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identify how the conditions vary regionally across the ice sheet. In this study, the daily MEaSUREs dataset is examined in relation to concurrent conditions using a simple linear regression analysis. The conditions are examined regionally by using defined GrIS basins. Atmospheric general circulation and cloud conditions are described using the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). Section 2 provides a description of the datasets used and the regression
identify how the conditions vary regionally across the ice sheet. In this study, the daily MEaSUREs dataset is examined in relation to concurrent conditions using a simple linear regression analysis. The conditions are examined regionally by using defined GrIS basins. Atmospheric general circulation and cloud conditions are described using the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). Section 2 provides a description of the datasets used and the regression
Assessing Sensitivity of MERRA-2 to AMSU-A in the Upper Stratosphere. (2012) , and Dee et al. (2011) . Furthermore, the impact of assimilating AMSU-A radiances into numerical weather prediction (NWP) models have been discussed in English et al. (2000) and Baker et al. (2005) . These data are one of the main contributors that have stabilized the climatology of troposphere and lower stratosphere ( Pawson 2012 ) in both the Modern-Era Retrospective Analysis for Research and Applications (MERRA), ( Rienecker et al. 2011 ) and its second version (MERRA-2) ( Gelaro et
. (2012) , and Dee et al. (2011) . Furthermore, the impact of assimilating AMSU-A radiances into numerical weather prediction (NWP) models have been discussed in English et al. (2000) and Baker et al. (2005) . These data are one of the main contributors that have stabilized the climatology of troposphere and lower stratosphere ( Pawson 2012 ) in both the Modern-Era Retrospective Analysis for Research and Applications (MERRA), ( Rienecker et al. 2011 ) and its second version (MERRA-2) ( Gelaro et
weight of the ensemble perturbations, Eq. (5) can be written as the unbiased linear regression equation: At this point we have made no assumption about the nature of the ensemble perturbations. MERRA-2 did not include an ensemble of aerosol forecasts, and this practical approach was developed to produce ensemble perturbations capable of producing realistic speciation and vertical structures for the mixing ratio analysis increments. The underlying assumption of our error covariance modeling exercise
weight of the ensemble perturbations, Eq. (5) can be written as the unbiased linear regression equation: At this point we have made no assumption about the nature of the ensemble perturbations. MERRA-2 did not include an ensemble of aerosol forecasts, and this practical approach was developed to produce ensemble perturbations capable of producing realistic speciation and vertical structures for the mixing ratio analysis increments. The underlying assumption of our error covariance modeling exercise
), though the diversity and ephemeral nature of the assimilated observations and remaining systematic biases in the forecast model each bring uncertainty to the eventual reanalysis data. During the analysis of the observations, the background forecast model is compared to the available observations, and the departure is reflected by the analysis increment. The analysis increment can vary in space and time, and even in the diurnal cycle (e.g., Bosilovich et al. 2015a ). In the atmospheric water budget
), though the diversity and ephemeral nature of the assimilated observations and remaining systematic biases in the forecast model each bring uncertainty to the eventual reanalysis data. During the analysis of the observations, the background forecast model is compared to the available observations, and the departure is reflected by the analysis increment. The analysis increment can vary in space and time, and even in the diurnal cycle (e.g., Bosilovich et al. 2015a ). In the atmospheric water budget
Earth Observing System, version 1 (GEOS-1), reanalysis ( Schubert et al. 1993 ) but advanced significantly with the more recent production of the Modern-Era Retrospective Analysis for Research and Applications (MERRA; Rienecker et al. 2011 ). MERRA encompassed the period 1979–2016 and was undertaken with two primary objectives: to place NASA’s Earth Observing System (EOS) satellite observations in a climate context and to improve the representation of the atmospheric branch of the hydrological
Earth Observing System, version 1 (GEOS-1), reanalysis ( Schubert et al. 1993 ) but advanced significantly with the more recent production of the Modern-Era Retrospective Analysis for Research and Applications (MERRA; Rienecker et al. 2011 ). MERRA encompassed the period 1979–2016 and was undertaken with two primary objectives: to place NASA’s Earth Observing System (EOS) satellite observations in a climate context and to improve the representation of the atmospheric branch of the hydrological
