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Abstract
The relationship between the microwave radiometer and the precipitation radar on the Tropical Rainfall Measuring Mission TRMM satellite is inherently complementary. Neither sensor by itself would be adequate to achieve the TRMM objectives but the match between the strengths and weaknesses of each sensor results in an extremely powerful payload. Here these strengths and weaknesses are discussed and a specific example is examined.
Abstract
The relationship between the microwave radiometer and the precipitation radar on the Tropical Rainfall Measuring Mission TRMM satellite is inherently complementary. Neither sensor by itself would be adequate to achieve the TRMM objectives but the match between the strengths and weaknesses of each sensor results in an extremely powerful payload. Here these strengths and weaknesses are discussed and a specific example is examined.
Abstract
The latent heat represented by atmospheric water vapor is extremely important to the energetics of the Earth system. Future satellite (NOAA and DMSP) will carry microwave radiometers designed to measure the profile of water vapor globally. The problem of retrieving water vapor from the measurements is highly nonlinear even in clear atmospheres and the addition of clouds only makes it more so. In this paper, an algorithm with several novel features, which will retrieve water vapor profiles from microwave radiometric measurements even in the presence of clouds, is developed. Simulations with this algorithm show a vertical resolution on the order of 3 km and that clouds are well handled in many, but not all, circumstances. The most surprising result is that clouds can actually improve the vertical resolution of the retrieval.
Abstract
The latent heat represented by atmospheric water vapor is extremely important to the energetics of the Earth system. Future satellite (NOAA and DMSP) will carry microwave radiometers designed to measure the profile of water vapor globally. The problem of retrieving water vapor from the measurements is highly nonlinear even in clear atmospheres and the addition of clouds only makes it more so. In this paper, an algorithm with several novel features, which will retrieve water vapor profiles from microwave radiometric measurements even in the presence of clouds, is developed. Simulations with this algorithm show a vertical resolution on the order of 3 km and that clouds are well handled in many, but not all, circumstances. The most surprising result is that clouds can actually improve the vertical resolution of the retrieval.
It is argued that because microwave radiation interacts much more strongly with hydrometeors than with cloud particles, microwave measurements from space offer a significant chance of making global precipitation estimates. Over oceans, passive microwave measurements are essentially attenuation measurements that can be very closely related to the rain rate independently of the details of the drop-size distribution. Over land, scattering of microwave radiation by the hydrometeors, especially in the ice phase, can be used to estimate rainfall. In scattering, the details of the drop-size distribution are very important and it is therefore more difficult to achieve a high degree of accuracy. The SSM/I (Special Sensor Microwave Imager), a passive microwave imaging sensor that will be launched soon, will have dual-polarized channels at 85.5 GHz that will be very sensitive to scattering by frozen hydrometeors. Other sensors being considered for the future space missions would extend our ability to estimate rain rates from space. The ideal spaceborne precipitation-measurement system would use the complementary strengths of passive microwave, radar, and visible/infrared measurements.
It is argued that because microwave radiation interacts much more strongly with hydrometeors than with cloud particles, microwave measurements from space offer a significant chance of making global precipitation estimates. Over oceans, passive microwave measurements are essentially attenuation measurements that can be very closely related to the rain rate independently of the details of the drop-size distribution. Over land, scattering of microwave radiation by the hydrometeors, especially in the ice phase, can be used to estimate rainfall. In scattering, the details of the drop-size distribution are very important and it is therefore more difficult to achieve a high degree of accuracy. The SSM/I (Special Sensor Microwave Imager), a passive microwave imaging sensor that will be launched soon, will have dual-polarized channels at 85.5 GHz that will be very sensitive to scattering by frozen hydrometeors. Other sensors being considered for the future space missions would extend our ability to estimate rain rates from space. The ideal spaceborne precipitation-measurement system would use the complementary strengths of passive microwave, radar, and visible/infrared measurements.
Abstract
In preparation for the launch of TRMM, new algorithms must be created that take advantage of the combined data from radar and microwave radiometers that will be on board the satellite. A microwave radiative transfer algorithm with a one-dimensional cloud model is created that incorporates data from radar and radiometers using data obtained from TCM-90 and TOGA COARE flown over the western Pacific in 1990 and 1993, respectively.
A hybrid cloud model (HCM) was developed using observations from TOGA COARE, TCM-90, and other field projects. The HCM is a physically based model that is not “tuned” by limited “ground truth.” Therefore, the HCM incorporated new microphysical data based on observations of clouds. Cloud observations changed the HCM in four ways. First, stratiform clouds with low rain rates were shown to have a low cloud liquid water content (<0.1 g m−3). Second, radar data showed a linear decrease in the logarithm of the backscatter of ice particles above the freezing level. Third, tropical clouds contained more small drops and fewer large drops than predicted by the Marshall–Palmer drop size distribution. Last, the angular distribution of reflected radiation from ocean surface appears to be specular.
