The High Asia Refined analysis (HAR)
The High Asia Refined analysis (HAR*) is an atmospheric dataset generated within the frame of the WET project (Variability and Trends in Water Balance Components of Benchmark Drainage Basins on the Tibetan Plateau) financed by the BMBF CAME Programme (Central Asia - Monsoon dynamics and Geo-ecosystems) and also supported by the TiP Project.
The HAR provides gridded fields such as temperature, precipitation and wind at 30 km resolution for Central Asia and 10 km for the Tibetan Plateau and surroundings (see Figure 1 - Model domains). The HAR intends to provide an estimate of the state of the atmosphere on an hourly basis during the last decade (starting in 2001). It can serve as a tool for studying climate variability and atmosphere related processes on the Tibetan Plateau, where other types of observations are scarce.
The dataset will be updated and actualised regularly, either to provide new time periods or new/updated products.
* The High Asia Refined analysis was previously called High Asia Reanalysis. Read here for further information.
A new version (HAR v2) is now available. Please read here for further information.
Description
The dataset is generated using the atmospheric model WRF version 3.3.1 (Weather Research and Forecast model). The simulations are comprised from consecutive model runs of 36 h time integration, the last 24 h of model output providing one day of the final dataset. The model is driven by global observations (Final Analysis data from the Global Forecasting System with additional sea surface temperature input). Thus, it remains really close to observations while adding fine scale details and acting as a "smart" physically-based interpolator (without further assimilation of observations during the simulation time). The output of the model is post-processed into product-files: one single file per variable and per year at various aggregation levels (see Table 1).
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Time Span: | October 2000 - October 2014 |
Production of HAR V1 is completed. Please note: last day of October 2014 is missing. |
| Spatial Resolution: | 30km, 10km | ||
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Temporal Resolution (Aggregations): |
hourly (h), daily (d), monthly (m), yearly (y) | ||
| Data Format: | compressed NetCDF 4 | ||
| Pressure Levels (hPa): | 1000, 975, 925, 900, 850, 800, 700, 650, 600, 550, 500, 450, 400, 350, 300, 250, 200, 150, 100, 75 |
Climate Maps
This modus allows you to view results of the HAR for both domains.
List of Variables
| Variable Name | Variable Description | Type | Unit |
| albedo | Albedo | 2d | - |
| et | Evapotranspiration | 2d | mm h-1 |
| grdflx | Ground Heat Flux | 2d | W m-2 |
| hfx | Upward Heat Flux at the Surface | 2d | W m-2 |
| lh | Latent Heat Flux at the Surface | 2d | W m-2 |
| lwdown | Downward Long Wave Flux at Ground Surface | 2d | W m-2 |
| lwup | Upward Long Wave Flux at Ground Surface | 2d | W m-2 |
| netrad | Net Radiation at Ground Surface | 2d | W m-2 |
| pblh | PBL Height | 2d | m |
| potevap | Potential Evaporation | 2d | W m-2 |
| prcp | Total Precipitation | 2d | mm h-1 |
| prcp_fr | Frozen Precipitation | 2d | mm h-1 |
| psfc | SFC Pressure | 2d | Pa |
| q2 | Specific Humidity at 2m | 2d | kg kg-1 |
| scld | total column clouds | 2d | - |
| slp | Sea Level Pressure | 2d | hPa |
| snowfall | Grid Scale Snow and Ice | 2d | mm h-1 |
| sst | Sea Surface Temperature | 2d | K |
| swdown | Downward Short Wave Flux at Ground Surface | 2d | W m-2 |
| swup | Upward Short Wave Flux at Ground Surface | 2d | W m-2 |
| t2 | Temperature at 2 m | 2d | K |
| tsk | Surface Skin Temperature | 2d | K |
| u10 | u at 10m | 2d | m s-1 |
| v10 | v at 10m | 2d | m s-1 |
| ws10 | 10 m Wind Speed | 2d | m s-1 |
| 3d Variables | |||
| geopotential | Full Model Geopotential on Mass Points | 3d_press | m2 s-2 |
| qliquid | Liquid Water Mixing Ratio | 3d_press | kg kg- |
| qsolid | Solid Water Mixing Ratio | 3d_press | kg kg-1 |
| qvapor | Water Vapor Mixing Ratio | 3d_press | kg kg-1 |
| theta | Potential Temperature (theta) | 3d_press | K |
| u | x-wind component | 3d_press | m s-1 |
| v | y-wind component | 3d_press | m s-1 |
| w | z-wind component | 3d_press | m s-1 |
| Static Variables | |||
| hgt | Terrain Height | static | m |
| lu_index | Land Use Category | static | - |
| cosalpha | Local cosine of map rotation | static | - |
| sinalpha | Local sine of map rotation | static | - |
| lai | Leaf area index | static | area/area |
| e | Coriolis cosine latitude term | static | s-1 |
| f | Coriolis sine latitude term | static | s-1 |
| isltyp | Dominant soil category | static | - |
| ivgtyp | Dominant vegetation category | static | - |
| vegfra | Vegetation fraction | static | - |
| landmask | Land mask (1 for land, 0 for water) | static | - |
| mapfac_m | Map scale factor on mass grid | static | - |
| mapfac_mx | map scale factor on mass grid, x direction | static | - |
| mapfac_my | map scale factor on mass grid, y direction | static | - |
Examples
 30 km Domain |
 30 km Domain |
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Data Access
When using the data please refer to Maussion et al. (2014), and read carefully the disclaimer available here.
