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- Author or Editor: Taikan Oki x
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Abstract
The diurnal cycle of precipitation is investigated using ground-based hourly observations for more than 10 years both in Japan and Malaysia. The diurnal cycle of precipitation in Japan is classified into three clusters. The first one has a peak in the morning, and the stations categorized into this cluster are located in coastal regions. The second cluster has two peaks in the morning and in the evening. These stations are located in an inland region. The morning peak in the above two clusters is dominant in June, when it is “baiu” in Japan. Baiu is the rainy season related to the southwest Asian monsoon. The third cluster is an exceptional case. No morning peak is observed in the stations of the third cluster and they have a comparatively strong evening peak.
In the case of the Malay Peninsula, the inland region has a pronounced peak of rainfall at 1600 LST; the magnitude exceeds the mean of each month by 200%. This evening peak is too sharp to be represented by a 24-h-cycle sine wave decomposed by Fourier transform. The intensity also becomes higher at the peak time (1500 LST). The magnitude of the diurnal cycle of mean intensity is larger than the annual cycle of monthly mean intensity. The morning peak of precipitation is observed during the southwest monsoon season on the west coast, and during the northeast monsoon season on the east coast. The intensity of precipitation is not significantly high during this period; namely, the increase of the probability or the duration of precipitation forms this morning peak. These evidences indicate the mechanism of the convective rainfall by the thermodynamic forcing in the evening, and the low-level convergence between the local land–sea breeze and the predominant monsoon wind in the morning.
Abstract
The diurnal cycle of precipitation is investigated using ground-based hourly observations for more than 10 years both in Japan and Malaysia. The diurnal cycle of precipitation in Japan is classified into three clusters. The first one has a peak in the morning, and the stations categorized into this cluster are located in coastal regions. The second cluster has two peaks in the morning and in the evening. These stations are located in an inland region. The morning peak in the above two clusters is dominant in June, when it is “baiu” in Japan. Baiu is the rainy season related to the southwest Asian monsoon. The third cluster is an exceptional case. No morning peak is observed in the stations of the third cluster and they have a comparatively strong evening peak.
In the case of the Malay Peninsula, the inland region has a pronounced peak of rainfall at 1600 LST; the magnitude exceeds the mean of each month by 200%. This evening peak is too sharp to be represented by a 24-h-cycle sine wave decomposed by Fourier transform. The intensity also becomes higher at the peak time (1500 LST). The magnitude of the diurnal cycle of mean intensity is larger than the annual cycle of monthly mean intensity. The morning peak of precipitation is observed during the southwest monsoon season on the west coast, and during the northeast monsoon season on the east coast. The intensity of precipitation is not significantly high during this period; namely, the increase of the probability or the duration of precipitation forms this morning peak. These evidences indicate the mechanism of the convective rainfall by the thermodynamic forcing in the evening, and the low-level convergence between the local land–sea breeze and the predominant monsoon wind in the morning.
Abstract
A normalized difference vegetation index (NDVI) cloud index (NCI) was derived from Pathfinder Advanced Very High Resolution Radiometer (AVHRR) daily NDVI data and compared with observed cloud amounts and a sunshine duration–cloud index (SCI) over an area of diverse land cover. Ground observations from 120 meteorological stations were significantly related to the daily NCI and the SCI, with R 2 values of 0.41 and 0.50, respectively. The daily NCI and interpolated cloud indices derived from ground observations over the 776 900 km2 study area were compared. The correlation coefficient between the NCI and the observed cloud amount was less than 0.6 for less than 20% of the area. The correlation coefficient between the NCI and the observed sunshine duration index was less than 0.6 for less than 10% of the area and less than 0.7 for 41% of the area. There were strong correlations for high elevations in summer, and correlations for low elevations in winter were weaker. A frozen soil surface or snow cover degrades the NDVI relationship to clouds. The NCI and observed cloud indices had high correlation coefficients in areas with diverse land uses, suggesting that the NCI may be useful in estimating cloudiness over a large region.
