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- Author or Editor: Alfred Chang x
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Abstract
The climatology of oceanic rain column height derived from 12 years (July 1987–June 1999) of Special Sensor Microwave Imager (SSM/I) data is presented. The estimation procedure is based on a technique developed by Wilheit et al. In the annual mean, the SSM/I-derived oceanic rain height shows a maximum of about 4.7 km in the Tropics and decreases toward the high latitudes to less than 3.5 km at 50°. Interannual variations exhibit seasonal dependency and show maxima of about 200–300 m in the oceanic dry zones and in the midlatitude storm track regions. The rain heights estimated from the morning passes of the SSM/I are lower than those computed from the afternoon passes by about 60 m in the Tropics but are higher north of 40°N. This small difference cannot change the conclusion about the morning maximum in rain rate. The nonsystematic error increases with decreasing rain column height and is estimated to be about 120 m for rain heights of 4–5 km and 200 m at 3.5 km. Comparison with the height of the 0°C isotherm derived from the Goddard Laboratory for Atmospheres general circulation model (GCM) results shows a mean zonal low bias (SSM/I lower than GCM freezing height) of about 200 m in the Tropics. Outside the Tropics, the SSM/I rain column heights are much higher, reaching a difference of 2 km at 50°N. The small bias in the Tropics is consistent with the notion that the melting layer extends over hundreds of meters below the freezing level. Outside the Tropics, the sampling of the SSM/I rain height and the inclusion of nonraining observations in GCM calculations may contribute to the large discrepancy. The freezing height is interpreted as the columnar water content and found to be consistent with columnar water vapor maps retrieved from SSM/I data.
Abstract
The climatology of oceanic rain column height derived from 12 years (July 1987–June 1999) of Special Sensor Microwave Imager (SSM/I) data is presented. The estimation procedure is based on a technique developed by Wilheit et al. In the annual mean, the SSM/I-derived oceanic rain height shows a maximum of about 4.7 km in the Tropics and decreases toward the high latitudes to less than 3.5 km at 50°. Interannual variations exhibit seasonal dependency and show maxima of about 200–300 m in the oceanic dry zones and in the midlatitude storm track regions. The rain heights estimated from the morning passes of the SSM/I are lower than those computed from the afternoon passes by about 60 m in the Tropics but are higher north of 40°N. This small difference cannot change the conclusion about the morning maximum in rain rate. The nonsystematic error increases with decreasing rain column height and is estimated to be about 120 m for rain heights of 4–5 km and 200 m at 3.5 km. Comparison with the height of the 0°C isotherm derived from the Goddard Laboratory for Atmospheres general circulation model (GCM) results shows a mean zonal low bias (SSM/I lower than GCM freezing height) of about 200 m in the Tropics. Outside the Tropics, the SSM/I rain column heights are much higher, reaching a difference of 2 km at 50°N. The small bias in the Tropics is consistent with the notion that the melting layer extends over hundreds of meters below the freezing level. Outside the Tropics, the sampling of the SSM/I rain height and the inclusion of nonraining observations in GCM calculations may contribute to the large discrepancy. The freezing height is interpreted as the columnar water content and found to be consistent with columnar water vapor maps retrieved from SSM/I data.
Abstract
About 10 yr (July 1987–December 1997 with December 1987 missing) of oceanic monthly rainfall based on data taken by the Special Sensor Microwave/Imager (SSM/I) on board the Defense Meteorological Satellite Program satellites have been computed. The technique, based on the work of Wilheit et al., includes improved parameterization of the beam-filling correction, a refined land mask and sea ice filter. Monthly means are calculated for both 5° and 2.5° latitude–longitude boxes.
Monthly means over the latitude band of 50°N–50°S and error statistics are presented. The time-averaged rain rate is 3.09 mm day−1 (std dev of 0.15 mm day−1) with an error of 38.0% (std dev of 3.0%) for the 5° monthly means over the 10-yr period. These statistics compare favorably with 3.00 mm day−1 (std dev of 0.19 mm day−1) and 46.7% (std dev of 3.4%) computed from the 2.5° monthly means for the period January 1992–December 1994. Examination of the different rain rate categories shows no distinct discontinuity, except for months with a large number of missing SSM/I data.
An independent estimate of the error using observations from two satellites shows an error of 31% (std dev of 2.7%), consistent with the 38% estimated using (a.m. and p.m.) data from one satellite alone. Error estimates (31%) based on the 5° means by averaging four neighboring 2.5° boxes are larger than those (23%) estimated by assuming the means for these neighboring boxes are independent, thus suggesting spatial dependence of the 2.5° means.
