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- Author or Editor: David L. Randel x
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Abstract
Refinements and improvements of an earlier technique to retrieve the cloud liquid water path (LWP) of nonprecipitating clouds over land surfaces using Special Sensor Microwave/Imager (SSM/I) 85.5-GHz measurements are presented. These techniques require estimates of the microwave surface emissivity, which are derived in clear-sky regions from SSM/I measurements and window infrared measurements from the Visible and Infrared Spin Scan Radiometer on GOES-7. A comparison of forward model calculations with SSM/I measurements in clear regions demonstrates that over a 7-day period the surface emissivities are stable.
To overcome limitations in the single-channel retrieval method under certain situations, a new method is developed that uses a normalized polarization difference (NPD) of the brightness temperatures. This method has the advantages of providing estimates of the LWP for low clouds and being extremely insensitive to the surface skin temperature. Radiative transfer simulations also show that the polarization difference at 37 GHz may be useful for retrievals in high water vapor environments and for large cloud LWP.
An intercomparison of the different retrieval methods over Platteville, Colorado, reveals large discrepancies for certain cases, but the NPD method is found to agree best with coincident ground-based microwave radiometer measurements of cloud LWP. This success is primarily due to the larger than average surface polarization differences near the Platteville site. While the NPD method shows promise in distinguishing between low, moderate, and high values of cloud LWP, a comprehensive validation effort is required to further evaluate its accuracy and limitations.
Abstract
Refinements and improvements of an earlier technique to retrieve the cloud liquid water path (LWP) of nonprecipitating clouds over land surfaces using Special Sensor Microwave/Imager (SSM/I) 85.5-GHz measurements are presented. These techniques require estimates of the microwave surface emissivity, which are derived in clear-sky regions from SSM/I measurements and window infrared measurements from the Visible and Infrared Spin Scan Radiometer on GOES-7. A comparison of forward model calculations with SSM/I measurements in clear regions demonstrates that over a 7-day period the surface emissivities are stable.
To overcome limitations in the single-channel retrieval method under certain situations, a new method is developed that uses a normalized polarization difference (NPD) of the brightness temperatures. This method has the advantages of providing estimates of the LWP for low clouds and being extremely insensitive to the surface skin temperature. Radiative transfer simulations also show that the polarization difference at 37 GHz may be useful for retrievals in high water vapor environments and for large cloud LWP.
An intercomparison of the different retrieval methods over Platteville, Colorado, reveals large discrepancies for certain cases, but the NPD method is found to agree best with coincident ground-based microwave radiometer measurements of cloud LWP. This success is primarily due to the larger than average surface polarization differences near the Platteville site. While the NPD method shows promise in distinguishing between low, moderate, and high values of cloud LWP, a comprehensive validation effort is required to further evaluate its accuracy and limitations.
Abstract
Several decades of continuous improvements in satellite precipitation algorithms have resulted in fairly accurate level-2 precipitation products for local-scale applications. Numerous studies have been carried out to quantify random and systematic errors at individual validation sites and regional networks. Understanding uncertainties at larger scales, however, has remained a challenge. Temporal changes in precipitation regional biases, regime morphology, sampling, and observation-vector information content, all play important roles in defining the accuracy of satellite rainfall retrievals. This study considers these contributors to offer a quantitative estimate of uncertainty in recently produced global precipitation climate data record. Generated from intercalibrated observations collected by a constellation of passive microwave (PMW) radiometers over the course of 30 years, this data record relies on Global Precipitation Measurement (GPM) mission enterprise PMW precipitation retrieval to offer a long-term global monthly precipitation estimates with corresponding uncertainty at 5° scales. To address changes in the information content across different constellation members the study develops synthetic datasets from GPM Microwave Imager (GMI) sensor, while sampling- and morphology-related uncertainties are quantified using GPM’s dual-frequency precipitation radar (DPR). Special attention is given to separating precipitation into self-similar states that appear to be consistent across environmental conditions. Results show that the variability of bias patterns can be explained by the relative occurrence of different precipitation states across the regions and used to calculate product’s uncertainty. It is found that at 5° spatial scale monthly mean precipitation uncertainties in tropics can exceed 10%.
Abstract
Several decades of continuous improvements in satellite precipitation algorithms have resulted in fairly accurate level-2 precipitation products for local-scale applications. Numerous studies have been carried out to quantify random and systematic errors at individual validation sites and regional networks. Understanding uncertainties at larger scales, however, has remained a challenge. Temporal changes in precipitation regional biases, regime morphology, sampling, and observation-vector information content, all play important roles in defining the accuracy of satellite rainfall retrievals. This study considers these contributors to offer a quantitative estimate of uncertainty in recently produced global precipitation climate data record. Generated from intercalibrated observations collected by a constellation of passive microwave (PMW) radiometers over the course of 30 years, this data record relies on Global Precipitation Measurement (GPM) mission enterprise PMW precipitation retrieval to offer a long-term global monthly precipitation estimates with corresponding uncertainty at 5° scales. To address changes in the information content across different constellation members the study develops synthetic datasets from GPM Microwave Imager (GMI) sensor, while sampling- and morphology-related uncertainties are quantified using GPM’s dual-frequency precipitation radar (DPR). Special attention is given to separating precipitation into self-similar states that appear to be consistent across environmental conditions. Results show that the variability of bias patterns can be explained by the relative occurrence of different precipitation states across the regions and used to calculate product’s uncertainty. It is found that at 5° spatial scale monthly mean precipitation uncertainties in tropics can exceed 10%.
