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. In this manuscript, the term “complex surfaces” refers to land surfaces whose emissivity is controlled by a large number of factors such as vegetation conditions, soil type and moisture ( Harrison et al. 2015 ), and (for frozen surfaces) the snow and/or ice properties ( Hirahara et al. 2020 ), but also surfaces whose geophysical properties are not homogeneous over the resolution of the PMW radiometer field of view including mixed land–water ( Gouweleeuw et al. 2012 ) and coastal boundaries ( Mega
. In this manuscript, the term “complex surfaces” refers to land surfaces whose emissivity is controlled by a large number of factors such as vegetation conditions, soil type and moisture ( Harrison et al. 2015 ), and (for frozen surfaces) the snow and/or ice properties ( Hirahara et al. 2020 ), but also surfaces whose geophysical properties are not homogeneous over the resolution of the PMW radiometer field of view including mixed land–water ( Gouweleeuw et al. 2012 ) and coastal boundaries ( Mega
Syst. , 8 , 66 – 95 , https://doi.org/10.1002/2015MS000469 . 10.1002/2015MS000469 Chong , M. , and D. Hauser , 1990 : A tropical squall line observed during the COPT 81 experiment in West Africa. Part III: Heat and moisture budgets . Mon. Wea. Rev. , 118 , 1696 – 1706 , https://doi.org/10.1175/1520-0493(1990)118<1696:ATSLOD>2.0.CO;2 . 10.1175/1520-0493(1990)118<1696:ATSLOD>2.0.CO;2 Choudhury , A. D. , and R. Krishnan , 2011 : Dynamical response of the South Asian monsoon trough
Syst. , 8 , 66 – 95 , https://doi.org/10.1002/2015MS000469 . 10.1002/2015MS000469 Chong , M. , and D. Hauser , 1990 : A tropical squall line observed during the COPT 81 experiment in West Africa. Part III: Heat and moisture budgets . Mon. Wea. Rev. , 118 , 1696 – 1706 , https://doi.org/10.1175/1520-0493(1990)118<1696:ATSLOD>2.0.CO;2 . 10.1175/1520-0493(1990)118<1696:ATSLOD>2.0.CO;2 Choudhury , A. D. , and R. Krishnan , 2011 : Dynamical response of the South Asian monsoon trough
several meteorological forcing variables, including surface precipitation rate, incoming shortwave and longwave radiations, air temperature, specific humidity, surface air pressure, and wind speeds in both horizontal and vertical directions. Using these forcing inputs, the LSM simulates runoff production (e.g., soil infiltration, surface and subsurface overflow, and channel inflow), soil moisture and temperature, canopy energy exchange, and soil moisture versus groundwater interaction, etc. ( Niu et
several meteorological forcing variables, including surface precipitation rate, incoming shortwave and longwave radiations, air temperature, specific humidity, surface air pressure, and wind speeds in both horizontal and vertical directions. Using these forcing inputs, the LSM simulates runoff production (e.g., soil infiltration, surface and subsurface overflow, and channel inflow), soil moisture and temperature, canopy energy exchange, and soil moisture versus groundwater interaction, etc. ( Niu et
. Climate conditions, topography, and regional land–atmosphere feedbacks drive these aspects of temporal persistence and spatial synchronicity of precipitation, which in turn influence soil moisture, flows, and vegetation. For example, the direction, speed, and size of a storm event moving across a basin can impact downstream flows and ecohydrologic processes. Goodwell and Kumar (2019) explored temporal precipitation persistence and predictability, addressing the extent to which the knowledge of past
. Climate conditions, topography, and regional land–atmosphere feedbacks drive these aspects of temporal persistence and spatial synchronicity of precipitation, which in turn influence soil moisture, flows, and vegetation. For example, the direction, speed, and size of a storm event moving across a basin can impact downstream flows and ecohydrologic processes. Goodwell and Kumar (2019) explored temporal precipitation persistence and predictability, addressing the extent to which the knowledge of past
, lower IM-F and GV-MRMS rainfall magnitudes (<0.5 mm h −1 ) are more often associated with larger distances to the coastline (range [90–110 km]), and they are slightly deviated from the 1:1 line (slight overestimation from IMERG). By contrast, higher rainfall magnitudes (>8 mm h −1 ) are recorded by GV-MRMS closer to the coastline (within 75 km) that IM-F more often and significantly underestimates. Moisture-laden westerlies from the ocean generate high precipitation when they encounter the high
, lower IM-F and GV-MRMS rainfall magnitudes (<0.5 mm h −1 ) are more often associated with larger distances to the coastline (range [90–110 km]), and they are slightly deviated from the 1:1 line (slight overestimation from IMERG). By contrast, higher rainfall magnitudes (>8 mm h −1 ) are recorded by GV-MRMS closer to the coastline (within 75 km) that IM-F more often and significantly underestimates. Moisture-laden westerlies from the ocean generate high precipitation when they encounter the high
