Shupeng Zhang

Evaluation of Satellite Rainfall Estimates over the Chinese Mainland

Benefiting from the high spatiotemporal resolution and near-global coverage, satellite-based precipitation products are applied in many research fields. However, the applications of these products may be limited due to lack of information on the uncertainties. To facilitate applications of these products, it is crucial to quantify and document their error characteristics. In this study, four satellite-based precipitation products (TRMM-3B42, TRMM-3B42RT, CMORPH, GSMaP) were evaluated using gauge-based rainfall analysis based on a high-density gauge network throughout the Chinese Mainland during 2003–2006. To quantitatively evaluate satellite-based precipitation products, continuous (e.g., ME, RMSE, CC) and categorical (e.g., POD, FAR) verification statistics were used in this study. The results are as follows: (1) GSMaP and CMORPH underestimated precipitation (about −0.53 and −0.14 mm/day, respectively); TRMM-3B42RT overestimated precipitation (about 0.73 mm/day); TRMM-3B42, which is the only dataset corrected by gauges, had the best estimation of precipitation amongst all four products; (2) GSMaP, CMORPH and TRMM-3B42RT overestimated the frequency of low-intensity rainfall events; TRMM-3B42 underestimated the frequency of low-intensity rainfall events; GSMaP underestimated the frequency of high-intensity rainfall events; TRMM-3B42RT tended to overestimate the frequency of high-intensity rainfall events; TRMM-3B42 and CMORPH produced estimations of high-intensity rainfall frequency that best aligned with observations; (3) All four satellite-based precipitation products performed better in summer than in winter. They also had better performance over wet southern region than dry northern or high altitude regions. Overall, this study documented error characteristics of four satellite-based precipitation products over the Chinese Mainland. The results help to understand features of these datasets for users and improve algorithms for algorithm developers in the future.

Mapping near-surface air temperature, pressure, relative humidity and wind speed over Mainland China with high spatiotemporal resolution

As part of a joint effort to construct an atmospheric forcing dataset for mainland China with high spatiotemporal resolution, a new approach is proposed to construct gridded near-surface temperature, relative humidity, wind speed and surface pressure with a resolution of 1 km×1 km. The approach comprises two steps: (1) fit a partial thin-plate smoothing spline with orography and reanalysis data as explanatory variables to ground-based observations for estimating a trend surface; (2) apply a simple kriging procedure to the residual for trend surface correction.The proposed approach is applied to observations collected at approximately 700 stations over mainland China. The generated forcing fields are compared with the corresponding components of the National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis dataset and the Princeton meteorological forcing dataset. The comparison shows that, both within the station network and within the resolutions of the two gridded datasets, the interpolation errors of the proposed approach are markedly smaller than the two gridded datasets.

Evaluation of Global Satellite Mapping of Precipitation Project Daily Precipitation Estimates over the Chinese Mainland

Two versions of Global Satellite Mapping of Precipitation (GSMaP) products (GSMaP-V4 and GSMaP-V5) are validated both in a single grid scale and in contiguous China by comparing to gauge-based rainfall analysis dataset. GSMaP products can capture spatial patterns and magnitude of rainfall in daily mean precipitation. They perform better in summer than in winter over the Chinese Mainland. They also have better estimation over the southeast than over the northwest of the Chinese Mainland. An apparent system underestimate is detected in both GSMaP products. The underestimation existing in the GSMaP-V4 has been largely improved in GSMaP-V5. The impacts of snow cover and vegetation fraction are also investigated. The result indicates that snow cover deeply impacts the POD and FAR of GSMaP products. NDVI may result in overestimated precipitation in sparse vegetation regions. These results implicate that it is useful to use some auxiliary data from other sensors (e.g., MODIS) to improve the quality of precipitation product.

Using Analysis State to Construct a Forecast Error Covariance Matrix in Ensemble Kalman Filter Assimilation

Correctly estimating the forecast error covariance matrix is a key step in any data assimilation scheme. If it is not correctly estimated, the assimilated states could be far from the true states. A popular method to address this problem is error covariance matrix inflation. That is, to multiply the forecast error covariance matrix by an appropriate factor. In this paper, analysis states are used to construct the forecast error covariance matrix and an adaptive estimation procedure associated with the error covariance matrix inflation technique is developed.The proposed assimilation scheme was tested on the Lorenz-96 model and 2D Shallow Water Equation model, both of which are associated with spatially correlated observational systems. The experiments showed that by introducing the proposed structure of the forecast error covariance matrix and applying its adaptive estimation procedure, the assimilation results were further improved.

Global carbon assimilation system using a local ensemble Kalman filter with multiple ecosystem models

In this paper, a global carbon assimilation system (GCAS) is developed for optimizing the global land surface carbon flux at 1° resolution using multiple ecosystem models. In GCAS, three ecosystem models, Boreal Ecosystem Productivity Simulator, Carnegie-Ames-Stanford Approach, and Community Atmosphere Biosphere Land Exchange, produce the prior fluxes, and an atmospheric transport model, Model for OZone And Related chemical Tracers, is used to calculate atmospheric CO2 concentrations resulting from these prior fluxes. A local ensemble Kalman filter is developed to assimilate atmospheric CO2 data observed at 92 stations to optimize the carbon flux for six land regions, and the Bayesian model averaging method is implemented in GCAS to calculate the weighted average of the optimized fluxes based on individual ecosystem models. The weights for the models are found according to the closeness of their forecasted CO2 concentration to observation. Results of this study show that the model weights vary in time and space, allowing for an optimum utilization of different strengths of different ecosystem models. It is also demonstrated that spatial localization is an effective technique to avoid spurious optimization results for regions that are not well constrained by the atmospheric data. Based on the multimodel optimized flux from GCAS, we found that the average global terrestrial carbon sink over the 2002–2008 period is 2.97 ± 1.1 PgC yr−1, and the sinks are 0.88 ± 0.52, 0.27 ± 0.33, 0.67 ± 0.39, 0.90 ± 0.68, 0.21 ± 0.31, and 0.04 ± 0.08 PgC yr−1 for the North America, South America, Africa, Eurasia, Tropical Asia, and Australia, respectively. This multimodel GCAS can be used to improve global carbon cycle estimation.

