Search Results
You are looking at 1 - 10 of 23 items for
- Author or Editor: R. Adler x
- Refine by Access: All Content x
Abstract
Data from the Nimbus-5 F-Electrically Scanning Microwave Radiometer (ESMR-5) have been used to calculate latent heat release (LHR) and other rainfall parameters for over 70 satelite observations of 21 tropical cyclones during 1973, 1974 and 1975 in the tropical North Pacific Ocean. The results indicate that the ESMR-5 measurements can be useful in determining the rainfall characteristics of these storms and appear to be potentially useful in monitoring as well as predicting their intensity. The ESMR-5 derived total tropical cyclone rainfall estimates agree favorably with previous estimates for both the disturbance and typhoon stages. The mean typhoon rainfall rate (1.9 mm h−1) is approximately twice that of disturbances (1.1 mm h−1).
Case studies suggest that tropical cyclone intensification is indicated by the increase in the ESMR-5 derived LHR, the increase in the relative contribution of the heavier rain rates (≥5 mm h−1) to the total storm rainfall, and the decrease in the radius of maximum rain rate from the cyclone center. It also appears evident from these case studies that by monitoring the trend of increasing LHR the first indication of tropical cyclone intensification may be obtained 1–2 days prior to the tropical cyclone reaching storm stage and often prior to the first reconnaissance aircraft observation. Further, the time of the maximum intensity of the tropical cyclone lags by 1–2 days the time of maximum LHR. The statistics of the western Pacific tropical cyclones confirm the case study results in that tropical cyclone intensity can be monitored from ESMR-5 derived rainfall parameters. As the mean tropical cyclone intensifies from disturbance to typhoon stage the average LHR increases steadily. The mean relative contribution of the heavier rate (≥5 mm−1) to the total storm rainfall increased from 0.24 at depression stage to 0.33 at storm stage and finally to 0.39 at typhoon stage. The radial distance of the maximum rain rate from the center decreases with intensification while the azimuthal distribution indicates a slight preference for maximum rain rate in the right half of the composite storm at all stages. The study also indicates that eastern Pacific hurricanes have less LHR, are more compact, and have less intense rainfall than western Pacific typhoons.
Abstract
Data from the Nimbus-5 F-Electrically Scanning Microwave Radiometer (ESMR-5) have been used to calculate latent heat release (LHR) and other rainfall parameters for over 70 satelite observations of 21 tropical cyclones during 1973, 1974 and 1975 in the tropical North Pacific Ocean. The results indicate that the ESMR-5 measurements can be useful in determining the rainfall characteristics of these storms and appear to be potentially useful in monitoring as well as predicting their intensity. The ESMR-5 derived total tropical cyclone rainfall estimates agree favorably with previous estimates for both the disturbance and typhoon stages. The mean typhoon rainfall rate (1.9 mm h−1) is approximately twice that of disturbances (1.1 mm h−1).
Case studies suggest that tropical cyclone intensification is indicated by the increase in the ESMR-5 derived LHR, the increase in the relative contribution of the heavier rain rates (≥5 mm h−1) to the total storm rainfall, and the decrease in the radius of maximum rain rate from the cyclone center. It also appears evident from these case studies that by monitoring the trend of increasing LHR the first indication of tropical cyclone intensification may be obtained 1–2 days prior to the tropical cyclone reaching storm stage and often prior to the first reconnaissance aircraft observation. Further, the time of the maximum intensity of the tropical cyclone lags by 1–2 days the time of maximum LHR. The statistics of the western Pacific tropical cyclones confirm the case study results in that tropical cyclone intensity can be monitored from ESMR-5 derived rainfall parameters. As the mean tropical cyclone intensifies from disturbance to typhoon stage the average LHR increases steadily. The mean relative contribution of the heavier rate (≥5 mm−1) to the total storm rainfall increased from 0.24 at depression stage to 0.33 at storm stage and finally to 0.39 at typhoon stage. The radial distance of the maximum rain rate from the center decreases with intensification while the azimuthal distribution indicates a slight preference for maximum rain rate in the right half of the composite storm at all stages. The study also indicates that eastern Pacific hurricanes have less LHR, are more compact, and have less intense rainfall than western Pacific typhoons.