Multiple regression analyses show that the error varies inversely as the square root of the number of samples but exhibits a somewhat weaker dependence on the mean rain rate. Regression analyses show a power law dependence of −0.255 to −0.265 on the rain rate for the 5° monthly means using data from a single satellite and a dependence of −0.366 for the 5° monthly means and −0.337 for the 2.5° monthly means based on two satellite measurements. The latter estimate is consistent with that obtained by Bell et al. using a different rainfall retrieval technique.
Abstract
About 10 yr (July 1987–December 1997 with December 1987 missing) of oceanic monthly rainfall based on data taken by the Special Sensor Microwave/Imager (SSM/I) on board the Defense Meteorological Satellite Program satellites have been computed. The technique, based on the work of Wilheit et al., includes improved parameterization of the beam-filling correction, a refined land mask and sea ice filter. Monthly means are calculated for both 5° and 2.5° latitude–longitude boxes.
Monthly means over the latitude band of 50°N–50°S and error statistics are presented. The time-averaged rain rate is 3.09 mm day−1 (std dev of 0.15 mm day−1) with an error of 38.0% (std dev of 3.0%) for the 5° monthly means over the 10-yr period. These statistics compare favorably with 3.00 mm day−1 (std dev of 0.19 mm day−1) and 46.7% (std dev of 3.4%) computed from the 2.5° monthly means for the period January 1992–December 1994. Examination of the different rain rate categories shows no distinct discontinuity, except for months with a large number of missing SSM/I data.
An independent estimate of the error using observations from two satellites shows an error of 31% (std dev of 2.7%), consistent with the 38% estimated using (a.m. and p.m.) data from one satellite alone. Error estimates (31%) based on the 5° means by averaging four neighboring 2.5° boxes are larger than those (23%) estimated by assuming the means for these neighboring boxes are independent, thus suggesting spatial dependence of the 2.5° means.
Multiple regression analyses show that the error varies inversely as the square root of the number of samples but exhibits a somewhat weaker dependence on the mean rain rate. Regression analyses show a power law dependence of −0.255 to −0.265 on the rain rate for the 5° monthly means using data from a single satellite and a dependence of −0.366 for the 5° monthly means and −0.337 for the 2.5° monthly means based on two satellite measurements. The latter estimate is consistent with that obtained by Bell et al. using a different rainfall retrieval technique.
Abstract
An algorithm for the estimation of monthly rain totals for 5° cells over the oceans from histograms of SSM/I brightness temperatures has been developed. Them are three novel features to this algorithm. First, it uses knowledge of the form of the rainfall intensity probability density function to augment the measurements. Second, a linear combination of the 19.35 and 22.235 GHz channels has been employed to reduce the impact of variability of water vapor. Third, an objective technique has been developed to estimate the rain layer thickness from the 19.35- and 22.235-GHz brightness temperature histograms. Comparison with climatologies and the GATE radar observations suggest that the estimates are reasonable in spite of not having a beam-filling correction. By-products of the retrievals indicate that the SSM/I instrument noise level and calibration stability am quite good.
Abstract
An algorithm for the estimation of monthly rain totals for 5° cells over the oceans from histograms of SSM/I brightness temperatures has been developed. Them are three novel features to this algorithm. First, it uses knowledge of the form of the rainfall intensity probability density function to augment the measurements. Second, a linear combination of the 19.35 and 22.235 GHz channels has been employed to reduce the impact of variability of water vapor. Third, an objective technique has been developed to estimate the rain layer thickness from the 19.35- and 22.235-GHz brightness temperature histograms. Comparison with climatologies and the GATE radar observations suggest that the estimates are reasonable in spite of not having a beam-filling correction. By-products of the retrievals indicate that the SSM/I instrument noise level and calibration stability am quite good.
Abstract
Global averages and random errors associated with the monthly oceanic rain rates derived from the Special Sensor Microwave/Imager (SSM/I) data using the technique developed by Wilheit et al. are computed. Accounting for the beam-filling bias, a global annual average rain rate of 1.26 m is computed. The error estimation scheme is based on the existence of independent (morning and afternoon) estimates of the monthly mean. Calculations show overall random errors of about 50%–60% for each 5° × 5° box. The results are insensitive to different sampling strategy (odd and even days of the month). Comparison of the SSM/I estimates with raingage data collected at the Pacific atoll stations showed a low bias of about 8%, a correlation of 0.7, and an rms difference of 55%.
Abstract
Global averages and random errors associated with the monthly oceanic rain rates derived from the Special Sensor Microwave/Imager (SSM/I) data using the technique developed by Wilheit et al. are computed. Accounting for the beam-filling bias, a global annual average rain rate of 1.26 m is computed. The error estimation scheme is based on the existence of independent (morning and afternoon) estimates of the monthly mean. Calculations show overall random errors of about 50%–60% for each 5° × 5° box. The results are insensitive to different sampling strategy (odd and even days of the month). Comparison of the SSM/I estimates with raingage data collected at the Pacific atoll stations showed a low bias of about 8%, a correlation of 0.7, and an rms difference of 55%.
