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- Author or Editor: R. Kidd x
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Abstract
This paper uses rainfall estimates retrieved from active and passive microwave data to investigate how spatially and temporally dependent algorithm biases affect the monitoring of the diurnal rainfall cycle. Microwave estimates used in this study are from the Tropical Rainfall Measuring Mission (TRMM) and include the precipitation radar (PR) near-surface (2A25), Goddard Profiling (GPROF) (2A12), and PR–TRMM Microwave Imager (TMI) (2B31) rain rates from the version 5 (v5) 3G68 product. A rainfall maximum is observed early evening over land, while oceans generally show a minimum in rainfall during the morning. Comparisons of annual and seasonal mean hourly rain rates and harmonics at both global and regional scales show significant differences between the algorithms. Relative and absolute biases over land vary according to the time of day. Clearly, these retrieval biases need accounting for, either in the physics of the algorithm or through the provision of accurate error estimates, to avoid erroneous climatic signals and the discrediting of satellite rainfall estimations.
Abstract
This paper uses rainfall estimates retrieved from active and passive microwave data to investigate how spatially and temporally dependent algorithm biases affect the monitoring of the diurnal rainfall cycle. Microwave estimates used in this study are from the Tropical Rainfall Measuring Mission (TRMM) and include the precipitation radar (PR) near-surface (2A25), Goddard Profiling (GPROF) (2A12), and PR–TRMM Microwave Imager (TMI) (2B31) rain rates from the version 5 (v5) 3G68 product. A rainfall maximum is observed early evening over land, while oceans generally show a minimum in rainfall during the morning. Comparisons of annual and seasonal mean hourly rain rates and harmonics at both global and regional scales show significant differences between the algorithms. Relative and absolute biases over land vary according to the time of day. Clearly, these retrieval biases need accounting for, either in the physics of the algorithm or through the provision of accurate error estimates, to avoid erroneous climatic signals and the discrediting of satellite rainfall estimations.
Abstract
This paper is a preliminary summary of results of a tropical cumulus seeding experiment. Individual supercooled cumuli were seeded by silver iodide pyrotechnics and studied before and after by radar, photography and multi-aircraft penetrations. The choice of “seeded” versus “control” cloud was made from sealed instructions opened on the seeding aircraft.
Twenty-two cases were studied on nine days, with fifteen seeded and seven controls, largely in matched pairs. Two-thirds of the properly seeded clouds underwent marked vertical growth while six-sevenths of the controls did not. It is suggested that this and sequel experiments can delineate conditions under which seeding may have different effects upon clouds.
Abstract
This paper is a preliminary summary of results of a tropical cumulus seeding experiment. Individual supercooled cumuli were seeded by silver iodide pyrotechnics and studied before and after by radar, photography and multi-aircraft penetrations. The choice of “seeded” versus “control” cloud was made from sealed instructions opened on the seeding aircraft.
Twenty-two cases were studied on nine days, with fifteen seeded and seven controls, largely in matched pairs. Two-thirds of the properly seeded clouds underwent marked vertical growth while six-sevenths of the controls did not. It is suggested that this and sequel experiments can delineate conditions under which seeding may have different effects upon clouds.
Abstract
This paper describes a new high-resolution multiplatform multisensor satellite rainfall product for southern Africa covering the period 1993–2002. The microwave infrared rainfall algorithm (MIRA) employed to generate the rainfall estimates combines high spatial and temporal resolution Meteosat infrared data with infrequent Special Sensor Microwave Imager (SSM/I) overpasses. A transfer function relating Meteosat thermal infrared cloud brightness temperatures to SSM/I rainfall estimates is derived using collocated data from the two instruments and then applied to the full coverage of the Meteosat data. An extensive continental-scale validation against synoptic station data of both the daily MIRA precipitation product and a normalized geostationary IR-only Geostationary Operational Environmental Satellite (GOES) precipitation index (GPI) demonstrates a consistent advantage using the former over the latter for rain delineation. Potential uses for the resulting high-resolution daily rainfall dataset are discussed.
Abstract
This paper describes a new high-resolution multiplatform multisensor satellite rainfall product for southern Africa covering the period 1993–2002. The microwave infrared rainfall algorithm (MIRA) employed to generate the rainfall estimates combines high spatial and temporal resolution Meteosat infrared data with infrequent Special Sensor Microwave Imager (SSM/I) overpasses. A transfer function relating Meteosat thermal infrared cloud brightness temperatures to SSM/I rainfall estimates is derived using collocated data from the two instruments and then applied to the full coverage of the Meteosat data. An extensive continental-scale validation against synoptic station data of both the daily MIRA precipitation product and a normalized geostationary IR-only Geostationary Operational Environmental Satellite (GOES) precipitation index (GPI) demonstrates a consistent advantage using the former over the latter for rain delineation. Potential uses for the resulting high-resolution daily rainfall dataset are discussed.
