Search Results
The various data types used in MERRA and the timeline of their availability are summarized in Fig. 3 . The complete listing of the data streams and their sources are provided in appendix B . The quality control procedures, the channels used for radiance assimilation, and the observation error characteristics are presented in R2008 . Fig . 3. Summary of the observing system used for MERRA. MERRA benefited from the observational assembly for the NCEP–NCAR reanalysis, the NCEP preparations for its
The various data types used in MERRA and the timeline of their availability are summarized in Fig. 3 . The complete listing of the data streams and their sources are provided in appendix B . The quality control procedures, the channels used for radiance assimilation, and the observation error characteristics are presented in R2008 . Fig . 3. Summary of the observing system used for MERRA. MERRA benefited from the observational assembly for the NCEP–NCAR reanalysis, the NCEP preparations for its
assimilation methods, increased availability of computing resource, growing types of observations available for assimilation, and improved observational quality control. While the data assimilation and numerical model components of the reanalysis system are fixed for the processing of the climate period (as proposed by Bengtsson and Shukla 1988 ; Trenberth and Olson 1988 ), the observing system changes greatly in time. A major change to the observational record concerns the onset of routine operational
assimilation methods, increased availability of computing resource, growing types of observations available for assimilation, and improved observational quality control. While the data assimilation and numerical model components of the reanalysis system are fixed for the processing of the climate period (as proposed by Bengtsson and Shukla 1988 ; Trenberth and Olson 1988 ), the observing system changes greatly in time. A major change to the observational record concerns the onset of routine operational
.1; Huffman et al. 2001 , 2009 ). The TRMM 3B42 algorithm merges high quality microwave precipitation retrievals from instruments that include the TRMM Combined Instrument (TCI), TRMM Microwave Imager (TMI), Special Sensor Microwave Image (SSM/I), AMSR-E, and Advanced Microwave Sounding Unit-B (AMSU-B) with adjusted precipitation estimates from geostationary observations of infrared brightness temperature ( Huffman and Bolvin 2009 ). The gridded P data are reported at 3-hourly intervals and 0.25° × 0
.1; Huffman et al. 2001 , 2009 ). The TRMM 3B42 algorithm merges high quality microwave precipitation retrievals from instruments that include the TRMM Combined Instrument (TCI), TRMM Microwave Imager (TMI), Special Sensor Microwave Image (SSM/I), AMSR-E, and Advanced Microwave Sounding Unit-B (AMSU-B) with adjusted precipitation estimates from geostationary observations of infrared brightness temperature ( Huffman and Bolvin 2009 ). The gridded P data are reported at 3-hourly intervals and 0.25° × 0
quality control steps that included automatic detection of problematic observations and a visual inspection of the time series. We excluded data that are obviously unrealistic (such as data outside of the physical range or data related to discontinuities in the time series that could not be explained by physical processes). We also excluded soil moisture measurements that were taken under frozen conditions (according to SCAN soil temperature measurements), or data affected by inconsistencies that are
quality control steps that included automatic detection of problematic observations and a visual inspection of the time series. We excluded data that are obviously unrealistic (such as data outside of the physical range or data related to discontinuities in the time series that could not be explained by physical processes). We also excluded soil moisture measurements that were taken under frozen conditions (according to SCAN soil temperature measurements), or data affected by inconsistencies that are
that MERRA does demonstrate improved skill of spatial distribution of precipitation, especially related to the tropical oceanic regions. Like other reanalysis efforts, MERRA ingests much of the conventional and satellite observational data stream and performs a series of extensive quality checks and bias corrections. An extensive review of these data streams and operational procedures can be found in Rienecker et al. (2008) . The MERRA incremental analysis update (IAU) methodology ( Bloom et al
that MERRA does demonstrate improved skill of spatial distribution of precipitation, especially related to the tropical oceanic regions. Like other reanalysis efforts, MERRA ingests much of the conventional and satellite observational data stream and performs a series of extensive quality checks and bias corrections. An extensive review of these data streams and operational procedures can be found in Rienecker et al. (2008) . The MERRA incremental analysis update (IAU) methodology ( Bloom et al