Applications of Dynamic Land Surface Information for Passive Microwave Precipitation RetrievalCongo rain forests. Given the relative uniformity of emissivity over the tropical rain forest, which tends to be less dynamic due to year-round vegetation and relative insensitivity to surface soil moisture changes under the heavy canopy, this presents further evidence that using a TPW value retrieved using the coincident observations versus ancillary data has the potential to improve the GPM PMW precipitation algorithm. The false alarms using the GPROF Class constraints are very similar to a Φ N
Congo rain forests. Given the relative uniformity of emissivity over the tropical rain forest, which tends to be less dynamic due to year-round vegetation and relative insensitivity to surface soil moisture changes under the heavy canopy, this presents further evidence that using a TPW value retrieved using the coincident observations versus ancillary data has the potential to improve the GPM PMW precipitation algorithm. The false alarms using the GPROF Class constraints are very similar to a Φ N
or soil moisture retrievals into a land surface model . Hydrol. Earth Syst. Sci. , 20 , 4895 – 4911 , https://doi.org/10.5194/hess-20-4895-2016 . 10.5194/hess-20-4895-2016 Evensen , G. , 2003 : The ensemble Kalman filter: Theoretical formulation and practical implementation . Ocean Dyn. , 53 , 343 – 367 , https://doi.org/10.1007/s10236-003-0036-9 . 10.1007/s10236-003-0036-9 Gaspari , G. , and S. E. Cohn , 1999 : Construction of correlation functions in two and three dimensions
or soil moisture retrievals into a land surface model . Hydrol. Earth Syst. Sci. , 20 , 4895 – 4911 , https://doi.org/10.5194/hess-20-4895-2016 . 10.5194/hess-20-4895-2016 Evensen , G. , 2003 : The ensemble Kalman filter: Theoretical formulation and practical implementation . Ocean Dyn. , 53 , 343 – 367 , https://doi.org/10.1007/s10236-003-0036-9 . 10.1007/s10236-003-0036-9 Gaspari , G. , and S. E. Cohn , 1999 : Construction of correlation functions in two and three dimensions
snow and ice properties ( Takbiri et al. 2019 ; Kummerow 2020 ). Over the ocean, PMW algorithms can separate the surface radiation signal from the emission signal for liquid hydrometeors. Over land, microwave emissivity is sensitive to soil moisture, particularly at lower frequencies, which obscures the liquid hydrometeor emission signals. Hence, coastal areas present high emissivity gradients that challenge GPROF2017 precipitation retrieval. These retrievals are then intercalibrated to CORRA (the
snow and ice properties ( Takbiri et al. 2019 ; Kummerow 2020 ). Over the ocean, PMW algorithms can separate the surface radiation signal from the emission signal for liquid hydrometeors. Over land, microwave emissivity is sensitive to soil moisture, particularly at lower frequencies, which obscures the liquid hydrometeor emission signals. Hence, coastal areas present high emissivity gradients that challenge GPROF2017 precipitation retrieval. These retrievals are then intercalibrated to CORRA (the
, https://doi.org/10.1002/2017RG000574 . 10.1002/2017RG000574 Trenberth , K. E. , A. Dai , R. M. Rasmussen , and D. B. Parsons , 2003 : The changing character of precipitation . Bull. Amer. Meteor. Soc. , 84 , 1205 – 1218 , https://doi.org/10.1175/BAMS-84-9-1205 . 10.1175/BAMS-84-9-1205 Trenberth , K. E. , J. T. Fasullo , and J. Mackaro , 2011 : Atmospheric moisture transports from ocean to land and global energy flows in reanalyses . J. Climate , 24 , 4907 – 4924 , https
, https://doi.org/10.1002/2017RG000574 . 10.1002/2017RG000574 Trenberth , K. E. , A. Dai , R. M. Rasmussen , and D. B. Parsons , 2003 : The changing character of precipitation . Bull. Amer. Meteor. Soc. , 84 , 1205 – 1218 , https://doi.org/10.1175/BAMS-84-9-1205 . 10.1175/BAMS-84-9-1205 Trenberth , K. E. , J. T. Fasullo , and J. Mackaro , 2011 : Atmospheric moisture transports from ocean to land and global energy flows in reanalyses . J. Climate , 24 , 4907 – 4924 , https
analysis for spectroscopic determination of subsurface moisture and water-table height in Northern peatland ecosystems . IEEE Trans. Geosci. Remote Sens. , 55 , 1526 – 1536 , https://doi.org/10.1109/TGRS.2016.2626460 . 10.1109/TGRS.2016.2626460 Bartlett , M. S. , 1950 : Periodogram analysis and continuous spectra . Biometrika , 37 , 1 – 16 , https://doi.org/10.2307/2332141 . 10.1093/biomet/37.1-2.1 Bloomfield , P. , and J. M. Davis , 1994 : Orthogonal rotation of complex principal
analysis for spectroscopic determination of subsurface moisture and water-table height in Northern peatland ecosystems . IEEE Trans. Geosci. Remote Sens. , 55 , 1526 – 1536 , https://doi.org/10.1109/TGRS.2016.2626460 . 10.1109/TGRS.2016.2626460 Bartlett , M. S. , 1950 : Periodogram analysis and continuous spectra . Biometrika , 37 , 1 – 16 , https://doi.org/10.2307/2332141 . 10.1093/biomet/37.1-2.1 Bloomfield , P. , and J. M. Davis , 1994 : Orthogonal rotation of complex principal
