# A New Algorithm for Simultaneous Retrieval of Aerosols and Marine Parameters
## 1 Introduction
The retrieved marine parameters included the chlorophyll concentration, the detrital/dissolved-matter absorption at 443 nm, and the backscattering coefficient at 443 nm. The algorithm in [19] employs an iterative, non-linear optimal estimation scheme, which uses the forward RTM at each iteration step to simulate TOA radiances and Jacobians associated with the current state vector, whose components are the five retrieval parameters. The use of the forward RTM at each step of the iteration process for computing TOA radiances and Jacobians associated with the current state vector is computationally demanding. Algorithms employing neural networks have been used in remote sensing of the atmosphere-ocean system because they are fast and can retrieve in one step both marine and atmospheric parameters. [PERSON] et al. [21] developed an algorithm which employs the Stuttgart Neural Network Simulator [22] for constructing and training the neural network. Their algorithm inverts the radiative transfer procedure to retrieve three ocean parameters (chlorophyll concentrations, suspended organic matter, and yellow substance) from normalized water-leaving radiance data of the ocean color and temperature sensor (OCTS) of the Advanced Earth Observing Satellite (ADEOS). [PERSON] et al. [23] developed the NeuroVaria neural network algorithm which retrieves atmospheric and ocean parameters by minimising the difference between the observed satellite reflectance and that computed from radiative transfer simulations using artificial neural networks. Their method takes care of absorbing aerosols by using the Junge power law [24] of particle size distribution, and the computation of the partial derivatives of the cost function is accomplished by using the gradient method. [PERSON] et al. [25] modified the NeuroVaria algorithm by [PERSON] et al. [23]. The improved algorithm is more efficient in minimizing the iterative cost function. [PERSON] and [PERSON] [26] developed an algorithm which employs neural networks to parameterize the inverse relation between concentrations and reflectances. Their algorithm applies to case II waters, and is capable of retrieving three marine (chlorophyll concentration, suspended matter and CDOM) parameters and one atmospheric (aerosol concentration) parameter. [PERSON] et al. [27] modified [PERSON] et al. [19] algorithm and developed an inversion scheme named \"Ocean Color: Simultaneous Marine and Aerosol Retrieval Tool\" (OC-SMART). The OC-SMART retrieval algorithm is faster than that in [19], because instead of employing a linearized forward RTM to compute required TOA radiances and Jacobians, it employs a radial basis function neural network (RBF-NN) to establish an analytical relationship between input/retrieval parameters and simulated TOA radiances. The RBF-NN also provides analytical expressions for the Jacobians. In the OC-SMART algorithm, the retrieval is based on optimal estimation combined with a Levenberg-Marquardt scheme for speeding up convergence when determining those parameters which provide the best fit to the TOA radiance measurements. The aim of this study is to develop a method for simultaneous retrieval of aerosol and water parameters in high-latitude coastal waters based on a coupled atmosphere-ocean radiative transfer forward model (CAO-LDISORT), and on aerosol and bio-optical models derived from in-situ data measurements. In this paper we present, a fast tool for simultaneous retrieval of two aerosol parameters (aerosol fine-mode fraction and aerosol volume fraction), and three marine parameters (chlorophyll concentration, mineral concentration, and CDOM absorption). For inverse modelling, the algorithm uses a multidimensional unconstrained nonlinear minimization function (fminsearch) based on the Nelder-Mead method [28; 29]. This method has an advantage over the other mentioned methods because it does not make use of Jacobians, and is tolerant of local minima. A more detailed description is given in Section 2.4.