Abstract
The development of a combined infrared and passive microwave satellite rainfall estimation technique is outlined. Infrared data from geostationary satellites are combined with polar-orbiting passive microwave estimates to provide 30-min rainfall estimates. Collocated infrared and passive microwave values are used to generate a database, which is accessed by a cumulative histogram matching approach to generate an infrared temperature–rain-rate relationship. The technique produces initial estimates at 30-min and 12-km resolution ready to be aggregated to the user requirements. A 4-month case study over Africa has been chosen to compare the results from this technique with those of some existing rainfall techniques. The results indicate that the technique outlined here has statistical scores that are similar to other infrared/passive microwave combined algorithms. Comparison with the Geostationary Operational Environmental Satellite (GOES) precipitation index shows that while these algorithms result in lower correlation scores, areal statistics are significantly better than either the infrared or passive microwave techniques alone.
Abstract
The development of a combined infrared and passive microwave satellite rainfall estimation technique is outlined. Infrared data from geostationary satellites are combined with polar-orbiting passive microwave estimates to provide 30-min rainfall estimates. Collocated infrared and passive microwave values are used to generate a database, which is accessed by a cumulative histogram matching approach to generate an infrared temperature–rain-rate relationship. The technique produces initial estimates at 30-min and 12-km resolution ready to be aggregated to the user requirements. A 4-month case study over Africa has been chosen to compare the results from this technique with those of some existing rainfall techniques. The results indicate that the technique outlined here has statistical scores that are similar to other infrared/passive microwave combined algorithms. Comparison with the Geostationary Operational Environmental Satellite (GOES) precipitation index shows that while these algorithms result in lower correlation scores, areal statistics are significantly better than either the infrared or passive microwave techniques alone.
Abstract
Satellite-derived high-resolution precipitation products (HRPP) have been developed to address the needs of the user community and are now available with 0.25° × 0.25° (or less) subdaily resolutions. This paper evaluates a number of commonly available satellite-derived HRPPs covering northwest Europe over a 6-yr period. Precipitation products include the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA), the Climate Prediction Center (CPC) morphing (CMORPH) technique, the CPC merged microwave technique, the Naval Research Laboratory (NRL) blended technique, and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) technique. In addition, the Geosynchronous Operational Environmental Satellite (GOES) precipitation index (GPI) and the European Centre for Medium-Range Weather Forecasting (ECMWF) operational forecast model products are included for comparison. Surface reference data from the European radar network is used as ground truth, supported by the Global Precipitation Climatology Centre (GPCC) precipitation gauge analysis and gauge data over the United Kingdom. Measures of correlation, bias ratio, probability of detection, and false alarm ratio are used to evaluate the products. Results show that satellite products generally exhibit a seasonal cycle in correlation, bias ratio, probability of detection, and false alarm ratio, with poorer statistics during the winter. The ECMWF model also shows a seasonal cycle in the correlation, although the results are poorer during the summer, while the bias ratio, probability of detection, and false alarm ratio are consistent through all seasons. Importantly, all the satellite HRPPs underestimate precipitation over northwest Europe in all seasons.
Abstract
Satellite-derived high-resolution precipitation products (HRPP) have been developed to address the needs of the user community and are now available with 0.25° × 0.25° (or less) subdaily resolutions. This paper evaluates a number of commonly available satellite-derived HRPPs covering northwest Europe over a 6-yr period. Precipitation products include the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA), the Climate Prediction Center (CPC) morphing (CMORPH) technique, the CPC merged microwave technique, the Naval Research Laboratory (NRL) blended technique, and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) technique. In addition, the Geosynchronous Operational Environmental Satellite (GOES) precipitation index (GPI) and the European Centre for Medium-Range Weather Forecasting (ECMWF) operational forecast model products are included for comparison. Surface reference data from the European radar network is used as ground truth, supported by the Global Precipitation Climatology Centre (GPCC) precipitation gauge analysis and gauge data over the United Kingdom. Measures of correlation, bias ratio, probability of detection, and false alarm ratio are used to evaluate the products. Results show that satellite products generally exhibit a seasonal cycle in correlation, bias ratio, probability of detection, and false alarm ratio, with poorer statistics during the winter. The ECMWF model also shows a seasonal cycle in the correlation, although the results are poorer during the summer, while the bias ratio, probability of detection, and false alarm ratio are consistent through all seasons. Importantly, all the satellite HRPPs underestimate precipitation over northwest Europe in all seasons.
