High Asia Refined Analysis Version 2 (HAR v2): a New Atmospheric Data Set for the Third Pole Region

2020 ◽  
Author(s):  
Xun Wang ◽  
Vanessa Tolksdorf ◽  
Marco Otto ◽  
Dieter Scherer
Keyword(s):  
Natural Hazards ◽  
Dynamical Downscaling ◽  
Data Access ◽  
Reanalysis Data ◽  
The Tibetan Plateau ◽  
Climate Data ◽  
Data Set ◽  
The Third ◽  
High Asia ◽  
Atmospheric Data

<p>Climatic-triggered natural hazards such as landslides and glacier lake outburst floods pose a threat to human lives in the third pole region. Availability of accurate climate data with high spatial and temporal resolution is crucial for better understanding climatic triggering mechanisms of these localized natural hazards. Within the framework of the project “Climatic and Tectonic Natural Hazard in Central Asia” (CaTeNA), High Asia Refined analysis version 2 (HAR v2) is under production, and is freely available upon request. HAR v2 is a regional atmospheric data set generated by dynamical downscaling of global ERA5 reanalysis data using the Weather Research and Forecasting (WRF) model. Compared to its predecessor (HAR), HAR v2 has an extended 10 km domain covering the Tibetan Plateau and the surrounding mountains, as well as a longer temporal coverage. It will be extended back to 1979, and will be continuously updated in the future. This presentation will contain the following aspects: (1) summarizing the WRF configuration; (2) validating HAR v2 against observational data; (3) comparing HAR v2 with other gridded data sets, such as the newly developed ERA5-Land reanalysis data; (4) providing information about data format, variable list, data access, etc.  </p>

scholarly journals Hydrologic modeling by means of a hybrid downscaling approach: an application to the Sai Gon–Dong Nai Rivers Basin

2021 ◽  
Author(s):  
T. Trinh ◽  
V. T. Nguyen ◽  
N. Do ◽  
K. Carr ◽  
D. H. Tran ◽  
...  
Keyword(s):  
Machine Learning ◽  
Dynamical Downscaling ◽  
Watershed Modeling ◽  
Reanalysis Data ◽  
Hydrologic Model ◽  
Substantial Contribution ◽  
Observation Data ◽  
Data Set ◽  
Hydrological Data ◽  
Calibration And Validation

Abstract The spatial and temporal availability and reliability of hydrological data are substantial contribution to the accuracy of watershed modeling; unfortunately, such data requirements are challenging and perhaps impossible in many regions of the world. In this study, hydrological conditions are simulated using the hydrologic model-WEHY, whose data input are obtained from a hybrid downscaling technique to provide reliable and high temporal and spatial resolution hydrological data. The hybrid downscaling technique is coupled a hydroclimate and a machine learning models; wherein the global atmospheric reanalysis data, including ERA-Interim, ERA-20C, and CFSR are used for initial and boundary conditions of dynamical downscaling utilizing the Weather Research and Forecasting model (WRF). The machine learning model (ANN) then follows to further downscale the WRF outputs to a finer resolution over the studied watershed. An application of the combination of mentioned techniques is applied to the third-largest river basin in Vietnam, the Sai Gon–Dong Nai Rivers Basin. The validation of hybrid model is in the ‘satisfactory’ range. After the estimation of geomorphology and land cover within the watershed, WEHY's calibration and validation are performed based on observed rainfall data. The simulation results matched well with flow observation data with respect to magnitude for both the rising and recession time segments. In comparison among the three selected reanalysis data sets, the best calibration and validation results were obtained from the CFSR data set. These results are closer to the observation data than those using only the dynamic downscaling technique in combination with the WEHY model.

