SPIE Asia-Pacific Remote Sensing最新文献

{"title":"An algorithm for retrieval of precipitation using microwave humidity sounder channels around 183 GHz","authors":"A. Varma, D. N. Piyush","doi":"10.1117/12.2222742","DOIUrl":"https://doi.org/10.1117/12.2222742","url":null,"abstract":"An algorithm is developed to identify precipitation affected pixels and quantitatively measure the precipitation using Megha-Tropiques humidity sounder (SAPHIR) channels around water vapor absorption line at 183 GHz. Based on observed brightness temperatures at all the six channels of the SAPHIR, a probabilistic rain identification algorithm is proposed. The rain thus identified is subjected to intensive testing using SAPHIR and PR collocated dataset, that showed that false alarm and missing rain is below 0.9 mm/h. Further a radiative transfer simulations supported rain retrieval algorithm is developed that explained a correlation of 0.7 and rmse of 0.81 mm/h. When both precipitation detection and retrieval algorithms are applied the correlation marginally deteriorates but rmse reduces to 0.55 mm/h. Further comparisons are made of monthly, daily and instantaneous rain over different geographical regions from SAPHIR with corresponding rain values from GSMap, TRMM-3B42 V7 and TRMM-TMI/PR, etc. The paper provides details of algorithm development and validation results.","PeriodicalId":165733,"journal":{"name":"SPIE Asia-Pacific Remote Sensing","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131743563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Gridded radar rainfall product for comparison with model rainfall","authors":"K. Jyothi, D. Devajyoti, D. Kumar, E. Rajagopal, T. N. Rao","doi":"10.1117/12.2223802","DOIUrl":"https://doi.org/10.1117/12.2223802","url":null,"abstract":"A tool for the entire Indian weather radar network using the static composite QI (Quality Index) map is generated. Various customized modules are used for this generation of the radar mosaic. The characterization of quality of DWR (Doppler weather Radar) data in terms of their QI is essential for assimilating the data into NWP (Numerical Weather Prediction) models. The static QI maps give a quick overview about the inherent errors in the DWR data. Quality control algorithms are applied for the generation of composite QI. The near real time access to the DWR data at NCMRWF enables the generation of an accumulated gridded radar rainfall product. This gridded rainfall map is useful for generating products like high resolution rainfall product, QPE (quantitative precipitation estimate) and for other applications. Results of some case studies shall be presented.","PeriodicalId":165733,"journal":{"name":"SPIE Asia-Pacific Remote Sensing","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131559252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Associative study of Absorbing Aerosol Index (AAI) and precipitation in India during monsoon season (2005 to 2014)","authors":"S. Dubey, Manu Mehta, Ankit Singh","doi":"10.1117/12.2228150","DOIUrl":"https://doi.org/10.1117/12.2228150","url":null,"abstract":"Based on their interaction with solar radiations, aerosols may be categorized as absorbing or scattering in nature. The absorbing aerosols are coarser and influence precipitation mainly due to microphysical effect (participating in the formation of Cloud Condensation Nuclei) and radiative forcing (by absorbing electromagnetic radiations). The prominent absorbing aerosols found in India are Black Carbon, soil dust, sand and mineral dust. Their size, distribution, and characteristics vary spatially and temporally. This paper aims at showing the spatio-temporal variation of Absorbing Aerosol Index (AAI) and precipitation over the four most polluted zones of Indian sub-continent (Indo-Gangetic plains 1, Indo-Gangetic plains 2, Central and Southern India) for monsoon season (June, July, August, September) during the last decade (2005 to 2014). Zonal averages AAI have been found to be exhibiting an increasing trend, hence region-wise correlations have been computed between AAI and precipitation during monsoon. Daily Absorption Aerosol Index (AAI) obtained from Aura OMI Aerosol Global Gridded Data Product-OMAEROe (V003) and monthly precipitation from TRMM 3B42-V7 gridded data have been used.","PeriodicalId":165733,"journal":{"name":"SPIE Asia-Pacific Remote Sensing","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129188514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigation on the monthly variation of cirrus optical properties over the Indian subcontinent using cloud-aerosol lidar and infrared pathfinder satellite observation (Calipso)","authors":"Reji k. Dhaman, M. Satyanarayana, G. S. Jayeshlal, V. P. Mahadevan Pillai, V. Krishnakumar","doi":"10.1117/12.2223653","DOIUrl":"https://doi.org/10.1117/12.2223653","url":null,"abstract":"Cirrus clouds have been identified as one of the atmospheric component which