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Yuichiro Oku, Hirohiko Ishikawa, and Zhongbo Su

Abstract

A Surface Energy Balance System (SEBS) originally developed for the NOAA Advanced Very High Resolution Radiometer was applied to Geostationary Meteorological Satellite (GMS)-5 Visible/Infrared Spin-Scan Radiometer data that were supplemented with other meteorological data. GMS-5, which is a geostationary satellite, recorded continuous hourly information. Surface temperatures obtained from the GMS-5 data were entered into SEBS to estimate the hourly regional distribution of the surface heat fluxes over the Tibetan Plateau. The estimated fluxes are verified by using corresponding field observations. The diurnal cycle of estimated fluxes agreed well with the field measurements. For example, the diurnal range of the estimated sensible heat flux decreases from June to August. This reflects the change of dry to wet surface characteristics resulting from frequent precipitation during the summer monsoon. Over the Tibetan Plateau, the diurnal range of the surface temperature is as large as the annual range, so that the resultant sensible heat flux has a large diurnal variation. Thus, the hourly estimation based on the GMS data may contribute to a better understanding of the land surface–atmosphere interaction in this critical area.

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Vahid Rahimpour Golroudbary, Yijian Zeng, Chris M. Mannaerts, and Zhongbo Su

Abstract

Knowledge of the response of extreme precipitation to urbanization is essential to ensure societal preparedness for the extreme events caused by climate change. To quantify this response, this study scales extreme precipitation according to temperature using the statistical quantile regression and binning methods for 231 rain gauges during the period of 1985–2014. The positive 3%–7% scaling rates were found at most stations. The nonstationary return levels of extreme precipitation are investigated using monthly blocks of the maximum daily precipitation, considering the dependency of precipitation on the dewpoint, atmospheric air temperatures, and the North Atlantic Oscillation (NAO) index. Consideration of Coordination of Information on the Environment (CORINE) land-cover types upwind of the stations in different directions classifies stations as urban and nonurban. The return levels for the maximum daily precipitation are greater over urban stations than those over nonurban stations especially after the spring months. This discrepancy was found by 5%–7% larger values in August for all of the classified station types. Analysis of the intensity–duration–frequency curves for urban and nonurban precipitation in August reveals that the assumption of stationarity leads to the underestimation of precipitation extremes due to the sensitivity of extreme precipitation to the nonstationary condition. The study concludes that nonstationary models should be used to estimate the return levels of extreme precipitation by considering the probable covariates such as the dewpoint and atmospheric air temperatures. In addition to the external forces, such as large-scale weather modes, circulation types, and temperature changes that drive extreme precipitation, urbanization could impact extreme precipitation in the Netherlands, particularly for short-duration events.

Open access
Margaret Kimani, Joost C. B. Hoedjes, and Zhongbo Su

Abstract

Rainfall variability affects agriculture planning and water resource management. In extreme flood and drought events, lives and property are destroyed. This study aims to improve East Africa’s seasonal rainfall prediction by determining the impact of the standard eight Real-time Multivariate Madden–Julian Oscillation (MJO) (RMM) phases on rainfall and using sea surface temperature (SST) response to test the predictability of the March–May (MAM) and October–December (OND) main rainfall seasons over a period of 33 years (1981–2013). Pearson correlation patterns, composite maps, and regression analyses were applied, and the Brier skill score (BSS) and correlation coefficients (CC) were utilized as validation metrics. Low correspondence of rainfall to MJO 1 and MJO 2 was observed except for the months of November and December. Seasonally, MAM and OND correlation patterns with MJO 2 revealed enhanced rainfall over the highlands and insignificant correspondence over coastal areas. Conversely, enhanced MJO 8 corresponded to suppressed rainfall during the June–August season over the coast and the eastern highlands. MAM rainfall was shown to be predictable using Maritime Continent SST indices, with a BSS of 0.41, while OND rainfall was shown to be predictable using Atlantic and Maritime Continent SSTs with a BSS of 0.62. Positive and negative MJO 2 corresponded, respectively, to enhanced and suppressed rainfall during the OND season and was confirmed to be related to, respectively, a positive and negative Indian Ocean dipole (IOD). An IOD year could possibly be triggered by changes in MJO 2 amplitudes observed as early peaks between February and June.

