Search Results
1. Introduction Satellite-borne passive microwave observations at L-band will become routinely available for the first time through the European Space Agency’s (ESA) Soil Moisture and Ocean Salinity mission (SMOS) foreseen in 2009. The sensitivity of L-band measurements to soil moisture has been thoroughly analyzed (e.g., Ulaby et al. 1986 ), and the applicability of soil moisture retrievals has been demonstrated over the previous decades (e.g., Jackson et al. 1999 ). In recent years, data
1. Introduction Satellite-borne passive microwave observations at L-band will become routinely available for the first time through the European Space Agency’s (ESA) Soil Moisture and Ocean Salinity mission (SMOS) foreseen in 2009. The sensitivity of L-band measurements to soil moisture has been thoroughly analyzed (e.g., Ulaby et al. 1986 ), and the applicability of soil moisture retrievals has been demonstrated over the previous decades (e.g., Jackson et al. 1999 ). In recent years, data
infrared (IR) images from low-Earth-orbiting (LEO) or geostationary (GEO) satellites provide regular observations of clouds from which estimates of precipitation may be generated. However, although precipitation originates from clouds, not all clouds produce precipitation. More importantly, the relationship between the cloud-top properties and the precipitation reaching the surface is indirect. Passive microwave (PM) radiometers allow a more direct measure of precipitation to be made since these
infrared (IR) images from low-Earth-orbiting (LEO) or geostationary (GEO) satellites provide regular observations of clouds from which estimates of precipitation may be generated. However, although precipitation originates from clouds, not all clouds produce precipitation. More importantly, the relationship between the cloud-top properties and the precipitation reaching the surface is indirect. Passive microwave (PM) radiometers allow a more direct measure of precipitation to be made since these
because that TB temporal variation is close to 0. The objective of this study is to present a new idea for enhancing precipitation retrievals by using TB temporal variation. We will explain where, when, and why TB temporal variation overcomes some of the limitations of the instantaneous TB for precipitation retrievals. This study is organized as follows. Section 2 describes the passive microwave observations from eight polar-orbiting satellites and the precipitation rate from the ground radar
because that TB temporal variation is close to 0. The objective of this study is to present a new idea for enhancing precipitation retrievals by using TB temporal variation. We will explain where, when, and why TB temporal variation overcomes some of the limitations of the instantaneous TB for precipitation retrievals. This study is organized as follows. Section 2 describes the passive microwave observations from eight polar-orbiting satellites and the precipitation rate from the ground radar
1. Introduction Global precipitation products capitalize upon the long period of record of satellite-based passive microwave (MW) radiometer observations ( Aonashi and Ferraro 2020 ). The passive MW brightness temperature (TB) represents the net top-of-atmosphere upwelling radiation, after taking into consideration the emission and scattering properties of hydrometeors within the top-to-bottom profile, including the contribution from the surface emissivity. The surface precipitation represents
1. Introduction Global precipitation products capitalize upon the long period of record of satellite-based passive microwave (MW) radiometer observations ( Aonashi and Ferraro 2020 ). The passive MW brightness temperature (TB) represents the net top-of-atmosphere upwelling radiation, after taking into consideration the emission and scattering properties of hydrometeors within the top-to-bottom profile, including the contribution from the surface emissivity. The surface precipitation represents
basins globally ( Hong et al. 2007 ; Hossain et al. 2007 ; Hossain and Lettenmaier 2006 ). Current Tropical Rainfall Measuring Mission (TRMM)-era and future Global Precipitation Measurement (GPM) mission combined precipitation products generally merge geostationary infrared data and polar-orbiting microwave data to take advantage of the frequent sampling of the infrared and the superior quality of the microwave. With respect to hydrometeorological prediction, GPM is intended to improve
basins globally ( Hong et al. 2007 ; Hossain et al. 2007 ; Hossain and Lettenmaier 2006 ). Current Tropical Rainfall Measuring Mission (TRMM)-era and future Global Precipitation Measurement (GPM) mission combined precipitation products generally merge geostationary infrared data and polar-orbiting microwave data to take advantage of the frequent sampling of the infrared and the superior quality of the microwave. With respect to hydrometeorological prediction, GPM is intended to improve
