Editorial Type:
Article
Article Type:
Research Article

A Two-Season Impact Study of Satellite and In Situ Data in the NCEP Global Data Assimilation System

Tom H. Zapotocny Cooperative Institute for Meteorological Satellite Studies, and Space Science and Engineering Center, University of Wisconsin—Madison, Madison, Wisconsin, and Joint Center for Satellite Data Assimilation, Camp Springs, Maryland

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James A. Jung Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin—Madison, Madison, Wisconsin, and Joint Center for Satellite Data Assimilation, Camp Springs, Maryland

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John F. Le Marshall University of Maryland, College Park, College Park, and Joint Center for Satellite Data Assimilation, Camp Springs, Maryland

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Russ E. Treadon National Centers for Environmental Prediction, and Joint Center for Satellite Data Assimilation, Camp Springs, Maryland

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Print Publication:
01 Aug 2007
DOI:
https://doi.org/10.1175/WAF1025.1
Page(s):
887–909
Restricted access

Abstract

Observing system experiments are used to quantify the contributions to the forecast made by conventional in situ and remotely sensed satellite data. The impact of each data type is assessed by comparing the analyses and forecasts based on an observing system using all data types. The analysis and forecast model used for these observing system experiments is the National Centers for Environmental Prediction (NCEP) Global Data Assimilation/Forecast System (GDAS/GFS). The case studies chosen consist of 45-day periods during January–February 2003 and August–September 2003. During these periods, a T254–64 layer version of NCEP’s Global Spectral Model was used. The control run utilizes NCEP’s operational database and consists of all data types routinely assimilated in the GDAS. The two experimental runs have either all the conventional in situ data denied (NoCon) or all the remotely sensed satellite data denied (NoSat). Differences between the control and experimental runs are accumulated over the 45-day periods and analyzed to demonstrate the forecast impact of these data types through 168 h. Anomaly correlations, forecast impacts, and hurricane track forecasts are evaluated for both experiments. Anomaly correlations of geopotential height are evaluated over the polar caps and midlatitudes of both the Northern and Southern Hemispheres for spectral waves 1–20. Forecast impacts related to conventional meteorological parameters are evaluated. The parameters examined include geopotential height, precipitable water, temperature, the u component of the wind, wind vector differences, and relative humidity. Comparisons are made on multiple pressure levels extending from 10 to 1000 hPa. Hurricane track forecasts are evaluated during August and September for both the Atlantic and eastern Pacific basins. The results demonstrate a positive forecast impact from both the conventional in situ and remotely sensed satellite data during both seasons in both hemispheres. The positive forecast impacts from the conventional and satellite data are of similar magnitude in the Northern Hemisphere; however, the contribution to forecast quality from satellite data is considerably larger than the conventional data in the Southern Hemisphere. The importance of satellite data also generally increases at longer forecast times relative to conventional data. Finally, the accuracy of hurricane track forecasts benefits from the inclusion of both conventional and satellite data.

Corresponding author address: James A. Jung, NOAA Science Center, 5200 Auth Rd., Camp Springs, MD 20746-4304. Email: jim.jung@noaa.gov

Abstract

Observing system experiments are used to quantify the contributions to the forecast made by conventional in situ and remotely sensed satellite data. The impact of each data type is assessed by comparing the analyses and forecasts based on an observing system using all data types. The analysis and forecast model used for these observing system experiments is the National Centers for Environmental Prediction (NCEP) Global Data Assimilation/Forecast System (GDAS/GFS). The case studies chosen consist of 45-day periods during January–February 2003 and August–September 2003. During these periods, a T254–64 layer version of NCEP’s Global Spectral Model was used. The control run utilizes NCEP’s operational database and consists of all data types routinely assimilated in the GDAS. The two experimental runs have either all the conventional in situ data denied (NoCon) or all the remotely sensed satellite data denied (NoSat). Differences between the control and experimental runs are accumulated over the 45-day periods and analyzed to demonstrate the forecast impact of these data types through 168 h. Anomaly correlations, forecast impacts, and hurricane track forecasts are evaluated for both experiments. Anomaly correlations of geopotential height are evaluated over the polar caps and midlatitudes of both the Northern and Southern Hemispheres for spectral waves 1–20. Forecast impacts related to conventional meteorological parameters are evaluated. The parameters examined include geopotential height, precipitable water, temperature, the u component of the wind, wind vector differences, and relative humidity. Comparisons are made on multiple pressure levels extending from 10 to 1000 hPa. Hurricane track forecasts are evaluated during August and September for both the Atlantic and eastern Pacific basins. The results demonstrate a positive forecast impact from both the conventional in situ and remotely sensed satellite data during both seasons in both hemispheres. The positive forecast impacts from the conventional and satellite data are of similar magnitude in the Northern Hemisphere; however, the contribution to forecast quality from satellite data is considerably larger than the conventional data in the Southern Hemisphere. The importance of satellite data also generally increases at longer forecast times relative to conventional data. Finally, the accuracy of hurricane track forecasts benefits from the inclusion of both conventional and satellite data.

Corresponding author address: James A. Jung, NOAA Science Center, 5200 Auth Rd., Camp Springs, MD 20746-4304. Email: jim.jung@noaa.gov

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