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issues ( Bauer et al. 2010 ). As expected, excluding cloud contaminated observations causes a significant lack of satellite data in the rainbands of tropical cyclones. Measurements from infrared instruments are restricted in the presence of convective clouds and thus do not provide much information on the state of the atmosphere. However, microwave measurements are less sensitive to clouds and are capable of providing information even in the presence of deep convective clouds such as in the case of
issues ( Bauer et al. 2010 ). As expected, excluding cloud contaminated observations causes a significant lack of satellite data in the rainbands of tropical cyclones. Measurements from infrared instruments are restricted in the presence of convective clouds and thus do not provide much information on the state of the atmosphere. However, microwave measurements are less sensitive to clouds and are capable of providing information even in the presence of deep convective clouds such as in the case of
on this type of surface ( English 2008 ). Studies focusing on the modeling of snow and sea ice emissivity were conducted within the framework of Concordiasi, to improve the use of microwave remote sensing observations in numerical weather prediction (NWP). Guedj et al. (2010) studied the impact of the reflection assumptions on the emissivity: specular, Lambertian, or using different specularity parameters following previous studies about the role of surface approximations on the emissivities
on this type of surface ( English 2008 ). Studies focusing on the modeling of snow and sea ice emissivity were conducted within the framework of Concordiasi, to improve the use of microwave remote sensing observations in numerical weather prediction (NWP). Guedj et al. (2010) studied the impact of the reflection assumptions on the emissivity: specular, Lambertian, or using different specularity parameters following previous studies about the role of surface approximations on the emissivities
Remote Sensing (ERS), the Adaptive Domain Environment for Operating Systems (ADEOS), the Quick Scatterometer (QuikSCAT), and the Meteorological Operational (MetOp) satellites or synthetic aperture radars (SAR) on board the Environmental Satellite ( Envisat ) and Radarsat satellites, synoptic observations of surface wind and atmospheric water content generally reveal storm structures with impressive detail. However, most microwave sensors suffer severe limitations when attempting to retrieve the
Remote Sensing (ERS), the Adaptive Domain Environment for Operating Systems (ADEOS), the Quick Scatterometer (QuikSCAT), and the Meteorological Operational (MetOp) satellites or synthetic aperture radars (SAR) on board the Environmental Satellite ( Envisat ) and Radarsat satellites, synoptic observations of surface wind and atmospheric water content generally reveal storm structures with impressive detail. However, most microwave sensors suffer severe limitations when attempting to retrieve the
from the Special Sensor Microwave Imager (SSM/I) and the Advanced Microwave Sounding Unit (AMSU-B). Over land, radiosondes, ground-based stations, and AMSU-B observations dominate. In the upper troposphere, also infrared sounding channels from the High-Resolution Infrared Radiation Sounder (HIRS), the Atmospheric Infrared Sounder (AIRS), and radiometers on board geostationary satellites provide significant information on humidity to the atmospheric analysis. When rainfall observations from the
from the Special Sensor Microwave Imager (SSM/I) and the Advanced Microwave Sounding Unit (AMSU-B). Over land, radiosondes, ground-based stations, and AMSU-B observations dominate. In the upper troposphere, also infrared sounding channels from the High-Resolution Infrared Radiation Sounder (HIRS), the Atmospheric Infrared Sounder (AIRS), and radiometers on board geostationary satellites provide significant information on humidity to the atmospheric analysis. When rainfall observations from the
satellite radiances in all atmospheric conditions, from clear skies to precipitation. A situation-dependent observation error model retains similar error specifications in clear conditions while assigning larger errors in cloudy conditions. At ECMWF, all-sky assimilation was first pioneered using radiances from microwave imagers and then microwave humidity sounders ( Bauer et al. 2010 ; Geer and Bauer 2010 ; Geer et al. 2014 ). This has gradually led to microwave observations that are sensitive to
satellite radiances in all atmospheric conditions, from clear skies to precipitation. A situation-dependent observation error model retains similar error specifications in clear conditions while assigning larger errors in cloudy conditions. At ECMWF, all-sky assimilation was first pioneered using radiances from microwave imagers and then microwave humidity sounders ( Bauer et al. 2010 ; Geer and Bauer 2010 ; Geer et al. 2014 ). This has gradually led to microwave observations that are sensitive to
