scholarly journals Evaluation of cloud effects on air temperature estimation using MODIS LST based on ground measurements over the Tibetan Plateau

2016 ◽  
Author(s):  
Hongbo Zhang ◽  
Fan Zhang ◽  
Guoqing Zhang ◽  
Xiaobo He ◽  
Lide Tian
Keyword(s):  
Tibetan Plateau ◽  
Air Temperature ◽  
Land Surface ◽  
Estimation Accuracy ◽  
Daily Maximum ◽  
The Tibetan Plateau ◽  
Cloud Detection ◽  
Air Temperatures ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) data have played a significant role in estimating the air temperature (Tair) due to the sparseness of ground measurements, especially for remote mountainous areas. Generally, two types of air temperatures are studied including daily maximum (Tmax) and minimum (Tmin) air temperatures. MODIS daytime and nighttime LST are often used as proxies for estimating Tmax and Tmin, respectively. The Tibetan Plateau (TP) has a high daily cloud cover fraction (> 45 %). The presence of clouds can affect the relationship between Tair and LST and can further affect the estimation accuracies. This study comprehensively analyzes the effects of clouds on Tair estimation based on MODIS LST using detailed half-hourly ground measurements and daily meteorological station observations collected from over the TP. Comparisons made between in-situ cloudiness observations and MODIS claimed clear-sky records show that erroneous rates of MODIS nighttime cloud detection are obviously higher than those achieved in daytime. Our validation of the MODIS LST values under different cloudiness constraining conditions shows that the accuracy of MODIS nighttime LST is severely affected by undetected clouds. Large errors introduced by undetected clouds are found to significantly affect the Tmin estimations based on nighttime LST through cloud effect tests. However, clouds are mainly found to affect Tmax estimation by affecting the essential relationship between Tmax and daytime LST. The obviously larger errors of Tmax estimation than those of Tmin could be attributed to larger MODIS daytime LST errors resulting from higher degrees of daytime LST heterogeneity within MODIS pixel than those of nighttime LST. Constraining all four MODIS observations per day to non-cloudy observations can efficiently screen samples to build a strong fit of Tmin estimation using MODIS nighttime LST. The present study reveals the effects of clouds on Tair estimation through MODIS LST and will thus help improve the estimation accuracy levels while alleviating the problems associated with severe data sparseness over the TP.

scholarly journals Evaluation of cloud effects on air temperature estimation using MODIS LST based on ground measurements over the Tibetan Plateau

2016 ◽  
Vol 16 (21) ◽  
pp. 13681-13696 ◽  
Author(s):  
Hongbo Zhang ◽  
Fan Zhang ◽  
Guoqing Zhang ◽  
Xiaobo He ◽  
Lide Tian
Keyword(s):  
Tibetan Plateau ◽  
Air Temperature ◽  
Land Surface ◽  
Estimation Accuracy ◽  
Daily Maximum ◽  
The Tibetan Plateau ◽  
Cloud Detection ◽  
Air Temperatures ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. Moderate Resolution Imaging Spectroradiometer (MODIS) daytime and nighttime land surface temperature (LST) data are often used as proxies for estimating daily maximum (Tmax) and minimum (Tmin) air temperatures, especially for remote mountainous areas due to the sparseness of ground measurements. However, the Tibetan Plateau (TP) has a high daily cloud cover fraction (> 45 %), which may affect the air temperature (Tair) estimation accuracy. This study comprehensively analyzes the effects of clouds on Tair estimation based on MODIS LST using detailed half-hourly ground measurements and daily meteorological station observations collected from the TP. It is shown that erroneous rates of MODIS nighttime cloud detection are obviously higher than those achieved in daytime. Large errors in MODIS nighttime LST data were found to be introduced by undetected clouds and thus reduce the Tmin estimation accuracy. However, for Tmax estimation, clouds are mainly found to reduce the estimation accuracy by affecting the essential relationship between Tmax and daytime LST. The errors of Tmax estimation are obviously larger than those of Tmin and could be attributed to larger MODIS daytime LST errors that result from higher degrees of LST heterogeneity within MODIS pixel compared to those of nighttime LST. Constraining MODIS observations to non-cloudy observations can efficiently screen data samples for accurate Tmin estimation using MODIS nighttime LST. As a result, the present study reveals the effects of clouds on Tmax and Tmin estimation through MODIS daytime and nighttime LST, respectively, so as to help improve the Tair estimation accuracy and alleviate the severe air temperature data sparseness issues over the TP.

