Tsung-Hua Kuo

Applying SPOT data to estimate the aerosol optical depth and air quality

Abstract The improvement in the structure function method for retrieving aerosol optical depth (AOD) with SPOT HRV data and its application in air quality monitoring are highlighted in this paper. Generally speaking, estimation of the aerosol optical depth will be affected by the temporal change of surface canopy, observation geometry and terrain effect when applying the contrast reduction method to the multi-temporal satellite image set. In order to reduce the errors induced by such effects, the single-directional structure function is replaced by the multi-directional mode, which can describe the real characteristics of the surface structure more completely. Comparison of the results with in-site observations show a significant improvement in the accuracy of the retrieved AOD. Furthermore, due to the linear relationship between aerosol optical depth and turbidity coefficient, satellite images can be employed for monitoring air quality. Application of the method is demonstrated with a case study situated around the northern Taiwan area.

Rainfall Intensity Estimation by Ground-Based Dual-Frequency Microwave Radiometers

Abstract Many investigators have used satellite data to derive rainfall intensity and to compare them with rain gauge data. However, there has always been a problem: what is the optimal time period for the two different types of data? A set of well-controlled data collected by ground-based dual-frequency microwave radiometers at the National Central University (24.9°N, 121.1°E) in Taiwan between January of 1996 and December of 1997 was used to find the answer. The results show that a 1-h interval would be the optimal time period and that hourly data will provide a better accuracy than other options (5, 10, or 30 min or 2 h). Two algorithms, the differential and the brightness temperature, were established to estimate rainfall intensity using ground-based dual-frequency microwave brightness temperature and rain gauge data. The results show that the root-mean-square error and the correlation coefficient are 0.63 mm h−1 and 0.88, respectively, for the differential method, and 0.91 mm h−1 and 0.71 for the bri...

A contrast and comparison of near-sea surface air temperature/humidity from GMS and SSM/I data with an improved algorithm

With data sets gained from Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I) microwave channels, Geostationary Meteorological Satellite (GMS-5) infrared channels, and ship-measured data, the statistical algorithms to estimate sea surface temperature and near-sea surface air humidity around Taiwan and the South China Sea areas are developed. Then a new, improved method to estimate near-sea surface air temperature based on the algorithm proposed by Konda et al. (1996) is established in this study. The results estimated with SSM/I data show that the root mean square error (RMSE) of SST, near-sea surface air humidity and air temperature over the oceans around Taiwan and the South China Sea are 1.2 K,1.43 g/kg, and 1.6 K, respectively. The results with GMS data are 1.7 K,1.71 g/kg and 1.7 K, respectively. The results also show that the improvements in the algorithm of Konda et al. simplify the computation scheme, improve the accuracy, and match the regional ocean-atmosphere properties in retrieving near-sea surface air temperature. The estimate produced using SSM/I and GMS data also show good consistency between them, both in temporal and spatial variations. Basically, the accuracy of this result implies strong potential for application of satellite data to relative studies and operational work in the ocean-atmosphere interaction.

The atmospheric correction algorithm of ROCSAT-1/OCI data

Abstract In this study, the Normalized Difference Vegetation Index (NDVI) and the band ratio of the total radiance at channels 670nm and 865nm were used to determine the sea surface albedo. The air mass character parameter and aerosol optical depth were then assessed by a simulated process. The pixel-by-pixel aerosol scattering radiance and water-leaving radiance are the main goals to retrieve in this study. As the Ocean Color Imager (OCI) is similar to the Sea-viewing Wide Field-of-view Sensor (Sea WiFS), a set of images were acquired both by Sea WiFS and OCI at the same temporal and spatial parameters. Their respective models-SeaWiFS Data Analysis System (SeaDAS) and Ocean Color Imager TRANsmittance / radiance computation code (OCITRAN) were employed to retrieve the water-leaving radiance so we could compare and evaluate the accuracy of OCITRAN. The results showed a high correlation (R>0.76) between the two models, proving that the OCITRAN algorithm established by this study is adaptable.