influence the radiative processes in the atmosphere and plays a key role in the Earth Radiation Budget. CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) is a joint NASA-CNES satellite mission designed to provide insight in understanding of the role of aerosols and clouds in the climate system. This paper reports the study on the variation of cirrus cloud optical properties of over the Indian sub - continent for a period of two years from January 2009 to December 2010, using cloud-aerosol lidar and infrared pathfinder satellite observations (Calipso). Indian Ocean and Indian continent is one of the regions where cirrus occurrence is maximum particularly during the monsoon periods. It is found that during the south-west monsoon periods there is a large cirrus cloud distribution over the southern Indian land masses. Also it is observed that the north-east monsoon periods had optical thick clouds hugging the coast line. The summer had large cloud formation in the Arabian Sea. It is also found that the land masses near to the sea had large cirrus presence. These cirrus clouds were of high altitude and optical depth. The dependence of cirrus cloud properties on cirrus cloud mid-cloud temperature and geometrical thickness are generally similar to the results derived from the ground-based lidar. However, the difference in macrophysical parameter variability shows the limits of space-borne-lidar and dissimilarities in regional climate variability and the nature and source of cloud nuclei in different geographical regions.","PeriodicalId":165733,"journal":{"name":"SPIE Asia-Pacific Remote Sensing","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126002081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of advanced technology microwave sounder data in the NCMRWF 4D-VAR data assimilation system","authors":"S. Rani, D. Srinivas, S. Mallick, J. George","doi":"10.1117/12.2223501","DOIUrl":"https://doi.org/10.1117/12.2223501","url":null,"abstract":"This study demonstrates the added benefits of assimilating the Advanced Technology Microwave Sounder (ATMS) radiances from the Suomi-NPP satellite in the NCMRWF Unified Model (NCUM). ATMS is a cross-track scanning microwave radiometer inherited the legacy of two very successful instrument namely, Advanced Microwave Sounding Unit-A (AMSU-A) and Microwave Humidity Sounder (MHS). ATMS has 22 channels: 11 temperature sounding channels around 50-60 GHz oxygen band and 6 moisture sounding channels around the 183GHz water vapour band in addition to 5 channels sensitive to the surface in clear conditions, or to water vapour, rain, and cloud when conditions are not clear (at 23, 31, 50, 51 and 89 GHz). Before operational assimilation of any new observation by NWP centres it is standard practice to assess data quality with respect to NWP model background (short-forecast) fields. Quality of all channels is estimated against the model background and the biases are computed and compared against that from the similar observations. The impact of the ATMS data on global analyses and forecasts is tested by adding the ATMS data in the NCUM Observation Processing system (OPS) and 4D-Var variational assimilation (VAR) system. This paper also discusses the pre-operational numerical experiments conducted to assess the impact of ATMS radiances in the NCUM assimilation system. It is noted that the performance of ATMS is stable and it contributes to the performance of the model, complimenting observations from other instruments.","PeriodicalId":165733,"journal":{"name":"SPIE Asia-Pacific Remote Sensing","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126599550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improvement of Systematic Bias of mean state and the intraseasonal variability of CFSv2 through superparameterization and revised cloud-convection-radiation parameterization","authors":"P. Mukhopadhyay, R. Phani Murali Krishna, B. Goswami, S. Abhik, Malay Ganai, M. Mahakur, M. Khairoutdinov, J. Dudhia","doi":"10.1117/12.2222982","DOIUrl":"https://doi.org/10.1117/12.2222982","url":null,"abstract":"Inspite of significant improvement in numerical model physics, resolution and numerics, the general circulation models (GCMs) find it difficult to simulate realistic seasonal and intraseasonal variabilities over global tropics and particularly over Indian summer monsoon (ISM) region. The bias is mainly attributed to the improper representation of physical processes. Among all the processes, the cloud and convective processes appear to play a major role in modulating model bias. In recent times, NCEP CFSv2 model is being adopted under Monsoon Mission for dynamical monsoon forecast over Indian region. The analyses of climate free run of CFSv2 in two resolutions namely at T126 and T382, show largely similar bias in simulating seasonal rainfall, in capturing the intraseasonal variability at different scales over the global tropics and also