scholarly journals Evaluation of Cloud Mask and Cloud Top Height from Fengyun-4A with MODIS Cloud Retrievals over the Tibetan Plateau

2021 ◽  
Vol 13 (8) ◽  
pp. 1418
Author(s):  
Wenjing Xu ◽  
Daren Lyu
Keyword(s):  
Tibetan Plateau ◽  
Climate Science ◽  
The Tibetan Plateau ◽  
Cloud Detection ◽  
Cloud Properties ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
New Generation ◽  
Cloud Mask

The Tibetan Plateau (TP) has profound thermal and dynamic influences on the atmospheric circulation, energy, and water cycles of the climate system, which make the clouds over the TP the forefront of atmospheric and climate science. However, the highest altitude and most complex terrain of the TP make the retrieval of cloud properties challenging. In order to understand the performance and limitations of cloud retrievals over the TP derived from the state-of-the-art Advanced Geosynchronous Radiation Imager (AGRI) onboard the new generation of Chinese Geostationary (GEO) meteorological satellites Fengyun-4 (FY-4), a three-month comparison was conducted between FY-4A/AGRI and the Moderate Resolution Imaging Spectroradiometer (MODIS) for both cloud detection and cloud top height (CTH) pixel-level retrievals. For cloud detection, the AGRI and MODIS cloud mask retrievals showed a fractional agreement of 0.93 for cloudy conditions and 0.73 for clear scenes. AGRI tended to miss lower CTH clouds due to the lack of thermal contrast between the clouds and the surface of the TP. For cloud top height retrievals, the comparison showed that on average, AGRI underestimated the CTH relative to MODIS by 1.366 ± 2.235 km, and their differences presented a trend of increasing with height.

scholarly journals Performance of GLASS and MODIS Satellite Albedo Products in Diagnosing Albedo Variations during Different Time Scales and Special Weather Conditions in the Tibetan Plateau

2020 ◽  
Vol 12 (15) ◽  
pp. 2456
Author(s):  
Yingying An ◽  
Xianhong Meng ◽  
Lin Zhao ◽  
Zhaoguo Li ◽  
Shaoying Wang ◽  
...  
Keyword(s):  
Tibetan Plateau ◽  
Land Surface ◽  
Surface Albedo ◽  
Weather Conditions ◽  
Glass Product ◽  
The Tibetan Plateau ◽  
Actual Surface ◽  
Multiple Observations ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer

Surface albedo is a crucial parameter in accurately and quantitatively estimating energy and water budget on the Tibetan Plateau (TP) and is also one of the largest radiative uncertainties in land surface modelling attempts. Based on an 8-year ground-based observation of the surface albedo over typical alpine meadows at Maqu and Maduo sites in the eastern TP, the performance of surface albedo products of Global LAnd Surface Satellite (GLASS) and Moderate Resolution Imaging Spectroradiometer (MODIS) in describing albedo variations at daily, 8-day, seasonal timescales, and during different special weather conditions were analyzed. Compared with the ground-based observation in Maqu, the 8-day albedo products from GLASS and MCD43B3 present maximum negative biases of −0.030 and −0.027 at Maqu, respectively. The black-sky albedo (BSA) of GLASS product coincides well with the ground-based observation in Maduo, with root mean square error (RMSE) of 0.092 and correlation coefficient (R) of 0.833, whereas that of MCD43B3 had an RMSE of 0.072 and R of 0.752. However, they are underestimated when the albedo is greater than 0.4. At the seasonal timescale, the BSA of GLASS and MCD43B3 underestimated the ground-based observation of Maqu by 0.015 in summer, while their white-sky albedo (WSA) are slightly overestimated and closer to the ground-based observation. In daily timescale, the response of surface albedo to soil moisture is different in semihumid and semiarid areas in summer. For both sites, the blue-sky-albedo of MCD43A3 has better agreement with the ground-based observation than GLASS and MCD43B3, as it improves the temporal resolution and calculates the albedo by weighting multiple observations within 16 days to be closer to the actual surface. However, even MCD43A3 could not capture the slowdown processes of albedo changes resulted by small snowfall processes or the snow aging due to cloud cover and inversion algorithms.

