Ground Observation Data Research Articles

Hydrologic impact of regional climate change for the snow‐fed and glacier‐fed river basins in the Republic of Tajikistan: statistical downscaling of global climate model projections

AbstractClimate change due to global warming is a public concern in Central Asia. Because of specific orography and climate conditions, the republic of Tajikistan is considered as the main glacial center of Central Asia. In this study, regional climate change impacts in the two large basins of Tajikistan, Pyanj and Vaksh River basins located in the upstream sector of the Amu Darya River basin are analysed. A statistical regression method with model output statistics corrections using the ground observation data, Willmott archived dataset and GSMaP satellite driven dataset, was developed and applied to the basins to downscale the Global Climate Model Projections at a 0.1‐degree grid and to assess the regional climate change impacts at subbasin scale. It was found that snow and glacier melting are of fundamental importance for the state of the future water resources and flooding at the target basins since the air temperature had a clearly increasing trend toward the future. It was also found that the snowfall will decrease, but the rainfall will increase because of the gradual increase in the air temperature. Such changes may result in an increase in flash floods during the winter and the early spring, and in significant changes in the hydrological regime during a year in the future. Furthermore, the risks of floods in the target basins may be slightly increasing because of the increase in the frequencies and magnitudes of high daily precipitation and the increase in the rapid snowmelt with high air temperatures toward the future. Copyright © 2012 John Wiley & Sons, Ltd.

Asian dust height and infrared optical depth retrievals over land from hyperspectral longwave infrared radiances

The dust top height and infrared optical depth over land are retrieved from the Atmospheric Infrared Sounder (AIRS) longwave infrared measurements by using a one‐dimensional‐variation retrieval algorithm for different Asian dust storms. By combining particle size measurements from a 10‐year ground observation data set from the Dunhuang Skynet station located to the east of the Taklimakan Desert in China and the Optical Properties of Aerosols and Clouds data set of optical properties, the mineral dust scattering and absorption coefficients are obtained and then used to compute brightness temperatures with RTTOV 9.3. The retrieved dust thermal infrared optical depths are compared with the Ozone Monitoring Instrument and Moderate Resolution Imaging Spectroradiometer (MODIS) products. The retrieved dust top heights are compared against the extinction backscatter profiles obtained from the Cloud‐Aerosol Lidar with Orthogonal Polarization lidar. Infrared optical depths from AIRS correlate favorably with visible optical depths from MODIS, and dust top heights agree reasonably with lidar observations for the single‐layered dust storms over the Taklimakan Desert.

Spatial mashup technology and real time data integration in geo-web application using open source GIS – a case study for disaster management

The Web 2.0 technologies and standards enable web as a platform by allowing the user participation in web application. In the realization of Web 2.0, new knowledge and services are created by combining information and services from different sources which are known as ‘mashups'. The present study focused on spatial mashup solution for disaster management using open source GIS, mobile applications, web services in web 2.0, Geo-RDBMS and XML which are in the central of intelligent geo web services. The geo-web application is developed to generate the actionable GIS products at user end during disaster event by consuming various data and information services from web and central server system and also real time ground observation data collected through a mobile device. The technological solution developed in this study is successfully demonstrated for disaster management in the Assam State of India during the floods in 2010.

