This PDF file contains the front matter associated with SPIE Proceedings Volume 8529, including the Title Page, Copyright information, Table of Contents, Introduction, and Conference Committee listing.

Bangladesh region is interesting regions in a core region of the Asian monsoon. The Bangladesh region is a very flat region facing to the Bay of Bengal. The precipitaion characteristics are studied using the long-term Tropical Rainfall Measuring Mission (TRMM) data. Over the Bangladesh, the stability of the atmosphere seems to affect the precipitation system in the vertical profiles. In the pre-monsoon season, rain rate increases with height in the lower part of the profile, while in the mature monsoon season, rain rate is nearly constant in the lower part of the profile. The structure of precipitation system is more persistent and homogenous in the mature monsoon season. The rain top is higher in premonsoon season than in mature monsoon season. The rain total is generally determined by rain frequency. The horizontal size of the precipitation systems is larger for pre-monsoon season than for mature monsoon season. In other words, the precipitation system is small but many in the mature monsoon season. This fact may be explained that the atmosphere is sufficently humid to be easily triggered by small liftings. These characteristics are reflected in the rain retrieval biases in precipitation radar and microwave radiometers in space.

Recent climate models consistently project a decreasing trend of global tropical cyclone (TC) frequency in the future due to global warming. In our recent 228 year long simulations from 1872 to 2099, a decreasing trend of global TC frequency is found not only in the future but also in the past during the twentieth century. The decreasing trend of TC frequency is closely related to a decreasing trend of upward mass flux in the tropics, and it is in turn closely related to an increasing trend of dry static stability. Both decreasing trend of upward mass flux and increasing trend of dry static stability are simulated in all climate models. However, some observational studies indicated that the dry static stability was increasing at much smaller rate than the climate models or even decreasing during the last 30 years. In this paper, we explore possible causes of this apparent discrepancy between the observations and models by comparing the 228 year simulations with several reanalysis data. It is found that the difference between the model and reanalysis is within the uncertainties among the different reanalysis data.

Accurate knowledge of vertical distributions of aerosol and cloud fields and their space-time variations are required to reduce the uncertainty in estimated climate forcing. Here, multi-sensor (both passive and active) data were used to construct the climatology of 3-D cloud and aerosol fields over the Indian monsoon region. Multilayer clouds are found to persist throughout the year, among which cumulus and stratocumulus dominate the low clouds and cirrus dominates the high clouds. A combination of passive stereo-technique (MISR) and radiometric technique (ISCPP) captures the multilayer cloud structure as revealed by active sensor CALIOP. Coexistence of low clouds throughout the year with high aerosol concentration beneath and above leads to a transition from increasing to decreasing cloud fraction with an increase in aerosol optical depth. Such transition is rapid in the monsoon season due to convergence of low clouds to form high clouds facilitated by high aerosol loading. Further, the regional climate model RegCM 4.1 has been used to examine aerosol-cloud interaction. The aerosol-induced changes of low cloud amount are under-estimated by the model. The observation-based seasonal climatology of aerosol and cloud fields presented here may help in improving the model simulations of cloud variability and associated rainfall.

Wind profile is fundamental in many atmospheric phenomena. Radiosonde, windprofiler, and Doppler lidar, have been developed for the wind measurement. Radiosonde and windprofiler are used to obtain wind profiles. About 1,300 weather stations launch radiosondes to obtain profiles of pressure, wind, temperature, and humidity. Most of the weather stations are on land, while the stations on the sea are very sparse. Spaceborne visible and infrared imagers and microwave scatterometers can obtain wind data only at a specific altitude. Current wind observations are not enough and their reliability in the global climate model and weather prediction must be improved. Many scientific groups anticipate the realization of a global observation system for three-dimensional wind measurements. The spaceborne Doppler lidar is regarded as one of the candidate sensors for the global wind measurements. The working group on Japanese spaceborne Doppler Lidar has been established to realize for wind measurements from space. In this paper, we describe the activities and goals of this working group.

Observing System Simulation Experiments (OSSEs) are a powerful tool used to assess the potential impact on numerical weather prediction skill from planned or hypothetical future observing systems. Over the last several years an international Joint OSSE collaboration has emerged centered on the use of NASA's and NOAA's data assimilation systems. A Nature Run provided by the European Centre for Medium Range Weather Forecasts (ECMWF) has undergone extensive validation, and a set of simulated reference observations have been subjected to a set of calibration experiments. One of the first candidate observing systems assessed by this system is a wind lidar based on the Global Wind Observing Sounder (GWOS) concept developed by NASA in response to the National Research Council (NRC) Decadal Survey. OSSEs were conducted at Joint Center for Satellite Data Assimilation (JCSDA) and positive impacts from GWOS on medium range weather forecast were demonstrated. For OSSEs, all major observations used for the data assimilation have to be simulated as a control observation in addition to the observations being tested by an OSSE. Simulation of control observations and OSSE calibration are the most significant initial investments for an OSSE before it can be used to evaluate the data impact of future instruments. The Nature Run data and control observation that were simulated at NOAA from the Nature Run are made available from a NASA portal and NCAR for international collaborative Joint OSSEs. Recent developments and plans for a JCSDA OSSE based on a 2012 observation system will be also described.