Abstract
A normalized difference vegetation index (NDVI) cloud index (NCI) was derived from Pathfinder Advanced Very High Resolution Radiometer (AVHRR) daily NDVI data and compared with observed cloud amounts and a sunshine duration–cloud index (SCI) over an area of diverse land cover. Ground observations from 120 meteorological stations were significantly related to the daily NCI and the SCI, with R 2 values of 0.41 and 0.50, respectively. The daily NCI and interpolated cloud indices derived from ground observations over the 776 900 km2 study area were compared. The correlation coefficient between the NCI and the observed cloud amount was less than 0.6 for less than 20% of the area. The correlation coefficient between the NCI and the observed sunshine duration index was less than 0.6 for less than 10% of the area and less than 0.7 for 41% of the area. There were strong correlations for high elevations in summer, and correlations for low elevations in winter were weaker. A frozen soil surface or snow cover degrades the NDVI relationship to clouds. The NCI and observed cloud indices had high correlation coefficients in areas with diverse land uses, suggesting that the NCI may be useful in estimating cloudiness over a large region.
Abstract
Seto et al. developed rain/no-rain classification (RNC) methods over land for the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI). In this study, the methods are modified for application to other microwave radiometers. The previous methods match TMI observations with TRMM precipitation radar (PR) observations, classify the TMI pixels into rain pixels and no-rain pixels, and then statistically summarize the observed brightness temperature at the no-rain pixels into a land surface brightness temperature database. In the modified methods, the probability distribution of brightness temperature under no-rain conditions is derived from unclassified TMI pixels without the use of PR. A test with the TMI shows that the modified (PR independent) methods are better than the RNC method developed for the Goddard profiling algorithm (GPROF; the standard algorithm for the TMI) while they are slightly poorer than corresponding previous (PR dependent) methods. M2d, one of the PR-independent methods, is applied to observations from the Advanced Microwave Scanning Radiometer for Earth Observing Satellite (AMSR-E), is evaluated for a matchup case with PR, and is evaluated for 1 yr with a rain gauge dataset in Japan. M2d is incorporated into a retrieval algorithm developed by the Global Satellite Mapping of Precipitation project to be applied for the AMSR-E. In latitudes above 30°N, the rain-rate retrieval is compared with a rain gauge dataset by the Global Precipitation Climatology Center. Without a snow mask, a large amount of false rainfall due to snow contamination occurs. Therefore, a simple snow mask using the 23.8-GHz channel is applied and the threshold of the mask is optimized. Between 30° and 60°N, the optimized snow mask forces the miss of an estimated 10% of the total rainfall.
Abstract
Seto et al. developed rain/no-rain classification (RNC) methods over land for the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI). In this study, the methods are modified for application to other microwave radiometers. The previous methods match TMI observations with TRMM precipitation radar (PR) observations, classify the TMI pixels into rain pixels and no-rain pixels, and then statistically summarize the observed brightness temperature at the no-rain pixels into a land surface brightness temperature database. In the modified methods, the probability distribution of brightness temperature under no-rain conditions is derived from unclassified TMI pixels without the use of PR. A test with the TMI shows that the modified (PR independent) methods are better than the RNC method developed for the Goddard profiling algorithm (GPROF; the standard algorithm for the TMI) while they are slightly poorer than corresponding previous (PR dependent) methods. M2d, one of the PR-independent methods, is applied to observations from the Advanced Microwave Scanning Radiometer for Earth Observing Satellite (AMSR-E), is evaluated for a matchup case with PR, and is evaluated for 1 yr with a rain gauge dataset in Japan. M2d is incorporated into a retrieval algorithm developed by the Global Satellite Mapping of Precipitation project to be applied for the AMSR-E. In latitudes above 30°N, the rain-rate retrieval is compared with a rain gauge dataset by the Global Precipitation Climatology Center. Without a snow mask, a large amount of false rainfall due to snow contamination occurs. Therefore, a simple snow mask using the 23.8-GHz channel is applied and the threshold of the mask is optimized. Between 30° and 60°N, the optimized snow mask forces the miss of an estimated 10% of the total rainfall.