GRACE 위성 중력자료를 활용한 한반도의 평균 수자원변화량 산정

대부분의 수문자료는 지상관측을 통해 얻어진다. 그러나 어떤 지역은 접근이 어렵거나 장기적인 관측에 어려움이 있기 때문에 지상관측을 대체하거나 보완할 새로운 방법이 요구된다. 시 공간적 한계를 극복할 수 있는 대안으로 미항공우주국 NASA에서 2002년 지구 중력장을 측정하는 GRACE(Gravity Recovery And Climate Experiment) 인공위성 자료가 존재한다. 본 연구에서는 GRACE Level-2 중력자료를 이용하여 공간평활화 반경별(0 km, 300 km, 500 km)로 한반도의 수자원변화량(GRACE-based TWSC)을 산정하였다. 산정된 결과의 타당성을 검증하기 위해, 지상 수문 관측자료를 이용한 수자원 변화량(land-based TWSC)과의 비교를 시도하였다. Land-based TWSC는 WAMIS의 강우량, 증발 산량 및 GLDAS의 유출량 자료를 이용해 계산하였다. GRACE-based TWSC와 land-based TWSC의 RMSE 검정 결과, 공간평활화 반경 500 km의 위성자료가 한반도에 가장 적합한 것으로 나타났다. 한반도의 월별 평균 TWSC는 0.986 cm/month로 나타났고, 이러한 큰 변화폭 때문에 안정적수자원 확보를 위한 대처 방안의 마련이 필요한 것으로 평가된다. Most hydrologic data are obtained by ground observations. New observation methods are needed for some regions to overcome difficulties in accessibility and durability of long-term observation. In 2002, NASA launched twin satellites named GRACE which were designed to measure the gravitational field of the earth. Using the GRACE monthly gravity level-2 data, we calculated terrestrial water storage change (TWSC) of the Korean peninsula in various spatial smoothing radii (0 km, 300 km, 500 km). For the validation of GRACE-based TWSC, we compared it with land-based TWSC which was obtained using the ground observation data: precipitation and evaporation from WAMIS, and runoff from GLDAS. According to the mean square-error test, GRACE-based TWSC best fits the land-based one at 500 km smoothing radius. The variation of the terrestrial water storage in the Korean peninsula turned out to be 0.986 cm/month, which means that appropriate measures should be prepared for sustainable water resources management.

The source of the steep plasma density gradient in middle latitudes during the 11–12 April 2001 storm

A steep plasma density gradient has been observed in the middle‐latitude F region during large geomagnetic storms. This phenomenon can be understood as a special form of the middle‐latitude ionization trough (hereafter trough), but its causal linkage has not yet been clarified. We investigate the association of the steep density gradient and the trough by comparing their morphologies and occurrence locations using the satellite and ground observation data during the 11–12 April 2001 storm. Steep density gradients are detected in the dusk sector at the equatorward edges of the aurora by the Defense Meteorological Satellite Program (DMSP) F13 spacecraft. The locations of the steep density gradients coincide with the locations of the ionospheric footprints of the plasmapause identified by the Imager for Magnetopause‐to‐Aurora Global Exploration satellite. These observations demonstrate that the steep density gradient is created at the typical location of the trough. However, the steep density gradient is not produced by the formation of an intense trough during the storm. The temporal evolution of the total electron content maps shows that the steep density gradient observed at dusk by DMSP is associated with the plasma density enhancement in the dayside and its corotation into the dusk sector. The severe plasma density enhancement in middle latitudes, in combination with the trough and presumably the plasma depletion in high latitudes by the neutral composition change, produces the steep density gradient in the subauroral region during the storm.

Backscattering characteristics of L-band polarimetric and optical satellite imagery over planted acacia forests in Sumatra, Indonesia

Backscattering characteristics of L-band synthetic aperture radar (SAR) polarization data over industrial plantation forests of the fast-growing Acacia mangium in Sumatra, Indonesia, were investigated by combined analysis of microwave and optical reflectance imagery acquired by the Advanced Land Observation Satellite (ALOS) and ground observation data of forest stand characteristics. Prior to the satellite data analysis, growth curves of the planted trees are analyzed using ground observation data. The results show that tree growth fits a sigmoidal curve with a maximum growth rate between 1 and 2 years of age, which gradually declines after that. Our statistical analyses using satellite and ground observation data provide the following findings: 1. The regression between SAR backscattering intensity and forest stand characteristics fits negative quadratic curves for the cross-polarization HV, which show higher correlation than those obtained for the co-polarizations (HH, VV); 2. A decreasing trend in optical normalized difference vegetation index (NDVI) values with age prevails for the trees 2 years and older; 3. A significant correlation is obtained between SAR backscattering and NDVI for the cross-polarization. These findings suggest that the L-band SAR cross-polarization is strongly affected by the acacia tree foliage.

