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The method developed for estimating rainfall over several sample areas over the main islands of Japan from IR imagery data of GMS is extended to estimate rainfall over a wider region including the Japanese islands and their adjacent ocean.In extended method, the proportional coefficients between rainfall and cold cloud fractional coverage, FC are assumed to be linear functions of latitudes and calculated for the 3 types of rainy clouds by using IR and rainfall data of AIP/1. The extended method gives a relatively good estimation of rainfall in applying to the whole AIP/1 data, with relative errors of 73% and 17% for daily and monthly estimation, respectively. Rainfall features of the Baiu season are then investigated through IR estimation for June 1989.The total monthly rainfall for the period is distributed in a band-like shape exteding from west to east, showing its maximum on 35ﾟN in the latitudinal section and a sudden decrease to the west of 130ﾟE in the longitudinal section. The latitudinal distributions of rainfall relative to the Baiu front show almost the same shape with their maximam at 1.25ﾟ north of the front at the surface over both land and ocean, but the peak amount is larger over the island than that over the ocean.

Rainfall features of the Baiu season estimated from GMS IR images are investigated for the Japanese islands and their adjacent ocean for June 1989. The total monthly rainfall for the period is distributed in a band-like shape extending from west to east, showing its maximum on 35°N in the latitudinal section and a sudden decrease to the west of 130°E in the longitudinal section. The latitudinal distributions of rainfall relative to the Baiu front show almost the same shape with the maximum at 1.25° north of the front at the surface over both land and ocean, but the peak amount is larger over the island than that over the ocean. The temporal changes of rainfall are dominated by a period of synoptic scale for all regions over both land and ocean, and secondly, by a peak corresponding to daily variation observed only over the continent. The rainfall region moves eastward along the Baiu front with almost same phase speed of about 7.5° longitudes per 12 hours over almost all regions.

The method developed for estimating rainfall over several sample areas over the main islands of Japan from IR imagery data of GMS is extended to estimate rainfall over a wider region including the Japanese islands and their adjacent ocean. In this method, rainfall estimation over a grid of 1.25° latitudes × 1.25° longitudes is made by assuming linear relations between hourly rainfall and cold cloud fractional coverage (FC) for the 3 rain cloud types (A: Cumulus; B: Cumulonimbus and C: Middle Clouds) and assuming rain free in cases of cloud amount less than 70% as well as cloud type D (Cirrus). The threshold temperatures to define FC for the 3 rain cloud types are kept the same as those used in Xie, that is, 245 K, 235 K and 255 K for each cloud type A, B and C, respectively. The proportional constants between hourly rainfall and FC are assumed to be functions of latitude and are calculated by using the rainfall data obtained by radar-AMeDAS composite and FC data of the grids over the Japanese islands. The resultant relative errors of rainfall estimation over the Japanese islands are 73% and 17% for daily and monthly estimation, respectively, which satisfy the useful level of the accuracy of rainfall data in climatic use.

A new method is proposed for estimation of rainfall in mid-latitudinal areas from GMS infrared imagery data. This method was developed by using the satellite and gage data in 5 areas of about 10^4 km^2 in Japan, taking account of the different features of rainfall from various types of clouds. In this method, clouds are classified into six types, S(Clear Sky), F(Fine), A(Cumulus), B(Cumulonimbus), C(Middle Clouds) and D(High Clouds), by using the discriminate analysis technique from IR imagery data. The cloud types S, F and D are assumed to be no-rain cases and in the cases of A, B and C, rainfall is assumed to be proportional to the cold cloud fractional coverage (FC). A proportional constant for each cloud type is determined from the regression equation between FC and 3-hr rainfall, where the threshold T_BB used to define FC is determined to give the highest correlation for each cloud type. This method gives a good rainfall estimation for 24 hr cumulative rainfall with the correlation coefficient of 0.651 and relative root mean square error (RRE) of 0.968. To apply this method to the Hokkaido area, the results should be adjusted by an empirically determined factor, because weather conditions are different in this area.

