Radar Description
Location and Data
Radars are located at Bamako, Manantalli, and Mopti. Reflectivity (dBZ) and velocity (VR) data from the radars are no longer available on this website.
Description
NCAR/RAL in coordination with Weather Modification Incorporated (WMI) has completed deploying three C-band weather radars in Bamako, Manantalli, and Mopti. The radars include the NCAR HIQ receivers, which offer Doppler capability.
The radars (1) continuously collect information on natural cloud characteristics, (2) help direct the operations with the cloud physics aircraft, and (3) provide general information to weather forecast personnel. NCAR has implemented analysis and display software systems (e.g. TITAN/CIDD/CTREC), along with possible hardware upgrades necessary for ingesting high-quality radar data.
An essential part of the radar analysis will be to determine the number of storms occurring over Mali. This is important in order to understand (1) the number of storms that occur naturally in the various regions around Mali, (2) the length of time that might be necessary in order to perform a later randomized experiment that would quantitatively describe the potential rainfall increase from seeding, (3) to assess the operational aircraft needs in treating these storms in a timely manner, and (4) to conduct a very preliminary estimate the overall area rainfall increases that might be possible from seeding. Using TITAN, the typical lifetimes, sizes, and intensities of rain events will be determined, in order to compare the Arabian storm climatology to those observed in other parts of the world.
The dynamical organization of storms responsible for the bulk of the rainfall will also be documented. A significant number of rainstorms are likely to be convective in nature (isolated storms as well as embedded). Among the convective storms, it is important to determine whether they are organized in individual convective units, or whether they often occur in organized lines. If there are a lot of line storms, then it may be necessary to adopt a different method of objectively characterizing the convective unit from what has been used in other recent seeding projects.
The software includes the NCAR Thunderstorm Identification Tracking Analysis and Nowcasting (TITAN), and the Configurable Integrated Data Display (CIDD), and Radar Echo Classifier (REC) software systems. Both the TITAN and CIDD displays have been implemented and will be used for viewing real-time and archived data. Using the TITAN system, data from the radars will be collected in volume-scan mode. TITAN identifies each storm seen by the radar, tags it with a specific identifier, determines the storm properties (such as height, volume, area, centroid, intensity, rainfall, speed of motion), and tracks it over time. The TITAN radar histories for a whole season can be stored on a single computer disk for later analysis and evaluation, making it an extremely useful archive and research tool. Overall, the radars will monitor the characteristics of the rainstorms to understand the following aspects: (1) the large-scale organization of the storms, (2) their frequency of occurrence and spatial distribution in the study area, (3) the temporal history, sizes, intensities and rainfall of individual storms, (4) the kinematic storm structures, and (5) divergence profiles from radar VAD profiles and aircraft measurements. An essential part of the radar analysis will be to determine the frequency of storms occurring over the various areas.
RT-FDDA Description
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
Confused? Maybe our RT-FDDA FAQs can help
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
How can I learn more about RT-FDDA?
Please refer to our page describing RT-FDDA for more information.