Abstract
An alternative formulation of the Langley plot relating observed solar irradiance, extraterrestrial solar irradiance, and air mass has been suggested to potentially improve radiometer calibration accuracy. In this study, results from the traditional and alternative plotting methods are compared using both simulated and measured data. The simulations indicate that their relative accuracies depend on the time scale of the atmospheric extinction fluctuations. The two methods are found to be essentially equivalent with the measured data.
Abstract
An alternative formulation of the Langley plot relating observed solar irradiance, extraterrestrial solar irradiance, and air mass has been suggested to potentially improve radiometer calibration accuracy. In this study, results from the traditional and alternative plotting methods are compared using both simulated and measured data. The simulations indicate that their relative accuracies depend on the time scale of the atmospheric extinction fluctuations. The two methods are found to be essentially equivalent with the measured data.
The Tropical Rainfall Measuring Mission (TRMM) satellite is planned for an operational duration of at least three years, beginning in the mid-1990's. The main scientific goals for it are to determine the distribution and variability of precipitation and latent-heat release on a monthly average over areas of about 105 km2, for use in improving short-term climate models, global circulation models and in understanding the hydrological cycle, particularly as it is affected by tropical oceanic rainfall and its variability.
The TRMM satellite's instrumentation will consist of the first quantitative spaceborne weather radar, a multichannel passive microwave radiometer and an AVHRR (Advanced Very High Resolution Radiometer). The satellite's orbit will be low altitude (about 320 km) for high resolution and low inclination (30° to 35°) in order to visit each sampling area in the tropics about twice daily at a different hour of the day. A strong validation effort is planned with several key ground sites to be instrumented with calibrated multiparameter rain radars.
Mission goals and science issues are summarized. Research progress on rain retrieval algorithms is described. Radar and passive microwave algorithms are discussed and the use of radiative models in conjunction with cloud dynamical-microphysical models is emphasized especially. Algorithms are being and will continue to be tested and improved using microwave instruments on high-altitude aircraft overflying precipitating convective systems, located in the range of well-calibrated radars.
The Tropical Rainfall Measuring Mission (TRMM) satellite is planned for an operational duration of at least three years, beginning in the mid-1990's. The main scientific goals for it are to determine the distribution and variability of precipitation and latent-heat release on a monthly average over areas of about 105 km2, for use in improving short-term climate models, global circulation models and in understanding the hydrological cycle, particularly as it is affected by tropical oceanic rainfall and its variability.
The TRMM satellite's instrumentation will consist of the first quantitative spaceborne weather radar, a multichannel passive microwave radiometer and an AVHRR (Advanced Very High Resolution Radiometer). The satellite's orbit will be low altitude (about 320 km) for high resolution and low inclination (30° to 35°) in order to visit each sampling area in the tropics about twice daily at a different hour of the day. A strong validation effort is planned with several key ground sites to be instrumented with calibrated multiparameter rain radars.
Mission goals and science issues are summarized. Research progress on rain retrieval algorithms is described. Radar and passive microwave algorithms are discussed and the use of radiative models in conjunction with cloud dynamical-microphysical models is emphasized especially. Algorithms are being and will continue to be tested and improved using microwave instruments on high-altitude aircraft overflying precipitating convective systems, located in the range of well-calibrated radars.
Abstract
This study proposes a method to quantify systematic and random components of the error associated with satellite precipitation products. Specifically, the Precipitation Uncertainties for Satellite Hydrology (PUSH) model is expanded to provide an estimate of those components of the root-mean-square error. The framework is tested on the TRMM Multisatellite Precipitation Analysis (TMPA) 3B42, real time (3B42RT), and 3B42, version 7 (3B42V7), products over the contiguous United States, using the NOAA Climate Prediction Center (CPC) Unified gauge product as reference. Results show that 3B42V7 exhibits much smaller errors than the real-time product and that the major component of the error associated with both TMPA 3B42 products is random, as the systematic error is almost completely removed by the bias adjustment applied to the two products. A strong dependence of both systematic and random error components on satellite rain rates—with larger error components at larger rain rates—is observed for both satellite products, which suggests that future satellite bias adjustment procedures should account for this dependence. The resulting error estimates and their random and systematic components allow inferences about the accuracy of these datasets and will enhance their deployment in numerous applications, from hydrological modeling and hazard mitigation to climate change studies and water management policy.
