How complex should a snow model be?

Transcription

1 How complex should a snow model be? Xu Liang 1 and Seongeun Jeong 2 1 Department of Civil and Environmental Engineering University of Pittsburgh 2 Department of Civil and Environmental Engineering University of California at Berkeley

2 Background Have we reached the limit of the snow model? (when a snow model does not perform well) How much improvement would we gain if the model is more complex? (tend to believe we would improve model performance by having better model parameters, including more physical processes, and improving numerical solutions) How complex should a model be?

3 Complexity of Snow Models Models Complexity References BATS snow module Simple Dickinson et al., NCAR Tech. Note, VIC 2-L snow module Simple Cherkauer and Lettenmaier, JGR, NOHRSC Snow Model Medium Rutter et al., J. Hydrometeor, Lynch-Stieglitz model Medium Lynch-Stieglitz, J. Clim., 1994 SAST snow model Medium Sun et al., JGR, 1999; Jin et al., Hydrol. Processes, CLM snow model Complex Dai et al., BAMS, SNTHERM snow model (U.S. Army Cold Regions Res. & Eng. Lab. Model) Very Complex Jordan, US ACE CRREL Special Rep , Anderson snow model Very Complex Anderson, NOAA Tech. Rep. NWS 19, 1973.

4 2-L Snow Module of VIC Model (VIC 2-L) Lone Pine Station Ebbetts Pass station SWE simulation (Pan et al., 2003, JGR) Galena Summit station SWE percentiles between estimated (red line), EnKF (green line) and SNOTEL (blue line) (Andreadis & Lettenmaier, 2006, AWR) VIC 2-L SWE simulations using DMIP2 precipitation and temperature with other meteorological forcing data from NLDAS.

5 Potential Causes or Problems Errors in forcing data Improper model parameters Simplified or improper physical processes Simplified numerical solutions or methods Combinations of some or all of the above Others

6 Ebbetts Pass, CA

7 Initial Findings & Hypothesis Given the normal range of forcing uncertainty, the impact on the simulation of SWE is not significant. SWE is still significantly underestimated with the best model parameters. There must be other significant uncertainties in the simulation of SWE that cannot be explained by typical uncertainty ranges associated with the forcing data, nor by the best model parameters. Hypothesis: Advanced description of snow physical processes and numerical solutions may reduce the large underestimation.

8 VIC Multi-layer (M-L) Snow Module To test the hypothesis that a complex model can perform better, we developed a more complex multi-layer (M-L) snow module for VIC. Incorporate more physical processes and better numerical structures that are not available in the simpler 2-L snow module of the VIC land surface model (LSM) The new multi-l module is fully coupled with the VIC LSM

9 Model Comparison Snow models BATS snow module (Simple) VIC 2-L snow module (Simple) NOHRSC snow model (Medium) Lynch-Stieglitz model (Medium) SAST snow model (Medium) CLM snow model (Complex) VIC M-L snow module (Complex) SNTHERM (Very Complex) Anderson snow model (Very Complex) Physical equations Solar radiation penetration 5 Water phases Snow density (function of) Liquid water treatment Surface EB 1 Not considered Ice Snow age N/A 1 layer EMB 2* Not considered Liquid, ice EMB N/A Liquid, ice EMB Not considered Liquid, ice EMB, W/IVD Considered Liquid, ice EMB Not considered Liquid, ice EMB, W/IVD Considered Liquid, ice EMB Considered Liquid, ice, vapor EMB Considered Liquid, ice, vapor Snow age, compaction Mass balance, compaction Constant LWHC 3 Constant LWHC 3 Layers 2 layers 3 layers Snow age Constant LWHC 3 3 layers Mass balance, compaction Mass balance, compaction Mass balance, compaction Mass balance, compaction Mass balance, compaction Function of snow density Function of fixed irreducible water Function of snow density Gravitational flow Function of snow density 3 layers M-L 4 M-L M-L M-L 1 EB: energy balance 2 EMB : energy mass balance 3 LWHC: liquid water holding capacity 4 M-L: multilayers, more than 3 layers 5 penetration into intermediate snow layer 6 W/IVD: with indirect consideration of vapor diffusion within conductivity * Excessive energy at the surface transferred to the lower layer

10 VIC M-L Snow Module Structure Number of layers are variable Snow and soil layers are considered as one system Solves the snow and soil temperatures simultaneously

11 SWE Comparison at a Shallow Snowpack Site VIC 2-L and M-L models show similar results of SWE simulation at Valdai, Russia, a relative shallow snowpack site.

