April 2010
Newly awarded Precipitation projects/connections to Modeling working group
Ana Barros/Duke University (connection to Lidard/Tao project)
Characterizing and Understanding the Space-Time Gradients and Vertical
Structure of Orographic Precipitation and Hydrologic Response in Mid-Latitude
Mountainous Regions - Observations and Process Studies in the Great Smoky
Mountains
The overarching research objective of this proposal is to elucidate the 4D structure of
orographic precipitation, and to build modeling capacity toward improved
hydrometeorological and hydrological prediction in mountainous regions. The proposed
research plan is organized in three major activities: (1) - Integration of Satellite and
Ground Validation Data and Process Studies - The focus is on investigating the vertical
structure of orographic precipitation systems in the Southern Appalachians. This activity
builds on ongoing research in the Southern Appalachians, specifically the groundvalidation and process studies opportunities provided by the Great Smoky Mountains
(GSM) National Park network facility; (2) - Dynamical Downscaling of Precipitation and
Flashflood Forecasting - Operational OSSEs (Observing System Simulator Experiments)
will be conducted for flashfloods in the Southern Appalachians for the 2008-2012 period
using a hierarchy of high-resolution atmospheric models and data assimilation; (3) Hydrometeorological Modeling and Interpretation Studies - The goal is to investigate
orographic land-atmosphere interactions and hydrometeorology, including vegetation
dynamics at both the event time-scale (vegetation wipe-out by earthflows) and at seasonal
time-scales using a high-resolution coupled land-cloud model, and to characterize
feedbacks among landform, land-cover and orographic precipitation regimes.
Rafael Bras/University of California, Irvine
Downscaling and Improvement of Predictability of Rainfall and Soil Moisture by
Assimilating GPM and SMAP Data Using a High Resolution Coupled Hydrometeorological Model
This proposal focuses on the development of a modeling and data assimilation framework
that will allow for downscaling of Global Precipitation Mission (GPM) and Soil Moisture
Active and Passive (SMAP) observations to produce precipitation and soil moisture
predictions at fine scales for hydrometeorological applications. The hypothesis of this
research is that a high resolution regional climate model coupled to a physically-based
representation of subgrid land-atmosphere feedbacks and used with data assimilation may
be an optimal approach for downscaling of coarse scale remotely sensed precipitation and
complementary soil moisture products.
The modeling framework with data assimilation capabilities consists of tRIBS-VEGGIE
(Triangulated Irregular Network (TIN)-based Real-time Integrated Basin Simulator with
VEGetation Generator for Interactive Evolution) serving as the lower boundary for an
advanced mesoscale regional atmospheric model known as the Weather Research and
Forecasting (WRF) model. tRIBS-VEGGIE (tRIBS hereafter) offers the unique capability
of capturing the fine structure of topography and its role in modulating the dynamics of
soil moisture and vegetation. This in turn leads to improvement in the representation the
sub-grid land surface fluxes in the coupled WRF-tRIBS model, which improves the
simulation of dynamics within the atmospheric boundary layer at fine spatial and time
resolutions. The original WRF model is designed for modeling mesoscale processes, but
has limited ability to represent the land surface energy and water fluxes at high resolution
and detailed topography. Accurate representation of the complex mesoscale feedback at
the land atmosphere boundary is proven to increase the predictive skill of precipitation at
fine spatial-temporal scales.
A key component of the downscaling-prediction framework is the data assimilation
function of satellite based observations. Specifically, we propose to assimilate GPM and
SMAP rainfall and soil moisture using 4DVAR and/or EnKF data assimilation
techniques. Assimilation of SMAP soil moisture within tRIBS has been proven to lead to
better estimation of energy fluxes at the land atmosphere boundary while assimilation of
rainfall within WRF and other atmospheric models has been proven useful. The
expectation is that assimilation of both precipitation and soil moisture products within the
proposed coupled atmospheric-landsurface model will result in further improvements of
in the estimation of the two complementary variables: rainfall and soil moisture. The
deliverables of this projects are (1) a methodology to downscale future GPM and SMAP
products and improve the predictability of precipitation and soil moisture and (2) an
index based on high resolution soil moisture estimates from tRIBS that will be useful for
flash flood forecasting and hopefully improve on existing operational procedures.
