The NCEP–DOE Atmospheric Model Intercomparison Project (AMIP-II) reanalysis is a follow-on project to the “50-year” (1948–present) NCEP–NCAR Reanalysis Project. NCEP–DOE AMIP-II re-analysis covers the “20-year” satellite period of 1979 to the present and uses an updated forecast model, updated data assimilation system, improved diagnostic outputs, and fixes for the known processing problems of the NCEP–NCAR reanalysis. Only minor differences are found in the primary analysis variables such as free atmospheric geopotential height and winds in the Northern Hemisphere extratropics, while significant improvements upon NCEP–NCAR reanalysis are made in land surface parameters and land–ocean fluxes. This analysis can be used as a supplement to the NCEP–NCAR reanalysis especially where the original analysis has problems. The differences between the two analyses also provide a measure of uncertainty in current analyses.

NCEP–DOE AMIP-II Reanalysis (R-2)

A Global Land Data Assimilation System (GLDAS) has been developed. Its purpose is to ingest satellite- and ground-based observational data products, using advanced land surface modeling and data assimilation techniques, in order to generate optimal fields of land surface states and fluxes. GLDAS is unique in that it is an uncoupled land surface modeling system that drives multiple models, integrates a huge quantity of observation-based data, runs globally at high resolution (0.25°), and produces results in near–real time (typically within 48 h of the present). GLDAS is also a test bed for innovative modeling and assimilation capabilities. A vegetation-based “tiling” approach is used to simulate subgrid-scale variability, with a 1-km global vegetation dataset as its basis. Soil and elevation parameters are based on high-resolution global datasets. Observation-based precipitation and downward radiation and output fields from the best available global coupled atmospheric data assimilation systems are employe...

The Global Land Data Assimilation System

Daytime measurements of reflected sunlight in the visible spectrum have been a staple of Earth-viewing radiometers since the advent of the environmental satellite platform. At night, these same optical-spectrum sensors have traditionally been limited to thermal infrared emission, which contains relatively poor information content for many important weather and climate parameters. These deficiencies have limited our ability to characterize the full diurnal behavior and processes of parameters relevant to improved monitoring, understanding and modeling of weather and climate processes. Visible-spectrum light information does exist during the nighttime hours, originating from a wide variety of sources, but its detection requires specialized technology. Such measurements have existed, in a limited way, on USA Department of Defense satellites, but the Suomi National Polar-orbiting Partnership (NPP) satellite, which carries a new Day/Night Band (DNB) radiometer, offers the first quantitative measurements of nocturnal visible and near-infrared light. Here, we demonstrate the expanded potential for nocturnal low-light visible applications enabled by the DNB. Via a combination of terrestrial and extraterrestrial light sources, such observations are always available—expanding many current existing applications while enabling entirely new capabilities. These novel low-light measurements open doors to a wealth of new interdisciplinary research topics while lighting a pathway toward the optimized design of follow-on satellite based low light visible sensors.

Illuminating the Capabilities of the Suomi National Polar-Orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band

An algorithm for the remote sensing of global cloud cover using multispectral radiance measurements from the Advanced Very High Resolution Radiometer (AVHRR) on board National Oceanic and Atmospheric Administration (NOAA) polar-orbiting satellites has been developed. The CLAVR-1 (Clouds from AVHRR-Phase I) algorithm classifies 2 × 2 pixel arrays from the Global Area Coverage (GAC) 4-km-resolution archived database into CLEAR, MIXED, and CLOUDY categories. The algorithm uses a sequence of multispectral contrast, spectral, and spatial signature threshold tests to perform the classification. The various tests and the derivation of their thresholds are presented. CLAVR-1 has evolved through experience in applying it to real-time NOAA-11 data, and retrospectively through the NOAA AVHRR Pathfinder Atmosphere project, where 16 years of data have been reprocessed into cloud, radiation budget, and aerosol climatologies. The classifications are evaluated regionally with image analysis, and it is concluded ...

Scientific basis and initial evaluation of the clavr-1 global clear/cloud classification algorithm for the advanced very high resolution radiometer

This note describes changes that have been made to the National Centers for Environmental Prediction ( NCEP ) operational ‘‘early’’ eta model. The changes are 1 ) an decrease in horizontal grid spacing from 80 to 48 km, 2 ) incorporation of a cloud prediction scheme, 3 ) replacement of the original static analysis system with a 12-h intermittent data assimilation system using the eta model, and 4 ) the use of satellite-sensed total column water data in the eta optimum interpolation analysis. When tested separately, each of the four changes improved model performance. A quantitative and subjective evaluation of the full upgrade package during March and April 1995 indicated that the 48-km eta model was more skillful than the operational 80-km model in predicting the intensity and movement of large-scale weather systems. In addition, the 48-km eta model was more skillful in predicting severe mesoscale precipitation events than either the 80-km eta model, the nested grid model, or the NCEP global spectral model during the March ‐ April 1995 period. The implementation of this new version of the operational early eta system was performed in October 1995.

/pdf/changes-to-the-operational-early-eta-analysis-forecast-5cyricy31i.pdf

Changes to the Operational ''Early'' Eta Analysis / Forecast System at the National Centers for Environmental Prediction

The Air Force Global Weather Central (AFGWC) Real-Time Nephanalysis (RTNEPH) is an automated cloud model that produces a 48-km gridded analysis of cloud amount, cloud type, and cloud height. Its primary input is imagery from polar-orbiting satellites. Six main programs make up the RTNEPH. These are the satellite data mapper, the surface temperature analysis and forecast model, the satellite data processor, the conventional data processor, the merge processor, and the bogus processor. The satellite data mapper remaps incoming polar-orbiter imagery to a polar-stereographic database. The surface temperature model produces an analysis and forecast of shelter and skin temperatures for comparison to satellite-measured infrared (IR) brightness temperatures. The satellite data processor reads in the new satellite data and produces a satellite-derived cloud analysis. The conventional data processor retrieves and reformats cloud information from airport observations. The merge processor combines the satell...

James T. Buntin

Papers

A Description of the Air Force Real-Time Nephanalysis Model