over the ocean where sea ice may be present, are being assimilated. The use of surface sensitive observations other than those over the ice-free ocean has only recently been a topic of research in NWP, with work being led by Karbou et al. (2005 , 2006) for the assimilation of surface-sensitive channels from the Advanced Microwave Sounding Unit (AMSU) over land. They compared using the operational system, which assigns an emissivity value based on surface type, to using a retrieved emissivity (i
over the ocean where sea ice may be present, are being assimilated. The use of surface sensitive observations other than those over the ice-free ocean has only recently been a topic of research in NWP, with work being led by Karbou et al. (2005 , 2006) for the assimilation of surface-sensitive channels from the Advanced Microwave Sounding Unit (AMSU) over land. They compared using the operational system, which assigns an emissivity value based on surface type, to using a retrieved emissivity (i
estimation for tropical cyclones in the developing and mature stages. Microwave scatterometers can also estimate the sea surface wind distribution in and around tropical cyclones ( Katsaros et al. 2001 ). By way of example, Sharp et al. (2002) and Gierach et al. (2007) inferred tropical cyclone genesis using the vorticity retrieved from observational data produced by the scatterometer of the Quick Scatterometer (QuikSCAT). Unfortunately, however, QuikSCAT observations were only available at most
estimation for tropical cyclones in the developing and mature stages. Microwave scatterometers can also estimate the sea surface wind distribution in and around tropical cyclones ( Katsaros et al. 2001 ). By way of example, Sharp et al. (2002) and Gierach et al. (2007) inferred tropical cyclone genesis using the vorticity retrieved from observational data produced by the scatterometer of the Quick Scatterometer (QuikSCAT). Unfortunately, however, QuikSCAT observations were only available at most
, DL performs best with at least tens of thousands of training samples, and model performance scales logarithmically with the training sample size ( Sun et al. 2017 ). Thus we have sought out the largest available dataset of TC observations in the 37- and 89-GHz bands. This is available in the Microwave Imagery from NRL TC (MINT) collection, which covers global conical scanner observations from 1987 to 2012. As described in Cossuth et al. (2013) , the dataset includes brightness temperatures from
, DL performs best with at least tens of thousands of training samples, and model performance scales logarithmically with the training sample size ( Sun et al. 2017 ). Thus we have sought out the largest available dataset of TC observations in the 37- and 89-GHz bands. This is available in the Microwave Imagery from NRL TC (MINT) collection, which covers global conical scanner observations from 1987 to 2012. As described in Cossuth et al. (2013) , the dataset includes brightness temperatures from
extended to ~100 km and assimilated observations, initially up to ~80 km ( Hoppel et al. 2008 ) and later up to ~92 km ( Eckermann et al. 2009 ). For these studies, mesospheric temperatures from two research satellite instruments were assimilated: the Sounding of the Atmosphere Using Broadband Emission Radiometry (SABER) limb sensor on the National Aeronautics and Space Administration (NASA) Thermosphere Ionosphere Mesosphere Energetics and Dynamics (TIMED) satellite, and the Microwave Limb Sounder
extended to ~100 km and assimilated observations, initially up to ~80 km ( Hoppel et al. 2008 ) and later up to ~92 km ( Eckermann et al. 2009 ). For these studies, mesospheric temperatures from two research satellite instruments were assimilated: the Sounding of the Atmosphere Using Broadband Emission Radiometry (SABER) limb sensor on the National Aeronautics and Space Administration (NASA) Thermosphere Ionosphere Mesosphere Energetics and Dynamics (TIMED) satellite, and the Microwave Limb Sounder
1. Introduction The Special Sensor Microwave Imager (SSM/I) was first flown on the Defense Meteorological Satellite Program (DMSP) F8 satellite in June 1987 ( Hollinger et al. 1987 ). Since then, six SSM/I sensors have been launched successfully, and currently there are three sensors onboard the DMSP F13 – 15 . Our analysis is based on the observations by the SSM/I onboard the DMSP F14 between 1997 and 2002. The scan direction of the SSM/I on DMSP F14 is from left to right with a spatial
1. Introduction The Special Sensor Microwave Imager (SSM/I) was first flown on the Defense Meteorological Satellite Program (DMSP) F8 satellite in June 1987 ( Hollinger et al. 1987 ). Since then, six SSM/I sensors have been launched successfully, and currently there are three sensors onboard the DMSP F13 – 15 . Our analysis is based on the observations by the SSM/I onboard the DMSP F14 between 1997 and 2002. The scan direction of the SSM/I on DMSP F14 is from left to right with a spatial