Free access
Xuelong Chen, Zhongbo Su, Yaoming Ma, James Cleverly, and Michael Liddell

Abstract

MODIS thermal sensors can provide us with global land surface temperature (LST) several times each day, but have difficulty in obtaining information from the land surface in cloudy situations. As a result, the monthly day or night LST products [Terra monthly day LST (TMD), Terra monthly night LST (TMN), Aqua monthly day LST (AMD), and Aqua monthly night LST (AMN)] are the average LST values calculated over a variable number of clear-sky days in a month. Is it possible to derive an accurate estimate of monthly mean LST based on averaging of the multidaily overpasses of MODIS sensors? In situ ground measurements and ERA-Interim reanalyses data, both of which provide continuous information in either clear or cloudy conditions, have been used to validate the approach. Using LST measurements from 156 ground flux towers, it was found that the three mean values , , and (mean biases of 0.19, 0.59, and 0.40 K, respectively) can all provide a reliable estimate of all-sky monthly mean LST. Of the three means, we recommend the use of for monthly mean LST in climate studies as it provides the most complete coverage. When retrievals from either Terra or Aqua are not available, then either or may be used to fill the gaps. The intrinsic error in the MODIS monthly mean LST cannot be explained from monthly mean view time, view angle, and clear-sky ratio. MODIS monthly LST calculated using this approach (RMSE = 2.65, mean bias < ±1 K) will have wide applicability for climate studies and numerical model evaluation.

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Xuelong Chen, Zhongbo Su, Yaoming Ma, Kun Yang, Jun Wen, and Yu Zhang

Abstract

Roughness height for heat transfer is a crucial parameter in the estimation of sensible heat flux. In this study, the performance of the Surface Energy Balance System (SEBS) has been tested and evaluated for typical land surfaces on the Tibetan Plateau on the basis of time series of observations at four sites with bare soil, sparse canopy, dense canopy, and snow surface, respectively. Both under- and overestimation at low and high sensible heat fluxes by SEBS was discovered. Through sensitivity analyses, it was identified that these biases are related to the SEBS parameterization of bare soil’s excess resistance to heat transfer (kB −1, where k is the von Kármán constant and B −1 is the Stanton number). The kB −1 of bare soil in SEBS was replaced. The results show that the revised model performs better than the original model.

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Lei Zhong, Zhongbo Su, Yaoming Ma, Mhd. Suhyb Salama, and José A. Sobrino

Abstract

Variations of land surface parameters over the Tibetan Plateau have great importance on local energy and water cycles, the Asian monsoon, and climate change studies. In this paper, the NOAA/NASA Pathfinder Advanced Very High Resolution Radiometer (AVHRR) Land (PAL) dataset is used to retrieve the land surface temperature (LST), the normalized difference vegetation index (NDVI), and albedo, from 1982 to 2000. Simultaneously, meteorological parameters and land surface heat fluxes are acquired from the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) dataset and the Global Land Data Assimilation System (GLDAS), respectively. Results show that from 1982 to 2000 both the LST and the surface air temperature increased on the Tibetan Plateau (TP). The rate of increase of the LST was 0.26±0.16 K decade−1 and that of the surface air temperature was 0.29 ± 0.16 K decade−1, which exceeded the increase in the Northern Hemisphere (0.054 K decade−1). The plateau-wide annual mean precipitation increased at 2.54 mm decade−1, which indicates that the TP is becoming wetter. The 10-m wind speed decreased at about 0.05±0.03 m s−1 decade−1 from 1982 to 2000, which manifests a steady decline of the Asian monsoon wind. Due to the diminishing ground–air temperature gradient and subdued surface wind speed, the sensible heat flux showed a decline of 3.37 ± 2.19 W m−2 decade−1. The seasonal cycle of land surface parameters could clearly be linked to the patterns of the Asian monsoon. The spatial patterns of sensible heat flux, latent heat flux, and their variance could also be recognized.

