Processing and regionalisation of meteorological input data
openAMUNDSEN simulates the processes for each model grid cell individually and is developed to use meteorological forcing from different sources. The standard application is to provide meteorological station data (point measurements). An option to directly read and use gridded meteorological data is currently in development.
Required meteorological input data:
- air temperature
- precipitation
- global radiation
- wind speed
- relative humidity
Optional meteorological input data:
- incoming longwave radiation
Regionalisation of station data
openAMUNDSEN provides a set of procedures suited specifically for topographically complex terrain to regionalise the point data to the model grid.
Regression-based approach including interpolation of the residuals (regression)
- can be applied to
temperature,precipitation,humidity,wind speed
For each model time step,
- a regression analysis between observations and the associated station elevation is performed to derive an elevation-dependent trend function.
- the derived function is applied upon a digital elevation model to create a trend field for a given meteorological variable (e.g., temperature as a function of elevation).
- the residuals for all station locations are calculated by subtracting the calculated regression value for the station elevation from the actual measurement for the current time step.
- the residuals from the station locations (raster cells) are interpolated to the grid using Inverse Distance Weighting (IDW) method.
- the interpolated residual field is added to the trend field, which results in elevation- and distance-dependent interpolated field ensuring a reproduction of the measured values at the station locations.
Monthly altitudinal gradients including interpolation of the residuals (fixed/fractional)
- can be applied to
temperature,precipitation,humidity,wind speed
This method is similar to the regression-based approach but uses prescribed monthly altitudinal gradients (lapse rates) either in absolute (fixed) or fractional (fractional) values for the first step. Fractional values are used for precipitation gradients.
MicroMet: precipitation regionalisation (adjustment factor)
- can be applied to
precipitation
Please see details about this method in Liston and Elder (2016).
Micromet: wind speed regionalisation using topography (Liston)
- can be applied to
wind speed
Please see details about this method in Liston and Elder (2016).
Estimation of cloudiness
-
can be applied to
cloudiness -
During daytime, cloud coverage is either determined by comparing potential to observed global radiation (method:
clear_sky_fraction, see also Sect. Radiation modeling) or it is estimated using atmospheric humidity following Liston and Elder (2016). -
During nighttime, cloudiness is either kept constant (method:
constant) or estimated using humidty (method:humidity) following Liston and Elder (2016)
Precipitation phase
Precipitation phase is determined by either air temperature (method: temp) or wet-bulb temperature (method: wet_bulb_temp). For both methods, a threshold with an enclosing temperature range is defined. Precipitation is determined as liquid above the upper end, and as solid below the lower end of the temperature range, respectively. Within the defined temperature range, the fractions of solid/liquid precipitation are linearly distributed between 100% liquid at the upper and 100 % solid at the lower end of the range with 50% liquid/solid precipitation at the threshold temperature.
Choose and configure method in openAMUNDSEN
The following example shows the options for choosing and configuring the regionalisation methods for each meteorological variable individually:
meteo:
# Spatial interpolation parameters
interpolation:
temperature:
trend_method: fixed # use fixed monthly temperature lapse rates
extrapolate: true
lapse_rate: # (°C m-1)
- -0.0026 # J
- -0.0035 # F
- -0.0047 # M
- -0.0053 # A
- -0.0052 # M
- -0.0053 # J
- -0.0049 # J
- -0.0047 # A
- -0.0042 # S
- -0.0033 # O
- -0.0035 # N
- -0.0031 # D
precipitation:
trend_method: fractional # use fixed monthly fractional precipitation gradients
extrapolate: true
lapse_rate: # (m-1)
- 0.00048 # J
- 0.00046 # F
- 0.00041 # M
- 0.00033 # A
- 0.00028 # M
- 0.00025 # J
- 0.00024 # J
- 0.00025 # A
- 0.00028 # S
- 0.00033 # O
- 0.00041 # N
- 0.00046 # D
humidity:
trend_method: fixed # use fixed monthly dew point temperature lapse rates
extrapolate: true
lapse_rate:
- -0.0044 # J
- -0.0046 # F
- -0.0049 # M
- -0.0048 # A
- -0.0046 # M
- -0.0047 # J
- -0.0043 # J
- -0.0042 # A
- -0.0045 # S
- -0.0044 # O
- -0.0047 # N
- -0.0046 # D
cloudiness:
day_method: clear_sky_fraction # use the ratio of measured vs. potential shortwave radiation to calculate cloudiness
night_method: humidity
# "constant" keep the last cloudiness value of the day during nighttime
wind_speed:
trend_method: regression
extrapolate: false
# Precipitation phase determination parameters
precipitation_phase:
method: wet_bulb_temp # use wet-bulb temperature for precipitation phase determination
threshold_temp: 273.65 # threshold temperature (K) in which 50% of precipitation falls as snow
temp_range: 1. # temperature range in which mixed precipitation can occur
measurement_height:
temperature: 2 # temperature measurement height (m)
wind: 10 # wind measurement height (m)
stability_correction: false # adjust turbulent fluxes for atmospheric stability
stability_adjustment_parameter: 5. # adjustment parameter for atmospheric stability correction
References
- Liston, G. E. and Elder, K. (2006): A meteorological distribution system for high-resolution terrestrial modeling (MicroMet). Journal of Hydrometeorology, 7(2), 217-234, https://doi.org/10.1175/JHM486.1.