Research Page
Precipitation and Fluxes
Advances in our understanding of global hydrological processes will require detailed precipitation estimates on a broad range of time and space scales. Satellite observations provide a critical contribution toward mapping global rainfall and its variability. Over long time periods, monthly records of precipitation will prove valuable in determining global and regional precipitation trends and possibly separating anthropogenic changes from the large natural variations in rainfall. On shorter time and space scales, global maps of rainfall and latent heating structure will prove very useful as validation tools for general circulation models as they attempt to forecast climate conditions. Assimilation of the precipitation information into global and regional models should produce more realistic simulations. On even smaller time and space scales, knowledge of surface rainfall will be useful for the improvement of surface hydrology models.
Physical Retrievals of Rainrate and Structure
Estimates of surface rain fluxes and precipitation vertical structure from satellite sensors can be used to characterize storm structure and intensity, and to indirectly infer distributions of vertical air motion and latent heat of condensation in precipitation systems. Therefore, in aggregate, these data are also important for global hydrological and energy cycle studies. The most accurate satellite estimates of precipitation rates and vertical structure are currently derived from a combination of spaceborne weather radar and passive microwave radiometer observations. The spaceborne radar data reveal the structure of precipitation at relative high spatial resolution, while the microwave radiometer observations provide a measure of the vertical column-integrated water content of the atmosphere due to water vapor, clouds, and precipitation. Since these atmospheric constituents attenuate the spaceborne radar pulses that are reflected by precipitation, the passive microwave measurements can be used to help "attenuation-correct" the radar observations. The greater relative accuracy of combined radar-radiometer estimates of precipitation makes them useful not only for direct science applications, but also for calibrating other satellite sensors that have lower accuracy but provide greater sampling in space and time.
Shown in the figure below is a schematic of the Global Precipitation Measurement (GPM) mission Core satellite, including the scanning of its Dual-frequency Precipitation Radar (DPR) and GPM Microwave Imager (GMI). The GPM Core mission launched on February 27, 2014. The DPR and GMI represent the next-generation of sensors that were flown on the Tropical Rainfall Measuring Mission (TRMM) satellite (1997–2015). The DPR's two channels generally yield more accurate precipitation particle size distribution and rain rate estimates. The GMI has additional higher-frequency microwave channels useful for light rain and snow detection.
Laboratory researchers are developing computer algorithms for diagnosing precipitation rates, vertical structure, and latent heating from a combination of the DPR and GMI data; see Grecu et al. (2004, 2009, 2011). In the pre-launch phase of GPM, these algorithms were applied to either TRMM data or synthesized GPM observations. Below are the surface rainfall rate fields in Hurricane Floyd (1999) and a Pacific wintertime cold-frontal band derived from TRMM data and a prototype algorithm designed for GPM. The performance of this algorithm is similar to previously-developed TRMM algorithms, but it has the advantage that it can incorporate the additional channel data from GPM to improve precipitation estimates.
Diagram illustrating the dimensions covered by the GPM Core Observatory's Dual-frequency Precipitation Radar (DPR) and GPM Microwave Imager (GMI) instruments. NOTE: As of May 2018 the KaPR swath width is now also 245km.
• Grecu, M . S. Olson, and E. N. Anagnostou, 2004: Retrieval of precipitation profiles from multiresolution, multifrequency, active and passive microwave observations. J. Appl. Meteor., 43, 562-575.
• Grecu M., W. S. Olson, C.-L. Shie, T. S. L'Ecuyer, and W.-K. Tao, 2009: Combining satellite microwave radiometer and radar observations to estimate atmospheric latent heating profiles. Journal of Climate, 22, 6356-6376.
• Grecu, M., L. Tian, W. S. Olson, and S. Tanelli, 2011: A robust dual-frequency radar profiling algorithm. J. Appl. Meteor. and Climatol., 50, 1543-1557.
Global Precipitation Estimates
Precipitation dataset users generally require levels of accuracy, coverage, and time/space resolution that are best addressed by combining estimates from several satellites, plus rain gauge data as possible. The MAPL precipitation group develops, produces, and analyzes such precipitation data sets, such as the TRMM Multi-satellite Precipitation Analysis (TMPA; Huffman et al. 2007). The TMPA is a high-resolution precipitation product (HRPP), focused on producing the best fine-scale quasi-global estimate for the TRMM era, specifically 3-hourly and monthly 0.25°x0.25° latitude/longitude gridded estimates on the latitude band 50°N-S for 1998-present. The main 3-hourly and monthly research products are computed two months after observation, while a near-real-time version of the 3-hourly product is computed about eight hours after observation. The latter provides critical input for a range of analyses, including floods, crops, and drought.
