Jiarui Dong

The Plumbing of Land Surface Models: Benchmarking Model Performance

The Protocol for the Analysis of Land Surface Models (PALS) Land Surface Model Benchmarking Evaluation Project (PLUMBER) was designed to be a land surface model (LSM) benchmarking intercomparison. Unlike the traditional methods of LSM evaluation or comparison, benchmarking uses a fundamentally different approach in that it sets expectations of performance in a range of metrics a priori-before model simulations are performed. This can lead to very different conclusions about LSM performance. For this study, both simple physically based models and empirical relationships were used as the benchmarks. Simulations were performed with 13 LSMs using atmospheric forcing for 20 sites, and then model performance relative to these benchmarks was examined. Results show that even for commonly used statistical metrics, the LSMs performance varies considerably when compared to the different benchmarks. All models outperform the simple physically based benchmarks, but for sensible heat flux the LSMs are themselves outperformed by an out-of-sample linear regression against downward shortwave radiation. While moisture information is clearly central to latent heat flux prediction, the LSMs are still outperformed by a three-variable nonlinear regression that uses instantaneous atmospheric humidity and temperature in addition to downward shortwave radiation. These results highlight the limitations of the prevailing paradigm of LSM evaluation that simply compares an LSM to observations and to other LSMs without a mechanism to objectively quantify the expectations of performance. The authors conclude that their results challenge the conceptual view of energy partitioning at the land surface.

Scanning multichannel microwave radiometer snow water equivalent assimilation

[1]xa0Accurate prediction of snowpack status is important for a range of environmental applications, yet model estimates are typically poor and in situ measurement coverage is inadequate. Moreover, remote sensing estimates are spatially and temporally limited due to complicating effects, including distance to open water, presence of wet snow, and presence of thick snow. However, through assimilation of remote sensing estimates into a land surface model, it is possible to capitalize on the strengths of both approaches. In order to achieve this, reliable estimates of the uncertainty in both remotely sensed and model simulated snow water equivalent (SWE) estimates are critical. For practical application, the remotely sensed SWE retrieval error is prescribed with a spatially constant but monthly varying value, with data omitted for (1) locations closer than 200 km to significant open water, (2) times and locations with model-predicted presence of liquid water in the snowpack, and (3) model SWE estimates greater than 100 mm. The model error is estimated using standard error propagation with a calibrated spatially and temporally constant model error contribution. A series of tests have been performed to assess the assimilation algorithm performance. Multiyear model simulations with and without remotely sensed SWE assimilation are presented and evaluated with in situ SWE observations. The SWE estimates from assimilation were found to be superior to both the model simulation and remotely sensed estimates alone, except when model SWE estimates rapidly and erroneously crossed the 100-mm SWE cutoff early in the snow season.

On the Relationship Between Temperature and MODIS Snow Cover Retrieval Errors in the Western U.S.

Understanding and quantifying satellite-based remotely sensed snow cover errors are critical for successful utilization of snow cover products. The Moderate Resolution Imaging Spectroradiometer (MODIS) Snow Covered Area (SCA) product errors have been previously recognized to be associated with factors such as cloud contamination, snow pack particles, vegetation cover, and topography; however, the quantitative relationship between the retrieval errors and these factors remains elusive. Joint analysis of MODIS SCA and land surface temperature (LST) products, and in-situ air temperature and snow water equivalent (SWE) measurements provides a unique look at the error structure of the recently developed MODIS SCA products. Analysis of the MODIS SCA data set over the period from 2000 to 2005 was undertaken for the California/Nevada and Colorado regions of the western United States. Both regions have extensive observational networks. For this study area, the MODIS SCA product demonstrates strong ability in detecting the presence of snow cover (80%). However, significant spatial and temporal variations in accuracy (from 75% in high roughness to 86% in low roughness regions and 45% in October to 94% in February) suggest that a proxy is required to adequately predict the MODIS SCA product errors. For the first time, we demonstrate a relationship between the errors in the MODIS SCA products and temperature in the western United States, and find that this relationship is well-represented by the cumulative double exponential distribution function. We have performed a fitting and validation experiment by deriving the relationship between temperature and the errors in the MODIS SCA product from 2000-2004 period and using 2005 to independently test our method. This relationship is shown to hold for both in-situ daily mean air temperature and MODIS LST. Both of them are useful indices in quantifying the errors in MODIS product for various hydrological applications.

