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.

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

Fig. 1: HAR domains in Central Asia with 30 und 10 km resolution © TU Berlin	Time Span:	October 2000 October 2014
	Spatial Resolution:	30km, 10km
	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	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	-

Examples

Fig. 2: Mean Precipitation January 2001-2012, 30 km Domain © Chair of Climatology	Fig. 3: Mean Precipitation July 2001-2012, 30 km Domain © Chair of Climatology
Fig. 4: Mean Annual Precipitation 2001-2012, 10 km Domain © Chair of Climatology	Fig. 5: Mean Annual Temperature 2001-2012, 10 km Domain © Chair of Climatology

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

Fachgebietsleiter:

Prof. Dr. Dieter Scherer

Lead and contact:

Dr. Marco Otto

MSc. Xun Wang

Links:

Data access

TiP Project Page

WET Project Page

Publications:

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.
Link

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.
Link

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
Link

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.
Link

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
Link

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.
Link

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.
Link

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.
Link

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.
Link

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.
Link