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1. Introduction For more than 40 yr, satellites have monitored the earth’s weather and climate with significant advancements in the quality and scope of the observations. However, no single measurement provides the necessary information to characterize all relevant atmospheric properties. For this reason it is desirable to combine multiple measurements often on different satellites with diverse viewing geometries and sampling characteristics. This collocation process can be time consuming if
1. Introduction For more than 40 yr, satellites have monitored the earth’s weather and climate with significant advancements in the quality and scope of the observations. However, no single measurement provides the necessary information to characterize all relevant atmospheric properties. For this reason it is desirable to combine multiple measurements often on different satellites with diverse viewing geometries and sampling characteristics. This collocation process can be time consuming if
transfer model (RTM). The relationships between the atmospheric variables in the model and the simulated BTs are then used to develop inversion procedures to retrieve cloud and precipitation fields from a set of satellite observations. An advantage of these mesoscale databases is that they provide profiles that have a more detailed description of the microphysics than the low-resolution numerical weather prediction (NWP) model can give, and that are associated with realistic synthetic BTs obtained from
transfer model (RTM). The relationships between the atmospheric variables in the model and the simulated BTs are then used to develop inversion procedures to retrieve cloud and precipitation fields from a set of satellite observations. An advantage of these mesoscale databases is that they provide profiles that have a more detailed description of the microphysics than the low-resolution numerical weather prediction (NWP) model can give, and that are associated with realistic synthetic BTs obtained from
( Hahn and Warren 2009 ), beginning in 1971 over land and 1954 over the ocean. Quantitative agreement between surface- and satellite-observed cloud amounts should not be expected because of the different ways in which clouds are defined. However, measures of deviations from the means such as seasonal cycles, decadal trends, and interannual variation may be usefully compared. Satellite cloud observations offer the advantage of uniform spatial and temporal coverage as well as a high geographical
( Hahn and Warren 2009 ), beginning in 1971 over land and 1954 over the ocean. Quantitative agreement between surface- and satellite-observed cloud amounts should not be expected because of the different ways in which clouds are defined. However, measures of deviations from the means such as seasonal cycles, decadal trends, and interannual variation may be usefully compared. Satellite cloud observations offer the advantage of uniform spatial and temporal coverage as well as a high geographical
fields; aerosol properties derived from MODIS observations and assimilated in an aerosol transport model; and radiative transfer modeling ( Charlock et al. 2005 ). Satellite estimates of surface fluxes are known to have shortcomings. Errors in the downward shortwave flux arise primarily from the absorption of sunlight by aerosols, and those in the downward longwave flux arise primarily from the lack of information on cloud properties, particularly their lower boundaries ( Charlock and Alberta 1996
fields; aerosol properties derived from MODIS observations and assimilated in an aerosol transport model; and radiative transfer modeling ( Charlock et al. 2005 ). Satellite estimates of surface fluxes are known to have shortcomings. Errors in the downward shortwave flux arise primarily from the absorption of sunlight by aerosols, and those in the downward longwave flux arise primarily from the lack of information on cloud properties, particularly their lower boundaries ( Charlock and Alberta 1996
1. Introduction Thanks to satellite-based observations, extreme weather events such as tropical cyclones (TCs) or explosive midlatitude storms and polar lows can be frequently observed and analyzed. These measurements are critical for short-term forecasting. Yet, prediction of TC intensity and potential destructiveness remains a major challenge. The Dvorak technique makes use of infrared and visible images from polar-orbiting and geostationary satellites for TC intensity analysis ( Dvorak 1975
1. Introduction Thanks to satellite-based observations, extreme weather events such as tropical cyclones (TCs) or explosive midlatitude storms and polar lows can be frequently observed and analyzed. These measurements are critical for short-term forecasting. Yet, prediction of TC intensity and potential destructiveness remains a major challenge. The Dvorak technique makes use of infrared and visible images from polar-orbiting and geostationary satellites for TC intensity analysis ( Dvorak 1975
