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Radar Data Assimilation

Radar Data Assimilation. For Severe Convective Weather By Kyle Ziolkowski . Outline. 1. Introduction What is radar data assimilation? Why is it important? 2. How is radar data assimilated? Different data assimilation techniques.

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Radar Data Assimilation

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  1. Radar Data Assimilation For Severe Convective Weather By Kyle Ziolkowski

  2. Outline • 1. Introduction • What is radar data assimilation? • Why is it important? • 2. How is radar data assimilated? • Different data assimilation techniques. • Transformation/relationships of radial velocity and reflectivity products to model variables • 3. Improved quality of QPF • Case examples

  3. Radar Data Assimilation Introduction

  4. 1. Introduction • One of the major issues of operational forecasting is the location and timing of precipitation also known as Quantitative Precipitation Forecasting (QPF). • Additionally, it is difficult to forecast the timing and location of deep moist convection. • Some higher resolution models (NAM, RAP, and the HRRR for example) have simulated reflectivity or composite reflectivity which attempt to forecast the timing and location of deep moist convection. • In order for these types of high resolution models to preform, they require lots of reliable data from the boundary layer in order to improve QPF, particularly QPF from convection.

  5. 1. Introduction • Radar data assimilation has long been viewed as a method to help improve QPF • Radars observe with high temporal resolution obtaining lots of information on the current state of the atmosphere, and are able to locate and track precipitation. • High spatial resolutions of radars on the order of a few kilometres match that of high resolution models. • Radiosondes and satellite information are sufficient for observing the synoptic setting. However they do not provide enough information in terms of convective scale processes. • With the advancements in computing power, and higher spatial coverage with radars, it is now possible to assimilate radar data to help improve QPF.

  6. Radar Data Assimilation How is Radar Data Assimilated

  7. 2. How Is Radar Data Assimilated • Lots of Data! • Roughly 3 million data points are observed every 5 minutes (Wang, 2013). • This number decreases after quality control procedures. • Ideal for Convective scale models • Radar data provided to the models is reflectivity and radial velocity data, and unfortunately these are not model variables. • Therefore relationships are derived for both in order for the model to ingest the data.

  8. 2. How Is Radar Data Assimilated • Radar data assimilation techniques. • Successive Correction • Newtonian Nudging • 3D-Var • 4D-Var • Ensemble Kalman Filter (EnKF) • Each have different methods/advantages/disadvantages when considering radar data assimilation

  9. 2. How Is Radar Data Assimilated • 3D-Var • Most widely used • Advantages: • Can directly assimilate variables that are not model variables • Uses a recursive filter which incorporates mass continuity into the cost function. Useful for radial wind components. • Information on total hydrometeors, and total water vapor helps balance other moisture variables to the microphysics schemes used. • Can include vertical velocity via the Richardson’s Eq. • Disadvantage – Struggles with crossbeam component winds and convective scale thermodynamic disturbances for background error statistics, which were originally designed for larger scale observations.

  10. 2. How Is Radar Data Assimilated • 4D-Var/EnKF • Can perform the same functions as 3D-Var but also incorporates the time component in which is useful for rapidly changing phenomena such as precipitation. • Can directly assimilate rainfall data from radars into the model without deriving it from divergence, moisture, and heating. • Disadvantage – Computationally expensive, but is becoming more widely used with the advancement of computing power.

  11. 2. How Is Radar Data Assimilated • Transformation of Radar data to Model Variables • First, the radar data needs to be converted from polar to Cartesian coordinates. • Reflectivity data • A relationship is created through the rain-water mixing ratio qr • Note: This equation assumes a Marshall-Palmer DSD, so it can be subject to errors based on this assumption • qr can be used to to find total cloud water/water vapor, and rain water. • Note: typically a warm rain microphysics scheme is applied to bridge hydrometeors to other variables • Issues: Model may not show rain where rain exists if the first guess field shows no RH in that region.

