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computed from similarity theory ( Monin and Obukhov 1954 ). The “Noah” (from NCEP–Oregon State University–U.S. Air Force–National Weather Service Office of Hydrologic Development) land surface model was used, which is based on Chen and Dudhia (2001) . It is a four-layer soil temperature and moisture model with canopy moisture and snow cover prediction. It provides sensible and latent heat fluxes to the boundary layer scheme. The soil temperature and moisture were also initialized from NCEP GFS
computed from similarity theory ( Monin and Obukhov 1954 ). The “Noah” (from NCEP–Oregon State University–U.S. Air Force–National Weather Service Office of Hydrologic Development) land surface model was used, which is based on Chen and Dudhia (2001) . It is a four-layer soil temperature and moisture model with canopy moisture and snow cover prediction. It provides sensible and latent heat fluxes to the boundary layer scheme. The soil temperature and moisture were also initialized from NCEP GFS
1. Introduction Remotely sensed measurements from meteorological satellite instruments play an extremely important role in providing valuable information on many key parameters of the global-scale hydrological cycle (water vapor, precipitation, snow cover, etc.). These satellite measurements supplement ground-based observations, especially in areas where in situ measurements are limited. Global precipitation is one of the most challenging parameters to retrieve, yet one of the most important of
1. Introduction Remotely sensed measurements from meteorological satellite instruments play an extremely important role in providing valuable information on many key parameters of the global-scale hydrological cycle (water vapor, precipitation, snow cover, etc.). These satellite measurements supplement ground-based observations, especially in areas where in situ measurements are limited. Global precipitation is one of the most challenging parameters to retrieve, yet one of the most important of
and N16 data for the years 2002–07. These results should facilitate comparisons with other sensors and datasets, and they include retrievals of annual accumulations of rain, snow, and convective and stratiform precipitation as well as two seasonal examples. Strong interannual variations are evident—in particular, in the Arctic where this algorithm successfully maps precipitation events for over four warm months each year despite extensive ice cover. Comparisons with Global Precipitation
and N16 data for the years 2002–07. These results should facilitate comparisons with other sensors and datasets, and they include retrievals of annual accumulations of rain, snow, and convective and stratiform precipitation as well as two seasonal examples. Strong interannual variations are evident—in particular, in the Arctic where this algorithm successfully maps precipitation events for over four warm months each year despite extensive ice cover. Comparisons with Global Precipitation
1. Introduction Snowfall composes a nonnegligible amount of the total precipitation that falls at many mid- and high-latitude locations and obviously has important hydrological and societal impacts. Snow also plays a crucial role in ice sheet dynamics, so knowledge of annual snowfall accumulations is extremely important to areas of the globe that are covered by large expanses of ice (e.g., Greenland, Antarctica, and alpine glacial regions). Additionally, the importance of obtaining robust
1. Introduction Snowfall composes a nonnegligible amount of the total precipitation that falls at many mid- and high-latitude locations and obviously has important hydrological and societal impacts. Snow also plays a crucial role in ice sheet dynamics, so knowledge of annual snowfall accumulations is extremely important to areas of the globe that are covered by large expanses of ice (e.g., Greenland, Antarctica, and alpine glacial regions). Additionally, the importance of obtaining robust
simulating the radiances measured by microwave radiometers ( Smith et al. 2002 ). Scattering parameters are precalculated using Mie theory and tabulated as a function of frequency, temperature, and hydrometeor type and density. The most important inputs to RTTOV-S CATT are the surface skin temperature and winds and the vertical profiles of pressure, temperature, moisture, cloud liquid water and ice, rain and snow fluxes, and cloud cover. Cloud overlap approaches are described in section 3 . d
simulating the radiances measured by microwave radiometers ( Smith et al. 2002 ). Scattering parameters are precalculated using Mie theory and tabulated as a function of frequency, temperature, and hydrometeor type and density. The most important inputs to RTTOV-S CATT are the surface skin temperature and winds and the vertical profiles of pressure, temperature, moisture, cloud liquid water and ice, rain and snow fluxes, and cloud cover. Cloud overlap approaches are described in section 3 . d
