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.

Important message (2020/10/09):

Due to a mistake in the WRF model, the units of potevap (potential evaporation) in our products are wrong. The units in potevap should be m per time step (e.g., m h-1 for the hourly product, m d-1 for the daily product, etc.) , instead of W m-2. We will replace all the product files of potevap and will generate new files with the correct unit as soon as possible. We apologize for any inconvenience caused.

A new version (HAR v2) is now available. Please read here for further information.


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). Production of HAR V1 is completed. Please note: last day of October 2014 is missing.

Click to enlarge!
Model domains
Fig. 1: HAR domains in Central Asia with 30 und 10 km resolution
Time Span: October
Spatial Resolution: 30km, 10km
Temporal Resolution
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 Water Vapor Mixing Ratio at 2 m 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-1
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 -


Click to enlarge!
Mean Precipitation January 2001-2012
Fig. 2: Mean Precipitation January 2001-2012,
30 km Domain

Click to enlarge!
Mean Precipitation July 2001-2012
Fig. 3: Mean Precipitation July 2001-2012,
30 km Domain

Click to enlarge!
Mean Annual Precipitation 2001-2012
Fig. 4: Mean Annual Precipitation 2001-2012, 10 km Domain

Click to enlarge!
Mean Annual Temperature 2001-2012
Fig. 5: Mean Annual Temperature 2001-2012, 10 km Domain

Data Access

When using the data please refer to Maussion et al. (2014), and read carefully the disclaimer available here.

Here you can find 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.

nach oben


Prof. Dr. Dieter Scherer


Wang, X., Tolksdorf, V. Otto, M., Scherer, D. (2020):
WRF-based Dynamical Downscaling of ERA5 Reanalysis Data for High Mountain Asia: Towards a New Version of the High Asia Refined Analysis. Int. J. Climatol., DOI: 10.1002/joc.6686.

Pritchard, D.M., Forsythe, N., Fowler, H.J., O'Donnell, G.M., Li, X.F. (2019):
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.

Curio, J. & Scherer, D.(2016):
Seasonality and spatial variability of dynamic precipitation controls on the Tibetan Plateau. Earth Syst. Dynam., DOI:10.5194/esd-7-767-2016

Curio, J., Maussion, F., Scherer, D.(2015):
A 12-year high-resolution climatology of atmospheric water transport over the Tibetan Plateau. Earth System Dynamics 6, 109-124. DOI:10.5194/esd-6-109-2015.

Maussion, F., Scherer, D., Mölg, T., Collier, E., Curio, J., Finkelnburg, R. (2014):
Precipitation seasonality and variability over the Tibetan Plateau as resolved by the High Asia Reanalysis. J. Climate. DOI:10.1175/JCLI-D-13-00282.1

Dietze, E., Maussion, F., Ahlborn, M., Diekmann, B., Hartmann, K., Henkel, K., Kasper, T., Lockot, G., Opitz, S., and Haberzettl, T. (2014):
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.

Mölg, T., Maussion, F., Scherer, D. (2014):
Mid-latitude westerlies as a driver of glacier variability in monsoonal High Asia. Nature Clim. Change 4(1), 68-73. DOI:10.1038/nclimate2055.

Kropacek, J., Maussion, F., Chen, F., Hoerz, S. and Hochschild, V. (2013):
Analysis of ice phenology of lakes on the Tibetan Plateau from MODIS data. The Cryosphere, 7, 287-301. DOI:10.5194/tc-7-287-2013.

Mölg, T., Maussion, F., Yang, W., Scherer, D. (2012):
The footprint of Asian monsoon dynamics in the mass and energy balance of a Tibetan glacier. The Cryosphere 6, 1445-1461. DOI:10.5194/tc-6-1445-2012.

Maussion, F., Scherer, D., Finkelnburg, R., Richters, J., Yang, W., Yao, T. (2011):
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 DOI:10.5194/hess-15-1795-2011.