The HCM is compared to the Wilheit et al. model (WILM). The HCM differs from the WILM at low rain rates by as much as 10 K. At high rain rates, the HCM and the WILM produce similar brightness temperatures. However, this result is fortuitous because both models have substantially different thermodynamics and microphysics incorporated into them. Next, the brightness temperatures generated by the HCM are compared to observations from TOGA COARE. It is found that the brightness temperatures produced by the HCM closely agree with the observations. This study shows that a plane-parallel microwave radiative transfer algorithm coupled with a cloud model based on microphysical observations can accurately simulate rainfall observed in the Tropics.
Abstract
In preparation for the launch of TRMM, new algorithms must be created that take advantage of the combined data from radar and microwave radiometers that will be on board the satellite. A microwave radiative transfer algorithm with a one-dimensional cloud model is created that incorporates data from radar and radiometers using data obtained from TCM-90 and TOGA COARE flown over the western Pacific in 1990 and 1993, respectively.
A hybrid cloud model (HCM) was developed using observations from TOGA COARE, TCM-90, and other field projects. The HCM is a physically based model that is not “tuned” by limited “ground truth.” Therefore, the HCM incorporated new microphysical data based on observations of clouds. Cloud observations changed the HCM in four ways. First, stratiform clouds with low rain rates were shown to have a low cloud liquid water content (<0.1 g m−3). Second, radar data showed a linear decrease in the logarithm of the backscatter of ice particles above the freezing level. Third, tropical clouds contained more small drops and fewer large drops than predicted by the Marshall–Palmer drop size distribution. Last, the angular distribution of reflected radiation from ocean surface appears to be specular.
The HCM is compared to the Wilheit et al. model (WILM). The HCM differs from the WILM at low rain rates by as much as 10 K. At high rain rates, the HCM and the WILM produce similar brightness temperatures. However, this result is fortuitous because both models have substantially different thermodynamics and microphysics incorporated into them. Next, the brightness temperatures generated by the HCM are compared to observations from TOGA COARE. It is found that the brightness temperatures produced by the HCM closely agree with the observations. This study shows that a plane-parallel microwave radiative transfer algorithm coupled with a cloud model based on microphysical observations can accurately simulate rainfall observed in the Tropics.
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
At 37 GHz, the frequency at which the Nimbus 6 Electrically Scanning Microwave Radiometer (ESMR 6) measures upwelling radiance, it has been shown theoretically that the atmospheric scattering and the relative independence on electromagnetic polarization of the radiances emerging from hydrometeors make it possible to monitor remotely active rainfall over land. In order to verify experimentally these theoretical findings and to develop an algorithm to monitor rainfall over land, the digitized ESMR 6 measurements were examined statistically.
Horizontally and vertically polarized brightness temperature pairs (TH,TV ) from ESMR 6 were sampled for areas of rainfall over land as determined from the rain recording stations and the WSR 57 radar, and areas of wet and dry ground (whose thermodynamic temperatures were greater than 5°C) over the southeastern United States. These three categories of brightness temperatures were found to be significantly different in the sense that the chances that the mean vectors of any two populations coincided were less than 1 in 100. Since these categories were significantly different, classification algorithms were then developed. Three decision rules were examined: the Fisher linear classifier, the Bayesian quadratic classifier, and a non-parametric linear classifier. The Bayesian algorithm was found to perform best, particularly at a higher confidence level. An independent test case analysis showed that a rainfall area delineated by the Bayesian classifier coincided well with the synoptic-scale rainfall area mapped by ground recording rain data and radar echoes.
Abstract
At 37 GHz, the frequency at which the Nimbus 6 Electrically Scanning Microwave Radiometer (ESMR 6) measures upwelling radiance, it has been shown theoretically that the atmospheric scattering and the relative independence on electromagnetic polarization of the radiances emerging from hydrometeors make it possible to monitor remotely active rainfall over land. In order to verify experimentally these theoretical findings and to develop an algorithm to monitor rainfall over land, the digitized ESMR 6 measurements were examined statistically.
Horizontally and vertically polarized brightness temperature pairs (TH,TV ) from ESMR 6 were sampled for areas of rainfall over land as determined from the rain recording stations and the WSR 57 radar, and areas of wet and dry ground (whose thermodynamic temperatures were greater than 5°C) over the southeastern United States. These three categories of brightness temperatures were found to be significantly different in the sense that the chances that the mean vectors of any two populations coincided were less than 1 in 100. Since these categories were significantly different, classification algorithms were then developed. Three decision rules were examined: the Fisher linear classifier, the Bayesian quadratic classifier, and a non-parametric linear classifier. The Bayesian algorithm was found to perform best, particularly at a higher confidence level. An independent test case analysis showed that a rainfall area delineated by the Bayesian classifier coincided well with the synoptic-scale rainfall area mapped by ground recording rain data and radar echoes.
