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- Author or Editor: Mark Morrissey x
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Abstract
This paper focuses on estimating the error uncertainty of the monthly 2.5° × 2.5° rainfall products of the Global Precipitation Climatology Project (GPCP) using rain gauge observations. Two kinds of GPCP products are evaluated: the satellite-only (MS) product, and the satellite–gauge (SG) merged product. The error variance separation (EVS) method has been proposed previously as a means of estimating the error uncertainty of the GPCP products. In this paper, the accuracy of the EVS results is examined for a variety of gauge densities. Three validation sites—two in North Dakota and one in Thailand—all with a large number of rain gauges, were selected. The very high density of the selected sites justifies the assumption that the errors are negligible if all gauges are used. Monte Carlo simulation studies were performed to evaluate sampling uncertainty for selected rain gauge network densities. Results are presented in terms of EVS error uncertainty normalized by the true error uncertainty. These results show that the accuracy of the EVS error uncertainty estimates for the SG product differs from that of the MS product. The key factors that affect the errors of the EVS results, such as the gauge density, the gauge network, and the sample size, have been identified and their influence has been quantified. One major finding of this study is that 8–10 gauges, at the 2.5° scale, are required as a minimum to get good error uncertainty estimates for the SG products from the EVS method. For eight or more gauges, the normalized error uncertainty is about 0.86 ± 0.10 (North Dakota: Box 1) and 0.95 ± 0.10 (North Dakota: Box 2). Results show that, despite its error, the EVS method performs better than the root-mean-square error (rmse) approach that ignores the rain gauge sampling error. For the MS products, both the EVS method and the rmse approach give negligible bias. As expected, results show that the SG products give better rainfall estimates than the MS products, according to most of the criteria used.
Abstract
This paper focuses on estimating the error uncertainty of the monthly 2.5° × 2.5° rainfall products of the Global Precipitation Climatology Project (GPCP) using rain gauge observations. Two kinds of GPCP products are evaluated: the satellite-only (MS) product, and the satellite–gauge (SG) merged product. The error variance separation (EVS) method has been proposed previously as a means of estimating the error uncertainty of the GPCP products. In this paper, the accuracy of the EVS results is examined for a variety of gauge densities. Three validation sites—two in North Dakota and one in Thailand—all with a large number of rain gauges, were selected. The very high density of the selected sites justifies the assumption that the errors are negligible if all gauges are used. Monte Carlo simulation studies were performed to evaluate sampling uncertainty for selected rain gauge network densities. Results are presented in terms of EVS error uncertainty normalized by the true error uncertainty. These results show that the accuracy of the EVS error uncertainty estimates for the SG product differs from that of the MS product. The key factors that affect the errors of the EVS results, such as the gauge density, the gauge network, and the sample size, have been identified and their influence has been quantified. One major finding of this study is that 8–10 gauges, at the 2.5° scale, are required as a minimum to get good error uncertainty estimates for the SG products from the EVS method. For eight or more gauges, the normalized error uncertainty is about 0.86 ± 0.10 (North Dakota: Box 1) and 0.95 ± 0.10 (North Dakota: Box 2). Results show that, despite its error, the EVS method performs better than the root-mean-square error (rmse) approach that ignores the rain gauge sampling error. For the MS products, both the EVS method and the rmse approach give negligible bias. As expected, results show that the SG products give better rainfall estimates than the MS products, according to most of the criteria used.
Abstract
The common use of remotely located, buoy-mounted capacitance rain gauges in the tropical oceans for satellite rainfall verification studies provides motivation for an in situ gauge bias assessment. A comparison of the biases in rainfall catchment between Pacific island tipping-bucket rain gauges and capacitance rain gauges mounted on moored buoys in the tropical Pacific is conducted using the relationship between the fractional time in rain and monthly rainfall. This study utilizes the widespread spatial homogeneity of this relationship in the tropics to assess the rain catchment of both types of gauges at given values for the fractional time in rain. The results indicate that the capacitance gauges are not statistically significantly biased relative to the island-based tipping-bucket gauges. In addition, given the relatively small error bounds about the bias estimates any real bias differences among all the tested gauges are likely to be quite small compared to monthly rainfall totals. Underestimates resulting from wind biases, which may be substantial, are not documented in this paper.