The group is also responsible for the Global Precipitation Climatology Project (GPCP; Adler et al. 2003, Huffman et al. 2009, Huffman et al. 2001) monthly and daily products, which are designed for the long-term homogeneity of Climate Data Record (CDR) standards. The monthly runs 1979–present at 2.5°x2.5° resolution, while the daily is October 1996–present at 1°x1°, both fully global. These data are important inputs for climate analysis and climate model validation. As examples, the long-term climatology of precipitation is shown in the upper panel in millimeters per day, while the time series of global-average land, ocean, and total precipitation is shown in the lower panel as blue, red, and black lines.
Both data sets are highly cited across the precipitation, climate, hydrological, agricultural, and disaster response communities. New versions of both data sets have recently been established, and plans are underway for additional improvements. For example, the group is leading the next-generation multi-satellite algorithm, known as the Integrated Multi-satellitE Retrievals for GPM (IMERG), for the Global Precipitation Measurement (GPM) mission.
References
• Adler, R.F., Huffman, G.J., A. Chang, R. Ferraro, P. Xie, J. Janowiak, B. Rudolf, U. Schneider, S. Curtis, D. Bolvin, A. Gruber, J. Susskind, P. Arkin, and E. Nelkin, 2003: The version-2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis (1979–Present), J. Hydrometeor., 4, 1147-1167.
• Huffman, G. J., R. F. Adler, M. Morrissey, D. Bolvin, S. Curtis, R. Joyce, B. McGavock, and J. Susskind, 2001: Global precipitation at one-degree daily resolution from multi-satellite observations, J. Hydrometeor., 2, 36-50.
• Huffman, G.J., R.F. Adler, D.T. Bolvin, G. Gu, 2009: Improving the Global Precipitation Record: GPCP Version 2.1. Geophys. Res. Lett., 36, L17808, doi:10.1029/2009GL040000. • Huffman, G.J., R.F. Adler, D.T. Bolvin, G. Gu, E.J. Nelkin, K.P. Bowman, Y. Hong, E.F. Stocker, D.B. Wolff, 2007: The TRMM Multi-satellite Precipitation Analysis: Quasi-Global, Multi-Year, Combined-Sensor Precipitation Estimates at Fine Scale. J. Hydrometeor., 8(1), 38-55.
GPCP climatology of global precipitation in millimeters per day, computed from the period 1979–2008
Time series of global-average GPCP monthly precipitation in millimeters per day for ocean (blue), land (red), and all (black) areas
Ocean Surface Fluxes
Accurate global sea surface fluxes measurements are crucial to understanding the global water and energy cycles. The oceanic evaporation that is a major component of the global oceanic fresh water flux is particularly useful for predicting oceanic circulation and transport. Remote sensing is a valuable tool for global monitoring of these flux measurements. The Goddard Satellite-based Surface Turbulent Fluxes (GSSTF) algorithm has been developed and applied to remote sensing research and applications. The early version GSSTF2 (a global 1°x1° daily dataset of July 1987–December 2000) was widely used by the scientific community for global energy and water cycle research, and regional and short period data analysis since its official release in 2001 (Chou et al. 1997; 2003). We were funded by the NASA MEaSUREs Program and produced a current version GSSTF2b (a global 1°x1° daily dataset of July 1987-December 2008) (Shie et al. 2009; Shie 2010a,b; Shie et al. 2010) using the improved and upgraded input datasets that included the updated Special Sensor Microwave Imagers (SSM/I) V6 (e.g., brightness temperature [TB]) and the NCEP-DOE Reanalysis II (e.g., sea surface temperature [SST] and 2m air temperature [Tair]). The GSSTF2b was found to generally agree better with the sounding observations than its counterpart GSSTF2 in all three flux components – latent heat flux (LHF), sensible heat flux (SHF), and wind stress (WST). Climate and weather scenarios such as the ENSO and the Monsoon activities were also genuinely revealed by the GSSTF2b fluxes. Brunke et al. (2011) also found that GSSTF2b performed well, especially in LHF and SHF, among the 11 accessed global ocean surface turbulent fluxes datasets including 6 reanalysis, 4 satellite-derived, and 1 combined. We further found that the gradually increased (positive) temporal trend shown in the globally averaged LHF of GSSTF2b, especially post 2001, was “systematically” related to the trends found in the SSM/I TBs, which was used to retrieve bottom layer precipitable water (WB) and specific humidity (Qa), and subsequently LHF. The associated TBs trends were later found due to the temporal variation/drifting (decreasing) of earth incidence angle (EIA) of the SSM/I satellites (Hilburn and Shie 2011; Shie and Hilburn 2011). We, therefore, have produced an improved and upgraded version GSSTF2c using the corrected TBs (Shie et al. 2011; Shie 2011). The temporal trends (% per year) of the globally-averaged parameters, i.e., WB; Qa; LHF, have thus been properly reduced from -0.27/-0.12; -0.20/-0.09; 1.12/0.76 in GSSTF2b-Set1/Set2 to -0.04; -0.03; 0.53 in GSSTF2c, respectively.