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M. Jahanzeb Malik, Rogier van der Velde, Zoltan Vekerdy, and Zhongbo Su

Abstract

This study assesses the impact of assimilating satellite-observed snow albedo on the Noah land surface model (LSM)-simulated fluxes and snow properties. A direct insertion technique is developed to assimilate snow albedo into Noah and is applied to three intensive study areas in North Park (Colorado) that are part of the 2002/03 Cold Land Processes Field Experiment (CLPX). The assimilated snow albedo products are 1) the standard Moderate Resolution Imaging Spectrometer (MODIS) product (MOD10A1) and 2) retrievals from MODIS observations with the recently developed Pattern-Based Semiempirical (PASS) approach. The performance of the Noah simulations, with and without assimilation, is evaluated using the in situ measurements of snow albedo, upward shortwave radiation, and snow depth. The results show that simulations with albedo assimilation agree better with the measurements. However, because of the limited impact of snow albedo updates after subsequent snowfall, the mean (or seasonal) error statistics decrease significantly for only two of the three CLPX sites. Though the simulated snow depth and duration for the snow season benefit from the assimilation, the greatest improvements are found in the simulated upward shortwave radiation, with root mean squared errors reduced by about 30%. As such, this study demonstrates that assimilation of satellite-observed snow albedo can improve LSM simulations, which may positively affect the representation of hydrological and surface energy budget processes in runoff and numerical weather prediction models.

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Donghai Zheng, Rogier van der Velde, Zhongbo Su, Jun Wen, Xin Wang, and Kun Yang

Abstract

This study evaluates the Noah land surface model (LSM) in its ability to simulate water and heat exchanges over frozen ground in a Tibetan meadow ecosystem. A comprehensive dataset including in situ micrometeorological and soil moisture–temperature profile measurements collected between November and March is utilized, and analyses of the measurements reveal that the measured soil freezing characteristics are better captured by 1) modifying the parameter b l implemented in the current Noah LSM that constrains the shape parameter of soil water retention curve utilized by the water potential freezing point depression equation to produce appropriate liquid water content θ liq under subzero temperature conditions and 2) neglecting the ice effect on soil-specific surface and thus matric potential via setting the empirical parameter that accounts for the effect of increase in specific surface of soil particles and ice–liquid water c k to zero. The numerical experiments performed with the Noah model run show that in comparison to the default Noah LSM, adoption of c k = 0 and site-specific b l values reduces the overestimation of θ liq across the soil profile. Implementation of augmentations such as the parameterization of diurnally varying thermal roughness length resolves the overestimation of daytime turbulent heat fluxes and underestimation of surface temperature. Further adoption of a new heat conductivity parameterization reduces the overestimation of nighttime surface temperature. An appropriate treatment of phase change efficiency that accounts for changing freezing rate with varying liquid water contents is also needed to reduce the temperature underestimation across soil profiles.

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Rogier van der Velde, Mhd. Suhyb Salama, Marcel D. van Helvoirt, Zhongbo Su, and Yaoming Ma

Abstract

Understanding the sources of uncertainty that cause deviations between simulated and satellite-observed states can facilitate optimal usage of these products via data assimilation or calibration techniques. A method is presented for separating uncertainties following from (i) scale differences between model grid and satellite footprint, (ii) residuals inherent to imperfect model and retrieval applications, and (iii) biases in the climatologies of simulations and retrievals. The method is applied to coarse (10 km) soil moisture simulations by the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5)–Noah regional climate model and 2.5 years of high-resolution (100 m) retrievals from the Advanced Synthetic Aperture Radar (ASAR) data collected over central Tibet. Suppression of the bias is performed via cumulative distribution function (CDF) matching. The other deviations are separated by taking the variance of the ASAR soil moisture at the coarse MM5 model grid as measure for the deviations caused by scale differences. Via decomposition of the uncertainty sources it is shown that the bias and the spatial-scale difference explain the majority (>70%) of the deviations between the two products, whereas the contribution of model–observation residuals is less than 30% on a monthly basis. Consequently, this study demonstrates that accounting for uncertainties caused by bias as well as spatial-scale difference is imperative for meaningful assimilation of high-resolution soil moisture products. On the other hand, the large uncertainties following from spatial-scale differences suggests that high-resolution soil moisture products have a potential of providing observation-based input for the subgrid spatial variability parameterizations within large-scale models.

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Donghai Zheng, Rogier van der Velde, Zhongbo Su, Martin J. Booij, Arjen Y. Hoekstra, and Jun Wen
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