Anthony Del Genio/NASA Goddard Institute for Space Studies
Using Satellite Precipitation Data to Improve the Diurnal Cycle of Rainfall and
Convective Lifecycles in GCMs
We propose to continue our research into the diurnal cycle and lifecycle of tropical
convective storms using TRMM data, ISCCP data, a cloud-resolving model (CRM), and
the GISS general circulation model (GCM). We will conduct a number of CRM
simulations for continental and maritime convective storms in different regions to
determine their ability to portray a realistic daily transition from shallow to deep
convection and local time of peak precipitation. The sensitivity of the diurnal evolution
and the vertical structure of hydrometeors to the parameterization of ice microphysics
will be tested and compared with TRMM observations. We will use TRMM data to
determine the relative importance of time of convection onset and convective cluster
lifetime in determining the time of peak precipitation. We will make improvements in
the GCM’s simulation of entrainment and convective ice and mixed-phase microphysics
and test the effect of these on the model’s diurnal cycle and vertical structure of
convective storms. We will use geostationary ISCCP data to document the evolution of
convective clusters and to assign lifecycle phases. TRMM radar and microwave rainfall
structure data and TRMM latent and radiative heating profile data will be mapped into
the appropriate ISCCP-determined lifecycle phase to construct composite lifecycles of
TRMM rainfall and heating structure. These will be used as the basis for the
development and evaluation of a mesoscale updraft parameterization for the GCM
Dusanka Zupanski/Colorado Satate University
Ensemble-based assimilation and downscaling of the GPM-like satellite
precipitation information
In a near future Global Precipitation Measurement (GPM) Mission will provide
precipitation observations with unprecedented accuracy and spatial/temporal coverage of
the globe. Currently operational and research experiences in using precipitation
information have mostly focused on a global model resolution with prescribed static
forecast error statistics, while a cloud-resolving high resolution and flow-dependent
forecast error information are needed for many GPM scientific applications such as
hydrology and precipitation estimates downscaled from rain-sensitive radiances. We
propose to develop an ensemble-based data assimilation system for assimilation and
downscaling of precipitation information from GPM observations. The proposal seeks to
bring a variety of observations from different instruments and platforms, a cloudresolving model and an ensemble assimilation methodology together to produce accurate
and dynamically downscaled precipitation estimates.
A prototype of the ensemble data assimilation system at cloud-resolving scales has been
developed jointly by NASA/GSFC and Colorado State University (CSU). The system
consists of components: (i) Weather Research and Forecasting (WRF) model with
multiple nesting capability and NASA cloud-microphysics (ii) NASA Satellite Data
Simulation Unit (SDSU), (iii) NOAA/NCEP's Gridpoint Statistical Interpolation (GSI)
forward observation operators and (iv) the CSU Maximum Likelihood Ensemble Filter
(MLEF) data assimilation algorithm. The assimilation system has capabilities of
assimilating precipitation-sensitive microwave radiances at pixel scale and estimating
flow-dependent and terrain-dependent forecast error covariance.
The work proposed in this research is as follows: (i) develop the prototype data
assimilation system into a validated operational-comparable system, (ii) produce
downscaled precipitation analyses by assimilating precipitation information into highresolution WRF model, using currently available TRMM TMI, SSM/I, AMSR-E, AMSU
and InfraRed (IR) cloudy radiances, and (iii) verify and improve the accuracy of
precipitation estimates using HydroMeteorological Testbeds (HMTs).
The proposed research belongs to the Precipitation Science Research Category 2.3
"Methodology development for improved applications of satellite products". Expected
benefits are in enhancing scientific and operational applications of the GPM observations
and improving weather, climate and hydrological forecasts. This research project can
serve as a pilot study for the development of Level 4 GPM precipitation products.