towers from across the continent allows for analysis from different vegetation types and climate regimes, including tundra, grassland, and evergreen forests. No gap-filling techniques were utilized on the flux tower observations, but the level-3 data used have quality flags that were used to selectively remove any data that had been deemed of questionable quality by the PI. Even though the questionable quality data are removed from the analysis, flux tower measurements are known to contain errors for
towers from across the continent allows for analysis from different vegetation types and climate regimes, including tundra, grassland, and evergreen forests. No gap-filling techniques were utilized on the flux tower observations, but the level-3 data used have quality flags that were used to selectively remove any data that had been deemed of questionable quality by the PI. Even though the questionable quality data are removed from the analysis, flux tower measurements are known to contain errors for
: Antarctic surface and subsurface snow and ice melt fluxes . J. Climate , 18 , 1469 – 1481 . Liu , H. , K. Jezek , B. Li , and Z. Zhao , cited 2001 : Radarsat Antarctic Mapping Project digital elevation model version 2 . [Available online at http://nsidc.org/data/nsidc-0082.html .] Magand , O. , C. Genthon , M. Fily , G. Krinner , G. Picard , M. Frezzotti , and A. Ekaykin , 2007 : An up-to-date quality-controlled surface mass balance data set for the 90°–180°E
: Antarctic surface and subsurface snow and ice melt fluxes . J. Climate , 18 , 1469 – 1481 . Liu , H. , K. Jezek , B. Li , and Z. Zhao , cited 2001 : Radarsat Antarctic Mapping Project digital elevation model version 2 . [Available online at http://nsidc.org/data/nsidc-0082.html .] Magand , O. , C. Genthon , M. Fily , G. Krinner , G. Picard , M. Frezzotti , and A. Ekaykin , 2007 : An up-to-date quality-controlled surface mass balance data set for the 90°–180°E
. Because of the quality of the MERRA temperature assimilation, the 20–90-day filtering as applied to MERRA data yields almost identical dates. The results of this process on tropospheric temperature can be seen in Fig. 1 where the MERRA filtered data are shown with the AMSU-A data with just the annual cycle removed. Elimination of lower-frequency interannual variability by the bandpass filter is readily seen. This procedure differs somewhat from Spencer et al. (2007) who chose their dates based on
. Because of the quality of the MERRA temperature assimilation, the 20–90-day filtering as applied to MERRA data yields almost identical dates. The results of this process on tropospheric temperature can be seen in Fig. 1 where the MERRA filtered data are shown with the AMSU-A data with just the annual cycle removed. Elimination of lower-frequency interannual variability by the bandpass filter is readily seen. This procedure differs somewhat from Spencer et al. (2007) who chose their dates based on
). In fact, the ERA-I is the only reanalysis that assimilates radiances affected by clouds and precipitation ( Dee et al. 2011 ) either directly or indirectly through a 1DVAR + 4DVAR approach ( Bauer et al. 2006a , b ). Additional improvements specifically found for the ERA-I over the ERA-40 include a better formulation of the background error constraint, a new humidity analysis, improvements in the quality control of the input data, and better model physics that include an improved radiative
). In fact, the ERA-I is the only reanalysis that assimilates radiances affected by clouds and precipitation ( Dee et al. 2011 ) either directly or indirectly through a 1DVAR + 4DVAR approach ( Bauer et al. 2006a , b ). Additional improvements specifically found for the ERA-I over the ERA-40 include a better formulation of the background error constraint, a new humidity analysis, improvements in the quality control of the input data, and better model physics that include an improved radiative
forecasting skill. An assessment of the mean JAS precipitation shows that MERRA produces an overall smaller bias with respect to GPCP compared with other reanalyses ( Fig. 5 ). Soil moisture gradients, a prominent factor controlling the AEJ, are difficult to assess and verify; however, the overall quality of precipitation distribution in the MERRA data appears satisfactory. A Hilbert–Huang spectral analysis performed on the datasets reveals that two fundamental time scales can be recognized: at 2.5–6 days
forecasting skill. An assessment of the mean JAS precipitation shows that MERRA produces an overall smaller bias with respect to GPCP compared with other reanalyses ( Fig. 5 ). Soil moisture gradients, a prominent factor controlling the AEJ, are difficult to assess and verify; however, the overall quality of precipitation distribution in the MERRA data appears satisfactory. A Hilbert–Huang spectral analysis performed on the datasets reveals that two fundamental time scales can be recognized: at 2.5–6 days