Abstract
The second WetNet Precipitation Intercomparison Project (PIP-2) evaluates the performance of 20 satellite precipitation retrieval algorithms, implemented for application with Special Sensor Microwave/Imager (SSM/I) passive microwave (PMW) measurements and run for a set of rainfall case studies at full resolution–instantaneous space–timescales. The cases are drawn from over the globe during all seasons, for a period of 7 yr, over a 60°N–17°S latitude range. Ground-based data were used for the intercomparisons, principally based on radar measurements but also including rain gauge measurements. The goals of PIP-2 are 1) to improve performance and accuracy of different SSM/I algorithms at full resolution–instantaneous scales by seeking a better understanding of the relationship between microphysical signatures in the PMW measurements and physical laws employed in the algorithms; 2) to evaluate the pros and cons of individual algorithms and their subsystems in order to seek optimal “front-end” combined algorithms; and 3) to demonstrate that PMW algorithms generate acceptable instantaneous rain estimates.
It is found that the bias uncertainty of many current PMW algorithms is on the order of ±30%. This level is below that of the radar and rain gauge data specially collected for the study, so that it is not possible to objectively select a best algorithm based on the ground data validation approach. By decomposing the intercomparisons into effects due to rain detection (screening) and effects due to brightness temperature–rain rate conversion, differences among the algorithms are partitioned by rain area and rain intensity. For ocean, the screening differences mainly affect the light rain rates, which do not contribute significantly to area-averaged rain rates. The major sources of differences in mean rain rates between individual algorithms stem from differences in how intense rain rates are calculated and the maximum rain rate allowed by a given algorithm. The general method of solution is not necessarily the determining factor in creating systematic rain-rate differences among groups of algorithms, as we find that the severity of the screen is the dominant factor in producing systematic group differences among land algorithms, while the input channel selection is the dominant factor in producing systematic group differences among ocean algorithms. The significance of these issues are examined through what is called “fan map” analysis.
The paper concludes with a discussion on the role of intercomparison projects in seeking improvements to algorithms, and a suggestion on why moving beyond the “ground truth” validation approach by use of a calibration-quality forward model would be a step forward in seeking objective evaluation of individual algorithm performance and optimal algorithm design.
Abstract
The second WetNet Precipitation Intercomparison Project (PIP-2) evaluates the performance of 20 satellite precipitation retrieval algorithms, implemented for application with Special Sensor Microwave/Imager (SSM/I) passive microwave (PMW) measurements and run for a set of rainfall case studies at full resolution–instantaneous space–timescales. The cases are drawn from over the globe during all seasons, for a period of 7 yr, over a 60°N–17°S latitude range. Ground-based data were used for the intercomparisons, principally based on radar measurements but also including rain gauge measurements. The goals of PIP-2 are 1) to improve performance and accuracy of different SSM/I algorithms at full resolution–instantaneous scales by seeking a better understanding of the relationship between microphysical signatures in the PMW measurements and physical laws employed in the algorithms; 2) to evaluate the pros and cons of individual algorithms and their subsystems in order to seek optimal “front-end” combined algorithms; and 3) to demonstrate that PMW algorithms generate acceptable instantaneous rain estimates.
It is found that the bias uncertainty of many current PMW algorithms is on the order of ±30%. This level is below that of the radar and rain gauge data specially collected for the study, so that it is not possible to objectively select a best algorithm based on the ground data validation approach. By decomposing the intercomparisons into effects due to rain detection (screening) and effects due to brightness temperature–rain rate conversion, differences among the algorithms are partitioned by rain area and rain intensity. For ocean, the screening differences mainly affect the light rain rates, which do not contribute significantly to area-averaged rain rates. The major sources of differences in mean rain rates between individual algorithms stem from differences in how intense rain rates are calculated and the maximum rain rate allowed by a given algorithm. The general method of solution is not necessarily the determining factor in creating systematic rain-rate differences among groups of algorithms, as we find that the severity of the screen is the dominant factor in producing systematic group differences among land algorithms, while the input channel selection is the dominant factor in producing systematic group differences among ocean algorithms. The significance of these issues are examined through what is called “fan map” analysis.
The paper concludes with a discussion on the role of intercomparison projects in seeking improvements to algorithms, and a suggestion on why moving beyond the “ground truth” validation approach by use of a calibration-quality forward model would be a step forward in seeking objective evaluation of individual algorithm performance and optimal algorithm design.