approaches to hydrometeorological problems include better estimation of initial soil moisture and temperature in mesoscale climatological models ( Jones et al. 2004 ; Huang et al. 2008 ), improved energy partitioning between latent and sensible heat fluxes ( Pipunic et al. 2008 ), and a concomitant higher skill in quantitative precipitation forecasts ( Koster et al. 2000 ). For example, it has been shown that updating soil moisture in a numerical weather model using passive microwave observations at
approaches to hydrometeorological problems include better estimation of initial soil moisture and temperature in mesoscale climatological models ( Jones et al. 2004 ; Huang et al. 2008 ), improved energy partitioning between latent and sensible heat fluxes ( Pipunic et al. 2008 ), and a concomitant higher skill in quantitative precipitation forecasts ( Koster et al. 2000 ). For example, it has been shown that updating soil moisture in a numerical weather model using passive microwave observations at
1. Introduction Microwave land surface emissivity (MLSE) is a fundamental parameter in physical overland rainfall retrieval algorithm development involving space-based passive microwave (PMW) radiometer observations since it influences the thermal emission and scattering of radiation at the surface. In general, MLSE retrieved from brightness temperature (TB) differs from the soil emissivity in a way that MLSE is an effective emissivity that includes the effects of vegetation. Despite its
1. Introduction Microwave land surface emissivity (MLSE) is a fundamental parameter in physical overland rainfall retrieval algorithm development involving space-based passive microwave (PMW) radiometer observations since it influences the thermal emission and scattering of radiation at the surface. In general, MLSE retrieved from brightness temperature (TB) differs from the soil emissivity in a way that MLSE is an effective emissivity that includes the effects of vegetation. Despite its
February 2002, IOP2 from 24 to 30 March 2002, IOP3 from 17 to 25 February 2003, and IOP4 from 25 March through 1 April 2003. In this paper, we summarize the CLPX airborne remote sensing datasets from four categories that span three spectral regions: gamma radiation observations, multi- and hyperspectral optical imaging and optical altimetry, and passive and active microwave. 2. Gamma radiation snow and soil moisture surveys Natural terrestrial gamma radiation is emitted from the potassium, uranium, and
February 2002, IOP2 from 24 to 30 March 2002, IOP3 from 17 to 25 February 2003, and IOP4 from 25 March through 1 April 2003. In this paper, we summarize the CLPX airborne remote sensing datasets from four categories that span three spectral regions: gamma radiation observations, multi- and hyperspectral optical imaging and optical altimetry, and passive and active microwave. 2. Gamma radiation snow and soil moisture surveys Natural terrestrial gamma radiation is emitted from the potassium, uranium, and
: Why are there large differences in the precipitation estimates from radar and passive microwave observations over different regions? Would these differences vary in precipitation systems with different properties? If so, how? How can we improve precipitation retrievals based on what we learn from these differences? Though these questions have been addressed in various ways in the past (e.g., Nesbitt et al. 2004 ; Shige et al. 2006 ; Seo et al. 2007 ; Wang et al. 2009 ), there is still lack of
: Why are there large differences in the precipitation estimates from radar and passive microwave observations over different regions? Would these differences vary in precipitation systems with different properties? If so, how? How can we improve precipitation retrievals based on what we learn from these differences? Though these questions have been addressed in various ways in the past (e.g., Nesbitt et al. 2004 ; Shige et al. 2006 ; Seo et al. 2007 ; Wang et al. 2009 ), there is still lack of
; Levizzani and Cattani 2019 ). Levizzani and Cattani (2019) and Levizzani et al. (2011) highlight issues impeding accurate remote sensing of snowfall, but suggest there is a framework for future improvements. Spaceborne passive microwave (PMW) instruments are attractive from a global snow perspective because they interact directly with the snow crystals—whether in the air or on the ground, have the ability to make observations through clouds both day and night, and have relatively frequent revisit
; Levizzani and Cattani 2019 ). Levizzani and Cattani (2019) and Levizzani et al. (2011) highlight issues impeding accurate remote sensing of snowfall, but suggest there is a framework for future improvements. Spaceborne passive microwave (PMW) instruments are attractive from a global snow perspective because they interact directly with the snow crystals—whether in the air or on the ground, have the ability to make observations through clouds both day and night, and have relatively frequent revisit
