Preparing for the AMSU

Diak, George R.; Kim, Dong-Soo; Whipple, Mark S.; Wu, Xiaohua

doi:10.1175/1520-0477(1992)073<1971:pfta>2.0.co;2

Cited by 28 publications

(8 citation statements)

References 4 publications

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“…These instruments provide nearly complete global coverage several times per day and serve the purpose of providing temperature and humidity sounding data over all regions for assimilation into NWP analyses. The four classes of instrument include the following: the Advanced MSU [(AMSU)-A and AMSU-B] instruments, launched as part of the payloads of the National Oceanic and Atmospheric Administration (NOAA)-15, -16, and -17 satellites starting in 1998 (Prigent et al 2000;Diak et al 1992); the Special Sensor Microwave Water Vapor Profiler (SSM/T2), first launched in November of 1991, on the Defense Meteorological Satellite Program (DMSP) F series of polar-orbiting satellites (Prigent et al 2000); the first in a series of Special Sensor Microwave Imager/Sounder (SSMIS) instruments, launched with DMSP on 18 October 2003 (Deblonde and English 2003); and the Microwave Humidity Sounder (MHS), launched on the NOAA-18 satellite on 20 May 2005. These SSMIS, SSM/T2, and MHS instruments have channels that are only slightly different than those of AMSU-B.…”

Section: A Importance Of Emissivitiesmentioning

confidence: 99%

Airborne Retrievals of Snow Microwave Emissivity at AMSU Frequencies Using ARTS/SCEM-UA

Harlow

2007

Journal of Applied Meteorology and Climatology

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The remote sounding, by satellite, of atmospheric temperature and humidity is an important source of data for assimilation into operational weather forecasting routines. For retrievals of these variables near the surface, wavebands with low optical depths are monitored to allow penetration through the overlying atmosphere. Brightness temperatures in these relatively transparent bands are also sensitive to the land surface emissivity and effective temperature. Inadequate understanding of these land surface emissivities is a major issue when assimilating Advanced Microwave Sounding Unit data for the land-covered portion of the globe. One approach for estimating the emissivity of snow-covered surfaces is an empirical model derived from satellite-based and land-based retrievals of emissivity for a variety of snow types. The Met Office's Hercules C-130 aircraft flew over snow-covered Arctic terrain of northern Finland during the Polar Experiment (POLEX) of March 2001. On these flights, microwave radiometers provided microwave brightness temperatures at 23.8, 50.3, 89.0, 157, and 183 GHz. The work presented here uses these data along with a robust multiparameter optimization routine [Shuffled Complex Evolution Metropolis (SCEM-UA)] coupled to the Atmospheric Radiative Transfer Simulator (ARTS) to retrieve emissivities at the measured frequencies. These results are then used to validate an empirical model. This latter model predicts 23.8-157-GHz emissivities with an RMSE of less than 0.02 and bias of less than 0.01 when compared with data at an incidence angle of 40°. Nonmonotonic behavior in the emissivity spectrum for this campaign, reported in earlier work, is confirmed by the retrievals presented here.

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Section: A Importance Of Emissivitiesmentioning

confidence: 99%

Airborne Retrievals of Snow Microwave Emissivity at AMSU Frequencies Using ARTS/SCEM-UA

Harlow

2007

Journal of Applied Meteorology and Climatology

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“…The prognostic version of ALEX has been embedded within the Cooperative Institute for Meteorological Satellite Studies (CIMSS) Regional Assimilation System (CRAS) for purposes of improving the model land surface representation (Diak et al, 1998). CRAS assimilates radiosonde and surface synoptic data at 1200 h UTC, along with satellite‐derived cloud data, into a regional forecast run at 40‐ to 80‐km spatial resolution and 48 h duration (Leslie et al, 1985; Diak, 1987; Diak et al, 1992; Wu et al, 1995).…”

