Search Results
1. Introduction Microwave links have been shown to be highly suitable for estimating path-averaged rainfall intensity ( Ruf et al. 1996 ; Rincon and Lang 2002 ; Holt et al. 2003 ; Rahimi et al. 2003 , 2004 ; Minda and Nakamura 2005 ; Krämer et al. 2005 ; Upton et al. 2005 ; Grum et al. 2005 ; Messer et al. 2006 ; Leijnse et al. 2007a , b ). This is due to the near linearity of the relationship between the variable measured by the link (the path-integrated attenuation) and the rainfall
1. Introduction Microwave links have been shown to be highly suitable for estimating path-averaged rainfall intensity ( Ruf et al. 1996 ; Rincon and Lang 2002 ; Holt et al. 2003 ; Rahimi et al. 2003 , 2004 ; Minda and Nakamura 2005 ; Krämer et al. 2005 ; Upton et al. 2005 ; Grum et al. 2005 ; Messer et al. 2006 ; Leijnse et al. 2007a , b ). This is due to the near linearity of the relationship between the variable measured by the link (the path-integrated attenuation) and the rainfall
and freeze–thaw status, with improved (<10 km) resolution over current satellite microwave remote sensing products available from the Special Sensor Microwave Imager (SSM/I), Earth Observing System (EOS) Advanced Microwave Scanning Radiometer (AMSR-E), and the Soil Moisture and Ocean Salinity (SMOS) mission. In the Level 4 soil moisture algorithm, SMAP observations will be assimilated within a land surface data assimilation system that is being developed in the Goddard Earth Observing System
and freeze–thaw status, with improved (<10 km) resolution over current satellite microwave remote sensing products available from the Special Sensor Microwave Imager (SSM/I), Earth Observing System (EOS) Advanced Microwave Scanning Radiometer (AMSR-E), and the Soil Moisture and Ocean Salinity (SMOS) mission. In the Level 4 soil moisture algorithm, SMAP observations will be assimilated within a land surface data assimilation system that is being developed in the Goddard Earth Observing System
water retrievals ( Onogi et al. 2005 ; Bosilovich et al. 2008 ) beginning in 1987 with the Special Sensor Microwave Imager (SSM/I) operational satellites. While analysis state variables are most closely related to observations, the variability of the physical processes and fluxes among reanalyses can be substantial ( Bosilovich et al. 2009 ). Much of the reanalysis data that provide information about the earth’s water and energy budgets come from the model physics, which has been categorized as
water retrievals ( Onogi et al. 2005 ; Bosilovich et al. 2008 ) beginning in 1987 with the Special Sensor Microwave Imager (SSM/I) operational satellites. While analysis state variables are most closely related to observations, the variability of the physical processes and fluxes among reanalyses can be substantial ( Bosilovich et al. 2009 ). Much of the reanalysis data that provide information about the earth’s water and energy budgets come from the model physics, which has been categorized as
; Bromwich et al. 2007 ). For example, the introduction of satellite atmospheric sounding data in late 1978 produced a jump in the Antarctic SMB simulated by the 40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-40) ( van de Berg et al. 2005 ; Bromwich et al. 2007 ). In mid-1987, JRA-25 precipitation exhibits a sudden drop over the Southern Ocean as a result of the assimilation of observations from the Special Sensor Microwave Imager (SSM/I) ( Bosilovich et al. 2006
; Bromwich et al. 2007 ). For example, the introduction of satellite atmospheric sounding data in late 1978 produced a jump in the Antarctic SMB simulated by the 40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-40) ( van de Berg et al. 2005 ; Bromwich et al. 2007 ). In mid-1987, JRA-25 precipitation exhibits a sudden drop over the Southern Ocean as a result of the assimilation of observations from the Special Sensor Microwave Imager (SSM/I) ( Bosilovich et al. 2006
processed in three separate streams, each spun up in two stages: a 2-yr analysis at 2° × 2.5° and then a 1-yr analysis on the MERRA grid. Unfortunately, some system changes were made between spinup and production; these included small changes to the model that should have had little impact on the analysis, but also updates to the spectral coefficients used in the CRTM and a correction to the quality control of the microwave observations. Since the spinup was primarily aimed at the root-zone soil
