This PDF file contains the front matter associated with SPIE Proceedings Volume 9265, including the Title Page, Copyright information, Table of Contents, Authors, Introduction (if any), and Conference Committee listing.

Satellite data is very important for model initialization and verification. A large number of satellite observations are currently assimilated into the Numerical Weather Prediction (NWP) systems at the National Centre for Medium Range Weather Forecasting (NCMRWF). Apart from Global meteorological observations from GTS, near-real time satellite observations are received at NCMRWF from other operational centres like ISRO, NOAA/NESDIS, EUMETCAST, etc. Recently India has become member of Asia-Pacific Regional ATOVS Retransmission Service (APRARS) for faster access to high resolution global satellite data useful for high resolution regional models. Indian HRPT at Chennai covers the APRARS data gap region over South East Asia. A robust data monitoring system has been implemented at NCMRWF to assess the quantity and quality of the data as well as the satellite sensor strength, before getting assimilated in the models. Validation of new satellite observations, especially from Indian satellites are being carried out against insitu observations and similar space borne platforms. After establishing the quality of the data, Observation System Experiments (OSEs) are being conducted to study their impact in the assimilation and forecast systems. OSEs have been carried out with the Oceansat-2 scatterometer winds and radiance data from Megha-Tropiques SAPHIR sensor. Daily rainfall analysis dataset is being generated by merging satellite estimates and in-situ observations. ASCAT soil wetness measurements from METOP satellite is being assimilated into the global model. Land surface parameters (LuLc and albedo) retrieved from Indian satellites are being explored for its possible usage in the global and regional models. OLR from Indian satellites are used for validating model outputs. This paper reviews the efforts made at NCMRWF in (i) assimilating the data from Indian/International satellites and (ii) generating useful products from the satellite data.

Information of temporal and spatial variation of aerodynamic roughness length is required in most land surface models. The current research presents a practical approach for determining spatially distributed vegetation aerodynamic roughness length with fine temporal and spatial resolution by combining remote sensing and ground measurements. The basic framework of Raupach (1992), with the bulk surface parameters revised by Jasinski et al. (2005) has been applied to optical remote sensing data of HJ-1A/1B missions. In addition, a method for estimating regional scale vegetation height was introduced, so the aerodynamic roughness length, which is more preferred by users than the height normalized form has been developed. Direct validation on different vegetation classes have finally been performed taking advantage of the data-dense field experiments of Heihe Watershed Allied Telemetry Experimental Research (HiWATER). The roughness model had an overall good performance on most of Eddy Covariance sites of HiWATER. However, deviations still existed on different sites, and these have been further analyzed.

Satellite-based scatterometers, for historical reasons, have been used mainly to derive the wind forcing term for oceanography applications in the form of the near-surface wind field. However, the scatterometer is sensitive to the surface roughness, which is related to the wind stress field, which is in turn related to the wind field at the bottom of the troposphere but not just at 10 meters above the surface { indeed, in organized systems such as tropical cyclones, the surface roughness is highly correlated with the wind at altitudes much higher than 10 meters. We show how to assimilate this data as a function of the vertical principal components of the wind rather than the oversimplified alternative. We derive the empirical correlations between simulated scatterometer observations and underlying columns of wind produced by a numerical weather prediction model and derive an observation operator based on these correlations. We then present the results of the subsequent assimilation.

