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Abstract
A statistical histogram method is developed to objectively determine sea-surface temperature from satellite high resolution window radiation measurements. The method involves inferring the distribution of surface radiances for the clear atmospheric case from observed histograms of generally cloud-contaminated radiances. The brightness temperature associated with the clear atmosphere modal peak radiance is the statistically most probable surface temperature. The reliability of the inferred surface temperature depends upon the number of cloud-free measurements available to define the clear mode. The method accounts for atmospheric attenuation and instrumental noise and also objectively discriminates cloud-free from cloud-contaminated observations.
The statistical histogram method is applied to 3.8 micrometer window radiation data obtained by the High Resolution Infrared Radiometer flown on the Nimbus 2 and Nimbus 3 satellites. Examples of sea temperatures inferred over both small and large areas are presented. Comparisons with conventional ship observations indicate that both bias and random errors of the inferred sea temperatures are less than 1°C.
Due to the apparent success of this statistical histogram technique, plans have been made to use it to obtain sea-surface temperatures on a global basis daily from operational high resolution infrared radiation measurements.
Abstract
A statistical histogram method is developed to objectively determine sea-surface temperature from satellite high resolution window radiation measurements. The method involves inferring the distribution of surface radiances for the clear atmospheric case from observed histograms of generally cloud-contaminated radiances. The brightness temperature associated with the clear atmosphere modal peak radiance is the statistically most probable surface temperature. The reliability of the inferred surface temperature depends upon the number of cloud-free measurements available to define the clear mode. The method accounts for atmospheric attenuation and instrumental noise and also objectively discriminates cloud-free from cloud-contaminated observations.
The statistical histogram method is applied to 3.8 micrometer window radiation data obtained by the High Resolution Infrared Radiometer flown on the Nimbus 2 and Nimbus 3 satellites. Examples of sea temperatures inferred over both small and large areas are presented. Comparisons with conventional ship observations indicate that both bias and random errors of the inferred sea temperatures are less than 1°C.
Due to the apparent success of this statistical histogram technique, plans have been made to use it to obtain sea-surface temperatures on a global basis daily from operational high resolution infrared radiation measurements.
Abstract
One primary goal of annual Spring Forecasting Experiments (SFEs), which are coorganized by NOAA’s National Severe Storms Laboratory and Storm Prediction Center and conducted in the National Oceanic and Atmospheric Administration’s (NOAA) Hazardous Weather Testbed, is documenting performance characteristics of experimental, convection-allowing modeling systems (CAMs). Since 2007, the number of CAMs (including CAM ensembles) examined in the SFEs has increased dramatically, peaking at six different CAM ensembles in 2015. Meanwhile, major advances have been made in creating, importing, processing, verifying, and developing tools for analyzing and visualizing these large and complex datasets. However, progress toward identifying optimal CAM ensemble configurations has been inhibited because the different CAM systems have been independently designed, making it difficult to attribute differences in performance characteristics. Thus, for the 2016 SFE, a much more coordinated effort among many collaborators was made by agreeing on a set of model specifications (e.g., model version, grid spacing, domain size, and physics) so that the simulations contributed by each collaborator could be combined to form one large, carefully designed ensemble known as the Community Leveraged Unified Ensemble (CLUE). The 2016 CLUE was composed of 65 members contributed by five research institutions and represents an unprecedented effort to enable an evidence-driven decision process to help guide NOAA’s operational modeling efforts. Eight unique experiments were designed within the CLUE framework to examine issues directly relevant to the design of NOAA’s future operational CAM-based ensembles. This article will highlight the CLUE design and present results from one of the experiments examining the impact of single versus multicore CAM ensemble configurations.
Abstract
One primary goal of annual Spring Forecasting Experiments (SFEs), which are coorganized by NOAA’s National Severe Storms Laboratory and Storm Prediction Center and conducted in the National Oceanic and Atmospheric Administration’s (NOAA) Hazardous Weather Testbed, is documenting performance characteristics of experimental, convection-allowing modeling systems (CAMs). Since 2007, the number of CAMs (including CAM ensembles) examined in the SFEs has increased dramatically, peaking at six different CAM ensembles in 2015. Meanwhile, major advances have been made in creating, importing, processing, verifying, and developing tools for analyzing and visualizing these large and complex datasets. However, progress toward identifying optimal CAM ensemble configurations has been inhibited because the different CAM systems have been independently designed, making it difficult to attribute differences in performance characteristics. Thus, for the 2016 SFE, a much more coordinated effort among many collaborators was made by agreeing on a set of model specifications (e.g., model version, grid spacing, domain size, and physics) so that the simulations contributed by each collaborator could be combined to form one large, carefully designed ensemble known as the Community Leveraged Unified Ensemble (CLUE). The 2016 CLUE was composed of 65 members contributed by five research institutions and represents an unprecedented effort to enable an evidence-driven decision process to help guide NOAA’s operational modeling efforts. Eight unique experiments were designed within the CLUE framework to examine issues directly relevant to the design of NOAA’s future operational CAM-based ensembles. This article will highlight the CLUE design and present results from one of the experiments examining the impact of single versus multicore CAM ensemble configurations.