scholarly journals Satellite observations of changes in snow-covered land surface albedo during spring in the Northern Hemisphere

2015 ◽  
Vol 9 (5) ◽  
pp. 1879-1893 ◽  
Author(s):  
K. Atlaskina ◽  
F. Berninger ◽  
G. de Leeuw
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Air Temperature ◽  
Land Surface ◽  
Surface Albedo ◽  
Vegetation Index ◽  
Precipitation Amount ◽  
Snow Cover Fraction ◽  
Air Temperatures ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. Thirteen years of Moderate Resolution Imaging Spectroradiometer (MODIS) surface albedo data for the Northern Hemisphere during the spring months (March–May) were analyzed to determine temporal and spatial changes over snow-covered land surfaces. Tendencies in land surface albedo change north of 50° N were analyzed using data on snow cover fraction, air temperature, vegetation index and precipitation. To this end, the study domain was divided into six smaller areas, based on their geographical position and climate similarity. Strong differences were observed between these areas. As expected, snow cover fraction (SCF) has a strong influence on the albedo in the study area and can explain 56 % of variation of albedo in March, 76 % in April and 92 % in May. Therefore the effects of other parameters were investigated only for areas with 100 % SCF. The second largest driver for snow-covered land surface albedo changes is the air temperature when it exceeds a value between −15 and −10 °C, depending on the region. At monthly mean air temperatures below this value no albedo changes are observed. The Enhanced Vegetation Index (EVI) and precipitation amount and frequency were independently examined as possible candidates to explain observed changes in albedo for areas with 100 % SCF. Amount and frequency of precipitation were identified to influence the albedo over some areas in Eurasia and North America, but no clear effects were observed in other areas. EVI is positively correlated with albedo in Chukotka Peninsula and negatively in eastern Siberia. For other regions the spatial variability of the correlation fields is too high to reach any conclusions.

scholarly journals Variations in Winter Surface Temperature of the Purog Kangri Ice Field, Qinghai–Tibetan Plateau, 2001–2018, Using MODIS Data

2020 ◽  
Vol 12 (7) ◽  
pp. 1133
Author(s):  
Yufan Qie ◽  
Ninglian Wang ◽  
Yuwei Wu ◽  
An’an Chen
Keyword(s):  
Tibetan Plateau ◽  
Surface Temperature ◽  
Land Surface ◽  
Remote Sensing Data ◽  
Warming Trend ◽  
Data Set ◽  
Moderate Resolution ◽  
Ice Field ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Meteorological Observations

In the context of global warming, the land surface temperature (LST) from remote sensing data is one of the most useful indicators to directly quantify the degree of climate warming in high-altitude mountainous areas where meteorological observations are sparse. Using the daily Moderate Resolution Imaging Spectroradiometer (MODIS) LST product (MOD11A1 V6) after eliminating pixels that might be contaminated by clouds, this paper analyzes temporal and spatial variations in the mean LST on the Purog Kangri ice field, Qinghai–Tibetan Plateau, in winter from 2001 to 2018. There was a large increasing trend in LST (0.116 ± 0.05 °C·a−1) on the Purog Kangri ice field during December, while there was no evident LST rising trend in January and February. In December, both the significantly decreased albedo (−0.002 a−1, based on the MOD10A1 V6 albedo product) on the ice field surface and the significantly increased number of clear days (0.322 d·a−1) may be the main reason for the significant warming trend in the ice field. In addition, although the two highest LST of December were observed in 2017 and 2018, a longer data set is needed to determine whether this is an anomaly or a hint of a warmer phase of the Purog Kangri ice field in December.