A new method is proposed for estimation of rainfall in mid-latitudinal area from GMS infrared imagery data. This method is developed by using the satellite and gage data in 5 areas of about 10^4 km^2 with similar meteorological conditions in Japan, taking account of the different features of rainfall from various types of clouds. In this method, cloud is classified by IR-parameters into six types, S(Clear Sky), F(Fine), A(cumulus), B(Cumulonimbus), C(Middle Clouds) and D(High Clouds), by using the discriminate analysis technique. The cloud types, S, F and D are assumed to be no-rain cases. In the case of A, B and C, the rainfall is assumed to be proportional to the cold cloud fractional coverage (FC), for which the proportional constant is determined by the regressional equation between FC and 3-hr rainfall, where the threshold value to define FC are determined to give the highest correlation between them. This method gives a good rainfall estimation for 24-hr rainfall with the correlation coefficient of 0.65 and relative root mean square error of 0.94. An application of this method to Hokkaido area also gives a good estimation of 24-hr rainfall if an adjustment factor is introduced, suggesting a possible application of this mothod to areas with different meteorological conditions.

Techniques estimating cloud amount and classifying cloud type from GMS IR and/or VIS imagery data have been developed and evaluated. Cloud amount is estimated by a Two-Threshold-Method (TTM) which takes account of the partially cloud-covered pixels. Cloud type is classified by a discriminant analysis technique. Fairly good agreement is obtained between the satellite estimated and the surface observed cloud amounts. The comparison between the satellite classified and ground observed cloud types shows correctness ratios of 52.9% and 47.7% for the daytime and nighttime respectively, but the distinction between cumulus and middle clouds seems insufficient. The techniques were applied to produce the time series of cloud amounts and types during the period from 3 to 11 Sept. 1980, and the results were acceptable.

Methods for estimating cloud amount and type from GMS IR imaegry data developed forthe daytime are applied to nighttime data. Cloud amount is estimated by a Two ThresholdMethod (TTM) which takes account of the ground related spectral peak and of the partiallycloud-covered pixels. Cloud type is classified by a discriminant analysis technique. The resultsshow a correlation coefficient of 0.659 and correctness ratio of 47.7% with comparison to theground observations. These values are a little worse than those in daytime (0.869 and 52.9%respectively), but suggesting the possibility to applying to all data. The methods are applied toproduce the time series of cloud amounts and types for the period from 3 to 11 Sept. 1980. Thevariation trend of satellite derived cloud appeared to reflect the change of weather systems andto agree with the surface cloud observation comparatively well.

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A new method is proposed for estimation of rainfall in mid-latitudinal area from GMS infrared imagery data. This method is developed by using the satellite and gage data in 5 areas of about 10^4 km^2 with similar meteorological conditions in Japan, taking account of the different features of rainfall from various types of clouds. In this method, cloud is classified by IR-parameters into six types, S(Clear Sky), F(Fine), A(cumulus), B(Cumulonimbus), C(Middle Clouds) and D(High Clouds), by using the discriminate analysis technique. The cloud types, S, F and D are assumed to be no-rain cases. In the case of A, B and C, the rainfall is assumed to be proportional to the cold cloud fractional coverage (FC), for which the proportional constant is determined by the regressional equation between FC and 3-hr rainfall, where the threshold value to define FC are determined to give the highest correlation between them. This method gives a good rainfall estimation for 24-hr rainfall with the correlation coefficient of 0.65 and relative root mean square error of 0.94. An application of this method to Hokkaido area also gives a good estimation of 24-hr rainfall if an adjustment factor is introduced, suggesting a possible application of this mothod to areas with different meteorological conditions.

Methods for estimating cloud amount and type from GMS IR imaegry data developed forthe daytime are applied to nighttime data. Cloud amount is estimated by a Two ThresholdMethod (TTM) which takes account of the ground related spectral peak and of the partiallycloud-covered pixels. Cloud type is classified by a discriminant analysis technique. The resultsshow a correlation coefficient of 0.659 and correctness ratio of 47.7% with comparison to theground observations. These values are a little worse than those in daytime (0.869 and 52.9%respectively), but suggesting the possibility to applying to all data. The methods are applied toproduce the time series of cloud amounts and types for the period from 3 to 11 Sept. 1980. Thevariation trend of satellite derived cloud appeared to reflect the change of weather systems andto agree with the surface cloud observation comparatively well.