Abstract
Three years of monthly rain rates over 5° × 5° latitude–longitude boxes have been calculated for oceanic regions 50°N–50°S from measurements taken by the Special Sensor Microwave/Imager on board the Defense Meteorological Satellite Program satellites using the technique developed by Wilheit et al. The annual and seasonal zonal-mean rain rates are larger than Jaeger's climatological estimates but are smaller than those estimated from the GOES precipitation index (GPI) for the same period. Regional comparison with the GPI showed that these rain rates are smaller in the north Indian Ocean and in the southern extratropics where the GPI is known to overestimate. The differences are also dominated by a jump at 170°W in the GPI rain rates across the mid Pacific Ocean. This jump is attributed to the fusion of different satellite measurements in producing the GPI.
Abstract
Three years of monthly rain rates over 5° × 5° latitude–longitude boxes have been calculated for oceanic regions 50°N–50°S from measurements taken by the Special Sensor Microwave/Imager on board the Defense Meteorological Satellite Program satellites using the technique developed by Wilheit et al. The annual and seasonal zonal-mean rain rates are larger than Jaeger's climatological estimates but are smaller than those estimated from the GOES precipitation index (GPI) for the same period. Regional comparison with the GPI showed that these rain rates are smaller in the north Indian Ocean and in the southern extratropics where the GPI is known to overestimate. The differences are also dominated by a jump at 170°W in the GPI rain rates across the mid Pacific Ocean. This jump is attributed to the fusion of different satellite measurements in producing the GPI.
Abstract
A study of differences between the morning and evening monthly rainfall for 5° × 5° cells over the oceans from the SSM/I data has been conducted. The monthly rainfalls are estimated from the technique given by Wilheit et al. The difference between the morning and evening monthly rainfall arises due to the various random errors involved in the retrieval process, the sampling error in the observations, and the diurnal component of oceanic rainfall. The diurnal component is weak but clearly visible when averaged over large areas and for long time periods. The analysis shows that morning rainfall is consistently greater than evening rainfall. The Northern Hemisphere seems to have a larger diurnal variation than does the Southern Hemisphere. The maximum ratio between the morning and evening monthly rainfall is 1.7 while 1.2 is the more typical value.
Abstract
A study of differences between the morning and evening monthly rainfall for 5° × 5° cells over the oceans from the SSM/I data has been conducted. The monthly rainfalls are estimated from the technique given by Wilheit et al. The difference between the morning and evening monthly rainfall arises due to the various random errors involved in the retrieval process, the sampling error in the observations, and the diurnal component of oceanic rainfall. The diurnal component is weak but clearly visible when averaged over large areas and for long time periods. The analysis shows that morning rainfall is consistently greater than evening rainfall. The Northern Hemisphere seems to have a larger diurnal variation than does the Southern Hemisphere. The maximum ratio between the morning and evening monthly rainfall is 1.7 while 1.2 is the more typical value.
The Global Precipitation Climatology Project (GPCP) has released the GPCP Version 1 Combined Precipitation Data Set, a global, monthly precipitation dataset covering the period July 1987 through December 1995. The primary product in the dataset is a merged analysis incorporating precipitation estimates from low-orbit-satellite microwave data, geosynchronous-orbit-satellite infrared data, and rain gauge observations. The dataset also contains the individual input fields, a combination of the microwave and infrared satellite estimates, and error estimates for each field. The data are provided on 2.5° × 2.5° latitude-longitude global grids. Preliminary analyses show general agreement with prior studies of global precipitation and extends prior studies of El Nino-Southern Oscillation precipitation patterns. At the regional scale there are systematic differences with standard climatologies.
The Global Precipitation Climatology Project (GPCP) has released the GPCP Version 1 Combined Precipitation Data Set, a global, monthly precipitation dataset covering the period July 1987 through December 1995. The primary product in the dataset is a merged analysis incorporating precipitation estimates from low-orbit-satellite microwave data, geosynchronous-orbit-satellite infrared data, and rain gauge observations. The dataset also contains the individual input fields, a combination of the microwave and infrared satellite estimates, and error estimates for each field. The data are provided on 2.5° × 2.5° latitude-longitude global grids. Preliminary analyses show general agreement with prior studies of global precipitation and extends prior studies of El Nino-Southern Oscillation precipitation patterns. At the regional scale there are systematic differences with standard climatologies.
