100 years. The interannual distribution of atmospheric CH4 has been studied in Peninsular Malaysia during the period 2003–2009 using Atmosphere Infrared Sounder (AIRS) data, onboard NASA's Aqua Satellite. The analysis of CH4 above five dispersed stations in the study area shows that the high CH4 growth rates observed at the end of each year can be attributed to the increased emissions from biomass burning and wetlands, and the reduced hydroxyl (OH) sink. In particular, we observe a quasi-biennial variation in CH4 emissions in Peninsular Malaysia, with varying magnitudes in peak emissions occurring in 2004, 2006, and 2008. The seasonal variation in the CH4 fluctuated significantly between northeast (NEM) and southwest (SWM) monsoon seasons. The CH4 value in the NEM season was higher than in the SWM season, and higher in the north regions, above the latitude 4°, than in the rest of area throughout the year. To study the CH4 distribution over peninsular Malaysia for 2009, monthly CH4 maps were generated using the Kriging interpolation technique. The AIRS data and satellite measurements are able to measure the increase in the atmospheric CH4 concentrations over different regions.

Precipitable Water Vapor Retrieval Using Neural Network from Infrared Hyperspectral Soundings

A neural network (NN) based algorithm for retrieval of precipitable water vapor (PWV) from the Atmospheric Infrared Sounder (AIRS) observations is proposed. An exact radial basis function (RBF) network is selected, in which the at-sensor brightness temperatures are the input variables, and PWV is the output variable. The training data sets for the RBF network are mainly simulated from the fast radiative transfer model (Community Radiative Transfer Model, CRTM) and the latest global assimilation data. The algorithm is validated by retrieving the PWV over west area in China using AIRS data. Compared with the AIRS PWV products, the RMSE of the PWV retrieved by our algorithm is 0.67 g/cm2, and a comparison between the retrieved PWV and radiosonde data is carried out. The result suggests that the RBF neural network based algorithm is applicable and feasible in actual conditions. Furthermore, spatial resolution of water vapor derived by RBF neural network is superior as compared to that of AIRS-L 2 standard product. Finally a PCA scheme is used for the preliminary investigation of the compression of AIRS high dimension observations.

Precipitable Water Vapor Retrieval Using Neural Network from Infrared Hyperspectral Soundings

A neural network (NN) based algorithm for retrieval of precipitable water vapor (PWV) from the Atmospheric Infrared Sounder (AIRS) observations is proposed. An exact radial basis function (RBF) network is selected, in which the at-sensor brightness temperatures are the input variables, and PWV is the output variable. The training data sets for the RBF network are mainly simulated from the fast radiative transfer model (Community Radiative Transfer Model, CRTM) and the latest global assimilation data. The algorithm is validated by retrieving the PWV over west area in China using AIRS data. Compared with the AIRS PWV products, the RMSE of the PWV retrieved by our algorithm is 0.67 g/cm2, and a comparison between the retrieved PWV and radiosonde data is carried out. The result suggests that the RBF neural network based algorithm is applicable and feasible in actual conditions. Furthermore, spatial resolution of water vapor derived by RBF neural network is superior as compared to that of AIRS-L 2 standard product. Finally a PCA scheme is used for the preliminary investigation of the compression of AIRS high dimension observations.

Relationship between marine boundary layer clouds and lower tropospheric stability observed by AIRS, CloudSat, and CALIOP

[1] Thirteen months of matched temperature and water vapor profiles from the Atmospheric Infrared Sounder (AIRS), collocated European Centre for Medium-Range Weather Forecasts (ECMWF) model analyses, National Centers for Environmental Prediction-National Center for Atmospheric Research reanalysis, and cloud profiles from the CloudSat and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instruments are investigated to quantify aspects of maritime boundary layer (MBL) clouds and their thermodynamic environment. This study highlights the strengths and limitations of this multisensor A-Train approach. The AIRS retrieval yield (percentage of high-quality temperature and water vapor profiles to the surface) over the oceans between 40°S and 40°N within MBL clouds is between 61% and 71% globally and is greater than 80%–90% throughout most of the subtropics. The lower tropospheric stability (LTS) and estimated inversion strength (EIS) are derived from AIRS temperature and water vapor profiles over the global oceans as well as from collocated ECMWF model analysis data. Positive values of EIS derived from AIRS well represent MBL conditions, demonstrating that AIRS contains quantitatively useful information with regard to relative changes in dynamic range of temperature and water vapor in the MBL. The relative magnitude and seasonality of LTS and EIS from the collocated satellite data set in stratocumulus regions are very similar to spatially and temporally collocated ECMWF model analyses, but differences are found between different subsampling criteria of reanalysis data. For coincident vertical profiles, the MBL in AIRS data shows a more smoothed structure than that of ECMWF. This multisensor investigation establishes a basis for using A-train observations to quantify elements of low cloud-climate feedback.

