The goal of research aircraft operations in the Asir region of Saudi Arabia is to address key scientific questions on the application of weather modification. Research aircraft operations will focus primarily on the characterization the effect of sub–cloud aerosol on the microphysics and dynamics of both natural and seeded clouds through cloud profiling and tracer experiments using two aircraft: an instrumented King Air B200 research aircraft and a Piper Cheyenne II seeding aircraft.
To attain the fundamental purpose of intensive observation period, specific objectives are pursued:
The research aircraft is the WMI King Air B200 (N825ST) equipped with instruments to conduct in–situ aerosol and cloud microphysical measurements. This aircraft will be dedicated to conduct atmospheric measurements during the observation period, with an additional Piper Cheyenne II aircraft used to conduct seeding trials.
The WMI King Air B200 (Figure 1) is equipped with specialized instruments that have the capability of measuring aerosol, and microphysical properties of clouds and their thermodynamic environment. These are used to document the composition of clouds and to diagnose the physical processes of precipitation development within them. A list of instruments and integration tasks are listed in Table 1.
The RT-FDDA system was developed to provide high-resolution short-term analyses/forecasts (0-12 h). However, recent advances in computing power have allowed for a much longer forecast cycle; up to 36 h at current operational sites given the present grid and model physics configuration. In contrast, the twice-daily MM5 runs were specifically designed to provide long term forecasts (24-48 h).
RT-FDDA employs a time-continuous assimilation of a variety of synoptic and asynoptic observation data including:
- METAR observations (includes "Specials")
- Ship/buoy observations
- Local surface observations
- WMO rawinsonde observations
- NESDIS satellite-derived winds
- ACARS aircraft observations
These data sets have time frequencies varying from 5 min to 3 h, and are assimilated into the RT-FDDA system at their particular valid time.
By comparison, the twice-daily MM5 forecasts are limited to incorporating those observation data available at the synoptic times. These data are only used to improve the first guess at the initial time of the forecast cycle. Therefore, the twice-daily MM5 forecasts have a strong dependence on errors in the first guess. However, because the RT-FDDA cycles execute over a long period of time , errors can accumulate in regions without much data, although we have not observed major problems in this regard.
RT-FDDA analyses/forecasts do not generally suffer from model 'spin up' issues. Thus at any time, the RT-FDDA forecasts contain realistic and detailed mesoscale atmospheric structures, including cloud and precipitation systems, and local thermally-forced circulations. It should be noted that RT-FDDA does not assimilate cloud/precipitation data. The diagnosed cloud and precipitation systems in the analysis cycles result from the vertical motion and humidity assimilated from the available data.
The twice-daily MM5 forecasts, by comparison, are initialized using a 'cold start' methodology. This means that they start with no cloud and precipitation systems, or local thermally-driven circulations. Therefore, a certain amount of model 'spin up' time is required for the atmosphere, as it is represented by the MM5, to begin responding to the mesoscale forcing resulting from variations in the local complex physiography.
In summary, the characteristics of the RT-FDDA system generally contribute to a superior analysis/forecast compared to the twice daily MM5 forecast system. However, the advantages of RT-FDDA over the MM5 tend to decrease as the length of the forecast increases. This is principally due to the fact that the lateral boundary conditions employed by the MM5 and RT-FDDA systems are quite similar, and tend to have a stronger influence as the forecast length increases.
Lastly, the RT-FDDA system is temporarily employing a simple surface energy physics package. However, the RT-FDDA development team is busily working toward coupling Oregon State University land surface model (OSU LSM) to system. The new system incorporates many recent research/test results by the NCAR RTFDDA developers. Some major improvements are listed as following:
- Land Surface Model (LSM): with more detailed and accurate soil physics than previous SLAB soil model.
- Increase of the vertical model level from 31 to 36 and keep the level-distribution density with height. In other words, the resolution is increased in all troposphere and with more improvement in PBL layer.
- An improved obs Quality_Control (QC) scheme that could effectively QC every kind of observations measured at any location, height and time. Previously only those obs that are located closed model 1st-guess levels were QC-ed.
- More strict QC constraints. Working together with 3), it makes the system high quality and reliability.
Special Thanks go to the following members of the 4DWX team for their assistance in putting this system together: Yubao Liu, Laurie Carson, Becky Ruttenburg
RT-FDDA Frequently Asked Questions
What is does RT-FDDA stand for?
RT-FDDA stands for Real Time Four Dimentional Data Assimilation.
What is RT-FDDA?
RT-FDDA is a mesoscale forecasting system based on either MM5 or WRF and employs a time-continuous assimilation of a variety of synoptic and asynoptic observation data including:
* METAR observations (includes "Specials")
* Ship/buoy observations
* Local surface observations
* WMO rawinsonde observations
* NESDIS satellite-derived winds
* ACARS aircraft observations
These data sets have time frequencies varying from 5 min to 3 h, and are assimilated into the RT-FDDA system at their particular valid time. This allows the model to be nudged closer to observations before the next forecast cycle commences. Please refer to our page describing RT-FDDA for more information.
When does RT-FDDA work best?
RT-FDDA works best when there are a large amount of observations available for assimilation.
How do I read the forecast graphics?
What does forecast cycle mean?
A forecast cycle is the time in UTC the model starts running again. There is usually analysis of the previous 6 hours of model output and assimilation of observations that have become available for this time. The model then begins its forecast. The forecast period can last for up to 36 hours. For this particular implementation of the system, the forecast cycles run every 6 hours at 00, 06, 12, and 18 UTC. If you see graphics up there with a forecast cycle time more than 12 hours old, consider the forecast to be somewhat stale.
What is a cold start?
A cold start is when the RT-FDDA system uses model grids other than it's own to start a forecast cycle. These grids are commonly Eta, GFS, Ruc, etc.
Where can I find out more about the parameterizations used for this forecast?
This system is based on the MM5 model. Please see the MM5 users documentation for more information. You may find this at:http://www.mmm.ucar.edu/mm5/mm5-home.html. A direct link to the discussion about model physics is:http://www.mmm.ucar.edu/mm5/documents/MM5_tut_Web_notes/MM5/mm5.htm