Validation of clear-sky radiances over oceans simulated with MODTRAN4.2 and global NCEP GDAS fields against nighttime NOAA15-18 and MetOp-A AVHRR data

An accurate and globally representative forward radiative transfer model (RTM) is needed to explore improvements in sea surface temperature (SST) retrievals from spaceborne infrared observations. This study evaluates the biases in top-of-atmosphere (TOA) brightness temperatures (BT) modeled with the moderate resolution transmission (MODTRAN4.2) band RTM, bounded by a Fresnel's reflective flat sea surface. This model is used to simulate global clear-sky Advanced Very High Resolution Radiometer (AVHRR) nighttime BTs from NOAA-15 through 18 and MetOp-A platforms for one full day of 18 February 2007. Inputs to RTM (SST fields and vertical profiles of atmospheric relative humidity, temperature, pressure, and geopotential height) are specified from the National Centers for Environmental Prediction's (NCEP) Global Data Assimilation System (GDAS) data. Model BTs in AVHRR channels 3B (3.7 μm), 4 (11 μm), and 5 (12 μm) are then compared with their respective measured counterparts, available in the NESDIS operational SST files. Ideally, the RTM should match the observations, but in fact, the modeled BTs are biased high with respect to the AVHRR BTs. The “Model minus Observation” (M − O) bias ranges from about 0 to 2 K, depending upon spectral band, view zenith angle, and sea and atmosphere state at the retrieval point. The bias asymptotically decreases towards confidently clear-sky conditions, but it never vanishes and invariably shows channel-specific dependencies on view zenith angle and geophysical conditions (e.g., column water vapor and sea-air temperature difference). Fuller exploration of the potential of the current RTM (e.g., adding global vertical aerosol profiles) or improvements to its input (NCEP SST and atmospheric profiles) may reduce this bias, but they cannot fully reconcile its spectral and angular structure. The fact that the M − O biases are closely reproducible for five AVHRR sensors flown onboard different platforms adds confidence in the validation approach employed in this study. We emphasize the need for establishing a globally adequate forward RTM for the use in SST modeling and retrievals. A first test of the RTM adequacy is its ability, when used in conjunction with the global fields from the numerical weather prediction models, to reproduce the TOA clear-sky radiances measured by satellite sensors.     

Separating surface emissivity and temperature using two-channel spectral indices and emissivity composites and comparison with a vegetation fraction method

The temperature-independent thermal infrared spectral indices (TISI) method is employed for the separation of land surface temperature (LST) and emissivity from surface radiances (atmospherically corrected satellite data). The daytime reflected solar irradiance and the surface emission at ∼3.8 μm have comparable magnitudes. Using surface radiances and a combination of day-night 2-channel TISI ratios, the ∼3.8 μm reflectivity is derived. For implementing the TISI method, coefficients for NOAA 9-16 AVHRR channels are obtained. A numerical analysis with simulated surface radiances shows that for most surface types (showing nearly Lambertian behavior) the achievable accuracy is ∼0.005 for emissivity (AVHRR channel-5) and ∼1.5 K for LST. Data from the European Centre for Medium-Range Weather Forecasts (ECMWF) is used for calculation of atmospheric attenuation. Comparisons are made over a part of central Europe on two different dates (seasons). Clouds pose a major problem to surface observations; hence, monthly emissivity composites are derived. Additionally, using TISI-based monthly composites of emissivities, a normalized difference vegetation index (NDVI)-based method is tuned to the particular study area and the results are intercompared. Once the coefficients are known, the NDVI method is easily implemented but holds well only for vegetated areas. The error of the NDVI-based emissivities (with respect to the TISI results) ranges between −0.038 and 0.032, but for vegetated areas the peak of the error-histogram is at ∼0.002. The algorithm for retrieving emissivity via TISI was validated with synthetic data. Due to the different spatial scales of satellite and surface measurements and the lack of homogeneous areas, which are representative for low-resolution pixels and ground measurements, ground-validation is a daunting task. However, for operational products ground-truth validation is necessary. Therefore, also an approach to identify suitable validation sites for meteorological satellite products in Europe is described.     

WLAN based energy efficient smart city design

The rapid growth of new technologies resulted in a new city model, known as the famous “Smart City”. The main aim in this paper is to create a paradigm for building an energy efficient smart city. Wireless local area network (WLAN) controller that will be used by the city will be constructed in such a manner that when there will be no request from any node to the access point (AP), the AP will be send from active mode to sleep mode. In Qualnet7.2, with the help of three types of energy model generic, mica z and mica motes the energy consumption in three modes transmit, receive and sleep mode is analysed, where it is seen that energy consumption in sleep mode is much less than in any other modes. In this paper, we propose an algorithm where it is shown that the energy consumption in sleep mode is less than in any other modes.