Research Areas |
- Rainfall Algorithm Development:
The Goddard PRofiling Algorithm (GPROF) was developed to retrieve both the
instantaneous rainfall as well as the rainfall vertical structure from
satellite passive microwave observations. It uses a Bayesian approach to
match the observed brightness temperatures to hydrometeor profiles derived
from cloud resolving models (CRMs). Research efforts to improve a number of
aspects of the retrieval algorithm are ongoing. These include incorporating
information from the TRMM precipitation radar into the a-priori database,
extending the current scheme to include land backgrounds, and developing a
parametric approach for use with a large constellation of radiometers with
diverse characteristics.
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- Rainfall Validation and Uncertainty Estimation:
In situ rainfall measurements are limited over oceans due to the extreme
paucity of measurements. As a result, estimates of uncertainties must
depend on comprehensive error modeling. Related research efforts encompass
a variety of aspects including the developing methods to propagate
uncertainties through the retrieval algorithm, analysis of observational
errors, and identifying biases associated with sensor differences and/or
meteorlogical regimes.
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- Climate Studies:
Long-term global rainfall estimates now extend over well over two decades
with high-quality satellite radiometer estimates approaching 20 years.
Unfortunately the applicability of these datasets to studies of changes in
the global hydrologic cycle are limited by regional and time-dependent
biases that are often larger than the climate signals of interest. These
biases are due to a combination of factors including changes in
precipitation systems between meteorological regimes, sensor calibration
errors, and sampling issues. Related research efforts are focused on
identifying these biases, quantifying their impact, and developing methods
to eliminate or reduce their influence.
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- Modeling Studies:
Continuing advances in Cloud Resolving Models now allow the prediction of
explicit cloud microphysics that can in many cases be verified at regional
scales. While observations can be used to validate these model results,
the precipitation structure observations can also be used in a synergistic
approach to examine model processes and improve these in preparation for
more comprehensive data assimilation efforts at these scales.
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