Generation of an improved precipitation data set from multisource information over the Tibetan Plateau

2021 ◽  
Author(s):  
Zhongkun Hong ◽  
Zhongying Han ◽  
Xueying Li ◽  
Di Long ◽  
Guoqiang Tang ◽  
...  
Keyword(s):  
Tibetan Plateau ◽  
Mean Squared Error ◽  
Critical Role ◽  
Reanalysis Data ◽  
The Tibetan Plateau ◽  
Efficiency Coefficient ◽  
Precipitation Data ◽  
Distributed Hydrological Model ◽  
Data Set ◽  
Daily Streamflow

AbstractPrecipitation over the Tibetan Plateau (TP) known as Asia’s water tower plays a critical role in regional water and energy cycles, largely affecting water availability for downstream countries. Rain gauges are indispensable in precipitation measurement, but are quite limited in the TP that features complex terrain and the harsh environment. Satellite and reanalysis precipitation products can provide complementary information for ground-based measurements, particularly over large poorly gauged areas. Here we optimally merged gauge, satellite, and reanalysis data by determining weights of various data sources using artificial neural networks (ANNs) and environmental variables including elevation, surface pressure, and wind speed. A Multi-Source Precipitation (MSP) data set was generated at a daily timescale and a spatial resolution of 0.1° across the TP for the 1998–2017 period. The correlation coefficient (CC) of daily precipitation between the MSP and gauge observations was highest (0.74) and the root mean squared error was the second lowest compared with four other satellite products, indicating the quality of the MSP and the effectiveness of the data merging approach. We further evaluated the hydrological utility of different precipitation products using a distributed hydrological model for the poorly gauged headwaters of the Yangtze and Yellow rivers in the TP. The MSP achieved the best Nash-Sutcliffe efficiency coefficient (over 0.8) and CC (over 0.9) for daily streamflow simulations during 2004–2014. In addition, the MSP performed best over the ungauged western TP based on multiple collocation evaluation. The merging method could be applicable to other data-scarce regions globally to provide high quality precipitation data for hydrological research.

scholarly journals A 12-year high-resolution climatology of atmospheric water transport over the Tibetan Plateau

2015 ◽  
Vol 6 (1) ◽  
pp. 109-124 ◽  
Author(s):  
J. Curio ◽  
F. Maussion ◽  
D. Scherer
Keyword(s):  
High Resolution ◽  
Tibetan Plateau ◽  
Water Transport ◽  
Summer Monsoon ◽  
East Asian ◽  
High Mountain ◽  
The Tibetan Plateau ◽  
Atmospheric Water ◽  
Data Set ◽  
High Asia

Abstract. The Tibetan Plateau (TP) plays a key role in the water cycle of high Asia and its downstream regions. The respective influence of the Indian and East Asian summer monsoon on TP precipitation and regional water resources, together with the detection of moisture transport pathways and source regions are the subject of recent research. In this study, we present a 12-year high-resolution climatology of the atmospheric water transport (AWT) over and towards the TP using a new data set, the High Asia Refined analysis (HAR), which better represents the complex topography of the TP and surrounding high mountain ranges than coarse-resolution data sets. We focus on spatiotemporal patterns, vertical distribution and transport through the TP boundaries. The results show that the mid-latitude westerlies have a higher share in summertime AWT over the TP than assumed so far. Water vapour (WV) transport constitutes the main part, whereby transport of water as cloud particles (CP) also plays a role in winter in the Karakoram and western Himalayan regions. High mountain valleys in the Himalayas facilitate AWT from the south, whereas the high mountain regions inhibit AWT to a large extent and limit the influence of the Indian summer monsoon. No transport from the East Asian monsoon to the TP could be detected. Our results show that 36.8 ± 6.3% of the atmospheric moisture needed for precipitation comes from outside the TP, while the remaining 63.2% is provided by local moisture recycling.

scholarly journals Assessment of Alaska Rain-on-Snow Events Using Dynamical Downscaling

2018 ◽  
Vol 57 (8) ◽  
pp. 1847-1863 ◽  
Author(s):  
Peter A. Bieniek ◽  
Uma S. Bhatt ◽  
John E. Walsh ◽  
Rick Lader ◽  
Brad Griffith ◽  
...  
Keyword(s):  
Dynamical Downscaling ◽  
Cold Season ◽  
Reanalysis Data ◽  
Seasonal Patterns ◽  
Climate Data ◽  
Coastal Regions ◽  
Winter Rainfall ◽  
Southwesterly Wind ◽  
Rain Events ◽  
Projected Changes

