Simulation of the AMSRE brightness temperatures and retrieval algorithm development

Microwave radiometers measure the outgoing emission of the underlying surface-atmosphere system in several frequencies v and polarizations. The measured parameter is antenna temperature (brightness temperature TB(v) after calibration). Contribution of the underlying surface in the measured TB(v) is defined by the relation TB(v) = TS Kv) where TS is the surface temperature and k is the emissivity. For the frequencies between about 7 and 90 GHz used by AMSR-E, the emissivity of the sea surface can be calculated quite accurately (especially for calm water) from a model describing spectrum of the dielectric permittivity of water. The behavior of k(v) for the various forms of ice and snow is less accurately known, and therefore often has to be empirically measured. IFOV of AMSR-E changes approximately between 5 and 60 km, implying that many different components of snow/ice/water can be present in one measurement. Atmospheric contribution to the Tb(v) depends mainly from the values of total cloud liquid water content Q and total water vapor content V. Variable weather conditions are responsible for large Tb(v) variations especially over the open ocean. A developed program of microwave radiative transfer allows computing the brightness temperatures TBV,H(v).

The radiative transfer equation (RTE) is the basis for the development of retrieval algorithms of the ocean surface and atmospheric parameters. Modeling of AMSR-E measurements over the open ocean was carried out with an updated microwave radiative transfer program. The program and calculations of the brightness temperatures were described in (Mitnik and Mitnik 2003). Some modifications were made in the program. In particular, formulas for dielectric permittivity of saline and fresh water which are used in calculation of the sea surface emissivity and cloud absorption respectively were taken from (Meissner and Wentz 2004). Dependence of the sea surface emissivity on wind speed was also corrected in accordance with (Meissner and Wentz 2004). A contracted form of the RTE can be written as:

Tbv,h(v,9,Ts,W) = [KwV,H(v,9,Ts,W)Ts] exp[-x(v)sec6] + TtBatm(v,6) + 7^*^,6) x [1 - KwV,H (v,6,t„ W)] exp[-x(v)sec6] + Tros[1 - KV,H(v,6,ts> W)] exp[-2x(v)sec6] (1)

where 6 is an incidence angle, Ts is sea surface temperature (SST), W is sea surface wind speed, kwv,h is the water surface emissivity at the vertical (V) and horizontal (H) polarization, Ts = ts + 273.16, where ts is SST in Celsius degrees, t(v) is the total atmospheric absorption, T^Batm(v,6) and are the upwelling and downwelling brightness temperatures of the atmosphere, respectively, Tcos = 2.7 K is brightness temperature of the cosmic background radiation.

To model atmospheric conditions observable in winter over the Okhotsk and Bering Seas the radiosonde (r/s) database was built up. Only r/s issued when SST tS < 2°C were included in the database. Total 410 cases measurements were selected: 69 sets from research vessels and 341 sets from six polar coastal and island stations. Every set consists of radiosonde, meteorological data (wind speed and direction, forms and amount of clouds) and the values of sea surface temperature. In the database, the total atmospheric water vapor content V varied from 0.63 to 24.5 kg/m2, total cloud liquid water content Q did not exceed 0.3 kg/m2 and wind speed W < 18.0 m/s. R/s atmospheric profiles were complimented by the cloud liquid water content profiles as in (Mitnik and Mitnik 2003).

The 7bv'h (v), the oceanic and atmospheric contributions to 7BV'H (v), total absorption by atmospheric gases and clouds and other parameters were computed by numerical integration of the RTE for the whole input database. For each r/s, the TBs (v) were computed for four values of sea surface wind and for r/s with cloudiness for two profiles of cloud liquid water content. As a result, the dataset used for the algorithm development comprised of 2,730 scenes. The randomly Gaussian distributed radiometer noises were added to 7Bs. The standard deviation of the radiometer error distribution was set equal to the noise level 0.5 K for all AMSR-E channels. Spectra of 7BV' H (v) computed for four r/s differing in V and Q values are presented in Fig. 1. Their analysis shows that the changes of atmospheric parameters that are observed during MCs passing result in the significant changes of the brightness temperatures compare to radiometer sensitivity.

Fig. 1. Spectra of the brightness temperature of the atmosphere-ocean system with vertical (solid lines) and horizontal (dotted lines) polarization computed for cloudless atmosphere (1) and three cloudy atmospheres (2-4) with various values of total water vapor content V and total cloud liquid water content Q. Arrows mark AMSR-E frequencies. 1 - V = 2.8 kg/m2, Q = 0 kg/m2; 2 - V = 3.6 kg/m2, Q = 0.14 kg/m2; 3 - V = 13.8 kg/m2, Q = 0.26 kg/m2; 4 - V = 19.3 kg/m2, Q = 0.56 kg/m2.

Fig. 1. Spectra of the brightness temperature of the atmosphere-ocean system with vertical (solid lines) and horizontal (dotted lines) polarization computed for cloudless atmosphere (1) and three cloudy atmospheres (2-4) with various values of total water vapor content V and total cloud liquid water content Q. Arrows mark AMSR-E frequencies. 1 - V = 2.8 kg/m2, Q = 0 kg/m2; 2 - V = 3.6 kg/m2, Q = 0.14 kg/m2; 3 - V = 13.8 kg/m2, Q = 0.26 kg/m2; 4 - V = 19.3 kg/m2, Q = 0.56 kg/m2.

Several versions of V and Q retrieval algorithms were constructed. They include linear and nonlinear regression, physically-based and Neutral Network-based algorithms (Mitnik and Mitnik 2003, 2006; Zabolotskikh et al. 2005, 2007). Fields of V and Q are important characteristics of the whole boundary layer as opposite to the visible and infrared images which represent a situation at the upper boundary of the marine boundary layer of the atmosphere and to the radar images, which represent a situation at its lower boundary. Knowledge of V and Q distributions in the area of origin and evolution of MCs can be used to improve their forecast as well as to validate the present high resolution models describing development of the marine boundary layer of the atmosphere during cold air outbreaks (Liu et al. 2004, 2006) and polar lows (Fu et al. 2004a, b).

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Renewable energy is energy that is generated from sunlight, rain, tides, geothermal heat and wind. These sources are naturally and constantly replenished, which is why they are deemed as renewable.

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