NESDIS/Center for Satellite Applications and Research (STAR) has been reprocessing and
recalibrating observations from the Microwave Sounding Unit (MSU) and Advanced Microwave Sounding Unit
(AMSU) to generate atmospheric temperature climate data record (CDR). To obtain reliable atmospheric
temperature trends from the dataset, diurnal drift errors due to orbital drift must be removed from the time series.
This adjustment is especially important for the MSU/AMSU mid-tropospheric temperature product over land
where diurnal-drift effect is large. In this study, we applied the diurnal anomalies developed by the Remote
Sensing Systems (RSS) to the STAR MSU/AMSU atmospheric temperatures CDR and examined how the
correction affects the trend and intersatellite biases over land. A scaling factor was introduced to multiply the
RSS diurnal anomalies to account for uncertainties in the dataset. The results show that the diurnal drift has
negligible effect on the mid-tropospheric temperature trends over oceans, which is consistent with previous
investigations. However, the trend over land is very sensitive to the magnitude of the scaling factor. The final
scaling factor was determined by minimizing intersatellite temperature differences over land. The trend values
corresponding to such a scaling factor for the 28-year (1979-2006) merged MSU T2 time series are 0.193
K/Decade over the global land and 0.180 K/Decade over the global ocean. The global mean T2 trend is 0.183
K/decade.
This study aims to analyze impacts of the NESDIS new product of green vegetation fraction (GVF) data on simulated
surface air temperature and surface fluxes over the continental United States (CONUS) using the Nonhydrostatic
Mesoscale Model (NMM) core of the Weather Research and Forecasting (WRF) system, i.e. WRF-NMM, coupled with
the Noah land surface model (LSM). The new global 0.144 by 0.144 degree GVF dataset is an AVHHR-based, near real-time
weekly dataset starting from 1982. It has better quality and a higher temporal resolution than the old monthly GVF
dataset that is currently used in the NOAA operational numerical weather prediction models. The new weekly
climatology GVF data shows a higher percentage of greenness fraction over most US areas than the old dataset, with the
largest differences by 20-40% over the southeast U.S., the northern Middle West, and the west coast of California in
summer. We have performed some case studies over CONUS during July 2006. In general, using the new GVF data
cools predicted surface temperature over most regions compared to the old data, with the largest cooling over regions
with the largest GVF increase. The latent heat increases significantly over most areas while the sensible heat decreases
slightly. These results are physically consistent as more of the net radiation is dissipated in form of latent heat via
enhanced evapotranspiration in response to increasing vegetation cover. Compared with observations, the new GVF
application reduces the WRF-NMM 2-m surface air temperature warm biases, 2-m relative humidity negative biases, and
their RMSEs.
NOAA/NESDIS/Center for Satellite Applications and Research has been reprocessing and
recalibrating the Microwave Sounding Unit (MSU) observations to generate atmospheric temperature dataset
with climate quality. So far, observations from the MSU channels 2, 3, and 4 for NOAA 10, 11, 12, and 14
have been recalibrated using a recently-developed SNO (simultaneously nadir overpasses) sequential nonlinear
calibration technique and a 20-year long deep-layer atmospheric temperature dataset from 1987 to 2006 has
been generated. However, when using the SNO nonlinear technique to intercalibrate the MSU instrument for
satellites before NOAA 10, one has to deal with the short overlap issue for satellites between NOAA 9 and
NOAA 10. In this study, by extending the spatial distance criterion for the SNO matchups, we generate more
SNO samplings for the short-overlapping satellites. We analyze the error characteristics of the SNO matchups
when the spatial distance is extended to as large as 650km. We also generate calibration coefficients using the
SNO nonlinear sequential intercalibration technique and then analyze how the intercalibration affects the SNO
biases with different separation distances. These analyses will be helpful in determining the final calibration
coefficients used for generating consistent MSU long-term temperature time series that will include all available
satellites.
The Microwave Sounding Unit (MSU) on board the National Oceanic and Atmospheric Administration
(NOAA) polar-orbiting satellites were designed to measure the atmospheric temperature from the surface to the
lower stratosphere under all weather conditions, excluding precipitation. Although the instrumental design and
calibration were made primarily for monitoring the atmospheric weather processes, the MSU observations have
been extensively used for detecting climate trend. However, calibration errors have been a major uncertainty in
climate trend detections. In order for the MSU data to be of high quality for climate trend and variability
research, we have recently recalibrated the MSU satellites NOAA 10, 11, 12, and 14 using simultaneous nadir
overpass (SNO) method. The calibration results in a well-merged 20-year radiance dataset for the MSU
channels 2, 3, and 4. Limb-correction is applied to adjust the incident angles of the footprints. The limbcorrected
radiances are further binned into 2.50 longitude by 2.50 latitude grids to generate deep-layer
temperature datasets for the mid-troposphere (T2), tropopause (T3), and lower-stratosphere (T4). The global
ocean averaged trends for the recalibrated T2, T3, and T4 are respectively 0.234±0.071 K/decade, 0.079±0.085
K/decade, and -0.414±0.287 K/decade for the 20-year time period from 1987 to 2006. Both the recalibrated
radiance and deep-layer temperature datasets are freely available through the NESDIS/STAR website
http://www.orbit.nesdis.noaa.gov/smcd/emb/mscat/mscatmain.htm
htm.
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