Search Results
WAM requires in-depth analysis of this multiscale variability of rainfall. Satellite observations are a powerful tool to cover these scales and to be used for these much needed meteorological investigations over the WAM where the pluviograph network is scarce. The recent generation of combined infrared (IR) and microwave (MW) products ( Hsu et al. 1997 ; Herman et al. 1997 ; Huffman et al. 2001 ; Joyce et al. 2004 ; Ushio et al. 2009 ; Huffman et al. 2007 ; Levizzani et al. 2007 ; Bergès et
WAM requires in-depth analysis of this multiscale variability of rainfall. Satellite observations are a powerful tool to cover these scales and to be used for these much needed meteorological investigations over the WAM where the pluviograph network is scarce. The recent generation of combined infrared (IR) and microwave (MW) products ( Hsu et al. 1997 ; Herman et al. 1997 ; Huffman et al. 2001 ; Joyce et al. 2004 ; Ushio et al. 2009 ; Huffman et al. 2007 ; Levizzani et al. 2007 ; Bergès et
1. Introduction Observations of clouds and precipitation processes in the microwave (MW) domain from space have been performed since the late 1980s ( Spencer et al. 1989 ). The launch of the Tropical Rainfall Measurement Mission (TRMM) satellite in 1997, carrying a Precipitation Radar (PR) along with the passive TRMM Microwave Imager (TMI), allowed an unprecedented amount of collocated MW multispectral atmospheric signatures and radar-derived vertical profiles of hydrometeor type and density
1. Introduction Observations of clouds and precipitation processes in the microwave (MW) domain from space have been performed since the late 1980s ( Spencer et al. 1989 ). The launch of the Tropical Rainfall Measurement Mission (TRMM) satellite in 1997, carrying a Precipitation Radar (PR) along with the passive TRMM Microwave Imager (TMI), allowed an unprecedented amount of collocated MW multispectral atmospheric signatures and radar-derived vertical profiles of hydrometeor type and density
thick clouds depolarize the upwelling background (polarized) microwaves ( Adams et al. 2008 ). Fig . 7. As in Fig. 6 , but for the radiation at 89 GHz. Since the microwave radiation from the underlying surface is highly polarized at 89 GHz, it is difficult to infer the contribution of ice clouds to 89-GHz PD based on the GMI observations. However, light rain would make the liquid precipitation layer almost opaque and unpolarized at 89 GHz. It is estimated with physics modeling that the PD over a
thick clouds depolarize the upwelling background (polarized) microwaves ( Adams et al. 2008 ). Fig . 7. As in Fig. 6 , but for the radiation at 89 GHz. Since the microwave radiation from the underlying surface is highly polarized at 89 GHz, it is difficult to infer the contribution of ice clouds to 89-GHz PD based on the GMI observations. However, light rain would make the liquid precipitation layer almost opaque and unpolarized at 89 GHz. It is estimated with physics modeling that the PD over a
been found to affect microwave sounder observations ( Lu et al. 2011 ; Lu and Bell 2013 ); however, center frequency stability specifications for AMSU-A are typically 5 MHz. In practice, stabilities are expected to be better than 1.5 MHz based on the thermal tuning coefficients and temperature stability of the local oscillator on orbit, although this has yet to be confirmed by careful analysis of on-orbit data. The value of 10 MHz for the frequency shift in Table 1 therefore represents a
been found to affect microwave sounder observations ( Lu et al. 2011 ; Lu and Bell 2013 ); however, center frequency stability specifications for AMSU-A are typically 5 MHz. In practice, stabilities are expected to be better than 1.5 MHz based on the thermal tuning coefficients and temperature stability of the local oscillator on orbit, although this has yet to be confirmed by careful analysis of on-orbit data. The value of 10 MHz for the frequency shift in Table 1 therefore represents a
discussion of the effects of truncation size, see Kneifel et al. (2011) . b. Wakasa Bay observations This work uses data from the 2003 Wakasa Bay Advanced Microwave Scanning Radiometer Precipitation Validation Campaign ( Lobl et al. 2007 ). The Wakasa Bay dataset is uniquely applicable to this study because it not only provides aircraft radar observations at similar frequencies considered by Kneifel et al. (2011) but also includes data from a variety of precipitation events, ranging from stratiform
discussion of the effects of truncation size, see Kneifel et al. (2011) . b. Wakasa Bay observations This work uses data from the 2003 Wakasa Bay Advanced Microwave Scanning Radiometer Precipitation Validation Campaign ( Lobl et al. 2007 ). The Wakasa Bay dataset is uniquely applicable to this study because it not only provides aircraft radar observations at similar frequencies considered by Kneifel et al. (2011) but also includes data from a variety of precipitation events, ranging from stratiform
, 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
A Passive Microwave Retrieval Algorithm with Minimal View-Angle Bias: Application to the TEMPEST-D CubeSat Mission1. Introduction Satellite-based passive microwave (PMW) radiometers have been used for several decades to measure atmospheric water vapor and bulk cloud properties such as total liquid water path and total ice water path (e.g., Wilheit and Chang 1980 ; Greenwald et al. 1993 ; Wentz 1997 ; Boukabara et al. 2010 ). PMW instruments also provide some of the most important observations for operational data assimilation ( Geer et al. 2017 ). Recently, rapid advances in miniaturized satellite
1. Introduction Satellite-based passive microwave (PMW) radiometers have been used for several decades to measure atmospheric water vapor and bulk cloud properties such as total liquid water path and total ice water path (e.g., Wilheit and Chang 1980 ; Greenwald et al. 1993 ; Wentz 1997 ; Boukabara et al. 2010 ). PMW instruments also provide some of the most important observations for operational data assimilation ( Geer et al. 2017 ). Recently, rapid advances in miniaturized satellite
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
radiometer measurements are available. We have developed an algorithm to estimate atmospheric parameters, in particular rain, from AMSR’s brightness temperatures to provide atmospheric correction for scatterometer wind retrieval from the collocated SeaWinds scatterometer on board ADEOS-II . Although our algorithm uses AMSR observations, it can be easily tailored to any conically scanning microwave radiometer with a similar set of channels [e.g., the Tropical Rainfall Measuring Mission (TRMM) Microwave
radiometer measurements are available. We have developed an algorithm to estimate atmospheric parameters, in particular rain, from AMSR’s brightness temperatures to provide atmospheric correction for scatterometer wind retrieval from the collocated SeaWinds scatterometer on board ADEOS-II . Although our algorithm uses AMSR observations, it can be easily tailored to any conically scanning microwave radiometer with a similar set of channels [e.g., the Tropical Rainfall Measuring Mission (TRMM) Microwave
National Centers for Environmental Prediction (NCEP) and the European Center for Medium-Range Weather Forecasts (ECMWF). It is widely accepted that direct assimilation of radiance observations from microwave temperature sounding channels can significantly improve the accuracy of global and regional weather analysis and forecasts ( Andersson et al. 1994 ; Courtier et al. 1998 ; Derber and Wu 1998 ; McNally et al. 2000 ; Kozo et al. 2005 ). An initial evaluation of FY-3A MWTS against NOAA-18 AMSU
National Centers for Environmental Prediction (NCEP) and the European Center for Medium-Range Weather Forecasts (ECMWF). It is widely accepted that direct assimilation of radiance observations from microwave temperature sounding channels can significantly improve the accuracy of global and regional weather analysis and forecasts ( Andersson et al. 1994 ; Courtier et al. 1998 ; Derber and Wu 1998 ; McNally et al. 2000 ; Kozo et al. 2005 ). An initial evaluation of FY-3A MWTS against NOAA-18 AMSU