in capturing tropical waves. Thus, the biases of CFSv2 indicate a deficiency in model’s parameterization of cloud and convective processes. Keeping this in background and also for the need to improve the model fidelity, two approaches have been adopted. Firstly, in the superparameterization, 32 cloud resolving models each with a horizontal resolution of 4 km are embedded in each GCM (CFSv2) grid and the conventional sub-grid scale convective parameterization is deactivated. This is done to demonstrate the role of resolving cloud processes which otherwise remain unresolved. The superparameterized CFSv2 (SP-CFS) is developed on a coarser version T62. The model is integrated for six and half years in climate free run mode being initialised from 16 May 2008. The analyses reveal that SP-CFS simulates a significantly improved mean state as compared to default CFS. The systematic bias of lesser rainfall over Indian land mass, colder troposphere has substantially been improved. Most importantly the convectively coupled equatorial waves and the eastward propagating MJO has been found to be simulated with more fidelity in SP-CFS. The reason of such betterment in model mean state has been found to be due to the systematic improvement in moisture field, temperature profile and moist instability. The model also has better simulated the cloud and rainfall relation. This initiative demonstrates the role of cloud processes on the mean state of coupled GCM. As the superparameterization approach is computationally expensive, so in another approach, the conventional Simplified Arakawa Schubert (SAS) scheme is replaced by a revised SAS scheme (RSAS) and also the old and simplified cloud scheme of Zhao-Karr (1997) has been replaced by WSM6 in CFSV2 (hereafter CFS-CR). The primary objective of such modifications is to improve the distribution of convective rain in the model by using RSAS and the grid-scale or the large scale nonconvective rain by WSM6. The WSM6 computes the tendency of six class (water vapour, cloud water, ice, snow, graupel, rain water) hydrometeors at each of the model grid and contributes in the low, middle and high ","PeriodicalId":165733,"journal":{"name":"SPIE Asia-Pacific Remote Sensing","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128227449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding hydrologic sensitivity and land-atmosphere interactions through remote sensing and high resolution regional model","authors":"Anil Kumar","doi":"10.1117/12.2218433","DOIUrl":"https://doi.org/10.1117/12.2218433","url":null,"abstract":"In this study we investigated the impact of land surface surface process & land-atmospheric interaction on weather and surface hydrology. The ultimate goal is to integrate remote sense data into numerical mesoscale weather prediction and regional climate model in order to improve prediction of the impacts of land-atmosphere interactions and land-surface processes on regional weather, and hydrology. We have used climatology based green vegetation fraction and 8-day Moderate Resolution Imaging Spectroradiometer (MODIS) based green vegetation fraction and integrated in the Land Information System to conduct uncoupled simulation to understand the impact on surface and hydrological parameters in the summer season. The vegetation response is also realized through coupled regional climate simulation in which climatological based greenness and 8-days varying vegetation is investigated and quantify the impact of vegetation on summertime precipitation process. This study has bought following findings (a) Satellite based vegetation indices captures vegetation temporal patterns more realistic than climatological vegetation data and detects early/late spring signature through vegetation indices, (b) Integrated satellite vegetation greenness input data in regional weather model resolved much better soil moisture and soil temperature including the diurnal cycle of surface heat fluxes and surface temperature in the simulation. Secondly, integration of the TRMM based satellite rainfall product into coupled hydrological and Atmospheric model and results shows better resolved soil moisture patterns in the remote regions of the Asia Mountain regions.","PeriodicalId":165733,"journal":{"name":"SPIE Asia-Pacific Remote Sensing","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133614287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Seasonal dynamics of circulation in Hooghly Estuary and its adjacent coastal oceans","authors":"Shashank Mishra, G. Nayak, R. Nayak, V. Dadhwal","doi":"10.1117/12.2223882","DOIUrl":"https://doi.org/10.1117/12.2223882","url":null,"abstract":"Hooghly is one of the major estuaries in Ganges, the largest and longest river in the Indian subcontinent. The Hooghly estuary is a coastal plain estuary lying approximately between 21°–23° N and 87°–89° E. We used a terrain following ocean model to study tide driven residual