GOES-9과 MTSAT-1R 위성 간의 일사량 산출의 연속성과 일관성 확보를 위한 구름 감쇠 계수의 조정

Surface insolation is one of the major indicators for climate research over the Earth system. For the climate research, long-term data and wide range of spatial coverage from the data observed by two or more of satellites of the same orbit are needed. It is important to improve the continuity and consistency of the derived products, such as surface insolation, from different satellites. In this study, surface insolations based on Geostationary Operational Environmental Satellite (GOES-9) and Multi-functional Transport Satellites (MTSAT-1R) were compared during overlap period using physical model of insolation to find ways to improve the consistency and continuity between two satellites through comparison of each channel data and ground observation data. The thermal infrared brightness temperature of two satellites show a relatively good agreement between two satellites : root- mean square error (RMSE)=5.595 Kelvin; Bias=2.065 Kelvin. Whereas, visible channels shown a quite different values, but it distributed similar tendency. And the surface insolations from two satellites are different from the ground observation data. To improve the quality of retrieved insolations, we have reproduced surface insolation of each satellite through adjustment of the Cloud Factor, and the Cloud Factor for GOES-9 satellite is modified based on the analysis result of difference channel data. As a result, the insolations estimated from GOES-9 for cloudy conditions show good agreement with MTSAT-1R and ground observation : RMSE=83.439 W m -2 Bias=27.296W m -2 . The result improved accuracy confirms that the modification of Cloud Factor for GOES-9 can improve the continuity and consistency of the insolations derived from two or more satellites.

Polynomial regression calculation of the Earth's position based on millisecond pulsar timing

Prior to achieving high precision navigation of a spacecraft using X-ray observations, a pulsar rotation model must be built and analysis of the precise position of the Earth should be performed using ground pulsar timing observations. We can simulate time-of-arrival ground observation data close to actual observed values before using pulsar timing observation data. Considering the correlation between the Earth's position and its short arc section of an orbit, we use polynomial regression to build the correlation. Regression coefficients can be calculated using the least square method, and a coordinate component series can also be obtained; that is, we can calculate Earth's position in the Barycentric Celestial Reference System according to pulse arrival time data and a precise pulsar rotation model. In order to set appropriate parameters before the actual timing observations for Earth positioning, we can calculate the influence of the spatial distribution of pulsars on errors in the positioning result and the influence of error source variation on positioning by simulation. It is significant that the threshold values of the observation and systematic errors can be established before an actual observation occurs; namely, we can determine the observation mode with small errors and reject the observed data with big errors, thus improving the positioning result.

Spatial and Temporal Variability of Nitrogen Deposition and Its Impacts on the Carbon Budget of China

Nitrogen deposition in different regions has different volume and rate. And the impaction of nitrogen deposition is also inconformity on the different ecosystems. In order to study the atmospheric deposition of nitrogen stress on the carbon cycle, we analyzed the history, present and future trends in the evolution of nitrogen deposition, using remote sensing data and models. At the mean while, a series of spatial and temporal nitrogen deposition data was established and install into the Integrated Biosphere Simulator (IBIS), in order to found out the effects of different nitrogen deposition levels on the carbon budget in China. GOME and SCIAMICHY remote sensing data provide us a long time series of nitrogen dioxide column concentration data which can be fitted by the sine function. So it was used to construct nitrogen deposition data associated with ground observation data and recent research results of nitrogen deposition (dry and wet). Along with the nitrogen deposition data, two climate change seniors (A2 and B1) were used to drive the IBIS model. Comparing impact of different nitrogen deposition level, six simulation experiments have been set. The results show that ecosystem responses to nitrogen deposition will be different under future climate change scenarios. In the aggregate, more nitrogen input may not be able to bring more NPP and NEP in the future. At the meanwhile, the responses of different vegetation types to nitrogen deposition will show significant differences.