Abstract
This study proposes a method to quantify systematic and random components of the error associated with satellite precipitation products. Specifically, the Precipitation Uncertainties for Satellite Hydrology (PUSH) model is expanded to provide an estimate of those components of the root-mean-square error. The framework is tested on the TRMM Multisatellite Precipitation Analysis (TMPA) 3B42, real time (3B42RT), and 3B42, version 7 (3B42V7), products over the contiguous United States, using the NOAA Climate Prediction Center (CPC) Unified gauge product as reference. Results show that 3B42V7 exhibits much smaller errors than the real-time product and that the major component of the error associated with both TMPA 3B42 products is random, as the systematic error is almost completely removed by the bias adjustment applied to the two products. A strong dependence of both systematic and random error components on satellite rain rates—with larger error components at larger rain rates—is observed for both satellite products, which suggests that future satellite bias adjustment procedures should account for this dependence. The resulting error estimates and their random and systematic components allow inferences about the accuracy of these datasets and will enhance their deployment in numerous applications, from hydrological modeling and hazard mitigation to climate change studies and water management policy.
Abstract
This paper describes a method to combine geosynchronous IR and low-orbit microwave data to estimate mean monthly rainfall useful for climate studies. The IR data have the advantage of high time resolution (important for rapidly changing precipitation patterns and for the detection of diurnal signals) but lack a strong physical connection between the remotely sensed signal and the surface rainfall. The microwave data provide a stronger relation between the radiance and the rainfall but provide poor time sampling of the rainfall signal.
The microwave technique uses the brightness temperature at 37 and 86 GHz from the Special Sensor Microwave/Imager instrument on board the Defense Meteorological Satellite Program (DMSP) satellite to define raining areas over water and land and uses the 86-GHz scattering signal to assign rain rate based on cloud model-microwave calculations. The microwave results are generally good for both individual swaths and monthly totals, except for a glaring underestimation of shallow, orographic rain systems over the southern coast of Japan. The IR techniques used are the GOES precipitation index of Arkin and Meisner and the convective-stratiform technique of Adler and Negri.
Initially the IR estimates are computed separately using hourly data from the Japanese Geostationary Meteorological Satellite. Calibration or adjustment factors are derived by dividing the microwave monthly estimate by a second IR estimate (made with the microwave sampling that simulates the observations from an IR radiometer on board the DMSP satellite). The spatial array of coefficients are then multiplied by the original IR monthly estimates (produced from all the hourly data) to produce the merged IR-Microwave monthly estimates. The results show that in areas where the base (microwave) technique performs well, that is, has a relatively small bias, the combined microwave-IR monthly total estimates have better error statistics than either the microwave or IR techniques individually.
Abstract
This paper describes a method to combine geosynchronous IR and low-orbit microwave data to estimate mean monthly rainfall useful for climate studies. The IR data have the advantage of high time resolution (important for rapidly changing precipitation patterns and for the detection of diurnal signals) but lack a strong physical connection between the remotely sensed signal and the surface rainfall. The microwave data provide a stronger relation between the radiance and the rainfall but provide poor time sampling of the rainfall signal.
The microwave technique uses the brightness temperature at 37 and 86 GHz from the Special Sensor Microwave/Imager instrument on board the Defense Meteorological Satellite Program (DMSP) satellite to define raining areas over water and land and uses the 86-GHz scattering signal to assign rain rate based on cloud model-microwave calculations. The microwave results are generally good for both individual swaths and monthly totals, except for a glaring underestimation of shallow, orographic rain systems over the southern coast of Japan. The IR techniques used are the GOES precipitation index of Arkin and Meisner and the convective-stratiform technique of Adler and Negri.
