dphase_mm5_2_ct: MM5 model forecast with 2 km horizontal resolution driven by ECMWF operational forecast run by UHOH for the MAP D-PHASE project
Description
Accurate initialisation of the water vapor field is important for NWP. With recent advances in Global Positioning System (GPS) atmospheric remote sensing, ground-based GPS receivers have become an important instrument that can provide high resolution water vapor measurements operationally at low cost with an accuracy of a few millimeters. The system can operate in all weather conditions.
During the COPS campaign, a dense network of GPS receivers was installed. This data has been assimilated in real-time into the MM5 4DVAR system to improve quantitative precipitation forecasts and process understanding. Operational forecasts initialised with 4DVAR and corresponding CONTROL forecasts, initialised only by the ECMWF forecast,ran the whole COPS/D-PHASE period to provide a basis for future statistical investigations.
This experiment contains only the innermost domain (2 km) of the forecast initialised with the operational ECMWF forecast only (CONTROL). The corresponding 4DVAR forecast can be found as experiment dphase_mm5_2_4d.
Assimilation run:
— 18 km horizontal resolution
— 36 level up tp 100 hPa
— 64x70 grid points
— MM5 3.4 (4DVAR version)
— Kuo convection scheme
— MRF PBL scheme
— Simple radiation
— Warm cloud Microphysics
— 3 hour assimilation window
Free forecast run: only innermost 2 km domain (CDOM) was archived)
— Triple 2-way nested 24h forecast (18, 6.2 km resolution)
— 36 level up to 100 hPa
— 64x70 points (18 km), 106x109 points (6 km), 169x184 points (2 km)
— MM5 3.7.4
— Kain Fritsch 2 cumulus (no Parameterisation in the 2 km domain)
— Reisner2 cloud Microphysics
— RRTM LW + Dudhia SW radiation
— MRF PBL scheme
— 5 layer soil model
Note: here the datasets differ in time resolution (DDOM:1h, CDOM 15 min) not in region.
Grid description:“CDOM”+“DDOM”:xinc/yinc:2.0 xnpole/ynpole:0.0
xfirst:6.0205 yfirst:47.0167 xsize:184.0 ysize:169.0
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Tags
- model-data
- regional-model-forecast