The NASA Earth Observing System Simulators Suite (NEOS³) is a modular framework of forward simulations tools for remote sensing of Earth’s Atmosphere from space. It was initiated as the Instrument Simulator Suite for Atmospheric Remote Sensing (ISSARS) under the NASA Advanced Information Systems Technology (AIST) program of the Earth Science Technology Office (ESTO) to enable science users to perform simulations based on advanced atmospheric and simple land surface models, and to rapidly integrate in a broad framework any experimental or innovative tools that they may have developed in this context. The name was changed to NEOS³ when the project was expanded to include more advanced modeling tools for the surface contributions, accounting for scattering and emission properties of layered surface (e.g., soil moisture, vegetation, snow and ice, subsurface layers). NEOS³ relies on a web-based graphic user interface, and a three-stage processing strategy to generate simulated measurements. The user has full control over a wide range of customizations both in terms of a priori assumptions and in terms of specific solvers or models used to calculate the measured signals.This presentation will demonstrate the general architecture, the configuration procedures and illustrate some sample products and the fundamental interface requirements for modules candidate for integration.

Monsoon depressions, that form during the Indian summer monsoon season (June to September) are known to be baroclinic disturbances (horizontal scale 2000 to 3000 km) and are driven by deep convection that carries a very large vertical slope towards cold air aloft in the upper troposphere. Deep convection is nearly always organized around the scale of these depressions. In the maintenance of the monsoon depression the generation of eddy kinetic energy on the scale of the monsoon depression is largely governed by the “in scale” covariance of heating and temperature and of vertical velocity and temperature over the region of the monsoon depression. There are normally about 6 to 8 monsoon depressions during a summer monsoon season. Recent years 2009, 2010 and 2011 saw very few (around 1, 0 and 1 per season respectively). The best numerical models such as those from ECMWF and US (GFS) carried many false alarms in their 3 to 5 day forecasts, more like 6 to 8 disturbances. Even in recent years with fewer observed monsoon depressions a much larger number of depressions is noted in ECMWF forecasts. These are fairly comprehensive models that carry vast data sets (surface and satellite based), detailed data assimilation, and are run at very high resolutions. The monsoon depression is well resolved by these respective horizontal resolutions in these models (at 15 and 35km). These models carry complete and detailed physical parameterizations. The false alarms in their forecasts leads us to suggest that some additional important ingredient may be missing in these current best state of the art models. This paper addresses the effects of pollution for the enhancement of cloud condensation nuclei and the resulting disruption of the organization of convection in monsoon depressions. Our specific studies make use of a high resolution mesoscale model (WRF/CHEM) to explore the impacts of the first and second aerosol indirect effects proposed by Twomey and Albrecht. We have conducted preliminary studies including examination of the evolution of radar reflectivity (computed inversely from the model hydrometeors) for normal and enhanced CCN effects (arising from enhanced monsoon pollution). The time lapse histories show a major disruption in the organization of convection of the monsoon depressions on the time scale of a week to ten days in these enhanced CCN scenarios.

Inverse estimation of surface C0₂ fluxes is performed with atmospheric transport model using ground-based and GOSAT observations. The NIES-retrieved C0₂ column mixing (X_c02) and column averaging kernel are provided by GOSAT Level 2 product v. 2.0 and PPDF-DOAS method. Monthly mean C0₂ fluxes for 64 regions are estimated together with a global mean offset between GOSAT data and ground-based data. We used the fixed-lag Kalman filter to infer monthly fluxes for 42 sub-continental terrestrial regions and 22 oceanic basins. We estimate fluxes and compare results obtained by two inverse modeling approaches. In basic approach adopted in GOSAT Level4 product v. 2.01, we use aggregation of the GOSAT observations into monthly mean over 5x5 degree grids, fluxes are estimated independently for each region, and NIES atmospheric transport model is used for forward simulation. In the alternative method, the model-observation misfit is estimated for each observation separately and fluxes are spatially correlated using EOF analysis of the simulated flux variability similar to geostatistical approach, while transport simulation is enhanced by coupling with a Lagrangian transport model Flexpart. Both methods use using the same set of prior fluxes and region maps. Daily net ecosystem exchange (NEE) is predicted by the Vegetation Integrative Simulator for Trace gases (VISIT) optimized to match seasonal cycle of the atmospheric C0₂ . Monthly ocean-atmosphere C0₂ fluxes are produced with an ocean pC02 data assimilation system. Biomass burning fluxes were provided by the Global Fire Emissions Database (GFED); and monthly fossil fuel C0₂ emissions are estimated with ODIAC inventory. The results of analyzing one year of the GOSAT data suggest that when both GOSAT and ground-based data are used together, fluxes in tropical and other remote regions with lower associated uncertainties are obtained than in the analysis using only ground-based data. With version 2.0 of L2 X_c02 the fluxes appear reasonable for many regions and seasons, however there is a need for improving the L2 bias correction, data filtering and the inverse modeling method to reduce estimated flux anomalies visible in some areas. We also observe that application of spatial flux correlations with EOF­ based approach reduces flux anomalies.