Yearly (1988-2008) Climatological Latent Heat Flux (Wm-2) of GSSTF2c (top) and difference of GSSTF2c and GSSTF2b (bottom)
References
• Brunke, A. M., Z. Wang, X. Zeng, M. Bosilovich, and C.-L. Shie, 2011: (GSSTF2b-related). J. Climate, Vol. 24, 5469-5493. doi: 10.1175/2011JCLI4223.1
• Chou, S.-H., C.-L. Shie, R.M. Atlas, and J. Ardizzone, 1997: (GSSTF1). JGR, 102, 12705-12726.
• Chou, S.-H., E. Nelkin, J. Ardizzone, R. M. Atlas, and C-L. Shie, 2003: (GSSTF2). J. Climate, 16, 3256-3273.
• Hilburn, K. A., and C.-L. Shie, 2011: (GSSTF2b & GSSTF2c). Remote Sensing Systems, 53 pp.
• Shie, C.-L., et al., 2009: (reviving GSSTF). AAS. 26, 1071-1080.
• Shie, C.-L., 2010a: (GSSTF2b). 17th AMS Conference on Satellite Meteo. and Oceano. (keynote speech)
• Shie, C.-L., 2010b: (GSSTF2b). Science Document GES/DISC, 18 pp.
• Shie, C.-L., et al., 2010: (GSSTF2b product).
• Shie, C.-L., 2011: (GSSTF2c). Science Document GES/DISC, 19 pp.
• Shie, C.-L., and K. L. Hilburn, 2011: (GSSTF2b & GSSTF2c). 2011 EUMETSAT Proceedings, 8 pp.
• Shie, C.-L., et al., 2011: (GSSTF2c product).
GSFC Disdrometer Data
Images of disdrometers and rain gauges on the roof of the Earth Sciences building at NASA's Goddard Space Flight Center
In support of NASA's Global Precipitation Measurement (GPM) Ground Validation program, two disdrometers were placed on top of the roof of Building 33 at NASA GSFC, as well as three tipping-bucket rain gauges. Read More
Physical Retrievals of Rainrate and Structure
Estimates of surface rain fluxes and precipitation vertical structure from satellite sensors can be used to characterize storm structure and intensity, and to indirectly infer distributions of vertical air motion and latent heat of condensation in precipitation systems. Therefore, in aggregate, these data are also important for global hydrological and energy cycle studies. The most accurate satellite estimates of precipitation rates and vertical structure are currently derived from a combination of spaceborne weather radar and passive microwave radiometer observations. The spaceborne radar data reveal the structure of precipitation at relative high spatial resolution, while the microwave radiometer observations provide a measure of the vertical column-integrated water content of the atmosphere due to water vapor, clouds, and precipitation. Since these atmospheric constituents attenuate the spaceborne radar pulses that are reflected by precipitation, the passive microwave measurements can be used to help "attenuation-correct" the radar observations. The greater relative accuracy of combined radar-radiometer estimates of precipitation makes them useful not only for direct science applications, but also for calibrating other satellite sensors that have lower accuracy but provide greater sampling in space and time.
Shown in the figure below is a schematic of the Global Precipitation Measurement (GPM) mission Core satellite, including the scanning of its Dual-frequency Precipitation Radar (DPR) and GPM Microwave Imager (GMI). The GPM Core mission launched on February 27, 2014. The DPR and GMI represent the next-generation of sensors that were flown on the Tropical Rainfall Measuring Mission (TRMM) satellite (1997–2015). The DPR's two channels generally yield more accurate precipitation particle size distribution and rain rate estimates. The GMI has additional higher-frequency microwave channels useful for light rain and snow detection.
Laboratory researchers are developing computer algorithms for diagnosing precipitation rates, vertical structure, and latent heating from a combination of the DPR and GMI data; see Grecu et al. (2004, 2009, 2011). In the pre-launch phase of GPM, these algorithms were applied to either TRMM data or synthesized GPM observations. Below are the surface rainfall rate fields in Hurricane Floyd (1999) and a Pacific wintertime cold-frontal band derived from TRMM data and a prototype algorithm designed for GPM. The performance of this algorithm is similar to previously-developed TRMM algorithms, but it has the advantage that it can incorporate the additional channel data from GPM to improve precipitation estimates.