Recent Trends in Land Surface Temperature on the Tibetan Plateau

2006 ◽  
Vol 19 (12) ◽  
pp. 2995-3003 ◽  
Author(s):  
Yuichiro Oku ◽  
Hirohiko Ishikawa ◽  
Shigenori Haginoya ◽  
Yaoming Ma
Keyword(s):  
Tibetan Plateau ◽  
Surface Temperature ◽  
Air Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Precipitation Amount ◽  
Surface Air Temperature ◽  
The Tibetan Plateau ◽  
Near Surface ◽  
Diurnal Range

Abstract The diurnal, seasonal, and interannual variations in land surface temperature (LST) on the Tibetan Plateau from 1996 to 2002 are analyzed using the hourly LST dataset obtained by Japanese Geostationary Meteorological Satellite 5 (GMS-5) observations. Comparing LST retrieved from GMS-5 with independent precipitation amount data demonstrates the consistent and complementary relationship between them. The results indicate an increase in the LST over this period. The daily minimum has risen faster than the daily maximum, resulting in a narrowing of the diurnal range of LST. This is in agreement with the observed trends in both global and plateau near-surface air temperature. Since the near-surface air temperature is mainly controlled by LST, this result ensures a warming trend in near-surface air temperature.

scholarly journals MODIS-derived interannual variability of the equilibrium-line altitude across the Tibetan Plateau

2016 ◽  
Vol 57 (71) ◽  
pp. 140-154 ◽  
Author(s):  
Marinka Spiess ◽  
Christoph Schneider ◽  
Fabien Maussion
Keyword(s):  
Tibetan Plateau ◽  
Large Scale ◽  
Equilibrium Line ◽  
Climate Indices ◽  
The Tibetan Plateau ◽  
Equilibrium Line Altitude ◽  
Winter Temperatures ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Positive Correlations

Abstract.Using the Moderate Resolution Imaging Spectroradiometer (MODIS) Level 1 radiance Swath Data (MOD02QKM) with a spatial resolution of 250 m, we derive snowlines during July–September 2001–12 for several mountain ranges distributed across the Tibetan Plateau (TP). Radiance bands 1 and 2 are projected to the study area and processed automatically. The discrimination between snow and ice is done using a k-mean cluster analysis and the snowlines are delineated based on a fixed percentile of the snow-cover altitude. The highest transient snowline altitude is then taken as a proxy for the equilibrium-line altitude (ELA). In the absence of measured glaciological, meteorological or hydrological data, our ELA time series enable better understanding of atmosphere-cryosphere couplings on the TP. Interannual ELA variability is linked to local and remote climate indices using a correlation analysis. Southerly flow and higher temperatures are linked with a higher ELA in most regions. Eastern and Trans-Himalayan sites show positive correlations between winter temperatures and ELA. As winter temperatures are substantially below zero, this suggests an enhancement of winter sublimation as opposed to a reduction in accumulation. It appears that large-scale atmospheric forcing has varying and sometimes opposite influences on the annual ELA in different regions on the TP.

Evaluation of WRF coupled with Noah using an improved albedo parameterization scheme during a severe snow event over the Tibetan Plateau

2020 ◽  
Author(s):  
Lian Liu ◽  
Massimo Menenti ◽  
Yaoming Ma ◽  
Weiqiang Ma
Keyword(s):  
Tibetan Plateau ◽  
Air Temperature ◽  
Land Surface ◽  
Large Impact ◽  
Parameterization Scheme ◽  
Surface Model ◽  
The Tibetan Plateau ◽  
Vegetation Fraction ◽  
Green Vegetation ◽  
Heavy Snow

