Use of numerical weather forecast predictions in soil moisture modelling

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1 Use of numerical weather forecast predictions in soil moisture modelling Ari Venäläinen Finnish Meteorological Institute Meteorological research

2 OBJECTIVE The weather forecast models are at their best when describing meteorological conditions above lowest tens of meters above surface. Nowadays also parameters like surface temperature, air moisture, wind speed or precipitation can be obtained from the models with accuracy that makes it possible to utilize this data as a data source for agricultural models. Really?

3 Weather Observations Network Soil moisture model Observations made on observing stations are interpolated onto 10 km*10 km grid using an objective interpolation method known as kriging. Air temperature Air humidity Solar radiation Wind speed Precipitation Potential evaporation is calculated for each grid-square with help of air temperature, air humidity, wind speed and solar radiation data using the so called Penman-Monteithequation Drying efficiency Water added to the surface layer (mm) Voumetric moisture Precipitation (mm) The change of volumetric moisture in a 60 mm thick surface layer is estimated from potential evaporation and precipitation data. The calculations are made using 3 hours time step.

4 The HIRLAM-weather forecast model Grid size 0.4 (~ 44 km) or 0.2 (~22 km) 31 vertical levels 3 or 2 minutes time step Length of the forecast up to 36 hours The international HIRLAM project is a cooperation of : Danish Meteorological Institute (DMI) (Denmark), Finnish Meteorological Institute (FMI) (Finland), Icelandic Meteorological Office (VI) (Iceland), Irish Meteorological Service (IMS) (Ireland), Royal Netherlands Meteorological Institute (KNMI) (The Netherlands), The Norwegian Meteorological Institute (DNMI) (Norway), Spanish Meteorological Institute (INM) (Spain), Swedish Meteorological and Hydrological Institute (SMHI) (Sweden) There is a research cooperation with Météo-France (France) IBM RS/6000 SP

5 In the present study the conditions at 11 geographical locations were analysed; the forecasted values of soil moisture were compared with values obtained using measured data. In case of one station (Ilomantsi) also forecasted air temperature, air humidity, radiation balance components and wind speed values are compared with measured data. Location of study areas

6 Surface moisture, May-August h forecast 12 h forecast The volumetric moisture of 6 cm thick surface layer is relatively conservative parameter unless there is precipitation and the forecasts up to 36-hours are good. The conditions in Northern Finland at Kilpisjärvi are relatively wet and the moisture is almost all the time quite near the uppermost limit of the model i.e. 50 %. At more southern places the moisture varies between 10 and 50 %. 36 h forecast

7 Evaporation, May-August h forecast 24 h forecast The forecasts of potential evaporation are relatively good. 36 h forecast

8 Precipitation, May-August h forecast 12 h forecast The forecasts of precipitation are still to some extent unreliable. However, it is good to remember that now we are comparing grid square values and the interpolated values are exactly not the same as the measured value. 36 h forecast

9 Evaporation (mm), precipitation (mm) and surface moisture forecasts at Ilomantsi, May-August 2001

10 Evaporation (3 hour sum, mm) calculated based on observations and 12 and 36 h forecasts at Ilomantsi, May-August The thicker line depicts the best fit linear linear regression. The forecasted evaporation sum values are slightly higher than the values based on measurements. The correlation between the forecasted and measured evaporation values is and for 12 and 36 hour forecasts respectively.

11 24-hour precipitation sum (mm) calculated based on forecasts and as obtained from interpolated meteorological data compared with a point measurement at Ilomantsi, May-August The correlation between forecasted and at Ilomantsi weather station measured 24 hour precipitation sum is In soil moisture calculations the grid square values are obtained from interpolated data and the correlation between the interpolated and forecasted values is The correlation between measured and interpolated values is

12 00 UTC+12h 00 UTC+24h Global radiation (Wm -2 ) forecasts at Ilomantsi, May-August UTC+36h In 12 and 36 hour forecasts the highest global radiation values have been predicted relatively correctly. 12 hour forecasts are slightly better than 36 hour forecasts. The 24 hour forecasts are naturally correct as in the middle of the night there is no global radiation

13 00 UTC+12h 00 UTC+24h Long-wave radiation (Wm -2 ) forecasts at Ilomantsi, May- August UTC+36h Daytime (12 and 36 hour) the predicted highest upward long-wave radiation values are greater than on measurements based values. The longwave radiation values even in case of observations are calculated using a parameterization utilizing air temperature, air humidity and cloudiness information.

14 00 UTC+12h 00 UTC+24h 00 UTC+36h Relative humidity (%) forecasts at Ilomantsi, May- August 2001 There is slight tendency that the daytime predicted relative humidity values are a little lower than the observed values. During the night, the values are close to 100%.

15 00 UTC+12h 00 UTC+24h 00 UTC+36h Air temperature (C ) forecasts at Ilomantsi, May-August 2001 Air temperature forecasts are good. The predicted daytime highest temperatures are a little higher that the observed.

16 00 UTC+12h 00 UTC+24h 00 UTC+36h Wind speed (ms -1 ) forecasts at Ilomantsi, May-August 2001 The forecasted wind speed values contain no systematic error though the scatter is relatively large

17 Measured and modeled soil moisture values at Ilomantsi cm 30 cm 50 cm 70 cm 90 cm Model (6 cm) Moisture /16/01 5/23/01 5/30/01 6/6/01 6/13/01 6/20/01 6/27/01 7/4/01 7/11/01 7/18/01 Date 7/25/01 8/1/01 8/8/01 8/15/01 8/22/01 8/29/01

18 DISCUSSION AND CONCLUSIONS According to this small study, the use of numerical weather forecast model data seems to be a useful alternative when input data for a soil moisture model is needed. The data used in this current study did not contain bad systematic errors when forecasted and measured values interpolated into the grid were compared. When soil moisture estimates are made then the quality of precipitation data is naturally very important and soil moisture modellers must ensure that this data is not biased.

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