between the first guess (model forecast) and the observations. Schubert and Chang (1996) employed a least squares analysis of the projection of the physical terms in the moisture budget onto ANA to infer errors in the GEOS reanalysis budget terms. Robertson et al. (2014) used the results of a principal component analysis (PCA) applied to MERRA ANA to regress out artifacts in the water and heat budget flux terms. That study showed that nonphysical modes of variability, due largely to increasing
between the first guess (model forecast) and the observations. Schubert and Chang (1996) employed a least squares analysis of the projection of the physical terms in the moisture budget onto ANA to infer errors in the GEOS reanalysis budget terms. Robertson et al. (2014) used the results of a principal component analysis (PCA) applied to MERRA ANA to regress out artifacts in the water and heat budget flux terms. That study showed that nonphysical modes of variability, due largely to increasing
°), the 35-yr time series analyzed here were randomly shuffled to produce 100 000 possible arrangements of the values and the linear regression analysis applied to those. A two-sided p value is derived by counting how many permuted slopes are larger than those derived from the reanalyses and dividing by the number of instances (100 000) in the permutation distributions. While spatial or temporal autocorrelation can generally make the results of permutation tests misleading (e.g., Wilks 2011
°), the 35-yr time series analyzed here were randomly shuffled to produce 100 000 possible arrangements of the values and the linear regression analysis applied to those. A two-sided p value is derived by counting how many permuted slopes are larger than those derived from the reanalyses and dividing by the number of instances (100 000) in the permutation distributions. While spatial or temporal autocorrelation can generally make the results of permutation tests misleading (e.g., Wilks 2011
extreme precipitation events in the northeastern United States, a composite analysis of extreme precipitation events, an analysis of the impact of tropical cyclones and closed low pressure systems on extreme precipitation events, and an indication of how the character of extreme events has changed over time. A summary and discussion is provided in section 4 . 2. Data and methods The primary tools for the analysis include observational and reanalysis products. Observations of precipitation are from
extreme precipitation events in the northeastern United States, a composite analysis of extreme precipitation events, an analysis of the impact of tropical cyclones and closed low pressure systems on extreme precipitation events, and an indication of how the character of extreme events has changed over time. A summary and discussion is provided in section 4 . 2. Data and methods The primary tools for the analysis include observational and reanalysis products. Observations of precipitation are from
1. Introduction This study presents an analysis of the atmospheric and oceanic signals over the tropics associated with the strong El Niño event that occurred in 2015/16. For the ocean, the study uses the Goddard Earth Observing System (GEOS) oceanic analysis ( Vernieres et al. 2012 ) that is driven by the Modern-Era Retrospective Analysis for Research and Applications (MERRA) atmospheric reanalysis ( Rienecker et al. 2011 ). Atmospheric fields in this work are from the updated MERRA-2
1. Introduction This study presents an analysis of the atmospheric and oceanic signals over the tropics associated with the strong El Niño event that occurred in 2015/16. For the ocean, the study uses the Goddard Earth Observing System (GEOS) oceanic analysis ( Vernieres et al. 2012 ) that is driven by the Modern-Era Retrospective Analysis for Research and Applications (MERRA) atmospheric reanalysis ( Rienecker et al. 2011 ). Atmospheric fields in this work are from the updated MERRA-2
1. Introduction The NASA Global Modeling and Assimilation Office recently released the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2; Gelaro et al. 2017 ). This new global reanalysis product replaces and extends the original MERRA atmospheric reanalysis ( Rienecker et al. 2011 ), as well as the MERRA-Land reanalysis ( Reichle et al. 2011 ). In addition to several other major advances, MERRA-2 uses observed precipitation in place of model
1. Introduction The NASA Global Modeling and Assimilation Office recently released the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2; Gelaro et al. 2017 ). This new global reanalysis product replaces and extends the original MERRA atmospheric reanalysis ( Rienecker et al. 2011 ), as well as the MERRA-Land reanalysis ( Reichle et al. 2011 ). In addition to several other major advances, MERRA-2 uses observed precipitation in place of model