Section: A Heirarchy Of Modelsmentioning

confidence: 99%

Upscaling and Downscaling—A Regional View of the Soil–Plant–Atmosphere Continuum

2003

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The strength of interaction among soil, plants, and atmosphere depends highly on scale. As the spatial scale of organized soil–plant behavior (e.g., soil drying and/or stomatal closure) increases, so does the influence the land surface has on atmospheric properties and circulations. Counterbalancing this is a system of feedback loops that serve to reduce the sensitivity of surface fluxes to changes in surface conditions. Model upscaling involves capturing land–atmosphere feedbacks and effects of land surface heterogeneity on surface fluxes and atmospheric boundary‐layer dynamics that become operative at progressively larger spatial scales. Conversely, by downscaling, we learn how to appropriately parameterize subgrid‐scale phenomena within large‐scale modeling frameworks. This paper discusses some of the major challenges faced today in properly describing system behavior at regional spatial scales. We focus on a suite of simple biophysical models, tied closely to remote sensing, that work synergistically from canopy to mesoscales. This suite includes a diagnostic regional‐scale model used for routine mapping of flux and moisture conditions across the United States at 10‐km resolution. A related approach disaggregates regional flux estimates to local scales (100–102 m) for comparison with ground‐based measurements or for use in site‐specific agricultural or resource management applications. Coupled with turbulence‐ and mesoscale atmospheric models, the core land surface representation provides means for assimilating remote sensing data into large‐eddy simulations and improving short‐range weather forecasts. This multiscale modeling framework is being utilized in a concerted research effort aimed at identifying scale‐relevant land–atmosphere feedbacks and representing surface heterogeneity efficiently and robustly in regional modeling schemes.

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“…Shelter‐level wind speed and air temperature data from the U.S. synoptic surface network were analyzed to a 40‐km grid using the analysis component of the CIMSS (Cooperative Institute for Meteorological Satellite Studies) Regional Assimilation System (CRAS) mesoscale forecast model [ Diak et al , 1992; Wu et al , 1995]. Air temperature is used only in the PET assessments, and the overall model is not very sensitive to this input [ Anderson et al , 1997].…”

Section: Model Input Datamentioning

confidence: 99%

A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 1. Model formulation

et al. 2007

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[1] Due to the influence of evaporation on land-surface temperature, thermal remote sensing data provide valuable information regarding the surface moisture status. The Atmosphere-Land Exchange Inverse (ALEXI) model uses the morning surface temperature rise, as measured from a geostationary satellite platform, to deduce surface energy and water fluxes at 5-10 km resolution over the continental United States. Recent improvements to the ALEXI model are described. Like most thermal remote sensing models, ALEXI is constrained to work under clear-sky conditions when the surface is visible to the satellite sensor, often leaving large gaps in the model output record. An algorithm for estimating fluxes during cloudy intervals is presented, defining a moisture stress function relating the fraction of potential evapotranspiration obtained from the model on clear days to estimates of the available water fraction in the soil surface layer and root zone. On cloudy days, this stress function is inverted to predict the soil and canopy fluxes. The method is evaluated using flux measurements representative at the watershed scale acquired in central Iowa with a dense flux tower network during the Soil Moisture Experiment of 2002 (SMEX02). The gap-filling algorithm reproduces observed fluxes with reasonable accuracy, yielding $20% errors in ET at the hourly timescale, and 15% errors at daily timesteps. In addition, modeled soil moisture shows reasonable response to major precipitation events. This algorithm is generic enough that it can easily be applied to other thermal energy balance models. With gap-filling, the ALEXI model can estimate hourly surface fluxes at every grid cell in the U.S. modeling domain in near real-time. A companion paper presents a climatological evaluation of ALEXI-derived evapotranspiration and moisture stress fields for the years 2002-2004.

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Preparing for the AMSU

Cited by 28 publications

References 4 publications

Airborne Retrievals of Snow Microwave Emissivity at AMSU Frequencies Using ARTS/SCEM-UA

Airborne Retrievals of Snow Microwave Emissivity at AMSU Frequencies Using ARTS/SCEM-UA

Upscaling and Downscaling—A Regional View of the Soil–Plant–Atmosphere Continuum

A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 1. Model formulation

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