Correction methods of MODIS cloud product based on ground observation data

Correction methods of MODIS cloud product based on ground observation data

Determining Angstrom Constants for Estimating Solar Radiation in Malawi

This paper discusses a procedure that was adopted for the development of a linear regression model for estimating solar radiation in Malawi. By making use of sunshine-hours data recorded at six selected meteorological stations in the country, namely: Salima, Makoka, Karonga, Bolero, Chileka and Mzimba over the period 1991-1995, a set of Angstrom constants were obtained and averaged in order to develop the linear regression model. This model has potential for generating ground observation data of solar radiation at any given location in the country using sunshine hours as the only required input. The Gunn-Bellan Spherical Pyranometer and the Campbell Stokes Sunshine Recorder were respectively used in the measurement of incident radiation (Ib) in J·cm–2/day (converted to MJ·m–2·day–1) and sunshine hours. An Angstrom model of monthly average Clearness Index with normalized sunshine duration was then developed for each of the six meteorological stations. The resulting linear regression model was applied in estimating monthly average daily solar radiation. Regression analysis between computed and measured radiation data was applied to assess the reliability of the generated Angstrom constants. The results generally show a high degree of agreement between the two variables, with correlation coefficients ranging from 0.63 to 0.90. Angstrom constants obtained at the six meteorological stations were thereafter averaged in order to develop a linear regression model for estimating solar radiation in Malawi. Solar radiation values obtained using this model were noted to be in good agreement with those developed for each of the six meteorological stations.

A Study on the 3D Visualization of Typhoons Using the COMS Data

The satellite Chollian was successfully launched on June 27, 2010 and is expected to perform its communication, oceanographic, and meteorological duties for seven years. The follow-up launch of the Chollian satellite is already being planned, and diverse studies are under way to enable the use of the Korean satellite data. Studies are also being actively conducted in and out of Korea to visualize the meteorological data on the open-source virtual globes. The meteorological data include ground observation, satellite, and digital-model data. In this study, an efficient three-dimensional technique was developed to visualize typhoons on the virtual globes using the Chollian satellite data. This study was conducted to provide service to the public via the scientific visualization of the satellite image data, and to create an efficient satellite image analysis environment for meteorological researchers.

A Markov Chain Geostatistical Framework for Land-Cover Classification With Uncertainty Assessment Based on Expert-Interpreted Pixels From Remotely Sensed Imagery

This paper introduces an expert interpretation-based Markov chain geostatistical (MCG) framework for classifying land-use/land-cover (LULC) classes from remotely sensed imagery. The framework uses the MCG method to classify uninformed pixels based on the informed pixels and quantify the associated uncertainty. The method consists of the following steps: 1) decide the number of LULC classes and define the physical meaning of each class; 2) obtain a data set of class labels from one or a time series of remotely sensed images through expert interpretation; 3) estimate transiogram models from the data set; and 4) use the Markov chain sequential simulation algorithm to conduct simulations that are conditional to the data set. The simulated results not only provide classified LULC maps but also quantify the uncertainty associated with the classification. A case study with three LULC classes shows that, with increasing number of informed pixels from 0.45% to 1.81% of the total pixels at the resolution of 4.8 m × 4.8 m, the optimal classification accuracy based on maximum probabilities increases from 88.13% to 99.23% and the averaged classification accuracy of realization maps increases from 81.84% to 97.18%. Although it is relatively labor intensive, such an expert interpretation and geostatistical simulation-based approach may provide a useful LULC classification method complementary to existing image processing methods, which usually account for limited expert knowledge and may not incorporate ground observation data or assess the uncertainty associated with classified data.