An observing System Simulation Experiment (OSSE) is a method to evaluate impacts of hypothetical observing systems on analysis and forecast accuracy in numerical weather prediction (NWP) systems. Since OSSE requires simulations of hypothetical observations, uncertainty of OSSE results is generally larger than that of observing system experiments (OSEs). To reduce such uncertainty, OSSEs for existing observing systems are often carried out as calibration of the OSSE system. The purpose of this study is to achieve reliable OSSE results based on results of OSSEs with multiple methods. There are three types of OSSE methods. The first one is the sensitivity observing system experiment (SOSE) based OSSE (SOSEOSSE). The second one is the ensemble of data assimilation cycles (ENDA) based OSSE (ENDA-OSSE). The third one is the nature-run (NR) based OSSE (NR-OSSE). These three OSSE methods have very different properties. The NROSSE evaluates hypothetical observations in a virtual (hypothetical) world, NR. The ENDA-OSSE is very simple method but has a sampling error problem due to a small size ensemble. The SOSE-OSSE requires a very highly accurate analysis field as a pseudo truth of the real atmosphere. We construct these three types of OSSE methods in the Japan meteorological Agency (JMA) global 4D-Var experimental system. In the conference, we will present initial results of these OSSE systems and their comparisons.

Trophic Level Index (TLI) calculated from several water quality monitoring indicators is often used to assess the general eutrophication state of inland-lake. In this paper, we proposed a data driven inland-lake eutrophication mapping method by using artificial neural network (ANN) to build relationship from remote sensing data and in-situ TLI sampling. Low spatial resolution remote sensing data (MODIS, 250-m and 500-m) and moderate spatial resolution remote sensing data (OLI, 30-m) together with in-situ observations are acquired to train the net. Result demonstrates that TLI obtained from medium-resolution remote sensing images is more accurate than which from low resolution remote sensing data, and more accurate than TLI calculated from the water quality factors retrieved from remote sensing images. This method provides an efficient way of mapping the TLI spatial distribution in-inland lake.

This study mainly presents recent progresses on persistent extreme precipitation events (PEPEs) in East China. A definition focusing both persistence and extremity of daily precipitation is firstly proposed. An identification method for quasi-stationary regional PEPEs is then designed. By utilizing the identified PEPEs in East China, typical circulation configurations from the lower to the upper troposphere are confirmed, followed by investigations of synoptic precursors for key components with lead time of 1-2 weeks. Two characteristic circulation patterns responsible for PEPEs in East China are identified: a double blocking high type and a single blocking high type. They may account for occurrence of nearly 80% PEPEs during last 60 years. For double blocking high type, about two weeks prior to PEPEs, two blockings developed and progressed towards the Ural Mountains and the Sea of Okhotsk, respectively. A northwestward progressive anomalous anticyclone conveying abundant moisture and eastward-extended South Asia High favoring divergence can be detected about one week in advance. A dominant summertime teleconnection over East Asia, East Asia/ Pacific (EAP) pattern, is deemed as another typical regime inducing PEPEs in the East China. Key elements of the EAP pattern initiated westward movement since one week prior to PEPEs. Eastward energy dispersion and poleward energy dispersion contributed to early development and subsequent maintenance of this teleconnection pattern, respectively. These typical circulation patterns and significant precursors may offer local forecasters some useful clues in identifying and predicting such high-impact precipitation events about 1-2 weeks in advance.

The distribution and transport of suspended sediment in the coastal areas has attracted more and more attention. Monitoring and modeling of the distribution and transport of suspended sediment is significant. The mutual promotion of remote sensing and numerical simulation plays an important role on the coastal water quality study. In this study, a method of coupling derived suspended sediment concentration (SSC) images with numerical model, based on GOCI and COHERENS (COupled Hydrodynamical-Ecological model for REgioNal and Shelf seas) model, is proposed to monitor the suspended sediment dynamics in the East China Sea. The retrieved SSC were extracted from GOCI images, to set as initial condition and employed to calibrate the parameters and validation for the hydrodynamic modeling and sediment transport modeling, respectively. The model is forced by considering tidal surface elevation at open sea boundary, river discharges, surface stress as a function of wind speed, air temperature, relative humidity and cloud coverage, bottom roughness and heat flux through sea surface. The model results are in accord with the in situ measurements. The results show that: (1) Numerical model which initialized with satellite-derived SSC data can quickly response to the changes of sediment concentration in real sense. (2) Remote sensing is helpful to calibrate and validate the model for simulating the suspended sediment concentration distribution. (3)The proposed approach can obtain reasonable simulated results in the East China Sea. (4) It is of great significance to combine remote sensing and numerical simulation together to study the water quality in the coastal areas.