  12. 2. How Is Radar Data Assimilated • Transformation of Radar data to Model Variables • Radial Velocity • Where: • (x,y,z) = model location • (xrad, yrad, zrad) = radar observation location • r = distance between the model grid point and the radar location • (u, v, w) = model velocity variables • VT = terminal velocity of rain

  13. 2. How Is Radar Data Assimilated • Transformation of Radar data to Model Variables • Relationship of VT to qr

  14. 2. How Is Radar Data Assimilated • Transformation of Radar data to Model Variables • We are interested in the information we can obtain for u, v and w and we can see their relationship from the radial velocity equation (mainly interested in the w component!). • Furthermore we can obtain vertical velocity from the Richardson Eq.

  15. 2. How Is Radar Data Assimilated • Transformation of Radar data to Model Variables • Rishardson Eq. • Richardson’s equation is a higher-order approximation of the continuity equation than the incompressible continuity equation or anelastic continuity equation. • It can build an efficient linkage between dynamic and thermodynamic fields because the thermodynamic equation is directly involved.

  16. 2. How Is Radar Data Assimilated • Notes: • Radar data assimilation is only as good as the radar processing! • Quality control is a major step when concerning radar data assimilation • Velocity dealiasing • Second trip echoes • Ground clutter • Anomalous Propagation

  17. Radar Data Assimilation Improved Quality of QPF

  18. Improvements to QPF • The assimilation of radar data has shown to improve the timing and the location of precipitation. • The figure by Xiao et al. shows the Threat and Bias scores averaged for a 24hr forecast over two seasons in Korea. • 2005, no radar data was being assimilated into the WRF • 2006, radar data was being assimilated (note the difference in bias and threat scores). (Xiao et al, 2008)

  19. Improvements to QPF • How does this match up against other models with/without radar data assimilation?

  20. (Sun, 2010)

  21. Improvements to QPF • We can see that radar data assimilation isn’t failsafe, but does improve the location of precipitation along with the shape. • How does radar data assimilation improve mesoscale forecasts?

  22. Improvements to QPF • The following is an experiment set up by Xue et al. (2014) and they show how radar data assimilation helps with the location and timing of convection. They also show that with increasing resolution, the detail in the storm structure becomes more realistic and that radial velocity is important to this fact. • The figure on the right is at a grid spacing of 1km and only assimilates data from the nearby KTLX radar.

  23. Improvements to QPF • Here the data is stopped being fed into the model and the model is now performing a “free-forecast” • Note how the structure is maintained and how it compares to the actual observations

  24. Improvements to QPF • Note how the radial velocity field shows the strong mesocyclone, but it is displaced slightly northward. • This could be due to other dynamics from the parent model

  25. Summary • Radar data assimilation doesn’t fully resolve the timing and location of precipitation but rather it helps improve the quality of the analysis field. • We can see from the examples that it does preform well but can fail • The analysis is only as good as the processing of the radar data! • Very complex process; lots of data being fed into the model at grid spacing's which are close to that of the model grid spacing’s.

  26. Thank You!

  27. References • Lord, S., DiMego, G., & Parrish, D. (2006). Progress on Radar Data Assimilation at the NCEP Environmental Modeling Centre. National Weather Service, National Centres for Environemtal Prediction. College Park, MD: NECP. • Smith, T., Gao, J., Calhoun, r., Stensrud, D., Manross, K., Ortega, K., et al. (2014). Examination of a Real-time 3D-Var Analysis System in the Hazardous Weather Testbed. Weather and Forecasting, 29 (1), 63-77. • Sun, J. (2005). Convective-scale assimilation of radar data: Progress and challenges . Quarterly Journal of th Royal Meteorological Society (131), 3439-3463. • Wang, H. (2013, January 10). Recent Development of WRF 3/4D-Var Radar Data Assimilation. Retrieved March 23, 2014, from American Meteorlogical Society : https://ams.confex.com/ams/93Annual/webprogram/Paper216169.html • Xiao, Q., Lim, E., Won, D.-J., Sun, J., Lee, W.-C., Lee, M.-S., et al. (2008, January). Doppler Radar Data Assimilation in KMA's Operational Forecasting. Bulletin of the American Meteorological Society , 39-43.

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