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products ( Savtchenko et al. 2004 ). VIIRS will collect data in 22 bands that will be used to create 23 data products ( Hutchison and Cracknell 2006 ). A key product created with both MODIS and VIIRS sensors is the cloud mask, which is generated using sophisticated logic that includes a series of cloud detection tests. Although the MODIS cloud mask (MCM) algorithm has evolved since the launch of MODIS on the Terra spacecraft in December 1999 ( Ackerman et al. 1997 , 2002 ), the VIIRS cloud mask (VCM
products ( Savtchenko et al. 2004 ). VIIRS will collect data in 22 bands that will be used to create 23 data products ( Hutchison and Cracknell 2006 ). A key product created with both MODIS and VIIRS sensors is the cloud mask, which is generated using sophisticated logic that includes a series of cloud detection tests. Although the MODIS cloud mask (MCM) algorithm has evolved since the launch of MODIS on the Terra spacecraft in December 1999 ( Ackerman et al. 1997 , 2002 ), the VIIRS cloud mask (VCM
1. Introduction Knowledge regarding the three-dimensional variability of precipitation is essential in the development of precipitation retrieval algorithms from satellite radiometer observations. This is because satellite radiometer observations cannot be uniquely associated with precipitation, and statistical information is required to determine optimal precipitation estimates. Spaceborne radar observations may be used to derive such information. For example, it is anticipated that in the
1. Introduction Knowledge regarding the three-dimensional variability of precipitation is essential in the development of precipitation retrieval algorithms from satellite radiometer observations. This is because satellite radiometer observations cannot be uniquely associated with precipitation, and statistical information is required to determine optimal precipitation estimates. Spaceborne radar observations may be used to derive such information. For example, it is anticipated that in the
1. Introduction Algorithms that can extract properties of storm cells 1 and track those properties over time provide information that is important to forecasters in assessing storm intensity, growth, and decay ( Wilson et al. 1998 ). However, associating storm cells across frames of remotely sensed images poses a difficult problem because storms evolve, split, and merge. Because storm-tracking algorithms are a key component of nowcasting systems, the problem of how to track storms has received
1. Introduction Algorithms that can extract properties of storm cells 1 and track those properties over time provide information that is important to forecasters in assessing storm intensity, growth, and decay ( Wilson et al. 1998 ). However, associating storm cells across frames of remotely sensed images poses a difficult problem because storms evolve, split, and merge. Because storm-tracking algorithms are a key component of nowcasting systems, the problem of how to track storms has received
The GPM Combined Algorithmuniformly calibrated rain algorithms for all radiometers in the GPM constellation ( Kummerow et al. 2011 ). The constellation of radiometers provides the temporal sampling necessary to achieve the mission objective. During the Tropical Rainfall Measuring Mission (TRMM) era, several algorithms for estimating precipitation from a combination of radar and microwave radiometer observations were developed. The TRMM observatory included a single-frequency (Ku band) cross-track scanning radar and a
uniformly calibrated rain algorithms for all radiometers in the GPM constellation ( Kummerow et al. 2011 ). The constellation of radiometers provides the temporal sampling necessary to achieve the mission objective. During the Tropical Rainfall Measuring Mission (TRMM) era, several algorithms for estimating precipitation from a combination of radar and microwave radiometer observations were developed. The TRMM observatory included a single-frequency (Ku band) cross-track scanning radar and a
least squares framework for implementing most variants of ensemble filters that have been described in the literature. In this framework, it is possible to completely describe an ensemble filter by discussing only the impact of a single scalar observation on a single state variable. Several filters have been implemented using this framework in both idealized and large models ( Zhang et al. 2005 ). However, the sequential nature of the algorithm has led to concerns that it cannot be practically
least squares framework for implementing most variants of ensemble filters that have been described in the literature. In this framework, it is possible to completely describe an ensemble filter by discussing only the impact of a single scalar observation on a single state variable. Several filters have been implemented using this framework in both idealized and large models ( Zhang et al. 2005 ). However, the sequential nature of the algorithm has led to concerns that it cannot be practically