12 Skin Temp. Comparison at a Shallow Snowpack Site Both 2-L and multi-l models simulate skin temp. well at the Valdai site, Russia

13 SWE Comparison at a Deep Snowpack Site (2000 Winter) Adjusted total ppt Adjusted solid ppt 2-L Obs. DMIP2 solid ppt M-L DMIP2 total ppt Adjusted (black) and SNOTEL ppt (red) DMIP2 total ppt Ebbetts Pass site, CA

14 SWE Comparison at a Deep Snowpack Site (2001 Winter) Adjusted solid ppt Adjusted total ppt Obs. DMIP2 total ppt DMIP2 solid ppt 2-L M-L Ebbetts Pass site, CA Adjusted and SNOTEL ppt DMIP2 total ppt

15 MKF-based Data Assimilation Framework Multiscale Kalman Filtering (MKF) based framework (Parada and Liang, 2004) is used to perform the data assimilation. MKF-based paradigm accounts for spatial correlation structures, error propagation over time, and dissimilar spatial resolutions of SWE data and model simulated SWE. Two SWE products, SNODAS (1 km resol.) and AMSR-E (25 km resol.), are used in this study. MKF-based framework from Parada and Liang, JGR, 2004.

16 SWE Data & Study Area Study Area Yosemite National Park Blue Lakes Ebbetts Pass East Fork Carson River Basin Boundary SNODAS (GOES, AVHRR, SSM/I, NOAH model) 1 km (~1/128 th degree) resolution, Re-gridded into 1/64 th degree AMSR-E 25 km (~1/4 th degree) resolution VIC snow module output (M-L) 1/8 th degree (~12 km)

17 Data Fusion Results (VIC M-L + SNODAS + AMSR-E, 3/1/04) VIC M-L at 1/8 degree Aggregated SNODAS at 1/8 degree Fused SWE (mm) at 1/8 degree AMSR-E at 1/4 degree SNODAS at 1/64 degree Fused SWE (mm) at 1/64 degree Mean SWE at ¼ deg: AMSR-E: 13.6 mm VIC: mm SNODAS: mm

18 Data Fusion Results (VIC M-L + SNODAS, 3/1/04) VIC M-L at 1/8 degree Aggregated SNODAS at 1/8 degree Fused SWE (mm) at 1/8 degree SNODAS at 1/64 degree Fused SWE (mm) at 1/64 degree

19 SWE (mm) Data Fusion Results (3/1/04) SNODAS+AMSR at 1/8 VIC+SNODAS at 1/8 VIC+SNODAS+AMSR at 1/8 SNODAS+AMSR at 1/64 VIC+SNODAS at 1/64 VIC+SNODAS+AMSR at 1/64

20 SWE Data & Study Area Study Area Yosemite National Park Blue Lakes Ebbetts Pass East Fork Carson River Basin Boundary SNODAS (GOES, AVHRR, SSM/I, NOAH model) 1 km (~1/128 th degree) resolution, Re-gridded into 1/64 th degree AMSR-E 25 km (~1/4 th degree) resolution VIC snow module output (M-L) 1/8 th degree (~12 km)

21 Data Fusion Results at Two SNOTEL Stations VIC+SNODAS+AMSR Ebbetts Pass, 2004 VIC+SNODAS SNODAS+AMSR Ebbetts Pass, 2004 Ebbetts Pass, 2004 Blue Lakes, 2004 Blue Lakes, 2004 SNODAS at 1/64 deg. SNOTEL obs. Fused at 1/64 deg. Fused at 1/8deg. SNODAS at 1/8 deg. VIC Blue Lakes, 2004

22 Summary/Conclusion Improvements can be obtained with the increase of a snow model complexity, but the improvements are quite limited, especially compared to the large discrepancy existed between the model simulation and observation due to significantly above normal range of uncertainties in the forcing data (e.g., precip.) in mountainous regions. The complexity of VIC 2-L snow module can capture important physical processes that the complex snow module VIC M-L captures. Reasonable SWE measurements are critical to uncover irregular uncertainties associated with snow accumulation phase in mountainous regions. It is important to conduct SWE data assimilation based on multiple reasonable data sets, which can significantly reduce the large uncertainty caused by uncertainties in the unusual forcing data in Mountainous region. Current AMSR-E SWE product significantly underestimates SWE over mountainous regions, and thus it is not useful for SWE assimilation. Great efforts are necessary to obtain multiple adequate SWE products through remote sensing techniques and better retrieval algorithms.

23 Thank You!

24 SWE Comparison at a Deep Snowpack Site (2002 Winter) Adjusted solid ppt Adjusted total ppt 2-L Obs. M-L DMIP2 total ppt DMIP2 solid ppt Adjusted (black) and SNOTEL ppt (red) DMIP2 total ppt Ebbetts Pass site, CA

25 Initial Conclusions based on 2-L and M-L For the shallow and deep snowpack sites, both 2-L and M-L models showed similar SWE results. This indicates that a snow model with the complexity of VIC 2-L can capture important physical processes that the VIC M-L captures. Other studies (e.g., Sun et al.,1999; Jin et al., 1999; Rutter et al., 2008) have shown that snow models with medium/complex complexity (e.g., NSM/SAST) can perform as well as the very complex one (e.g., SNTHERM). So, with our study, we can infer that VIC 2-L can capture most of the important physical processes that a very complex snow model can. In Mts., the uncertainty associated with precip. is much larger than the typical variation range of the precip., and much larger than the improvements that a more complex model can offer. Thus, to overcome such problems, it is very important to have adequate SWE measurements/products for conducting data assimilation, so that the accumulated SWE is not significantly affected by the large uncertainty in the precip.