processed in three separate streams, each spun up in two stages: a 2-yr analysis at 2° × 2.5° and then a 1-yr analysis on the MERRA grid. Unfortunately, some system changes were made between spinup and production; these included small changes to the model that should have had little impact on the analysis, but also updates to the spectral coefficients used in the CRTM and a correction to the quality control of the microwave observations. Since the spinup was primarily aimed at the root-zone soil
.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
. 2003 ) to evaluate and correct the MERRA precipitation estimates. The GPCP data are available as pentad averages from 1979 to 2009 on a 2.5° × 2.5° global grid and are based on the merging of satellite measurements (infrared and microwave) with global rain gauge observations from the Global Precipitation Climatology Centre. Specifically, the GPCP pentad product is computed by adjusting the pentad estimates from the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center
. 2003 ) to evaluate and correct the MERRA precipitation estimates. The GPCP data are available as pentad averages from 1979 to 2009 on a 2.5° × 2.5° global grid and are based on the merging of satellite measurements (infrared and microwave) with global rain gauge observations from the Global Precipitation Climatology Centre. Specifically, the GPCP pentad product is computed by adjusting the pentad estimates from the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center
) and turbulent flux parameterizations ( Josey 2001 ; Renfrew et al. 2002 ). The primary driver of errors in the fluxes may vary regionally and seasonally. Time-varying, gridded estimates of the turbulent heat fluxes can also be produced through the use of satellite-based observations, in situ observations, and/or combinations of these measurements. Satellite-based products are based heavily on passive or active microwave measurements to derive bulk variables needed to compute the surface fluxes
) and turbulent flux parameterizations ( Josey 2001 ; Renfrew et al. 2002 ). The primary driver of errors in the fluxes may vary regionally and seasonally. Time-varying, gridded estimates of the turbulent heat fluxes can also be produced through the use of satellite-based observations, in situ observations, and/or combinations of these measurements. Satellite-based products are based heavily on passive or active microwave measurements to derive bulk variables needed to compute the surface fluxes
-surface retrieval of air temperature and specific humidity using multisensory microwave satellite observations . J. Geophys. Res. , 111 , D10306 , doi:10.1029/2005JD006431 . Josey , S. A. , E. C. Kent , and P. K. Taylor , 1999 : New insights into the ocean heat budget closure problem from analysis of the SOC air–sea flux climatology . J. Climate , 12 , 2856 – 2880 . Kalnay , E. , and Coauthors , 1996 : The NCEP/NCAR 40-Year Reanalysis Project . Bull. Amer. Meteor. Soc. , 77 , 437 – 471
-surface retrieval of air temperature and specific humidity using multisensory microwave satellite observations . J. Geophys. Res. , 111 , D10306 , doi:10.1029/2005JD006431 . Josey , S. A. , E. C. Kent , and P. K. Taylor , 1999 : New insights into the ocean heat budget closure problem from analysis of the SOC air–sea flux climatology . J. Climate , 12 , 2856 – 2880 . Kalnay , E. , and Coauthors , 1996 : The NCEP/NCAR 40-Year Reanalysis Project . Bull. Amer. Meteor. Soc. , 77 , 437 – 471
Microwave Imager (TMI), the Special Sensor Microwave Imager (SSM/I), the Advanced Microwave Scanning Radiometer (AMSR), and AMSU passive microwave estimates using a joint TRMM radar–TMI product (2B31) and is subsequently used to calibrate additional infrared brightness temperature observations from geostationary satellite. The 0.25° × 0.25°, 3-h resolution native product was averaged to 2.5° × 2.5° daily means for this study. Other TRMM-related data include column-integrated water vapor from the TRMM
Microwave Imager (TMI), the Special Sensor Microwave Imager (SSM/I), the Advanced Microwave Scanning Radiometer (AMSR), and AMSU passive microwave estimates using a joint TRMM radar–TMI product (2B31) and is subsequently used to calibrate additional infrared brightness temperature observations from geostationary satellite. The 0.25° × 0.25°, 3-h resolution native product was averaged to 2.5° × 2.5° daily means for this study. Other TRMM-related data include column-integrated water vapor from the TRMM