Parameterization of a diagnostic carbon cycle model for continental scale application

Diagnostic carbon cycle models depend on parameterization to establish model sensitivity to climate variables and site factors. Here we acquired meteorological and carbon flux data from a diverse set (N = 18) of eddy covariance (EC) flux towers, along with MODIS data on FPAR (the fraction of incident photosynthetically active radiation that is absorbed by the plant canopy) at the sites, and used the data to develop a parameter set for the application of a diagnostic carbon cycle model over North America. The parameter optimization approach relied on goodness of fit between model simulations and tower estimates of gross primary production and net ecosystem production (NEP). Parameters such as light use efficiency (LUE) and base rate of heterotrophic respiration varied widely between sites representing different plant functional types (PFTs), thus supporting the value of stratification by PFT when parameterizing the model. Where multiple EC sites were available within a PFT, overall prediction error and bias in mean NEP was reduced by cross-site optimization as opposed to reliance on a single site. Optimization with the MODIS Enhanced Vegetation Index (EVI) instead of MODIS FPAR resulted in a similar goodness of fit, however, LUE values were pushed to levels that were not physiologically realistic when using EVI. The increasing availability of gap-filled EC tower data is rapidly improving the opportunities for direct coupling of satellite and ground observational data for parameterizing of diagnostic carbon flux models.

Evaluation of EDI derived from the exponential evapotranspiration model for monitoring China’s surface drought

Drought has become the most severe natural disaster in many provinces of China. In this paper, evaporative drought index (EDI) has been used to monitor China’s surface dryness conditions based on the exponential evapotranspiration (ET) model and Hargreaves equation from JAXA-MODIS Insolation products, GEWEX, NCEP-2 and MODIS NDVI data. The exponential ET model based on the surface net radiation, vegetation index, mean air temperature and diurnal air temperature range (DTaR) has been developed to estimate surface ET of China and has been independently validated using ground-measured data collected from two sites (Arou and Miyun) in China, indicating that the bias varies from −5.96 to 5.02 W/m2. The good agreement between daily estimated and ground-measured ET using ground observation data collected from all 22 sites further supports the validity of the exponential ET model for regional ET estimation. Moreover, EDI is closely correlated to the average soil moisture at 0–10 cm soil depth of the Yongning site with coefficient of determination of R2 = 0.52. The spatio-temporal patterns of monthly ET and EDI from April to September of 2004 over China are explored and the result indicates EDI is accordant with the precipitation by comparing the 15-day smoothed EDI with precipitation over six representative sites. The EDI based on the exponential ET model by integrating energy fluxes in response to soil moisture stress has demonstrated its validity for monitoring China’s surface drought events.

Influence of atmospheric correction and number of sampling points on the accuracy of water clarity assessment using remote sensing application

The main objectives of the research described in this paper are to assess the influence of atmospheric correction and the number of sampling points on the accuracy of lake water clarity using remote sensing. For this purpose field experiments were carried out at Bung Boraphet – the largest fresh water lake in Central Thailand. Two clarity parameters, secchi disk transparency (SDT), at 80 locations for three events on April 15th 2008 and March 24th and 31st 2009, and suspended sediment concentration (SSC) for the last two events were collected. These field data were collected nearly contemporaneous or contemporaneous to three Landsat 5 TM images, which were acquired on April 13th 2008 and March 24th and 31st 2009, respectively. Digital numbers of the Thematic Mapper (TM) image bands were transformed to at-sensor radiance by a radiometric correction process and then atmospheric correction was made using the Second Simulation of Satellite Signal in the Solar Spectrum (6S) code to acquire the surface reflectance. Linear regression equations between the log-transformed of clarity parameters and surface reflectance of each band and their band ratios at the same pixels were then determined. The band combinations TM1/TM3 and TM1 to estimate ln(SDT), and TM3/TM1 and TM1 to determine ln(SSC) for all satellite images were selected. These equations were later used to estimate the SDT and SSC values distributed across the whole lake. The results showed that lake wide average values of SDT for the three images are approximately 37, 97 and 81 cm, respectively, while the values of SSC for the second and third images are around 11 and 13 mg/l, respectively. The application of atmospheric correction to the TM data has proved to have the effect on the average values of SDT and SSC, and especially on their maximum and minimum values. Finally, it is possible to reduce the ground observation data of SDT and SSC substantially and still obtain reliable empirical relationships for the estimation of lake wide SDT and SSC values from TM data within the lake.