Abstract
The frequencies flown on the Special Sensor Microwave Imager (SSM/I) are sensitive to liquid water near the earth's surface. These frequencies are primarily atmospheric window channels, which receive the majority of their radiation from the surface. Liquid water near the surface depresses the emissivity as a function of wavelength. The relationship between brightness temperatures at different frequencies is used to dynamically derive the amount of liquid water in each SSM/I observation at 1/3° resolution. These data are averaged at 1° resolution throughout the globe for each month during the period of 1992–97, and the 6-yr monthly means and the monthly anomalies of the wetness index are computed from this base period. To quantify the relationship between precipitation and surface wetness, these anomalies are compared with precipitation anomalies derived from the Global Precipitation Climate Program. The analysis was performed for six agricultural regions across six continents. There is generally a good correspondence between the two variables. The correlation generally increases when the wetness index is compared with precipitation anomalies accumulated over a 2-month period. These results indicate that the wetness index has a strong correspondence to the upper layer of the soil moisture in many cultivated areas of the world. The region in southeastern Australia had the best relationship, with a correlation coefficient of 0.76. The Sahel, France, and Argentina showed that the wetness index had memory of precipitation anomalies from the previous months. The memory is shorter for southeastern Australia and central China. The weakest correlations occurred over the southeastern United States, where the surface is covered by dense vegetation. The unique signal, strengths, and weaknesses of the wetness index in each of the six study regions are discussed.
Abstract
The frequencies flown on the Special Sensor Microwave Imager (SSM/I) are sensitive to liquid water near the earth's surface. These frequencies are primarily atmospheric window channels, which receive the majority of their radiation from the surface. Liquid water near the surface depresses the emissivity as a function of wavelength. The relationship between brightness temperatures at different frequencies is used to dynamically derive the amount of liquid water in each SSM/I observation at 1/3° resolution. These data are averaged at 1° resolution throughout the globe for each month during the period of 1992–97, and the 6-yr monthly means and the monthly anomalies of the wetness index are computed from this base period. To quantify the relationship between precipitation and surface wetness, these anomalies are compared with precipitation anomalies derived from the Global Precipitation Climate Program. The analysis was performed for six agricultural regions across six continents. There is generally a good correspondence between the two variables. The correlation generally increases when the wetness index is compared with precipitation anomalies accumulated over a 2-month period. These results indicate that the wetness index has a strong correspondence to the upper layer of the soil moisture in many cultivated areas of the world. The region in southeastern Australia had the best relationship, with a correlation coefficient of 0.76. The Sahel, France, and Argentina showed that the wetness index had memory of precipitation anomalies from the previous months. The memory is shorter for southeastern Australia and central China. The weakest correlations occurred over the southeastern United States, where the surface is covered by dense vegetation. The unique signal, strengths, and weaknesses of the wetness index in each of the six study regions are discussed.
Abstract
The Global Precipitation Climatology Project (GPCP) Version-2 Monthly Precipitation Analysis is described. This globally complete, monthly analysis of surface precipitation at 2.5° latitude × 2.5° longitude resolution is available from January 1979 to the present. It is a merged analysis that incorporates precipitation estimates from low-orbit satellite microwave data, geosynchronous-orbit satellite infrared data, and surface rain gauge observations. The merging approach utilizes the higher accuracy of the low-orbit microwave observations to calibrate, or adjust, the more frequent geosynchronous infrared observations. The dataset is extended back into the premicrowave era (before mid-1987) by using infrared-only observations calibrated to the microwave-based analysis of the later years. The combined satellite-based product is adjusted by the rain gauge analysis. The dataset archive also contains the individual input fields, a combined satellite estimate, and error estimates for each field. This monthly analysis is the foundation for the GPCP suite of products, including those at finer temporal resolution. The 23-yr GPCP climatology is characterized, along with time and space variations of precipitation.
Abstract
The Global Precipitation Climatology Project (GPCP) Version-2 Monthly Precipitation Analysis is described. This globally complete, monthly analysis of surface precipitation at 2.5° latitude × 2.5° longitude resolution is available from January 1979 to the present. It is a merged analysis that incorporates precipitation estimates from low-orbit satellite microwave data, geosynchronous-orbit satellite infrared data, and surface rain gauge observations. The merging approach utilizes the higher accuracy of the low-orbit microwave observations to calibrate, or adjust, the more frequent geosynchronous infrared observations. The dataset is extended back into the premicrowave era (before mid-1987) by using infrared-only observations calibrated to the microwave-based analysis of the later years. The combined satellite-based product is adjusted by the rain gauge analysis. The dataset archive also contains the individual input fields, a combined satellite estimate, and error estimates for each field. This monthly analysis is the foundation for the GPCP suite of products, including those at finer temporal resolution. The 23-yr GPCP climatology is characterized, along with time and space variations of precipitation.