Diagram illustrating the dimensions covered by the GPM Core Observatory's Dual-frequency Precipitation Radar (DPR) and GPM Microwave Imager (GMI) instruments. NOTE: As of May 2018 the KaPR swath width is now also 245km.
• Grecu, M . S. Olson, and E. N. Anagnostou, 2004: Retrieval of precipitation profiles from multiresolution, multifrequency, active and passive microwave observations. J. Appl. Meteor., 43, 562-575.
• Grecu M., W. S. Olson, C.-L. Shie, T. S. L'Ecuyer, and W.-K. Tao, 2009: Combining satellite microwave radiometer and radar observations to estimate atmospheric latent heating profiles. Journal of Climate, 22, 6356-6376.
• Grecu, M., L. Tian, W. S. Olson, and S. Tanelli, 2011: A robust dual-frequency radar profiling algorithm. J. Appl. Meteor. and Climatol., 50, 1543-1557.
Global Precipitation Estimates
Precipitation dataset users generally require levels of accuracy, coverage, and time/space resolution that are best addressed by combining estimates from several satellites, plus rain gauge data as possible. The MAPL precipitation group develops, produces, and analyzes such precipitation data sets, such as the TRMM Multi-satellite Precipitation Analysis (TMPA; Huffman et al. 2007). The TMPA is a high-resolution precipitation product (HRPP), focused on producing the best fine-scale quasi-global estimate for the TRMM era, specifically 3-hourly and monthly 0.25°x0.25° latitude/longitude gridded estimates on the latitude band 50°N-S for 1998-present. The main 3-hourly and monthly research products are computed two months after observation, while a near-real-time version of the 3-hourly product is computed about eight hours after observation. The latter provides critical input for a range of analyses, including floods, crops, and drought.
The group is also responsible for the Global Precipitation Climatology Project (GPCP; Adler et al. 2003, Huffman et al. 2009, Huffman et al. 2001) monthly and daily products, which are designed for the long-term homogeneity of Climate Data Record (CDR) standards. The monthly runs 1979–present at 2.5°x2.5° resolution, while the daily is October 1996–present at 1°x1°, both fully global. These data are important inputs for climate analysis and climate model validation. As examples, the long-term climatology of precipitation is shown in the upper panel in millimeters per day, while the time series of global-average land, ocean, and total precipitation is shown in the lower panel as blue, red, and black lines.
Both data sets are highly cited across the precipitation, climate, hydrological, agricultural, and disaster response communities. New versions of both data sets have recently been established, and plans are underway for additional improvements. For example, the group is leading the next-generation multi-satellite algorithm, known as the Integrated Multi-satellitE Retrievals for GPM (IMERG), for the Global Precipitation Measurement (GPM) mission.
References
• Adler, R.F., Huffman, G.J., A. Chang, R. Ferraro, P. Xie, J. Janowiak, B. Rudolf, U. Schneider, S. Curtis, D. Bolvin, A. Gruber, J. Susskind, P. Arkin, and E. Nelkin, 2003: The version-2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis (1979–Present), J. Hydrometeor., 4, 1147-1167.
• Huffman, G. J., R. F. Adler, M. Morrissey, D. Bolvin, S. Curtis, R. Joyce, B. McGavock, and J. Susskind, 2001: Global precipitation at one-degree daily resolution from multi-satellite observations, J. Hydrometeor., 2, 36-50.
• Huffman, G.J., R.F. Adler, D.T. Bolvin, G. Gu, 2009: Improving the Global Precipitation Record: GPCP Version 2.1. Geophys. Res. Lett., 36, L17808, doi:10.1029/2009GL040000. • Huffman, G.J., R.F. Adler, D.T. Bolvin, G. Gu, E.J. Nelkin, K.P. Bowman, Y. Hong, E.F. Stocker, D.B. Wolff, 2007: The TRMM Multi-satellite Precipitation Analysis: Quasi-Global, Multi-Year, Combined-Sensor Precipitation Estimates at Fine Scale. J. Hydrometeor., 8(1), 38-55.