Here it goes to the data download page
The field of applications for this dataset is rich and unexplored; in the first place, we demonstrate the feasibility of various applications in hydrological and glaciological modelling. We are also looking forward to new collaborations going towards a better understanding of atmosphere-related processes on the Tibetan Plateau. Please contact Marco Otto if you have any question.
Selected Publications
Maussion, F., D. Scherer, T. Mölg, E. Collier, J. Curio, and R. Finkelnburg: Precipitation seasonality and variability over the Tibetan Plateau as resolved by the High Asia Reanalysis, J. Climate, 27, 1910-1927, doi:10.1175/JCLI-D-13-00282.1, 2014.
Maussion, F., Scherer, D., Finkelnburg, R., Richters, J., Yang, W. and Yao, T.:
WRF simulation of a precipitation event over the Tibetan Plateau, China - an assessment using remote sensing
and ground observations,
Hydrol. Earth Syst. Sci., 15, 1795-1817, 2011.
Link to the paper
Curio, J., Maussion, F., Scherer, D.:
A 12-year high-resolution climatology of atmospheric water transport over the Tibetan Plateau, Earth Syst. Dynam., 6, 109-124, doi:10.5194/esd-6-109-2015, 2015.
Link to the paper
Curio, J. and Scherer, D.:
Seasonality and spatial variability of dynamic precipitation controls on the Tibetan Plateau, Earth Syst. Dynam., 7, 767-782, doi:10.5194/esd-7-767-2016, 2016.
Link to the paper
Mölg, T., Maussion, F., Scherer, D.: Mid-latitude westerlies as a driver of glacier variability in monsoonal High Asia, Nature Climate Change, 4, 68-73, doi:10.1038/nclimate2055, 2014.
Dietze, E., Maussion, F., Ahlborn, M., Diekmann, B., Hartmann, K., Henkel, K., Kasper, T., Lockot, G., Opitz, S., and Haberzettl, T.: Sediment transport processes across the Tibetan Plateau inferred from robust grain-size end members in lake sediments, Clim. Past, 10, 91-106, doi:10.5194/cp-10-91-2014, 2014.
Mölg, T., Maussion, F., Yang, W. and Scherer, D.:
The footprint of Asian monsoon dynamics in the mass and energy balance of a Tibetan glacier,
The Cryosphere, 6, 1445-1461, 2012.
Link to the paper
Kropacek, J., Maussion, F., Chen, F., Hoerz, S. and Hochschild, V.:
Analysis of ice phenology of lakes on the Tibetan Plateau from MODIS data,
The Cryosphere, 7, 287-301, 2013.
Link to the paper
Pritchard, D.M., Forsythe, N., Fowler, H.J., O'Donnell, G.M. and Li, X.F.:
Evaluation of Upper Indus near-surface climate representation by WRF in the high Asia refined analysis. ,
TJ. Hydrometeorol., 20(3), 467-487, doi:10.1175/jhm-d-18-0030.1, 2019.
Link to the paper
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