Abstract
The common use of remotely located, buoy-mounted capacitance rain gauges in the tropical oceans for satellite rainfall verification studies provides motivation for an in situ gauge bias assessment. A comparison of the biases in rainfall catchment between Pacific island tipping-bucket rain gauges and capacitance rain gauges mounted on moored buoys in the tropical Pacific is conducted using the relationship between the fractional time in rain and monthly rainfall. This study utilizes the widespread spatial homogeneity of this relationship in the tropics to assess the rain catchment of both types of gauges at given values for the fractional time in rain. The results indicate that the capacitance gauges are not statistically significantly biased relative to the island-based tipping-bucket gauges. In addition, given the relatively small error bounds about the bias estimates any real bias differences among all the tested gauges are likely to be quite small compared to monthly rainfall totals. Underestimates resulting from wind biases, which may be substantial, are not documented in this paper.
Abstract
A Monte Carlo simulation study is conducted to investigate the performance of the area-threshold method of estimating mean areas rainfall. The study uses a stochastic space-time model of rainfall as the true rainfall-field generator. Simple schemes of simulating radar observations of the simulated rainfall fields are employed. The schemes address both random and systematic components of the radar rainfall-estimation process. The results of the area-threshold method are compared to the results based on conventional averaging of radar-estimated point rainfall observations. The results demonstrate that when the exponent parameter in the Z–R relationship has small uncertainty (about ±10%), the conventional method works better than the area-threshold method. When the errors are higher (±20%), the area-threshold method with optimum threshold in the 5–10 mm h−1 range performs best. For even higher errors in the Z–R relationship, the area-threshold method with a low threshold provides the best performance.
Abstract
A Monte Carlo simulation study is conducted to investigate the performance of the area-threshold method of estimating mean areas rainfall. The study uses a stochastic space-time model of rainfall as the true rainfall-field generator. Simple schemes of simulating radar observations of the simulated rainfall fields are employed. The schemes address both random and systematic components of the radar rainfall-estimation process. The results of the area-threshold method are compared to the results based on conventional averaging of radar-estimated point rainfall observations. The results demonstrate that when the exponent parameter in the Z–R relationship has small uncertainty (about ±10%), the conventional method works better than the area-threshold method. When the errors are higher (±20%), the area-threshold method with optimum threshold in the 5–10 mm h−1 range performs best. For even higher errors in the Z–R relationship, the area-threshold method with a low threshold provides the best performance.
Abstract
Very few (if any) in situ measurements of rainfall are available in the Pacific ITCZ east of the Line Islands (157°W). Hence, climatological datasets, which are assembled from various in situ sources, and satellite-derived analyses of precipitation are frequently relied upon to provide information on the distribution of rainfall in this important region. A substantial amount of disagreement exists among these information sources as demonstrated in this paper. In particular, the east–west gradient of estimated rainfall intensity in the eastern Pacific ITCZ is quite different during the Northern Hemisphere warm season among six different satellite algorithms (one infrared and five microwave) and two climatologies that are examined. Some of these data suggest that a local minimum in rainfall intensity is located near 140°W in the Pacific ITCZ during northern summer, with increasing intensity toward the east and west, while the others depict steadily decreasing rainfall intensity from west of the Americas to the date line. Conversely, all of the precipitation estimates that are examined depict a rainfall maximum in the Pacific ITCZ near 140°W during the Northern Hemisphere cool season, although the magnitudes vary substantially among them.
The authors examine estimates of seasonal mean rainfall over the eastern Pacific ITCZ (cast of the date line) from two rainfall climatologies and six satellite precipitation estimation techniques during July 1987 through June 1990. Inconsistencies among the precipitation analyses are investigated by examining several independent datasets that include atmospheric circulation data, sea surface temperature data, and ship reports of weather type.