Initially the IR estimates are computed separately using hourly data from the Japanese Geostationary Meteorological Satellite. Calibration or adjustment factors are derived by dividing the microwave monthly estimate by a second IR estimate (made with the microwave sampling that simulates the observations from an IR radiometer on board the DMSP satellite). The spatial array of coefficients are then multiplied by the original IR monthly estimates (produced from all the hourly data) to produce the merged IR-Microwave monthly estimates. The results show that in areas where the base (microwave) technique performs well, that is, has a relatively small bias, the combined microwave-IR monthly total estimates have better error statistics than either the microwave or IR techniques individually.
Abstract
Retrospective-analysis (or reanalysis) systems merge observations and models to provide global four-dimensional earth system data encompassing many physical and dynamical processes. Precipitation is one critical diagnostic that is not only sensitive to the observing system and model physics, but also reflects the general circulation. Climate records of observed precipitation through a merged satellite and gauge dataset provide a reference for comparison, though not without their own uncertainty. In this study, five reanalyses precipitation fields are compared with two observed data products to assess the strengths and weaknesses of the reanalyses. Taylor diagrams show the skill of the reanalyses relative to the reference dataset. While there is a general sense that the reanalyses precipitation data are improving in recent systems, it is not always the case. In some ocean regions, NCEP–NCAR reanalysis spatial patterns are closer to observed precipitation than NCEP–Department of Energy. The 40-yr ECMWF reanalysis (ERA-40) produces reasonable comparisons over Northern Hemisphere continents, but less so in the tropical oceans. On the other hand, the most recent reanalysis, the Japanese 25-yr reanalysis (JRA-25), shows good comparisons in both the Northern Hemisphere continents and the tropical oceans but contains distinct variation according to the available observing systems. The statistics and methods used are also tested on short experiments from a data assimilation system proposed to perform a satellite-era reanalysis.
Abstract
Retrospective-analysis (or reanalysis) systems merge observations and models to provide global four-dimensional earth system data encompassing many physical and dynamical processes. Precipitation is one critical diagnostic that is not only sensitive to the observing system and model physics, but also reflects the general circulation. Climate records of observed precipitation through a merged satellite and gauge dataset provide a reference for comparison, though not without their own uncertainty. In this study, five reanalyses precipitation fields are compared with two observed data products to assess the strengths and weaknesses of the reanalyses. Taylor diagrams show the skill of the reanalyses relative to the reference dataset. While there is a general sense that the reanalyses precipitation data are improving in recent systems, it is not always the case. In some ocean regions, NCEP–NCAR reanalysis spatial patterns are closer to observed precipitation than NCEP–Department of Energy. The 40-yr ECMWF reanalysis (ERA-40) produces reasonable comparisons over Northern Hemisphere continents, but less so in the tropical oceans. On the other hand, the most recent reanalysis, the Japanese 25-yr reanalysis (JRA-25), shows good comparisons in both the Northern Hemisphere continents and the tropical oceans but contains distinct variation according to the available observing systems. The statistics and methods used are also tested on short experiments from a data assimilation system proposed to perform a satellite-era reanalysis.
Abstract
A three-year climatology of satellite-estimated rainfall for the warm season for the southwest United States and Mexico has been derived from data from the Special Sensor Microwave Imager (SSM/1). The microwave data have been stratified by month (June, July, August), yew (1988, 1989, 1990), and time of day (morning and evening orbits). A rain algorithm was employed that relates 86-GHz brightness temperatures to rain rate using a coupled cloud-radiative transfer model.
Results identify an early evening maximum in rainfall along the western slope of the Sierra Madre Occidental during all three months. A prominent morning rainfall maximum was found off the western Mexican coast near Mazatlan in July and August. Substantial differences between morning and evening estimates were noted. To the extent that three years constitute a climatology, results of interannual variability are presented. Results are compared and contrasted to high-resolution (8 km, hourly) infrared cloud climatologies, which consist of the frequency of occurrence of cloud colder than −38°C and −58°C. This comparison has broad implications for the estimation of rainfall by simple (cloud threshold) techniques.