rainfall, clouds, water vapor, snow cover, and sea ice derived from SSM/I measurements. Bull. Amer. Meteor. Soc. , 77 , 891 – 905 . Ferraro , R. R. , E. A. Smith , W. Berg , and G. Huffman , 1998 : A review of screening techniques for passive microwave precipitation retrieval algorithms. J. Atmos. Sci. , 55 , 1583 – 1600 . Huffman , G. J. , and Coauthors , 1996 : The Global Precipitation Climatology Project (GPCP) Combined Precipitation Data Set. Bull. Amer. Meteor. Soc. , 78
rainfall, clouds, water vapor, snow cover, and sea ice derived from SSM/I measurements. Bull. Amer. Meteor. Soc. , 77 , 891 – 905 . Ferraro , R. R. , E. A. Smith , W. Berg , and G. Huffman , 1998 : A review of screening techniques for passive microwave precipitation retrieval algorithms. J. Atmos. Sci. , 55 , 1583 – 1600 . Huffman , G. J. , and Coauthors , 1996 : The Global Precipitation Climatology Project (GPCP) Combined Precipitation Data Set. Bull. Amer. Meteor. Soc. , 78
1. Introduction The desert locust affects a vast area of about 28 million km 2 that extends from the Atlantic coast of Africa to eastern India and from northern Turkey to Tanzania in the south ( FAO 2007 ). The breeding areas, also known as desert locust recession regions ( FAO 2007 ), are limited to an area of about 16 million km 2 , which covers arid and semiarid regions. From time to time, locusts form swarms that fly or are carried by wind to greater distances and wipe out crops and
1. Introduction The desert locust affects a vast area of about 28 million km 2 that extends from the Atlantic coast of Africa to eastern India and from northern Turkey to Tanzania in the south ( FAO 2007 ). The breeding areas, also known as desert locust recession regions ( FAO 2007 ), are limited to an area of about 16 million km 2 , which covers arid and semiarid regions. From time to time, locusts form swarms that fly or are carried by wind to greater distances and wipe out crops and
RTTOV. Land surface emissivity is particularly difficult to simulate because of the complex interaction of electromagnetic radiation with soil, vegetation, and snow cover as a function of a large number of unknown state variables. Therefore, land emissivity was modeled based on climatologies derived from SSM/I observations and integrated NWP and satellite products ( Prigent et al. 1997 ). Surface emissivity maps retrieved from all seven SSM/I channels were employed to interpolate SSM/I emissivities
RTTOV. Land surface emissivity is particularly difficult to simulate because of the complex interaction of electromagnetic radiation with soil, vegetation, and snow cover as a function of a large number of unknown state variables. Therefore, land emissivity was modeled based on climatologies derived from SSM/I observations and integrated NWP and satellite products ( Prigent et al. 1997 ). Surface emissivity maps retrieved from all seven SSM/I channels were employed to interpolate SSM/I emissivities
profiling algorithm ( L’Ecuyer and Stephens 2002 ). This will be developed further below. 1) Cloud particle radar model Under the assumption that the Mie regime is valid, the cloud particle radiative properties at 94 GHz, defined by (1) and (2) , are computed as where X can be either the extinction, scattering, or backscatter coefficients, while Q X is the corresponding Mie efficiency. The results are stored in the form of discretized lookup tables (LUT) for both snow and rain, as functions of
profiling algorithm ( L’Ecuyer and Stephens 2002 ). This will be developed further below. 1) Cloud particle radar model Under the assumption that the Mie regime is valid, the cloud particle radiative properties at 94 GHz, defined by (1) and (2) , are computed as where X can be either the extinction, scattering, or backscatter coefficients, while Q X is the corresponding Mie efficiency. The results are stored in the form of discretized lookup tables (LUT) for both snow and rain, as functions of
calibration of DMSP SSM/Is: F-8 to F-14, 1987-1997. IEEE Trans. Geosci. Remote Sens. , 37 , 418 – 439 . Ebert , E. E. , and M. J. Manton , 1998 : Performance of satellite rainfall estimation algorithms during TOGA COARE. J. Atmos. Sci. , 55 , 1537 – 1557 . Ferraro , R. , F. Weng , N. C. Grody , and A. Basist , 1996 : An eight-year (1987–1994) time series of rainfall, clouds, water vapor, snow cover, and sea ice derived from SSM/I measurements. Bull. Amer. Meteor. Soc. , 77
calibration of DMSP SSM/Is: F-8 to F-14, 1987-1997. IEEE Trans. Geosci. Remote Sens. , 37 , 418 – 439 . Ebert , E. E. , and M. J. Manton , 1998 : Performance of satellite rainfall estimation algorithms during TOGA COARE. J. Atmos. Sci. , 55 , 1537 – 1557 . Ferraro , R. , F. Weng , N. C. Grody , and A. Basist , 1996 : An eight-year (1987–1994) time series of rainfall, clouds, water vapor, snow cover, and sea ice derived from SSM/I measurements. Bull. Amer. Meteor. Soc. , 77