AbstractThe ice formed by cold-season rainfall or rain on snow (ROS) has striking impacts on the economy and ecology of Alaska. An understanding of the atmospheric drivers of ROS events is required to better predict them and plan for environmental change. The spatially/temporally sparse network of stations in Alaska makes studying such events challenging, and gridded reanalysis or remote sensing products are necessary to fill the gaps. Recently developed dynamically downscaled climate data provide a new suite of high-resolution variables for investigating historical and projected ROS events across all of Alaska from 1979 to 2100. The dynamically downscaled reanalysis data of ERA-Interim replicated the seasonal patterns of ROS events but tended to produce more rain events than in station observations. However, dynamical downscaling reduced the bias toward more rain events in the coarse reanalysis. ROS occurred most frequently over southwestern and southern coastal regions. Extreme events with the heaviest rainfall generally coincided with anomalous high pressure centered to the south/southeast of the locations receiving the event and warm-air advection from the resulting southwesterly wind flow. ROS events were projected to increase in frequency overall and for extremes across most of the region but were expected to decline over southwestern/southern Alaska. Increases in frequency were projected as a result of more frequent winter rainfall, but the number of ROS events may ultimately decline in some areas as a result of temperatures rising above the freezing threshold. These projected changes in ROS can significantly affect wildlife, vegetation, and human activities across the Alaska landscape.

scholarly journals High-resolution atmospheric reconstruction for Europe 1948–2012: coastDat2

2014 ◽  
Vol 6 (1) ◽  
pp. 147-164 ◽  
Author(s):  
B. Geyer
Keyword(s):  
Climate Model ◽  
Numerical Models ◽  
Regional Climate ◽  
Reanalysis Data ◽  
Data Sets ◽  
Data Set ◽  
European Continent ◽  
Atmospheric Data ◽  
The Baltic ◽  
Horizontal Grid

Abstract. The coastDat data sets were produced to give a consistent and homogeneous database mainly for assessing weather statistics and climate changes since 1948, e.g., in frequencies of extremes for Europe, especially in data sparse regions. A sequence of numerical models was employed to reconstruct all aspects of marine climate (such as storms, waves, surges, etc.) over many decades. The acronym coastDat stands for the set of consistent ocean and atmospheric data, where the atmospheric data where used as forcing for the reconstruction of the sea state. Here, we describe the atmospheric part of coastDat2 (Geyer and Rockel, 2013; doi:10.1594/WDCC/coastDat-2_COSMO-CLM). It consists of a regional climate reconstruction for the entire European continent, including the Baltic Sea and North Sea and parts of the Atlantic. The simulation was done for 1948 to 2012 with the regional climate model COSMO-CLM (CCLM) and a horizontal grid size of 0.22 degree in rotated coordinates. Global reanalysis data of NCEP1 were used as forcing and spectral nudging was applied. To meet the demands on the coastDat data set about 70 variables are stored hourly.

scholarly journals Precipitation Seasonality and Variability over the Tibetan Plateau as Resolved by the High Asia Reanalysis*

2014 ◽  
Vol 27 (5) ◽  
pp. 1910-1927 ◽  
Author(s):  
Fabien Maussion ◽  
Dieter Scherer ◽  
Thomas Mölg ◽  
Emily Collier ◽  
Julia Curio ◽  
...  
Keyword(s):  
High Resolution ◽  
Tibetan Plateau ◽  
Dynamical Downscaling ◽  
Global Analysis ◽  
Rain Gauge ◽  
Future Research ◽  
The Tibetan Plateau ◽  
Precipitation Frequency ◽  
Precipitation Seasonality ◽  
High Asia