The purpose of this study is to select a suitable ocean wind inversion method for FY-3C (MWRI). Based on the traditional empirical model of sea surface wind speed inversion, and in the case of small sample size of FY-3C satellite load regression analysis, this paper analyzes the channel differences between the FY-3C satellite microwave radiation imager (MWRI) and TMI onboard the TRMM. The paper also analyzes the influence of these differences on the channel in terms of receiving temperature, including channel frequency f, sensitivity ΔK and scaling precision K. Then, the limited range of new model coefficient regression analysis is determined, the regression methods of the finite field are proposed, and the empirical model of wind speed inversion applicable to MWRI is obtained, The method corrected by 2014 FY3C observation data and buoy data, and then by anti-electromagnetic interference geostationary communications satellite designed to fit in the FY-3C (MWRI). which achieves strong results. Compared to the TAO buoy data, the RMS of the new model is 1.18 m/s. In addition, the schematic diagram of the global ocean surface wind speed inversion is provided.1

To demonstrate image quality and sensor’s performance for target detection before the satellite launched, it is necessary to establish an end-to-end model that express the detection probability in terms of atmosphere effects, the sensor, and optical scattering properties of target. It is difficult to develop an accurate 3D radiation transfer model for scene including complex target, especially for large scale scene. It is beneficial to process separately the target and large scale background. Radiance from sea background can be solved exactly with atmospheric-ocean coupling radiation transfer model. However for ship target, it is only but sufficient to using the sample model. In the model the illuminated light is separate into direct sunlight and sky light, and the sensor received radiance is radiance scatted from target and attenuated by atmosphere. High spatial/spectral resolution image simulated with Monte Carlo ray tracing method is used as input for modeling space-borne imagery, which is economic for demonstrating sensor’s performance at different conditions and multiple scattering can also be considered. Bidirectional reflectance distribution function (BRDF) is introduced to characterize the light scattering model of the ship sample material.

To study the spatial and temporal distribution characteristics of near-surface CO₂ concentration over China, the data of GOSAT L4B and auxiliary data of Mt Waliguan background observations, population density, total energy consumption (coal) and GDP in 2009 were applied to this study. The ArcGIS Geostatistical Analytical Method was used. The ground-based validation was processed by comparing GOSAT data with Mt Waliguan background observations. The variation characteristics of the near-surface CO₂ concentration over China was analysed spatially and temporally. The results show that: GOSAT retrieved near-surface products are consistent with Mt Waliguan ground-based measurement; Near-surface CO₂ concentration over China is relatively concentrated, and has significant differences between the East and the West, with a overall characteristic that CO₂ concentration in the east of China is high and in the west is low; Near-surface CO₂ concentration over China has a significant seasonal variation characteristic, and the monthly average concentration rise to the highest value of 396.512 ppmv in April (spring), which is significantly higher than other seasons, decline to the lowest value of 382.781 ppmv in July (summer); All relationships illustrate a big uncertainty, resulting a conclusion that the reasons causing the spatial distribution of near-surface CO₂ concentration may be varied, could not be easily determined as anthropogenic or natural ressons, which need further study.

Global Land Data Assimilation System (GLDAS) is often used as a test bed for innovative modeling and assimilation capabilities. So its reliability is very important, the validation and the evaluation of product from GLDAS often use in-situ observations. Now remote sensing has been an important observation source, but satellite observations have not directly relation with the output of GLDAS, their relationships are so complex even fuzzy with many parameters. This study analyzed the reliability of soil moisture and land surface temperature simulated by GLDAS using Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) data for the periods of one year based on fuzzy comprehensive evaluation theory, Results demonstrate that both of them are relatively believable, and the land surface temperature is more reliable than the soil moisture.