Abstract
A physically based retrieval algorithm is presented that retrieves water vapor profiles from Special Sensor Microwave/Temperature-2 (SSM/T-2) passive microwave brightness temperature measurements. This method can use SSM/T-2 data alone or in conjunction with data from the Special Sensor Microwave/Imager (SSM/I). Several SSM/I channels, as well as total integrated water vapor (TIWV) retrieved from SSM/I, are tested to see if they add value to the retrieval. In the retrieval process, TIWV is formally treated as a separate channel. It is found that using the SSM/I TIWV increases the yield of the retrieval (the percentage of retrieved profiles whose brightness temperatures agree with the observations, on average, to within the noise level of the instrument), as well as reduces the average normalized brightness temperature error. Also, use of the TIWV allows the omission of the SSM/T-2 150-GHz channel data without a significant impact on results. It is shown that by using the SSM/I TIWV, retrievals can be made further into areas of precipitation and heavy clouds than when using data from only the SSM/T-2. Examples of retrieved profiles are shown to agree with the general features of profiles from radiosondes and the European Centre for Medium-Range Weather Forecasts analyses.
Abstract
A physically based retrieval algorithm is presented that retrieves water vapor profiles from Special Sensor Microwave/Temperature-2 (SSM/T-2) passive microwave brightness temperature measurements. This method can use SSM/T-2 data alone or in conjunction with data from the Special Sensor Microwave/Imager (SSM/I). Several SSM/I channels, as well as total integrated water vapor (TIWV) retrieved from SSM/I, are tested to see if they add value to the retrieval. In the retrieval process, TIWV is formally treated as a separate channel. It is found that using the SSM/I TIWV increases the yield of the retrieval (the percentage of retrieved profiles whose brightness temperatures agree with the observations, on average, to within the noise level of the instrument), as well as reduces the average normalized brightness temperature error. Also, use of the TIWV allows the omission of the SSM/T-2 150-GHz channel data without a significant impact on results. It is shown that by using the SSM/I TIWV, retrievals can be made further into areas of precipitation and heavy clouds than when using data from only the SSM/T-2. Examples of retrieved profiles are shown to agree with the general features of profiles from radiosondes and the European Centre for Medium-Range Weather Forecasts analyses.
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
A physical–statistical monthly rainfall retrieval algorithm has been developed using multichannel brightness temperatures from the Special Sensor Microwave/Imager (SSM/I). Since an emission-based retrieval algorithm gives the most physically direct estimation of rainfall over oceans, instantaneous rain rates are retrieved using brightness temperature–rain rate (T–R) relationships derived from a radiative transfer model. The retrieved rain rates, however, are only reliable and useful over a portion of a whole dynamic range of rain rate due to limitations of the emission-based algorithm. When monthly rainfall in a 5° × 5° box is estimated, the instantaneous rain-rate samples are actually truncated. The method used in this study assumes that monthly rainfall intensity in a 5° × 5° box has a mixed lognormal distribution. Thus, the contribution of the rain rates outside of the dynamic range can be estimated by extrapolation. Coefficients of the mixed lognormal distribution are determined by fitting the truncated rain-rate samples to the lognormal form using a maximum likelihood estimate method. The beamfilling error is corrected by a multiplicative factor generated from simulation studies. Comparison between the monthly rainfall estimated from the SSM/I and Pacific atoll data indicates that the algorithm works very well in tropical areas. Although this algorithm is tested on SSM/I data, it is also suited for the Tropical Rainfall Measuring Mission data, which should have a larger dynamic range with 10.7-GHz channels added.
Abstract
A physical–statistical monthly rainfall retrieval algorithm has been developed using multichannel brightness temperatures from the Special Sensor Microwave/Imager (SSM/I). Since an emission-based retrieval algorithm gives the most physically direct estimation of rainfall over oceans, instantaneous rain rates are retrieved using brightness temperature–rain rate (T–R) relationships derived from a radiative transfer model. The retrieved rain rates, however, are only reliable and useful over a portion of a whole dynamic range of rain rate due to limitations of the emission-based algorithm. When monthly rainfall in a 5° × 5° box is estimated, the instantaneous rain-rate samples are actually truncated. The method used in this study assumes that monthly rainfall intensity in a 5° × 5° box has a mixed lognormal distribution. Thus, the contribution of the rain rates outside of the dynamic range can be estimated by extrapolation. Coefficients of the mixed lognormal distribution are determined by fitting the truncated rain-rate samples to the lognormal form using a maximum likelihood estimate method. The beamfilling error is corrected by a multiplicative factor generated from simulation studies. Comparison between the monthly rainfall estimated from the SSM/I and Pacific atoll data indicates that the algorithm works very well in tropical areas. Although this algorithm is tested on SSM/I data, it is also suited for the Tropical Rainfall Measuring Mission data, which should have a larger dynamic range with 10.7-GHz channels added.
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.