A new method is developed to estimate the total cloud amount from GMS imagery databy the Two-Threshold-Method (TTM) taking account of the spectral peaks due to the groundsurface and of the pixels totally covered by cloud. The approximate agreement is obtainedbetween the estimated values and the ground-observed cloud amounts over an area of about10 4 km2. The possibility of classification of cloud types from GMS IR data into low, middle, high and convective clouds is studied and found that they can be distinguished from each otherby TBB spectral parameters except the middle and convective clouds.

Rainfall estimation in midlatitude by the use of geostationary meteorological satellite (GMS)infrared and visible imagery data was studied. A preliminary study of the relationship betweenground observed cloudiness and rainfall reveals that the upper layer cloud like cirrus causes norain and that rainfall by convective cloud is roughly proportional to the cloud amount. The rela-tion between types of clouds and IR-VIS 2-dimensional frequency distributions shows that upperlayer cloud can be identified by the peak with an albedo less than 0.4, and that the groundobserved cloud amount is well proportional to the fractional coverage of the satellite data. Basedupon these results, a twe-step rain estimation method is developed; determination of rain/no-rainby peak position, followed by calculation of the rainfall with regression equation using the frac-tional coverage.

A new method is developed to estimate the total cloud amount from GMS imagery databy the Two-Threshold-Method (TTM) taking account of the spectral peaks due to the groundsurface and of the pixels totally covered by cloud. The approximate agreement is obtainedbetween the estimated values and the ground-observed cloud amounts over an area of about10 4 km2. The possibility of classification of cloud types from GMS IR data into low, middle, high and convective clouds is studied and found that they can be distinguished from each otherby TBB spectral parameters except the middle and convective clouds.

Rainfall estimation in midlatitude by the use of geostationary meteorological satellite (GMS)infrared and visible imagery data was studied. A preliminary study of the relationship betweenground observed cloudiness and rainfall reveals that the upper layer cloud like cirrus causes norain and that rainfall by convective cloud is roughly proportional to the cloud amount. The rela-tion between types of clouds and IR-VIS 2-dimensional frequency distributions shows that upperlayer cloud can be identified by the peak with an albedo less than 0.4, and that the groundobserved cloud amount is well proportional to the fractional coverage of the satellite data. Basedupon these results, a twe-step rain estimation method is developed; determination of rain/no-rainby peak position, followed by calculation of the rainfall with regression equation using the frac-tional coverage.

To improve monitoring of extreme weather and climate events from space, the World Meteorological Organization (WMO) initiated the space-based weather and climate extremes monitoring demonstration project (SEMDP). Presently, SEMDP is focused on drought and heavy precipitation monitoring over Southeast Asia and the Pacific. Space-based data and derived products form critical part of meteorological services’ operations for weather monitoring; however, satellite products are still not fully utilized for climate applications. Using SEMDP satellite-derived precipitation products, it would be possible to monitor extreme precipitation events with uniform spatial coverage and over various time periods – pentad, weekly, 10 days, monthly and longer time-scales. In this chapter, SEMDP satellite-derived precipitation products over the Asia-Pacific region produced by the Earth Observation Research Center/Japan Aerospace Exploration Agency (EORC/JAXA) and the Climate Prediction Center/National Oceanic and Atmospheric Administration (CPC/NOAA) are introduced. Case studies for monitoring (i) drought in Australia in July-October 2007 and September 2018 and (ii) heavy precipitation over Australia in December 2010 and Thailand and the Peninsular Malaysia in November-December 2014 which caused widespread flooding are also presented. Satellite observations are compared with in situ data to demonstrate value of satellite-derived estimates of precipitation for drought and heavy rainfall monitoring.