Seven years of observations of mid-tropospheric CO 2 from the Atmospheric Infrared Sounder

The Atmospheric Infrared Sounder (AIRS) on the EOS Aqua Spacecraft was launched on May 4, 2002. AIRS acquires hyperspectral infrared radiances in the 3.7–15.4 μm spectral region with spectral resolving power of better than 1200. The AIRS was designed to measure temperature and water vapor profiles and cloud properties for improvement in weather forecast and improved parameterization of climate processes. Currently a subset of AIRS Level 1B Radiance Products is assimilated by NWP centers, resulting in significant forecast improvement. Scientists have also demonstrated accurate retrievals of minor gases from AIRS including carbon monoxide, methane, and ozone. The excellent sensitivity and stability of the AIRS instrument has recently allowed the AIRS team to successfully retrieve carbon dioxide (CO 2) concentrations in the mid-troposphere (8–10 km) with a horizontal resolution of 100 km and accuracy better than 2 ppm. The AIRS mid-tropospheric CO 2 yield is 15,000 measurements per 24-h period over land and ocean, day and night for clear and cloudy scenes. The AIRS CO 2 accuracy has been validated against a variety of mid-tropospheric aircraft measurements as well as upward looking interferometers. Findings from the AIRS data include higher than expected variability in the mid-troposphere, the presence of a seasonally variable belt of enhanced CO 2 in the southern hemisphere, and observations of impact of atmospheric dynamics on the CO 2 concentrations in the mid-troposphere including the effects of El Nino/La Nina and the Arctic polar vortex. The full mid-tropospheric AIRS CO 2 data set is now available at the NASA GES/DISC for the 8 year time span since AIRS became operational.

A study of upper troposphere and lower stratosphere water vapor above the Tibetan Plateau using AIRS and MLS data

AbstractWe use water vapor data from Atmospheric Infrared Sounder (AIRS) and Aura Microwave Limb Sounder (MLS) combined with meteorological data from ECMWF Interim (ERA‐Interim) reanalysis, to study some aspects of stratosphere–troposphere exchange (STE) near the Tibetan Plateau. Apart from a distinctive region of high water vapor in the lower stratosphere (LS) in summer near the southern Tibetan Plateau (between 30°N and 35°N), the water vapor in the upper troposphere and lower stratosphere (UTLS) adjacent to the northern Tibetan Plateau (40°N–45°N) is found to be relatively higher than the surrounding regions of the same latitude in March and April. This relatively high water vapor in the northern Tibetan Plateau UTLS is proposed to be associated with approaching cold surges from the north and forced lifting of air by high orography. Another interesting feature detected in this study is the region of low water vapor values on the order of 5–7 ppmv, that is more pronounced from May to September at around 200 hPa and located at 30°N–40°N western Tibetan Plateau. This low water vapor region is found to be related to an anticyclone developed at the western Tibetan Plateau which causes sinking of dry air from the stratosphere resulting lower water vapor values in the upper troposphere. Copyright © 2011 Royal Meteorological Society

Influence of the Saharan Air Layer on Atlantic tropical cyclone formation during the period 1–12 September 2003

Atmospheric Infrared Sounder (AIRS) data show that the Saharan air layer (SAL) is a dry, warm, and well-mixed layer between 950 and 500 hPa over the tropical Atlantic, extending westward from the African coast to the Caribbean Sea. The formations of both Hurricane Isabel and Tropical Depression 14 (TD14) were accompanied with outbreaks of SAL air during the period 1–12 September 2003, although TD14 failed to develop into a named tropical cyclone. The influence of the SAL on their formations is investigated by examining data from satellite observations and numerical simulations, in which AIRS data are incorporated into the MM5 model through the nudging technique.