Abstract Because of the scarcity of meteorological observations, the precipitation climate on the Tibetan Plateau and surrounding regions (TP) has been insufficiently documented so far. In this study, the characteristics and basic features of precipitation on the TP during an 11-yr period (2001–11) are described on monthly-to-annual time scales. For this purpose, a new high-resolution atmospheric dataset is analyzed, the High Asia Reanalysis (HAR), generated by dynamical downscaling of global analysis data using the Weather Research and Forecasting (WRF) model. The HAR precipitation data at 30- and 10-km resolutions are compared with both rain gauge observations and satellite-based precipitation estimates from the Tropical Rainfall Measurement Mission (TRMM). It is found that the HAR reproduces previously reported spatial patterns and seasonality of precipitation and that the high-resolution data add value regarding snowfall retrieval, precipitation frequency, and orographic precipitation. It is demonstrated that this process-based approach, despite some unavoidable shortcomings, can improve the understanding of the processes that lead to precipitation on the TP. Analysis focuses on precipitation amounts, type, seasonality, and interannual variability. Special attention is given to the links between the observed patterns and regional atmospheric circulation. As an example of an application of the HAR, a new classification of glaciers on the TP according to their accumulation regimes is proposed, which illustrates the strong spatial variability of precipitation seasonality. Finally, directions for future research are identified based on the HAR, which has the potential to be a useful dataset for climate, glaciological, and hydrological impact studies.

scholarly journals Prediction of Wide Range Two-Dimensional Refractivity Using an IDW Interpolation Method from High-Altitude Refractivity Data of Multiple Meteorological Observatories

2021 ◽  
Vol 11 (4) ◽  
pp. 1431
Author(s):  
Sungsik Wang ◽  
Tae Heung Lim ◽  
Kyoungsoo Oh ◽  
Chulhun Seo ◽  
Hosung Choo
Keyword(s):  
High Altitude ◽  
Korean Peninsula ◽  
Interpolation Method ◽  
Atmospheric Condition ◽  
Propagation Path ◽  
Two Dimensional ◽  
Data Set ◽  
Wide Range ◽  
Atmospheric Data ◽  
Terrain Surface

This article proposes a method for the prediction of wide range two-dimensional refractivity for synthetic aperture radar (SAR) applications, using an inverse distance weighted (IDW) interpolation of high-altitude radio refractivity data from multiple meteorological observatories. The radio refractivity is extracted from an atmospheric data set of twenty meteorological observatories around the Korean Peninsula along a given altitude. Then, from the sparse refractive data, the two-dimensional regional radio refractivity of the entire Korean Peninsula is derived using the IDW interpolation, in consideration of the curvature of the Earth. The refractivities of the four seasons in 2019 are derived at the locations of seven meteorological observatories within the Korean Peninsula, using the refractivity data from the other nineteen observatories. The atmospheric refractivities on 15 February 2019 are then evaluated across the entire Korean Peninsula, using the atmospheric data collected from the twenty meteorological observatories. We found that the proposed IDW interpolation has the lowest average, the lowest average root-mean-square error (RMSE) of ∇M (gradient of M), and more continuous results than other methods. To compare the resulting IDW refractivity interpolation for airborne SAR applications, all the propagation path losses across Pohang and Heuksando are obtained using the standard atmospheric condition of ∇M = 118 and the observation-based interpolated atmospheric conditions on 15 February 2019. On the terrain surface ranging from 90 km to 190 km, the average path losses in the standard and derived conditions are 179.7 dB and 182.1 dB, respectively. Finally, based on the air-to-ground scenario in the SAR application, two-dimensional illuminated field intensities on the terrain surface are illustrated.

scholarly journals Study on the changes in the East Asian precipitation in the mid-1990s using a high-resolution global downscaled atmospheric data set

2014 ◽  
Vol 119 (5) ◽  
pp. 2279-2293 ◽  
Author(s):  
Eun-Chul Chang ◽  
Sang-Wook Yeh ◽  
Song-You Hong ◽  
Jung-Eun Kim ◽  
Renguang Wu ◽  
...  
Keyword(s):  
High Resolution ◽  
East Asian ◽  
Data Set ◽  
Atmospheric Data
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