GPCP climatology of global precipitation in millimeters per day, computed from the period 1979–2008
Time series of global-average GPCP monthly precipitation in millimeters per day for ocean (blue), land (red), and all (black) areas
Ocean Surface Fluxes
Accurate global sea surface fluxes measurements are crucial to understanding the global water and energy cycles. The oceanic evaporation that is a major component of the global oceanic fresh water flux is particularly useful for predicting oceanic circulation and transport. Remote sensing is a valuable tool for global monitoring of these flux measurements. The Goddard Satellite-based Surface Turbulent Fluxes (GSSTF) algorithm has been developed and applied to remote sensing research and applications. The early version GSSTF2 (a global 1°x1° daily dataset of July 1987–December 2000) was widely used by the scientific community for global energy and water cycle research, and regional and short period data analysis since its official release in 2001 (Chou et al. 1997; 2003). We were funded by the NASA MEaSUREs Program and produced a current version GSSTF2b (a global 1°x1° daily dataset of July 1987-December 2008) (Shie et al. 2009; Shie 2010a,b; Shie et al. 2010) using the improved and upgraded input datasets that included the updated Special Sensor Microwave Imagers (SSM/I) V6 (e.g., brightness temperature [TB]) and the NCEP-DOE Reanalysis II (e.g., sea surface temperature [SST] and 2m air temperature [Tair]). The GSSTF2b was found to generally agree better with the sounding observations than its counterpart GSSTF2 in all three flux components – latent heat flux (LHF), sensible heat flux (SHF), and wind stress (WST). Climate and weather scenarios such as the ENSO and the Monsoon activities were also genuinely revealed by the GSSTF2b fluxes. Brunke et al. (2011) also found that GSSTF2b performed well, especially in LHF and SHF, among the 11 accessed global ocean surface turbulent fluxes datasets including 6 reanalysis, 4 satellite-derived, and 1 combined. We further found that the gradually increased (positive) temporal trend shown in the globally averaged LHF of GSSTF2b, especially post 2001, was “systematically” related to the trends found in the SSM/I TBs, which was used to retrieve bottom layer precipitable water (WB) and specific humidity (Qa), and subsequently LHF. The associated TBs trends were later found due to the temporal variation/drifting (decreasing) of earth incidence angle (EIA) of the SSM/I satellites (Hilburn and Shie 2011; Shie and Hilburn 2011). We, therefore, have produced an improved and upgraded version GSSTF2c using the corrected TBs (Shie et al. 2011; Shie 2011). The temporal trends (% per year) of the globally-averaged parameters, i.e., WB; Qa; LHF, have thus been properly reduced from -0.27/-0.12; -0.20/-0.09; 1.12/0.76 in GSSTF2b-Set1/Set2 to -0.04; -0.03; 0.53 in GSSTF2c, respectively.
Yearly (1988-2008) Climatological Latent Heat Flux (Wm-2) of GSSTF2c (top) and difference of GSSTF2c and GSSTF2b (bottom)
References
• Brunke, A. M., Z. Wang, X. Zeng, M. Bosilovich, and C.-L. Shie, 2011: (GSSTF2b-related). J. Climate, Vol. 24, 5469-5493. doi: 10.1175/2011JCLI4223.1
• Chou, S.-H., C.-L. Shie, R.M. Atlas, and J. Ardizzone, 1997: (GSSTF1). JGR, 102, 12705-12726.
• Chou, S.-H., E. Nelkin, J. Ardizzone, R. M. Atlas, and C-L. Shie, 2003: (GSSTF2). J. Climate, 16, 3256-3273.
• Hilburn, K. A., and C.-L. Shie, 2011: (GSSTF2b & GSSTF2c). Remote Sensing Systems, 53 pp.
• Shie, C.-L., et al., 2009: (reviving GSSTF). AAS. 26, 1071-1080.
• Shie, C.-L., 2010a: (GSSTF2b). 17th AMS Conference on Satellite Meteo. and Oceano. (keynote speech)
• Shie, C.-L., 2010b: (GSSTF2b). Science Document GES/DISC, 18 pp.
• Shie, C.-L., et al., 2010: (GSSTF2b product).
• Shie, C.-L., 2011: (GSSTF2c). Science Document GES/DISC, 19 pp.
• Shie, C.-L., and K. L. Hilburn, 2011: (GSSTF2b & GSSTF2c). 2011 EUMETSAT Proceedings, 8 pp.
• Shie, C.-L., et al., 2011: (GSSTF2c product).
GSFC Disdrometer Data
Images of disdrometers and rain gauges on the roof of the Earth Sciences building at NASA's Goddard Space Flight Center
In support of NASA's Global Precipitation Measurement (GPM) Ground Validation program, two disdrometers were placed on top of the roof of Building 33 at NASA GSFC, as well as three tipping-bucket rain gauges. Read More