Incorporating root hydraulic redistribution and compensatory water uptake in the Common Land Model: Effects on site-level and global land modeling

Treatment of plant water uptake through the roots remains a significant issue in land surface models. Most current land surface models calculate the root water uptake (RWU) by extracting soil water in different soil layers based on the relative soil water availability and the root fraction of each layer within the rooting zone. This approach is also used as the default in the Common Land Model (CoLM). This approach often significantly underestimates plant transpiration during dry periods. Therefore, more realistic RWU functions are needed in land surface models. In this study, the modified CoLM with root hydraulic redistribution (HR) and compensatory water uptake (CWU) was evaluated against the CoLM with the default approach by comparing the observed and simulated latent and sensible heat fluxes observed from three sites that experience seasonal drought over the measured periods. We found that the CoLM using the default RWU significantly underestimated latent heat fluxes and overestimated the sensible heat fluxes over dry periods, whereas those biases were significantly reduced by the CoLM with HR and CWU functions. We also ran global offline simulations using the revised CoLM to evaluate the performance of these alternative RWU functions on the global scale. Compared with the estimated latent heat fluxes from the FLUXNET-MTE model product, CoLM with HR and CWU functions significantly improved the estimated latent heat fluxes over the Amazon, Southern Africa and Central Asia during their dry seasons. Therefore, we recommend the implementation of HR and CWU in land surface models.

Optimization of Terrestrial Ecosystem Model Parameters Using Atmospheric CO2 Concentration Data With the Global Carbon Assimilation System (GCAS)

The Global Carbon Assimilation System (GCAS) that assimilates ground-based atmospheric CO2 data is used to estimate several key parameters in a terrestrial ecosystem model for the purpose of improving carbon cycle simulation. The optimized parameters are the leaf maximum carboxylation rate at 25°C ( Vmax25), the temperature sensitivity of ecosystem respiration (Q10) and the soil carbon pool size. The optimization is performed at the global scale at 1°resolution for the period from 2002 to 2008. The results indicate that vegetation from tropical zones has lower Vmax25 values than vegetation in temperate regions. Relatively high values of Q10 are derived over high/mid latitude regions. Both Vmax25 and Q10 exhibit pronounced seasonal variations at mid-high latitudes. The maxima in Vmax25 occur during growing seasons, while the minima appear during non-growing seasons. Q10 values decrease with increasing temperature. The seasonal variabilities of Vmax25 and Q10 are larger at higher latitudes. Optimized Vmax25 and Q10 show little seasonal variabilities at tropical regions. The seasonal variabilities of Vmax25 are consistent with the variabilities of LAI for evergreen conifers and broadleaf evergreen forests. Variations in leaf nitrogen and leaf chlorophyll contents may partly explain the variations in Vmax25. The spatial distribution of the total soil carbon pool size after optimization is compared favorably with the gridded Global Soil Dataset for Earth System. The results also suggest that atmospheric CO2 data is a source of information that can be tapped to gain spatially and temporally meaningful information for key ecosystem parameters that are representative at the regional and global scales.

Reexamination and further development of two‐stream canopy radiative transfer models for global land modeling

Four representative two-stream canopy radiative transfer models were examined and intercompared using the same configuration. Based on the comparison results, two modifications were introduced to the widely used Dickinson-Sellers model and then incorporated into the Community Land Model (CLM4.5). The modified model was tested against Monte-Carlo simulations and produced significant improvements in the simulated canopy transmittance and albedo values. In direct comparison with MODIS albedo data, the modified model shows good performance over most snow/ice-free vegetated areas, especially for regions that are covered by dense canopy. The modified model shows seasonally dependent behavior mainly in the near-infrared band. Thus, the improvements are not present in all seasons. Large biases are still noticeable in sparsely vegetated areas, in particular for the snow/ice covered regions, that is possibly related to the model, the land surface input data, or even the observations themselves. Further studies focusing on the impact of the seasonal changes in leaf optical properties, the parameterizations for snow/ice covered regions and the case of sparsely vegetated areas, are recommended.

Mapping Daily Precipitation over China Based on TRMM Multisatellite Precipitation Analysis and Gauge Data

Precipitation is an important part of hydrologic cycle in global scale or regional scale. Although satellite-based observation is able to provide spatial and temporal distribution of precipitation, the satellite measurements tend to show systematic bias. A fusion method for mapping precipitation is introduced in this paper. The thin plate smoothing spline interpolation method is introduced to combine rainfall gauge and TRMM Multisatellite Precipitation Analysis (TMPA) data and map daily precipitation over china mainland region. With combined gauge data, the new daily precipitation provide more reliable high temporal/spatial resolution precipitation estimates.