observations globally. However, limitations of contemporary operational environmental satellite observing systems (e.g., passive optical and microwave spectrum radiometers) to provide detailed, vertically resolved cloud information remains one of the main roadblocks to advancing our understanding of cloud processes. Recognizing the importance of cloud vertical structure information, researchers have pushed the limits of passive sensor technology. Wang et al. (2009) estimate cirrus cloud particle size
observations globally. However, limitations of contemporary operational environmental satellite observing systems (e.g., passive optical and microwave spectrum radiometers) to provide detailed, vertically resolved cloud information remains one of the main roadblocks to advancing our understanding of cloud processes. Recognizing the importance of cloud vertical structure information, researchers have pushed the limits of passive sensor technology. Wang et al. (2009) estimate cirrus cloud particle size
from these different perspectives, several methods for estimating latent heating from satellite observations have been developed. Tao et al. (1990) and Smith et al. (1994) used satellite estimates of precipitation vertical structure to infer latent heating rates in discrete atmospheric layers, assuming that the net flux of precipitation into or out of a given layer is balanced by microphysical processes under steady-state conditions. Tao et al. (1993) later simplified their approach by
from these different perspectives, several methods for estimating latent heating from satellite observations have been developed. Tao et al. (1990) and Smith et al. (1994) used satellite estimates of precipitation vertical structure to infer latent heating rates in discrete atmospheric layers, assuming that the net flux of precipitation into or out of a given layer is balanced by microphysical processes under steady-state conditions. Tao et al. (1993) later simplified their approach by
(DOE) Atmospheric Radiation Measurement (ARM) Program Southern Great Plains (SGP) Central Facility at Lamont, Oklahoma, and 3) to demonstrate the utility of 6-min NOAA Wind Profiler Network observations for satellite-derived AMV validation. Section 2 provides background information on past efforts to extract mesoscale flow patterns from satellite, in addition to a description of issues associated with AMV validation. Section 3 highlights the qualitative application of mesoscale AMVs to weather
(DOE) Atmospheric Radiation Measurement (ARM) Program Southern Great Plains (SGP) Central Facility at Lamont, Oklahoma, and 3) to demonstrate the utility of 6-min NOAA Wind Profiler Network observations for satellite-derived AMV validation. Section 2 provides background information on past efforts to extract mesoscale flow patterns from satellite, in addition to a description of issues associated with AMV validation. Section 3 highlights the qualitative application of mesoscale AMVs to weather
over time, or to intercalibrate multiple instruments. This paper concentrates on the “inversion for calibration,” which deals with the transformation of the satellite observations to improve the retrieval process. However, the methodology presented in this study is very general and this introduction mentions various aspects of calibration. A reliable instrument calibration is a prerequisite for any retrieval scheme. The calibration has to be stable over a long time period and free from any biases
over time, or to intercalibrate multiple instruments. This paper concentrates on the “inversion for calibration,” which deals with the transformation of the satellite observations to improve the retrieval process. However, the methodology presented in this study is very general and this introduction mentions various aspects of calibration. A reliable instrument calibration is a prerequisite for any retrieval scheme. The calibration has to be stable over a long time period and free from any biases
1. Introduction Radio occultation (RO) observations have recently (since late 2006) been shown to have a positive impact on global numerical weather prediction, complementing infrared (IR) and microwave (MW) observations from satellites ( Cardinali 2009 ; Anthes 2011 ; Bonavita 2014 ; Healy 2008 , 2013 ; Poli et al. 2009 ; Cucurull 2010 ; Radnóti et al. 2010 ; Rennie 2010 ; Aparicio and Deblonde 2008 ; Bauer et al. 2014 ). A radio occultation occurs when a receiver on a low
1. Introduction Radio occultation (RO) observations have recently (since late 2006) been shown to have a positive impact on global numerical weather prediction, complementing infrared (IR) and microwave (MW) observations from satellites ( Cardinali 2009 ; Anthes 2011 ; Bonavita 2014 ; Healy 2008 , 2013 ; Poli et al. 2009 ; Cucurull 2010 ; Radnóti et al. 2010 ; Rennie 2010 ; Aparicio and Deblonde 2008 ; Bauer et al. 2014 ). A radio occultation occurs when a receiver on a low