Abstract
Very few (if any) in situ measurements of rainfall are available in the Pacific ITCZ east of the Line Islands (157°W). Hence, climatological datasets, which are assembled from various in situ sources, and satellite-derived analyses of precipitation are frequently relied upon to provide information on the distribution of rainfall in this important region. A substantial amount of disagreement exists among these information sources as demonstrated in this paper. In particular, the east–west gradient of estimated rainfall intensity in the eastern Pacific ITCZ is quite different during the Northern Hemisphere warm season among six different satellite algorithms (one infrared and five microwave) and two climatologies that are examined. Some of these data suggest that a local minimum in rainfall intensity is located near 140°W in the Pacific ITCZ during northern summer, with increasing intensity toward the east and west, while the others depict steadily decreasing rainfall intensity from west of the Americas to the date line. Conversely, all of the precipitation estimates that are examined depict a rainfall maximum in the Pacific ITCZ near 140°W during the Northern Hemisphere cool season, although the magnitudes vary substantially among them.
The authors examine estimates of seasonal mean rainfall over the eastern Pacific ITCZ (cast of the date line) from two rainfall climatologies and six satellite precipitation estimation techniques during July 1987 through June 1990. Inconsistencies among the precipitation analyses are investigated by examining several independent datasets that include atmospheric circulation data, sea surface temperature data, and ship reports of weather type.
Abstract
The One-Degree Daily (1DD) technique is described for producing globally complete daily estimates of precipitation on a 1° × 1° lat/long grid from currently available observational data. Where possible (40°N–40°S), the Threshold-Matched Precipitation Index (TMPI) provides precipitation estimates in which the 3-hourly infrared brightness temperatures (IR T b ) are compared with a threshold and all “cold” pixels are given a single precipitation rate. This approach is an adaptation of the Geostationary Operational Environmental Satellite Precipitation Index, but for the TMPI the IR T b threshold and conditional rain rate are set locally by month from Special Sensor Microwave Imager–based precipitation frequency and the Global Precipitation Climatology Project (GPCP) satellite–gauge (SG) combined monthly precipitation estimate, respectively. At higher latitudes the 1DD features a rescaled daily Television and Infrared Observation Satellite Operational Vertical Sounder (TOVS) precipitation. The frequency of rain days in the TOVS is scaled down to match that in the TMPI at the data boundaries, and the resulting nonzero TOVS values are scaled locally to sum to the SG (which is a globally complete monthly product).
The GPCP has approved the 1DD as an official product, and data have been produced for 1997 through 1999, with production continuing a few months behind real time (to allow access to monthly input data). The time series of the daily 1DD global images shows good continuity in time and across the data boundaries. Various examples are shown to illustrate uses. Validation for individual gridbox values shows a very high mean absolute error, but it improves quickly when users perform time/space averaging according to their own requirements.
Abstract
The One-Degree Daily (1DD) technique is described for producing globally complete daily estimates of precipitation on a 1° × 1° lat/long grid from currently available observational data. Where possible (40°N–40°S), the Threshold-Matched Precipitation Index (TMPI) provides precipitation estimates in which the 3-hourly infrared brightness temperatures (IR T b ) are compared with a threshold and all “cold” pixels are given a single precipitation rate. This approach is an adaptation of the Geostationary Operational Environmental Satellite Precipitation Index, but for the TMPI the IR T b threshold and conditional rain rate are set locally by month from Special Sensor Microwave Imager–based precipitation frequency and the Global Precipitation Climatology Project (GPCP) satellite–gauge (SG) combined monthly precipitation estimate, respectively. At higher latitudes the 1DD features a rescaled daily Television and Infrared Observation Satellite Operational Vertical Sounder (TOVS) precipitation. The frequency of rain days in the TOVS is scaled down to match that in the TMPI at the data boundaries, and the resulting nonzero TOVS values are scaled locally to sum to the SG (which is a globally complete monthly product).
The GPCP has approved the 1DD as an official product, and data have been produced for 1997 through 1999, with production continuing a few months behind real time (to allow access to monthly input data). The time series of the daily 1DD global images shows good continuity in time and across the data boundaries. Various examples are shown to illustrate uses. Validation for individual gridbox values shows a very high mean absolute error, but it improves quickly when users perform time/space averaging according to their own requirements.