Uncertainties, Correlations, and Optimal Blends of Drought Indices from the NLDAS Multiple Land Surface Model Ensemble

AbstractThis study analyzed uncertainties and correlations over the United States among four ensemble-mean North American Land Data Assimilation System (NLDAS) percentile-based drought indices derived from monthly mean evapotranspiration ET, total runoff Q, top 1-m soil moisture SM1, and total column soil moisture SMT. The results show that the uncertainty is smallest for SM1, largest for SMT, and moderate for ET and Q. The strongest correlation is between SM1 and SMT, and the weakest correlation is between ET and Q. The correlation between ET and SM1 (SMT) is strongest in arid–semiarid regions, and the correlation between Q and SM1 (SMT) is strongest in more humid regions in the Pacific Northwest and the Southeast. Drought frequency analysis shows that SM1 has the most frequent drought occurrence, followed by SMT, Q, and ET. The study compared the NLDAS drought indices (a research product) with the U.S. Drought Monitor (USDM; an operational product) in terms of drought area percentage derived from each p...

Impacts of vegetation and cold season processes on soil moisture and climate relationships over Eurasia

[1] A number of modeling studies have addressed soil moisture persistence and its effects on the atmosphere. Such analyses are particularly valuable for seasonal to interannual prediction. In this study, we perform an observation-based study to further investigate the impacts of vegetation and cold season processes on soil moisture persistence and climate feedbacks. The joint analysis of independent meteorological, soil moisture and land cover measurements, without the use of a model, in the former Soviet Union provides a unique look at soil moisture–climate relationships at seasonal to interannual timescales. Averaged data over the growing season show a strong consistency between soil moisture and precipitation over grassland dominant regions, suggesting that precipitation anomalies are a dominant control of soil moisture at interannual timescales. Investigation of soil moisture persistence at the seasonal timescale shows a strong correlation between soil moisture in spring and the subsequent precipitation in summer over forest dominant regions and between cold season precipitation accumulation in winter and soil moisture in the following spring. Our findings can be explained by the theory proposed by Koster and Suarez (2001) and are consistent with the results from other modeling studies. Although it is hard to obtain the statistical meaningful conclusions because of the short data records, our results show the potential role of vegetation and cold season processes in land-atmosphere interactions. Further modeling studies and analyses using long in situ data records are necessary to fully verify our results.

Using Air Temperature to Quantitatively Predict the MODIS Fractional Snow Cover Retrieval Errors over the Continental United States

AbstractUnderstanding and quantifying satellite-based, remotely sensed snow cover uncertainty are critical for its successful utilization. The Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover errors have been previously recognized to be associated with factors such as cloud contamination, snowpack grain sizes, vegetation cover, and topography; however, the quantitative relationship between the retrieval errors and these factors remains elusive. Joint analysis of the MODIS fractional snow cover (FSC) from Collection 6 (C6) and in situ air temperature and snow water equivalent measurements provides a unique look at the error structure of the MODIS C6 FSC products. Analysis of the MODIS FSC dataset over the period from 2000 to 2005 was undertaken over the continental United States (CONUS) with an extensive observational network. When compared to MODIS Collection 5 (C5) snow cover area, the MODIS C6 FSC product demonstrates a substantial improvement in detecting the presence of snow cover in N...

Mapping random and systematic errors of satellite-derived snow-water equivalent observations in Eurasia

Passive microwave sensors onboard satellites can provide global snow water equivalent (SWE) observations day or night, even under cloudy conditions. However, there are both systematic (bias) and random errors associated with the passive microwave measurements. While these errors are well known, they have thus far not been adequately quantified. In this study, unbiased SWE maps, random error maps and systematic error maps of Eurasia for the 1990-1991 snow season (November-April) have been examined. Dense vegetation, especially in the taiga region, and large snow crystals (>0.3 mm in radius), found in areas where the temperature/vapor gradients are greatest, (in the taiga and tundra regions) are the major source of systematic error. Assumptions about how snow crystals evolve with the progression of the season also contribute to the errors. In general, while random errors for North America and Eurasia are comparable, systematic errors are not as great for Eurasia as those observed for North America. Understanding remote sensing retrieval errors is important for correct interpretation of observations, and successful assimilation of observations into numerical models.