scholarly journals Detecting Long-Term Trends in Precipitable Water over the Tibetan Plateau by Synthesis of Station and MODIS Observations*

2015 ◽  
Vol 28 (4) ◽  
pp. 1707-1722 ◽  
Author(s):  
Ning Lu ◽  
Kevin E. Trenberth ◽  
Jun Qin ◽  
Kun Yang ◽  
Ling Yao
Keyword(s):  
Tibetan Plateau ◽  
Bayesian Estimation ◽  
Precipitable Water ◽  
The Tibetan Plateau ◽  
Estimation Model ◽  
Spatial Coverage ◽  
Moderate Resolution ◽  
Long Term Trends ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract Long-term trends in precipitable water (PW) are an important component of climate change assessments for the Tibetan Plateau (TP). PW products from Moderate Resolution Imaging Spectroradiometer (MODIS) are able to provide good spatial coverage of PW over the TP but limited in time coverage, while the meteorological stations in the TP can estimate long-term PW but unevenly distributed. To detect the decadal trend in PW over the TP, Bayesian inference theory is used to construct long-term and spatially continuous PW data for the TP based on the station and MODIS observations. The prior information on the monthly-mean PW from MODIS and the 63 stations over the TP for 2000–06 is used to get the posterior probability knowledge that is utilized to build a Bayesian estimation model. This model is then operated to estimate continuous monthly-mean PW for 1970–2011 and its performance is evaluated using the monthly MODIS PW anomalies (2007–11) and annual GPS PW anomalies (1995–2011), with RMSEs below 0.65 mm, to demonstrate that the model estimation can reproduce the PW variability over the TP in both space and time. Annual PW series show a significant increasing trend of 0.19 mm decade−1 for the TP during the 42 years. The most significant PW increase of 0.47 mm decade−1 occurs for 1986–99 and an insignificant decrease occurs for 2000–11. From the comparison of the PW data from JRA-55, ERA-40, ERA-Interim, MERRA, NCEP-2, and ISCCP, it is found that none of them are able to show the actual long-term trends and variability in PW for the TP as the Bayesian estimation.

scholarly journals Long-term aerosol optical depth datasets over China retrieved from satellite data

2011 ◽  
Vol 4 (6) ◽  
pp. 6643-6678 ◽  
Author(s):  
Y. Xue ◽  
H. Xu ◽  
Y. Li ◽  
L. Yang ◽  
L. Mei ◽  
...  
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Dust Storms ◽  
The Tibetan Plateau ◽  
Hysplit Model ◽  
The North ◽  
Moderate Resolution ◽  
Dust Weather ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. Nine years of daily aerosol optical depth (AOD) measurements have been derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) data using the Synergetic Retrieval of Aerosol Properties (SRAP) method over China for the period from August 2002 to August 2011, comprising AODs at 470, 550, and 660 nm. Then, the variation over China over the nine years was determined from the derived AOD data. Preliminary daily results show the agreement between the Aerosol Robotic Network (AERONET) AOD data and the derived AOD data. From 1219 daily collocations, representing mutually cloud-free conditions, we find that more than 54% of SRAP-MODIS retrieved AOD values comparing with AERONET-observed values within an expected error envelop of 20%. From 222 monthly averaged collocations, representing mutually cloud-free conditions, we find that more than 63% of SRAP-MODIS retrieved AOD values comparing with AERONET-observed values within an expected error envelop of 15% and more than 70% within an expected error envelop of 20%. In addition, the long-term SRAP AOD dataset has been implemented in analysing case studies involving dust storms, haze and the characteristics of AOD variation over China over the past nine years. It was found that areas in China with high AOD values generally appear in the Inner Mongolia, the North China Plain, Tarim Basin, the Sichuan Basin, the Tibetan Plateau and the middle and lower reaches of the Yangtze River and area with low AOD values generally appear in the Fujian Province, the Yungui Plateau, and northeast plain. The seasonal averaged AOD results indicate that AOD values generally reach their maximum in spring and their minimum in winter. The yearly mean and monthly mean SRAP AOD were also used to study the spatial and temporal aerosol distributions over China. The results indicate that the AOD over China exhibited no obvious change. Monthly averaged AOD in August in Beijing experienced one decreasing processes from 2006 to 2010, especially after 2007. The monthly mean AOD decreased from 0.46 in 2007 to 0.29 in 2010. SRAP AODs were used to study one haze case and dust case. Combining AOD data from the SRAP AOD dataset and HYSPLIT model can forecast the transport of haze. SRAP AOD data are also sensitive enough to reflect the occurrence and intensity of dust weather. Thus, the SRAP AOD dataset can be used to precisely reflect the spatial distribution, concentration distribution and transmission path of dust.