By sampling the infrared data to approximate the time and space resolution of the microwave, we produce ratios (or adjustment factors) by which we can adjust the infrared rain estimation schemes. This produces a combined micro wave/infrared rain algorithm for monthly rainfall. Using a limited set of raingage data as ground truth, an improvement (lower bias and root-mean-square error) was demonstrated by this combined technique when compared to either method alone. The diurnal variability of convection during July 1990 was examined using hourly rain estimates from the GOES precipitation index and the convective stratiform technique, revealing a maximum in estimated rainfall from 1800 to 2100 local time. It is in this time period when the SSM/1 evening orbit occurs. A high-resolution topographic database was available to aid in interpreting the influence of topography on the rainfall patterns.
Abstract
A three-year climatology of satellite-estimated rainfall for the warm season for the southwest United States and Mexico has been derived from data from the Special Sensor Microwave Imager (SSM/1). The microwave data have been stratified by month (June, July, August), yew (1988, 1989, 1990), and time of day (morning and evening orbits). A rain algorithm was employed that relates 86-GHz brightness temperatures to rain rate using a coupled cloud-radiative transfer model.
Results identify an early evening maximum in rainfall along the western slope of the Sierra Madre Occidental during all three months. A prominent morning rainfall maximum was found off the western Mexican coast near Mazatlan in July and August. Substantial differences between morning and evening estimates were noted. To the extent that three years constitute a climatology, results of interannual variability are presented. Results are compared and contrasted to high-resolution (8 km, hourly) infrared cloud climatologies, which consist of the frequency of occurrence of cloud colder than −38°C and −58°C. This comparison has broad implications for the estimation of rainfall by simple (cloud threshold) techniques.
By sampling the infrared data to approximate the time and space resolution of the microwave, we produce ratios (or adjustment factors) by which we can adjust the infrared rain estimation schemes. This produces a combined micro wave/infrared rain algorithm for monthly rainfall. Using a limited set of raingage data as ground truth, an improvement (lower bias and root-mean-square error) was demonstrated by this combined technique when compared to either method alone. The diurnal variability of convection during July 1990 was examined using hourly rain estimates from the GOES precipitation index and the convective stratiform technique, revealing a maximum in estimated rainfall from 1800 to 2100 local time. It is in this time period when the SSM/1 evening orbit occurs. A high-resolution topographic database was available to aid in interpreting the influence of topography on the rainfall patterns.
Abstract
The “satellite-gauge-model” (SGM) technique is described for combining precipitation estimates from microwave satellite data, infrared satellite data, rain gauge analyses, and numerical weather prediction models into improved estimates of global precipitation. Throughout, monthly estimates on a 2.5° × 2.5° lat-long grid are employed. First, a multisatellite product is developed using a combination of low-orbit microwave and geosynchronous-orbit infrared data in the latitude range 40°N–40–S (the adjusted geosynchronous precipitation index) and low-orbit microwave data alone at higher latitudes. Then the rain gauge analysis is brought in, weighting each field by its inverse relative error variance to produce a nearly global, observationally based precipitation estimate. To produce a complete global estimate, the numerical model results are used to fill data voids in the combined satellite-gauge estimate. Our sequential approach to combining estimates allows a user to select the multisatellite estimate, the satellite-gauge estimate, or the full SGM estimate (observationally based estimates plus the model information). The primary limitation in the method is imperfections in the estimation of relative error for the individual fields.
The SGM results for one year of data (July 1987 to June 1988) show important differences from the individual estimates, including model estimates as well as climatological estimates. In general, the SGM results are drier in the subtropics than the model and climatological results, reflecting the relatively dry microwave estimates that dominate the SGM in oceanic regions
Abstract
The “satellite-gauge-model” (SGM) technique is described for combining precipitation estimates from microwave satellite data, infrared satellite data, rain gauge analyses, and numerical weather prediction models into improved estimates of global precipitation. Throughout, monthly estimates on a 2.5° × 2.5° lat-long grid are employed. First, a multisatellite product is developed using a combination of low-orbit microwave and geosynchronous-orbit infrared data in the latitude range 40°N–40–S (the adjusted geosynchronous precipitation index) and low-orbit microwave data alone at higher latitudes. Then the rain gauge analysis is brought in, weighting each field by its inverse relative error variance to produce a nearly global, observationally based precipitation estimate. To produce a complete global estimate, the numerical model results are used to fill data voids in the combined satellite-gauge estimate. Our sequential approach to combining estimates allows a user to select the multisatellite estimate, the satellite-gauge estimate, or the full SGM estimate (observationally based estimates plus the model information). The primary limitation in the method is imperfections in the estimation of relative error for the individual fields.