budgets, verification and data assimilation in general circulation model simulations, and hydrologic applications. The accumulated rainfall estimations are required for precipitation climatological research ( Huffman et al. 1997 ; Kidd 2001 ; Mark et al. 2001 ). The methods for estimating precipitation from satellite observations have been evolving and improving for decades. The early rain retrieval algorithms with satellite visible (VIS) and infrared (IR) measurements applied statistical regression
budgets, verification and data assimilation in general circulation model simulations, and hydrologic applications. The accumulated rainfall estimations are required for precipitation climatological research ( Huffman et al. 1997 ; Kidd 2001 ; Mark et al. 2001 ). The methods for estimating precipitation from satellite observations have been evolving and improving for decades. The early rain retrieval algorithms with satellite visible (VIS) and infrared (IR) measurements applied statistical regression
satisfied a suitable fluctuation–dissipation theorem (FDT), then climate response to small external forcing could be calculated by estimating suitable statistics in the present climate. For the general FDT, see Deker and Haake (1975) , Risken (1989) , and Majda et al. (2005 , hereafter MAG05) . The topic of this paper is the use of a new algorithm ( Abramov and Majda 2007 , 2008 , hereafter AM07 , AM08 ) for calculating the FDT response to address the above issues in a prototype setting for the
satisfied a suitable fluctuation–dissipation theorem (FDT), then climate response to small external forcing could be calculated by estimating suitable statistics in the present climate. For the general FDT, see Deker and Haake (1975) , Risken (1989) , and Majda et al. (2005 , hereafter MAG05) . The topic of this paper is the use of a new algorithm ( Abramov and Majda 2007 , 2008 , hereafter AM07 , AM08 ) for calculating the FDT response to address the above issues in a prototype setting for the
) measurements of reflected and earth-emitted radiation in three broadband channels: a shortwave (SW) channel (0.2–5.0 μ m), a total channel (from 0.2 to >100 μ m), and a thermal infrared (IR) window channel (8–12 μ m). An extensive modeling effort is subsequently used with TOA measurements for deriving surface SW and longwave (LW) fluxes and corresponding flux profiles at multiple levels in the atmosphere. The three LW algorithms discussed in this study are part of the surface-only flux algorithms (SOFA
) measurements of reflected and earth-emitted radiation in three broadband channels: a shortwave (SW) channel (0.2–5.0 μ m), a total channel (from 0.2 to >100 μ m), and a thermal infrared (IR) window channel (8–12 μ m). An extensive modeling effort is subsequently used with TOA measurements for deriving surface SW and longwave (LW) fluxes and corresponding flux profiles at multiple levels in the atmosphere. The three LW algorithms discussed in this study are part of the surface-only flux algorithms (SOFA
maximum wind (RMW), and the center location of a TC from Doppler radar data, and could be applied in an operational environment. However, it was highly affected by the asymmetry of circulations and strong mean flows across the vortex. Marks et al. (1992) used the “simplex” algorithm ( Nelder and Mead 1965 ) to find the center that maximizes the tangential circulations encompassing the observed RMW at different altitudes. They found that the center was 3–6 km to the right of that determined
maximum wind (RMW), and the center location of a TC from Doppler radar data, and could be applied in an operational environment. However, it was highly affected by the asymmetry of circulations and strong mean flows across the vortex. Marks et al. (1992) used the “simplex” algorithm ( Nelder and Mead 1965 ) to find the center that maximizes the tangential circulations encompassing the observed RMW at different altitudes. They found that the center was 3–6 km to the right of that determined
forecasts of streamflow ( Faures et al. 1995 ; Frank et al. 1999 ; Ogden et al. 2000 ; Droegemeier et al. 2000 ). The accuracy and timeliness of flash flood and river flood forecasts are limited by the accuracy of radar-derived precipitation. Steiner and Smith (2002) provide an excellent summary of existing techniques to mitigate radar returns from ground clutter. They conclude that their algorithm yields improvement but fails in situations with strong, widespread clear air echoes and when anomalous
forecasts of streamflow ( Faures et al. 1995 ; Frank et al. 1999 ; Ogden et al. 2000 ; Droegemeier et al. 2000 ). The accuracy and timeliness of flash flood and river flood forecasts are limited by the accuracy of radar-derived precipitation. Steiner and Smith (2002) provide an excellent summary of existing techniques to mitigate radar returns from ground clutter. They conclude that their algorithm yields improvement but fails in situations with strong, widespread clear air echoes and when anomalous