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.

scholarly journals Ice extent in sub-arctic fjords and coastal areas from 2001 to 2019 analyzed from MODIS imagery

2020 ◽  
Vol 61 (82) ◽  
pp. 210-226
Author(s):  
Megan O'Sadnick ◽  
Chris Petrich ◽  
Camilla Brekke ◽  
Jofrid Skarðhamar
Keyword(s):  
Coastal Areas ◽  
Degree Days ◽  
Weather Variables ◽  
Modis Imagery ◽  
Moderate Resolution ◽  
Long Term Trends ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Arctic Fjords

AbstractResults examining variations in the ice extent along the Norwegian coastline based on the analysis of Moderate Resolution Imaging Spectroradiometer (MODIS) images from 2001 to 2019, February through May, are presented. A total of 386 fjords and coastal areas were outlined and grouped into ten regions to assess seasonal and long-term trends in ice extent. In addition, three fjords were examined to investigate how ice extent may vary over short distances (<100 km). Of the 386 outlined, 47 fjords/coastal areas held >5 km2 of ice at least once between 2001 and 2019. Over this span of time, no statistically significant trend in ice extent is found for all ten regions; however, variations between regions and years are evident. Ice extent is assessed through comparison to three weather variables – freezing degree days (FDD), daily new snowfall and daily freshwater supply from rainfall plus snowmelt. Six out of ten regions are significantly positively correlated (p < 0.05) to FDD. In addition, ice in two regions is significantly positively correlated to daily new snowfall, and in one region negatively correlated to rainfall plus snowmelt. The importance of fjord geometry and bathymetry as well as other weather variables including wind is discussed.

scholarly journals Investigation of the ice surface albedo in the Tibetan Plateau lakes based on the field observation and MODIS products

2018 ◽  
Vol 64 (245) ◽  
pp. 506-516 ◽  
Author(s):  
ZHAOGUO LI ◽  
YINHUAN AO ◽  
SHIHUA LYU ◽  
JIAHE LANG ◽  
LIJUAN WEN ◽  
...  
Keyword(s):  
Tibetan Plateau ◽  
Surface Temperature ◽  
Time Windows ◽  
The Tibetan Plateau ◽  
Lake Ice ◽  
Ice Surface ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Ice Surface Temperature ◽  
Relationship Of

ABSTRACTThe Tibetan Plateau (TP) lakes are sensitive to climate change due to ice-albedo feedback, but almost no study has paid attention to the ice albedo of TP lakes and its potential impacts. Here we present a recent field experiment for observing the lake ice albedo in the TP, and evaluate the applicability of the Moderate Resolution Imaging Spectroradiometer (MODIS) products as well as ice-albedo parameterizations. Most of the observed lake ice albedos on TP are <0.12, and the clear blue ice albedo is only 0.075, much lower than reported in the previous studies. Even that of ice covered with snow patches is only 0.212. MOD10A1 albedo product has the best agreement with observations, followed by those of MYD10A1. MCD43A3 product is consistently higher than the observations. Due to an error of snow flag and inconsistent time windows in MCD43A2 and MCD43A3, at certain times, the albedo of the ice without snow is even higher than that covered with snow. When the solar zenith angle is not considered, there is no significant correlation between the albedo and the ice surface temperature. None of the existing ice-albedo parameterizations can reproduce well the observed relationship of the albedo and surface temperature.

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 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.

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