This paper briefly discusses the mobile ground-based incoherent Doppler wind lidar system, with iodine filters as receiving frequency discriminators, developed by the Ocean Remote Sensing Laboratory, Ocean University of Qingdao, China. The presented result of wind profiles in October and November 2000, retrieved from the combined Mie and Rayleigh backscattering, is the first report to our knowledge of wind measurements in the troposphere by such a system, where the required independent measurement of aerosol-scattering ratio can also be performed. A second iodine vapor filter was used to lock the laser to absolute frequency reference for both wind and aerosol-scattering ratio measurements. Intercomparison experiments of the lidar wind profile measurements were performed with pilot balloons. Results showed that the standard deviation of wind speed and wind direction, for the 2-4 km altitude range, were 0.985 m/s and 17.9 degrees, respectively.

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[1] In the present paper, we report on a method to retrieve the pigment concentration in Case I waters from ocean color. The method is derived from radiative transfer (RT) simulations and subsequent application of artificial neural network (ANN) techniques. Information on absorption and total scattering of pure seawater, colored dissolved organic matter, and marine particles are mostly taken from published measurements or parameterizations. Additionally, a new model relating the backscattering of marine particles to pigment concentration and wavelength is introduced. The such defined inherent optical properties are input to a RT code in order to generate a synthetic data set of remote sensing reflectance spectra. This synthetic data set is then used for the training of a set of ANNs with the aim to approximate the functional relationship between ocean color and pigment concentration. The different ANNs are obtained by systematic variations of input parameters, architecture, and noise level added to the training data. The performance of each individual ANN-based pigment retrieval scheme is assessed by applying it to the remote sensing reflectance spectra contained in the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Bio-optical Algorithm MiniWorkshop (SeaBAM) data set and comparing the retrieved pigment concentrations to those actually measured. The most successful ANN compares favorably with commonly used empirical pigment retrieval schemes. Compared, e.g., to the SeaWiFS algorithms OC2B and OC4, the square of the correlation coefficient r 2 is increased from 0.924 (OC2B), respectively, 0.928 (OC4) to 0.934 (ANN). The root mean square error of the retrieved log-transformed pigment concentration drops from 0.156 for OC2B, respectively, 0.151 for OC4 to 0.148 for the ANN-based pigment retrieval scheme. Furthermore, the latter shows a higher resistance against noisy input data.

A mobile Doppler lidar based on an injection-seeded diode-pumped Nd:YAG pulsed laser with a high repetition rate was developed to measure the sea surface wind (SSW) with high spatial and temporal resolution. The system was operated during the 2007 Qingdao International Regatta to measure the distribution of SSW in the racing area in real time with 50-100 m horizontal resolution and 2-10 min temporal resolution. An observation of nonuniform distribution of SSW is presented. The lidar results are compared with both buoy and wind tower measurements, which show good agreement. This lidar can be used advantageously for the 2008 Olympic sailing games as well as for observing mesoscale and microscale meteorology processes.

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Atmospheric line-of-sight (LOS) wind measurement by means of incoherent Cabannes-Mie lidar with three frequency analyzers with nearly the same maximum transmission of ~80% that could be fielded at different wavelengths is analytically considered. These frequency analyzers are (a) a double-edge Fabry-Perot interferometer (FPI) at 1064 nm (IR-FPI), (b) a double-edge Fabry-Perot interferometer at 355 nm (UV-FPI), and (c) an iodine vapor filter (IVF) at 532 nm with two different methods, using either one absorption edge, single edge (se-IVF), or both absorption edges, double edge (de-IVF). The effect of the backscattered aerosol mixing ratio, R(b), defined as the ratio of the aerosol volume backscatter coefficient to molecular volume backscatter coefficient, on LOS wind uncertainty is discussed. Assuming a known aerosol mixing ratio, R(b), and 100,000 photons owing to Cabannes scattering to the receiver, in shot-noise-limited detection without sky background, the LOS wind uncertainty of the UV-FPI in the aerosol-free air (R(b)=0), is lower by ~16% than that of de-IVF, which has the lowest uncertainty for R(b) between 0.02 and 0.08; for R(b)>0.08, the IR-FPI yielded the lowest wind uncertainty. The wind uncertainty for se-IVF is always higher than that of de-IVF, but by less than a factor of 2 under all aerosol conditions, if the split between the reference and measurement channels is optimized. The design flexibility, which allows the desensitization of either aerosol or molecular scattering, exists only with the FPI system, leading to the common practice of using IR-FPI for the planetary boundary layer and using UV-FPI for higher altitudes. Without this design flexibility, there is little choice but to use a single wavelength IVF system at 532 nm for all atmospheric altitudes.

The East China Sea is a typical case 2 water environment, where concentrations of phytoplankton pigments, suspended matter, and chromophoric dissolved organic matter (CDOM) are all higher than those in the open oceans, because of the discharge from the Yangtze River and the Yellow River. By using a hyperspectral semianalytical model, we simulated a set of remote-sensing reflectance for a variety of chlorophyll, suspended matter, and CDOM concentrations. From this simulated data set, a new algorithm for the retrieval of chlorophyll concentration from remote-sensing reflectance is proposed. For this method, we took into account the 682-nm spectral channel in addition to the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) channels. When this algorithm was applied to a field data set, the chlorophyll concentrations retrieved through the new algorithm were consistent with field measurements to within a small error of 18%, in contrast with that of 147% between the SeaWiFS ocean chlorophyll 2 algorithm and the in situ observation.

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