Implementation of a cloud-based microphysical precipitation model for data assimilation: model formulation and sensitivity analysis James McPhee and Steven A. Margulis Contact:
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Parameterization required
1. Abstract In this work we formulate a precipitation model driven by cloud microphysical parameters whose primordial objective is to provide a robust spatial and temporal foundation for downscaling remotely sensed precipitation data. The precipitation model is based on a documented one-dimensional station precipitation model (SPM) that derives liquid mass balance of a cloud layer and surface rainfall rate from thermodynamic principles and surface estimates of temperature, pressure and dewpoint temperature. We modify the SPM in order to use GOES-based cloud microphysical parameters obtained with the VISST/SIST algorithm, in order to obtain fine scale (4 x 4 km, half hourly) precipitation estimates over a spatial domain. The computed rainfall rates subsequently serve as a prior estimate for a data assimilation procedure that seeks to merge different remotely sensed precipitation products, which are available at various spatial and temporal scales. This presentation deals with the model formulation, incorporation of cloud data and parametric sensitivity analysis needed in order to determine prior uncertainties associated with the simulated rainfall rates.
MODEL INPUT Cloud bulk microphysical parameters obtained from NASA Langley Research Center’s VISST product
To estimate precipitation rates at fine temporal and spatial resolution at a global scale
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To incorporate information from diverse remote sensing platforms
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To incorporate strengths from various existing retrievals algorithms
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To assess estimated rainfall uncertainty
•Updraft strength parameterized in terms of pressure difference between cloud top and cloud bottom •Evaporation-induced liquid mass reduction in subcloud layer depends on temperature and relative humidity
Summary of required model parameters Symbol
Units
α
s-1
3500 (liq) 1500 (ice)
Controls terminal velocity vs. diameter relation
β
[-]
0.2
Updraft velocity distribution
C1
[-]
7x105 (liq) 1.4x105 (ice)
Controls critical diameter relation
800
Lowest pressure level allowed for cloud base
Data assimilation framework for merging various sources of information -> ROBUST DOWNSCALING prior
state
Reference value
ε2
mb
ε3
m-1
1.0
Controls Ptop vs v relation
Ensemble Kalman Filter (EnKF) requires estimates obtained with a simulation model
variable
γmax
[-]
1.5
Upper bound on γ parameter for inversion from VISST data
•
Incorporation of fine-resolution remotely sensed microphysical cloud parameters into a one-dimensional precipitation model -> Microphysical Cloud-based Precipitation Model (MCPM)
rmid
%
80
Rel. humidity middle-level clouds
rhi
•
• Fraction of water output at cloud base reaches the ground because of evaporation and updraft
Application to winter (February 2002) and summer (August 2002) conditions Half-hourly estimates on 4 x 4 km grid over Southern Great Plains domain
Comparison NLDAS precipitation forcing field
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February results show general overestimation of precipitation rates. Spurious peaks occur mostly for twilight conditions (evening) when GOES retrievals are less reliable
• •
• Linear vertical profile of inverse mean drop diameter • Wedge-shape vertical profile of updraft velocity
Rel. humidity high-level clouds
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• Mass balance in unit cloud column • Updraft: provides moisture for cloud formation + carries small hydrometeors upward
60
• low clouds: adiabatic ascent •middle- and high-level clouds:stable subcloud layer Key Assumptions • Single-layer clouds • Wedge-shape updraft velocity distribution • Linear inverse mean diameter profile, diameter decreases with height • Subcloud layer assigned representative temperature, pressure and relative humidity
5. Model Application •
MCPM Conceptualization (after Georgakakos and Bras, 1984)
%
Reproduced from Houze, 1993
Comment
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4. Model formulation
Conceptual model of Mesoscale Convective System
•Reduction coefficient a function of updraft strength (NV) and evaporation losses (ND)
3. Methodology •
X vp P= "p Zc ! Directly from VISST data
2. Research objectives •
5.2 Sample results for specific times
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August results show better agreement in terms of max precipitation For August, many storm events occurring during the night (first half of UTC day) are not properly represented. Spatial features of storm events are captured satisfactorily
5.1 Time series average over entire SGP domain
6. Conclusions •
Ensemble data assimilation framework for merging existing precipitation products
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Data assimilation requires prior estimates of the state variable, obtained from simulation model
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Parsimonious one-dimensional precipitation model driven by satellite-based cloud data
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Model captures successfully spatial features of storm events.
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Further refinement needed for better estimation of rainfall magnitude
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Simulation quality linked with cloud data. Ongoing research on improving results for times when VISST retrieval is less reliable
7. References •
Cosgrove, B., D. Lohmann, K. Mitchell, P. Houser, E. Wood, J. Schaake, A. Robock, C. Marshall, J. Sheffield, Q. Duan, L. Luo, R. Higgins, R. Pinker, J. Tarpley, and J. Meng (2003), Real-time and retrospective forcing in the north american land data assimilation system (nldas) project, Journal of Geophys. Res.-Atmospheres, 108 (D22), 8842.
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Georgakakos, K., and R. Bras (1984a), A hydrologically useful station precipitation model.1. formulation, Water Resour. Res., 20 (11), 1585 -1596.
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Houze, R. A., Jr. (1993), Cloud Dynamics, International Geophysics Series, vol. 53, Academic Press, San Diego, CA.
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Minnis, P., D. P. Kratz, J. A. Coakley, III, M. D. King, D. Garber, P. Heck, S. Mayor, D. F. Young, and R. Arduini (1995), Cloud optical retrieval (subsystem 4.3). clouds and the earth’s radiant energy system (ceres) algorithm theoretical basis document, volume iii: Cloud analyses and radiance inversions (subsystem 4), RP 1376, NASA.
8. Acknowledgments This work was funded by the National Aeronautics and Space Administration (NASA) under grant NNG04GP74G and the National Science Foundation under Grant EAR0333133
Hydrology and Water Resources
