Albedo, fAPAR, Land Surface Temperature, Light Use Efficiency (LUE), NDVI , Precipitation, Solar radiation, Statics and Weather data

Weather data

Biomass production and evapotranspiration are driven by meteorological conditions. The transmissivity of the atmosphere affects the available solar radiation at the land surface while precipitation, temperature, wind speed and relative humidity are important factors for evapotranspiration.

WaPOR application

Weather data is an important element for calculating biomass production and evapotranspiration. It is indirectly connected to most data components.

Although these parameters are routinely measured by most meteorological stations around the world the number of meteorological stations in the WaPOR area of interest is relatively small and data generally not up to date. WaPOR therefore derives temperature, specific humidity and wind speed from global atmospheric models which use both synoptic observations and global climate models to produce hourly grids for a large number of atmospheric variables. The advantages of these models are a good coverage of the whole project area and a high consistency over time and space. Drawback is the relatively low resolution of these data sources.

Air temperature, humidity and wind speed are produced for all levels. These intermediate data components are produced as daily meteorological grids that are used as input to calculate Evaporation, Transpiration, RET and NPP and as instantaneous (interpolated hourly) data components (at the moment of satellite overpass) to calculate relative soil moisture and soil moisture stress.

These intermediate data components are not published through WaPOR.

Table 14: Overview of intermediate data components related to weather

Methodology

For NRT processing the meteorological data is derived from GEOS-5 while for final data processing the meteorological data is derived from ERA5 (instantaneous data) and AgERA5 (daily data).

Weather shows large variation over short distances, particularly in mountainous areas. Characterizing this variability is difficult without detailed monitoring with many ground stations. Temperature is strongly affected by elevation. In general, temperature decreases 6˚C for every km of increasing elevation. The average input data temperature values are at 0.10-0.25 degrees resolution (i.e. pixel values representing the average temperature within an area of approximately 10-25km) do not sufficiently take the effect of topography and elevation into account in mountainous areas. The temperature data is therefore resampled on the basis of elevation.

Operational approach

Weather data used as input to the evapotranspiration calculations are aggregated to an average daily value and instantaneous weather data used as input for soil moisture is derived by linear interpolation of the hourly input data. Daily minimum and maximum values are extracted from the time series where necessary. The coarse spatial resolution of the weather data is resampled to the level resolution and extent using a bilinear interpolation method.

This elevation correction is done in two steps:
1. The average elevation of the input meteo pixel is calculated by resampling the DEM to the spatial resolution of the meteogrid. The input temperature data is then assumed to be representative for this elevation.
2. The difference in elevation between a smoothed coarse scale DEM (>10 km) and a high resolution DEM (100 m) is used to upscale the coarse scale air temperature to a higher resolution. A default lapse rate of -6 °C km-1 is used to account for elevation differences.

At Level 3, the NPP is calculated using the actual Level 2 minimum and maximum temperatures as weather input. They are both resampled at 30 meters using bilinear interpolation before use.

Challenges

The quality and resolution of the input data has a strong impact on the output data. The meteorological data has a relatively low spatial resolution. Although some adjustments can be made to improve input meteorological data, they are generally based on coarse resolution products. Furthermore wind speed is sometimes unrealistically low.

Functions and workflow

The meteorological data is further processed in the following workflows:
- The Reference Evapotranspiration (RET) and Evapotranspiration (E, T, I) workflows use daily meteorological data
- The Soil Moisture workflow uses instantaneous meteorological data
- The NPP workflow uses daily meteorological data (mean, max and min air temperature and solar radiation) and indirectly (through the soil moisture stress) instantaneous meteorological data