Improved tropical rainfall potential forecasting for mountainous regions

In many geographic regions like Taiwan, people suffer from the disastrous effects of tropical cyclones year after year. For instance, the heavy rainfall from these events often triggers mudslides and other associated problems. Furthermore, there appears to be an increasing trend for stronger tropical cyclones.1 Indeed, typhoon Morakot (in 2009) brought record-breaking rainfall floods, the likes of which had not been witnessed in Taiwan for more than 50 years. This typhoon caused the death of more than 680 people (about 500 were killed in a single mudslide). There is thus a great need to develop a method that can be used to forecast the potential of tropical rainfall in both a quick and accurate manner (especially over mountainous regions as these are associated with the highest flood and mudslide risks). Assessing a tropical cyclone’s rainfall potential, however, can be very difficult because the interaction between the cyclone and inland rugged terrains is very complex. This is unfortunately the case for Taiwan because more than two-thirds of its area is covered by mountainous terrain (up to 3000m in altitude). For potential rainfall forecasting methods, the use of satellite data is the optimal choice, and the possibility of estimating tropical cyclone intensity was first demonstrated in the 1980s.2 In addition, continued satellite channel and resolution improvements have dramatically enhanced the application potential of such satellite data. The tropical rainfall potential (TRaP) technique3, 4 is one of the most practical methods for tropical cyclone potential rainfall operations that has so far been proposed. With this technique it is assumed that the tropical cyclone rainfall potential can be calculated by summing together all the rainfall rates derived from microwave satellite data (shifted with respect to Figure 1. Comparison of (a) the observed rainfall rate (Obs. RR) over Taiwan’s Central Mountain Range during the 2009 Morakot typhoon and the rainfall potential obtained from the (b) improved tropical rainfall potential (I-TRaP) model and (c) original TRaP methodology. These results (in mm) are for the period between 00:00UTC (coordinated universal time) on 7 August 2009 and 00:00UTC on 9 August 2009. Black triangles denote the peak rainfall locations. (d) The relationship between the gauge-measured rainfall and the I-TRaP results. r: Correlation coefficient. MB: Mean bias. RMSE: Root mean square error.

Applying satellite remote sensing technique in disastrous rainfall systems around Taiwan

Many people in Asia regions have been suffering from disastrous rainfalls year by year. The rainfall from typhoons or tropical cyclones (TCs) is one of their key water supply sources, but from another perspective such TCs may also bring forth unexpected heavy rainfall, thereby causing flash floods, mudslides or other disasters. So far we cannot stop or change a TC route or intensity via present techniques. Instead, however we could significantly mitigate the possible heavy casualties and economic losses if we can earlier know a TC’s formation and can estimate its rainfall amount and distribution more accurate before its landfalling. In light of these problems, this short article presents methods to detect a TC’s formation as earlier and to delineate its rainfall potential pattern more accurate in advance. For this first part, the satellite-retrieved air-sea parameters are obtained and used to estimate the thermal and dynamic energy fields and variation over open oceans to delineate the high-possibility typhoon occurring ocean areas and cloud clusters. For the second part, an improved tropical rainfall potential (TRaP) model is proposed with better assumptions then the original TRaP for TC rainfall band rotations, rainfall amount estimation, and topographic effect correction, to obtain more accurate TC rainfall distributions, especially for hilly and mountainous areas, such as Taiwan.

INVESTIGATION OF PERMANENT AEROSOL SOURCE REGIONS OVER ASIA USING MULTI-YEAR MODIS OBSERVATIONS

This work examines the permanent aerosol source regions over Asia by analyzing 7-years data of aerosol optical thickness (AOT) product from MODerate Imaging Spectroradiometer (MODIS) onboard Aqua and Terra satellites. The analysis is carried out by taking the average AOT map during the years 2002-2008 over the region in different seasons, in which the permanent source regions will appear pronounced whereas the locations influenced by transport or any emissions that last for shorter time period will be smoothened. The results show four such main permanent source regions. Angstrom Exponent (AE) aerosol product is used to infer about the possible type and size of aerosols over the source regions. The average AOT trends over the source regions in different seasons during 2002-2008 are examined and the results are discussed based on the corresponding variations of meteorological parameters derived from National Center for Environmental Prediction - National Center for Atmospheric Research (NCEP-NCAR) reanalysis data. In addition, the trends in AOT variation during the period 2002-2008 over certain selected stations over the area are also discussed.

A Heat Island Observation via MODIS and Concurrent Helicopter-Borne IR Imager

In this study, observations and aerial surveys were conducted in delineating the heat island patterns. Concurrent datasets from the satellite-borne MODIS sensor and helicopter-borne th ermal IR imager were employed. The sampling data in this experiment covered an area exceeding 100 km^2 in Taiwans western plain. A more than 10°C temperature difference between different sampling points could be observed in this area. The relationship between the surface temperature and vegetation index values was also showed. Results showed that the surface canopy and the air humidity together played an important role in the surface temperature increase.