circulations, seasonal mean flow patterns and its energetics in the Hooghly estuary and adjacent coastal oceans on the north eastern continental shelf of India. The model is driven by tidal levels at open ocean end and winds at the air-sea interface. The sources of forcing fields for tides were from FES2012, winds from ECMWF. Harmonic analysis is carried out to compute the tidal and non-tidal components of currents and sea level from the model solutions. The de-tidal components were averaged for the entire period of simulation to describe residual and mean-seasonal circulations in the regions. We used tide-gauge, SARAL-ALTIKA along track sea level measurements to evaluate model solutions. Satellite measure Chla were used along with simulated currents to describe important features of the circulations in the region.","PeriodicalId":165733,"journal":{"name":"SPIE Asia-Pacific Remote Sensing","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134209932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An automated fog monitoring system for the Indo-Gangetic Plains based on satellite measurements","authors":"D. Patil, R. Chourey, S. Rizvi, Manoj K. Singh, R. Gautam","doi":"10.1117/12.2228006","DOIUrl":"https://doi.org/10.1117/12.2228006","url":null,"abstract":"Fog is a meteorological phenomenon that causes reduction in regional visibility and affects air quality, thus leading to various societal and economic implications, especially disrupting air and rail transportation. The persistent and widespread winter fog impacts the entire the Indo-Gangetic Plains (IGP), as frequently observed in satellite imagery. The IGP is a densely populated region in south Asia, inhabiting about 1/6th of the world’s population, with a strong upward pollution trend. In this study, we have used multi-spectral radiances and aerosol/cloud retrievals from Terra/Aqua MODIS data for developing an automated web-based fog monitoring system over the IGP. Using our previous and existing methodologies, and ongoing algorithm development for the detection of fog and retrieval of associated microphysical properties (e.g. fog droplet effective radius), we characterize the widespread fog detection during both daytime and nighttime. Specifically, for the night time fog detection, the algorithm employs a satellite-based bi-spectral brightness temperature difference technique between two spectral channels: MODIS band-22 (3.9μm) and band-31 (10.75μm). Further, we are extending our algorithm development to geostationary satellites, for providing continuous monitoring of the spatial-temporal variation of fog. We anticipate that the ongoing and future development of a fog monitoring system would be of assistance to air, rail and vehicular transportation management, as well as for dissemination of fog information to government agencies and general public. The outputs of fog detection algorithm and related aerosol/cloud parameters are operationally disseminated via http://fogsouthasia.com/.","PeriodicalId":165733,"journal":{"name":"SPIE Asia-Pacific Remote Sensing","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125248321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Forecasting of monsoon heavy rains: challenges in NWP","authors":"K. Sharma, R. Ashrit, G. Iyengar, R. Bhatla, E. Rajagopal","doi":"10.1117/12.2223646","DOIUrl":"https://doi.org/10.1117/12.2223646","url":null,"abstract":"Last decade has seen a tremendous improvement in the forecasting skill of numerical weather prediction (NWP) models. This is attributed to increased sophistication in NWP models, which resolve complex physical processes, advanced data assimilation, increased grid resolution and satellite observations. However, prediction of heavy rains is still a challenge since the models exhibit large error in amounts as well as spatial and temporal distribution. Two state-of-art NWP models have been investigated over the Indian monsoon region to assess their ability in predicting the heavy rainfall events. The unified model operational at National Center for Medium Range Weather Forecasting (NCUM) and the unified model operational at the Australian Bureau of Meteorology (Australian Community Climate and Earth-System Simulator — Global (ACCESS-G)) are used in this study. The recent (JJAS 2015) Indian monsoon season witnessed 6 depressions and 2 cyclonic storms which resulted in heavy rains and flooding. The CRA method of verification allows the decomposition of forecast errors in terms of error in the rainfall volume, pattern and location. The case by case study using CRA technique shows that contribution to the rainfall errors come from pattern and displacement is large while contribution due to error in predicted rainfall volume is least.","PeriodicalId":165733,"journal":{"name":"SPIE Asia-Pacific Remote Sensing","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132614907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}