RELATIONS OF HOURLY PRECIPITATION EXTREMES AND TEMPERATURE OVER JAPAN BASED ON GROUND OBSERVATIONAL RECORDS

Changes of the intensity and the frequency of extreme precipitation under climate change are great concerns to scientists and policy makers. We analyzed the 99th percentiles of 1-hour precipitation for each temperature bin based on the ground observational data. The result showed that, first: the 99th percentiles of 1-hour precipitation tend to increase exponentially with the rising temperature, second: the rate of the precipitation increase was almost same as the Clausius-Clapeyron like relation for most part of Japan, except that the rate was larger for lower latitude region, third : the 99th precipitation intensities tend to have a peak at higher temperature.

DEVELOPMENT OF AN INTEGRATED MODEL FOR RAINFALL-RUNOFF/FLOOD INUNDATION SIMULATION AND ECONOMIC LOSS ESTIMATNION FOCUSING ON SAYO TOWN, HYOGO, JAPAN

This paper deals with the development of an integrated model for rainfall-runoff/flood inundation simulation and economic loss estimation, especially focusing on Sayo Town, Hyogo Prefecture, Japan where there was a severe flood disaster due to a heavy rainfall event on 9-10 August 2009. Firstly, the rainfall over the Sayogawa river catchment at the time is analyzed by using the composite radar and Radar-AMeDAS reanalysis data by the Japan Meteorological Agency in addition to the ground observation data. Second, the rainfall-runoff and flood inundation processes are simulated using the integrated model with the Radar-AMeDAS reanalysis data. The simulated inundation area shows the good agreement with the inundation record. Then, the economic loss estimation based on the simulated inundation area and depth is carried out and compared with the prospected value reported by the municipal government. The house/building economic loss estimated is 20.4 billion Japanese Yen, which agrees well with the reported value 18.5 billion Japanese Yen.

Linearization of NDVI Based on its Relationship with Vegetation Fraction

The Normalized Difference Vegetation Index (NDVI) is widely used for global monitoring of land surface vegetation dynamics from space. However, it is well documented that the NDVI approaches saturation asymptotically over highly vegetated areas. In this study, a linearized NDVI (LNDVI) is derived by introducing a linearity-adjustment factor, b, into the NDVI equation to improve the linearity of the relationship with vegetation fraction and mitigate the saturation problem encountered by NDVI. The linearity of the LNDVI is demonstrated using a ground-observed data set and a modelsimulated data set. A functional relationship and consistence of LNDVI with other NDVI adaptations are found, providing independent justification of the value of the NDVI adaptations. Due to its improved linearity with vegetation fraction, this index would provide more accurate monitoring of vegetation dynamics and estimation of biophysical parameters. The LNDVI can be derived from historical NDVI datasets directly without knowledge of input reflectances.

Precursory signals using satellite and ground data associated with the Wenchuan Earthquake of 12 May 2008

Multi-satellite sensor and ground observation data were analysed soon after the Wenchuan Earthquake of magnitude 7.9 (according to the USGS) or 8.0 (according to Xinhua, the Chinese News Agency) that occurred on 12 May 2008. This earthquake was felt by millions of people living in a number of countries; it took 65 000 lives and made millions of people homeless, damaged small and large infrastructure, and large surface manifestations were observed on the surface. Soon after the earthquake multi-satellite sensor and ground observatory data were analysed to study changes in ground, meteorological and atmospheric parameters. A detailed analysis of these data shows pronounced anomalous changes prior to the Wenchuan Earthquake event. Analysis of Atmospheric InfraRed Sounder (AIRS) data at different pressure levels clearly shows characteristic behaviour of the air temperature and relative humidity compared to other days. Such changes are observed up to 500 hPa, with maximal change at the lower levels. Changes in the surface, atmosphere, meteorological and ionospheric parameters prior to the Wenchuan Earthquake are complementary to one another and show the existence of a strong coupling between land, atmosphere and ionosphere, associated with the Wenchuan Earthquake.