Recent issues of climate changes and natural disasters have brought many changes in world energy utilization. Especially due to the Japan's earthquake and tsunami, potential of nuclear power have made negative. And thus many countries are looking for a new renewable energy that can replace. Of which solar energy has emerged as a useful alternative. Under these circumstances, it is highly desirable that detailed information about the availability of solar radiation on the surface is essential for the optimum design and study of solar energy systems. And its components at a given location are very essential. Hence the solar radiation data is one of the key parameters required to be monitored at any meteorological station. But solar radiation measurements are not easily available due to the cost and maintenance requirements of the measuring equipment. Therefore, solar resource modeling or mapping is one of the essential tools for proper design, planning, maintenance and pricing of solar energy system. In this study, the feasibility of a regression model using image fusion for the prediction of solar energy potential in Republic of Korea was investigated. Meteorological and geographical data of 22 cities in South Korea for period of 10 years (2001–2011) were used. Meteorological and geographical data (latitude, longitude, altitude, month, sunshine duration, temperature, and relative humidity) were used as inputs to the model, while the regional solar radiation was used as the output of the model. The model for evaluating the spatial and temporal solar radiation was executed for South Korea. The annual mean solar radiation estimates in South Korea vary from a minimum of 5.48 MJ/m²/day to a maximum of 19.51 MJ/m²/day. Our proposed annual mean solar radiation is 13.5 MJ/m²/day. These compare favorably with the observed data as expected. This study has shown that a simple method can accurately predict solar radiation potential in South Korea.

Monthly precipitation data from 66 rain gauge stations in northern China are analyzed for the period 1976–2011. Variations in droughts and wet spells are described using the standardized precipitation index (SPI). Empirical orthogonal functions and a global wavelet spectral analysis are applied to capture modes of spatio-temporal variability in droughts over northern China. Time series of monthly sea surface temperatures (SST) and the Multivariate El Niño Southern Oscillation Index (MEI) are presented, and cross wavelet and wavelet coherence transforms are carried out to investigate possible mechanisms behind variations in droughts and wet spells. From 1976 onwards, the northern parts of northern China have experienced an increase in the frequency of droughts, while the southern parts of northern China have experienced a decrease in the frequency of droughts. The north–south variability of droughts and wet spells is characterized by interannual timescales of 3.3 years and 7.0–11.0 years. The former timescale is closely related with the MEI, while the latter is closely related with sea surface temperature anomalies (SSTA) over the North Pacific. Most parts of northern China experienced an increase in the frequency of droughts during the periods 1980–2000 and 2004–present, and a decrease in the frequency of droughts during the period 2000–2004. The variability of drought in northern China peaks at timescales of 16.0–32.0 and 3.5–4.0 years. The first of these timescales shows a significant correlation with SSTA over the Indian Ocean. The eastern parts of northern China have experienced a decrease in the frequency of droughts since 1976, while the western parts of northern China have experienced an increase in the frequency of droughts. The east–west variability of droughts and wet spells is characterized by interannual timescales of 3.3-8.0 years, which are related with SSTA over the Indian Ocean warm pool.

We studied the semi-diurnal variation of surface rainfall over southern Africa and the Amazon simulated by a global cloud system resolving model (Nonhydrostatic ICosahedral Atmospheric Model; NICAM) under realistic conditions with land-sea contrast. This semi-diurnal variation was found to be consistent with the Tropical rainfall Measuring Mission (TRMM) and Meteosat-8 observations. The timing of the primary afternoon rainfall peak by the NICAM coincides with TRMM/PR primary afternoon peak, and the secondary early morning peak by the NICAM simulation agree with the TRMM/PR observations within two hours. Mean size of deep convection (DC), defined by 213K of the Meteosat-8 infrared data shows semi-diurnal variation, although the number of DC show diurnal variation with coincident peak with TRMM/PR primary peak. The semi-diurnal variation of the mean size of DC and number of DC is simulated with small secondary peak over southern Africa by NICAM, whose DC is defined by OLR smaller than 112 W m^-2 (corresponding to 213 K in cumulative frequency).