<p>Snow falls frequently over the Tibetan Plateau, and is a vital component of the widespread cryosphere which has vital feedback to climate change. Snowfall and the subsequent evolution of the snowpack have a large effect on surface energy balance and water cycle. Albedo, the main determinant of net radiation flux, is a major driver of land surface processes. However, the current widely used Noah land surface model does not describe snow albedo correctly, although it keeps snow-related variables i.e. snow cover and age into account. In our study, the impact of an improved albedo parameterization scheme in WRF coupled with Noah was investigated. In the improved albedo scheme, albedo was parameterized as functions of snow depth and age which was developed using remote sensing retrievals of albedo. Numerical experiments were conducted to model a severe snow event in March 2017. The performance of WRF coupled with Noah applying the improved albedo scheme was compared with that of applying the default albedo scheme and with that of WRF coupled with CLM applying CLM’s complex albedo scheme. First, the improved albedo scheme largely reduces the WRF coupled with Noah albedo overestimation in the southeastern Tibetan Plateau, remarkably reducing the large cold bias estimates by 0.7 ℃ air temperature RMSE. Second, the improved albedo scheme gives the highest correlationship between the satellite-derived and the model estimated albedo, contributing to achieve the SWE spatial pattern, heavy snow belt and maximum SWE estimates in eastern Tibetan Plateau. Remarkable underestimation of albedo in WRF coupled with CLM contributes to regional maximum SWE underestimation and failure in heavy snow belt estimates.</p><p>In addition, WRF default land cover and green vegetation fraction were out of date but played a large impact on estimates of air temperature, albedo and SWE. Updated land parameters led to improve the model performance in simulating the severe snow event, by reducing albedo RMSE by 1%-4%. The choice of the algorithm to retrieve green vegetation fraction had a large impact on the accuracy of green vegetation fraction retrievals. It remains open to debate the optimal algorithm to estimate land surface properties in the complex topographic Tibetan Plateau.</p>

scholarly journals Diagnostic and model dependent uncertainty of simulated Tibetan permafrost area

2016 ◽  
Vol 10 (1) ◽  
pp. 287-306 ◽  
Author(s):  
W. Wang ◽  
A. Rinke ◽  
J. C. Moore ◽  
X. Cui ◽  
D. Ji ◽  
...  
Keyword(s):  
Tibetan Plateau ◽  
Soil Temperature ◽  
Air Temperature ◽  
Land Surface ◽  
Ground Temperature ◽  
Diagnostic Methods ◽  
The Tibetan Plateau ◽  
Temperature Profiles ◽  
Land Surface Models ◽  
Surface Models

Abstract. We perform a land-surface model intercomparison to investigate how the simulation of permafrost area on the Tibetan Plateau (TP) varies among six modern stand-alone land-surface models (CLM4.5, CoLM, ISBA, JULES, LPJ-GUESS, UVic). We also examine the variability in simulated permafrost area and distribution introduced by five different methods of diagnosing permafrost (from modeled monthly ground temperature, mean annual ground and air temperatures, air and surface frost indexes). There is good agreement (99 to 135  ×  104 km2) between the two diagnostic methods based on air temperature which are also consistent with the observation-based estimate of actual permafrost area (101  × 104 km2). However the uncertainty (1 to 128  ×  104 km2) using the three methods that require simulation of ground temperature is much greater. Moreover simulated permafrost distribution on the TP is generally only fair to poor for these three methods (diagnosis of permafrost from monthly, and mean annual ground temperature, and surface frost index), while permafrost distribution using air-temperature-based methods is generally good. Model evaluation at field sites highlights specific problems in process simulations likely related to soil texture specification, vegetation types and snow cover. Models are particularly poor at simulating permafrost distribution using the definition that soil temperature remains at or below 0 °C for 24 consecutive months, which requires reliable simulation of both mean annual ground temperatures and seasonal cycle, and hence is relatively demanding. Although models can produce better permafrost maps using mean annual ground temperature and surface frost index, analysis of simulated soil temperature profiles reveals substantial biases. The current generation of land-surface models need to reduce biases in simulated soil temperature profiles before reliable contemporary permafrost maps and predictions of changes in future permafrost distribution can be made for the Tibetan Plateau.

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