The SGM results for one year of data (July 1987 to June 1988) show important differences from the individual estimates, including model estimates as well as climatological estimates. In general, the SGM results are drier in the subtropics than the model and climatological results, reflecting the relatively dry microwave estimates that dominate the SGM in oceanic regions
Abstract
This study proposes a new framework, Precipitation Uncertainties for Satellite Hydrology (PUSH), to provide time-varying, global estimates of errors for high-time-resolution, multisatellite precipitation products using a technique calibrated with high-quality validation data. Errors are estimated for the widely used Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42 product at daily/0.25° resolution, using the NOAA Climate Prediction Center (CPC) Unified gauge dataset as the benchmark. PUSH estimates the probability distribution of reference precipitation given the satellite observation, from which the error can be computed as the difference (or ratio) between the satellite product and the estimated reference. The framework proposes different modeling approaches for each combination of rain and no-rain cases: correct no-precipitation detection (both satellite and gauges measure no precipitation), missed precipitation (satellite records a zero, but the gauges detect precipitation), false alarm (satellite detects precipitation, but the reference is zero), and hit (both satellite and gauges detect precipitation). Each case is explored and explicitly modeled to create a unified approach that combines all four scenarios. Results show that the estimated probability distributions are able to reproduce the probability density functions of the benchmark precipitation, in terms of both expected values and quantiles of the distribution. The spatial pattern of the error is also adequately reproduced by PUSH, and good agreement between observed and estimated errors is observed. The model is also able to capture missed precipitation and false detection uncertainties, whose contribution to the total error can be significant. The resulting error estimates could be attached to the corresponding high-resolution satellite precipitation products.
Abstract
This study proposes a new framework, Precipitation Uncertainties for Satellite Hydrology (PUSH), to provide time-varying, global estimates of errors for high-time-resolution, multisatellite precipitation products using a technique calibrated with high-quality validation data. Errors are estimated for the widely used Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42 product at daily/0.25° resolution, using the NOAA Climate Prediction Center (CPC) Unified gauge dataset as the benchmark. PUSH estimates the probability distribution of reference precipitation given the satellite observation, from which the error can be computed as the difference (or ratio) between the satellite product and the estimated reference. The framework proposes different modeling approaches for each combination of rain and no-rain cases: correct no-precipitation detection (both satellite and gauges measure no precipitation), missed precipitation (satellite records a zero, but the gauges detect precipitation), false alarm (satellite detects precipitation, but the reference is zero), and hit (both satellite and gauges detect precipitation). Each case is explored and explicitly modeled to create a unified approach that combines all four scenarios. Results show that the estimated probability distributions are able to reproduce the probability density functions of the benchmark precipitation, in terms of both expected values and quantiles of the distribution. The spatial pattern of the error is also adequately reproduced by PUSH, and good agreement between observed and estimated errors is observed. The model is also able to capture missed precipitation and false detection uncertainties, whose contribution to the total error can be significant. The resulting error estimates could be attached to the corresponding high-resolution satellite precipitation products.
Abstract
Dr. Joanne Simpson's nine specific research contributions to the field of meteorology during her 50-year career—1) the hot tower hypothesis, 2) hurricanes, 3) airflow and clouds over heated islands, 4) cloud models, 5) trade winds and their role in cumulus development, 6) air–sea interaction, 7) cloud–cloud interactions and mergers, 8) waterspouts, and 9) TRMM science—will be described and discussed in this paper.
Abstract
Dr. Joanne Simpson's nine specific research contributions to the field of meteorology during her 50-year career—1) the hot tower hypothesis, 2) hurricanes, 3) airflow and clouds over heated islands, 4) cloud models, 5) trade winds and their role in cumulus development, 6) air–sea interaction, 7) cloud–cloud interactions and mergers, 8) waterspouts, and 9) TRMM science—will be described and discussed in this paper.
